Cancer and Energy Balance, Epidemiology and Overview (Energy Balance and Cancer)

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Energy Balance and Cancer

Series Editor: Nathan A. Berger, Case Western Reserve University, Cleveland, OH, USA

For further volumes: http://www.springer.com/series/8282

Nathan A. Berger Editor

Cancer and Energy Balance, Epidemiology and Overview

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Editor Nathan A. Berger School of Medicine Center for Science, Health & Society Case Western Reserve University 10900 Euclid Avenue Cleveland OH 44106 USA [email protected]

ISBN 978-1-4419-5514-2 e-ISBN 978-1-4419-5515-9 DOI 10.1007/978-1-4419-5515-9 Springer New York Dordrecht Heidelberg London Library of Congress Control Number: 2010923799 © Springer Science+Business Media, LLC 2010 All rights reserved. This work may not be translated or copied in whole or in part without the written permission of the publisher (Springer Science+Business Media, LLC, 233 Spring Street, New York, NY 10013, USA), except for brief excerpts in connection with reviews or scholarly analysis. Use in connection with any form of information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed is forbidden. The use in this publication of trade names, trademarks, service marks, and similar terms, even if they are not identified as such, is not to be taken as an expression of opinion as to whether or not they are subject to proprietary rights. While the advice and information in this book are believed to be true and accurate at the date of going to press, neither the authors nor the editors nor the publisher can accept any legal responsibility for any errors or omissions that may be made. The publisher makes no warranty, express or implied, with respect to the material contained herein. Printed on acid-free paper Springer is part of Springer Science+Business Media (www.springer.com)

Preface: Associations and Challenges

In a series of landmark articles published in the New England Journal of Medicine [1, 2] and Nature Reviews [3] between 1999 and 2004, Dr. Eugenia “Jeanne” Calle alerted the entire scientific and medical community to the epidemiologic evidence providing definitive support for the association between body mass, all cause mortality, and cancer mortality. Based on results from a prospectively studied cohort of more than 900,000 US adults in the American Cancer Society, Cancer Prevention II Study, Jeanne identified the association of increased body mass index with death rate for all cancers combined, as well as for specific malignancies in both men and women. The ACS study showed that elevated body mass was associated with higher death rates from cancers of the esophagus, colon and rectum, liver, gallbladder, pancreas, kidney, non-Hodgkin’s lymphoma, and multiple myeloma. Trends were identified also for association of elevated BMI with deaths from prostate and stomach cancer in men and postmenopausal breast, uterus, cervix, and ovarian cancer in women. She put forth the alarming statistics that 14% of cancers in men and 20% of cancers in women were associated with obesity, all of which were of even greater concern due to the rising prevalence of overweight and obesity in the United States and on a worldwide basis. These studies clearly stressed the importance of obesity control to prevent the relative burdens of obesity-related morbidity and mortality. In subsequent years, Jeanne conducted important studies defining specific tumor types associated with overweight and obesity, investigated the contribution of specific nutrients to this problem, initiated studies to examine the interaction of adiposity with hormones and hormone-dependent malignancies, conducted studies on the impact of weight gain as opposed to established obesity on carcinogenesis and instituted studies on the importance of physical activity on adiposity and cancer. Jeanne earned her Ph.D. in Preventive Medicine from the Ohio State University. She subsequently worked at Oak Ridge National Laboratories, The Centers for Disease Control and The American Cancer Society where she ultimately became Vice President of Epidemiology. In her 2003 article she advocated that maintaining a BMI less than 25.0 might prevent 90,000 cancer deaths per year in the United States. Before her tragic death on February 17, 2009, Jeanne had become a leading spokesperson for obesity control and increased physical activity to prevent cancer incidence and mortality.

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On July 8, 2004, the National Cancer Institute responded to the alarming association of obesity and cancer by issuing RFA-CA-05-010 to establish the Transdisciplinary Research on Energetics and Cancer (TREC) Centers in nutrition, energetics, energy balance, physical activity, and cancer. The primary mission of the TREC Centers was to “foster collaboration among transdisciplinary teams of scientists with the goal of accelerating progress toward reducing cancer incidence, morbidity and mortality associated with obesity, low levels of physical activity and poor diet.” This initiative was spearheaded at NCI by Linda Nebeling, Ph.D., M.P.H., RD, Chief, Health Promotion Research Branch, NCI, and her associates Robert Croyle, Ph.D., Director, Division of Cancer Control and Population Sciences, Rachel Ballard-Barbash, M.D, M.P.H. Associate Director, Applied Research Program and John Milner, Ph.D., Chief, Nutritional Science Research Group, Division of Cancer Prevention, NCI. After a competitive grant review process, TREC Centers were established at five institutions, Case Western Reserve University, University of Southern California, University of Minnesota, and Fred Hutchinson Cancer Research Center, along with a coordinating center at the Fred Hutchinson Cancer Research Center. Independent and collaborative research and training activities at the TREC Centers coupled with a series of interactive and national meetings have significantly accelerated development of progress focused on this critical area of cancer prevention and control. From the beginning, it was clear that the problems of energy balance and cancers would not be solved by individual scientists or even by interdisciplinary or multidisciplinary teams, but that a new transdisciplinary approach might lead to significant progress. This book series was stimulated by the need to encourage communication among investigators representing the multiple disciplines engaged in addressing this problem. Many of the authors in this introductory volume are TREC investigators or collaborators and, although they wrote their chapters from the perspective of their individual disciplines, they are very much aware of the need for transdisciplinary communication and research efforts and are already engaged in leading these initiatives. In her keynote address to a meeting entitled Energy Balance and Cancer: Mechanisms and Mediators sponsored by the American Association for Cancer Research, NCI, and TREC on October 24–26, 2008, in Lansdowne, Virginia, Jeanne Calle reviewed the relation between obesity and cancer, provided evidence for a growing list of obesity-related cancers, and stressed the need for accelerated research to identify the mechanisms involved. Just as she labored to prove the link between body mass and cancer, it remains for us to understand and break that link. Cleveland, OH, USA

Nathan A. Berger

References 1. Calle EE, Thun MJ, Petrelli HM, Rodriguez C, Heath CW Jr. (1999). Body mass and mortality in a prospective cohort of U.S. adults. N Engl J Med;341:1097–105


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2. Calle EE, Rodrigueez C, Walker-Thurmond KA, Thun MJ (2003). Oveweight, obesity and mortality from cancer in a prospectively studied cohort of U.S. Adults. N Engl J Med;348: 1625–38 3. Calle EE, Kaaks R (2004). Overweight, obesity and cancer: epidemiological evidence and proposed mechanisms. Nature Rev Cancer, 4:579–591

Contents

Preface: Associations and Challenges . . . . . . . . . . . . . . . . . . .

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Contributors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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1 Obesity and Cancer Epidemiology . . . . . . . . . . . . . . . . . . Rachel Ballard-Barbash, David Berrigan, Nancy Potischman, and Emily Dowling

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2 Obesity and Cancer: Epidemiology in Racial/Ethnic Minorities . Colleen Doyle

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3 Obesity and Cancer in Asia . . . . . . . . . . . . . . . . . . . . . . Wanghong Xu and Charles E. Matthews

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4 Genetic Epidemiology of Obesity and Cancer . . . . . . . . . . . . Courtney Gray-McGuire, Indra Adrianto, Thuan Nguyen, and Chee Paul Lin

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5 Obesity and Cancer: Overview of Mechanisms . . . . . . . . . . . Nora L. Nock and Nathan A. Berger

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6 Caloric Restriction and Cancer . . . . . . . . . . . . . . . . . . . Fei Xue and Karin B. Michels

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7 Physical Activity and Cancer . . . . . . . . . . . . . . . . . . . . . Leslie Bernstein, Yani Lu, and Katherine D. Henderson

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8 Energy Balance, Cancer Prognosis, and Survivorship . . . . . . . Melinda L. Irwin

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9 Behavior, Energy Balance, and Cancer: An Overview . . . . . . . Donna Spruijt-Metz, Selena T. Nguyen-Rodriguez, and Jaimie N. Davis

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10 Geographic and Contextual Effects on Energy Balance-Related Behaviors and Cancer . . . . . . . . . . . . . . . David Berrigan, Robin McKinnon, Genevieve Dunton, Lan Huang, and Rachel Ballard-Barbash Subject Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Contributors

Indra Adrianto Arthritis and Immunology Research Program, Oklahoma Medical Research Foundation, Oklahoma City, OK, USA, [email protected] Rachel Ballard-Barbash Applied Research Program, Division of Cancer Control and Population Sciences, National Cancer Institute, Bethesda, MD, USA, [email protected] Nathan A. Berger Center for Science, Health and Society, Case Western Reserve University, Cleveland, OH, USA, [email protected] Leslie Bernstein Division of Cancer Etiology, Department of Population Sciences, Beckman Research Institute, City of Hope Comprehensive Cancer Center, Duarte, CA, USA, [email protected] David Berrigan Division of Cancer Control and Population Sciences, , National Cancer Institute, Bethesda MD 20892, USA, [email protected] Jaimie N. Davis Keck School of Medicine, Institute for Health Promotion and Disease Prevention Research, University of Southern California, Los Angeles, CA, USA, [email protected] Emily Dowling Applied Research Program, Division of Cancer Control and Population Sciences National Cancer Institute, Bethesda, MD, USA, [email protected] Colleen Doyle Nutrition and Physical Activity, American Cancer Society, Oklahoma City, OK 73123–1538, USA, [email protected] Genevieve Dunton Department of Preventive Medicine, Institute for Health Promotion and Disease Prevention Research, University of Southern California, Los Angeles, CA, USA, [email protected] Courtney Gray-McGuire Arthritis and Immunology Research Program, Oklahoma Medical Research Foundation, Oklahoma City, OK, USA, [email protected]

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Contributors

Katherine D. Henderson Division of Cancer Etiology, Department of Population Sciences Beckman Research Institute and City of Hope National Medical Center, Duarte, CA, USA, [email protected] Lan Huang Physiology & Biophysics and Cell Biology, University of California Irvine, Irvine, CA, USA, [email protected] Melinda L. Irwin Epidemiology and Public Health, Yale School of Public Health, New Haven, CT, USA, [email protected] Chee Paul Lin Arthritis and Immunology Research Program, Oklahoma Medical Research Foundation, Oklahoma City, OK, USA, [email protected] Yani Lu Department of Population Sciences, Beckman Research Institute, City of Hope Comprehensive Cancer Center, Duarte, CA, USA; Department of Preventive Medicine, University of Southern California Keck School of Medicine, Los Angeles, CA, USA, [email protected] Charles E Matthews Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD 20892–7344, USA, [email protected] Robin McKinnon Risk Factor Monitoring and Methods Branch, Applied Research Program, Division of Cancer Control and Population Sciences , National Cancer Institute, Bethesda MD 20892, USA, [email protected] Karin B. Michels Epidemiology, Harvard Medical School, Boston, MA, USA, [email protected] Thuan Nguyen Arthritis and Immunology Research Program, Oklahoma Medical Research Foundation, Oklahoma City, OK, USA, [email protected] Selena T. Nguyen-Rodriguez Keck School of Medicine, Institute for Health Promotion and Disease Prevention Research, University of Southern California, Los Angeles, CA, USA, [email protected] Nora L. Nock Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, OH, USA, [email protected] Nancy Potischman Applied Research Program, Division of Cancer Control and Population Sciences National Cancer Institute, Bethesda, MD, USA, [email protected] Donna Spruijt-Metz Keck School of Medicine, Institute for Health Promotion and Disease Prevention Research, University of Southern California, Los Angeles, CA, USA, [email protected] Wanghong Xu Department of Epidemiology, School of Public Health, Fudan University, Shanghai, China, [email protected] Fei Xue i3 Drug Safety, Ingenix, United Health Groups, Waltham, MA, USA, [email protected]

Chapter 1

Obesity and Cancer Epidemiology Rachel Ballard-Barbash, David Berrigan, Nancy Potischman, and Emily Dowling

Abstract Evidence has expanded extensively in the past two decades on the association between body mass index (BMI) and other measures of body composition and weight gain with many cancers. Evidence is convincing for obesity as a risk factor for cancers of the esophagus, pancreas, colon and rectum, postmenopausal breast, endometrium, kidney, and thyroid and as probable for cancer of the gallbladder. Although not yet definitive, research is expanding rapidly for a number of other rare cancers and suggests associations for obesity and cancers of the ovary and liver and for several types of lymphoid and hematological malignancies. Associations between obesity and lung and head and neck cancers are confounded by tobacco use. An important shift in research has been the effort to examine the combined effect of overweight/obesity, physical inactivity, and poor diet. Generally, studies that have examined these combinations of factors have found much greater increases in risk among people who have these adverse health profiles. A number of mechanisms are being explored related to obesity and cancer, including changes in sex hormones, insulin-related growth factors, inflammation, immune function, and other growth factors. Data on racial/ethnic groups other than non-Hispanic whites and Asians are limited for most cancers, but suggest there may be some differences in BMI and cancer associations in some subgroups. The continued global epidemics of obesity and diabetes mellitus are likely to contribute to global increases in a number of obesity-related cancers.

R. Ballard-Barbash (B) Applied Research Program, Division of Cancer Control and Population Sciences, National Cancer Institute, Bethesda, MD, USA e-mail: [email protected]

N.A. Berger (ed.), Cancer and Energy Balance, Epidemiology and Overview, Energy Balance and Cancer 2, DOI 10.1007/978-1-4419-5515-9_1, C Springer Science+Business Media, LLC 2010

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R. Ballard-Barbash et al.

1 Introduction Evidence has expanded extensively in the past two decades on the association between body mass index (BMI) and other measures of body composition and weight gain with many cancers. The most recent World Cancer Research Fund/American Institute for Cancer Research report identifies evidence that body fatness increases risk of cancers of the esophagus, pancreas, colon and rectum, postmenopausal breast, endometrium, and kidney and as probable for cancer of the gallbladder [172]. In addition, the associations of BMI, central adiposity, and weight gain are being examined relative to many other cancers resulting in emerging evidence of other associations. The growing evidence about the role of obesity on cancer risk and survival has coincided with the expanding global epidemic of obesity. The World Health Organization (WHO) estimated that in 2005, many countries had prevalence rates of obesity of more than 30%, with rates of obesity much higher in women compared to men. Furthermore, WHO estimates that by 2015, prevalence rates of obesity will have continued to increase, particularly in North and South America, reaching more than 45% in many countries on those continents, with rates in men equal to or surpassing rates in women in some countries (Fig. 1.1) [173]. The convergence of the evidence on the potential role of obesity on cancer risk and the rising global epidemic of obesity has led to focused attention on the influence of obesity on cancer risk and survival. This chapter will focus on the evidence of the association of obesity with cancer incidence from observational epidemiologic research, briefly summarizing evidence on the potential mechanisms that have been explored in human studies and noting promising future research directions. At present, there is no evidence from randomized controlled trials on the influence of weight loss on cancer risk. Data from the Swedish Obesity Study, a prospective, non-randomized trial of bariatric surgery involving over 4,000 obese subjects, found statistically significant lower overall mortality rates among subjects undergoing bariatric surgery than those who did not. Cancer deaths were lower among those undergoing bariatric surgery; however, the study was not powered to examine differences in cancer alone [152]. Several other studies have been published since 2007 using other cohorts of bariatric surgical patients and have reported statistically significant reductions in cancer mortality following bariatric surgery [2, 29]. Similar to results for the Swedish Obesity Study, deaths from causes not due to chronic diseases, such as accidents and suicide, appear to be higher among the bariatric surgery patients compared to obese patients not undergoing bariatric surgery. The reductions in cancer mortality are reported to be observed irrespective of cancer site, while reductions in cancer incidence appear to be limited to cancer that have been associated with obesity in prior research [3]. Investigators are beginning to explore possible mechanisms for these effects in small samples of patients following bariatric surgery. Evidence on obesity and cancer mortality and survival are addressed in another chapter of this book. Because of space limitations, the evidence on height and cancer risk is not included in this chapter but has been well summarized in recent reviews [54, 145, 164, 94, 143, 147, 178].

Legend

>45.0

4000.02 km

Estimated % of population with BMI> = 30, Age-standardised to WHO World population.

8.0–14.9 15.0–29.9 30.0–44.9

45.0

4000.02 km

Estimated % of population with BMI> = 30, Age-standardised to WHO World population.

15.0-29.9

45.0 30.0–44.9 Estimated % of population with BMI> = 30, Age-standardised to WHO World population.

45.0 15.0-29.9 30.0–44.9 Estimated % of population with BMI> = 30, Age-standardised to WHO World population.

23.7 kg/m2 ; Fig. 3.2). The results suggest that both greater central adiposity and

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W. Xu and C.E. Matthews

5.4

Odds Ratio*

6

4.6

3.8

3.5

5

2.4

3.0

4

2.2

2.0 3

1.9

2

1.1 2.1

1

1.0

1.1

1.6

0

< 21.4

2.7

2.2

21.5-23.7

23.8-26.2

Body mass index (kg/m 2)

> 26.2

> 0.855 0.815-0.855 0.783-0.814 < 0.782

Waist:Hip Ratio

Fig. 3.2 Association between endometrial cancer and body mass index and waist:hip ratio, Shanghai, China 1997–2001. Adapted from Xu et al. American Journal of Epidemiology 161: 939, 2005 ∗ Adjusted for age, education, year of menstruation, and number of pregnancies

elevated BMI levels are important risk factors for endometrial cancer and support the recommendation to include waist circumference measurements as a practical tool to assess abdominal fat, in conjunction with measures of overall adiposity as indicated by BMI in both clinical and research settings [57]. Breast cancer offers a second example, in the form of more heterogeneous study results among premenopausal women, of a potentially interesting difference in the association between adiposity and cancer among Asian women that may relate to the importance of central adiposity as a risk factor for certain cancers. In both Caucasian and Asian populations, the association between elevated adiposity and postmenopausal breast cancer risk has been consistently observed. For example, several studies have shown a significantly greater risk of breast cancer with higher level of BMI among postmenopausal Asian women [5, 7]. However, in contrast to numerous studies among Caucasian women – which indicate either a reduced risk for heavier women or no increase in risk – several studies among Asian women, but not all [68–70], suggest that elevated adiposity may be associated with increased risk for premenopausal breast cancer [71–73]. Gilani et al. observed a positive association between BMI and breast cancer in young Pakistani women (< 45 years). Women with a BMI ≥ 30 compared to women with values < 25 were at more than a five-fold greater risk, but the risk for overweight women was not statistically significant [71]. Similarly among urban and rural women in South India, Matthew et al. observed that premenopausal women with a BMI above 25 kg/m2 were at a 30–50% greater risk than their counterparts with BMIs below this level [72]. Chow et al. also reported an increased risk of 30–50% among premenopausal Chinese women with higher BMIs, although the results were not statistically significant [73].

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Obesity and Cancer in Asia

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The heterogeneity among these reports may be an artifact of the case–control study design which must often employ measures of body weight obtained after diagnosis and treatment have been completed. It is possible that weight gain associated with treatment [74] could bias risk estimates from these studies, although similar weight gain has not been observed in Asian women [75]. Alternatively, it may be that among premenopausal Asian women, the accumulation of abdominal body fat may play a more important role in breast cancer than overall obesity. In a prospective case–control study involving 1,086 Chinese women in Singapore, central obesity as indicated by women with a larger WHR was associated with highest risk for breast cancer, with OR being 9.18 (95% confidence interval, 4.8–17.5) comparing the last and first quintile, whereas BMI did not significantly predict risk for breast cancer [68]. In a prospective cohort study of 11,889 women conducted in Taiwan, central adiposity reflected by hip circumference was a significant predictor of breast cancer [76]. At least two studies in Asian women have reported that upper body fat accumulation as measured by WHR was positively associated with breast cancer in premenopausal women [69, 77], which is consistent with a recent meta-analysis of available case–control and cohort studies [78]. Future studies in Asian women, preferably from prospective cohorts, are needed to further clarify the potential for overall adiposity (BMI) and fat distribution to be positively associated with premenopausal breast cancer. While there are some differences in the association between obesity and cancer in Asian populations, there are also a number of similarities with Caucasian populations. We next briefly review the evidence linking obesity to colorectal, prostate, ovarian, pancreatic, gallbladder, esophageal, and gastric cancers in Asian adults. In terms of colorectal disease, several studies have reported an increased risk of colon cancer for Asian men with high BMIs [4, 10–12, 79], with an exception of a prospective study in Japanese men for which no positive association was observed for obesity and excessive weight gain with colon cancer death [80]. Among women, the associations observed in Asian studies have been inconsistent. The relationship between BMI and colorectal cancer risk in Asian women has been found to be stronger [7, 80], weaker [10, 81], or absent [11, 12], and as anticipated the associations varied with age and menopausal status [7, 10]. Higher BMI levels were associated with increased risk of colon cancer among premenopausal Chinese women, but elevated BMI levels appeared to reduce risk among postmenopausal women [10]. Abdominal obesity is also an important risk factor in colon cancer. Although few studies have yet directly compared waist circumference to colon cancer risk among Asians [82], several studies have linked abdominal obesity with an increased risk of colorectal adenoma, a precursor of colorectal cancer [83–85]. Evidence is more limited for the relationship between obesity and prostate or ovarian cancer in Asian populations. One case–control study conducted in China did not observe a significant association of prostate cancer with height, usual adult weight, or pre-adult and usual adult BMI. However, there was a positive association between abdominal adiposity and clinical prostate cancer [86]. Results suggest that abdominal fat should be carefully evaluated in future prostate cancer studies among Asian men. Similar to the situation in Western populations, it is unclear

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whether obesity influences ovarian cancer risk in Asian women. Some studies report an increased risk among obese women [87, 88], whereas others have found no association [89]. Conflicting findings also exist regarding the association between obesity and risk of pancreatic cancer in Asian populations. For example, a case–control study of pancreatic cancer in Shanghai, China, reported a positive relationship between BMI and this disease [90]. Similarly, a large prospective cohort study of Japanese men and women observed that BMI at baseline was associated with a non-significant increase in the risk of death from pancreatic cancer, but only in women. Among men, obesity at age 20 years, defined as a BMI of 30 or more, was associated with a 3.5fold increase in risk for pancreatic cancer death compared with men with a normal BMI [91]. In contrast, two Korean [9, 92] and one Japanese [7] cohort studies found no association. In a pooled analysis of 30 cohort studies including 22 from Asian countries, central obesity, as opposed to overall adiposity as indicated by BMI, was observed to be positively associated with pancreatic cancer. The age-adjusted hazard ratio for pancreatic cancer death was 1.76 (95% CI: 1.15–2.69) for a 2 cm increase in waist circumference [93]. Gallbladder cancer also has been found to be associated with obesity in Asians. Both overall and abdominal obesity, including obesity in early adulthood, have been associated with an increased risk of gallbladder cancer among Chinese adults [94]. In Japanese women, higher BMI levels were also significantly associated with increased risk [7]. Similarly, among Korean men, obesity (BMI ≥ 30 kg/m2 ) was associated with increased risk for this outcome [12]; however, another Korean study found no association [9]. As we noted previously, rates of esophageal, gastric, and liver cancer are trending downward in many Asian countries as food safety has improved and public health efforts to minimize other infectious exposures have been implemented. Nevertheless, the emergence of obesity as a possible risk factor for cancers at these sites has the potential to mitigate some gains in preventing these cancers. In the United States and other Western countries, overweight and obese adults are about two times more likely to develop esophageal adenocarcinoma and cancer of the gastric cardia than adults that maintain a healthy body weight [95, 96]. In contrast, in recent studies conducted in Asia, inverse associations between obesity and risk for squamous cell carcinoma of the esophagus have been observed [9, 97–99], associations between esophageal adenocarcinoma are absent, and associations with gastric cardia adenocarcinoma are not consistent [100, 101]. For example, a study conducted in Shanghai, China [100], reported a positive association between obesity and cancer of the gastric cardia, while Zhang et al. reported an inverse association between BMI and gastric cardia adenocarcinoma in a hospital-based study in Beijing, China [101]. Clearly, additional studies are needed to clarify these findings. Other cancers linked to obesity among Asians include kidney cancer [81, 102], liver cancer [12], papillary cancer in the thyroid, small-cell cancer in the lung, nonHodgkin’s lymphoma, melanoma [9], and leukemia [81]. However, results from these studies are far from conclusive and further studies are needed to evaluate these associations in Asian populations.

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It should be noted that, similar to the findings in Western populations [103, 104], associations between obesity and cancer may differ among subsets of the population due to modifying effects of other risk factors [10, 57, 105]. Obesity itself can also function as a modifier of cancer risk, and this has been observed in Western [106, 107] and Asian populations [108, 109]. Given that distributions of risk factors for cancer can differ between and within populations, the risk of cancer associated with overweight or obesity might also differ because of modifications by these other factors. This may explain some of the variability in association observed in Asian studies. Furthermore, the changes in characteristics of populations have been suggested to have an influence on relative risk estimates for cancer with regards to obesity due to effect measure modification [110]. Therefore, changes in diet and lifestyles in Asia may not only result in increased prevalence of obesity in populations but these changes have the potential to also modify the relation between obesity and cancer risk.

4.2 Obesity and Cancer Survivorship Obesity also may be an indicator of prognosis among Asian cancer survivors, although the overall evidence is limited at present and elevated adiposity has been associated with both longer and shorter survival following a cancer diagnosis. Consistent with the result from a randomized trial of International Breast Cancer Study Group [111], a follow-up study of 1,455 breast cancer survivors aged 25–64 years in Shanghai, China, found that being overweight at cancer diagnosis or soon afterward, as measured by BMI, was associated with poorer overall survival and disease-free survival [112]. Interestingly, neither WHR nor waist circumference was independently associated with survival in this study [112]. A study among Japanese breast cancer survivors reported that obesity was associated with significantly shorter survival. The estimated survival probability for women with BMI of 20 at the time of operation was about 12% higher than that with BMI of 24 over 10 or more years of follow-up [113]. In a Korean study of survival following colorectal cancer, BMI and visceral adiposity were found to have no influence on overall colorectal cancer survival but overweight was observed to be inversely associated cumulative disease-free survival, and increased visceral adiposity was a significant predictor of poor disease-free survival in patients with resectable colorectal cancer [114]. As noted previously, the direction and magnitude of association between BMI and cancer survival among the studies conducted in Asia depend on the type or severity, or stage, of the cancer. In a Korean study including 14,578 men with cancer, higher BMI levels were associated with longer survival in head and neck (HR, 0.54; 95% CI, 0.39 to 0.74) and esophageal (HR, 0.44; 95% CI, 0.28–0.68) cancers, but not with other cancers [115]. In another study, mean survival was observed to be longer for individuals with stage 2 gastric cancers and lower BMI levels (1667 vs. 1322 days, P = 0.02), but survival was longer for cases with stage 3a disease

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and a higher BMI level (1431 vs. 943 days, P = 0.01) [116]. Favorable associations between high BMI on the prognosis of Japanese patients with renal cell carcinoma also have been reported [102]. It may be that there is a survival advantage to having greater energy reserves in the face of a diagnosis with later stage cancer.

5 Possible Biological Mechanisms The biologic mechanisms through which obesity influences the natural history of cancers are not completely understood in either Caucasian or Asian populations, but there are few reasons to anticipate major differences in these mechanisms between the two populations. A detailed review of the mechanisms linking obesity to cancer is beyond the scope of this chapter and detailed descriptions of these mechanisms may be found in other chapters of this book. In this section we briefly review selected mechanistic studies that may link obesity to cancer in Asian adults. One of the mechanisms that has been postulated to explain the obesity–cancer association emphasizes the role of systemic inflammation that can be associated with elevated adiposity [117, 118]. Several studies conducted among Asian adults lend support to this hypothesis. For example, in healthy Japanese men, those with abdominal obesity (waist circumference ≥ 85 cm) were observed to have higher levels of serum high sensitivity C-reactive protein and interleukin-6 and lower levels of adiponectin than men without abdominal obesity [119]. Evidence is also available for positive associations between inflammatory biomarkers and risk for cancers of the breast [120], endometrium [105], colon [121], and stomach [122]. Alterations in sex hormones (e.g., estrogen, progesterone, and androgens) associated with obesity may also explain the increased risk for hormone-sensitive cancers, such as breast, endometrial, and prostate cancers. Among postmenopausal women with elevated BMI levels, estrogen levels can be 50–100% higher than in lean women [123] and elevated estrogen levels are believed to account for the increased risk of postmenopausal breast cancer [124, 125]. For endometrial cancer, lifetime exposure to hormones and high levels of estrogen in obese women may be contributing factors [126]. Sex hormone-binding globulin, the major carrier protein for certain sex hormones in the plasma, also may play a role in the altered risk for these cancers in obese women [124–126]. The influence of sex hormone levels associated with elevated adiposity may differ for certain cancers. For example, while obesity has been consistently associated with increased risk of colon cancer in men [4, 9–12, 80], studies among women are more mixed, perhaps because of the protective effect of estrogens on colorectal cancer. In a study conducted in China, an elevated risk was observed in Chinese men, but among women, menopausal status was found to be a strong effect modifier of BMI–colon cancer association [10]. Results suggest that elevated estrogen levels associated with elevated adiposity in postmenopausal women may confer some protection against colon cancer among Chinese women. Studies of these relationships in the Chinese population may be

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particularly insightful because historically use of postmenopausal hormones has been quite low (2,000 British children showed an overall 9% increase in cancer incidence per standard deviation increase in BMI, with effects three times larger for smoking-related cancers. There is also evidence from an Israeli case–control study which indicated that being in the upper quartile of BMI at age 18 years was associated with a 42% increase for ovarian cancer [24]. However, the mechanisms underlying the association between childhood obesity and cancer are not well understood, particularly in conjunction with genetic data. In one case– control study of 40 obese and 40 non-obese prepubertal children, the obese group showed higher levels of IGF-1, insulin, and lower sex-binding hormone than that of the non-obese group [182]. This suggests that high levels of growth factors and altered sex hormone profiles are present in obese children and the exposures to an adverse metabolic milieu may begin early in life.

1 Genes for Obesity This public health concern has led to an increase in the number of studies aimed to isolate genes associated with obesity, but implications of the importance of heredity in obesity began several decades ago. Twin studies [127] indicated an important role of genetic factors, supported by several family studies, even in somewhat isolated populations. In a study of a Hutterite group, Paganini-Hill et al. [146] found evidence for a major gene in the determination of ‘bulk factor.’ In a study in the Danish Adoption Register, Stunkard et al. [185] found a strong relation between weight classes, thin, median weight, overweight, or obese, and the body mass index of the biologic parents but no significant correlation between the weight class of adoptees and the body mass index of their adoptive parents. Another twin study subjected 12 pairs of identical male twins to overfeeding by 1,000 kcal per day, 6 days a week, for a period of 100 days. The variance between pairs in response to overfeeding was about three times greater than that within pairs. With respect to the changes in regional fat distribution and amount of abdominal visceral fat, the differences were particularly striking, with six times as much variance between pairs compared to within pairs [14]. These results supported involvement of genetic factors in both storage of energy as either fat or lean tissue and the various determinants of resting expenditure of energy. Several family studies have also been conducted which support a genetic contribution to obesity. A study of nuclear families with school-aged children in northern Italy that included 67 families with children classified as obese and 112 families with non-obese children and their parents and sibs suggested a dominant major gene with a weak effect [225]. Recessive inheritance was implicated in a study of a relative fat pattern index (RFPI), i.e., the ratio of subscapular skinfold thickness to the sum of subscapular and suprailiac skinfold thicknesses which included 774

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adults from 59 pedigrees ascertained through cases of cardiovascular disease [75]. This same study suggested that the variance in RFPI is 42.3% due to the major locus, 9.5% due to polygenic inheritance, and 48.2% due to random environmental effects. Moll et al. [130] in a study investigating the role of genetic and environmental factors in determining variability in ponderosity (body weight relative to height) also suggested a single recessive locus with major effect. Ponderosity was measured by body mass index (BMI; kg/m2 ) in the mothers, fathers, and sibs of 284 school children in Muscatine, Iowa, and the major locus accounted for almost 35% of the adjusted variation in BMI. Polygenic loci accounted for an additional 42% of the variation. Approximately 23% of the adjusted variation was not explained by genetic factors. Several family-based linkage studies have been conducted, some of which have focused on special populations or specific genetic effects. For example, a genomewide linkage scan was conducted in the somewhat isolated and homogeneous population of Pima Indians (which also has a very high incidence of obesity). This study found single-marker linkages to percentage body fat using sib pair analysis for quantitative traits. From these analyses, the best evidence of genes influencing body fat came from markers at chromosome 11q21-q22 and 3p24.2-p22 [142]. Other studies have aimed to identify the interplay between the many regions identified as a part of linkage and association studies. For example, Dong et al. [50] evaluated potential epistatic interactions using independent obese-affected sibling pairs in 244 families. Both the affected sib pair-specific IBD-sharing probability and the family-specific NPL score revealed that there were strong positive correlations between the effects on chromosome 10q (88–97 cM) and 20q (65–83 cM). To detect potentially imprinted, obesity-related genetic loci, these same investigators [49] performed genome-wide parent-of-origin linkage analyses. They studied a European-American sample of 1,297 individuals from 260 families and then replicated in two smaller, independent samples. For discrete trait analysis, they found evidence for a maternal effect in 10p12 across the three samples, with both multipoint LOD scores over 4.0 in the pooled sample. For quantitative trait analysis, they found a maternal effect in region 12q24 (multipoint LOD of 4.01 for BMI and 3.69 for waist circumference) and a paternal effect (multipoint LOD of 3.72 for BMI) in region 13q32 in Caucasians. Few of the genetic effects suggested for obesity have been replicated and are generally accepted. One of those is the FTO locus on chromosome 16q12.2. Frayling et al. [55] identified an association between obesity and a common variant in this gene as a part of a genome-wide association study of 1,924 type II diabetes patients and 2,938 controls from the United Kingdom for close to 500,000 SNPs. SNPs in the FTO region were confirmed as a part of the same study using an additional 3,757 type II diabetics and 5,346 controls. The diabetes risk alleles at FTO were strongly associated with increased BMI, and further analysis showed that the association of the FTO SNPs with type II diabetes was mediated through an increased risk for BMI. Frayling et al. [55] analyzing an additional 19,424 white European adults confirmed association of the A allele of rs9939609 with increased BMI from seven population-based studies and 10,172 white European children from two

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population-based studies. In all adult population-based studies, the risk for higher BMI was additive, such that those homozygous for the A allele had a higher BMI than those heterozygous for the A allele and the low-risk T allele. The attributable risk for rs9939609 was approximately 20% for BMI >30 and approximately 13% for being overweight (BMI more than 25 kg/m2 ). Study of at-risk children showed that rs9939609 was associated with increased BMI and obesity by the age of 7 years. Further sequence analysis of 47 patients with a BMI of more than 40 kg/m2 did not reveal any obvious functional variants in the FTO coding region, minimal splice sites, or 3 UTR. In a study of almost 3,000 affected individuals and over 5,000 controls, Dina et al. [47] identified two potentially functional SNPs in intron 1 of the FTO gene that were consistently strongly associated with early-onset and severe obesity. The at-risk haplotype yielded a proportion of attributable risk of 22% for common obesity. An additional study of 2,726 children found that the A allele of the FTO variant rs9939609 was associated with increased weight and increased fat mass but not lean mass [25]. The authors concluded that the FTO variant that confers a predisposition to obesity may play a role in the control of food intake and food choice, perhaps involving a hyperphagic phenotype or a preference for energy-rich foods. Other GWAS have also highlighted the role of FTO but have identified unique loci as well. Loos et al. [116], for example, performed a meta-analysis of data from four European population-based studies and three case–control studies, involving a total of 16,876 individuals of European descent, and confirmed the association between FTO and BMI as well as found a significant association between rs17782313, near the melanocortin-4 receptor (MC4R), and BMI in adults and children. Likewise, Thorleifsson et al. [189] conducted a GWAS for both weight and body mass index (BMI) in a sample of over 30,000 Caucasian and 1,100 African Americans and combined the results with previously published results from the Diabetes Genetics Initiative (DGI) on 3,024 Scandinavians. They too confirmed previously identified variants close to or in the FTO, MC4R, BDNF, and SH2B1 genes, in addition to variants at seven loci not previously connected with obesity and not specific to any given pathway. Additional genes, including APOE and TGF-beta-1, have been associated with the obesity phenotypes of fat mass, percentage fat mass, and lean mass and ENPP1 and PCSK1 with childhood and adult obesity and increased risk of glucose intolerance and type II diabetes. Long et al. [113] analyzed several SNPs of each gene in 1,873 subjects from 405 white families to test for linkage or association with BMI, fat mass, percentage fat mass, or lean mass. A significant linkage disequilibrium was observed between pairs of SNPs within each gene. Within-family association was observed in the APOE gene for percentage fat mass and fat mass and was found between lean mass and the TGFB1 gene. Finally, Meyre et al. [128] analyzed genome-wide association data from 1,380 Europeans with early-onset and morbid adult obesity and 1,416 age-matched normal weight controls and confirmed association at one of three SNPs in a risk haplotype in ENPP1, with replication in an additional 14,186 European individuals.

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2 Genetically Influenced Environmental Risk Factors As mentioned, there are many factors that contribute to obesity, each with their own genetic susceptibility. While genes specific to fat mass and weight have been identified, it is most likely the interplay between the genes predisposing to the risk factor and the genes for obesity that lead to the many complications associated with this complex disease. Among these environmental risk factors are diet, exercise, sleep, and other mechanisms such as viruses and thermoregulation. Some argue that although genetic susceptibility may explain up to 40% of the obesity phenotype, technological, lifestyle, and cultural changes over the past 50 years are the most likely causes of the recent obesity epidemic. This section addresses both the environmental risk factors for obesity and the molecular mechanisms with which they are most likely associated.

2.1 Food Intake and Appetite Regulation Diet and eating habits play an important role in controlling the weight of a person [163, 159]. Individuals with improper balance of caloric intake over the short or long term may develop a high risk of obesity. Excessive intake of energy-dense foods and sugary drinks combined with decreased physical expenditure are involved in the growing obesity epidemic. Dietary intakes, particular nutrients, and foods are all associated with both obesity and several types of cancer. Specifically, people at risk have a very high fat content in their diets, low fiber, and eat fewer fruits and vegetables. In addition, there is often a lack of exercise in their lifestyle. The World Health Organization (WHO) also suggests that up to one-third of the cancer burden could be reduced by implementing cancer prevention strategies, which include making simple changes in diet and lifestyle [220]. 2.1.1 Genes for Food Intake and Appetite Regulation Interestingly, there have been several studies implicating genes as contributors of food intake and appetite regulation. Previous studies have shown that the body regulates energy intake and consumption through hormonal signals from the adipose stores to the hypothalamus, which sends the neuronal signals to the brain to reduce appetite and increase energy expenditure. Thus the central nervous system plays a critical role in the regulation of blood glucose. A study by Schwartz et al. emphasized that there are two key components of hypothalamic neurons involved in body weight regulation: (a) the anorexigenic melanocortin neurons which express proopiomelancortin (POMC) and reduce appetite while elevating energy expenditure and (b) the orexigenic neurons that express neuropeptide Y (NPY) which have the opposite effects on the central energy metabolism [173]. Further studies by Lee et al. showed that imbalances from either POMC or NPY neuronal genes can lead to an inappropriate increase in

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appetite and reduction in energy expenditure [105]. Studies led by Cone et al. also showed that leptin acts directly on two distinct classes of neurons: one class that co-expresses anorexigenic peptide POMC and CART (cocaine and amphetaminerelated transcript) which reduce food intake, while the other co-expresses the orexigenic peptides NPY and AgRP (agouti-related protein) which increases food intake. These neuronal peptides are regulated by leptin which sends a signal directly to the hypothalamus. These studies have also shown that MC3 and MC4 receptors, expressed in the brain and CNS, play a central role in the control of body weight [35]. In addition to the genes in the central nervous system, genes in the endocrine system have also shown to be a critical mediator in energy balance and body weight regulation. The laboratory of J.M. Friedman cloned the obese gene (ob) in mice and demonstrated that leptin-deficient mice (ob/ob) given leptin are capable of sustained weight loss. Similar studies from other groups found that leptin also plays a major role in energy balance and intake in that it belongs to a family of proteins secreted by adipocytes [192] that signals certain neuronal factors in the hypothalamus to reduce appetite because of fat stores and increase energy expense. Leptin belong to a family of adipokines, which includes resistin, adiponectin, FIAF (fasting-induced adipose factor), visfatin, vaspin, and a few unexpected candidates including nerve growth factors. The diabetes gene was cloned and identified as the leptin receptor shortly after the cloning of the leptin gene [188, 28, 31]. The leptin receptor (ObRb) was shown to be an important mediator of many different signaling cascades, such as STAT3, IFG-1, PI3K, SOC3, PTP1B (protein tyrosine phosphatase 1B). The importance of the central nervous system and likely genes controlling eating behavior was highlighted in a study by Willer et al., which performed a metaanalysis of 15 genome-wide association studies for BMI (n > 32,000) and followed up top signals in 14 additional cohorts (n > 59,000). They strongly confirmed FTO and MC4R, known obesity genes, but also identified six additional loci: TMEM18, KCTD15, GNPDA2, SH2B1, MTCH2, and NEGR1, all genes which are known to be highly expressed or to act in the central nervous system, emphasizing, as in rare monogenic forms of obesity, the role of the central nervous system in predisposition to obesity [210].

2.2 Physical Activity Physical activity is one of the most effective ways to both prevent and control obesity. With industrialization, urbanization, and mechanization, there are increasing numbers of people adapted to sedentary ways of life. It suggests that people who are not spending adequate time in maintaining a minimal level of physical activity to balance their caloric intake and caloric expenditure may obtain a positive energy balance that gives rise to obesity and increases the risk for cancer. However, the efficiency with which individuals expend calories, accumulate lean or fat mass is also

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highly influenced by genes. We present here a description of the risk associated with lack of exercise and also review both animal and human studies implicating genes in the regulation of calorie expenditure and efficiency of exercises, including fitness phenotypes. The prevalence of a sedentary lifestyle in youth is particular alarming, the most recent Youth Risk Behavior Survey (2007) indicated that 35% of high school students watched TV and 25% played video game/computer games or spent more than 3 h per average school day on the computer for something unrelated to school work. The study also reported 65% of the students did not meet recommended levels of physical activity and 46% did not attend physical education classes [193]. These behaviors may potentially be the risk factor to obesity in youth. A longitudinal study was conducted to assess the relationship between sedentary behavior and overweight in adolescent girls. The results showed an inverse association between sedentary behavior and light activity over the time period from 6th to 8th grade. Over the 2-year time period, adolescent girls shift their time from light activity to more sedentary activity. However, the changes in sedentary and light activity were not linked to the changes in BMI [63]. A longer follow-up may be required to observe the relationship between sedentary activity and BMI. The sedentary behavior and physical activity are not mutually exclusive; the association exists between sedentary behavior, physical activity, and obesity. In theory, young adults may be highly active and highly sedentary. For example, youth could be involved in both moderate to high physical activity and sedentary activity over the course of the day. The sedentary activity is particularly evident on weekends. Likewise, youth could also fit into other subgroups such as those who are highly active and low sedentary, low active and low sedentary, or low active and highly sedentary. A cross-sectional study showed that low-active, high-sedentary boys were 1.6 times more likely to be overweight than high-active, low-sedentary boys. The low-active, high-sedentary girls are two-fold more likely to be overweight than high-active, low-sedentary girls. However, high-active, high-sedentary girls were 1.91 times more likely to be overweight than were high-active, low-sedentary girls. These results indicate that sedentary behavior may moderate the relationship between physical activity and overweight [216]. Of course, as mentioned previously, we know that molecular mechanisms underlying the efficiency with which an individual responds to physical activity likely play a role in this relationship as well. Therefore, we present here a discussion of the role of genes in human physical performance and health-related fitness. The physical performance phenotypes include cardiorespiratory endurance and muscle strength whereas the health-related fitness phenotypes are grouped into four categories: hemodynamics; anthropometry and body composition; insulin and glucose metabolism; and blood lipid, lipoprotein, and hemostatic factors [16]. Animal and human studies using case–control and other designs are described. The mechanism of the effect of exercise on inflammatory processes has been well characterized and calorie restriction, physical activity alone, or in combination are known to reduce certain inflammatory markers. In general, acute inflammation is a process that is beneficial to the host by providing protection from invading

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pathogens and initiating wound healing, whereas chronic systemic inflammation, which typically accompanies obesity, is a low-grade pervasive form of inflammation that damages the endothelial linings of arteries and a variety of tissues and organs that causes disruptive effects to the nervous, endocrine, and other systems. Several cross-sectional studies showed an association between physical inactivity and systemic inflammation. The cancer risk is lowered by the effect of physical activity in reduction of chronic inflammation, as exercise-induced increases in systemic IL-6 may result in reduced pro-inflammatory mediators and elevated anti-inflammatory factors [85]. 2.2.1 Genes for Physical Activity Animal studies can be used to investigate the genetic analysis of complex phenotypes. The main advantage of animal studies on the genetics of performance and fitness phenotypes over human studies is the control on several factors including heterogeneity, breeding, variation, physiological studies, therapies, and hypothesis testing. A study by Ways et al. [206] applied the quantitative trait locus (QTL) mapping to identify genes contributing to maximal exercise endurance in mice. They found three QTLs linked with exercise endurance in an F2 rat model derived from Copenhagen (COP) and DA inbred progenitors. They found a significant linkage of exercise endurance with D16Rat17 (LOD of score of 4.0), a suggestive linkage with D16Rat55 (LOD score of 2.9) on chromosome 16, and a suggestive linkage with D3Rat56 (LOD score of 2.2) on chromosome 3. Lightfoot et al. [111] conducted a QTL study on F2 mice derived from an intercross of two inbred strains with high maximal exercise endurance (Balb/cJ) or low maximal exercise endurance (DBA/2 J) run on a treadmill to test their exercise endurance. They identified a significant QTL on chromosome X for all mice and a suggestive QTL in the female mice on chromosome 8 from selective mapping. From fine mapping, their findings were confirmed. A significant QTL was found at 57.9 cM (DXMIT31, LOD score of 2.26) on chromosome X. Suggestive QTLs were found for DXMit121 (LOD score of 2.13), DXMIT5 (LOD score of 2.13) and DXMIT236 (LOD score of 2.10) on chromosome X in all mice, and D8Mit359 (LOD score of 1.19) on chromosome 8 in the female mice. A recent study by MacArthur et al. [124] revealed that alpha actinin-3 deficiency alters skeletal muscle metabolism and increases endurance performance in a knockout mouse model. Homozygous knockout (Actn3–/– ) mice that do not have alpha actinin-3 protein were compared to wild-type (Actn3+/+ ) littermate controls. They found that Actn3–/– mice have more intense staining muscle sections than Actn3+/+ mice. In the intrinsic exercise capacity test, where mice were placed on a motorized treadmill at increasing speeds until they were exhausted, Actn3–/– mice ran on average 33% further than Actn3+/+ mice. They then suggested that the ACTN3 577X allele also influences muscle metabolism in humans. The role of the angiotensin-converting enzyme (ACE) insertion/deletion (I/D) polymorphism on endurance performance has been extensively investigated using

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case–control studies [7, 8, 34, 59, 82, 120, 132, 137, 139, 170, 195]. The ACE is involved in the degradation of vasodilator bradykinin and converts angiotensin I to the vasoconstrictor angiotensin II that influences blood pressure [7, 132]. Recent case–control studies have identified a higher frequency of the I allele in the ACE locus in British high-altitude mountaineers [132], Australian rowers [59], British Olympic-standard distance runners [137], and South African triathlon athletes [34] compared to controls. An increased frequency of the homozygous II ACE genotype has been reported in Italian road cyclists, track and field endurance athletes, and cross-country skiers [170]; Russian swimmers, skiers, triathlon athletes, and track and field athletes [139]; Spanish cyclists, long-distance runners, and handball players (P = 0.0009) [7]; and Turkish university athletes [195]. On the other hand, an excess of the deletion (D) allele in the ACE locus has been found among elite short distance swimmers [137, 218] and sprinters [137] that require more power than endurance. Additional studies examining aerobic phenotypes implicate several other genes. A case–control study by Wolfarth et al. [215] revealed significant differences in allele and genotype frequencies for the alpha-2A-adrenoceptor gene (ADRA2A) between elite endurance athletes and sedentary controls. Another study by Wolfarth et al. [214] found a significant difference in the genotype distribution between athletes and controls, indicating a significant association between the Arg16Gly single nucleotide polymorphism in the beta2-adrenergic receptor (ADRB2) gene and endurance performance. Other case–control studies found genes that associated with endurance performance including the adenosine monophosphate deaminase 1 (AMPD1) [165], the peroxisome proliferator-activated receptor-gamma coactivator 1 alpha (PPARGC1A) [120], the bradykinin beta 2 receptor (BDKRB2) [169], and peroxisome proliferator-activated receptor alpha (PPARalpha) [5]. Hagberg et al. [68] found that, in postmenopausal women, the ACE insertion/insertion (II) genotype group had a significant higher maximal O2 consumption (VO2max ) than the ACE deletion/deletion (DD) genotype group after assessing the effect of physical activities levels. They also reported that the ACE II genotype group had a significantly higher VO2max than the ACE insertion/deletion (ID) genotype group. The ACE II genotype group also had a higher maximal arterial–venous O2 difference (a-vDO2 ) compared to D-allele carriers. Similar findings have also been reported by Hagberg et al. [69], Zhao et al. [224], and Kasikcioglu et al. [94]. Abraham et al. [3] studied 57 patients with congestive heart failure and found that the ACE II genotype group had a significantly higher maximum exercise time on the treadmill and VO2peak than the ACE ID and DD genotype groups. A study on 33 patients with chronic obstructive pulmonary disease (COPD) by Kanazawa et al. [93] revealed that the mean pulmonary arterial pressure (mPAP) after exercise was significantly higher in patients with the DD genotype than in those with the II genotype. Pulmonary vascular resistance (PVR) after exercise was also significantly higher in patients with the DD genotype than in those with the II genotype or ID genotype. Furthermore, DO2 after exercise was significantly lower in patients with the DD genotype than in those with the II genotype or ID genotype. No significant difference was found in PVR or DO2 between placebo or nifedipine administered

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controls or all genotype groups. However, they found that nifedipine administration significantly reduced mPAP after exercise in all groups. Another study in 232 patients with heart failure [203] examined polymorphisms of ADRB2 in relation to exercise capacity. They reported that patients with the Ile164 polymorphism had a lower VO2peak than patients with Thr164. Liggett et al. [110] suggested that patients with Ile164 progress more quickly to death or transplantation than those with Thr164. McCole et al. [126] revealed an association between ADRB2 and a-vDO2 in postmenopausal women during treadmill exercise. They found that the Gln/Gln homozygote women had higher a-vDO2 than the Glu/Glu carriers during submaximal and maximal exercise. The same study indicated that the Gln carriers had higher VO2max than the Glu carriers. Moore et al. [133] investigated 63 non-obese postmenopausal Caucasian women and found that VO2max was lower in ADRB2 Glu27Glu than in ADRB2 Glu27Gln and Gln27Gln genotype women. Hence, they suggested that the Gln27Gln and Gln27Glu genotypes might associate with elite endurance performance in older women. Wagoner et al. [204] studied polymorphisms of ADRB1 in 263 patients with congestive heart failure in relation to exercise capacity and found that patients homozygous for Gly389 had significantly lower VO2peak compared with those with Arg389. Sandilands et al. [168] also confirmed the Wagoner study although the differences between 389R and 389G homozygotes in the Sandilands study were somewhat larger than that reported by Wagoner et al. [204]. Defoor et al. [41] conducted the CAREGENE (cardiac rehabilitation and genetics of exercise performance) study on 935 patients with coronary artery disease (CAD). They found that patients with the Gly49Gly genotype of ADRB1 had significantly higher covariate-adjusted aerobic power at baseline than those with Ser49Ser and Ser49Gly. Lopez-Alarcon et al. [117] examined the association of a genetic polymorphism of the insulin-like growth factor, IGF-I, on body composition, exercise performance, and exercise economy in 114 premenopausal women. After adjusting for African admixture, they found that IGF-I was negatively associated with lean body mass and lean leg mass, but not with leg strength, and IGF-I carriers had a longer time on the treadmill after adjusting for AFADM. A negative relationship was found between oxygen uptake during cycling. Hersh et al. [79] studied genetic associations for COPD-related phenotypes, including measures of exercise capacity, pulmonary function, and respiratory symptoms in 304 COPD patients. They found an association of single nucleotide polymorphisms (SNPs) in the microsomal epoxide hydrolase (EPHX1) and in the latent transforming growth factor-beta binding protein-4 (LTBP4) maximal output on cardiopulmonary exercise testing (Wmax ). Hautala et al. [76] examined peroxisome proliferator-activated receptor-delta (PPARdelta) gene polymorphisms in relation to cardiorespiratory fitness and plasma lipid responses to endurance training. They found a lower training response in maximal power output (Wmax ) in the exon 4 +15 C/C homozygotes compared with the heterozygotes and the T/T homozygotes in black subjects, and a similar finding was reported in white subjects.

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They also found that the exon 4 +15 C/C homozygotes had a smaller traininginduced increase in maximal oxygen consumption (P = 0.028) compared with the C/T and T/T genotypes in black subjects. The HERITAGE family study [162] investigated quantitative trait loci for maximal exercise capacity phenotypes and their responses to a standardized 20-week endurance training program in sedentary black and white subjects. They found promising linkages in the sedentary state for VO2max on chromosome 11p15 in Caucasians and for Wmax on chromosome 10q23 in Caucasians, and on chromosome 1p31 in African Americans for VO2max and chromosome 5q23 in Caucasians for Wmax for their responsiveness to training. They also reported suggestive evidence of linkage on 13q33 and 18q12 for baseline Wmax in whites and on 1p31, 7q32, and 7q36 for baseline VO2max in blacks. The suggestive evidence of linkage of VO2max training response was reported on 16q22 and 20q13.1 in blacks and on 4q27, 7q34, and 13q12 in whites. Genes for anaerobic activity have also been identified, implicating the interplay of both aerobic and anaerobic components of physical performance. Ahmetov et al. [5] found an increasing linear trend of the C allele with increasing anaerobic components of physical performance by studying the intron 7 G/C polymorphism in the peroxisome proliferator-activated receptor alpha (PPARA) gene in Russian athletes. They also reported that GG genotype frequencies in enduranceoriented and power-oriented athletes were significantly different compared to controls. Furthermore, the muscle fiber analysis revealed that the CC genotype carriers had significantly lower percentages of slow-twitch fibers compared to the GG genotype carriers. An examination of the alpha actinin-3 (ACTN3) gene locus and its nonsense R577X in African athletes by Yang et al. [221] did not find any significant genotype frequency differences between Nigerian sprinters and controls [16]. A study by Oh [144] on the distribution of the I/D polymorphism in the ACE gene in elite Korean athletes also did not reveal a significant difference between athletes and controls. Moran et al. [135] investigated 40 m sprint performance in 992 adolescent Greeks and found that there was a significant association (P = 0.003) between the ACTN3 R577X polymorphism and the sprint time, with the 577R allele contributing the faster times. Vincent et al. [201] examined the ACTN3 R577X polymorphism in relation to isometric and isokinetic knee extension strength in 90 young males and found significantly higher relative dynamic quadriceps torques at 300◦ /s in RR carriers compared with XX carriers (P = 0.04). This same group studied [135] the associations of the ACE polymorphisms with physical, physiological, and skill parameters in 1,027 teenage Greeks. They reported a strong association (P < 0.001) between the ACE I/D (insertion/deletion) polymorphism and both handgrip strength and vertical jump in females where homozygotes for the I-allele exhibiting higher performance-related phenotype scores. However, they did not find a significant association in the males for either performance measure. A study by van Rossum et al. [200] revealed that carriers of the ER22/23EK polymorphism in the glucocorticoid receptor gene were taller, had more lean body mass, greater thigh circumference, and more muscle strength in arms and legs compared to

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noncarriers in males at 36 years of age. In females at the age of 36 years, waist and hip circumferences were smaller in ER22/23EK carriers, but there was no difference in body mass index compared to noncarriers. A linkage study by Huygens et al. [86] was performed in 329 young male sibs from 146 families using a polymorphic marker in RB1 (D13S153 on 13q14.2) for trunk strength. They reported evidence for linkage between locus D13S153 at 13q14.2 and several measurements of trunk flexion with LOD scores between 1.62 and 2.78, but did not find evidence for linkage with trunk extension. The next study by Huygens et al. [87] examined the potential role of the myostatin (GDF8) pathway in relation to muscle strength and estimated muscle cross-sectional area in 329 young male sibs with a candidate gene approach. They observed linkage patterns between knee extension and flexion peak torque with markers D2S118 (GDF8), D6S1051 (CDKN1A), and D11S4138 (MYOD1), and a maximum LOD score of 2.63 (P = 0.0002) was reported with D2S118.

2.3 Sleep Several recent studies have linked lack of sleep with obesity and other chronic health problems, yet millions of people do not get enough sleep and many suffer from sleep deprivation. Insufficient sleep makes an individual more vulnerable to obesity. The causes of sleep deprivation vary; the problems can directly or indirectly be linked to abnormalities in physiological systems such as the brain and nervous system, cardiovascular system, metabolic functions, and immune system. Furthermore unhealthy conditions, disorders, and diseases also contribute to sleeplessness. The percentage of adults getting , whereas substrates that inhibit or interfere with subsequent pathways or activities are indicated by a − − −| .

as the α- and β-insulin dimer and the free 30 amino acids (C-peptide). Insulin facilitates glucose uptake and utilization in muscle, liver, and adipose tissue to provide energy, while concomitantly decreasing the amount of glucose produced by

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the liver. Under normal conditions, insulin acts by directly binding to its receptor and through a myriad of signaling molecules (e.g., IRS-1, Akt, and to a lesser extent IRS-2), causing movement of glucose transporters (e.g., GLUT4 in skeletal muscle) to the cell membrane to bring glucose into the cell for subsequent use in energy metabolism pathways such as glycolysis and glycogen and fatty acid synthesis [9]. However, obesity causes a decreased sensitivity to the action of insulin (insulin resistance), leading to a compensatory increase in the production of insulin (hyperinsulinemia) [23]. Serum insulin levels fluctuate with the level of blood glucose and the state of fasting; therefore, surrogates of pancreatic insulin secretion, such as C-peptide, may be preferred in epidemiological studies because C-peptide is not removed by the liver, has a slower metabolic clearance, and lacks cross-reactivity with antibodies [24]. Since C-peptide is cleaved along with insulin from proinsulin and secreted into the circulation in equimolar amounts when insulin is required, it is used as a surrogate marker for insulin secretion. Higher circulating levels of C-peptide have been associated with obesity [25] and with an increased risk of endometrial [26], postmenopausal breast [27], colorectal [28], and pancreatic [29] cancers. However, a recent meta-analysis concluded that only colorectal and pancreatic cancer patients have increased pre-diagnostic blood levels of insulin and glucose [30], and effects in carcinogenesis may only be relevant in the postprandial state [29]. In terms of the potential mechanistic role(s) of insulin in carcinogenesis, chronic hyperinsulinemia may lead to increased cell proliferation (mitogenesis) and inhibition of programmed cell death (apoptosis). This may occur directly through the effects of insulin binding to its receptor or indirectly by decreasing the levels of insulin-like growth factor-binding proteins (IGFBP-1 and -2), resulting in higher levels of bioavailable IGF-1 [19, 31]. As shown in Fig. 5.2, insulin activity at the cellular level is initiated by binding to its receptor (INSR), one of several cell membrane receptor tyrosine kinases, which upon phosphorylation initiate a cascade of events through the insulin receptor substrate (IRS-1) and phosphoinositide 3-kinase (PI3K)/Akt pathway leading to activation of mammalian target of rapamycin (mTOR), which serves as a central regulator of cell metabolism through several downstream pathways. However, insulin may only be directly mitogenic at levels higher than those typically found in normal physiological conditions [32]. Interestingly, IRS-1-associated PI3K activity has been found to be markedly impaired in obese women and GLUT4 protein levels substantially decreased in obese compared to lean subjects [33]. As shown in Fig. 5.2, activated PI3K then converts phosphatidylinositol 4,5-bisphosphate (PI(4,5)P2) to phosphatidylinositol 3,4,5-triphosphate (PIP3), which recruits Akt and phosphoinositide-dependent kinase-1 (PDK1) to the plasma membrane and, then, PDK1 phosphorylates and activates Akt [34]. Aberrant Akt signaling is observed in many cancers and may induce upregulation of mTOR, which controls protein synthesis in response to growth factor signaling and nutrient availability [35]. Activation of the PI3K/AKT pathway is also associated with deactivation of glycogen synthetase kinase 3B (GSK3B), resulting in upregulation of cyclin D1 and increased cell proliferation (Fig. 5.2).

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Recent evidence suggests mTOR is also regulated by TNF-α and Wnt, both of which have critical roles in the development of many human neoplasias [35]. AMPactivated protein kinase (AMPK) responds to cellular AMP/ATP ratios and when AMP is high (or ATP is low) the tuberous sclerosis complex (TSC) is activated, which, in turn, inhibits mTOR signaling [36]. Insulin may also inhibit sex hormonebinding globulin (SHBG) production [37], leading to an increase in the amount of bioavailable steroid hormones (see Section 3 for further discussion).

2.2 Insulin-Like Growth Factor (IGF) The IGF system is complex involving multiple ligands (IGF-1, IGF-2), receptors (IGF-1R, IGF-2R), and binding proteins (IGFBP-1, 2, 3, 4, 5, 6), which are important for regulating normal tissue growth and regeneration. Although both IGF-1 and IGF-2 are involved in prenatal growth, only IGF-I appears to be relevant in postnatal growth [38]. IGF-1 may have a beneficial effect on glucose homeostasis, since IGF-1 can bind to insulin receptors, stimulating insulin-like actions and enhancing insulin sensitivity; however, IGF-1 has a low affinity for these insulin receptors and, therefore, may only act indirectly through suppression of growth hormone (GH), an insulin antagonist [39]. Circulating levels of IGF-1 are determined primarily by hepatic synthesis, which is regulated by nutrient status and GH [40] while IGF-1 levels in extrahepatic tissues are determined by a plethora of factors including GH, IGFBPs, and local levels of IGF-1 [41]. Approximately 80–90% of IGF-I is bound to IGFBP-3, the most abundant of the six IGFBP isoforms found in humans [42], thereby reducing its bioavailability. However, high circulating levels of insulin can decrease the production of IGFBP-1 and 2, resulting in a larger amount of bioavailable IGF-1 [19]. IGF-1 levels are typically measured in serum or plasma to estimate the amount of bioavailable IGF-1. Most, but not all, epidemiological studies suggest a nonlinear relationship between IGF-1 and body fatness, as measured by BMI and waist circumference. Lean subjects (BMI < 25 kg/m2 ) exhibit positive associations between IGF-I levels and body fatness while overweight and obese subjects (BMI > 25 kg/m2 ) show inverse associations between IGF-1 levels and body fatness [44]. The divergence observed may be due, in part, to differences in laboratory methods, the use of plasma versus serum, the presence of type II diabetes [45], decreased circulating levels of GH with increased levels of body fatness [44], or current use of hormone replacement therapy [46]. Associations between levels of IGFBP1 and IGFBP-3 and body fatness are also inconsistent across studies; however, most suggest an inverse association between IGF-binding proteins and body fatness and a positive association between the ratio of IGF-1 to IGFBPs (IGF-1:IGFBP-3; IGF-1:IGFBP-1) and body fatness [44, 46]. Associations between circulating levels of IGF-1 and IGF-binding proteins vary across different cancer types. A recent meta-analysis involving 21 studies (3,609 cases and 7,137 controls) showed that higher IGF-1 levels are associated with an

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increased risk of premenopausal breast and prostate cancers [47]. Increased circulating concentrations of IGF-1 and acromegaly, a condition arising from sustained hypersecretion of GH and elevated IGF-1, have also been associated with increased risk of colonic neoplasia [48]. Although IGF-binding proteins are generally believed to be inversely associated with increased cancer risk, the studies, even within the same cancer type, are too divergent to draw any general conclusions [49]. The level of bioavailable IGF-1 is believed to be a critical factor in carcinogenesis because IGF-1 binding to its receptor (IGF-1R) induces a variety of actions, such as mitogenesis, anti-apoptosis, and pro-angiogenesis, which favor tumor growth. As shown in Fig. 5.2, the signaling cascades induced by IGF-1 binding to IGF-R1, a transmembrane tyrosine kinase receptor, are similar to those discussed for insulin and include PI3K and extracellular signal-regulated kinase (ERK) mitogen-activated protein kinase (MAPK) pathways [50]. In vitro studies have consistently shown that IGF-1 enhances the growth of cancer cells in various cancer cell lines [51] and IGF-R1 has been found to be overexpressed in many tumors [52]. IGF-1 may also act indirectly through interactions with the tumor suppressor gene, p53 [53, 54]. Furthermore, IGF-I may also promote migration of cancer cells through the activation of IGF-1R [38], which may be important in tumor metastases. On the other hand, the binding of IGF-1 to IGFBP-3 can invoke apoptosis through interactions with caspase-7 and caspase-8 [43]. Recent evidence suggests cross talk between IGF-1 and other factors potentially involved in obesity-related carcinogenesis including leptin, estrogen, and prolactin which may synergize the processes associated with neoplastic progression in human cancer cells, particularly breast cancer cells [55–57]. In particular, cross talk between IGF-1 and estrogen receptor (ER) signaling pathways (discussed further in Section 3) results in synergistic growth, whereby estrogen enhances IGF signaling by inducing expression of IGF-R1 and downstream signaling molecules (IRS-1, IRS-2), which, in turn, result in enhanced tyrosine phosphorylation of IRS-1 followed by enhanced MAPK activation [58].

3 Sex Steroid Hormones Steroid hormones are lipids characterized by a carbon skeleton with four fused rings and a variety of functional side groups. Corticosteroids are synthesized primarily by the adrenal glands and endogenous sex steroid hormones including estrogens (estrone (E1), estradiol (E2)), androgens (testosterone, androstenedione, dihydrotestosterone (DHT), dehydroepiandrosterone (DHEA)), and progestogens (progesterone), which are responsible for the development of secondary sex characteristics and are synthesized primarily in gonadal tissues. In females, estrogen and progesterone are made primarily in the ovary (and in the placenta during pregnancy). Estrogens promote the development of female sex characteristics as well as help regulate the menstrual cycle by thickening the endometrium. Progesterone converts the endometrium to its secretory stage (primarily to prepare

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the uterus for implantation) and, therefore, levels are relatively low during the pre-ovulatory phase of the menstrual cycle, rise after ovulation, and remain elevated during the luteal phase. Estrogen and progestin production by the ovaries are regulated by hypothalamic production of follicle-stimulating hormone (FSH) and luteinizing hormone (LH). In males, androgens function to support sperm production and development of testes, which are the predominant organ synthesizing testosterone. Higher levels of androgens may be responsible for the increased skeletal muscle mass observed in males [59] compared to females. Estrogens and androgens also play a role in the development and regulation of metabolic-related systems including the hypothalamus–pituitary axis (HPA), bone metabolism, and cardiovascular systems [60]. Although estrogens and progestogens are generally considered ‘female sex hormones’ and androgens considered ‘male sex hormones,’ both types are present in each gender at differing levels. Sex steroid hormones are ultimately synthesized from cholesterol and, notably, estrogen can be synthesized from testosterone via aromatase enzymes as shown in Fig. 5.1. This peripheral estrogen synthesis may be an important mechanism in postmenopausal women, particularly those who are obese, since excess central adiposity has been associated with increased levels of aromatase in postmenopausal but not premenopausal women [61]. Furthermore, obesity has been associated with higher levels of testosterone in postmenopausal women [62]. In contrast, in men, obesity as measured by BMI and waist circumference has been associated with lower levels of total and free testosterone [63, 64]. Levels of sex hormone-binding globulin (SHBG), which can bind and deactivate bioavailable estrogens and androgens (implications of which are discussed further below), are also affected by levels of adiposity with obese individuals (BMI > 30.0 kg/m2 ) having as much as 50% less SHBG than thin individuals (BMI < 22.0 kg/m2 ) [65, 66]. Weight loss has also been associated with lower levels of estrone and total and bioavailable estradiol in obese breast cancer survivors [67]. Not surprisingly, sex steroid hormones have been associated with cancers that are considered ‘hormone’ dependent including breast, uterine, and ovarian. A pooled analysis of nine prospective cohort studies showed that higher levels of circulating estrogens (estrone, estradiol) and androgens (testosterone, androstenedione, DHEA) and lower levels of SHBG were associated with an increased risk of postmenopausal breast cancer, and this risk may be driven predominantly by the parallel increase in circulating estradiol with increased BMI [62]. Similarly, higher levels of testosterone and androstenedione were associated with increased premenopausal breast cancer risk in the European Prospective Investigation into Cancer and Nutrition (EPIC) study but they found no association with SHBG [68]. Interestingly, higher levels of androgens (androstenedione, testosterone) have been associated with increased endometrial cancer risk in both pre- and postmenopausal women, but increased levels of estrogens (estradiol, estrone) only appear to increase postmenopausal endometrial cancer risk [69]. The relationship between sex steroid hormones and prostate cancer is more complex. Epidemiological studies of associations between circulating levels of

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androgens and prostate cancer have been inconsistent and a recent pooled analysis of 18 prospective studies (3,886 cases and 6,438 healthy controls) showed no association between serum androgen levels and prostate cancer risk [70]. However, Hsing et al. [71] argue that circulating levels of testosterone do not adequately reflect androgen action in the prostate and that dihydrotestosterone (DHT) and the androgen receptor (AR) have key roles in prostate carcinogenesis. Furthermore, obese men appear to have a differential risk for prostate cancer depending on the aggressiveness of the disease – with a decreased risk observed in men with low-grade prostate cancer and an increased risk in men with high-grade disease [72], which may be driven by lower serum levels of testosterone and SHBG in obese compared to normal weight men as discussed above. Mechanistically, sex steroid hormones could have multiple functions in obesityrelated carcinogenesis. First and foremost, when estrogen binds to its receptor (ER), it activates nuclear and transcriptional processes, regulating intracellular signaling pathways such as MAPK, which may stimulate initiated cancer cells in the Go /G1 resting phase to progress through the G1 –S phase and complete cell division, leading to tumor progression. In addition, estrogens may also be involved in cancer initiation because their metabolism may produce multiple forms of DNA damage, which, if not repaired, could lead to DNA mutation(s). Specifically, aromatase (CYP19) metabolizes testosterone to estradiol (E2) (Fig. 5.1) and androstendione to estrone (E1), which can be further metabolized to E2 by CYP17-β HSD; thus, enhanced expression of aromatase from excess adipose tissue (obesity) could lead to higher levels of unbound estradiol. The E2 may then be further metabolized by CYP1A1 and CYP1B1 to 2-OH-E2 and 4-OH-E2, respectively, and, if these metabolites, particularly 4-OH-E2, are not methylated and detoxified by COMT, they may generate E2-quinones, which could directly bind to DNA (creating bulky DNA adducts). Otherwise, the E2-quinones could enter into a futile redox cycle, generating substantial quantities of reactive oxygen species (ROS) that could cause oxidative DNA base lesions (see Section 8; Table 5.1). Estrogens may also interact with the IGF system (see Section 2) and inhibit apoptosis, at least in the endometrium [19]. Furthermore, progesterone levels, which would normally diminish the proliferative actions of estrogens in the endometrium by stimulating the breakdown of estradiol and inducing the synthesis of IGFBP-1 [19, 69], become altered during menopause transitioning (perimenopause) and during chronic anovulation, which may occur as a result of polycystic ovary syndrome (PCOS) [69], yielding variable periods of ‘unopposed estrogen.’ The prevailing hypothesis of ‘unopposed estrogen’ is that unbound, biologically active estrogen leads to increased mitogenesis of endometrial tissue, thus increasing endometrial cancer risk [61]. Interestingly, a recent study using urinary biomarkers of estrogen and progesterone concluded that the total number of days of ‘unopposed estrogen’ exposure may be much greater than previously appreciated and that variation in the amount of time spent in the menopause transition may be an important risk factor for ovarian, breast, and endometrial cancers [73]. However, additional cohort studies evaluating estrogen and progesterone urinary biomarkers in obese women as

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they transition through menopause may lend additional insight to the risk associated with longer bouts of ‘unopposed estrogen.’ The mechanistic role for androgens in hormonal cancers is complex. Androgens may either stimulate or inhibit proliferation of normal epithelial and cancer mammary cells [74]. The underlying mechanisms driving these opposing actions are not well understood but may depend, in part, on the availability of estrogens. In the absence of estrogens, androgens stimulate breast cancer cell growth via binding to ER-α but, in the presence of estrogens, androgens act as anti-estrogens by inhibiting growth of breast cancer cells via the androgen receptor [74]. The ‘androgen hypothesis’ for ovarian cancer, which speculates that androgens stimulate epithelial cell proliferation, was initially proposed by Risch in 1998 [75]; however, direct evidence to support this hypothesis is lacking [76]. Furthermore, although androgen ablation has been the primary non-surgical treatment for prostate cancer, many prostate tumors eventually become refractory to this treatment, which could involve the differential proliferative, apoptotic, and angiogenic events following androgen activation of its receptor [77]. Other factors that may contribute to enhanced androgen receptor (AR) signaling in an androgen-depleted environment include mutations in the AR that allow activation by other steroid hormones such as progesterone and estrogens [78]. Nevertheless, the presence of elevated levels of both estrogens and androgens may increase the risk of breast, endometrial, and prostate cancers, since treating rodents with both estrogens and androgens appears to have a synergistic effect on uterine and mammary cancers in female mice [79] and prostatic hyperplasia in male rats [80]. There is also evidence for cross talk between sex steroid hormones and other putative mechanisms in obesity-related carcinogenesis. For example, cross talk between estrogen and IGF-1 appears to enhance the transcriptional activation of the ER leading to even higher levels of cellular proliferation than either acting alone, and the function of ER-α may be required to maintain IGF signaling [57]. Estrogen and insulin may act synergistically to promote cell cycle progression through the differential regulation of c-Myc and cyclin D1, which are downstream targets of Wnt signaling [81]. There is also evidence for cross talk between the androgen receptor and cytokines (e.g., IL-6) and growth factors (e.g., IGF-1R) [78], which may enhance AR activation of AKT and MAPK signaling pathways and exacerbate tumor progression.

4 Adipokines and Gut Hormones Polypeptide hormones derived from adipocytes are known as ‘adipokines’ and over 50 different adipokines have been identified [82]. The most abundant and wellstudied adipokines with a potential role in obesity-related carcinogenesis include leptin, adiponectin, resistin, and visfatin, which we discuss in further detail in this section. We also discuss ghrelin, which is not secreted by adipoytes but by the stomach mucosa, in this section (cytokines and chemokines are discussed in Section 5).

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4.1 Leptin Leptin is a 16-kDa protein hormone that is secreted primarily by white adipocytes in a proportion nearly linear to the level of adipose tissue [83]. Leptin plays a key role in regulating energy balance by binding to its receptors, which are members of the cytokine family of transmembrane receptors, located in the hypothalamus. Although at least six leptin receptor isoforms exist primarily as a result of mRNA alternative splicing, only the long form (OB-Rb), which has an intracellular domain, appears to be involved in hypothalamic regulation of energy systems in humans [84]. Leptin acts by inhibiting the activity of orexigenic pathway neurons such as neuropeptide Y (NPY) and agouti-related peptide (AGRP) and, acts by increasing the activity of anorexigenic pathway neurons such as cocaine- and amphetamine-regulated transcript (CART) and proopiomelanocortin (POMC), leading to elevated levels of α-melanocyte-stimulating hormone (α-MSH) that, in turn, decrease appetite by binding to melanocortin receptors [85]. Thus, as adiposity increases, compensatory levels of leptin should rise to decrease dietary intake (via orexogenic pathways) and enhance energy expenditure (via anorexigenic pathways). However, leptin may be involved in more than just regulating energy systems as it appears to play a role in the development and maintenance of reproductive tissues [86] and in innate and adaptive immune responses [87]. Leptin has also been shown to be expressed in colorectal and mammary epithelial tissues [88]. Although leptin was not formally discovered until 1994 by Friedman and colleagues [89], the effects of a deficiency in leptin were observed four decades earlier in a colony of mice that developed obesity through a random mutation, which was designated by the symbol ‘ob’ [90]. Specifically, they observed that mice homozygous for this mutation (ob/ob) had a rapid increase in weight, which became noticeable at 4–6 weeks of age, and grew to about four times the weight of normal animals [90]. In humans, mutations in the leptin (LEP) and leptin receptor (LEPR) genes contribute to severe obesity and other rare metabolic disorders (see Chapter 4). Epidemiological studies have shown that obese individuals have higher circulating levels of leptin compared to normal weight individuals and may become insensitive (resistant) to the action of leptin [91], much like individuals with type 2 diabetes become resistant to insulin action. Furthermore, some research has shown that circulating leptin levels may be higher in women than in men [92], which may be attributed, in part, to higher levels of subcutaneous fat in females compared to males [93, 94]. The association between circulating levels of leptin and cancer is inconsistent across cancer types. A recent review indicated that only 3 out of 10 studies reported a positive association between increased leptin levels and increased breast cancer risk [95]. The potential relationship between leptin and endometrial cancer has not been well studied but there is some evidence that higher serum leptin levels are associated with increased risk of endometrial cancer and endometrial hyperplasia (EH) compared to levels found in women with a normal endometrium [96]. Higher circulating

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levels of leptin have also been associated with increased prostate cancer risk, particularly more aggressive prostate cancer [97], but not all studies have observed this effect [98]. Several studies have observed an association between higher leptin levels and increased risk of colon cancer [99, 100] and colorectal adenomas [101, 102]; however, these associations were only found to be statistically significant in men and not women [99, 101]. Why leptin may play a more important role in men compared to women in manifesting certain cancers is not well understood. Some researchers have suggested that heterogeneity in endogenous sex steroid hormones and certain LEPR genotypes may be partially responsible for the observed sex differences in the relationship between obesity and colorectal adenoma risk [101]. However, leptin concentrations appear to be higher in subcutaneous compared to visceral fat [106] and, as mentioned briefly above, women have a higher proportion of subcutaneous fat than men [93, 94]. Moreover, leptin has been shown to upregulate the transcription of aromatase [107] and estrogen may be involved in stimulating leptin secretion [108]. Thus, one might expect leptin to have a greater role in women, particularly obese postmenopausal women, and not men. Nevertheless, as indicated in Fig. 5.1, leptin has been shown to exert mitogenic, anti-apoptotic, and angiogenic effects in several cell lines [95, 109, 110, 111] and, therefore, may be involved in tumor growth and metastasis. Mechanistic studies have also shown that human colon cancer cell lines treated with leptin produce higher numbers of cells, an effect which is most likely mediated through the PI3K pathway [103], and that preneoplastic colon epithelial cells treated with leptin orchestrate VEGF-driven angiogenesis [104]. Higher serum leptin levels have also been associated with an increased risk of Barrett s esophagus, a precursor condition to esophageal adenocarcinoma, but this association was only statistically significant among men and not women [105]. Leptin’s role in breast cancer may be unique in that it may also depend on the specific leptin receptor isoform that leptin binds to, which may invoke a different signaling pathway. For example, ER-positive MCF-7 and T47D cell lines express substantial quantities of the long form of the leptin receptor (LRb) but ER-negative breast cancer cell lines express only the short form. This, in turn, may lead to differences in the cell cycle signaling pathway activated since LRb can activate both STAT and MAPK, whereas the short form of the leptin receptor primarily activates only MAPK (Fig. 5.2) [95]. Interestingly, studies conducted with A-Zip/F-1 ‘fatless’ mice, which have no white adipose tissue or detectable serum levels of leptin or adiponectin but display accelerated tumor formation, suggest that adipokines may not play an integral role in the enhancement of tumor development [112]. Furthermore, the MMTV-Wnt-1 transplantable breast cancer model showed a lower incidence of breast cancer and generated smaller tumors in ob/ob leptin-deficient mice compared to wild-type control mice (Reizes O, Hursting SD, 2009 Failure of breast cancer progression in leptin deficient mice, Personal Communication), which supports an important role for leptin in breast tumor development.

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4.2 Adiponectin Adiponectin is a 30-kDa protein hormone produced by mature adipocytes, which is abundant in the serum at a concentration ranging between 1 to 15 μg/ml. Adiponectin has a complex assembly involving a low molecular weight (LMW) trimer, a middle molecular weight (MMW) hexamer, and a high molecular weight (HMW) multimer, with the LMW oligomers being the most predominant form found in the circulation and the HMW oligomers representing the most abundant intracellular form [113]. As shown in Fig. 5.2, adiponectin is involved in the regulation of a number of metabolic processes including glucose and fatty acid catabolism and may be an important insulin-sensitizing agent since adiponectin replacement experimentally is able to diminish insulin resistance [114]. Adiponectin (ADIPO) acts by binding to its receptors ADIPO-R1 and ADIPO-R2, which are most highly expressed in the skeletal muscle and liver, respectively [115]. This binding leads to the activation of AMP protein kinase (AMPK) pathway, subsequent inhibition of acetyl CoA carboxylase and an increase in fatty acid β-oxidation [116]. Increased glucose utilization and fatty acid oxidation in skeletal muscle together with inhibition of liver gluconeogenesis may result in increased insulin sensitivity [117]. Adiponectin levels correlate negatively with obesity. Specifically, lower levels of adiponectin have been associated with increased waist circumference, visceral fat, and obesity in both adults and children [116, 118]. Furthermore, weight loss appears to significantly increase circulating levels of adiponectin in obese women [119]. However, adiponectin levels were not associated with a change in weight over 6 years in a cohort of older (50–77 years of age at baseline), non-diabetic men and women [120]. Future studies would benefit from researchers clearly identifying which oligomeric form(s) of adiponectin are being measured, particularly since the HMW form may be a better marker of ectopic fat [113]. The epidemiological evidence for an inverse association between adiponectin levels and cancer risk has been fairly consistent across cancer types. For example, lower concentrations of adiponectin have been associated with increased risk of endometrial cancer in premenopausal and postmenopausal women [121] and with an increased risk of endometrial cancer and endometrial hyperplasia in obese women [122]. Lower adiponectin levels have been fairly consistently associated with an increased risk of breast cancer in postmenopausal but not premenopausal women [123, 124]. Although there are far fewer reports, lower adiponectin levels have been observed to increase colorectal cancer in men [125] and may have prognostic significance in non-metastatic disease [126]. The inverse association between adiponectin and colon cancer is further supported by mechanistic studies which demonstrate that adiponectin deficiency induced by azoxymethane enhances colorectal carcinogenesis and liver tumor formation in mice [127]. Lower adiponectin levels have been associated with prostate cancer and may be substantially lower in more aggressive or advanced forms of the disease [128, 129]. A protective role for adiponectin in carcinogenesis is not well developed but may involve upregulation of the peroxisome proliferator-activated receptor alpha

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(PPAR-α) pathway, which controls cellular proliferation and differentiation, including adipocyte differentiation [117]. Tumor inhibition may also involve inactivation of the MAPK pathway and/or anti-angiogenesis via caspase-mediated apoptosis of endothelial cells [130]. Adiponectin also appears to interact with inflammatoryrelated factors secreted by adipocytes including TNF-α and IL-6 (see Section 5). TNF-α and IL-6 can decrease adiponectin expression and adiponectin may be able to counteract the pro-inflammatory effects of TNF-α and IL-6 [131, 132]. Since adiponectin is an insulin-sensitizing agent, it may also act indirectly through insulin-related carcinogenic mechanisms (see Section 2). However, adiponectin’s putative role in carcinogenesis has recently been challenged by researchers who have observed accelerated tumor formation in A-Zip/F-1 ‘fatless’ mice despite the fact that these mice have no detectable levels of adiponectin (or leptin) [112].

4.3 Resistin Resistin is a 12.5-kDa polypeptide hormone produced by adipocytes in rodents and immunocompetent cells in humans [133]. The physiological role for resistin in humans remains controversial. Although initially thought to provide a link between inflammation and insulin resistance, resistin now appears to have a greater role in inflammatory-related processes rather than in insulin sensitivity [134]. In particular, resistin has been shown to increase transcriptional events leading to higher expression of pro-inflammatory cytokines including TNF-α and IL-6 [135]. Although resistin is not secreted by adipocytes in humans, serum levels of resistin have been shown to be elevated in obese compared to lean subjects [136]. In addition, resistin levels have been positively correlated with the homeostasis model assessment ratio (HOMA-R), a marker for insulin resistance as determined by fasting insulin and glucose levels, in some [137] but not all [136] studies. However, circulating resistin levels have not been associated with type II diabetes risk after adjustment for BMI [138]. Interestingly, resistin levels appear to differ by gender with significantly higher levels observed in women compared to men [137, 138]. The potential association between circulating resistin levels and various cancer types has not been well explored and studies published thus far have been in fairly small sample sizes. One study in Korean women reported that higher serum resistin levels were associated with breast cancer and that resistin levels increased with tumor grade [139]. Additional support for resistin’s potential role in breast cancer comes from a study in Chinese women whereby serum levels of resistin were found to be significantly higher in breast cancer patients versus healthy controls and in patients with lymph node metastasis (LNM) compared to those without LNM [140]. Higher resistin levels have been observed in colon cancer patients compared to healthy controls but levels were not significantly different between patients with colon cancer and those with colon adenomas [102]. Serum resistin levels did not differ significantly in prostate cancer patients compared to men with benign prostate hyperplasia [141].

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The potential mechanisms for resistin in cancer are not well understood but may involve inflammatory (see Section 5) and angiogenic pathways. mRNA expression of resistin has been shown to be induced by TNF-α and IL-6, and insulin sensitizers with anti-inflammatory properties, including a synthetic PPARγ agonist (rosiglitazone), as well as aspirin, have been shown to suppress resistin expression in macrophages [135]. Furthermore, resistin induces human endothelial cell proliferation and migration and, upregulates expression of vascular endothelial growth factor receptors (VEGFR-1, VEGFR-2) and matrix metalloproteinases (MMP-1, MMP-2) at both the mRNA and protein levels [142], suggesting a potential role for resistin in tumor progression and metastasis.

4.4 Visfatin Visfatin occurs in both intracellular and extracellular forms with some serum visfatin appearing at 50 kDa but most is found at 100 kDa, suggesting that it primarily circulates in multimeric forms. Visfatin is a relatively new member of the adipokine family, which is believed to be secreted by visceral fat and to mimic the effects of insulin [143]. Visfatin was first identified as pre-B cell colony-enhancing factor (PBEF) based on its ability to work in association with IL-7 and as a stem cell factor to increase pre-B cell colony-forming activity [144, 145]. Visfatin has additional pro-inflammatory activities including stimulation of cytokines IL-6 and IL-8 in cultured amniotic cells. Visfatin is elevated in obese children, adolescents, and women with polycystic ovary syndrome and in patients with type 2 diabetes mellitus [144–146]. Increased visfatin has also been correlated with increased HDL, cholesterol, leptin, and IL-6 [144–146]. Serum visfatin is increased in patients with inflammatory bowel disease and sepsis. Visfatin has also been implicated in inducing vascular endothelial growth factor [144–146], and elevated levels have been observed in patients with colorectal cancer [147, 148]. Visfatin stimulates glucose uptake and, although initially thought to bind to and activate the insulin receptor, this has not been substantiated [149]. Nevertheless, visfatin is associated with phosphorylation of several components of the insulin signaling pathway including the insulin receptor, IRS-1, and IRS-2, as well as with glucose utilization in selected cell types [145]. Both visfatin and PBEF are identical to the circulating, extracellular form of the enzyme, nicotinamide phosporibosyltransferase (Nampt), which converts nicotinic acid to nicotinamide mononucleotide (NMN), the rate-limiting step for enzymatic synthesis of NAD, a critical cofactor for oxidation–reduction processes, DNA repair process, and many other signaling processes [139]. Therefore, it is probable that many of the observed functions for visfatin are related to a regulatory function determined by effects on NMN or NAD levels. For example, the Nampt-mediated synthesis of NAD content could serve to regulate glucose-stimulated insulin secretion which could contribute to its systemic effect on regulation of glucose levels. Interestingly, NMN biosynthesis and

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NAD levels have been implicated in regulation of the NAD-dependent deacetylase, SIRT1, which modulates circadian rhythm [150] and may contribute to sleep disturbances, an important determinant of obesity [144, 149].

4.5 Ghrelin Ghrelin, a 28-amino acid peptide which is acylated at the serine 3 position with an octanoyl group from the ghrelin gene, is predominantly secreted from the stomach mucosa. Ghrelin acts by binding to its type 1a growth hormone secretagogue receptor (GHSR1a) to release growth hormone and acts as an orexigenic signal from the gut to the brain to stimulate appetite by activating NPY and AGRP neurons in the arcuate nucleus of the hypothamalus [151]. During fasting, ghrelin levels rise and fall to a nadir within 1 h of eating [151]. In addition to the stomach and hypothalamus, ghrelin has been shown to be expressed in several other tissues including the small and large intestines, pancreas, and testes [151] and may be involved in functions regulating tissue growth and development. Non-acylated forms of the ghrelin gene product such as obestatin, a 23-amino acid peptide, appear to have opposite physiological actions (e.g., induce satiety/decrease food intake); however, the receptors for non-acylated forms and their mechanisms of action have not been well established [152] and are not discussed further here. In contrast to leptin, serum levels of ghrelin are generally inversely associated with BMI and body fat and may differ by gender. Serum levels of ghrelin appear to be higher in women compared to men, particularly during the follicular phase of menstruation [151]. Studies have also shown that fasting serum ghrelin concentrations are significantly lower in overweight and obese compared to normal weight (BMI < 25 kg/m2 ) adults [153]. This evidence, however, does not support overt dysregulation of ghrelin in obesity and, therefore, evaluating changes in fasting ghrelin levels with weight loss (or gain) may be more informative. In contrast to studies involving gastric banding, which have shown no change in ghrelin levels with weight loss (median weight loss of 45.7%) [154], ghrelin levels have been observed to decrease significantly after substantial weight loss (mean weight loss of 62.5%) following Roux-en-Y gastric bypass surgery [155]. This suggests drastic physical change in the gastrointestinal tract may be required to induce ghrelin-related appetite suppression. Reports evaluating serum levels of ghrelin in cancer patients compared to healthy controls are scant. One study reported that serum levels of ghrelin were not significantly different in prostate cancer patients compared to men with benign prostatic hyperplasia [156]. Plasma ghrelin levels were reported to be higher among breast and colon cancer patients with cachexia, which is a complex state characterized by the loss of muscle mass and adipose tissue together with anorexia, compared to noncachectic patients [157]. Putative mechanisms for ghrelin in cancer are lacking. However, ghrelin and its receptors have been observed to be upregulated in breast, endometrial, prostate

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and pancreatic cancer tissue and/or cell lines, and these increases in ghrelin have been shown to increase cancer cell proliferation [151, 158]. Thus, ghrelin could play a role in tumor progression but additional studies are clearly needed to better understand its role in carcinogenesis.

5 Inflammatory Factors: Cytokines and Chemokines Cytokines are a group of diverse polypeptides secreted by multiple cell types, which are involved in a variety of cell signaling, particularly in inflammatory and immunological processes. The work of Hotamisligil and colleagues [159] conducted over 15 years ago, which first described elevated cytokine (tumor necrosis factor alpha (TNF-α)) levels in diet-induced obese rodents, has helped pave the way for a paradigm shift in the way adipose tissue is viewed. Instead of adipose tissue being thought of as just a site of energy storage, it is viewed as a biologically active endocrine organ. It is now widely accepted that obesity induces a state of chronic, low-grade inflammation resulting in the increase of several inflammatoryrelated molecules such as TNF-α, interleukin-6 (IL-6), C-reactive protein (CRP), and monocyte chemoattractant protein-1 (MCP-1). It is also widely accepted that chronic inflammation is associated with processes that contribute to the onset and/or progression of several types of cancer including colorectal [160], prostate [161], and esophageal [162]. Therefore, it is quite plausible that the chronic inflammation is a mediator of the relationship between obesity and carcinogenesis. In the sections that follow, we present the most well-studied cytokines and chemokines released by adipose tissue and discuss their putative role(s) in the carcinogenic process. However, it is important to note that secretion of a substance from adipose tissue does not necessarily imply the substance is synthesized by adipocytes. Recent evidence shows that obese individuals have an increased infiltration of macrophages into their adipose tissue and that these macrophages, which are mononuclear phagocytes whose primary function is to provide defense against invading foreign organisms, may be the primary source of the inflammatory cytokines [163]. However, it is not clear what actually causes the increased macrophage infiltration. Hypothesized processes responsible for macrophage invasion include altered adipocyte size and adipokine signaling, nutritional induction of metabolic endotoxemia or reduced angiogenesis, and local adipose cell hypoxia [163]. MCP-1 (discussed further below) has also been shown to contribute to macrophage infiltration into adipose tissue [164].

5.1 Tumor Necrosis Factor Alpha (TNF-α) The cytokine TNF-α is produced primarily as a 212-amino acid type II transmembrane protein, which forms a 51-kDa soluble homotrimeric cytokine (sTNF) and a 17-kDa protomer. TNF-α is produced mainly by macrophages and is secreted

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in response to lipopolysaccharide (a component of gram-negative bacteria) and interleukin-1 (IL-1). TNF-α has two receptors, TNF-R1 and TNF-R2. Although TNF-R1 is expressed in most tissues and can be fully activated by both the membrane-bound and soluble form, TNF-R2 is found only in immune system cells and can activate only the membrane-bound form. The binding of TNF-α to TNFR1 leads to the binding of TNF-RSF1A-associated death domain (TRADD) and the subsequent activation of nuclear factor kappa B (NF-κB), MAPK, or caspase-8 signaling cascades, which induce transcription of proteins involved in inflammatory response, cell proliferation, cell differentiation, and apoptosis (discussed further below). TNF-α is expressed in adipocytes but whether or not it is secreted independent of the presence of macrophages in human white adipose tissue has been debated [165]. TNF-α expression in white adipose tissue was first identified in obese rodents, where TNF-α expression was higher and shown to regulate insulin action in ob/ob mice [159]. Obese mice lacking TNF-α or its receptor were found to be protected against developing insulin resistance [166]. TNF-α appears to have a role in human adipocytes as a regulator of the synthesis and release of other cytokines (e.g., IL-6) as well as a regulator of apoptosis [165]. TNF-α may also alter adipocyte-related energy metabolism by inhibiting lipoprotein lipase, stimulating hormone-sensitive lipase, inducing uncoupling protein expression, and downregulating insulin-stimulated glucose uptake [167]. The extent to which TNF-α produced by human adipocytes is released into the circulation has also been a matter of debate; however, several studies have shown that TNF-α levels are higher in obese compared to normal weight subjects [85, 168]. Furthermore, TNF-α has been shown to decrease substantially with weight loss in obese subjects [169, 170]. Epidemiological studies examining associations between circulating levels of TNF-α and cancer outcomes are relatively scant, perhaps, because understanding the specific source of its production is much more informative than circulating levels which may arise due to infection, tissue damage, or various disease states. Nevertheless, the Health Aging and Body Composition cohort study of older adults (70–79 years) found that higher baseline plasma levels of TNF-α were more strongly associated with death from any cancer than incidence of any cancer at an average follow-up of 5.5 years but, site-specific analyses revealed no association between TNF-α levels with colon, breast, or prostate cancer [171]. Although no other studies examining TNF-α on colorectal cancer have been reported, higher levels of TNF-α have recently been associated with increased colorectal adenomas [172]. Moreover, significantly higher levels of serum concentrations of TNF-α have been observed in metastatic compared to localized prostate cancer [173], lending more evidence for the prognostic value of this marker. However, serum concentrations of TNF-α were not found to be significantly different in a small sample of overweight and obese breast cancer patients compared to BMI-matched controls [174]. Although TNF-α is toxic to tumor cells at high doses [175], physiological levels may be involved in several mechanisms linking obesity to carcinogenesis. First, exposure to TNF-α promotes insulin resistance by activating

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pro-inflammatory pathways that modify glucose uptake in adipocytes (and myocytes) and by impairing insulin signaling at the level of the insulin receptor substrate (IRS) proteins mainly through the expression of protein tyrosine phosphatase (PTP)1B [176]. Furthermore, it has been hypothesized that the absence or inhibition of PTP1B in insulin target tissues could confer protection against insulin resistance induced by TNF-α [176]. As mentioned above, the binding of TNF-α to TNF-R1 induces a series of intracellular events that results in the activation of NF-κB, phosphatidylinositol 3-kinase (PI3K)/Akt, and c-Jun NH(2)terminal kinase (JNK), which can inhibit cell apoptosis [177]. However, JNK may induce pro- or anti-apoptotic functions, depending on the cell type, nature of the death stimulus, duration of its activation, and the activity of other signaling pathways [178]. In particular, peroxisome proliferator-activated receptors (PPAR) may antagonize the activities of NF-κB. In addition, TNF-α modulates the synthesis of other enzymes in the adipose milieu including IL-6 [179] and estrogen through the stimulation of aromatase [180], which suggests a role for TNF-α in obesity-related carcinogenesis, particularly among postmenopausal women.

5.2 Interleukin-6 (IL-6) Interleukin-6 (IL-6), which is also known as interferon-beta 2, is a 26-kDa protein with a myriad of functions. IL-6 is secreted by macrophages to stimulate B-cell immune response and by osteoblasts to stimulate osteoclast formation. IL-6 is also known as a ‘myokine,’ a cytokine produced from muscle, which increases in response to muscle contraction. In muscle and adipose tissue, IL-6 stimulates energy mobilization leading to increased body temperature. IL-6 binding to its receptor (IL-6R) activates a signaling cascade through Janus kinases (JAK) and signal transducers and activators of transcription (STAT) factors. Although IL-6 has receptors in the hypothalamus of mice, suggesting it has a direct role in the regulation of energy homeostasis, no IL-6 receptors in the human hypothalamus have been identified [181, 182]. A soluble form of the receptor (sIL-6R), however, has been identified in human serum, and IL-6 binding to these soluble receptors appears to yield neuronal interaction. Circulating levels of IL-6 have been shown to be elevated in obese compared to normal weight subjects [165]. Fasting plasma IL-6 concentrations have also been positively correlated with percent body fat [183]. Moreover, weight loss results in a significantly decreased levels of IL-6 [172, 173]. Circulating levels of IL-6 have been associated with several cancers but, as with TNF-α, the association seems to be stronger in predicting cancer mortality than cancer incidence. For example, although higher baseline plasma levels of IL-6 were associated with death from any cancer, they were not associated with incident colon, breast, or prostate cancer [171]. Furthermore, significantly higher levels of serum concentrations of IL-6 have been observed in metastatic compared to localized prostate [173] and breast [184] cancers; and, may have prognostic value, particularly in predicting breast cancer survival [185]. IL-6 levels have been shown to

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be higher in a small sample of overweight and obese breast cancer patients compared to BMI-matched controls [174]. Higher plasma levels of IL-6 have also been associated with the risk of colorectal adenomas [172]. IL-6 is known to stimulate cell growth and inhibit apoptosis [186]; however, IL-6 may have tumor-promoting or tumor-inhibitory effects depending on the presence of other modulating factors. For example, IL-6 expression induced by treatment with leptin has been reported to promote cell proliferation in an Apc Min/+ colon epithelial cell line [187] and, thus, may play more of a role in the progression of mutated cells. IL-6 expression is induced by hypoxia and subsequently able to upregulate vascular endothelial growth factor (VEGF) transcription, suggesting a potential role for IL-6 as an angiogenic factor that facilitates the production and the distribution of VEGF to metastatic sites [185]. Furthermore, because IL-6 promotes osteoclast formation and inhibits dendritic cell proliferation, it may play a role in metastatic growth [188].

5.3 C-Reactive Protein (CRP) C-reactive protein (CRP) is a 117.5-kDa protein released by hepatocytes and adipocytes. Because it is released in response to inflammation, high-sensitivity CRP (hs-CRP) often serves as a non-specific prognostic marker of systemic inflammation for cardiovascular disease with levels >3 mg/L suggesting elevated risk for myocardial infarction and stroke [189]. Higher levels of CRP have been associated with obesity in adults, irrespective of ethnicity [190]. Furthermore, CRP levels decreased significantly among obese women who lost, on average, 10% of their body weight over 12 months [119]. Similar associations showing decreased levels of CRP with 10% or more weight loss were also observed in an earlier study of obese premenopausal women [191]. Plasma CRP concentrations have been shown to be significantly higher in colorectal cancer patients in several prospective studies [192]; however, this association may be driven by those who developed colon and not rectal cancer [193]. Plasma concentrations of CRP were not associated with prostate cancer risk in two case– control studies [194, 195] or in four prospective studies [196]. CRP levels were not significantly associated with clinical characteristics in a cohort of breast cancer survivors at 31 months following diagnosis [197]. Authors of a review evaluating CRP in various cancers concluded that the limited number of prospective studies conducted provided no strong evidence for a causal role of CRP in cancer [196]. However, additional prospective studies are needed to determine if CRP has prognostic value in colon, prostate, and breast carcinogenesis. The role for CRP in obesity-related carcinogenesis is likely mediated through chronic inflammation (Fig. 5.1). CRP upregulates the expression of adhesion molecules and stimulates the release of IL-6 and TNF-α [198], which suggests CRP may activate mechanisms similar to those previously described for IL-6 and TNF-α.

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5.4 Monocyte Chemotactic Protein-1 (MCP-1) Monocyte chemotactic protein-1 (MCP-1), also known as the chemokine C–C motif (CCL2), is a low molecular weight 13-kDa protein. Although its main function is chemotaxis to monocytic cells and recruitment and activation of leukocytes during inflammation, MCP-1 is also produced by smooth muscle cells, bone (osteoblasts, osteoclasts, bone marrow), and endothelial cells. MCP-1 and its receptor, chemokine receptor 2 (CCR2), have been shown to be upregulated in obese subjects (Fig. 5.1) [199]. Visceral adipose tissue, which tends to accumulate around the organs in the abdominal region, has been shown to have higher numbers of macrophages and to express more MCP-1 compared to subcutaneous fat tissue in obese individuals [200], which may contribute to the increased secretion of inflammatory factors from adipose tissue. Higher serum levels of MCP-1 have been associated with less advanced disease in breast cancer patients [201]. In contrast, higher levels of MCP-1 have been associated with advanced stage prostate cancer [202]. MCP-1 appears to be actively involved in the carcinogenic process as MCP-1 and its receptor, CCR2, are upregulated in prostate, breast, and other cancer cells [202]. MCP-1 may be involved in modifying obesity-related carcinogenesis via insulin resistance mechanisms as MCP-1 can lead to the inhibition of serine phosphorylation of IRS-1 and impaired uptake of glucose in human skeletal muscle [163] and possibly other tissues. Interestingly, knockout of either MCP1 in db/db mice [164] or the MCP1 receptor (CCR2) in C57BL/6 J mice fed a high-fat diet [203] attenuated obesity and afforded protection against insulin resistance. MCP-1 has also been shown to play a role in tumor-induced osteoclastogenesis and bone resorption in prostate cancer [202] and may, therefore, play a role in metastasis of other tissue tumors.

6 Angiogenic Factors 6.1 Vascular Endothelial Growth Factor (VEGF) Vascular endothelial growth factor (VEGF) represents a family of growth factors that regulate angiogenesis, which is the growth of blood vessels from pre-existing vasculature. Angiogenesis is critical for nutrient and gas supply to all tissues and, therefore, an important requirement of tumor growth and metastasis. VEGF-A is the most well-studied isoform, which acts as a vasodilator increasing microvascular permeability and stimulates monocyte migration. VEGF is produced in many tissues with high levels in the prostate gland [204]. VEGF-A can bind to receptors (primarily VEGFR-2) stimulating a tyrosine kinase signaling pathway that leads to angiogenesis. Under conditions of low oxygen or ‘hypoxia,’ hypoxia-inducible factor (HIF-1) stimulates the release of VEGF (Fig. 5.2).

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Circulating levels of VEGF have been observed to be higher with increasing BMI in obese and overweight compared to lean subjects [205]. Serum VEGF-A levels were also found to be significantly higher in morbidly obese patients undergoing bariatric surgery compared to lean individuals with levels being significantly lower after weight loss 9–12 months after surgery [206]. Although several smaller clinical studies have shown that VEGF levels are associated with more advanced prostate cancer, pre-diagnostic plasma VEGF levels were not associated with development of prostate cancer in a larger nested case– control study derived from the Physicians’ Health Study cohort [204]. Studies evaluating VEGF levels and breast cancer risk have also been a bit conflicting with some studies reporting that higher levels are associated with more advanced disease [207] and others finding no association [208]. Similar patterns of association with more advanced disease have also been observed in colorectal cancer; however, the association may depend on the use of serum versus plasma, with serum levels being a better predictor of angiogenic tumor activity and colorectal cancer survival [209]. The formation of new blood vessels is essential for tumor growth and, therefore, VEGF may play an intuitive role in the progression of cancer; however, its role in obesity-related carcinogenesis is, perhaps, less obvious. Leptin, which is secreted by adipocytes (see Section 3), has been shown to induce preneoplastic colon epithelial cells to VEGF-driven angiogenesis, which provides a specific mechanism for obesity-associated colon cancer [103]. Cross talk with other obesity-related factors has been reported, whereby dietary restriction in rats reduced autocrine/paracrine IGF-1 expression, which contributed to reduced VEGF expression and signaling and inhibition of prostate tumor angiogenesis [210].

7 Fatty Acid Metabolism: Eicosanoids and Prostaglandins Eicosanoids are signaling molecules synthesized through the oxygenation of 20 carbon omega-6 (ω-6) or omega-3 (ω-3) fatty acids that regulate inflammation and immunity. There are four families of eicosanoids (prostaglandins, prostacyclins, thromboxanes, leukotrienes), which are characterized by the attachment of differing functional groups at distinct locations [211]. Prostaglandin synthesis is initiated by the metabolism of arachidonic acid, an ω-6 fatty acid with 20 carbons and 4 double bonds in cis (ω-6 20:4), by cyclooxygenase enzymes (COX-1, COX-2), which generates the formation of the five-member prostane ring, involving carbons 8–12, from the arachidonic acid molecule. As discussed in more detail below, another pathway of eicosanoid metabolism, independent of COX enzymes, is through free radical catalysis of arachidonic acid resulting in isoprostanes. Lipid peroxidation is necessary for initiation of COX activity [212], which leads to the formation of the prostaglandin precursor, PGH2 . Then, PGH2 generates series-2 prostanoids, most notably, PGE2 . PGE2 is involved in the downregulation of lipolysis in adipocytes

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and adipocyte differentiation. Moreover, PGE2 binding to its protein-coupled receptor, EP2, activates the PI3K/AKT pathway, while its binding to EP4 phosphorylates the epidermal growth factor receptor (EGFR) leading to cellular proliferation and anti-apoptosis [213]. PGE2 can be degraded by 15-prostaglandin dehydrogenase (15-PGDH) to downregulate these pathways [213]. Interestingly, knockout mice with a homozygous deletion of 15-PGDH have an increased susceptibility to colon cancer [214]. Higher PGE2 levels have been associated with higher BMI [215] but large-scale epidemiological studies evaluating PGE2 levels in obesity are lacking. Explants grown in tissue culture from a small study of obese humans have shown increased formation of PGE2 compared to adipocytes derived from normal individuals [216], lending some evidence, albeit indirect, that higher PGE2 levels may be secreted by adipocytes in obese subjects. PGE2 levels have been examined in tissue samples extracted from cancer patients. Higher PGE2 levels have been observed in colon tumors compared to normal colonic mucosa [217, 218]. Elevated PGE2 levels have also been found in lung cancer [219, 220]. However, PGE2 levels have not been consistently associated with prostate or breast cancers [221]. Mechanistically, PGE2 levels have been shown to increase the growth and motility of colon cancer cells, interfere with apoptosis, stimulate angiogenesis [213, 222], and stimulate production of IL-6 [223]. Furthermore, non-steroidal antiinflammatory drugs (NSAIDs), which act by inhibiting COX-1 and COX-2, have been shown to substantially lower the risk of colon cancer [213, 224]. However, the use of COX inhibitors has been limited by the relatively recent observation that their use leads to increased adverse cardiovascular events. Interestingly, PGE2 has been shown to stimulate estrogen biosynthesis and a strong linear association between aromatase and COX-1 and COX-2 expression has been observed in breast cancer specimens, which may occur via increases in intracellular cyclic AMP levels [225], suggesting multiple potential roles for PGE2 in breast cancer.

8 Reactive Oxygen Species and Oxidative Stress The term ‘oxidative stress’ is used to define disruption of redox homeostasis or, more specifically, an imbalance in the rate at which the intracellular content of free radicals increases relative to the rate at which the cell scavenges free radicals. Free radicals are generated from many endogenous processes as well as through the metabolism of exogenous agents and include reactive oxygen species (ROS) and reactive nitrogen oxide species (RNOS). In terms of ROS production, the mitochondrial electron transport chain is a major endogenous source of the superoxide anion (O2 –˙), which results from electron leakage between complex I and III [226]. As mentioned in Section 3, the superoxide anion can also be generated from the futile redox cycling of E2 quinones, which may be formed during the metabolism of endogenous estrogen. Similarly, many common

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lipophilic exogenous chemicals (e.g., polycyclic aromatic hydrocarbons (PAH), which are found in cigarette smoke, fuel exhaust, and grilled meats) can generate large quantities of the superoxide anion by futile redox cycling of their quinones [227]. The membrane-associated reduced nicotinamide adenine dinucleotide phosphate (NAD(P)H) oxidase, cytochrome c, and xanthine oxidase complexes can also generate ROS. Nitric oxide synthase (NOS) isozymes (mitochondrial (mtNOS), endothelial (eNOS), inducible (iNOS)) can generate nitric oxide (NO),˙ a RNOS [228]. Notably, the superoxide anion (O2 –˙) can react with nitric oxide (NO)˙ to form the highly reactive RNOS, peroxynitrite (ONOO– ) [229]. If free radicals are not neutralized, they have the capacity to attack proteins, lipids, and nucleic acids (DNA), which may modulate their structure and function and lead to dysregulated mRNA expression and DNA mutations. Therefore, biological systems have developed several lines of defense against ROS and RNOS. In the first line of defense, the superoxide anion and hydrogen peroxide can be scavenged non-enzymatically by vitamin C and E, respectively [230]. O2 –˙ can be metabolized by superoxide dismutase (SOD) to yield hydrogen peroxide (H2 O2 ) and O2 , and, then, the H2 O2 can be neutralized by catalase (CAT) to yield H2 O and O2 [231]. H2 O2 can also be neutralized to O2 through coupled reactions involving catalysis by glutathione peroxidase (GPX) in the presence of reduced glutathione (GSH) [232]. On the other hand, H2 O2 may be converted to the highly reactive hydroxyl radical, HO,˙ in the presence of iron (Fe(II) and Fe(III)) through the Fenton reaction or via the Haber–Weiss reaction [233]. Macrophages, which may invade adipose tissue in obese individuals, may also generate hydrogen peroxide (H2 O2 ). Because free radicals are generally short lived, direct measurement is not feasible and surrogate measures are used in an attempt to quantify oxidative stress levels. The markers fall into oxidized proteins, nucleic acids (DNA), and lipids categories; and, while some represent the final end product of damage, others represent interim products, which can result in further (secondary) effects as discussed below.

8.1 Biomarkers of Oxidative Protein Damage Oxidized proteins are often functionally inactive; however, cells are generally very successful in removing oxidized proteins through proteolysis [234]. Protein carbonyl levels have been used as a biomarker of protein oxidation but a variety of mechanisms can lead to protein carbonyls [234], which limits their usefulness as a marker of oxidative stress.

8.2 Biomarkers of Oxidative Lipid Damage The attack of free radicals on lipids results in lipid peroxidation. Polyunsaturated fatty acids (PUFAs) with two or more double bonds are subject to greater oxidation from free radicals than saturated and monounsaturated fatty acids because of

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instability in the hydrogen atom adjacent to the double bond [235]. Oxidized lowdensity lipoprotein cholesterol (Ox-LDL) is a commonly used biomarker of lipid peroxidation, which has been reported to be positively correlated with abdominal visceral adipose tissue in males [236]. Higher circulating Ox-LDL levels have also been associated with increased waist circumference in both men and women independent of body mass index [237]. Furthermore, circulating Ox-LDL levels have been shown to decrease after weight loss in morbidly obese patients following laproscopic banding surgery [238]. Studies measuring Ox-LDL in cancer patients are limited. In one small study, Ox-LDL levels were observed to be higher among breast and ovarian cancer patients compared to healthy control subjects [239]. When comparing the highest quartile to the lowest quartile, serum Ox-LDL levels were found to be associated with increased colorectal cancer risk in a Japanese cohort [240]. Lipid peroxyl radicals can produce aldehydes such as malondialdehyde (MDA), which can cause DNA damage. Increased levels of malondialdehyde (MDA) have been reported in tumor tissue from colorectal cancer patients compared with normal mucosa from the same subjects [217], which point toward an initiating versus a promoting mechanism. Furthermore, oxidized LDL has been shown to downregulate base excision repair (BER) activity in extracts of mouse monocytes [241], which could potentially amplify oxidative DNA damage since BER is the primary repair mechanism for oxidative DNA damage.

8.3 Biomarkers of Oxidative Nucleic Acid (DNA) Damage By inducing hydroxylation of the C-8 position of 2 -deoxyguanosine, ROS can create DNA damage (Fig. 5.1) in the form of the oxidative DNA base lesion, 8oxo-deoxyguanosine (8-oxo-dG). Although 20 oxidative base lesions have been identified, 8-oxo-dG is the most abundant DNA lesion caused by ROS and it is highly mutagenic [242]. 8-Oxo-dG is a reliable marker of oxidative DNA damage in tissue or urine [242]. RNOS (NO ˙and ONOO–) have also been observed to attack DNA bases and induce single-strand breaks [243]. Although levels of 8-oxo-dG have not been well studied in obesity, urinary 8oxo-dG excretion has been observed to be positively correlated with BMI in subjects with obstructive sleep apnea who were either overweight or obese [244]. Another study reported 8-oxo-dG levels were associated with body fat and higher in muscle obtained from self-reported weight gainers ( >5 kg) compared with weight maintainers (≤ 4 kg) in a small sample of hernia patients [245]. Cancer cells generally have higher levels of ROS and oxidative DNA lesions compared to normal cells [246]. In particular, human colon and breast cancer cells have increased steady-state levels of ROS (O2 –˙; H2 O2 ) relative to their respective normal cells [247]. Increased levels of 8-oxo-dG have also been observed in various tumors [248]. 8-Oxo-dG levels were significantly reduced in postmenopausal breast cancer survivors with the highest quartile of plasma carotenoids compared to those

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with the lowest quartile [249]. An association between urinary 8-oxo-dG levels and breast cancer risk was observed in another study but only after cases that underwent radiation treatment were removed from the analysis [250]. The increased ROS levels in cancer cells may result from endogenous and exogenous sources discussed above or from increased metabolic activity, mitochondrial malfunction, or infiltration of the tumor by inflammatory cells [246]. Regardless of how they are generated, ROS can attack and damage DNA and, therefore, may play a role in cancer cell initiation if the DNA base lesions induced are not repaired properly by DNA repair systems. Although base excision repair (BER) is the primary mechanism for 8-oxo-dG repair, nucleotide excision repair (NER), which is a more complex process that typically repairs larger lesions, may also be involved in repairing oxidative DNA lesions. Furthermore, since the mitochondrial electron transport chain is a major source of ROS generation, the vulnerability of the mitochondrial DNA (mtDNA) to ROS-induced damage may amplify oxidative stress in cancer cells; however, studies supporting the level of ROS-induced mtDNA damage in obesity are lacking. In addition to direct mutagenic effects, ROS may also play a role in tumor progression. For example, ROS are a necessary component of the signal transduction mechanisms by which many growth factors and cytokines activate the PI3K pathway and elicit their cellular responses [251]. Although a low to moderate level of ROS can activate the PI3K signaling pathway and promote cell survival (Fig. 5.2), severe or chronic oxidative stress levels may inhibit the PI3K pathway leading to apoptosis [251]. ROS also regulate autophagy (Fig. 5.2), a self-digestion process that degrades intracellular structures in response to stress, through several distinct mechanisms involving catalase and the mitochondrial electron transport chain [252]. Interestingly, ROS-regulated autophagy can be blocked by mTOR under nutrient-rich conditions [246]. Increased oxidative stress may also result in dysregulation of mRNA expression in mice and tissue culture. Treatment of 3T3-L1 adipocytes with H2 O2 was found to downregulate mRNA expression of adiponectin and PPARγ and upregulate mRNA expression of plasminogen activator inhibitor-1 (PAI-1), IL-6 and MCP-1, suggesting that ROS can affect multiple factors, albeit indirectly, involved in obesity and the Metabolic Syndrome (discussed in Section 9). Anti-oxidants may also be particularly important in obesity-related carcinogenesis because obesity may create ‘malnubesity,’ a condition whereby substantially lower levels of micronutrients are available relative to the level of macronutrients, leading to inefficiencies in oxidative energy metabolism [253]. Decreased concentrations of plasma anti-oxidants such as vitamin E, a known ROS scavenger, have been correlated with obesity [254]. Moreover, decreased enzymatic activity of anti-oxidants such as SOD and CAT have been reported in obese compared to non-obese individuals [255] and in some cancer cells compared to non-cancer cells [246]. Futhermore, anti-oxidant deficiencies may alter DNA repair mechanisms which are highly dependent on micronutrients. Interestingly, co-treatment of mouse monocytes with oxidized LDL and anti-oxidants (vitamins C and E) prevented the downregulation of BER [241].

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8.4 Biomarkers of Systemic Oxidative Stress: F2-Isoprostanes Isoprostanes are prostaglandin-like bioactive compounds that are synthesized independent of cyclooxygenases (COX) through free-radical catalysis of arachidonic acid in a multi-step process involving the removal of a labile hydrogen, addition of oxygen, formation of peroxy radicals, and resulting in endoperoxide intermediates designated as either 5-, 8-, 12-, or 15-series isomers based on the carbon atom to which the side chain hydroxyl group is positioned [235]. Isoprostanes are cleaved from phospholipids by phospholipases and, then, circulate in plasma with subsequent excretion in urine. Due to its chemical stability, the urinary metabolite 8-iso-prostaglandin F2α (8-iso-PGF2α) has been identified as a reliable index of in vivo oxidative stress and ensuing lipid peroxidation [256]. Increased urinary levels of 8-iso-PGF2α have been observed in obese compared to non-obese women, particularly in those with android (defined as a BMI > 28 kg/m2 and a waist-to-hip ratio (WHR) ≥ 0.86) compared to gynoid (defined as a BMI > 28 kg/m2 and a WHR < 0.86) obesity [257]. Obese men were found to have significantly higher plasma concentrations of 8-iso-PGF2α than non-obese men [258]. Increased urinary isoprostanes have also been associated with increased visceral adipose tissue [259] and a 5 kg/m2 increase in BMI has been associated with a 9.9% increase in urinary isoprostane levels [260]. Studies examining 8-iso-PGF2α levels in cancer patients are limited. Increased isoprostanes have been associated with increased breast cancer using immunoassay [250] and mass spectrometry [261] techniques. This association was even greater among women with higher BMI values [261]. Interestingly, 8-iso-PGF2α levels were found to be significantly decreased in breast cancer patients who lost weight after participation in a 12-month low-fat (15% of energy from fat) dietary intervention program [262]. However, urinary 8-iso-PGF2α levels were not found to be significantly different in a small study of prostate cancer cases compared to controls [263]. Although mechanistic studies are lacking, 8-iso-PGF2α levels have been observed to be significantly higher in estradiol-induced mammary tumors than levels in control mammary tissue from age-matched rats leading to proliferative changes in the breast tissue after only 7 days, with the first palpable tumor appearing 128 days after estradiol exposure, suggesting that the oxidative changes occurred prior to tumor development [264].

9 Metabolic Syndrome The Metabolic Syndrome, also known as the insulin resistance syndrome and syndrome ‘X,’ is characterized by multiple core traits including insulin resistance, impaired glucose tolerance, obesity (central body), hypertension, and dyslipidemias (Fig. 5.1). Recent research suggests that the Metabolic Syndrome may also consist of additional factors including a prothrombotic state characterized by high PAI-1

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and a pro-inflammatory state characterized by elevated C-reactive protein [265, 266]. Disturbed sleep has also been identified as a component of the Metabolic Syndrome, prompting the term syndrome ‘Z’ [267]. The Metabolic Syndrome has been associated with diseases such as diabetes, cardiovascular disease, and non-alcoholic fatty liver disease as well as certain cancers [268, 269]. Patients with the Metabolic Syndrome have a higher risk of colon adenomas and carcinomas [270] and pancreatic cancer [271]. Furthermore, women with type 2 diabetes have a 16% higher risk of developing breast cancer compared to nondiabetics, and this risk is greatest in postmenopausal women [268]. Although there is limited direct evidence for the role of the Metabolic Syndrome in breast cancer, the relation is supported indirectly by the multiple and consistent associations observed between individual components of the syndrome (insulin resistance, dyslipidemia, abdominal obesity) and increased postmenopausal breast cancer risk [269, 271]. Studies in prostate cancer have produced equivocal findings, perhaps, due to the opposing associations observed between BMI and less advanced (decreased risk) compared to more advanced (increased risk) prostate cancer [269]. The role of the Metabolic Syndrome in cancer clearly depends on a multitude of genetic and environmental factors. When mice with the Apc Min/+ mutation are fed a high-fat diet promoting obesity and the Metabolic Syndrome, they show more rapid progression of intestinal polyps than mice without the Metabolic Syndrome (Doerner S, Nadeau JH, 2009, Diet dependent growth of intestinal polyps in Apc Min/+ mice, Unpublished Work). The same mice without the Apc Min/+ mutation, that are susceptible to diet-induced obesity and Metabolic Syndrome, go on to develop NAFLD and then hepatocellular carcinoma (HCC) at even later ages [272]. The importance of understanding the role of the Metabolic Syndrome in cancer stems from the fact that this syndrome involves several factors previously discussed that have been shown to independently affect tumor growth (see Sections 2, 3, 4, and 5). However, these factors need to be considered in aggregate, since interruption of any one component, such as blocking the cellular growth stimulated by insulin or IGF, is unlikely to completely block cancer promotion by other factors involved in the Metabolic Syndrome. On the other hand, it may be possible to simultaneously eliminate multiple individual risk factors for cancer by ablating the Metabolic Syndrome through weight loss induced by diet/caloric restriction and exercise and, perhaps, where necessary, bariatric surgery. Interestingly, recent studies have shown that bariatric surgery for obesity not only improves multiple metabolic abnormalities but also reduces the incidence of cancer and cancer-related deaths [273–275] see Section 11.

10 Dietary Intake of Carcinogens and DNA Damage Although the concept has not been well explored, it is plausible that excess dietary intake of carcinogens may occur commensurate with obesity and may be compounded by ‘malnubesity’ (decreased micronutrient relative to macronutrient level)

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leading to increased risk of carcinogenesis. For example, increased dietary intake of pan-fried and grilled fish, chicken, and red meats can increase levels of exposure to carcinogenic xenobiotic agents such as heterocyclic amines (HCAs) and polycyclic aromatic hydrocarbons (PAHs). Furthermore, the lipophilic nature of these compounds may lead to bioaccumulation in body fat, which may be later released during periods of stress or weight loss, which may increase risk in ‘yo-yo’ dieters. In this section, we discuss the most mutagenic and commonly studied dietary carcinogens with a potential role in obesity-related carcinogenesis.

10.1 Heterocyclic Amines (HCAs) The most well-studied and abundant HCAs are 2-amino-3,8-dimethylimidazo[4,5f]quinoxaline (MeIQx) and 2-amino-1-methyl-6-phenylimidazo[4,5-b]pyridine (PhIP), with PhIP being the most abundant. Bioactivation of HCAs including PhIP to carcinogenic species is initiated by N-oxidation of the compound, which is catalyzed by cytochrome P4501A2 and subsequent acetylation or sulfation of the N-hydroxy–PhIP is catalyzed by N-acetyltransferases (NAT) or sulfotransferases (SULT), which generate N-acetoxy- or N-sulfonyloxy–PhIP electrophilic compounds that bind covalently to DNA to form PhIP adducts [276]. PhIP–DNA adducts and other bulky adducts formed from HCAs can lead to mutations if they are not repaired by NER processes. Investigations between HCA levels and body composition, particularly body fat, are limited. A single study in a Swedish cohort examined HCA and BMI levels and found that increased dietary intake of HCA levels was associated with increased BMI, where obese women and men had a significantly higher risk of being in the highest HCA quintile compared to average weight women and men [277]. Many studies have used meat intake as a surrogate measure of HCA intake level. A meta-analysis involving 15 cohort studies (7,367 cases) found that an increase of 120 g/day of red meat was associated with a 28% increased risk of colorectal cancer [278]. Interestingly, a recent study observed a 47% increased risk of colorectal adenomas when comparing the highest to the lowest quartile of dietary intake of PhIP, but no statistically significant association was observed for MeIQx [279]. A study conducted in the Agricultural Health Study cohort recently reported that intake of well or very well done meat was associated with a 1.26-fold increased risk of incident prostate cancer and a 1.97-fold increased risk of advanced disease when the highest tertile was compared with the lowest; however, they found no statistically significant association with estimated levels of individual HCAs including PhIP and MeIQx [280]. In addition, PhIP–DNA adducts in men undergoing radical prostatectomy for prostate cancer treatment who had increased grilled red meat consumption had higher PhIP-DNA adduct levels in prostate tumor but not adjacent non-tumor prostate cells [276]. In a recent review between dietary factors and breast cancer risk, one cohort study observed a positive association between HCA levels and breast cancer [281].

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PhIP has been shown to increase mutation frequency and tumor incidence in animal models [280]. PhIP-DNA adducts have been found in prostate and other organ cells [276]. Moreover, the mutations induced by these DNA adducts can occur in genes controlling key homeostatic mechanisms such as tumor suppression (e.g., p53) or tumor promotion (e.g., KRAS), potentially leading to accelerated tumor growth.

10.2 Polycyclic Aromatic Hydrocarbons (PAHs) Polycyclic aromatic hydrocarbons (PAHs) are large aromatic planar compounds that comprise a class of over 200 chemicals with three or more benzene rings. The most notable PAH is benzo(a)pyrene (b(a)p), which is classified as a known human carcinogen. PAH are metabolized by the cytochrome P450 enzyme system into reactive electrophiles, which may be detoxified and made water soluble through conjugation with glutathione or glucuronic acid; however, if these reactive electrophilic PAH species are not detoxified they may bind to DNA and form PAH–DNA adducts [282]. If these bulky adducts are not repaired by NER systems, they may cause mutations in the DNA. Human exposure to PAHs can come from many sources including cigarette smoking, fuel combustion/diesel exhaust, and our diet through the consumption primarily of cooked meats, but appreciable levels have also been found in grains and leafy vegetables [283]. Similar to HCAs, dietary intake of PAHs can be estimated using food questionnaires together with a database that has quantified levels of PAHs, most notable b(a)p, in various foods [284]. Studies examining PAH levels and body composition are lacking. However, one study found that BMI was inversely associated with the presence of detectable PAH (b(a)p)–DNA adducts [285]. As mentioned above, a study conducted in the Agricultural Health Study cohort found that intake of well or very well done meat was associated with a 1.26-fold increased risk of incident prostate cancer and a 1.97-fold increased risk of advanced disease when the highest tertile was compared with the lowest. However, they found no statistically significant association with estimated levels of individual PAHs including b(a)p [280]. PAH–DNA adducts have been associated with prostate cancer cells and this association differed by race as well as variation in genes involved in PAH metabolism, conjugation, and repair [286]. In a pooled analysis (873 cases; 941 controls), detectable compared to non-detectable PAH–DNA adducts in peripheral mononuclear cells were found to be associated with increased breast cancer risk but no dose–response with quantile levels was observed [287]. A recent study observed that higher intake of b(a)p was associated with increased risk of colorectal adenomas [288]. PAH can induce PAH–DNA adducts that can lead to mutations, primarily G to T transversions, if they are not properly repaired by NER systems [282]. B[a]p

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has been shown to preferentially form adducts with DNA bases in the p53 tumor suppresser gene which are frequently mutated in cancer [282] and, therefore, may exacerbate cancer progression.

11 Therapeutic Opportunities: Diet, Exercise, and Pharmaceuticals Given that obesity is a causal risk factor for certain cancers, diet and exercise are potential modifiable factors that could prevent carcinogenesis. However, there is little in the way of randomized control trial evidence that weight loss can prevent cancer incidence or mortality other than that found in bariatric studies where weight reduction has been associated with decreased cancer mortality [270, 271].

11.1 Diet In animal models, restricting calories by 10–40% results in decreased cell proliferation and increased apoptosis [51]. As discussed within each of the previous sections, weight loss may modify many of the factors involved in obesity-related mechanisms. However, caloric restriction alone does not appear to be feasible for sustaining long-term weight loss in obese humans and strategies involving caloric restriction and physical activity (as well as possibly other psychosocial aspects) seem much more promising.

11.2 Exercise Exercise may alter several factors involved in the various mechanisms discussed in the preceding sections including insulin–IGF axis and AMPK pathways. Increased insulin action afforded by exercise is due to increased movement of glucose transporters (e.g., GLUT4 in skeletal muscle) to cell membranes. Other processes may involve the upregulation of enzymes responsible for the phosphorylation, storage, and oxidation of glucose as well as a greater capillary density and possibly conversion of fast-twitch glycolytic fibers (Type IIb) to fast-twitch oxidative fibers (Type IIa) [289], depending on the modality of training. Strength training in particular has been shown to reduce fasting insulin, fasting glucose, and IGF-I levels; however, no consistent change in IGFBP1 or IGFBP3 has been observed [290]. Exercise also activates AMPK, a fuel-sensing enzyme, in skeletal muscle and possibly adipose tissue and other organs in humans, which, in turn, stimulates energy-generating processes (e.g., glucose uptake and fatty acid oxidation) and decreases energy-consuming processes (e.g., protein and lipid synthesis) (Fig. 5.2) [291].

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The relationship between exercise and estrogen and androgen levels is not clear and may be affected by many factors including age, gender, body composition, exercise prescription, and pre-trial training status. However, most studies seem to show that exercise increases SHBG levels, which would decrease the availability of biologically active estrogens and androgens [66, 292]. In terms of cytokines and other factors secreted by adipocytes, aerobic exercise training studies have demonstrated reductions in hs-CRP concentrations ranging from 16% to 41%, an effect that may be independent of baseline levels of CRP, body composition, or weight loss [293]. Furthermore, IL-6, the first ‘myokine’ which is a cytokine produced and released by contracting skeletal muscle fibers, increases up to 100-fold in the circulation during physical exercise [294]. Regular exercise also induces anti-inflammatory effects by suppressing TNFα production, which together with changes in IL-6 may allow exercise to serve as a means to control the low-grade systemic inflammation [295, 296] associated with obesity. An acute bout of exercise has been shown to decrease plasma VEGF [297]; however, much additional research is needed to understand how obesity-related angiogenic factors are modified in humans as a result of chronic exercise. A transgenic mouse model containing the Apc Min/+ mutation and a lifelong enhanced exercise phenotype showed delayed development of tumors and prolonged survival compared to mice without the enhanced exercise phenotype. This may be attributed, in part, to lower levels of insulin, leptin, and IL-6 (Berger NA, Hanson R, Nadeau JH, 2009, Effect of PEPCK_Cmus transgenic mice on enhanced exercise and colon polyps in Apc Min/+ mice, Unpublished Work). Regular physical activity has been shown to decrease plasma levels of OxLDL [298]. Decreased oxidative damage to lipids as well as to DNA may occur as a result of the upregulation of GSH and SOD observed with chronic exercise [299]. Chronic exercise also increases the number and activity of natural killer (NK) and lymphokine-activated killer cells [300], which would enhance innate immunity and potentially reduce ensuing inflammation-mediated oxidative stress damage. Physical activity has been shown to decrease gut transit time, possibly through increased vagal tone and subsequent increased peristalsis [301, 302]. Decreased transit time would decrease contact time of colon mucosa with carcinogens as well as decrease potential exposure of carcinogens to other cells from reuptake through the portal vein. High levels of physical activity have been associated with decreased PGE2 levels and increased prostaglandin F2α levels, which may enhance gut motility [215, 303].

11.3 Pharmaceuticals Pharmacologic investigations targeting the obesity–cancer link focus on either promoting weight loss or disrupting the cell growth/tumor promotion process.

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Investigational drugs targeting obesity by catabolic (anorexigenic) pathways include leptin, agonists of melanocortin receptor-4, 5-HT and dopamine, beta-3 adrenergic receptor agonists, adiponectin derivatives, and glucagon-like peptide-1 and those targeting anabolic (orexigenic) pathways consist of the ghrelin receptor, neuropeptide Y receptor and melanin-concentrating hormone-1 antagonists, peroxisome proliferator-activated receptor-gamma and -beta/delta agonists, and cannabinoid-1 receptor antagonists [304]. Sibutramine is a serotonin and noradrenaline reuptake inhibitor, which has been studied in a large number of randomized, double-blind, placebo-controlled trials lasting up to 24 months or more. Sibutramine has been found to induce weight loss in a dose–response manner (e.g., weight losses of 1.2, 6.1 and 8.8% at 10, 20, and 30 mg doses, respectively); however, this drug has been shown to cause many adverse effects including increasing diastolic blood pressure and, thus, is not utilized in patients with hypertension [305]. Orlistat, which is a lipase inhibitor that works in the gastrointestinal tract to reduce the body’s absorption of fat, has been well studied and shown to decrease weight as well as LDL cholesterol, systolic and diastolic blood pressure, and fasting insulin and glucose; however, it reduces the absorption of fat-soluble vitamins such as A, D, E, and K, and beta carotene [305]. In terms of pharmaceuticals that aim to interrupt the cell growth/tumorpromoting processes, PPAR agonists appear effective in treating insulin resistance and in treating various types of cancers by inhibiting proliferation through arrest at the G1 phase of the cell cycle; however, their usefulness is limited since they increase the risk of myocardial infarction and death from cardiovascular diseases [306]. Antibodies to the IGF-1 receptor have been used to interfere with the mitogenic effects of IGF-1 in tissue culture [307] and against a variety of tumors in clinical trials [308, 309]. Metformin, used as an ‘insulin sensitizer’ for treatment of type 2 diabetes mellitus, functions as an activator of AMP kinase, which inhibits activity of the mTOR pathway, thereby, reducing tumor growth rates [308, 310]. Metformin has also been shown to reduce the incidence of cancer in patients with diabetes and is currently in clinical trials for cancer prevention and treatment [308]. Furthermore, in women receiving neoadjuvant chemotherapy for early stage breast cancer, the concomitant administration of metformin for treatment of type II diabetes mellitus was associated with a three-fold increase in pathologic response rate; however, metformin administration was not associated with prolongation of relapse-free survival [311, 312]. Although the use of metformin by these women during neoadjuvant chemotherapy produced some degree of weight loss, the anti-cancer effects of metformin are likely not driven by the modest, short-term weight loss. Other agents in various stages of preclinical and clinical evaluation and use targeting the PI3K–mTOR pathway include LY294002 and everolimus [313]. Everolimus (RAD001) is currently in clinical use for treatment of renal cell carcinoma, which has been associated with obesity [314]. Newer agents with dual PI3K/mTOR inhibitor activity which are now in preclinical development may be more effective and include PI-103 and NVP-BEZ235. PI-103 has been shown to have anti-tumor effects against chordomas [315] and NVP-BEZ235 has been shown to have anti-tumor effects against trastuzumab-resistant breast cancer [316].

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Acknowledgments Support for this work was derived in part from NIH Grants K07CA129162, to Nora Nock and U54 CA116867 and P30 CA043703 to Nathan Berger.

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Chapter 6

Caloric Restriction and Cancer Fei Xue and Karin B. Michels

Abstract In various animal models, caloric restriction is the most effective and reproducible intervention to extend life span and to reduce risks of aging-related chronic diseases, particularly cancer. Findings from human studies based on ecologic comparisons, the Norwegian famine during World War II, and patients with anorexia nervosa suggest that caloric restriction reduces cancer risk, especially the risk of breast cancer. In contrast, transient and abrupt caloric restriction with malnutrition followed by compensatory overnutrition may counter any protection conferred. Several earlier hypotheses for the biological mechanisms underlying the association between caloric restriction and longer life span and decreased cancer risk such as retarded growth and development, reduced metabolism rate, endocrinological changes, and decreased accumulation of oxidative damage were refuted by laboratory results. More recent findings suggest a hormesis hypothesis proposing that caloric restriction conveys a low-intensity biological stress on organisms, which may elicit an adaptive response of enhanced maintenance and repair. The identification of a new class of caloric restriction mimetic molecules that target the SIR2 family of longevity-promoting enzymes may provide a novel intervention for the prevention and treatment of cancer and other aging-related chronic diseases. Epigenetic mechanisms may also play a role.

1 Caloric Restriction Studies in Animals In 1935, McCay and colleagues published data suggesting that restricted diets after weaning may increase the mean and maximum life spans in laboratory rats [59]. In this study of 106 white rats, 34 rats were allowed all the feed desired and grew K.B. Michels (B) Obstetrics and Gynecology Epidemiology Center, Department of Obstetrics, Gynecology and Reproductive Biology, Brigham and Women’s Hospital, Harvard Medical School, 221 Longwood Avenue, Boston, MA 02115, USA; and Department of Epidemiology, Harvard School of Public Health, Boston, MA, USA e-mail: [email protected]


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normally, 36 were restricted in food intake to limit weight gain to 10 g every 2–3 months from the time of weaning, and 36 were allowed sufficient feed to permit normal growth for 2 weeks after weaning and then were restricted in food intake. Relative to the group on an ad libitum diet (483 ± 59 days), the two groups with restricted feeding were more likely to attain extreme ages, and the male rats on a restricted diet had a longer mean life span (820 ± 113 days) [59]. During the following 70 years, McCay’s findings have been confirmed in various animal species, ranging from non-mammalian species such as fish and flies to rodents and mice [97, 93, 62, 64, 65]. Evidence from additional studies suggested that the increased survival among rodents by food restriction was due to caloric restriction rather than restriction of specific nutrients (e.g., protein or fat or minerals) [63]. This extension of the life span of rodents was found to be attained along with retarded age-related pathology and diseases such as cancer, and it was proposed that caloric restriction increases survival by delaying these processes rather than altering aging and senescence [62]. While some studies suggest that caloric restriction preferentially affects physiological aging processes [61, 98, 52], a large body of laboratory studies indicate that caloric restriction can prevent pathology of chronic diseases, including cancer. The first observations on the effect of caloric restriction on cancer occurrence were made in 1909 by Moreschi and in 1914 by Rous; both studies demonstrated that animals on a lower calorie diet showed reduced growth of transplanted tumors than ad libitum-fed controls. Subsequently, Tannenbaum et al. showed that underfeeding inhibited the development of spontaneous or induced tumors in several different mouse strains [85]. Since the earlier studies of caloric restriction involved underfeeding of the same diet as that for controls, it seemed likely that deprivation of essential nutrients rather than caloric restriction was related to cancer growth. To address this question, Tannenbaum formulated a diet of known energy content and studied levels of energy present in various diets in relation to cancer risk [86] and found that at the same level of energy intake, a diet high in fat was related to higher tumor incidence [88]. This finding was later confirmed by Boutwell et al. [8]. In addition, Tannenbaum showed that caloric restriction was more effective in reducing cancer risk during the promotion rather than the initiation stage of tumorigenesis and caloric restriction during the initiation stage was ineffective if followed by an ad libitum diet [87]. Subsequently, data from decades of research among rodents consistently suggested that caloric restriction lowers the incidence of a variety of spontaneous as well as induced or transplanted tumors and that this effect is proportional to the extent of caloric restriction [49, 43, 74, 51]. A meta-analysis of 14 animal experiments reported a 55% reduction of the incidence of spontaneous mammary tumors associated with caloric restriction ranging from 23 to 40% in mice [18]. Kritchevsky et al. and Klurfeld et al. found that 40% caloric restriction effectively inhibited the growth of induced tumors among animals [49, 43]. In addition to the established tumor-inhibiting effect of 40% caloric restriction, it was found that caloric restriction of 10, 20, and 30% in 7,12-Dimethylbenz(a)anthracene (DMBA)-treated female Sprague-Dawley rats was related to a reduced tumor incidence or tumor multiplicity [44]. Though energy restriction in experimental animal studies can be achieved in

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several ways and sources of energy may include various nutrients such as fat, carbohydrates, and protein, the effect of caloric restriction on cancer risk was found to be independent of dietary components [2, 74, 44, 45, 51]. Klurfeld et al. reported a significantly reduced tumor incidence among DMBA-treated female rats fed a diet containing 20 or 26.7% fat under conditions of 25% energy restriction compared to DMBA-treated rats fed with diets containing 5, 15, or 20% fat ad libitum [45]. Compared with consistent results from short-lived mammals such as rodents, less data are available for non-human primates. Preliminary results suggest that caloric restriction may prevent or delay the onset and decrease the mortality due to several aging-related diseases, including cancer, among non-human primates (rhesus and squirrel monkeys) [79, 54]. In a large non-human primate study at the National Institute on Aging and another similar study from the University of Wisconsin, rhesus and squirrel monkeys with caloric restriction had substantially lower number and types of neoplasia than controls [54].

2 Caloric Restriction Studies in Humans 2.1 Ecologic Studies In an ecologic study, Armstrong and Doll assessed the correlation between total per capita caloric intake and cancer incidence and mortality based on concurrent food and cancer data from 33 countries [5]. The site and sex-specific correlation coefficient suggested a linear and statistically significant relation for several cancer sites: it was 0.57 for breast cancer, 0.66 for colon cancer, 0.56 for rectal cancer, 0.65 for endometrial cancer, and 0.64 for kidney cancer among women; among men the coefficient was 0.60 for colon cancer, 0.75 for rectal cancer, 0.55 for kidney cancer, and 0.56 for cancer of the nervous system [5]. Another similar study compared per capita food consumption with cancer mortality among 19 countries and reported a correlation coefficient between total calorie intake and cancer of 0.77 for breast cancer, 0.60 for prostate cancer, and 0.74 and 0.71 for male and female colon cancer, respectively [47]. Several cancer sites are more prevalent in developed countries than in developing countries (breast cancer, colon cancer, prostate cancer, pancreatic cancer, endometrial cancer, ovarian cancer, bladder cancer, kidney cancer, and leukemia) [1]; however, distributions are changing with the increase in obesity worldwide. Cancer incidence increases among migrants from low-incidence countries to high-incidence countries, suggesting the importance of environmental and lifestyle factors in cancer etiology [1].

2.2 Anorexia Nervosa Studies A human starvation study was conducted in the 1940s investigating the mechanism of famine edema, in which famine edema was produced experimentally in normal

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men who lost a quarter of their body weight while subsisting for 6 months on a European type of semi-starvation diet [40]. Though extracellular water to cellular tissue was roughly doubled among these study subjects, there were no signs of renal or cardiac failure, suggesting famine edema is not simply a result of hypoproteinemia or of renal or cardiac failure [40]. Caloric restriction as an intervention in humans is generally considered infeasible and unethical. Voluntary starvation found in patients who suffer from anorexia nervosa provides an opportunity to examine the effect of low caloric intake on cancer incidence. Anorexia nervosa is an eating disorder that occurs primarily during adolescence and early adulthood and is characterized by very low caloric intake, low intake of all micro- and macro nutrients, low body mass index, and amenorrhea. The relation of anorexia nervosa to subsequent cancer incidence has been assessed in two studies [69, 70] (Table 6.1). In a retrospective cohort of hospitalized anorexia nervosa patients identified from the population-based Danish Psychiatric Case Register, Mellemkjaer et al. reported a slight but not statistically significant reduction in the overall cancer incidence among women (standardized incidence ratio [SIR] = 0.80; 95% CI 0.52–1.18) as compared with that of the general population [69]. In a retrospective cohort study conducted in Sweden, women hospitalized for anorexia nervosa prior to age 40 identified from the Swedish inpatient registry had a 53% lower incidence of breast cancer later in life (SIR = 0.47; 95% CI 0.19–0.97) compared to the general population [70]. The reduced breast cancer incidence was more pronounced among parous women with anorexia nervosa (SIR=0.24, 95% CI 0.03–0.87) than among nulliparous women (SIR=0.77, 95% CI 0.25–1.79). In this study, overall cancer risk was not significantly reduced among women who had suffered from anorexia nervosa; however, parous women who had experienced anorexia tended to have a lower risk (SIR=0.66, 95% CI 0.38–1.07) [70]. While anorexia nervosa is generally associated with an extended period of very low caloric intake early in life, some affected patients also exercise excessively. Therefore, strenuous physical activity has to be considered as alternative or partial explanation for the inverse association between caloric intake and cancer incidence.

2.3 Famine Studies Observations made following famines allow a unique opportunity to investigate the influence of caloric restriction on cancer incidence. Individuals who experience time periods of famine generally have a substantially reduced caloric intake but may also experience malnutrition as they lack relevant amounts of micro- and macronutrients. In Norway, average caloric intake during World War II dropped from 3,475 kcal daily in 1939 to a minimum of 2,700 kcal in late 1944 and early 1945 [91, 90, 73, 77] (Table 6.2). Findings from studies based on the Norwegian famine data consistently suggested that caloric restriction during the war was associated with a reduced risk of breast cancer later in life, though the exposure to caloric restriction was not assessed at individual levels in these studies. In particular, the incidence of breast cancer was found to be lower than expected among women who experienced

Mellemkjaer, 2001, Denmark

Study (author, year, country)

Retrospective cohort

Design

Cancer of all sites

Outcome 25 cases of all cancer, 2,151 women and 186 men with anorexia nervosa (population)

No. of cases and controls or population Anorexia nervosa identified in the Psychiatric Case Registry and the National Registry of Patients

Exposure Observed number of cases in anorexia nervosa patients divided by the expected number of cases in general population

Exposure categories

Table 6.1 Epidemiologic studies on anorexia nervosa and risk of cancer

SIR: All malignant neoplasms: 0.8 (0.52–1.2) Cancer sites: Buccal cavity: 3.2 (0.1–17.4) Digestive organs: 1.3 (0.3–3.9) Lung: 2.2 (0.5–6.4) Breast: 0.8 (0.3–1.7) Female genital organs: 0.3 (0.0–1.2) Urinary system: 1.6 (0.0–8.7) Skin: 0.6 (0.2–1.5) Brain and nervous system: 0.6 (0.0–3.2) Thyroid gland: 1.8 (0.0–10.0) Non-Hodgkin’s lymphoma: 3.0 (0.4–10.8)

OR/HR/SIR (95%) and P for trend/interaction

Standardized by age, gender, and calendar time

Adjustment for covariates

6 Caloric Restriction and Cancer 185

Design

Retrospective cohort


Michels and Ekbom, 2004

Cancer of all sites and breast cancer

Outcome 52 cases of all cancer and 7 cases of breast cancer, 7,303 women with anorexia nervosa (population)

No. of cases and controls or population Hospitalization and treated for anorexia nervosa identified in the Swedish Inpatient Registry

Exposure

Table 6.1 (continued)

Observed number of cases in anorexia nervosa patients divided by the expected number of cases in general population

Exposure categories SIR: All cancer: Overall: 0.92 (0.69–1.21) Parous: 0.66 (0.38–1.07) Nulliparous: 1.12 (0.78–1.55) Breast cancer: 0.47 (0.19–1.97) Parous: 0.24 (0.03–0.87) Nulliparous: 0.77 (0.25–1.79)


Standardized by age and calendar time


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puberty during the war [90]. When residential history in areas with and without food production was used as the proxy to severity of exposure to caloric restriction, a lower incidence of breast cancer was found among women who resided in the non-food-producing compared to those in food-producing areas [77]. Furthermore, when height was assessed as a proxy of early nutritional status, height was more strongly associated with breast cancer incidence among women who were born or who went through peri-pubertal growth during the famine years [91, 73]. These results suggest the greater nutritional diversity in early life during famine years, as reflected by variations in height, would be of particular importance for women in their later risk of breast cancer. During the end of World War II, the densely populated Western parts of the Netherlands were cut off from food supply and daily rations per capita dropped from about 1,500 kcal to about 700 kcal between September 1944 and May 1945 with about 500 kcal at the height of the famine [21]. In the initial Dutch Famine Study “place of residence” during the famine was assessed as a proxy for individual exposure level; residents of the Western area who were exposed to famine had a higher risk of breast cancer than those who resided in unexposed areas [17]. In a subsequent study, in which the individual level of exposure to famine was assessed using a severity score derived from a questionnaire inquiring about hunger, cold, and weight loss, women severely exposed to famine had a higher risk of breast cancer (hazard ratio [HR]=1.48, 95% confidence interval [CI] 1.09–2.01) relative to unexposed women; the association was stronger for women exposed at ages 2–9 compared to women exposed at older ages (≥10 years) [21]. Overall cancer incidence among the Dutch famine population was 25% higher among women who experienced severe famine (95% CI 1–55) than the unexposed, but this increase was largely driven by the increase in breast cancer incidence and the association was attenuated after breast cancer was excluded (HR=1.12, 95% CI 0.87–1.43) [24]. The involuntary starvation of individuals during the famine and the voluntary starvation among individuals suffering from anorexia nervosa may have different physiologic effects. Food deprivation during the famine was relatively brief and spanned less than 1 year. It started and ended abruptly and was followed by availability of sufficient food. It is likely that the individuals exposed to famine compensated for the starvation by a feasting period. Indeed, individuals exposed to the Dutch famine had a higher body mass index (BMI) 25 years later than individuals who were not exposed, and the BMI was higher the more extreme the starvation had been: the average body mass index of subjects with severe exposure to famine (mean=26.1 kg/m2 ) was found to be higher than those with moderate exposure (mean=25.8 kg/m2 ) or no exposure (mean=25.7 kg/m2 ) [21]. Accordingly, exposure to famine was associated with elevated postmenopausal serum levels of insulin-like growth factor (IGF)-I [22, 23]. In contrast, women suffering from anorexia nervosa usually fast for extended periods of time, often years, and their deprivation is not following by overeating, but their caloric intake may approach normal ranges at best. This difference in behavior patterns may affect hormone levels differentially and explain the opposite association of exposure to the Dutch famine and to anorexia nervosa with breast cancer incidence.

Dirx, 1999, The Netherlands


Cohort

Design

Breast cancer

Outcome 1,009 cases, 62,573 (population)

No. of cases and controls or population Residence in the hunger winter (1944–1945) and war years (1940–1944) and fathers’ employment status (1932–1940) as indicators of caloric restriction

Exposure 1932–1940: Father had a job Father had no job 1940–1944: Rural area in 1942 City in 1942 1944–1945: Non-West Western rural area Western city

Exposure categories

Table 6.2 Epidemiologic studies on famine and risk of cancer

1.1 (0.9–1.4)

1.0 1.3 (1.0–1.7)

1.0 (0.8–1.2)

1.0

1.0 1.0 (0.7–1.3)


Age, age at menopause, parity, age at first birth, maternal breast cancer, breast cancer in sister(s), benign breast cancer disease, alcohol use, energy consumption, education, age at menarche, height



Nilsen and Vatten, 2001, Norway


Cohort

Design

Breast cancer

Outcome 215 cases, 25,204 (population)

No. of cases and controls or population Differences in achieved adult height as a reflection of differences in childhood nutrition

Exposure


Tertiles (cutoff=162 cm and 167 cm) of height in birth cohorts 1925–1929: T1 T2 T3 1930–1934: T1 T2 T3 1935–1940: T1 T2 T3 1940–1945: T1 T2 T3 ≥1946: T1 T2 T3

Exposure categories

1.0 1.1 (0.5–2.4) 1.0 (0.5–2.3)

1.0 1.1 (0.5–2.7) 2.5 (1.2–5.5)

1.0 0.8 (0.3–1.9) 1.3 (0.5–3.1)

1.0 0.9 (0.5–2.0) 0.9 (0.4–2.3)

1.0 0.9 (0.5–1.6) 0.5 (0.2–1.1)


Adjusted for age at study entry. Further adjustment for BMI, smoking, and physical activity did not change the results



Robsahm and Tretli, 2002, Norway


Cohort

Design

Breast cancer

Outcome 7,311 cases, 597,906 (population)

No. of cases and controls or population Residence in areas with and without food production according to the main income sources for the municipality in 1968

Exposure

Table 6.2 (continued) OR/HR/SIR (95%) and P for trend/interaction 1.0 1.17 (1.1–1.24)

Exposure categories Food No food

Age, age at first childbirth, education, occupational physical activity, birth cohort



Nested case– cohort

Nested case– cohort

Elias, 2005, The Netherlands

Design

Elias, 2004, The Netherlands


Cancer of all sites

Breast cancer

Outcome

718 cases of cancer of all sites, including 459 cases exclusive of breast cancer 2,352 (sub-cohort) randomly selected from 15,396 women

585 cases, 2,352 (sub-cohort) randomly selected from 15,396 women

No. of cases and controls or population OR/HR/SIR (95%) and P for trend/interaction 1.0 1.13 (0.92–1.38) 1.48 (1.09–2.01) 0.016

1.0 1.10 (0.96–1.27) 1.25 (1.01–1.55) 0.03 1.0 1.07 (0.91–1.26) 1.12 (0.87–1.43) 0.318

Exposure categories Not exposed Moderately exposed Severely exposed P for trend

All cancer sites: Unexposed Moderately exposed Severely exposed P for trend All cancer sites, exclusive of breast cancer: Unexposed Moderately exposed Severely exposed

Exposure Famine score assessing personal experience with hunger, cold, and weight loss during famine

Famine score assessing personal experience with hunger, cold, and weight loss during famine


Age, age squared, body mass index, height, socioeconomic status, age at menarche, parity, age at birth of first child, and family history of breast cancer (first-degree relative) Age, age squared, body mass index, height, socioeconomic status, cigarette smoking habits



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3 Potential Mechanisms A number of biologic and metabolic responses to caloric restriction may influence cancer risk. Reduced cell proliferation and DNA synthesis and enhanced apoptosis and DNA repair limit the number of preneoplastic lesions. Caloric restriction also alters levels of several hormones and growth factors, including estrogen, insulin, prolactin, leptin, IGF-I, and insulin - like growth factor binding protein (IGFBP)-3, which may affect hormone-related cancers [51, 2, 94, 39, 34]. Despite the consistency of findings on extended life span and reduced risk of cancer and other diseases among animals exposed to caloric restriction, the underlying mechanisms have remained largely unknown. Early on, caloric restriction was thought to extend life span by retarding the growth and development of animals [59]. However, observations that caloric restriction also extended the life span of adults suggest that the effect of caloric restriction was not mediated by limiting growth and development [48]. Sacher later proposed that caloric restriction reduces metabolic rate and may thereby extend survival among rodents [81]. However, this hypothesis was contradicted by a series of subsequent laboratory tests [67, 68]. In two groups of 6-month-old male rats fed ad libitum or maintained on a life-prolonging foodrestriction regimen for 4.5 months, the metabolic rate per kilogram lean body mass measured by O2 consumption was found to be the same for both groups [67]. In another study examining changes in metabolic rate immediately after restriction of food, both metabolism rate at normal daily living conditions and basal metabolic rate decreased after feeding was restricted to 60%, but this decrease was transient, so that within a few weeks metabolic rate of restricted rats was the same as that of rats fed ad libitum [68]. Precise measurements of metabolic rate are more difficult to achieve in humans compared to controlled animal studies. Although individuals have been observed to have physiologic or pathologic changes under conditions of extended starvation or anorexia nervosa, relevant studies were not designed to assess metabolic rate; onset and continuity of metabolic rate change during caloric restriction were not as carefully monitored. Endocrinologic changes were also considered as possible mechanisms for the reduced caner risk observed. Caloric restriction was found to alter levels of a number of hormones and growth factors, including increased glucocorticoids and IGFBP-3 levels and decreased levels of IGF-1, insulin, prolactin, estrogen, and leptin, all of which may reduce the risk of cancer [74, 2, 51, 94, 39]. Although the endocrine system plays an important role in cancer etiology, evidence on the effect of caloric restriction on cancer risk through the endocrinologic changes remains conflicting [82]. For instance, though IGF-1 and insulin levels were found to be lower in both animals with caloric restriction and in dwarf mice, Bartke et al. have reported that the life span of the dwarf mice can be further increased by caloric restriction [6], suggesting caloric restriction can further increase life span at least partially through mechanisms other than reduced IGF-1 level. Caloric restriction may also reduce cancer risk by decreasing the accumulation of oxidative damage [99, 100] due to reduced production of mitochondrial free radicals [25], enhanced protective

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mechanisms, including altering levels of antioxidant enzymes [96, 30], detoxication enzymes [4], plasma membrane antioxidant system [16, 35], and DNA repair capacity [29, 10, 84]. Nonetheless, the oxidative damage attenuation hypothesis remains elusive because findings from several other studies have suggested that oxidative stress does not alter the aging process in animals [66]. Hormesis refers to the phenomenon in which a usually detrimental environmental agent such as radiation or chemical substance provides beneficial effects when administered at low intensities or concentrations [28, 66]. The beneficial effects include increased longevity, retardation of senescent deterioration, retardation of age-associated diseases, and enhanced coping with intense stressors [66]. Caloric restriction may be one such low-intensity stressors as it induces daily elevation of circadian peak plasma-free corticosterone levels throughout the life span in both rats and mice [80, 31]. The activation of the intracellular cell-autonomous signaling pathways in response to biological stress and low nutrition would protect cells and tissues and regulate glucose, fat, and protein metabolism [82]. Increased expression of SIR2, a conserved longevity factor involved in the response to caloric restriction, was found to lengthen life span by acting on biological processes that promote survival under conditions of scarcity in yeast [32]. The homologs of SIR2 were found in a wide array of organisms in addition to yeast, ranging from bacteria [89, 78] to mammals, including humans [26, 27]. At the cellular level, SIRT1, a homolog of the SIR2 longevity factor, was found to be induced in tissues of rats with caloric restriction [13] and has been suggested to be involved in the epigenetic regulation of gene expression in cancer cells [41]. In many cancers, SIRT1 localizes to the promoters of several aberrantly silenced tumor suppressor genes whose DNA is hypermethylated [38], and the inhibition of SIRT1 increases H4-K16 and H3-K9 acetylation at endogenous promoters and suffices to induce gene re-expression in breast and colon cancer cells [75]. In addition, SIRT1 was also found to deacetylate non-histone proteins, including various transcription factors involved in growth regulation [56], DNA repair [9, 72], and apoptosis [58, 92], in the fundamental progression of cancer. The acetylation of H3K56 is increased in multiple types of cancer and SIRT1 and SIRT2 deactylate H3K56ac [15a].

4 Discussion Through nearly a century’s research, caloric restriction has been shown to reduce the incidence of spontaneous and induced tumors and increase the life span in animal models. This effect was independent of dietary composition and the period of life when caloric restriction occurred. In contrast to the abundant and consistent evidence from animal models, only limited data directly assessing the impact of caloric restriction are available from human studies, largely due to the difficulties of systematic follow-up of starving populations. It is likely that sustained rather than transient caloric restriction reduces cancer risk and expands life span. While reduction of caloric intake up to 60% in animals has been associated with a reduced risk of cancer [2], caloric restriction

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among rats followed by ad libitum feeding was found to lead to rapid weight gain with concomitant appearance of tumors [50]. In another study, an 8-day period of re-feeding following chronic dietary energy restriction reversed the anti-cancer effects of caloric restriction in rats [101]. This situation is similar to the shortterm caloric restriction of the Dutch famine, where subjects may have compensated for their nutritional deprivation by overeating after the end of the food embargo. Unlike individuals in the Norway famine study who experienced a mild caloric restriction or patients with anorexia nervosa who voluntarily reduced their calorie intake, victims who experienced the Dutch famine are more likely to have subsequent compensatory eating behavior and gain extra weight, which may compromise the anti-cancer effect of caloric restriction, since higher body mass index increases cancer risk. Recently, mimetic molecules of caloric restriction have been proposed as a possible preventive intervention [82]. One example of such mimetic molecules is 2-deoxyglucose (2DG), which is a synthetic glucose analog that inhibits the glycolytic enzyme phosphohexose isomerase [82]. Although 2DG has effects similar to caloric restriction, including suppressed tumor growth, reduced insulin, and increased glucocorticoids in rodents [36], the chronic use of it may enhance the risk of congestive heart failure [36]. Another family of mimetic molecules is biguanides, including metformin, buformin, and phenformin, which may inhibit the incidence of cancer by stimulating AMP-dependent kinase, modulating appetite, glucose, and insulin metabolism [11]; however, these compounds may increase the risk of lactic acidosis [60]. A new class of promising caloric restriction mimetics with fewer side effects are molecules that target the SIR2 family of longevity-promoting enzymes (sirtuins). Howitz et al. identified 18 small molecules from plants, including resveratrol, butein, and piceatannol, that were shown to potentiate human SIRT1 activity in vitro and in vivo [33]. Among them, resveratrol, a polyphenol that is found in many plants species, had the strongest stimulatory activity [82]. Resveratrol is found in grapes [55] and other plants, including peanuts and Itadori tea, a Japanese traditional herbal remedy, which may be the richest known source of resveratrol [76]. Many epidemiologic and clinical studies on red wine, grapes, and grape juice have suggested that resveratrol may aid in disease prevention [12]. The level of total resveratrol in red seedless table grape skin was found to be 2780 μg/g [76]. Red wine contains an average of 1.9 ± 1.7 mg/l trans-resveratrol (8.2 ± 7.5 μM), ranging from non-detectable levels to 14.3 mg/l (62.7 μM) trans-resveratrol depending on grape type [83]. By mimicking caloric restriction, resveratrol extended the life span of a variety of species ranging from yeast to Caenorhabditis elegans and Drosophila melanogaster [95]. In in vitro studies, resveratrol was found to protect mammalian cells in vitro and in vivo from oxidative damage at a concentration of 10–50 μM [53] or 10–100 μM [3], from gamma radiation at a dosage of 100 μM [33], and from Bax-mediated apoptosis [14]. Furthermore, in preliminary in vitro experiments and animal studies, resveratrol was found to act as an antioxidant and anti-mutagenic agent and selectively suppresses the transcriptional activation of cytochrome P-450 1A1 and

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inhibits the formation of carcinogen-induced preneoplastic lesions and was effective in treating esophageal cancer, breast cancer, and liver cancer, with a wide range of median effective dose (3.7–85 μM) according to various anti-inflammatory pathways [37, 7]. Resveratrol was also found to be effective in treating several other diseases, including oral herpes [19, 20], chronic obstructive pulmonary disease [15], hyperlipidemia [71], and ischemic events [42], through its antioxidant and anti-inflammatory effects in some animals and in vitro studies. The other two sirtuinactivating compounds, including quercetin and butein, were also effective against several diseases related to aging, including ischemic heart disease, cerebrovascular disease, cancer, asthma, and diabetes [46, 57]. Future studies may elucidate the long-term efficacy and potential side effects of these caloric restriction mimicking molecules and examine possible synergistic effects with lifestyle factors, e.g., diet and physical activity.

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Chapter 7

Physical Activity and Cancer Leslie Bernstein, Yani Lu, and Katherine D. Henderson

Abstract This chapter provides a synopsis of conclusions from existing epidemiologic literature on the association between physical activity and cancer risk and considers potential biological mechanisms underlying observed associations. Understanding the relationships between physical activity and risk of cancer will offer clues to the etiologic underpinnings of cancer development that should have important public health implications. Case–control studies and cohort studies, which provide most of the epidemiological evidence on physical activity and cancer risk, usually rely on widely varying self-reported measures of activity. Ideally, activity levels are represented by type (recreational or occupational), duration, frequency, and intensity of activity. The overall health benefits of participating in regular physical activity are widely documented and include reductions in risk of cardiovascular disease, diabetes, osteoporosis, obesity, depression, fatigue, and reduced overall mortality rates. The evidence linking physical activity and cancer risk is quite strong for breast cancer and colon cancer. Evidence that physical activity influences endometrial cancer is increasing. Results are still not confirmed or are conflicting for cancer at other sites. Although it is clear that public health recommendations for appropriate changes in activity levels are needed, we have no exact physical activity prescriptions to give the public. Many questions remain to be answered: What are the ages when physical activity provides its greatest benefit? What types of activity will provide the greatest protection against cancer? What activity patterns (intensity, frequency and/or duration of activity) are optimal? Knowledge about the mechanisms involved in the relationship between physical activity and each cancer type will be important in understanding the etiology of these cancers and in formulating public health recommendations.

L. Bernstein (B) Division of Cancer Etiology, City of Hope National Medical Center, Duarte, CA, USA e-mail: [email protected]


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1 Introduction This chapter provides an overview of the epidemiologic studies on the association between physical activity and risk of cancer at several organ sites. The relationship between energy balance, prognosis, and survivorship is addressed in Chapter 8. Because the literature regarding the epidemiology of physical activity and cancer risk is extensive, this is not a comprehensive review, but rather a synopsis of conclusions available from comprehensive reports [1–3] and meta-analyses, highlighting key results from several important research studies. Potential biological mechanisms explaining observed associations are considered, and areas for future research are discussed. Few established risk factors for cancer are modifiable. Physical activity, particularly participation in recreational forms of exercise activity, offers one potential lifestyle modification that may impact risk for cancer at several organ sites. Understanding the relationships between physical activity and risk of these cancers will offer clues to the etiologic underpinnings of cancer development and will have important public health implications. An emphasis on physical activity began in 1956 when President Eisenhower established the President’s Council on Youth Fitness, a national campaign emphasizing physical fitness objectives in physical education. This was expanded during the Kennedy administration in 1960 to include adult fitness, military fitness, and community recreation. Over the years a series of guidelines on the optimal levels of physical activity to be performed by children, adolescents, and adults has been published by several organizations. The most recent recommendation of the American College of Sports Medicine indicates that between 150 and 300 minutes of moderate intensity physical activity is needed to prevent weight gain and promote modest weight loss and health [4]. Interest in whether physical activity impacts cancer risk began in the early 1980s. One of the earliest studies used cancer registry data to determine whether a man’s occupational physical activity level as reported at diagnosis influenced his colon cancer risk [5]. Although the measure of occupational activity used was crude and not clearly temporally relevant, this study showed that men working in sedentary occupations had greater colon cancer risk than men whose jobs required substantial physical activity. In the ensuing years, physical activity has been studied in far greater detail in relation to many types of cancer by investigators throughout the world as it offers a workable approach for risk modification and ultimately, one hopes, cancer prevention [1, 6]. Physical activity is defined as any movement of the body that results in energy expenditure [7]. In this review, we consider studies of recreational physical activity (exercise activity) or occupational activity (including household activity) and cancer risk. In assessing how different types of activity influence risk, it is important to consider not only duration and frequency of activity, but also the intensity of activity that is being performed. For example, occupational activity may occur over a longer period of time than recreational activity, but occupational activity might require less

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energy expenditure per hour than bouts of vigorous or moderate recreational physical activity [8]. The distinction between recreational and occupational activity is also important because occupational physical activity, with increasing mechanization and technological advances, has decreased, particularly in developed areas of the world, leading to an overall decrease in energy expenditure through physical activity. Case–control studies and cohort studies provide most of the epidemiological evidence on physical activity and cancer risk. In these studies, activity levels are usually self-reported. These measures vary widely. They may be extremely comprehensive, obtaining detailed lifelong diaries of physical activity that represent lifetime histories of activity, they may be based on questionnaire items about activities at defined ages or time points in life, or they may only include questions on current or recent usual activity. Ideally activity levels are represented by objective measures of type (recreational or occupational), duration, frequency, and intensity of activity. While some studies have measured overall activity, others have asked respondents to classify their own activity as vigorous or moderate and report their extent of participation in each. Some studies have assessed sedentary time, representing hours of sitting per day, in addition to time spent in exercise or other physical activities [9, 10]. More comprehensive assessments take a lifetime or long-term history of individual activities using carefully designed in-person interviews which make use of memory probes by creating lifetime calendars of key life events [11]. One challenge regarding collection of information on physical activities in epidemiologic studies is the lack of a “gold standard” measure for the complex behavior of physical activity. Even in the best-designed studies (i.e., those using in-person interviews or those collecting information prior to disease onset), random errors in measurement of physical activity may lead to non-differential misclassification of exposure with respect to outcome status and thus an underestimation of the association between physical activity and cancer risk. Meta-analyses use a statistical approach to combine effect estimates from a series of studies addressing a research question to provide one summary effect estimate and its confidence interval. When interpreting results from meta-analyses, one must consider the myriad of methods for collecting physical activity data across studies. Meta-analyses often lack standardization with respect to the comparison group and generally present a comparison of the “most” active to the “least” active in a study. These categories will differ by study even if the study data collection methods are similar, unless a standard set of definitions is used for “most” and “least” active. In these studies, physical activity may represent a mixture of current activity, recent activity, past activity at a certain age, activity over a particular age range, or lifetime activity; and activity may be defined as sports activities, occupational activities, or other activities such as gardening. Many epidemiologic studies of physical activity convert information into metabolic equivalents (METs) of energy expenditure using an estimate of energy expenditure per hour for each activity [12] to summarize intensity, frequency, and duration across all physical activities. Such measures are not commonly applied in meta-analyses. Although meta-analyses provide some insight into the potential for physical activity to influence cancer risk, the summary

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relative risk estimates they provide are limited in detail and are somewhat difficult to interpret or use in making public health recommendations. The overall health benefits of participating in regular physical activity are widely documented and include reductions in risk of cardiovascular disease, diabetes, osteoporosis, obesity, depression and fatigue, and reduced overall mortality rates [13]. In 2001, the International Agency for Research on Cancer convened a committee to assess the relationship between cancer and two modifiable cancer risk factors, inactivity and obesity, and to determine the strength of the evidence by cancer site [1]. Evidence that a physically active lifestyle lowers colon cancer risk was considered to be the strongest and was classified as “convincing.” Evidence for breast cancer was also strong; a lower breast cancer risk, in the range of 20–40%, was reported for the most physically active versus the least physically active women. This review also considered the available evidence for other cancer sites and for these cancers, concluding that the current level of evidence was either limited or conflicting. This chapter focuses on the association between physical activity and risk of breast cancer, colon cancer, endometrial cancer, prostate cancer, lung cancer, and ovarian cancer and provides a short summary of available evidence for other, less well-studied cancer sites.

2 Breast Cancer Results from case–control studies [1, 14–18] and cohort studies [1, 19–24] have shown that invasive breast cancer risk is reduced by 13–50% among physically active women. One of the earliest studies, a case–control study of women 40 years or younger, showed a dramatic reduction in risk (approximately 50%) among women who averaged about 4 hours of activity per week during their reproductive years [14]. Similarly, among postmenopausal women, those with higher amounts of recreational physical activity during their lifetimes have lower breast cancer risk [15, 16]. These results have been confirmed in more than 50 studies and have been observed in different demographic subgroups of the population such as Asian American women [17] and African American women [18]. A study that focused solely on in situ breast cancer showed similar reductions in risk [25]. The Women’s Health Initiative Observational Study and the American Cancer Society Cancer Prevention Study II (CPS II) Nutrition Cohort have reported 22% and 29% reduced risk of breast cancer, respectively [19, 20]. In the Nurses’ Health Study cohort, risk of breast cancer was 18% lower among women engaging in moderate or vigorous activity for at least 7 hours a week compared to women that exercised less than 1 hour per week as adults [26]. For the California Teachers Study cohort, Dallal and colleagues reported that invasive breast cancer risk was inversely associated with long-term vigorous activity as was risk of in situ breast cancer [22]. Participation in activities like aerobics, running, distance swimming, and cycling on hills was accumulated from high school through age 54 years in this study. The protective effect of long-term vigorous activity on risk of invasive breast cancer was more

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pronounced in younger women (< 55 years). Both vigorous and moderate longterm recreational activities were associated with reduced risk of estrogen receptor (ER)-negative but not ER-positive invasive breast cancer. The California Teachers Study results for receptor status were confirmed by the National Institutes of Health (NIH)-American Association of Retired Persons (AARP) Diet and Health Study which used information on frequency of moderate-to-vigorous activity each week at the time women were recruited into the study [27]. Despite results from these two large studies, receptor status-specific results have not been consistent across all studies. One of the earliest reports, based on two case–control studies from Los Angeles, showed greater reductions for ER-negative, progesterone receptor (PR)-negative breast cancer compared to ER-positive, PR-positive breast cancer, but reductions in risk for the two subtypes were not statistically significantly different [28]. A recent case–control study from Germany found that the breast cancer– physical activity association was restricted to ER-positive, PR-positive breast cancer [29], whereas, in the Breast Cancer Detection Demonstration Project Follow-Up Study, which showed an overall inverse association among postmenopausal women participating in vigorous activity, results did not differ by hormone receptor status [24]. Not all studies have found that recreational physical activity impacts breast cancer risk. In the European Prospective Investigation into Cancer and Nutrition (EPIC) study [23], increased physical activity in the form of household activity (highest vs. lowest quartile) was associated with a reduction in breast cancer risk among postmenopausal and premenopausal women, but neither recreational activity nor occupational activity was significantly associated with breast cancer risk. A meta-analysis of 19 cohort and 29 case–control studies published prior to publication of the EPIC, California Teachers, and NIH-AARP studies has provided strong evidence for an inverse association between physical activity and postmenopausal breast cancer [30]. For premenopausal breast cancer the evidence was considered weaker. The authors reported that evidence for a dose–response relationship, on the order of a 6% decrease in risk for each additional hour of physical activity per week, was observed in approximately half of the “higher quality” studies that reported a decreased risk. The totality of results for breast cancer has begun to clarify the protective role of physical activity, suggesting that long-term vigorous activity is required for the effect to be apparent. The issue of whether physical activity affects risk of ER-negative breast cancer is clearly important because current chemopreventive approaches are not effective against ER-negative breast cancer.

3 Colon Cancer The epidemiological literature suggests that increased physical activity is protective for colon cancer [1, 31]. In early studies, this effect was observed more consistently in men than in women. In cohort studies, a greater benefit among men is still observed, although results for case–control studies indicate similar exercise benefits

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for colon cancer among men and women [31]. Risk reductions from a meta-analysis comparing individuals in the highest physical activity category to the lowest category found a 31% reduction in colon cancer risk for case–control studies and a 17% reduction in risk for cohort studies [31]. Among men, reductions in colon cancer risk have been associated with both occupational and leisure-time physical activity [1]. One factor that might account for weaker results of studies of physical activity on colon cancer risk among older women is use of hormone therapy, which has been shown to reduce colon cancer risk [32]. This possibility has been investigated in the California Teachers Study. Mai and colleagues [33] reported that combined lifetime moderate and vigorous leisure-time physical activity was modestly associated with decreased risk of colon cancer. Women who exercised at least 4 hours per week during their reproductive years had a 25% lower risk of colon cancer relative to women who exercised no more than 30 minutes per week. Importantly, among postmenopausal women, those who had never used hormone therapy experienced a substantial 46% decrease in colon cancer risk if they averaged at least 4 hours of exercise per week, whereas those who had used hormone therapy experienced no benefit from exercise, but retained a benefit from having used hormone therapy that was comparable to 4 hours of activity per week. It has been argued that distal and proximal colon cancers have distinct etiologies [34]. Several studies of physical activity and colon cancer have examined risk by anatomic subsite, but results are inconsistent with regard to whether the association is stronger for distal tumors or for proximal tumors [35–39]. In contrast to colon cancer, nearly all studies have failed to show a relationship between physical activity and rectal cancer risk [1]. However, the NIH-AARP Diet and Health Study shows a modest reduction in rectal cancer risk for men but not for women after 6.9 years of follow-up [39]. Sedentary behavior itself may be associated with colon cancer risk; in the NIHAARP Diet and Health Study cohort, men who spent at least 9 waking hours per day watching television had 56% greater risk of colon cancer than men who spent less than 3 hours per day in this sedentary behavior [39]. The questions of what type and what amount of exercise to recommend as a public health intervention are still ill defined for colon cancer. Based on the NIH-AARP Diet and Health Study results, sedentary behavior and physical activity appear to be independent predictors of colon cancer risk [39], suggesting that reduction of sitting time and increasing moderate-to-vigorous physical activity are both important. Few studies have evaluated different types of activities; several have looked at whether walking alone is sufficient to lower risk [38, 40], but results are mixed.

4 Endometrial Cancer The evidence showing that regular physical activity lowers endometrial cancer risk is accumulating, but is not currently considered convincing. Results supporting this association [1, 10, 41–44] are not as definitive as those for obesity and colon cancer

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[1, 2] or for physical activity and breast cancer (see Section 2). Studies to date have suggested that risk of endometrial cancer is decreased 20–40% in women who are in the highest category of physical activity [1, 41], with a median relative risk of 0.73 overall and 0.70 among studies that have adjusted for body mass index in their statistical analyses. The lack of change in relative risk estimates, upon adjustment for body mass index, suggests that physical activity is an independent risk factor for endometrial cancer. Although physical activity is associated with decreased risk of endometrial cancer in both normal weight and obese women, two recent studies have suggested that this association is more pronounced for obese women [10, 44]. Three recent studies have also reported an increased risk of endometrial cancer in sedentary women [10, 45, 46]. Two meta-analyses of the physical activity–endometrial cancer association identify some inconsistency in dose–response relationships, documenting the importance of differences in activity type (total, occupational, household, recreational) and intensity [41, 42]. Little evidence exists on how long-term or lifetime physical activity and activity patterns in different life periods influence endometrial cancer risk; some studies have suggested that recent activity and long-term activity may be more important than activity in the distant past [42].

5 Prostate Cancer More than 20 studies have assessed the potential association between physical activity and prostate cancer [1]. Regardless of the varied methods, population bases, and sample sizes used in these studies, the majority have suggested a modest reduction in risk with increasing level of physical activity [1]. Exercise also appears to lower risk for benign prostatic hyperplasia [47, 48]. In a review of the literature, Friedenreich and Orenstein concluded that prostate cancer risk is reduced 10–30% when comparing the most active men to the least active men; further, they suggested that it may be high levels of physical activity earlier in life that are most relevant to this disease [49]. Physical activity during adolescence and overall lifetime vigorous activity were modestly related to lower prostate cancer risk in a population-based case–control study of advanced prostate cancer (stage T2 or greater) conducted in Canada [50]. Cohort studies, which have essentially observed no association between adult levels of physical activity and overall prostate cancer risk, have shown reduced risk for advanced disease [51–53] and for fatal prostate cancer [51, 53] with high levels of physical activity, suggesting that regular vigorous activity may impact progression of prostate cancer. It is important to consider several caveats regarding these results including small numbers of exposed cases [51] and the potential for confounding by prostatespecific antigen (PSA) testing [52, 53]. One would expect that PSA testing should increase the incidence of prostate cancer overall, but decrease the incidence of tumors of advanced stage and high Gleason score. In a study based on the American

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Cancer Society CPS II cohort, Patel and colleagues [52] reported that a history of PSA testing was more common among physically active men than among inactive men. No association between physical activity and risk of aggressive prostate cancer was reported in another cohort study [54]; however, investigators conducting this study reported a statistically significant interaction between physical activity and body mass index on prostate cancer risk, with the protective effect of physical activity limited to normal weight men. This result suggests that overall energy balance may play a role in prostate cancer etiology.

6 Lung Cancer The existence of a relationship between physical activity and lung cancer risk is controversial. Physical activity may be protective for lung cancer, yet this effect is not considered well established [1]. A meta-analysis of nine studies published between 1989 and 2003 reported a 13% decreased risk for lung cancer associated with moderate recreational physical activity and a 30% decreased risk associated with vigorous activity [55]. The impact appeared to be slightly stronger among women than among men. Although these effects have been observed among smokers, and after considering measures of pack-years smoked, it is still possible that controls for confounding factors by smoking status and smoking intensity was incomplete in these studies, and that the lower risk of lung cancer reflects unmeasured differences in smoking habits. If there is a causal relationship between physical activity and lung cancer, we would expect to see the protective effect of physical activity among never smokers, yet studies to date have not shown such an association. In the NIH-AARP Diet and Health Study [56], a cohort study with 6,745 lung carcinoma patients identified during an average of 7.2 years of follow-up, physical activity at the time of study initiation was inversely associated with lung cancer risk among former and current smokers, but was not associated with risk of lung cancer overall or with risk of any individual histologic subtype among never smokers. Further, among current or past smokers, increasing physical activity was associated with decreasing risk of adenocarcinomas, but not of other cell types. These results are consistent with those from two other studies presenting data stratified by smoking status [57, 58], which appeared after the meta-analysis publication. Thus the protective effect of physical activity observed among smokers may be due to residual confounding by cigarette smoking.

7 Ovarian Cancer The literature on risk of ovarian cancer in relation to physical activity has been inconclusive. More than 18 studies have assessed the impact of physical activity on

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risk [1, 9, 59, 60]. Existing studies have shown reductions in risk of ovarian cancer with increasing activity ranging from 10 to 67% or no statistically significant effect. In a meta-analysis of 12 studies, ovarian cancer risk was 21% lower in case–control studies and 19% lower in cohort studies among women who exercised the most compared to those with the least amount of exercise, suggesting a modest inverse association [60]. The results among postmenopausal women from the American Cancer Society CPS II cohort showed no evidence that activity was associated with ovarian cancer risk; but in this study, a history of sedentary behavior (sitting ≥ 6 hours a day versus < 3 hours a day) was associated with more than a 50% increase in risk [9]. A similar positive association between sedentary hours and ovarian cancer risk has been reported in one case–control study [61]. Three cohort studies published after the meta-analysis, one in which most women were premenopausal [62] and two in which most women were postmenopausal [63, 64], have reported no association between physical activity and ovarian cancer risk.

8 Other Cancers Limited data exist on the relationship between physical activity and risk of other types of cancer; but, given the growing literature for cancers of the breast, colon, endometrium, ovary, prostate, and lung, physical activity is now being evaluated in relation to several other cancers.

8.1 Gastric Cancer The risk of gastric cancers occurring over 18 years of follow-up in a populationbased cohort of residents in Nord-Trondelag County in Norway was examined in relation to physical activity [65]. When participants were classified according to their overall activity, taking frequency, intensity, and duration of activity into account, risk for gastric cancer declined with increasing level of activity; this reduction in risk was most apparent for non-cardia gastric cancers where individuals with moderate and high levels of activity had one-half the risk of those with no activity. Similarly, in the NIH-AARP Diet and Health Study cohort, risk of distal gastric adenocarcinomas was more than 30% lower among cohort members who reported participating in any level of physical activity at cohort initiation [66]. One interpretation of these results is that protection results from minimal activity; another is that the results are due to residual confounding by a healthy lifestyle among active individuals. It is also possible that some subclinical disease process results in inactivity among those at high risk for distal gastric cancer and results in an apparent marked reduction in risk for active individuals. Although the NIH-AARP Diet and Health Study showed an impact of physical activity on risk of adenocarcinomas of the distal stomach and esophagus (see Section 8.2), no impact on risk of gastric cardia adenocarcinomas was observed [66]. A modest, but not statistically significant,

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decreased risk of both distal gastric cancer and gastric cardia cancer was observed in a case–control study that assessed lifetime occupational activity in relation to gastric cancer [67].

8.2 Esophageal Cancer One case–control study and one cohort study have examined risk of adenocarcinoma of the esophagus in relation to physical activity [66, 67]. In the case–control study, the activity measure was based on lifetime occupational activity with occupations classified as involving vigorous, moderate or sedentary activity [67]. Lifetime occupational activity was modestly related to risk of adenocarcinoma of the esophagus. A more pertinent measure, average annual level of occupational activity before age 65 years, was more strongly related to risk. Study participants in the highest occupational activity group experienced a roughly 40% reduction in risk of esophageal adenocarcinoma compared with those in the lowest occupational activity category. Results from the NIH-AARP Diet and Health Study are consistent with those from the case–control study and showed that participants who engaged in physical activity at least five times per week at cohort entry had a 25% lower risk of esophageal adenocarcinoma [66]. No beneficial effect was noted for squamous cell esophageal cancer [66].

8.3 Renal Cell Cancer At least six cohort studies have explored the relationship between renal cell cancer and physical activity, in part because of the known deleterious effects of high body mass index and hypertension on the risk of renal cell cancer [1, 68]. Despite this rationale for a potential association, no association between physical activity and renal cell cancer has been established [1]. In a large Canadian case–control study, physical activity was not associated with renal cell cancer among men or women [69]. In contrast, in the Hawaii and Los Angeles Multiethnic Cohort, physical activity was associated with renal cell cancer risk among women, but no association was found for men [68]. As pointed out by the authors, given the marked impact of obesity on risk in women, this could be an artifact of inadequate control of the confounding effects of adiposity.

8.4 Pancreatic Cancer Pancreatic cancer is generally diagnosed at an advanced stage. Therefore, case– control studies are likely to exclude patients who die soon after diagnosis or are too ill to participate. Prospective studies require long duration of follow-up to accrue sufficient numbers of pancreatic cancer cases to examine risk factors and rule out

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changes in behavior due to early symptoms in the first years of follow-up. Despite this, at least 12 prospective studies have examined risk of pancreatic cancer in relation to physical activity (summarized in [70]). Given the mixed results from these studies, current evidence does not support an association between physical activity and risk of pancreatic cancer.

9 Mechanisms Mechanisms by which physical activity can influence cancer risk, including those discussed in Chapter 5 of the current volume, may vary by cancer site. In brief, changes in estrogen and progesterone exposure over a woman’s lifetime are the main biological mechanisms considered in linking physical activity to risk of breast cancer. For example, evidence has shown that exercise influences exposure to estrogen and progesterone during a woman’s reproductive years by lowering body fat in youth [71] which can delay the age when first menses occurs [72]. Physical activity can also disrupt menstrual cyclicity which manifests across a continuum ranging from luteal phase defects to anovulation to secondary amenorrhea [73]. This may reflect an imbalance between energy expenditure and energy intake. In adolescence, even moderate levels of physical activity increase the likelihood of experiencing anovulatory menstrual cycles [74]. Having fewer ovulatory menstrual cycles is likely to reduce a woman’s cumulative lifetime exposure to endogenous ovarian hormones (estradiol and progesterone) [75]. Physical activity can reduce weight and weight gain in adulthood, both of which are associated with greater risk of breast cancer after menopause. In the postmenopausal woman, adipose tissue is the primary source of endogenous estrogen via aromatization of androstenedione to estrone [76]; thus, heavier postmenopausal women have higher levels of circulating estrogen than women with less adipose tissue, and these higher levels of estrogen likely increase breast cancer risk. Physical activity can also have a direct impact on circulating estrogen levels among postmenopausal women [77]. Another mechanism by which exercise might influence breast cancer risk is through regulation of melatonin levels; Knight and colleagues [78] have shown that exercise increases melatonin levels in women, which would be expected to lower breast cancer risk. Exposure to unopposed estrogen is the major cause of endometrial cancer [79]. The likely pathway by which physical activity influences endometrial cancer risk is by altering endogenous hormone profiles. Physical activity may counter the proliferative effects of estrogen either directly or by influencing body weight or weight gain. By lowering body fat after menopause, when ovarian production of estradiol and progesterone ceases, women have little or no exposure to androstenedione-derived estrogen. Studies have linked elevated insulin levels and diabetes to endometrial cancer risk [79]. Because physical activity also influences insulin sensitivity, this may also

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explain a potential association between physical activity and endometrial cancer risk. The impact of physical activity on circulating hormone profiles may also influence ovarian cancer risk and prostate cancer risk. The mechanisms cited above for breast cancer and endometrial cancer may affect ovarian cancer risk; however, risk may also involve changes in gonadotropin exposure. For prostate cancer, proposed mechanisms include paradoxical effects of testosterone on low-grade versus more advanced prostate cancer and alterations in insulin and circulating insulin-like growth factor (IGF)-I profiles [80]. It has been suggested that physical activity reduces colon cancer risk by stimulating stool transit through the colon, thereby decreasing the exposure of the colonic mucosa to carcinogens in the stool [81]. Physical activity may alter the control of cellular proliferation and apoptosis. A year-long randomized controlled clinical trial using a home-based exercise intervention confirmed this for men, showing that moderate to vigorous intensity exercise (6 hours/week) increased the levels of the pro-apoptotic protein, BAX, at the base of colonic crypts; but the same was not true for women [82]. In fact, results for women were in the opposite direction [82]. Colon mucosal prostaglandin concentrations did differ between the exercise and control arms of this clinical trial [83]. The insulin and IGF pathways have been proposed to mediate associations with colon cancer risk [84]. High insulin levels may promote cell proliferation and tumor growth in the colon [84] and may suppress expression of IGF-binding proteins 1 and 2 leading to increased bioavailable IGF-I levels [85], which have been associated with higher risk of colon cancer [86]. Inflammation is a pathway that may mediate the relationship between physical activity and a number of cancers, and in particular colon cancer and breast cancer. Chronic colon conditions, such as inflammatory bowel disease, are associated with increased incidence of colon cancer [84]. Further, use of aspirin and other non-steroidal anti-inflammatory drugs reduce colon cancer risk [84]. Recent evidence suggests that physical activity may reduce inflammation [87]. Muscle-derived interleukin-6 (IL-6) is the major cytokine that is affected by acute vigorous and moderate exercise, increasing exponentially in response to exercise. Although IL-6 levels decline following exercise, other anti-inflammatory cytokines induced by IL6 increase. IL-6 has inhibitory effects on proinflammatory cytokine proteins [88]. This evidence of the anti-inflammatory impact of physical activity is supported by studies showing that an individual’s level of physical activity (light, moderate, or vigorous) is inversely associated with level of circulating C-reactive protein, a marker of inflammation [89–91].

10 Summary In summary, the evidence regarding physical activity and cancer risk is quite strong for breast cancer and colon cancer. Evidence that physical activity influences endometrial cancer risk is increasing. Results are still mixed for ovarian cancer and

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those for prostate cancer may be influenced by PSA testing. The association with lung cancer requires careful assessment of the possibility that smoking patterns may account for differences in activity levels. Although it is clear that recommendations for appropriate changes in activity levels are important public health messages, we still have no exact physical activity prescriptions to give the public. Many questions remain to be answered: What are the ages when physical activity provides its greatest benefit? What types of activity will provide the greatest protection against cancer? What activity patterns (intensity, frequency and/or duration of activity) are optimal? What is the role of the built environment in affecting physical activity levels? Knowledge about the mechanisms involved in the relationship between physical activity and each cancer type will be important in understanding the etiology of these cancers and formulating public health recommendations.

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Chapter 8

Energy Balance, Cancer Prognosis, and Survivorship Melinda L. Irwin

Abstract An increasing number of men and women are being diagnosed with cancer and many cancer survivors are seeking lifestyle-based approaches to improve survival. Physical activity and diet are modifiable behaviors with a multitude of health benefits, and an increasing number of publications have shown a strong relationship between physical activity, diet, weight, and cancer survival. The purpose of this chapter is to review (1) the evidence supporting the effect of weight, physical activity, and diet on cancer prognosis and survivorship; (2) mechanisms mediating the observed associations between energy balance and improved prognosis; and (3) approaches to favorably changing physical activity, diet, and weight to improve cancer prognosis and survivorship. Since a majority of cancer survivors are overweight, and not participating in recommended levels of physical activity or eating a prudent diet, improving these behaviors has the potential to benefit a large number of cancer survivors.

1 Introduction There has been significant research directed toward improving survival and quality of life in men and women diagnosed with cancer. One of the most common questions cancer survivors ask is “What can I do to improve my survival?” The impact of energy balance, or specifically weight, physical activity and diet, may have on cancer survival is of growing interest to scientists, clinicians, professional organizations, and survivors. Physical activity and diet are modifiable behaviors with a multitude of health benefits, and an increasing number of publications have shown a strong relationship between physical activity, diet, weight, and cancer survival [1–8].

M.L. Irwin (B) Epidemiology and Public Health, Yale School of Public Health, New Haven, CT, USA e-mail: [email protected]


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The purpose of this chapter is to review (1) the evidence examining the impact of weight, physical activity, and diet on cancer prognosis and survivorship; (2) mechanisms mediating the observed associations between energy balance and improved prognosis; and (3) approaches to favorably changing physical activity, diet, and weight to improve cancer prognosis and survivorship. Since a majority of cancer survivors are overweight, and not participating in recommended levels of physical activity or eating a prudent diet [9–11], improving these behaviors has the potential to benefit a large number of cancer survivors.

2 Obesity at Initial Diagnosis and Cancer Survival Many observational studies have looked at the relationship between weight at diagnosis and cancer outcomes, and the vast majority of these have demonstrated an increased risk of cancer recurrence and death in men and women who are overweight or obese at the time of cancer diagnosis [5–8]. In a study conducted by the American Cancer Society, obesity in adult men and women was associated with increased mortality from cancers of the colon, breast, endometrium, kidney (renal cell), esophagus (adenocarcinoma), gastric cardia, pancreas, prostate, gallbladder, and liver [8]. Estimates from this study suggest 14% of all cancer deaths in men and 20% of all cancer deaths in women from a range of cancer types are attributable to overweight and obesity. Furthermore, there was a 52 and 88% increase in the risk of all cancer death for men and women, respectively, who were severely obese (BMI ≥ 40 kg/m2 ) compared with men and women who were normal body weight (BMI < 25 kg/m2 ). Recently, two studies observed associations between obesity and increased risk for recurrence and death from colon and prostate cancer. Dignam and colleagues investigated the association between BMI at diagnosis and risk of recurrence, second primary cancer, and mortality in 4288 colon cancer patients [12]. A BMI greater than 35.0 kg/m2 at diagnosis was associated with a 38 and 49% increased risk of recurrence or death, respectively, as compared to a BMI less than 25 kg/m2 . Wright and colleagues examined BMI in relation to prostate cancer mortality in 287,760 men in the NIH-AARP Diet and Health Study [13]. A significant two-fold elevation in prostate cancer mortality was observed in men with BMI levels greater than 35 kg/m2 as compared with men with BMI levels less than 25 kg/m2 . Given that breast cancer is the most frequently diagnosed invasive cancer among women and that rates of obesity are increasing among women and breast cancer survivors, there has been significant research directed toward the relationship between obesity and breast cancer prognosis. A meta-analysis of these observational studies demonstrated a hazard ratio for breast cancer recurrence at 5 years of 1.78 (95% CI 1.5–2.11) and for breast cancer death at 10 years of 1.36 (95% CI 1.19–1.55) for women in higher BMI categories compared with women at lower BMI categories [14]. The findings that obesity is associated with cancer mortality are apparent even after adjustment for stage at diagnosis and adequacy of treatment.

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Associations between obesity and breast cancer prognosis in younger women appear to be even stronger; Daling and colleagues [15] reported that women younger than 45 years of age who had invasive breast cancer and a BMI > 25 kg/m2 were 2.5 times as likely to die of their disease within 5 years of diagnosis compared with women with breast cancer and a BMI < 21 kg/m2 .

3 Weight Gain and Cancer Survival Epidemiological studies have also shown that weight gain after a breast cancer diagnosis is associated with an increased risk for recurrence and death compared with maintaining normal weight after diagnosis [16]. This is especially worrisome given the fact that, especially among women treated for breast cancer, a majority of them gain a significant amount of weight in the year following breast cancer diagnosis, and return to pre-diagnosis weight is rare [9]. Analyses from the Nurses’ Health Study showed that weight gain after diagnosis (∼5–10 lbs) was related to approximately 50% higher rates of breast cancer recurrence and death [16]. The findings were especially apparent in women who never smoked, among women with earlier stage disease or those who were normal weight before diagnosis. While these findings are intriguing, not all studies have observed an association between obesity or weight gain and poor survival. Caan and colleagues did not observe an association between post-diagnosis weight gain and breast cancer recurrence risk in the first 5–7 years post-diagnosis [17]. Meyerhardt and colleagues, using data from the Cancer and Leukemia Group B (CALGB) 89803 study of 1,053 patients who had stage III colon cancer, demonstrated no association between BMI or weight change and survival in colon cancer patients [18]. It is unknown if chemotherapy dose specifications may account for the differences between these studies and the studies showing an increased risk of death with higher BMI and weight gain. Thus, obesity is either associated with poor prognosis or may be associated with receiving inadequate chemotherapy doses. However, Buist and colleagues examined the association between BMI and receipt of appropriate primary tumor therapy and adjuvant therapy in 897 women diagnosed with breast cancer. They found that receipt of appropriate primary therapy and adjuvant therapy was not associated with BMI in women treated for breast cancer, implying that the majority of studies that have shown an association between obesity and poor prognosis may in fact be true [19]. One final concern recently raised by Daniell and colleagues is that being obese, compared with being normal weight, prior to and at cancer diagnosis is associated with earlier tumor metastasis, or more rapid growth of node metastases before diagnosis, as well as differences in hormone receptor status [20]. Thus, these genetic differences in tumors among obese patients may have already influenced the growth of metastic tissue before their initial diagnosis. Therefore, weight loss after diagnosis may not influence prognosis because of the already established genetic alterations. However, without a methodologically strong weight loss trial conducted in overweight and obese cancer survivors, we are unable to definitively

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know whether weight loss impacts survival or not. Regardless, obesity and weight gain still have adverse effects on risk of other new cancers and overall survival. Specifically, there is evidence that cancer survivors die of non-cancer causes at a higher rate than persons in the general population (deaths being primarily from cardiovascular disease and diabetes) [21]. Therefore, surviving cancer requires not only treating the primary cancer, but also avoiding second cancers for which patients are at increased risk. To improve overall survival, it is critically important for cancer survivors to prevent obesity. One of the primary methods for preventing or treating obesity and weight gain is by eating a healthy diet and increasing physical activity levels. Physical activity in particular has also been presented as a therapeutic strategy to address both the psychological and the physical concerns faced by cancer survivors. In summary, given the strong observational evidence suggesting that being overweight and obese is associated with poor prognosis in breast, colon, and prostate cancers, there is a need to develop clinical trials testing the effect of intentional weight loss upon cancer recurrence and mortality. If, e.g., 10% weight loss is associated with an improved disease-free survival rate, then perhaps more behavior/ lifestyle change programs will be available to cancer survivors. However, even if a benefit of weight loss on disease-free survival is not observed, there are additional benefits of weight loss including reduced therapy-related complications, improved quality of life, and reduced risk of death from other causes. Oncologists are, therefore, encouraged to counsel patients on maintaining a healthy weight via increased physical activity and eating a prudent diet high in fruits and vegetables, whole grains, and poultry.

4 Physical Activity and Cancer Survival Numerous observational studies have recently been published demonstrating that participation in moderate-intensity recreational physical activity after diagnosis is associated with improved survival in women who develop breast cancer [1–4]. These studies have demonstrated an approximate 50% reduction in the risk of total deaths and risk of breast cancer deaths in women who are physically active after breast cancer diagnosis compared with women reporting no recreational physical activity. These studies also showed that the decreased risk of death associated with physical activity was observed in pre- and post-menopausal women, overweight and normal weight women, and women with stage I–III disease. While any amount of recreational physical activity performed after diagnosis has been associated with a decreased risk of death, the maximal benefit occurred in women who performed the equivalent of brisk walking 3 hrs per week. The type of physical activity assessed in these studies was sports/recreational physical activity; however, one study showed similar yet slightly attenuated associations for any moderate-intensity physical activity (e.g., heavy household work, gardening, occupational activities) [2]. While most of the studies included samples of breast cancer survivors that were primarily non-Hispanic white, one study showed a similar association in African American and Hispanic women [2]. Furthermore, there is

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little reason to believe that the biological mechanisms by which physical activity could improve survival would differ by race/ethnicity. Given that women who are more physically active after diagnosis may have been similarly active before diagnosis, these studies cannot exclude the possibility that physically active individuals who develop breast cancer acquire tumors that are biologically less aggressive. Therefore, being physically active prior to diagnosis may have been associated with an earlier disease stage. Two studies, assessing physical activity in the year prior to diagnosis, observed nonsignificant reduced risks of breast cancer death with higher levels of pre-diagnosis physical activity [2, 3]. However, one study examined change in physical activity from before to after breast cancer diagnosis; and observed increased risk of death associated with decreasing physical activity [2]. Furthermore, compared with women who remained physically inactive both before and after diagnosis, increasing physical activity after diagnosis was associated with a reduced risk of death. These finding emphasizes the importance of maintaining or increasing physical activity levels after a diagnosis of breast cancer to gain the maximum benefits of physical activity on survival. Two large observational studies have also shown that participation in 3 hr/wk of moderate-intensity recreational physical activity after a diagnosis of colon cancer is associated with a 50–63% reduction in the risk of total death and 39–59% reduction in the risk of colon cancer death [22, 23]. The inverse relations between postdiagnosis physical activity and colon cancer mortality remained largely unchanged across strata of sex, BMI, age, disease stage, or year since diagnosis. In summary, these observational findings of post-diagnosis physical activity and improved survival suggest that exercise may confer additional improvements in breast and colon cancer survival beyond surgery, radiation, and chemotherapy. However, despite this growing body of observational evidence suggesting a strong link between physical activity and cancer survival, there is still the potential for confounding by unknown or poorly characterized variables. For example, physical activity may be a marker of overall health behaviors including adherence to adjuvant treatments. Thus, randomized controlled trials testing the effects of physical activity on cancer survival and/or surrogate/biological markers mediating the association between physical activity and survival are necessary and would provide critical information for cancer survivors about whether and how much lifestyle change can affect their prognosis. While a trial of physical activity on cancer survival has yet to be done, a small number of randomized trials of exercise on surrogate/biological markers of survival have been published. Refer to the Biological Mechanisms section 7 below for a review of these studies.

5 Nutrition and Cancer Survival With the growing population of cancer survivors, it is now recognized that evidence regarding nutrition, cancer prognosis, and survivorship is needed. At present, there are hundreds of studies that have investigated nutritional factors in the etiology of various cancers, but fewer that have evaluated nutrition in relation to survival. Several new cohort studies of cancer survivors that include post-diagnosis dietary

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assessments are now underway as reviewed by Kushi and colleagues [24]. A recent publication by Kwan and colleagues from the Life After Cancer Epidemiology Study examined dietary patterns and breast cancer survival. They observed a prudent dietary pattern (i.e., high intake of fruits, vegetables, whole grains, and poultry) was associated with a 43% reduced risk of overall death (p = 0.02). In contrast, a Western diet (high intakes of red and processed meats and refined grains) was related to a 53% increased risk of overall death (p = 0.05) [25]. Two important large-scale multi-site studies evaluated dietary interventions in breast cancer survivors. The Women’s Intervention Nutrition Study (WINS) was a randomized trial of a dietary intervention designed to reduce fat intake in 2437 women with resected, early-stage breast cancer [26]. The low-fat diet group consumed an estimated 33 g total fat/day in comparison with 51 g total fat/day in the usual care group. An interim analysis based on 5 years of follow-up reported that the intervention group had a lower risk of relapse events (HR = 0.76, 95% CI 0.60–0.98). Of particular interest, when the results were stratified by the estrogen receptor (ER) status (positive vs. negative) of the women’s first breast cancer, a more substantial benefit was observed in women who had ER-negative breast cancer (RR: 0.58, 95% CI: 0.37, 0.91). Women with ER-positive breast cancer who were randomized to the low-fat diet also had fewer second breast cancers, but the result was not statistically significant (RR: 0.85, 95% CI: 0.63, 1.14). However, the updated results from the WINS study (presented at the San Antonio Breast Conference in December 2006, www.sabcs.org ) demonstrated a non-significant improvement in disease-free survival in the intervention group compared with usual care. The Women’s Healthy Eating and Living Study (WHEL) was a randomized clinical trial of high fruit and vegetable and low-fat diet versus usual diet in breast cancer survivors. This trial demonstrated that women randomized to a high fruit and vegetable diet increased consumption of fruits and vegetables, as supported by marked increases in plasma carotenoids, which are biomarkers of fruit and vegetable intake [27]. Despite this apparent adherence, and a 7.3-year follow-up period, rates of second breast cancer were similar in the two dietary arms of this trial (HR = 0.96, 95% CI 0.80–1.14) and mortality was lower, but not significantly (HR = 0.91, 95% CI 0.72–1.15). The women in the non-intervention arm of this study were consuming 3.8 servings of vegetables and 3.4 servings of fruit at baseline; therefore, this trial was evaluating incremental benefits in cancer survival for consuming very high intakes versus high intakes. The observational evidence commonly suggests that the incremental benefits are largest when intervening in populations with low intakes, so the results of this trial should be interpreted in their proper context (no additional benefit to consuming more than 5 servings a day). These two trials are by far the largest trials of nutritional interventions in cancer survivors; however, some smaller trials of dietary interventions as well as some larger randomized trials of nutrient supplements have been done as recently reviewed [28] by Davies et al. This review includes trials in cancer patients as well as patients with premalignant end points such as adenomatous polyps. The authors noted that all-cause mortality was nonsignificantly lower in trials of a “healthy diet”

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(OR 0.90, 95% CI 0.46–1.77). Analyses of nutrient supplements, including antioxidant supplements, showed no clear evidence of benefit or harm in cancer survivors. However, it should be emphasized to patients that high-dose nutrient supplements are not recommended for generalized cancer prevention, and some studies of cancer survivors have observed adverse effects from nutrient supplements. As an example, Bairati and colleagues observed a higher rate of second primary cancers and poorer cancer-free survival in head and neck cancer patients randomized to receive alphatocopherol as compared with placebo [29]. The meta-analysis, published in 2006, concluded that there is an urgent need to better understand the effects of diet and nutrient supplements on cancer outcomes. In regards to supplements, vitamin D and its potential association with prognosis is receiving a lot of attention. Recent research suggests breast cancer patients might fare worse if they suffer from vitamin D deficiency. These results were presented at the 2008 annual meeting of the American Society of Clinical Oncology by Goodwin and colleagues [30]. They measured vitamin 25-OH D levels in the blood of 512 newly diagnosed breast cancer patients and followed them for 12 years. Compared with women with adequate vitamin D, women with deficient levels had significantly less disease-free survival (hazard ratio [HR] 1.94, p = 0.02) and overall survival (HR 1.73, p = 0.02). The prognostic significance of vitamin D levels was independent of patient age or weight, tumor stage, or tumor grade. In summary, because cancer survivors are often at heightened risk for non-cancer chronic diseases such as coronary heart disease, and frequently live a long time and may die of other diseases, it is obviously prudent to emphasize dietary patterns associated with lowered all-cause mortality. Future trials of low calorie diets with physical activity are necessary to better understand the impact of weight loss, diet, and physical activity on disease-free survival.

6 Impact of Both Nutrition and Physical Activity on Cancer Survival A recent observational analysis of the non-intervention arm of the WHEL study suggested that the combination of fruit and vegetable intake with physical activity was beneficial [4]. Specifically, breast cancer survivors who consumed 5+ daily servings of fruits and vegetables and who exercised an amount equivalent to walking 30 min a day, 6 days per week, had a significant survival advantage. This was not seen in women who engaged in only one behavior (diet or physical activity); rather, the combined influence of diet and physical activity was associated with risk reduction. Notably, only a minority of breast cancer survivors engaged in both health-promoting behaviors, suggesting obvious opportunities for survivorship interventions. Given the lack of randomized trials of both diet and exercise or weight loss on survival in men and women diagnosed with cancer, a couple small-scale trials of diet and exercise on surrogate markers of survival (serum hormones and body composition) have recently been initiated. Mefferd and colleagues recently published results

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from a cognitive behavioral therapy intervention for weight loss through exercise and diet modification on risk factors for recurrence of breast cancer [31]. Eighty-five overweight breast cancer survivors were randomly assigned to a once weekly, 16-week intervention, or wait-list control group. Significant differences in weight, BMI, and percent body fat were observed between intervention and control groups (p < 0.05). Their results indicate that 16 weeks of a cognitive behavioral therapy program for weight management may reduce obesity in overweight breast cancer survivors.

7 Mechanisms Linking Energy Balance to Cancer Survival The beneficial effects of weight loss, healthy eating, and physical activity on improved prognosis after a cancer diagnosis may be mediated through beneficial changes in metabolic (insulin) and sex hormones (androgens and estrogens), growth factors (insulin-like growth factor (IGF)-I and IGFBP-3), adipokines (leptin, adiponectin), and/or inflammation (C-reactive protein) [32]. Specifically, there is increasing evidence that high insulin levels strongly increase the risk of breast and colon cancer recurrence and death. Three recent studies have observed an approximate three-fold increased risk of all-cause mortality among women with high insulin levels, measured approximately 2 years after breast cancer diagnosis, relative to women with low insulin levels [33]. The strong association between fasting insulin levels and death has led a number of oncologists and scientists to consider the targeting of insulin as a therapeutic modality in breast cancer, particularly because insulin can be modified by lifestyle and pharmacologic interventions. Similar to insulin, IGF-I has potent mitogenic and antiapoptotic properties in normal and malignant epithelial cells, whereas IGFBP-3 can either stimulate or suppress cellular proliferation by restricting IGF-I’s availability and biological activity. For insulin, some mitogenic effects may be mediated by interaction with IGF-I receptors, as hyperinsulinemia promotes the synthesis and activity of IGF-I. Although the data are not consistent, high levels of IGF-I and low levels of IGFBP-3 have been associated with an increased risk of breast cancer and adverse prognostic factors; however, a study by Goodwin and colleagues found high levels, rather than low levels, of IGFBP-3 predicted distant recurrence of breast cancer in postmenopausal women [34]. Most recently, two studies have demonstrated decreased serum insulin levels in breast cancer survivors in response to recommended amounts of physical activity [35, 36], and one of the studies also observed beneficial changes in IGFs [36]. Thus, physical activity may improve prognosis via favorable changes in insulin and IGFs. Obesity, a high insulin level and altered IGF levels are also associated with a less favorable sex hormone profile [5, 8, 38, 40]. Sex steroid hormones have powerful mitogenic and proliferative influences and are strongly associated with the development and control of breast cancer. A number of clinical trials show that estrogen ablation increases survival following a diagnosis of breast cancer [37]. Changes in sex hormones are perhaps the most consistently cited potential mechanism for

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the association between physical activity and improved breast cancer survival. The primary mechanism of reduced calorie diets and physical activity influencing sex hormones in postmenopausal women is via decreased body fat, a substrate for estrogen and testosterone production, which results in less tissue capable of aromatization of the adrenal androgens to estrogens. Observational and clinical trials have shown favorable effects of healthy eating, physical activity, and weight loss on sex hormone concentrations [38–40]. Preliminary in vitro and epidemiologic data have also suggested a link between adipokines (e.g., leptin and adiponectin), inflammatory markers (e.g., C-reactive protein), and poor cancer prognosis [41]. Adipokines exhibit strong associations with body mass index, abdominal fat mass, and hyperinsulinemia. In addition, several adipokines including leptin promote angiogenesis, which is essential for cancer development and progression and can stimulate estrogen biosynthesis by the induction of aromatase activity. C-reactive protein (CRP) is a well-known systemic marker for inflammation that is produced by the liver, and is only present during episodes of chronic inflammation, and has been shown to be associated with worse prognosis. While these hormones may be related to cancer survival, very few studies have been published examining the impact of weight loss, diet, or exercise on adipokines and inflammation in cancer survivors. Future studies need to examine the effect of energy balance on adipokines and inflammation. In summary, with numerous publications showing statistically and clinically significant associations between energy balance and poor prognosis, it becomes increasingly important to identify modifiable factors that improve survival and can be used as surrogate markers of survival. Although much further research is needed, evidence to date suggests that weight loss, healthy eating, and physical activity favorably modify surrogate markers of cancer survival.

8 Energy Balance and Psycho-Social/Quality of Life Benefits Because of the continually improving survival rates, resulting in a large population of over 11 million cancer survivors in the United States alone, the psychological well-being and physical functioning of survivors is important from a public health standpoint. Furthermore, treatment advances, new chemotherapeutic agents, hormone therapies, and biologic therapy have, at least in part, altered the psychological impact of a diagnosis of cancer. As a result of the cancer diagnosis, surgery, and adjuvant treatments, some cancer survivors experience fatigue, depression, anxiety, reduced overall quality of life, (QOL) and weight gain. Recent systematic reviews and meta-analyses have reported clear benefits of physical activity for improved quality of life, reduced fatigue, and cardiovascular fitness among cancer survivors [42]. In one of the largest studies to date, Courneya and colleagues examined the effects of aerobic exercise alone, resistance exercise alone, or usual care on quality of life in 242 breast cancer survivors initiating chemotherapy [43]. There were significant favorable effects of both aerobic and resistance exercise on multiple outcomes including self-esteem, fitness, and body

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composition, as well as increased chemotherapy completion rates compared with usual care. Furthermore, no significant adverse events were reported; lymphedema did not increase or was not exacerbated by aerobic or resistance exercise. Courneya and colleagues also completed a similar trial of aerobic exercise vs. usual care in breast cancer survivors who had completed adjuvant treatment and observed similar favorable effects of exercise on overall quality of life [44]. Overall, these, and other, studies have demonstrated that exercise is safe in cancer survivors and produces beneficial effects on quality of life and cancer-related symptoms with no adverse side effects. Most recently, Segal and colleagues examined the effect of 24 weeks of aerobic or resistance exercise compared with usual care on fatigue and quality of life in 121 prostate cancer patients initiating radiotherapy with or without androgen deprivation therapy [45]. Both resistance and aerobic exercise mitigated fatigue in prostate cancer survivors, and resistance training generated longer-term improvements and additional benefits for QOL. Lastly, Demark-Wahnefried and colleagues are currently conducting a trial that tests whether a home-based multi-behavior intervention focused on exercise and including a low saturated fat, plant-based diet, would improve physical functioning in 641 older, long-term (> 5 year post-diagnosis) survivors of breast, prostate, or colorectal cancer [46]. In summary, given that many existing cancer therapies are costly and have significant side effects that can result in long-term morbidity and even mortality in cancer patients, non-pharmacologic methods to lower cancer recurrence and death, especially those that are also associated with improvements in quality of life, depression, and fatigue, may offer an attractive addition to the currently available treatment options. Additionally, men and women who have survived cancer have an increased risk for developing cardiovascular disease [21], thus an intervention that might have a positive impact on this outcome would be beneficial for cancer survivors.

9 Approaches to Improving Weight, Nutrition, and Physical Activity Behaviors in Cancer Survivors Physical activity and nutrition counseling have not traditionally been a part of the cancer treatment/survivorship program. This is despite the fact that weight gain is common after a cancer diagnosis. Further, a large proportion of cancer survivors do not perform regular physical activity, and many cancer survivors decrease their physical activity levels after diagnosis. Suboptimal diets have also been observed in men and women diagnosed with cancer. Nutritional needs of cancer survivors vary, depending upon their stage in the cancer continuum. Many things can impact cancer survivors’ nutritional status (i.e., reduced appetite and alterations in taste due to chemotherapy), and dietary strategies are available to help cancer patients through the treatment and recovery process. Given, physical activity and nutrition programs carry tremendous potential to affect length and quality of survival in a positive manner and prevent or control morbidity associated with cancer or its treatment, the motivation to maintain a positive behavior change may be higher among cancer

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survivors than men and women without cancer. Survey studies have shown that cancer survivors want to receive information about lifestyle and cancer survivorship [47]. Survey studies have shown that oncologists agree that exercise, diet, and weight loss counseling is beneficial for cancer survivors during and after treatment [48]. Thus, oncologists appear to have a favorable attitude toward recommending exercise, healthy eating, and weight loss to cancer survivors, yet several barriers, such as not being aware of the benefits of these behaviors or referral opportunities, may prevent them from providing counseling. Most recently, working together, the American Cancer Society and the American College of Sports Medicine developed a certification called “the certified cancer exercise trainer” for personal trainers, physical therapists, nurse practitioners, or other health professionals to become certified in counseling and training cancer survivors in how to exercise safely and at recommended levels. These “Certified Cancer Exercise Trainers” are knowledgeable about the potential physical limitations associated with surgery and treatment and have the skills and abilities to help cancer survivors overcome some of the recent and late effects of surgery and treatment (go to www.acsm.org or www.cancer.org for more information on physical activity and diet counseling referral opportunities). In summary, oncologists should discuss with their patients the benefits of physical activity, healthy eating, and weight loss after a diagnosis of cancer, and also reassure them that exercise is safe and associated with improved overall quality of life. While large-scale trials of dietary-induced weight loss and physical activity on survival have yet to be conducted, cancer survivors should seek out the growing number of opportunities that exist toward being physically activity and eating healthy, and oncologists should also become aware of the benefits of these healthy behaviors after a cancer diagnosis, as well as existing referral networks.

10 Conclusions An increasing number of men and women are being diagnosed with cancer, and many cancer survivors are seeking lifestyle-based approaches to improve survival. There are, clearly, many questions to be answered concerning who would benefit from lifestyle change, when these behavior changes would be most beneficial, and what type of diet and exercise program would be most valuable. Future research must be done both to establish the efficacy and effectiveness of weight control, nutrition, and physical activity to lower cancer recurrence and death and to understand the biologic mechanisms through which these methods impact cancer development and malignant potential. However, until these studies are conducted, it may well be a benefit for men and women with cancer to maintain a healthy weight, eat a prudent diet of fruits and vegetables, whole grains and poultry and to exercise 30 min per day. Since a majority of cancer survivors are not currently participating in recommended levels of physical activity or eating a healthy diet, these targeted therapies have the potential to benefit a large number of cancer survivors. Thus, oncologists and primary-care physicians should be encouraged to counsel cancer survivors proactively about nutrition, exercise, and weight control.

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Future research might also benefit from specifically targeting those survivors who are experiencing psychosocial impairment or reduced quality of life. Despite overall improvements in the health and well-being of cancer survivors, quality of life remains a major concern for certain subgroups of survivors, including young survivors, survivors with a lower level of education, survivors who are diagnosed with later-stage cancer and those who undergo chemotherapy, hormone therapy, or extensive and debilitating treatment regimens. Improved understanding in these research areas will pave the way for physical activity and dietary interventions and programs to become a routine component of cancer treatment and recovery and will hopefully provide the necessary evidence to convince policy makers to include weight loss, exercise, and nutrition counseling in cancer management, and encourage second party payers to reimburse cancer survivors for receipt of their counseling.

References 1. Holmes MD, Chen WY, Feskanich D, Kroenke CH, Colditz GA (2005). Physical activity and survival after breast cancer diagnosis. JAMA, 293(20):2479–2486. 2. Irwin ML (2008). Influence of pre- and post-diagnosis physical activity on survival in breast cancer survivors: the health, eating, activity, and lifestyle (HEAL) study. J Clinic Oncol, 26(24):1–7. 3. Holick CN, Newcomb PA, Trentham-Dietz A, et al. (2008). Physical activity and survival after diagnosis of invasive breast cancer. Cancer Epidemiol Biomarkers Prev, 17(2):379–386. 4. Pierce JP, Stefanick ML, Flatt SW, et al. (2007). Greater survival after breast cancer in physically active women with high vegetable-fruit intake regardless of obesity. J Clinic Oncol, 25(17):2345–2351. 5. Doyle C, Kushi LH, Byers T, et al. (2006). Nutrition and physical activity during and after cancer treatment: an American cancer society guide for informed choices. CA Cancer J Clin, 56(6):323–353. 6. Daling JR, Malone KE, Doody DR, Johnson LG, Gralow JR, Porter PL (2001). Relation of body mass index to tumor markers and survival among young women with invasive ductal breast carcinoma. Cancer, 92(4):720–729. 7. Chlebowski RT, Aiello E, McTiernan A (2002). Weight loss in breast cancer patient management. J Clin Oncol, 20(4):1128–1143. 8. Calle EE, Rodriguez C, Walker-Thurmond K, Thun MJ (2003). Overweight, obesity, and mortality from cancer in a prospectively studied cohort of U.S. adults. N Engl J Med, 348(17):1625–1638. 9. Irwin ML, McTiernan A, Baumgartner RN, et al. (2005). Changes in body fat and weight after a breast cancer diagnosis: influence of demographic, prognostic, and lifestyle factors. J Clin Oncol, 23(4):774–782. 10. Irwin ML, Crumley D, McTiernan A, et al. (2003). Physical activity levels before and after a diagnosis of breast carcinoma: the health, eating, activity, and lifestyle (HEAL) study. Cancer, 97(7):1746–1757. 11. Blanchard CM, Courneya KS, Stein K (2008). Cancer survivors’ adherence to lifestyle behavior recommendations and associations with health-related quality of life: results from the American Cancer Society’s SCS-II. J Clin Oncol, 26(13): 2198–2204. 12. Dignam JJ, Polite BN, Yothers G, et al. (2006). Body mass index and outcomes in patients who receive adjuvant chemotherapy for colon cancer. J Natl Cancer Inst, 98(22):1647–1654. 13. Wright ME, Chang SC, Schatzkin A, et al. (2007). Prospective study of adiposity and weight change in relation to prostate cancer incidence and mortality. Cancer, 109(4):675–684.

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36. Irwin ML, Varma K, Alvarez-Reeves, et al. (2009). Randomized controlled exercise trial on insulin and IGFs in breast cancer survivors: the Yale exercise and survivorship study. Cancer Epidemiol Biomarker Prev, 18(1): 306–313. 37. Howell A, Cuzick J, Baum M, et al. (2005). Results of the ATAC (Arimidex, Tamoxifen, Alone or in Combination) trial after completion of 5 years’ adjuvant treatment for breast cancer. Lancet, 365(9453):60–62. 38. McTiernan A, Tworoger SS, Ulrich CM, et al. (2004). Effect of exercise on serum estrogens in postmenopausal women: a 12-month randomized clinical trial. Cancer Res, 64(8):2923–2928. 39. World Cancer Research Fund, American Institution for Cancer Research (2007). Food, nutrition, physical activity, and the prevention of cancer: a global perspective. The Second Expert Report. AICR, Washington, DC. 40. McTiernan A, Rajan K, Tworoger S, et al. (2003). Adiposity and sex hormones in postmenopausal breast cancer survivors. J Clin Oncol, 21:1961–1966. 41. Pierce BL, Neuhouser ML, Wener MH, et al. (2009). Correlates of circulating C-reactive protein and serum amyloid A concentrations in breast cancer survivors. Breast Cancer Res Treat, 114:155–167. 42. Bicego D, Brown K, Ruddick M, et al. (2008). Effects of exercise on quality of life in women living with breast cancer: A systematic review. Breast J, Dec 12. 43. Courneya KS, Segal RJ, Mackey JR, et al. (2007). Effects of aerobic and resistance exercise in breast cancer patients receiving adjuvant chemotherapy: a multicenter randomized controlled trial. J Clin Oncol, 25(28):4396–4404. 44. Courneya KS, Mackey JR, Bell GJ, Jones LW, Field CJ, Fairey AS (2003). Randomized controlled trial of exercise training in postmenopausal breast cancer survivors: cardiopulmonary and quality of life outcomes. J Clin Oncol, 21(9):1660–1668. 45. Segal RJ, Reid RD, Courneya KS, et al. (2009). Randomized controlled trial of resistance or aerobic exercise in men receiving radiation therapy for prostate cancer. J Clin Oncol, 27: 344–351. 46. Snyder DC, Morey MC, Sloane R, et al. (2008). Reach out to ENhancE Wellness in Older Cancer Survivors (RENEW): design, methods and recruitment challenges of a home-based exercise and diet intervention to improve physical function among long-term survivors of breast, prostte, and colorectal cancer. Psychooncology, 18(4):429–439. 47. Jones LW, Courneya KS (2002). Exercise counseling and programming preferences of cancer survivors. Cancer Pract, 10(4):208–215. 48. Jones LW, Courneya KS, Peddle C, Mackey JR (2005). Oncologists’ opinions towards recommending exercise to patients with cancer: a Canadian national survey. Support Care Cancer, 13(11):929–937.

Chapter 9

Behavior, Energy Balance, and Cancer: An Overview Donna Spruijt-Metz, Selena T. Nguyen-Rodriguez, and Jaimie N. Davis

1 Overview: Physical Activity, Diet, and Sleep Impact on Both Obesity and Cancer There are arguably only three lifestyle behaviors that are imperative for survival. We can, for instance, survive without working, reading, listening to music, writing, traveling, or driving a car. However, humans (and animals) have to eat, move, and sleep in order to maintain life. All three of these behaviors have been linked to obesity as well as cancer. Thus, not only do obesity and cancer share some common mechanisms, consequences, and reciprocal influences, as shown in previous chapters, they also appear to be influenced by the same lifestyle behaviors. More than 66% of adults, 18 years or older, are overweight or obese (defined as a body mass index (BMI) of 25 or over) [1]. More than 1 million Americans were diagnosed with cancer in 2008, and over 35% of this cancer is preventable through exercise, diet, and obesity reduction [2–8]. It stands to reason that if people know that healthy diet, physically active lifestyles, and sufficient sleep are linked to lower weight and decreased cancer risk, they would attempt to change these behaviors. In a recent nation-wide study, 47% of adults aged ≥20 years said they tried to lose weight during the preceding 12 months [9]. Consumers spend more than $35 billion a year on weight loss products and programs to change diet and physical activity [10]. However, physical activity, diet, and sleep are difficult to change and are strongly influenced by a host of determinants including habit, stress, and the social and built environment. Therefore, although these behavioral domains seem to be under volitional control, they have proven notoriously difficult to change, even for brief periods of time, and long-term change has proven elusive. Each of these behavioral domains encompasses a broad range of specific behaviors, some of which have much stronger empirically documented relationships with obesity and cancer

D. Spruijt-Metz (B) Keck School of Medicine, Institute for Health Promotion and Disease Prevention Research, University of Southern California, Los Angeles, CA, USA e-mail: [email protected]


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than others. Current knowledge on relationships between specific behaviors should inform choices of intervention targets. Therefore, this chapter is designed to provide up-to-date information on the relationships of specific behaviors with obesity and cancer in order to inform the design of future interventions to prevent obesity and some forms of cancer. In this chapter we will (1) review the basic tenets of interventions to change lifestyle behaviors, (2) review the relationships of specific components of these three key behavioral domains with obesity and cancer risk, (3) discuss challenges involved in interventions to change these behaviors, and (4) examine the effects of behavior changes on obesity and cancer outcomes. Each section provides examples of studies that focus on specific types of physical activity, diet, or sleep behaviors that have been associated with any form of cancer risk, and is not exhaustive, but meant to illustrate the current state of the science in each field.

2 Intervening to Change Lifestyle Behaviors Figure 9.1 shows a flowchart of choices that need to be made in the course of intervention development. Each of the top boxes will generate a cascade of choices as shown in the corresponding lower boxes and combine to inform the study design.

Population(s) •Ethnicity •Gender •Socioeconomic status •Age •Health status •Cognitive ability

Behavior(s) • Specific behaviors • # of behaviors • Outcome Measures

Setting(s) • Workplace • Schools • Home •Community Settings • Public space • Laboratory • Combination

Theory(s) • Appropriate for behaviors • Appropriate for target population • Measure Theoretical Constructs • Intervene on Theoretical Constructs

Level(s) (Intervention unit) • Individual • Family • Group • Environment • Policy Makers

Modality(s) • Phone • In-person • Text messages • Web • Mass Media • Pharmaceutical

Outcomes • Did behavior change? • How much–clinical vs. statistical significance • Correlated Changes?

• • • •

Intensity Number of contacts Length of contacts Length of Intervention Participant burden (travel time, measurement burden, homework, etc)

Evaluation • Did intervention work as planned? • How much did it cost? • How to improve in future?

Delivery • Research Team • Professional Trainer • Nutritionist • Medical Professional • etc.

Study Design • Number of measurement points • Time to follow-up • Crossover, Control/Comparison Groups, Matching, other design considerations

Fig. 9.1 Intervention decision node flowchart – choices to be made in the development of lifestyle interventions

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The combination of elements will dictate, among other things, (1) the success of the intervention at changing the targeted behaviors, (2) the ability to detect behavior change, and (3) the ability to explain behavior change. Interventions based on relevant behavioral theories that incorporate possible mediators and moderators of behavioral change into the study design, outcome measures, and statistical analyses are more informative and arguably more effective [11–13]. Moderators are variables not targeted for intervention and in most cases not expected to change, but which could interact with other factors to influence the outcomes of the intervention. In Fig. 9.1, the first box represents frequent moderators of intervention effects. For instance, several interventions to change diet and physical activity have been more successful in one gender than another [14], in which case gender moderates the effect of the intervention. Mediators are constructs that are hypothesized to fall in the causal pathway between intervention components and behavior. For instance, increases in self-efficacy to increase fruit and vegetable intake could serve as a mediator between improved dietary knowledge and increases in fruit and vegetable intake. Theories of behavior comprise sets of moderators and mediators and can be population specific. For instance, a behavioral theory that posits rational decision making as the main mediator in dietary behaviors might not be suitable for development of an intervention targeting adolescent populations [13]. A review of theories of health-related behaviors is beyond the scope of this chapter. See, for instance, Sallis et al. [15] and Spruijt-Metz and Saelens [13] for reviews of theories and correlates of physical activity in youth, Wendel-Vos et al. [16] and Trost et al. [17] for correlates of physical activity in adults, Shaikh et al. [18], for correlates of dietary intake in adults, and Blanchette et al. [19] and McClain et al. [20] for correlates of dietary intake in youth. The existing literature on psychosocial factors and sleep tends to focus on the psychosocial effects of sleep disturbance (i.e., psychosocial factors are the outcome rather than the predictor). For example, children who experience more sleep problems have more depressive symptoms [21] and their mothers experience more negative mood and parenting stress [22]. In a study of older adults, sleep disturbances were found to be related to depression [23]. The study of psychosocial correlates affecting sleep behavior is a new area of research, and to the best of our knowledge, an overview of these correlates has not yet been published. Aspects of the built environment can both mediate and moderate physical activity, diet, and sleep behaviors. There are many aspects of the built environment that might influence energy balance-related behaviors, such as proximity and availability and costs of healthy food choices [24], proximity of fast-food restaurants, parks, and safe places to play [25], noise levels, and connectivity [26]. These have yet to be fully enumerated and consistently incorporated into behavioral theories [27]. An important correlate of the built environment that also influences energy balance-related behaviors is socioeconomic status. Lower socioeconomic status has been linked to more “obesogenic” environments and an increased risk of obesity in both adult and child populations [28]. Chapter 10 of this book reviews the evidence for interrelationships between aspects of the built environment and energy balance-related behaviors.

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3 Physical Activity, Sedentary Behavior, and Cancer Risk Chapter 7 in this volume documents the epidemiological evidence linking physical activity to cancer risk and prevention. Physical activity seems to exert preventive effects at several stages of carcinogenesis, including tumor initiation and progression. There is convincing evidence for preventive effects of physical activity on breast, colon, endometrial, prostate, and lung cancer [3]. The biological mechanisms that underlie the protective effects of physical activity are not clear [2], although some have been characterized to certain extent. These include reduction in body fat stores, activity-related changes in sex-hormone levels, improved immune function, improved insulin dynamics, activity-induced changes in insulin-like growth factors, reductions in inflammation, and enhanced DNA repair systems [2, 29].

3.1 Domains, Levels, and Measures of Physical Activity One of the difficulties in understanding the mechanisms by which physical activity influences cancer is that physical activity is not one unified behavior. Physical activity is defined as body movement that is produced by the contraction of muscle that increases energy expenditure above the baseline level [30]. All domains of physical activity are included in this definition, including leisure-time physical activity, occupational physical activity, transportation physical activity, household chores, and any other activity other than complete body stillness [12, 31]. There are literally hundreds of different physical activity domains [32, 33]. A recent review of 47 physical activity interventions found 99 distinct physical activity outcomes in the 47 studies reviewed [12]. Different physical activity behaviors, or domains, can have different physiological and biological effects. Walking, leisure activities, work-related activities, organized sports such as basketball, swimming, strength training, and aerobic exercise – these and other activities may affect various biological mechanisms and thus affect cancer in different ways. Furthermore, many of these domains have some overlap. Brisk walking, for instance, is subsumed under the rubric of moderate physical activity, but may not have the same physiological effect or ease of execution as other domains that would also fit under that same rubric. Sedentary behavior, once conceptualized as the absence of physical activity, is now seen as a distinct realm of behaviors such as time spent watching TV, in motorized transportation, or sitting at a desk [34]. Time spent in sedentary behavior has been linked to obesity [35] and cancer risk [36] independently of other domains of physical activity. Within each domain of physical activity there are at least four levels of activity: (1) Intensity, which is the ratio of working metabolic rate to resting metabolic rate (metabolic equivalent tasks or METs), where one MET, or metabolic equivalent, represents the metabolic rate of an individual at rest and is estimated at 3.5 ml of oxygen consumed per kilogram of body mass per minute or 1 kcal/kg/h [32]. METs are frequently used to classify physical activity into light (6 METs) activity [37]. Sometimes MET-hours are used to

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quantify activity levels. MET-hours are METs per hour of each activity multiplied by hours per week of each activity; (2) Energy expenditure, which is the amount of kilocalories (kcals) or kilocalories per kilogram of body weight expended in any activity; (3) Duration – time spent in a particular activity; and (4) Frequency with which a particular activity is undertaken – how often in a day, week, month, or year. Thus, the effect of any activity on cancer and obesity might be influenced by properties of the activity itself (for instance, is it weight-bearing, aerobic, which muscles does it use?), the intensity at which it is practiced, the energy expended, the time spent doing that activity, and/or the frequency with which that activity is undertaken. Domains and levels of physical activity in which an individual participates in the course of a day, a week, or some other period of time combine to reveal patterns of physical activity. The protective effects of any specific activity will be related to the level at which the activity is undertaken, including duration and intensity, and in other cases, frequency and energy expenditure. Finally, each domain and level of physical activity can be measured in many different ways, including questionnaires and diaries, objective measures such as pedometers and accelerometers, and observation [31]. The choice of measures depends upon the research design and outcome of interest, study budget, and population to be studied. Many studies use a combination of these modes of measurement to assess the same behavioral outcome. For instance, if total physical activity is a target, this might be measured objectively (by accelerometry) and subjectively (by questionnaire). Each of these measures may be more or less reliable and valid, and may or may not show strong agreement with each other. The mode of measurement may influence research findings. Therefore, to examine the effects of physical activity on cancer or obesity, the behavioral domain, level, and mode of measurement needs to be taken into consideration.

3.2 Physical Activity Domains and Cancer Risk Chapter 7 of this volume gives a complete epidemiological overview of the relationships between any type of physical activity and specific cancers. This section, on the other hand, provides examples of specific types of physical activity that have been associated with any form of cancer risk. To illustrate physical activity type– cancer relationships here, examples of some significant relationships are reviewed. Although many types of physical activity have been related to reduced breast cancer risk, we only highlight a few here. Walking: Walking is a common and safe physical activity behavior that has been related to a myriad of health benefits in people of all ages. Walking to school is associated with lower BMI in youth [38]. In a case–control study in adults in Shanghai in 931 colon cancer patients and 1,552 randomly selected controls examined lifetime commuting to work and cancer risk. Walking for more than 30 minutes per day reduced colon cancer risk by 29% in men and 43% in women [39]. In a prospective study of 74,171 women aged 50–79 [40], women who participated in

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1.25–2.5 hours of brisk walking per week had an 18% decreased incidence of breast cancer compared with less active women. The protective domain of physical activity was walking; the intensity was brisk (3.8 METs or moderate physical activity) and the duration was between 1.25 and 2.5 hours. All of these parameters may be important to achieve effective prevention. Bicycling: In the case–control study by Hou et al. mentioned above [39], bicycling for transportation purposes was also associated with colon cancer. Risk was reduced consistently with an increasing average time spent bicycling for both men and women, with risk reduction of nearly 59% for those who rode a bicycle for more than 2 hours per day. Decreased risk of breast cancer with increasing cycling activity levels has also been reported in premenopausal and postmenopausal women [41]. Leisure time or recreational physical activity: Leisure time or recreational physical activity is a composite that can encompass sports, biking, walking, household chores, gardening, and leisure-time sports, depending upon the physical activity measure used. McTiernan et al. [42] found that >7 hours of moderate to vigorous recreational physical activity per week was protective against breast cancer incidence (RR = 0.79, 95% CI, 0.63–0.99) in a sample of 74,171 US postmenopausal women aged 50–79. Recreational physical activity was a composite of time spent in activities such as (but not limited to) aerobics, aerobic dancing, jogging, tennis, swimming laps, biking outdoors, using an exercise machine, calisthenics, easy swimming, and popular, folk or slow dancing, bowling, and golf. Another study in 685 colon cancer cases and 2,434 control subjects found significantly lower risks of colon cancer with as little as 10 minutes of leisure-time physical activity 2–6 times per week [43]. Occupational physical activity: A large European cohort study examined the relative risk of breast cancer between women whose jobs involved walking, lifting, or heavy manual labor and women whose jobs were sedentary. The study found a 52% reduction in breast cancer risk for women who reported doing heavy manual labor [44]. It should be noted that these are potentially confounded by health at baseline, which was not controlled. Women in better health might be more capable of performing heavy manual labor, therefore it is unclear if heavy manual labor or superior health actually reduced cancer risk. Household physical activity: Household physical activity, like recreational and occupational physical activity, is defined variously and measures are not uniform or necessarily comparable. Household physical activity is, however, for many women a substantial percentage of their overall physical activity. One study found that household activity was associated with a significantly reduced risk of breast cancer in postmenopausal (HR, 0.81; 95% confidence interval, 0.70–0.93, highest versus the lowest quartile; p = 0.001) and premenopausal (HR, 0.71; 95% confidence interval, 0.55–0.90, highest versus lowest quartile; p = 0.003) women [45]. MET-hours, moderate, and vigorous physical activity: Almost any activity can be used to calculate MET-hours (METs per hour of each activity multiplied by hours per week of each activity) to understand time spent in moderate to vigorous physical activity, depending on the measurement instruments used. In a

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large cohort of 416,277 men and women across 10 European countries, Steindorf et al. [46] found that women who participated in vigorous non-occupational physical activity for between 13.5 and 33.5 MET-hours a week had a significantly reduced risk for lung cancer (RR = 0.6, 95% CI, 0.40–0.89), as did men who participated in at least 18 MET-hours a week of sports (RR = 0.71, 95% CI, 0.50–0.98). Energy expenditure: In the Harvard Alumni Health Study of 17,148 men, energy expenditure (assessed by kilojoules per week (4.2 kJ=1 kcal) spent in physical activity from blocks walked, flights climbed, and participation in sports or recreational activities [47]) was associated with reduced risk in colon cancer [48]. Risk ratios (95% CI) for colon cancer, with inactive set at 1.00, was 0.52 (0.28–0.94) for moderately active participants and 0.50 (0.27–0.93) for highly active participants. Aerobic physical activity: The 2008 Physical Activity Guidelines synthesized the current data for the protective effects of aerobic physical activity, finding that those who participated in aerobic physical activity for approximately 3–4 hours per week at moderate or greater levels of intensity had on average a 30% reduction in colon cancer risk and a 20–40% lower risk of breast cancer, compared with those who were sedentary [49]. Strength training: Although strength training has not been directly linked to cancer prevention, it has been shown to affect important cancer biomarkers including body fat, waist circumference, fasting insulin, fasting glucose, insulin-like growth factor I (IGF-I), and several IGF-binding proteins [50]. Sedentary behaviors: Sedentary behavior is no longer considered merely a lack of activity, but has been defined as a class of behaviors, such as television viewing. One study examined the association between physical activity, sedentary behavior, and ovarian cancer risk in the American Cancer Society Cancer Prevention Study II Nutrition Cohort, a prospective study of cancer incidence and mortality [51]. Baseline information was collected in 1992. From 1992 to 2001, 314 ovarian cancer cases were found among 59,695 postmenopausal women who were cancer free at enrollment. In this large cohort, there were no associations between past physical activity or recreational physical activity at baseline and risk of ovarian cancer. However, a prolonged duration of sedentary behavior (for 6 vs.

Cancer and Energy Balance, Epidemiology and Overview (Energy Balance and Cancer)

Energy Balance and Gastrointestinal Cancer

Insulin Resistance and Cancer: Epidemiology, Cellular and Molecular Mechanisms and Clinical Implications (Energy Balance and Cancer 1)

Neuro-endocrinology of Growth and Energy Balance

Sensory and Metabolic Control of Energy Balance

Cancer Epidemiology and Prevention

Cancer Epidemiology: Principles and Methods

Peptides in Energy Balance and Obesity (Frontiers in Nutritional Science)

Epidemiology of lung cancer

Cancer Epidemiology. Modifiable Factors

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Fluid and Electrolyte Balance

Fluid and Electrolyte Balance

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Epidemiology, Pathogenesis, and Prevention of Head and Neck Cancer

Cancer Epidemiology, Host Susceptibility Factors

The Cancer Degradome: Proteases and Cancer Biology

Cancer Genome and Tumor Microenvironment (Cancer Genetics)

Cancer Vaccines and Immunotherapy (Cambridge Cancer Series)

Bioinformatics in Cancer and Cancer Therapy

Cancer Vaccines and Immunotherapy (Cambridge Cancer Series)

Chemokines and Cancer (Contemporary Cancer Research)

The Cancer Degradome: Proteases and Cancer Biology

Cancer and Energy Balance, Epidemiology and Overview (Energy Balance and Cancer)

Energy Balance and Gastrointestinal Cancer

Insulin Resistance and Cancer: Epidemiology, Cellular and Molecular Mechanisms and Clinical Implications (Energy Balance and Cancer 1)

Neuro-endocrinology of Growth and Energy Balance

Sensory and Metabolic Control of Energy Balance

Cancer Epidemiology and Prevention

Cancer Epidemiology: Principles and Methods

Peptides in Energy Balance and Obesity (Frontiers in Nutritional Science)

Epidemiology of lung cancer

Cancer Epidemiology. Modifiable Factors

Balance

Fluid and Electrolyte Balance

Fluid and Electrolyte Balance

Balance

Balance

Balance

Balance

Balance

Balance

Epidemiology, Pathogenesis, and Prevention of Head and Neck Cancer

Cancer Epidemiology, Host Susceptibility Factors

The Cancer Degradome: Proteases and Cancer Biology

Cancer Genome and Tumor Microenvironment (Cancer Genetics)

Cancer Vaccines and Immunotherapy (Cambridge Cancer Series)

Bioinformatics in Cancer and Cancer Therapy

Cancer Vaccines and Immunotherapy (Cambridge Cancer Series)

Chemokines and Cancer (Contemporary Cancer Research)

The Cancer Degradome: Proteases and Cancer Biology

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