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Journal of Clinical Oncology, Vol 22, No 22 (November 15), 2004: pp. 4507-4513 © 2004 American Society of Clinical Oncology. DOI: 10.1200/JCO.2004.04.119 Insulin, Physical Activity, and Caloric Intake in Postmenopausal Women: Breast Cancer ImplicationsFrom the Los Angeles Biomedical Research Institute at Harbor-UCLA, Torrance; Stanford University, Palo Alto, CA; Fred Hutchinson Cancer Research Center, Seattle, WA; MedStar Research Institute, Hyattsville, MD; and Albert Einstein College of Medicine, Bronx, NY Address reprint requests to R.T. Chlebowski, MD, PhD, Los Angeles Biomedical Research Institute at Harbor-UCLA, 1124 W Carson St, Building J-3, Torrance, CA 90502; e-mail: rchlebow{at}whi.org
PURPOSE: Increased physical activity and programs to reduce body mass index (BMI) with both increased physical activity and decreased caloric intake have been proposed to reduce insulin as a potential mediator of breast cancer and other chronic diseases. However, there are few data on the relative contribution of physical activity, caloric intake, and BMI to fasting insulin levels. MATERIALS AND METHODS: An ethnically diverse subsample of 2,996 mostly healthy postmenopausal women with no prior cancer history was randomly identified from the 161,809 participants in the Women's Health Initiative clinical trials and observational study. Information was collected on diet, recreational physical activity, and anthropometrics including BMI. Fasting insulin levels were determined. Using a cross-sectional design, insulin levels were then compared across quintiles of caloric intake and physical activity in linear regression model analyses controlled for BMI and other factors. RESULTS: Lower BMI (P < .0001), higher levels of physical activity (P < .0001), and lower caloric intake (P < .02) were all independently associated with significantly lower mean fasting insulin levels throughout the range of observed values. Insulin levels of 8.74 µU/mL ± 4.16 SD were seen in the highest physical activity and lowest caloric intake quintile compared with insulin levels of 15.08 µU/mL ± 16.32 SD in the lowest physical activity and highest caloric intake quintile (P < .0001). CONCLUSION: These findings suggest that reduction in BMI achieved by increasing physical activity, reducing caloric intake, or both, should lower insulin levels, providing support for clinical trials evaluating insulin level change and breast cancer risk.
Obesity1,2 and low physical activity3,4 have each been associated with increased breast cancer risk in postmenopausal women, whereas caloric restriction has been linked to lower breast cancer incidence.5 Higher fasting insulin levels has been proposed as a potential mediating factor of these observations because higher insulin levels are associated with obesity6 and low physical activity.7 Therefore, weight loss programs incorporating both increased physical activity and decreased caloric intake, as well as exercise programs without emphasis on weight loss, have been proposed as strategies to reduce risk of breast cancer.8,9 However, the relative contributions of body mass index (BMI), caloric intake, and physical activity on insulin levels in postmenopausal women have received limited attention and are poorly understood. To determine relationships among fasting insulin levels and physical activity, caloric intake, and BMI in postmenopausal women, these parameters were examined in an ethnically diverse sample of women randomly identified from the 161,860 Women's Health Initiative (WHI) participants. We posed the hypothesis that both a decrease in energy intake and an increase in physical activity would be associated with lower fasting insulin levels.
The WHI is a large, longitudinal, multicenter study investigating postmenopausal women's health, which includes an observational study (OS) and set of randomized clinical trials (CTs) evaluating hormone therapy, dietary modification, and calcium and vitamin D supplementation.10 Eligible patients are postmenopausal, age 50 to 79 years, and unlikely to move or die within 3 years; all participants provided informed consent. CT entry had additional eligibility requirements related to safety and potential adherence (study design and eligibility criteria are described elsewhere).11 A total of 161,809 participants enrolled onto either the OS (n = 93,676) or CT (n = 68,133) between October 1, 1993 and December 31, 1998.12 This report describes associations among parameters measured at baseline. The study population of 2,996 postmenopausal women was randomly identified, with six-fold oversampling for ethnic minorities, for the purpose of collecting blood samples on a subset of women enrolled onto either the OS or the CTs of the WHI. Informed consent was obtained from all participants and institutional human research committees at each clinical center approved the study.
Information on diet, physical activity, and medication use was collected by questionnaires. Women reporting diabetes treated with pills or insulin were excluded. Dietary intake was determined by a semiquantitative Food Frequency Questionnaire targeting dietary intake in the previous 3 months13 and analyzed using the Minnesota Nutrient Data System software (Minneapolis, MN) to compute daily energy and nutrient intakes. Physical activity was determined by a questionnaire that addressed the frequency, duration, and intensity of a woman's participation in different forms of physical activity each week. Total energy expended from recreational and walking physical activity per week per kilogram was calculated by multiplying an assigned energy expenditure level for each category of activity by the hours exercised per week and summing values for all of the types of activities (described in detail previously by McTiernan et al).3 Anthropometric measures including body weight were collected by clinic staff using standardized techniques with BMI calculated as weight (in kilograms) divided by height (in square meters) and categorized as normal (< 25), overweight (25 to 29), and obese ( Blood samples were obtained after at least an 8-hour overnight fast. Serum samples were frozen at –70°C and shipped to the WHI central storage facility. Serum insulin was measured in a stepwise sandwich enzyme immunosorbent assay procedure14 by Medical Research Laboratories (Highland Heights, KY) in a blinded fashion. The assay was monitored externally for quality assurance by the Diabetes Diagnostic Laboratory, which is the US reference laboratory for the Diabetes Collaborative Clinical Trial. Monthly interassay coefficients of variation were 4.7% to 9.5% and 3.2% to 7.9% at mean concentrations of 26.6 and 80.6 µU/mL, respectively. Quality control included use of two frozen specimen pools to monitor the stability of results over time and blinded split samples to monitor assay precision and reproducibility.
Statistical Methods
Linear regression was also used to examine the associations among insulin level energy intake and physical activity. Participants in the first and fifth quintiles of caloric intake were included in the final model development. Values for the dependent variable, insulin, were log transformed, and an indicator variable for the highest quintile of energy intake was included. Physical activity was coded as described above. Adjustment was made for race or ethnicity, age, BMI, current smoking, alcohol intake
Study population age and characteristics are listed in Table 1. The study population was ethnically diverse, with more than 70% overweight or obese. The demographic and categorizing information of women in the various caloric intake and physical activity quintiles were similar (data not shown).
Macronutrient intake by quintile of total caloric intake is listed in Table 2. The intakes of fat, protein, and carbohydrate were each closely correlated with total caloric intake (Spearman correlation coefficients of 0.90, 0.88, and 0.90 respectively). When macronutrient intake was tested for trend by quintile of total caloric intake, a statistically significant (P < .0001) increasing trend in percentage of calories from fat was seen with increasing total calories and a statistically significant inverse (P < .0001) trend was seen between percentage of calories from protein and carbohydrates with total calories (Table 2). Thus, as caloric intake increased, the percentage of calories from fat increased and the percentage of calories from protein and carbohydrate decreased.
Fasting insulin levels by BMI category (normal, overweight, and obese) and physical activity and caloric intake quintiles are listed in Table 3. Lower insulin levels were seen in women with lower BMI, higher levels of physical activity, and lower caloric intake. BMI (P < .0001), physical activity (P < .0001), and caloric intake (P < .0001) were all significantly associated with insulin levels after adjustment for race or ethnicity, age, smoking, alcohol intake, and study component. In addition, regression models were conducted that serially excluded consideration of one of the three covariates (caloric intake, physical activity, or BMI). In all three models the two remaining covariates were significantly associated with insulin levels (P < .005, data not shown).
The associations among insulin level with quintiles of physical activity and caloric intake are listed in Tables 4 and 5 . The mean in each of the 25 cells in Table 4 is based on values from 90 to 141 women. As shown, high physical activity was associated with lower insulin levels in all quintiles of caloric intake. Similarly, low caloric intake was associated with lower insulin levels in all quintiles of physical activity. Mean fasting insulin ranged from 8.74 µU/mL ± 4.16 SD in the highest physical activity and lowest caloric intake quintile to 15.08 µU/mL ± 16.32 SD in the lowest physical activity and highest caloric intake quintile. Figure 1 plots the difference between the mean insulin level in the lowest caloric intake and highest physical activity quintile and all other quintile categories. No interaction was seen with respect to the association of lower insulin levels with lower caloric intake and with higher physical activity, indicating that the relationship of physical activity to insulin level is the same in each quintile of caloric intake examined. Similar, but less strong, trends for associations among insulin level with physical activity and caloric intake quintiles were seen in each of the three categories of BMI, but power was limited by the smaller sample size.
A final regression model examined relationships among BMI, physical activity, and caloric intake with insulin levels. Higher physical activity and lower caloric intake were each associated with significantly lower mean fasting insulin levels independent of BMI. The linear regression model revealed a highly significant association (P = .0001, 4 degrees of freedom test) between insulin level and the physical activity quintiles, after adjustment for total caloric intake and the remaining covariates. Caloric intake was also significantly associated with insulin levels in the same model (P = .021). Similar results were seen when BMI was considered as a categoric variable instead of a log-transformed variable. The analyses were essentially unchanged if body weight was substituted for BMI (data not shown). Interactions among physical activity, caloric intake, and BMI were evaluated in models adjusted for age, smoking, race or ethnicity, alcohol, and WHI study component: for BMI and physical activity interaction, P = .18; for physical activity and caloric intake interaction, P = .82. BMI was significantly associated with caloric intake, P = .01. There was no interaction with race (P = .73).
In a large cohort of postmenopausal women, lower BMI, higher levels of physical activity, and lower caloric intake were all independently related to lower fasting insulin levels. In analyses controlled for BMI, lower caloric intake and higher physical activity were independently associated with significantly lower fasting insulin levels throughout the entire range of observed values. Small, short-term randomized trials of modest7 or relatively intense exercise15,16 and/or weight loss with16 or without17 exercise or reduction in total fat and refined carbohydrate intake18 have in general15-17 reported insulin reduction with exercise or weight loss. These studies have rarely examined the influence of modest changes in physical activity or caloric intake or the relative contribution of these interventions to insulin change. The current results from a large population of predominantly inactive postmenopausal women suggest that even relatively modest increases in physical activity or decreases in caloric intake could contribute to lower insulin levels. The actual amount of recreational physical activity reported by this cohort of postmenopausal women was quite low. No recreational or walking physical activity whatsoever was reported by one fifth of the women. The fourth most active quintile (with 8.75 to 17.5 kcal/wk/kg of recreational energy expenditure) represents about only 28 min/wk of brisk walking equivalent. Despite this narrow range of recreational physical activity differences, insulin levels were approximately 40% lower in the highest physical activity and lowest caloric intake compared with the lowest physical activity and highest caloric intake groups. Reduced caloric intake and increased physical activity are now recognized cornerstones of effective weight loss strategies.19,20 Given that higher insulin levels have been proposed as a potential mediator of an increased risk of two of the three most common malignancies in postmenopausal women, namely breast cancer and colorectal cancer,21 these findings support consideration of a weight loss strategy for cancer risk reduction trials. A comprehensive review of insulin and cancer risk is beyond the scope of this report. Although not without controversy,22-24 the preponderance of studies suggest that insulin may influence cancers that are more commonly seen in Western populations.21,25 In breast cancer case-control studies, Brunning et al26 reported a dose-response between c-peptide, a marker of pancreatic insulin secretion, and breast cancer in both pre- and postmenopausal women. Del Giudice et al27 related higher insulin levels to premenopausal breast cancer risk, whereas Yang et al28 found an increased breast cancer risk with increasing levels of c-peptide as well. More recently, Hirose et al29 reported insulin levels as significant predictors of postmenopausal breast cancer and a high ratio of c-peptide to fructosamine, suggestive of insulin resistance, was significantly related to both breast hyperplasia and breast cancer incidence in another case-control analysis.30 In a cross-sectional study involving 3,868 postmenopausal women with 151 prevalent breast cancers, hyperinsulinemia was positively associated with breast cancer risk, an association unaltered by consideration of BMI.31 A related parameter, higher fasting glucose, has been associated with breast cancer risk in a prospective study.32 Most recently, in a large cohort of 38,823 Norwegian women, low serum high-density lipoprotein cholesterol, described by the authors as part of the metabolic syndrome, was associated with increased postmenopausal breast cancer risk.33 Other studies suggest interaction between insulin and other breast cancer risk factors. An analysis from 400 case-control pairs from the Shanghai Breast Cancer Study suggested that insulin resistance and insulin-like growth factors may synergistically increase breast cancer risk,34 whereas Yu et al35 reported a synergistic effect on breast cancer risk for insulin-like growth factor-I with hormones estrone or testosterone in both pre and postmenopausal women. The associations seen among physical activity, caloric intake, and insulin have relevance for women with established cancers as well. In a population of 535 newly diagnosed breast cancer patients, both higher fasting insulin levels and obesity independently predicted increased recurrence risk and decreased survival.36 This observation was supported by Borugian et al,37 who reported that high levels of insulin were associated with significantly worse survival in a cohort of 603 breast cancer patients. Most recently, a moderate increase in physical activity has been associated with reduced recurrence risk in women diagnosed with breast cancer in the Nurses Health Study cohort.38 Other studies support the association of body weight with clinical outcome in women with breast cancer. In a recent review, a statistically significant association between obesity and recurrence or survival was seen in 26 of 34 studies.9 In an earlier meta-analysis, the hazard ratio for effect of body weight on recurrence was 1.78 (95% CI, 1.50 to 2.11).8 Most recently, Dignam et al39 reported a significant increase in all-cause mortality comparing obese versus nonobese breast cancer patients receiving adjuvant tamoxifen therapy. Similarity, in International Breast Cancer Study Group Trials involving 6,792 breast cancer patients, BMI significantly influenced overall survival (P = .03), but not disease-free survival.40 The strengths of this report include the large number of fasting blood samples from a study population with a diverse racial or ethnic and age composition, and the ability to evaluate modest differences in caloric intake and physical activity using standardized data collection instruments and procedures. The cross-sectional design is a limitation precluding causal conclusions. Given the close correlation of all macronutrient intakes with total caloric intake, the relative contribution of individual macronutrients to the insulin levels differences cannot be separated. In summary, the results of this study suggest that strategies to reduce BMI and body weight involving either or both increasing physical activity and decreasing caloric intake will reduce insulin levels. These observations support prospective intervention trials incorporating these lifestyle changes to test hypotheses relating insulin to cancer risk.
The following authors or their immediate family members have indicated a financial interest. No conflict exists for drugs or devices used in a study if they are not being evaluated as part of the investigation. Consultant/Advisory Role: Rowan T. Chlebowski, AstraZeneca, Novartis, Pfizer. Honoraria: Rowan T. Chlebowski, AstraZeneca. For a detailed description of these categories, or for more information about ASCO's conflict of interest policy, please refer to the Author Disclosure Declaration form and the Disclosures of Potential Conflicts of Interest section of Information for Contributors found in the front of every issue.
The dedicated efforts of the WHI participants and of the WHI investigators and staff at the Clinical Center and Clinical Coordinating Center (CCC) are acknowledged. The contributions of Melinda Irwin, PhD (Yale University) and Aimee Loar from the CCC warrant specific acknowledgment. A full listing of the WHI investigators can be found at http://www.whi.org.
Supported by the National Institutes of Health (NIH) Department of Health and Human Services, National Heart, Lung and Blood Institute, with additional support from NIH GCRC MO1RR0045. Authors' disclosures of potential conflicts of interest are found at the end of this article.
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Copyright © 2004 by the American Society of Clinical Oncology, Online ISSN: 1527-7755. Print ISSN: 0732-183X
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ABSTRACT
INTRODUCTION
30). Details of medication use including current or prior menopausal hormone therapy were collected via an interviewer-administered questionnaire. 
