Abstract
Adiposity is an established risk factor for postmenopausal breast cancer. Recent data suggest that high insulin levels in overweight women may play a major role in this relationship, due to insulin’s mitogenic/anti-apoptotic activity. However, whether overweight women who are metabolically healthy (i.e. normal insulin sensitivity) have elevated risk of breast cancer is unknown. We investigated whether overweight women with normal insulin sensitivity (i.e., homeostasis model assessment of insulin resistance [HOMA-IR] index, or fasting insulin level, within the lowest quartile [q1]) have increased breast cancer risk. Subjects were incident breast cancer cases (N=497) and a subcohort (N=2,830) of Women’s Health Initiative (WHI) participants with available fasting insulin and glucose levels. In multivariate Cox models, metabolically healthy overweight women, defined using HOMA-IR, were not at elevated risk of breast cancer compared to metabolically healthy normal weight women (hazard ratio [HR]HOMA-IR=0.96; 95% confidence interval [CI],0.64-1.42). In contrast, the risk among women with high (q3-4) HOMA-IRs was elevated whether they were overweight (HRHOMA-IR=1.76; 95% CI,1.19-2.60) or normal weight (HRHOMA-IR=1.80; 95% CI,0.88-3.70). Similarly, using fasting insulin to define metabolic health, metabolically unhealthy women (insulin q3-4) were at higher risk of breast cancer regardless of whether they were normal weight (HRinsulin=2.06; 95% CI,1.01-4.22) or overweight (HRinsulin=2.01; 95% CI,1.35-2.99), whereas metabolically healthy overweight women did not have significantly increased risk of breast cancer (HRinsulin=0.96; 95% CI,0.64-1.42) relative to metabolically healthy normal weight women. Metabolic health (e.g., HOMA-IR or fasting insulin) may be more biologically relevant and more useful for breast cancer risk stratification, than adiposity per se.
INTRODUCTION
Excess body weight is a well-established risk factor for breast cancer in postmenopausal women.(1, 2) Until recently, this relationship was largely thought to be attributable to the prevalence of higher estrogen levels in overweight women which is an established risk factor for postmenopausal breast cancer. (3, 4) However, being overweight is also associated with high levels of insulin, which has mitogenic and anti-apoptotic activity(5, 6) and recent cohort data have linked insulin levels with breast cancer risk.(7, 8) A large prospective study, for example, reported a highly significant 2.4-fold increased risk of breast cancer among postmenopausal women with insulin levels in the highest relative to the lowest quartile, after adjusting for multiple breast cancer risk factors, including serum estradiol.(7) In subsequent analyses using mediation analysis methods, it was reported that the obesity – breast cancer association is more greatly attributable to insulin than to estradiol levels.(9)
These observations lead to an important but yet untested clinical corollary to the insulin – breast cancer association, namely, that those overweight women with high insulin levels but not those with normal insulin levels will be at increased risk of breast cancer relative to healthy normal weight women. Indeed, a metabolically healthy obese phenotype has been posited to be relevant for cardiovascular disease risk, (10-12) and there is accumulating evidence that individuals who are overweight (body mass index [BMI] ≥25 kg/M2) but who have normal insulin sensitivity (e.g., a low quartile of homeostasis model assessment of insulin resistance [HOMA-IR] index) have little, if any, excess risk of cardiovascular events. (13-16) Therefore, we compared the risk of incident postmenopausal breast cancer among metabolically healthy overweight women to that in metabolically healthy normal weight women.
METHODS
Study Population
The data analyzed were from two separate prospective studies of incident postmenopausal breast cancer and fasting serum insulin and glucose levels based in the Women’s Health Initiative (WHI), a large prospective cohort study of postmenopausal women aged 50-79 years at enrolment.(17) WHI has both an observational component (N=93,676) and a clinical trial component (N=68,132) with three arms: hormone therapy (HT), dietary modification, and vitamin D/calcium supplementation. All components were conducted at the same clinical centers and shared relevant methods including a standardized blood collection protocol. All WHI participants were aged 50-79 years at baseline and were recruited at 40 clinical centers across the United States between October 1, 1993 and December 31, 1998. At baseline, a physical examination that included measurement of height and weight, and collection of fasting blood, was conducted. Incident cancer was then ascertained through annual or semi-annual self-administered questionnaires or by self-report, and were subsequently confirmed through centralized review of all pathology reports, discharge and consultant summaries, operative and radiology reports, and tumor registry abstracts.
The first of the two WHI breast cancer studies used was a case-cohort investigation of non-diabetic women based in the observational component of WHI that included 835 incident cases diagnosed over a mean of 8.2 years of follow-up and a randomly selected subcohort of 816 women present at the baseline visit.(7) The second study was a conventional cohort investigation involving a random sample of approximately 1% of women in the observational (N=1,054) and 6% in the clinical trial component (N=4,396), who were asked to provide extra blood for serologic studies.(8) A total of 190 incident breast cancer cases in this second study were diagnosed over 8 years of follow-up. The combined comparison group from both studies (each of which were randomly selected subjects) is referred to, herein, as the subcohort. As in prior reports, women who were either diabetic or currently using hormone therapy (HT) were excluded, due to the impact of these factors on insulin levels,(7, 18) leaving 497 cases and a subcohort of 2,830 women. All subjects included in the current analysis had fasting insulin and glucose levels available.
Categorization of Metabolic Health
We compared the risk of incident postmenopausal breast cancer among metabolically healthy normal weight women (BMI 18-24.9 kg/m2 and HOMA-IR-q1) to that in metabolically unhealthy overweight women (BMI ≥25 kg/m2 and HOMA-IR quartiles 3 and 4 [HOMA-IR-q3+4]), metabolically healthy overweight women (BMI ≥ 25 kg/m2 and HOMA-IR-q1), as well as metabolically unhealthy normal weight women (BMI 18-24.9 kg/m2 and HOMA-IR-q3+4) – similar to the design of prior studies of cardiovascular disease.(14-16) HOMA-IR-q2 was excluded to make the two strata discrete (non-abutting) categories. HOMA-IR is a standard measure of insulin resistance and is defined by a formula that incorporates both insulin and glucose levels ([fasting insulin (IU/mL) × fasting glucose (mg/dL)] /22.5). However, as our hypotheses focused particularly on the impact of insulin on breast cancer risk, we also a priori used insulin quartile itself to distinguish metabolically healthy from unhealthy women.
Statistical Analyses
Hazard ratios (HRs) and 95% confidence intervals (CI) for the association of metabolic health subtypes with incident breast cancer were estimated using Cox proportional hazards regression modeling that employed the Self-Prentice method for robust standard error estimates (to account for the case-cohort design), with time from study enrollment as the underlying time metric. (19) Statistical analyses adjusted for established breast cancer risk factors, namely, age (50–54 [referent], 55–59, 60–64, 65–69, 70–74, or 75–79 years), ethnicity (white [referent], black, Hispanic, or Asian/other), age at menarche (≤10, 11–12[referent], or ≥13 years) and menopause (≤42 [referent], 43–48, 49–51, or ≥52 years), parity (0 [referent], 1, or ≥2 live births), first degree relative with breast cancer (yes or no), education (high school or lower [referent], college, or postgraduate education), alcohol consumption (assessed as the number of servings per week during the preceding 3 months (none [referent], <3, or ≥3), physical activity (assessed as metabolic equivalent tasks per hour per week [METs; defined as the caloric need per kilogram of body weight per hour of activity divided by the caloric need per kilogram of body weight per hour at rest] and categorized as quartiles (<3.75, 3.75–9.82, 9.83–18.74, ≥18.75), as well as which of the two WHI studies each subject was enrolled in (observational study or clinical trial) and, among those who participated in the clinical trials, which specific clinical trial arm they were assigned to (hormone therapy [estrogen-alone, estrogen plus progestin], calcium/vitamin D and dietary modification) and whether they were a member of the placebo or treatment group. In addition, caloric intake, total carbohydrate, saturated fat and glycemic load and index were also considered as potential confounding variables in the analysis but their inclusion in the multivariable model did not meaningfully alter the regression coefficients and were therefore not included in the final models. Individuals were censored at diagnosis of breast cancer, death, or at the end of follow-up. Data from each of the two contributing WHI studies of insulin, glucose and breast cancer were combined and were analyzed using a case-cohort approach with each study permitted to retain its individual baseline hazards function. (19) The proportionality of the data was verified by graphical inspection and by Schoenfeld residuals. All tests of statistical significance were two sided, and P values less than .05 were considered statistically significant. All analyses were performed using SAS statistical software (version 9.1, Cary, NC).
RESULTS
Table 1 shows selected baseline characteristics of the cases in the analysis as well as the non-cases in the subcohort. The two groups did not differ significantly by ethnicity, BMI or age at menarche. However, cases (median age = 65) were on average older than non-cases in the subcohort (median age = 63), more likely to be college educated, to be nulliparous, have a later age at menopause, to have a first degree relative with breast cancer, and also consumed more alcohol and engaged in more physical activity.
Table 1.
Selected baseline characteristics of the study population
| Variable§ | Cases (N=497) | Subcohort (N=2,830) | P-value¶ |
|---|---|---|---|
| Age (years) | 65 (59 to 70) | 63 (57 to 69) | <.0001 |
| Women’s Health Initiative, N(%) | |||
| Observational Study Cohort | 401 (80.7) | 756 (26.7) | N/A |
| Clinical Trial Cohort | 96 (19.3) | 2074 (73.3) | |
| Ethnicity, N (%) | |||
| White | 394 (79.3) | 1591 (56.4) | <.0001 |
| Black | 60 (12.1) | 634 (22.4) | |
| Hispanic | 22 (4.4) | 340 (12.0) | |
| Asian/other | 19 (3.8) | 263 (9.2) | |
| Missing | 2 (0.4) | 2 (0.1) | |
| Body mass index (BMI; Kg/M2), N(%) | 0.17 | ||
| Normal (BMI<25.0) | 155 (31.2) | 762 (26.9) | |
| Overweight (25.0-<30.0) | 165 (33.2) | 998 (35.3) | |
| Obese (≥30.0) | 177 (35.6) | 1070 (37.8) | |
| Age at Menarche, N (%) | 0.44 | ||
| ≤10 | 41 (8.2) | 193 (6.8) | |
| 11-12 | 203 (40.8) | 1101 (38.9) | |
| 13+ | 250 (50.3) | 1523 (53.8) | |
| Missing | 3 (0.7) | 13 (0.5) | |
| Age at Menopause, N(%) | 0.001 | ||
| ≤42 | 72 (14.5) | 541 (19.1) | |
| 43-48 | 106 (21.3) | 629 (22.2) | |
| 49-51 | 133 (26.8) | 616 (21.8) | |
| ≥52 | 142 (28.6) | 675 (23.9) | |
| Missing | 44 (8.8) | 369 (13.0) | |
| Parity, N(%) | 0.04 | ||
| 0 | 79 (15.9) | 334 (11.8) | |
| 1 | 39 (7.8) | 254 (9.0) | |
| ≥2 | 375 (75.5) | 2223 (78.6) | |
| Missing | 4 (0.8) | 19 (0.6) | |
| Family history of breast cancer, (N%), | <.0001 | ||
| Yes | 138 (27.8) | 440 (15.5) | |
| No | 185 (37.2) | 2164 (76.5) | |
| Missing | 174 (35.0) | 226 (8.0) | |
| Smoking status, N (%) | 0.001 | ||
| Never | 254 (51.1) | 1519 (53.7) | |
| Former | 213 (42,9) | 1021 (36.1) | |
| Current | 22 (4.4) | 250 (8.8) | |
| Missing | 8 (1.6) | 40 (1.4) | |
| Education history, N (%) | <.0001 | ||
| High school and less | 144 (29.0) | 1125 (39.8) | |
| College | 203 (40.8) | 982 (34.7) | |
| Postgraduate education | 143 (28.8) | 701 (24.8) | |
| Missing | 7 (1.4) | 22 (0.7) | |
| Alcohol (servings per week) | 0.4 (0.0 to 3.5) | 0.2 (0.0 to 1.4) | <.0001 |
| Physical activity (METs‡) | 8.29 (2.00 to 17.50) | 5.75 (0.50 to 15.00) | <.0001 |
P-values derived from Wilcoxon rank sum test for continuous data and Pearson’s chi-square for categorical data.
MET, metabolic equivalent tasks (defined as the caloric need per kilogram of body weight per hour of activity divided by the caloric need per kilogram of body weight per hour at rest) per hour per week.
Values are medians (inter-quartile range) unless otherwise stated.
In multivariate Cox proportional hazards models, metabolically unhealthy overweight was associated with a significantly increased risk of incident breast cancer (HRHOMA-IR=1.76; 95% CI, 1.19-2.60; P=0.005) compared to metabolically healthy normal weight women (Table 2). Further, a similar association was observed for metabolically unhealthy normal weight though the relationship did not reach statistical significance (HRHOMA-IR=1.80; 95% CI, 0.88-3.70; P=0.11). No relationship, however, was observed between breast cancer and metabolically healthy overweight (HRHOMA-IR=0.96; 95% CI, 0.64-1.42; P=0.83) compared to metabolically healthy normal weight women. In addition, the hazard ratio directly contrasting breast cancer risk in overweight women who were metabolically unhealthy versus healthy was HR=1.84 (95% CI, 1.38-2.45; p<0.0001).
Table 2.
The Associations of Incident Postmenopausal Breast Cancer Risk with Metabolic Health Defined by HOMA-IR or Insulin Levels, Stratified by Body Mass Index (BMI) Category
|
HOMA-IR based definition of Metabolic Health
| |||||
|---|---|---|---|---|---|
| BMI Category | N (Cases/Controls) | Age-adjusted HR (95% CI) | P-Value | Multivariate HR† (95% CI) | P-Value |
| Normal Weight (<25Kg/M2) | |||||
| Metabolically Healthy1 | 113/356 | 1.00 (Referent) | 1.00 (Referent) | ||
| Metabolically Unhealthy2 | 18/182 | 1.68 (0.85-3.33) | 0.13 | 1.80 (0.88-3.70) | 0.11 |
| Overweight (≥25 Kg/M2) | |||||
| Metabolically Healthy1 | 87/339 | 0.93 (0.64-1.34) | 0.68 | 0.96 (0.64-1.42) | 0.83 |
| Metabolically Unhealthy2 | 169/1238 | 1.60 (1.12-2.28) | 0.01 | 1.76 (1.19-2.60) | 0.005 |
|
| |||||
|
Insulin based definition of Metabolic Health
| |||||
| BMI Category | N (Cases/Controls) | Age-adjusted HR (95% CI) | P-Value | Multivariate HR (95% CI) | P-Value |
| Normal Weight (<25Kg/M2) | |||||
| Metabolically Healthy3 | 108/352 | 1.00 (Referent) | 1.00 (Referent) | ||
| Metabolically Unhealthy4 | 19/180 | 1.86 (0.95-3.65) | 0.07 | 2.06 (1.01-4.22) | 0.048 |
| Overweight (≥25 Kg/M2) | |||||
| Metabolically Healthy3 | 86/329 | 0.93 (0.64-1.35) | 0.71 | 0.96 (0.64-1.42) | 0.82 |
| Metabolically Unhealthy4 | 175/1250 | 1.86 (1.30-2.66) | 0.001 | 2.01 (1.35-2.99) | 0.001 |
HOMA-IR (quartile 1);
HOMA-IR (quartiles 3+4);
Insulin (quartile 1);
Insulin (quartiles 3+4); Adjusted for age, ethnicity, age at menarche and menopause, parity, first degree relative with breast cancer, education, alcohol consumption, physical activity, which of the two WHI studies each subject was enrolled in and, among those who participated in the clinical trials, which specific clinical trial arm they were assigned to and whether they were a member of the placebo or treatment group.
We additionally used insulin quartile to differentiate metabolically healthy (q1) versus unhealthy (q3+q4) women. Statistically significant associations between breast cancer risk and metabolic health were observed, regardless of whether women were normal weight (HRinsulin=2.06; 95% CI, 1.01-4.22; P=0.048) or overweight (HRinsulin=2.01; 95% CI, 1.35-2.99; P=0.001), whereas metabolically healthy overweight women did not have significantly increased risk of breast cancer (HRinsulin=0.96; 95% CI, 0.64-1.42; P=0.82) relative to metabolically healthy normal weight women. Further, the HR directly contrasting cancer risk in overweight women who were metabolically unhealthy versus healthy (based on insulin) was HR=2.11 (95% CI: 1.58-2.81; p<0.0001).
CONCLUSION
Overall, the results from this study suggest that metabolic health status (as defined by HOMA-IR or fasting insulin levels) and not adiposity per se, may be the relevant factor associated with risk of postmenopausal breast cancer. These findings are consistent with recent reports that overweight individuals with normal insulin sensitivity are not at increased risk of cardiovascular disease, and collectively provide further evidence of the existence of a healthy obese phenotype.(10-12) Our findings also support the hypothesis that hyperinsulinemia is a significant risk factor for breast cancer, independent of adiposity and that insulin, or a closely-related mechanism, may be driving development of breast tumors. Several prospective studies have now reported significant, positive associations between fasting insulin or C-peptide (a marker of insulin secretion) (7, 8, 20-22) and there is evidence that insulin plays a significant role in mediating the obesity-breast cancer relationship.(7,9)
We note that our conclusions are limited by the fact that only a single insulin and glucose measurement were available from the study participants and that multiple measurements over time would enable a more precise assessment of long-term metabolic health. Further, our sample size was not large enough to further stratify by breast cancer subtypes such as those defined by estrogen receptor expression. Given potential cross-talk between estrogen and insulin signaling, it is possible that the association of metabolic health with breast cancer varies by breast tumor estrogen receptor subtype and future studies should be of sufficient sample size to examine this hypothesis with adequate precision.
In conclusion, the current findings raise the possibility that HOMA-IR or fasting insulin levels may be useful in combination with other predictors of breast cancer risk in efforts to individualize breast cancer screening practices.
Acknowledgments
WHI Investigators
Program Office: (National Heart, Lung, and Blood Institute, Bethesda, Maryland) Elizabeth Nabel, Jacques Rossouw, Shari Ludlam, Linda Pottern, Joan McGowan, Leslie Ford, and Nancy Geller.
Clinical Coordinating Center: (Fred Hutchinson Cancer Research Center, Seattle, WA) Ross Prentice, Garnet Anderson, Andrea LaCroix, Charles L. Kooperberg, Ruth E. Patterson, Anne McTiernan; (Wake Forest University School of Medicine, Winston-Salem, NC) Sally Shumaker; (Medical Research Labs, Highland Heights, KY) Evan Stein; (University of California at San Francisco, San Francisco, CA) Steven Cummings.
Clinical Centers: (Albert Einstein College of Medicine, Bronx, NY) Sylvia Wassertheil-Smoller; (Baylor College of Medicine, Houston, TX) Jennifer Hays; (Brigham and Women’s Hospital, Harvard Medical School, Boston, MA) JoAnn Manson; (Brown University, Providence, RI) Annlouise R. Assaf; (Emory University, Atlanta, GA) Lawrence Phillips; (Fred Hutchinson Cancer Research Center, Seattle, WA) Shirley Beresford; (George Washington University Medical Center, Washington, DC) Judith Hsia; (Los Angeles Biomedical Research Institute at Harbor- UCLA Medical Center, Torrance, CA) Rowan Chlebowski; (Kaiser Permanente Center for Health Research, Portland, OR) Evelyn Whitlock; (Kaiser Permanente Division of Research, Oakland, CA) Bette Caan; (Medical College of Wisconsin, Milwaukee, WI) Jane Morley Kotchen; (MedStar Research Institute/Howard University, Washington, DC) Barbara V. Howard; (Northwestern University, Chicago/Evanston, IL) Linda Van Horn; (Rush Medical Center, Chicago, IL) Henry Black; (Stanford Prevention Research Center, Stanford, CA) Marcia L. Stefanick; (State University of New York at Stony Brook, Stony Brook, NY) Dorothy Lane; (The Ohio State University, Columbus, OH) Rebecca Jackson; (University of Alabama at Birmingham, Birmingham, AL) Cora E. Lewis; (University of Arizona, Tucson/Phoenix, AZ) Tamsen Bassford; (University at Buffalo, Buffalo, NY) Jean Wactawski-Wende; (University of California at Davis, Sacramento, CA) John Robbins; (University of California at Irvine, CA) F. Allan Hubbell; (University of California at Los Angeles, Los Angeles, CA) Howard Judd; (University of California at San Diego, LaJolla/Chula Vista, CA) Robert D. Langer; (University of Cincinnati, Cincinnati, OH) Margery Gass; (University of Florida, Gainesville/Jacksonville, FL) Marian Limacher; (University of Hawaii, Honolulu, HI) David Curb; (University of Iowa, Iowa City/Davenport, IA) Robert Wallace; (University of Massachusetts/Fallon Clinic, Worcester, MA) Judith Ockene; (University of Medicine and Dentistry of New Jersey, Newark, NJ) Norman Lasser; (University of Miami, Miami, FL) Mary Jo O’Sullivan; (University of Minnesota, Minneapolis, MN) Karen Margolis; (University of Nevada, Reno, NV) Robert Brunner; (University of North Carolina, Chapel Hill, NC) Gerardo Heiss; (University of Pittsburgh, Pittsburgh, PA) Lewis Kuller; (University of Tennessee, Memphis, TN) Karen C. Johnson; (University of Texas Health Science Center, San Antonio, TX) Robert Brzyski; (University of Wisconsin, Madison, WI) Gloria E. Sarto; (Wake Forest University School of Medicine, Winston-Salem, NC) Mara Vitolins; (Wayne State University School of Medicine/Hutzel Hospital, Detroit, MI) Susan Hendrix.
Funding
This study was funded by National Cancer Institute grant R01-CA93881-01 (to H.Strickler). The WHI program is funded by the National Heart, Lung, and Blood Institute, National Institutes of Health, U.S. Department of Health and Human Services through contracts N01WH22110, 24152, 32100-2, 32105-6, 32108-9, 32111-13, 32115, 32118-32119, 32122, 42107-26, 42129-32, and 44221.
Footnotes
The authors disclose no potential conflicts of interest
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