Skip to main content
NIHPA Author Manuscripts logoLink to NIHPA Author Manuscripts
. Author manuscript; available in PMC: 2016 Jan 1.
Published in final edited form as: Cancer Epidemiol Biomarkers Prev. 2014 Oct 28;24(1):128–137. doi: 10.1158/1055-9965.EPI-14-0560

Body size throughout adult life influences postmenopausal breast cancer risk among Hispanic women: The Breast Cancer Health Disparities Study

Esther M John 1,2, Meera Sangaramoorthy 1, Lisa M Hines 3, Mariana C Stern 4, Kathy B Baumgartner 5, Anna R Giuliano 6, Roger K Wolff 7, Martha L Slattery 7
PMCID: PMC4295775  NIHMSID: NIHMS638809  PMID: 25352523

Abstract

Background

Few studies have assessed the association of body size with postmenopausal breast cancer (BC) risk in Hispanic women. Findings are inconsistent and appear to contradict those reported for non-Hispanic White (NHW) women.

Methods

We pooled interview and anthropometric data for 2,023 Hispanic and 2,384 NHW women from two U.S. population-based case-control studies. Using logistic regression analysis, we examined associations of overall and abdominal adiposity with risk of postmenopausal BC defined by estrogen receptor (ER) and progesterone receptor (PR) status.

Results

Weight gain was associated with increased risk of ER+PR+ BC in Hispanics not currently using menopausal hormone therapy (HT), but only among those with a low young-adult body mass index (BMI). In the subset of Hispanics with data on genetic ancestry, the association with weight gain was limited to women with lower Indigenous American ancestry. Young-adult BMI was inversely associated with both ER+PR+ and ER-PR- BC for both ethnicities combined, with similar, although non-significant, inverse trends in Hispanics and NHWs. Among all Hispanics, regardless of HT use, height was associated with risk of ER-PR-BC and hip circumference with risk of BC overall.

Conclusions

Body size throughout adult life is associated with BC risk among postmenopausal Hispanic women, as has been reported for NHW women. Associations were specific for BC subtypes defined by hormone receptor status.

Impact

Avoiding weight gain and maintaining a healthy weight are important strategies to reduce the risk of postmenopausal ER+PR+ BC, the most common BC subtype.

Keywords: Breast cancer, BMI, body size, estrogen receptor status, epidemiology, genetic ancestry, Hispanics, Latinas, postmenopausal, progesterone receptor status, weight gain

Introduction

In studies of non-Hispanic White (NHW) women, both overall and abdominal adiposity have been associated with increased risk of postmenopausal breast cancer (BC) (1, 2). Data on body size associations are sparse and inconsistent for U.S. Hispanic (37) and Mexican (8) women, who have a higher prevalence of obesity than NHWs (9). Most studies in Hispanics had small sample sizes, and most did not consider tumor hormone receptor status (10) or factors that modify body size associations, such as use of hormone therapy (HT) (1113) or young-adult body size (14, 15). Only three studies (4, 7, 8) have assessed associations with abdominal adiposity, and there remains uncertainty about the independent effects of abdominal vs. overall adiposity.

We pooled data for U.S. Hispanic and NHW women to assess associations of body size with risk of postmenopausal BC defined by hormone receptor status and evaluate the modifying effects of HT use and young-adult body mass index (BMI). We also investigated whether associations with body size varied by genetic ancestry, given the higher prevalence of obesity in women with higher Indigenous American (IA) ancestry (16).

Materials and Methods

The study population consists of U.S. Hispanic, Native American (NA) and NHW women included in the Breast Cancer Health Disparities Study (17) that pooled interview and anthropometric data from two population-based case-control studies. The study was approved by the institutional review board at each institution. All study participants provided written informed consent.

San Francisco Bay Area Breast Cancer Study (SFBCS)

Through the Greater Bay Area Cancer Registry, the SFBCS identified 17,537 cases aged 35–79 years diagnosed with a first primary invasive BC between April 1995 and April 2002 (18, 19). Controls were identified through random-digit dialing and were frequency matched to cases on race/ethnicity and the expected 5-year age distribution of cases. A telephone screening interview assessing self-identified race/ethnicity and eligibility for several studies was completed by 89% of 15,573 cases contacted (alive, valid address, no physician refusal) and 92% of 3,547 controls contacted. Hispanic cases diagnosed from 1995–2002, African American cases diagnosed from 1995–1999, and a 10% random sample of NHW cases diagnosed from 1995–1999 were eligible for an in-person interview. NHW cases were sampled, given the large number of incident cases during the ascertainment period. This analysis is based on interview data obtained for 1,715 cases, including 1,119 (89%) Hispanics and 596 (86%) NHWs, and 2,108 controls, including 1,462 (88%) Hispanics and 646 (83%) NHWs. Median time between diagnosis and interview was 14.2 months (range 5.0 – 46.5) for Hispanic cases, and 16.4 months (range 6.5 – 45.8) for NHW cases. Findings on body size associations for African Americans are reported elsewhere (7).

4-Corners Breast Cancer Study (4-CBCS)

The 4-CBCS was conducted in Hispanic, NA and NHW women living in non-reservation areas in Arizona, Colorado, New Mexico, and Utah (4). A total of 5,256 cases aged 25–79 years and diagnosed with in situ or invasive BC between October 1999 and May 2004 were identified through the state-wide cancer registries. Controls were selected from the populations living in the four states and were frequency matched to cases on race/ethnicity and expected 5-year age distribution. Of 3,761 cases contacted, 2,556 completed the in-person interview, including 873 Hispanics/NAs (63%) and 1,683 NHWs (71%). Of 6,152 controls contacted, 2,605 completed the interview, including 936 (36%) Hispanics/NAs and 1,669 (47%) NHWs. We combined the small number of NAs (55 cases, 73 controls) with Hispanics, and restricted cases to those with a first primary invasive breast cancer (662 Hispanics/NAs, 1,246 NHWs). Median time between diagnosis and interview was 19.7 months (range 3.4 – 58.3) for Hispanic cases and 16.7 months (range 2.5 – 59.0) for NHW cases.

Data Collection and Harmonization

In-person interviews were conducted in English or Spanish by trained bilingual interviewers using similar structured questionnaires translated into Spanish. Information on risk factors was collected up to the referent year, defined as the calendar year prior to diagnosis for cases or selection into the study for controls. Standing height (with shoes removed) and weight (with light clothing) were measured at the time of interview, using a portable stadiometer and scale, respectively. Waist and hip circumferences were measured using a linen tape (in SFBCS) or a flexible tape (in 4-CBCS). In SFBCS, height was measured to the nearest millimeter; weight was measured to the nearest 0.2 kilogram (kg). Three measurements of height and two of weight were taken and averaged (20). In 4-CBCS, height was measured to the nearest 0.25 inches (in), weight was measured to the nearest 0.5 pound (lb), and waist and hip circumferences were measured to the nearest 0.5 in. Two measurements of height and weight were taken (if they differed by >0.5 in or >1.0 lb, respectively, a third measurement was taken) and averaged (4). Data on estrogen receptor (ER) and progesterone receptor (PR) status were obtained from the respective cancer registries for most postmenopausal cases (85% in SFBCS, 76% in 4-CBCS).

The data from the two studies were harmonized and pooled, and common analytic variables were generated (17). Postmenopausal status was defined according to study-specific definitions. Women who were taking menopausal HT and were still having periods were classified as postmenopausal if they were at or above the 95th percentile of age for race/ethnicity of those who reported having a natural menopause (i.e., ≥12 months since their last period) within their study. This age (in years) was 55 for NHW and 56 for Hispanics in SFBCS, and 58 for NHW and 56 for Hispanics in 4-CBCS. For Hispanics, an acculturation index based on language spoken was created (low: Spanish only; moderate: more Spanish than English or Spanish and English equally; high: more English than Spanish or English only).

Body Size Variables

Current BMI was calculated as weight (kg) divided by height (m) squared, based on height measured at interview and self-reported weight in the reference year. Since cases may have experienced disease- or treatment-related weight gain or loss, we used self-reported weight before diagnosis for the BMI calculation. For individuals without self-reported weight, measured weight was used (1% of cases, 2% of controls) and for those who declined the height measurement, self-reported height was used (3% of cases, 2% of controls). In SFBCS, young-adult BMI was based on self-reported weight at age 25–30 years for cases diagnosed before May 1998 and their matched controls, or on self-reported weight at age 20–29 years for cases diagnosed in May 1998 or later and their matched controls. In 4-CBCS, young-adult BMI was calculated as the average of weights reported at ages 15 years and 30 years. Weight gain was calculated as the difference between self-reported young-adult weight and self-reported weight in the reference year (or measured weight at interview if self-reported weight was not available). We calculated WHR as a measure of body fat distribution that reflects both adipose tissue (waist circumference) and muscle mass (hip circumference), and WHtR as a measure of visceral adiposity independent of height (21). Current BMI was classified as underweight to normal weight (<25.0 kg/m2), overweight (25.0–29.9 kg/m2), or obese (≥30.0 kg/m2). All other body size variables were categorized according to the tertile or quartile distribution among postmenopausal controls. To facilitate the comparison of OR estimates between Hispanics and NHWs, we used the same cut-points for the two groups. Given the different distributions of body size characteristics, we also used ethnicity-specific quantiles.

Genetic Ancestry

We estimated genetic ancestry for a subset of study participants with available DNA. In SFBCS, biospecimens were collected only for cases diagnosed in April 1997 or later and their matched controls. Genetic admixture of European and Indigenous American (IA) ancestry was estimated based on 104 ancestry informative markers (AIMs) (17).

Statistical Analyses

Odds ratios (OR) and 95% confidence intervals (CI) were calculated using unconditional logistic regression. Given prior findings of effect modification by HT use (12, 22, 23), we stratified the analyses by HT use (current vs. former or never use). Polytomous logistic regression was used to compare the major subtypes, ER+PR+ and ER-PR- BC, to a common control group. Hispanic women were also stratified by median IA ancestry (44%) among Hispanic postmenopausal controls.

Multivariate analyses were adjusted for age (continuous) and study, and factors significantly associated with BC risk in our dataset. For overall and ER+PR+ BC, analyses were adjusted for education, BC family history in first-degree relatives, age at menarche, number of full-term pregnancies, age at first full-term pregnancy, lifetime duration of breast-feeding, and alcohol consumption. Variables were categorized as noted in the footnotes of the tables. For ER-PR-BC, analyses were adjusted for BC family history and age at menarche. Analyses that were not stratified by HT use were also adjusted for HT use. Analyses in Hispanics were additionally adjusted for language acculturation. Additional adjustment for genetic ancestry did not alter the results. Linear trends were assessed across ordinal values of categorical variables. Significant differences in ORs between case groups were tested using the Wald statistic P-value, calculated from the polytomous regression model. Two-sided P-values are reported for tests of trend and tests of heterogeneity, with P-values <0.05 considered statistically significant.

Analyses in postmenopausal women were based on 2,023 Hispanics (759 cases, 1,264 controls) and 2,384 NHWs (937 cases, 1,447 controls), after excluding subjects with missing data on covariates (91 cases, 231 controls) or ER/PR status (448 cases). Analyses by genetic ancestry were based on 1,338 Hispanics (493 cases, 845 controls). Because of concern about treatment- or disease-related weight gain among cases, we restricted the analysis of abdominal adiposity to cases and controls with anthropometric measurements taken <12 months after diagnosis or selection into the study (400 cases, 1,740 controls), hereafter referred to as the reduced dataset. Statistical analyses were conducted using SAS version 9.3 software (SAS Institute, Inc., Cary, NC).

Results

Compared to controls, higher proportions of cases had a higher education, BC family history in first-degree relatives, young age at menarche, no or few full-term pregnancies, late age at first full-term pregnancy, no or short duration of breast-feeding, a history of current HT use, and higher English language acculturation (among Hispanics) (Supplemental Table 1). Hispanic controls were shorter and had higher overall and abdominal body size measures than NHW controls (Supplemental Table 2). Hispanic controls with higher IA ancestry (>44%) had shorter height, higher current BMI, higher WHR, and lower WHtR.

Height, overall adiposity and breast cancer risk by ER/PR status

Among women currently using HT (554 cases, 920 controls), height and overall adiposity measures were not associated with ER+PR+ BC risk (data not shown). In women not currently using HT (Table 1), height was not related to risk of ER+PR+ BC and young-adult BMI, independent of current BMI, was associated with reduced risk among the two ethnicities combined (Ptrend=0.04), with similar inverse trends among Hispanics and NHWs, although of borderline significance among Hispanics only (high vs. low quartile: OR=0.61, Ptrend=0.07). There was no association with current BMI in either ethnic group. Weight gain, adjusted for current BMI, was associated with increased risk of ER+PR+ BC among Hispanics only (high vs. low quartile: OR=1.68, Ptrend=0.04), but the association was limited to those with a low (below the median) young-adult BMI (per 5 kg: OR= 1.42, 95% CI=1.09–1.86). Women with both elevated young-adult BMI and current obesity were not at increased risk of ER+PR+ BC in either ethnic group. In the reduced dataset, OR estimates for overall adiposity were not altered after additional adjustment for hip circumference (among Hispanics) or WHR (among NHWs), two measures associated with BC risk (Supplemental Tables 3 and 4).

Table 1.

Height and Overall Adiposity Associations with ER+PR+ Breast Cancer in Postmenopausal Women not currently Using Hormone Therapy, by Ethnicity

All Hispanics Non-Hispanic Whites
ER+PR+
Cases
n=586
Controls
n=1,775
ER+PR+
Cases
n=294
Controls
n=961
ER+PR+
Cases
n=292
Controls
n=814
n n OR (95%CI) 1 n n OR (95%CI) 1 n n OR (95%CI) 2
Current height (m) 3,4
    Q1: <153.2 139 442 1.0 111 354 1.0 28 88 1.0
    Q2: 153.2–157.7 138 443 0.95 0.72–1.26 77 283 0.85 0.61–1.20 61 160 1.22 0.72–2.09
    Q3: 157.8–162.9 147 443 0.97 0.73–1.30 72 213 1.01 0.71–1.44 75 230 1.05 0.63–1.76
    Q4: >162.9 161 440 1.06 0.78–1.45 33 108 0.92 0.57–1.48 128 332 1.26 0.76–2.09
P trend = 0.65 P trend = 0.88 P trend = 0.47
Current height (m) 4,5
    Quartile 1 65 240 1.0 69 213 1.0
    Quartile 2 81 251 1.16 0.79–1.71 78 192 1.24 0.84–1.83
    Quartile 3 62 228 0.97 0.65–1.46 66 203 1.01 0.68–1.51
    Quartile 4 85 239 1.26 0.85–1.88 79 202 1.23 0.82–1.85
P trend = 0.42 P trend = 0.52
    Per 5 cm 1.07 0.98–1.16 1.09 0.97–1.22 1.04 0.93–1.17
Young-adult BMI (kg/m2) 3,6,7
    Q1: <20.4 159 426 1.0 67 161 1.0 92 265 1.0
    Q2: 20.4–22.1 133 424 0.84 0.64–1.11 55 208 0.61 0.40–0.94 78 216 1.03 0.71–1.49
    Q3: 22.2–24.4 145 427 0.87 0.66–1.16 74 256 0.69 0.46–1.05 71 171 1.07 0.72–1.59
    Q4: >24.4 121 426 0.69 0.50–0.95 74 281 0.61 0.40–0.95 47 145 0.68 0.42–1.11
P trend = 0.04 P trend = 0.07 P trend = 0.27
Young-adult BMI (kg/m2) 6,7,8
    Quartile 1 85 227 1.0 71 201 1.0
    Quartile 2 59 226 0.69 0.47–1.02 70 197 0.99 0.66–1.47
    Quartile 3 65 226 0.79 0.53–1.17 79 199 1.01 0.67–1.52
    Quartile 4 61 227 0.71 0.46–1.09 68 200 0.73 0.46–1.14
P trend = 0.18 P trend = 0.23
    Per 5 kg/m2 0.81 0.68–0.96 0.83 0.66–1.04 0.77 0.59–1.00
Current BMI (kg/m2) 9,10
    <25.0 158 517 1.0 57 180 1.0 101 337 1.0
    25.0–29.9 202 615 1.02 0.77–1.35 99 364 0.81 0.52–1.25 103 251 1.16 0.79–1.70
    ≥30.0 225 635 0.96 0.67–1.37 137 413 0.91 0.55–1.51 88 222 0.89 0.52–1.52
P trend = 0.80 P trend = 0.84 P trend = 0.77
    Per 5 kg/m2 0.87 0.74–1.02 0.81 0.65–1.01 0.94 0.74–1.19
Weight gain (kg) 3,7,11
    Q1: <8.6 128 388 1.0 52 188 1.0 76 200 1.0
    Q2: 8.6–14.5 100 396 0.83 0.61–1.13 53 218 0.96 0.62–1.50 47 178 0.71 0.46–1.10
    Q3: 14.6–22.7 124 348 1.17 0.86–1.61 64 202 1.32 0.84–2.09 60 146 1.05 0.67–1.65
    Q4: >22.7 165 418 1.40 0.96–2.06 82 234 1.68 0.98–2.89 83 184 1.15 0.66–2.00
P trend = 0.04 P trend = 0.04 P trend = 0.49
Weight gain (kg) 7,11,12
    Quartile 1 62 232 1.0 62 177 1.0
    Quartile 2 51 192 1.06 0.69–1.63 49 162 0.91 0.58–1.43
    Quartile 3 65 212 1.31 0.85–2.02 72 185 1.10 0.70–1.72
    Quartile 4 73 206 1.75 1.02–3.02 83 184 1.29 0.73–2.29
P trend = 0.04 P trend = 0.36
    Per 5 kg 1.09 1.01–1.18 1.11 1.00–1.22 1.08 0.96–1.21
Young-adult BMI (kg/m2) 6,13 and weight gain (kg) 7,11
    <22.2 / per 5kg 292 850 1.26 1.05–1.50 122 369 1.42 1.09–1.86 170 481 1.15 0.90–1.47
    ≥22.2 / per 5kg 266 853 1.05 0.93–1.19 148 537 1.03 0.87–1.21 118 316 1.09 0.89–1.33
1

Odds ratios and 95% confidence intervals, adjusted for age (years, continuous), study (SFBCS, 4-CBCS), ethnicity/ English language acculturation (low, moderate, high, non-Hispanic white), education (less than high school, high school graduate, post high school education), family history of breast cancer in first degree relatives (no, yes), age at menarche (<12, 12, 13, ≥14), number of full-term pregnancies (nulliparous, 1–2, 3–4, ≥5), age at first full-term pregnancy (<20, 20–24, 25–29, ≥30, nulliparous), lifetime number of months of breastfeeding (nulliparous, 0, 1–6, 7–12, 13–24, >24), and average alcohol consumption in reference year (g/day; 0, 0.1–4.9, 5–9.9, 10–19.9, ≥20).

2

Adjusted for all variables above except English language acculturation.

3

Based on quartiles among postmenopausal controls not currently using hormone therapy.

4

Based on measured height at interview (or self-reported adult height when measured height was not available).

5

Based on ethnicity-specific quartiles among postmenopausal controls not currently using hormone therapy in each ethnic group. Quartile cutpoints by ethnicity are <151.2, 151.2–154.9, 155.0–159.4, and >159.4 for Hispanics, and <156.9, 156.9–161.5, 161.6–165.5, and >165.5 for NHWs.

6

Based on self-reported averaged weight at age 15 and age 30 for 4-CBCS cases and controls, self-reported weight in the 20's for SFBCS cases and controls (between ages 25–30 for cases diagnosed from April 1995 to April 1998 and matched controls and between ages 20–29 for cases diagnosed from May 1998 to April 2002 and matched controls), and measured height at interview (or self-reported adult height when measured height was not available).

7

Adjusted additionally for current BMI (continuous).

8

Based on ethnicity-specific quartiles among postmenopausal controls not currently using hormone therapy in each ethnic group. Quartile cutpoints by ethnicity are <21.0, 21.0–22.7, 22.8–25.1, and >25.1 for Hispanics, and <19.9, 19.9–21.5, 21.6–23.3, and >23.3 for NHWs.

9

Based on self-reported weight in reference year (or measured weight at interview when self-reported weight in reference year was not available) and measured height at interview (or self-reported adult height when measured height was not available).

10

Adjusted additionally for weight gain (continuous).

11

Based on self-reported weight in reference year (or measured weight at interview when self-reported weight was not available) minus self-reported young-adult weight.

12

Based on ethnicity-specific quartiles among postmenopausal controls not currently using hormone therapy in each ethnic group. Quartile cutpoints by ethnicity are <9.1, 9.1–15.4, 15.5–23.6, and >23.6 for Hispanics, and <7.1, 7.1–13.6, 13.7–22.7, and >22.7 for NHWs.

13

Based on median among postmenopausal controls not currently using hormone therapy.

Height and overall adiposity associations with ER-PR- BC did not significantly vary by HT use; therefore, results shown in Table 2 are not stratified by HT use. Height was marginally associated with increased risk among Hispanics (per 5 cm: OR =1.16, 95% CI=1.00–1.34). Young-adult BMI was inversely associated with risk among both ethnicities combined (high vs. low tertile: OR=0.67, Ptrend=0.03), with similar inverse trends for Hispanics and NHWs, but of borderline significance among NHWs only (Ptrend=0.05). A suggestive inverse trend was also seen for current BMI, with similar findings for Hispanics (per 5 kg/m2: OR =0.76, 95% CI=0.57–1.01) and NHWs (per 5 kg/m2: OR =0.63, 95% CI=0.43–0.92). Weight gain was associated with increased risk in NHWs only (per 5 kg: OR =1.18, 95% CI=1.02–1.37).

Table 2.

Height and Overall Adiposity Associations with ER-PR- Breast Cancer in Postmenopausal Women, by Ethnicity

All Hispanics Non-Hispanic Whites
ER-PR-
Cases
n=286
Controls
n=2,711
ER-PR-
Cases
n=153
Controls
n=1,264
ER-PR-
Cases
n=133
Controls
n=1,447
n n OR (95%CI) 1 n n OR (95%CI) 1 n n OR (95%CI)2
Current height (m) 3,4
    T1: <155.3 79 890 1.0 70 654 1.0 9 236 1.0
    T2: 155.3–161.8 106 895 1.41 1.02–1.95 57 417 1.24 0.84–1.82 49 478 2.44 1.17–5.09
    T3: >161.8 100 917 1.40 0.98–2.00 25 190 1.25 0.76–2.07 75 726 2.32 1.13–4.78
P trend = 0.07 P trend = 0.28 P trend = 0.08
Current height (m) 4,5
    Tertile 1 57 504 1.0 28 429 1.0
    Tertile 2 47 404 1.25 0.80–1.95 51 489 1.27 0.80–2.02
    Tertile 3 48 353 1.47 0.94–2.30 54 522 1.27 0.80–2.02
P trend = 0.10 P trend = 0.34
    Per 5 cm 1.12 1.01–1.25 1.16 1.00–1.34 1.11 0.96–1.29
Young-adult BMI (kg/m2) 3,6,7
    T1: <20.6 103 865 1.0 42 287 1.0 61 578 1.0
    T2: 20.6–23.0 89 866 0.81 0.59–1.10 51 391 0.90 0.57–1.42 38 475 0.69 0.44–1.07
    T3: >23.0 86 893 0.67 0.47–0.96 53 520 0.69 0.42–1.12 33 373 0.60 0.35–1.03
P trend = 0.03 P trend = 0.13 P trend = 0.05
Young-adult BMI (kg/m2) 6,7,8
    Tertile 1 47 348 1.0 49 464 1.0
    Tertile 2 48 389 0.92 0.59–1.44 43 481 0.79 0.51–1.23
    Tertile 3 51 461 0.79 0.49–1.28 40 480 0.58 0.35–0.97
P trend = 0.34 P trend = 0.04
    Per 5 kg/m2 0.87 0.70–1.09 0.86 0.64–1.17 0.85 0.61–1.19
Current BMI (kg/m2) 9,10
    <25.0 92 885 1.0 45 267 1.0 47 618 1.0
    25.0–29.9 98 900 0.91 0.64–1.29 53 478 0.71 0.43–1.16 45 422 1.00 0.61–1.66
    ≥30.0 95 915 0.66 0.41–1.05 54 515 0.60 0.32–1.11 41 400 0.57 0.28–1.17
P trend = 0.09 P trend = 0.11 P trend = 0.16
    Per 5 kg/m2 0.82 0.66–1.00 0.76 0.57–1.01 0.63 0.43–0.92
Weight gain (kg) 3,7,11
    T1: <10.2 68 771 1.0 44 340 1.0 24 431 1.0
    T2: 10.2–20.4 95 798 1.49 1.06–2.11 55 380 1.37 0.87–2.17 40 418 1.80 1.04–3.11
    T3: >20.4 91 810 1.61 1.02–2.53 36 390 1.08 0.57–2.04 55 420 2.74 1.39–5.41
P trend = 0.03 P trend = 0.66 P trend < 0.01
Weight gain (kg) 7,11,12
    Tertile 1 47 363 1.0 24 430 1.0
    Tertile 2 52 357 1.37 0.87–2.17 38 403 1.77 1.02–3.08
    Tertile 3 36 390 1.08 0.57–2.02 57 435 2.76 1.41–5.40
P trend = 0.67 P trend < 0.01
    Per 5 kg 1.10 1.00–1.22 1.04 0.91–1.20 1.18 1.02–1.37
Young-adult BMI (kg/m2) 6,13 and weight gain (kg) 7,11
    <21.8 / per 5kg 143 1312 1.11 0.88–1.40 62 480 0.86 0.57–1.29 81 832 1.23 0.92–1.64
    ≥21.8 / per 5kg 135 1311 1.13 0.97–1.32 84 718 1.20 0.97–1.50 51 593 1.09 0.85–1.40
1

Odds ratios and 95% confidence intervals, adjusted for age (years, continuous), study (SFBCS, 4-CBCS), ethnicity/ English language acculturation (low, moderate, high, non-Hispanic white), family history of breast cancer in first degree relatives (no, yes), age at menarche (<12, 12, 13, ≥14), and use of menopausal hormone therapy (never, past, current, unknown).

2

Adjusted for all variables above except English language acculturation.

3

Based on tertiles among all postmenopausal controls.

4

Based on measured height at interview (or self-reported adult height when measured height was not available).

5

Based on ethnicity-specific tertiles among all postmenopausal controls in each ethnic group. Tertile cutpoints by ethnicity are <152.8, 152.8–157.8, and >157.8 for Hispanics, and <158.8, 158.8–164.5, and >164.5 for NHWs.

6

Based on self-reported averaged weight at age 15 and age 30 for 4-CBCS cases and controls, self-reported weight in the 20's for SFBCS cases and controls (between ages 25–30 for cases diagnosed from April 1995 to April 1998 and matched controls and between ages 20–29 for cases diagnosed from May 1998 to April 2002 and matched controls), and measured height at interview (or self-reported adult height when measured height was not available).

7

Adjusted additionally for current BMI (continuous).

8

Based on ethnicity-specific tertiles among all postmenopausal controls in each ethnic group. Tertile cutpoints by ethnicity are <21.2, 21.2–23.8, and >23.8 for Hispanics, and <20.2, 20.2–22.2, and >22.2 for NHWs.

9

Based on self-reported weight in reference year (or measured weight at interview when self-reported weight in reference year was not available) and measured height at interview (or self-reported adult height when measured height was not available).

10

Adjusted additionally for weight gain (continuous).

11

Based on self-reported weight in reference year (or measured weight at interview when self-reported weight was not available) minus self-reported young-adult weight.

12

Based on ethnicity-specific tertiles among all postmenopausal controls in each ethnic group. Tertile cutpoints by ethnicity are <10.7, 10.7–20.4, and >20.4 for Hispanics, and <10.0, 10.0–19.3, and >19.3 for NHWs.

13

Based on median among all postmenopausal controls.

Abdominal adiposity and breast cancer risk

In the reduced dataset, associations with abdominal adiposity did not significantly differ by ER/PR status. For all BCs combined (Table 3), there were no associations with waist circumference, WHR, and WHtR in either population. Among Hispanics, hip circumference was associated with a two-fold increased risk (high vs. low tertile: OR=2.03, Ptrend=0.04) (Table 3). This association was limited to women not currently using HT (Supplemental Table 3), although the interaction by HT was not statistically significant. Among NHWs, there was evidence of significant interaction by HT use for WHR (Pinteraction <0.01), with an increased risk limited to those not currently using HT (Supplemental Table 4).

Table 3.

Abdominal Adiposity Associations with Breast Cancer Risk in Postmenopausal Women Measured <12 Months after Diagnosis/Selection, by Ethnicity

All Hispanics Non-Hispanic Whites

Cases
n=400
Controls
n=1,740
Cases
n=127
Controls
n=913
Cases
n=273
Controls
n=827
n n OR (95%CI) 1 n n OR (95%CI) 1 n n OR (95%CI) 2
Waist (cm) 3
    T1: <83.2 126 544 1.0 33 214 1.0 93 330 1.0
    T2: 83.2–94.6 120 551 1.19 0.86–1.63 43 317 1.03 0.59–1.79 77 234 1.20 0.81–1.77
    T3: >94.6 136 559 1.40 0.93–2.10 46 352 1.23 0.62–2.43 90 207 1.44 0.86–2.43
    P trend = 0.11 P trend = 0.54 P trend = 0.16
Waist (cm) 4
    Tertile 1 45 292 1.0 77 253 1.0
    Tertile 2 37 291 0.97 0.57–1.64 71 256 0.85 0.57–1.28
    Tertile 3 40 300 1.23 0.63–2.41 112 262 1.24 0.75–2.06
P trend = 0.58 P trend = 0.47
    Per 2 cm 1.02 0.99–1.05 1.02 0.97–1.07 1.01 0.98–1.05
Hip (cm) 3
    T1: <102.3 123 546 1.0 37 276 1.0 86 270 1.0
    T2: 102.3–111.8 115 549 0.98 0.72–1.34 38 272 1.43 0.84–2.43 77 277 0.80 0.54–1.19
    T3: >111.8 144 558 1.31 0.87–1.96 47 334 2.03 1.05–3.94 97 224 1.02 0.60–1.73
  P trend = 0.23 P trend = 0.04 P trend = 0.92
Hip (cm) 5
    Tertile 1 39 295 1.0 82 257 1.0
    Tertile 2 39 287 1.53 0.90–2.60 68 254 0.77 0.51–1.15
    Tertile 3 44 300 2.31 1.17–4.56 110 260 1.04 0.63–1.70
P trend = 0.02 P trend = 0.98
    Per 2 cm 1.01 0.98–1.04 1.02 0.97–1.08 1.00 0.96–1.05
Waist-to-hip ratio 3
    T1: <0.80 147 546 1.0 33 200 1.0 114 346
    T2: 0.80–0.86 106 544 0.88 0.65–1.19 39 302 0.88 0.52–1.50 67 242 0.84 0.58–1.22
    T3: >0.86 129 563 1.10 0.81–1.50 50 380 0.87 0.52–1.47 79 183 1.22 0.83–1.81
P trend = 0.55 P trend = 0.62 P trend = 0.39
Waist-to-hip ratio 6
    Tertile 1 47 292 1.0 76 252
    Tertile 2 32 290 0.78 0.47–1.31 79 257 1.00 0.68–1.47
    Tertile 3 43 300 0.93 0.57–1.53 105 262 1.32 0.89–1.96
P trend = 0.79 P trend = 0.16
    Per 0.1 1.09 0.91–1.31 0.99 0.72–1.37 1.10 0.87–1.38
Waist-to-height ratio 3
    T1: <0.53 150 545 1.0 34 180 1.0 116 365 1.0
    T2: 0.53–0.60 108 547 0.91 0.66–1.25 39 316 0.77 0.44–1.36 69 231 0.88 0.60–1.31
    T3: >0.60 124 562 0.98 0.65–1.49 49 387 0.87 0.44–1.75 75 175 0.99 0.58–1.70
P trend = 0.89 P trend = 0.74 P trend = 0.87
Waist-to-height ratio 7
    Tertile 1 49 290 1.0 82 254 1.0
    Tertile 2 37 290 0.86 0.51–1.46 74 254 0.80 0.53–1.20
    Tertile 3 36 301 0.86 0.43–1.71 104 263 0.91 0.54–1.54
P trend = 0.64 P trend = 0.69
    Per 0.1 1.07 0.85–1.35 1.03 0.70–1.52 1.07 0.80–1.43
1

Odds ratios and 95% confidence intervals, adjusted for age (years, continuous), study (SFBCS, 4-CBCS), ethnicity/ English language acculturation (low, moderate, high, non-Hispanic white), education (less than high school, high school graduate, post high school education), family history of breast cancer in first degree relatives (no, yes), age at menarche (<12, 12, 13, ≥14), number of full-term pregnancies (nulliparous, 1–2, 3–4, ≥5), age at first full-term pregnancy (<20, 20–24, 25–29, ≥30, nulliparous), lifetime number of months of breastfeeding (nulliparous, 0, 1–6, 7–12, 13–24, >24), use of menopausal hormone therapy (never, past, current, unknown), average alcohol consumption in reference year (g/day; 0, 0.1–4.9, 5–9.9, 10–19.9, ≥20), and current BMI (continuous).

2

Adjusted for all variables above except English language acculturation.

3

Based on tertiles among postmenopausal controls measured <12 months after selection into the study.

4

Based on ethnicity-specific tertiles among postmenopausal controls measured <12 months after selection into the study in each ethnic group. Tertile cutpoints by ethnicity are <86.4, 86.4–96.6, and >96.6 for Hispanics and <80.1, 80.1–91.8, and >91.8 for NHWs.

5

Based on ethnicity-specific tertiles among postmenopausal controls measured <12 months after selection into the study in each ethnic group. Tertile cutpoints by ethnicity are <102.9, 102.9–112.7, and >112.7 for Hispanics and <101.6, 101.6–110.5, and >110.5 for NHWs.

6

Based on ethnicity-specific tertiles among postmenopausal controls measured <12 months after selection into the study in each ethnic group. Tertile cutpoints by ethnicity are <0.82, 0.82–0.87, and >0.87 for Hispanics and <0.77, 0.77–0.84, and >0.84 for NHWs.

7

Based on ethnicity-specific tertiles among postmenopausal controls measured <12 months after selection into the study in each ethnic group. Tertile cutpoints by ethnicity are <0.56, 0.56–0.63, and >0.63 for Hispanics and <0.50, 0.50–0.57, and >0.57 for NHWs.

Overall adiposity, genetic ancestry and breast cancer among Hispanics

In the subset of cases and controls with information on genetic ancestry, we found that the overall adiposity associations in Hispanic women were limited to those with lower IA ancestry (Supplemental Table 5). Among non-current HT users, weight gain was associated with a three-fold increased risk of ER+PR+ BC (high vs. low tertile: OR=3.46, Ptrend=0.01), whereas current obesity was associated with a significantly reduced risk (≥30 vs. <25 kg/m2: OR=0.31, Ptrend=0.02). Similarly, for young-adult BMI, an inverse association with BC risk (all subtypes combined) was found only among Hispanics with lower IA ancestry (high vs. low tertile: OR=0.42, 95% CI=0.23–0.77, Ptrend<0.01) (data not shown). None of the interactions by genetic ancestry, however, reached statistical significance. For abdominal adiposity, the reduced dataset was too small to assess interactions by genetic ancestry.

Discussion

In this pooled analysis of over 2,000 postmenopausal Hispanic women, BC risk was associated with several body size measures and associations were specific for BC subtypes defined by hormone receptor status. Among Hispanics not currently using HT, weight gain was associated with an increased risk of ER+PR+ BC among those with a low young-adult BMI. Suggestive inverse trends for young-adult BMI were found for both ER+PR+ and ER-PR- BC. Among all Hispanics, regardless of HT use, height was associated with ER-PR- BC risk and hip circumference with BC risk overall.

Data on the association of height and overall adiposity with postmenopausal BC risk in Hispanic women are sparse, with reports from one cohort study (6) and four case-control studies (3, 4, 7, 8), two of which were included in this pooled analysis (4, 7). For height, we found no association with ER+PR+ BC and a suggestive positive association with ER-PR- BC among Hispanic women. The Mexico study reported a positive association (8), whereas the Multiethnic Cohort found no association (6). Neither study considered ER/PR status. For all BCs combined, regardless of HT use, we found a positive association with height in Hispanics only (high vs. low quartile: OR=1.51, 95% CI=1.12–2.03, Ptrend <0.01). Studies in NHW women reported positive associations with overall BC (24, 25) or ER+PR+ BC (15, 26, 27).

As reported for NHW women (6, 13, 15, 22, 28), we found that weight gain was a better predictor of risk in Hispanics than current BMI and that the association was limited to ER+PR+ BC and women not currently using HT, consistent with recent meta-analyses of weight gain and BMI by hormone receptor status (10, 29, 30) or HT use (29, 30). However, the association with weight gain was seen only in Hispanics with a low young-adult BMI. In women with both elevated young-adult BMI and current obesity there was no evidence of association, consistent with reports for NHW women (15, 28, 31), and likely explained by the residual protective effect associated with obesity prior to menopause. Other studies in Hispanics reported non-significant elevations in risk of ER+PR+ BC (3), or no associations for BC overall (6). In the Mexico study, increasing body shape silhouette size since childhood was strongly associated with increased risk of postmenopausal BC (8). In NHW women, we failed to find associations with current BMI or weight gain, contrary to other studies in NHWs (1, 2).

Our findings for Hispanics emphasize the importance of considering young-adult BMI when evaluating associations with weight gain in populations with a high prevalence of young-adult overweight and obesity. Positive associations between postmenopausal BC and overall adiposity may be masked in contemporary studies where the prevalence of young-adult obesity is higher than in past studies. Adult weight gain is a marker of body fat deposition (32), which serves as a source of estrogen production in postmenopausal women through the conversion of androgen to estrogen in adipose tissue (33), resulting in higher circulating concentrations of estrogens (34). Obesity may also affect BC risk through other pathways, including effects on hyperinsulinemia and glucose levels, insulin and insulin-like growth factors, cytokines, and adipokines (2, 35).

We found inverse associations with young-adult BMI for both ER+PR+ BC (among non-HT users) and ER-PR- BC, with similar inverse trends in Hispanics and NHWs. For all BCs combined, results were similar for Hispanics (high vs. low quartile: OR=0.66, 95% CI=0.46–0.95, Ptrend=0.07) and NHWs (OR=0.52, 95% CI=0.34–0.81, Ptrend<0.01) not currently using HT, and in agreement with reports for NHWs (12, 14, 22, 28, 3639). Inverse associations have also been reported for childhood and adolescent obesity (3841), including Hispanics in our studies (4, 5). Together, these findings suggest that early-life and young-adult adiposity exert a strong and long-lasting influence on BC risk that extends into the postmenopausal years. Underlying mechanisms, however, remain uncertain.

The role of overall adiposity in relation to ER-PR- BC is not well understood; data on the association with current BMI and weight gain are not consistent. Our finding of suggestive inverse associations with current BMI both in Hispanics and NHWs agrees with some studies (28, 42), but not others (10, 15, 31). For weight gain, we found a positive association among NHWs only, consistent with some (15, 43) but not other (29) reports. Compared to ER+PR+ BC, few risk factors have been identified for ER-PR- BC, a subtype that is more common in Hispanics than NHWs (44). Thus, further investigation of the role of overall adiposity in studies with larger numbers of ER-PR- BC cases is warranted.

We found no associations of waist circumference, WHR, and WHtR with BC risk among Hispanics. The Mexico study reported inverse associations with waist circumference and WHR (8), but limited to women with <10 years since menopause; among those with ≥10 years since menopause, there was no association with abdominal obesity. The sample size for our analysis was limited because we included only women with anthropometric measurements taken <12 months after diagnosis/selection because of concern about treatment- or disease-related weight gain among cases (45), especially in the abdominal area (46). The Mexico study took anthropometric measurements shortly after diagnosis (8). Unlike the Mexico study (8), we found a positive association between hip circumference and BC risk overall among Hispanic women, but no association among NHWs. Some studies in NHWs reported positive associations with hip circumference (12, 47), although the evidence is not consistent (48, 49).

For NHW women, we found that WHR was the only abdominal obesity measure significantly associated with BC risk, but only among those not currently using HT. This finding is in agreement with both case-control (23, 50, 51) and prospective (28, 5254) studies, including a meta-analysis (55). In contrast, some case-control (56) and prospective (12, 47, 49, 5759) studies found no association with WHR in NHW women. In some studies, associations with abdominal obesity were limited to (12, 23, 28, 54) or stronger in (47) non-current HT users, but not all studies evaluated the potential modifying effects of HT use. The data are also mixed for the association with specific BC subtypes, with reports of positive associations with waist (15, 47, 60), WHR (61) or WHtR (15) limited to ER+PR+ or ER+ BC; association with WHR independent of hormone receptor status (50); or no associations with WHR (49, 60, 62) and waist circumference (48, 49, 62), regardless of hormone receptor status. Thus, the data on the relation between abdominal adiposity and BC risk in NHW women are inconclusive.

In postmenopausal women, waist circumference has been associated with sex hormone-binding globulin (SHBG) and free estradiol and testosterone levels, independently of BMI (63), and low SHBG levels have been more strongly associated with abdominal adiposity than overall adiposity (64). In a prospective study, adjustment for serum estrogen attenuated the association between waist circumference and BC risk somewhat, although an increased risk remained (65), suggesting that other metabolic or hormonal factors, such as insulin resistance or other growth-related factors may play a role (66, 67).

Our findings suggest that genetic ancestry may modify the body size associations in Hispanic women. The inverse associations with young-adult BMI and current BMI and the positive association with weight gain were limited to Hispanics with lower IA ancestry. Our sample size, however, was too small to consider weight gain in relation to young-adult BMI. The 4-CBCS is the only other study that examined variations in body size associations by genetic ancestry, but used different AIMs (4). Larger studies will be needed to determine whether body size associations vary by genetic ancestry among Hispanic women.

Our study has some limitations and several strengths. Participation was less than optimal and differed between the SFBCS and 4-CBCS, although the results for Hispanics were generally consistent. The evaluation of several modifying factors jointly resulted in limited sample sizes and the many comparisons may have led to potentially false-positive results. Nevertheless, the analyses were hypothesis-driven, building upon prior findings. Past weight was based on self-report and exposure misclassification may have attenuated the associations due to inaccurate recall. However, the correlation between self-reported and measured weight was high both in postmenopausal cases (r=0.87) and controls (r=0.91) and similar in Hispanic and NHW cases (r=0.84 and r=0.90, respectively) and controls (r=0.91 and r=0.91, respectively). Furthermore, a sensitivity analysis in women with both measured and self-reported weight and height found similar associations with BMI based on self-reported or measured weight and height. The use of BMI as a measure of body fat does not distinguish between lean and fat mass (68) or between individuals with the same BMI but differing percent fat mass (69). The relation between body fat and BMI has been shown to vary by race/ethnicity (7072). Nevertheless, an analysis of the Women’s Health Initiative reported similar associations for measurement-based BMI and dual-energy-X-ray-absorptiometry (DXA) based body fat measures (73). We had to rely on waist and hip circumferences measured after diagnosis and had no data available on pre-diagnostic measures. To minimize exposure misclassification due to treatment- or disease-related weight gain, we restricted the analyses of abdominal adiposity to women with anthropometric measurements <12 months after diagnosis or selection into the study.

The main strengths include the population-based design, the use of standardized protocols to take body measurements rather than relying on self-report or self-measurement, and the comprehensive assessment of other BC risk factors by in-person interview. Our pooled sample size was considerably larger than previous studies in U.S. Hispanics which allowed us to evaluate the role of several modifying factors. The availability of information on hormone receptor status for most cases allowed us to investigate the role of body size for specific subtypes.

In conclusion, our pooled analysis shows that weight gain is an important risk factor for postmenopausal ER+PR+ BC in Hispanic women with a low young-adult BMI, and that a high young-adult BMI is inversely associated with BC risk, regardless of ER/PR status. These findings emphasize that body size throughout life should be considered when assessing postmenopausal BC risk. In light of the high prevalence of overweight and obesity, particularly among Hispanics, avoiding weight gain and maintaining a healthy weight are important strategies to reduce the risk of ER+PR+ BC, the most common BC subtype. For ER-PR- BC, the role of overweight and obesity throughout adult life warrants further investigation in larger studies, given our finding of suggestive inverse associations.

Supplementary Material

1

Acknowledgements

We acknowledge the contributions of the following individuals to the study: Sandra Edwards and Jennifer Herrick for data harmonization oversight, Erica Wolff and Michael Hoffman for laboratory support, Jocelyn Koo for data management for the San Francisco Bay Area Breast Cancer Study, Dr. Tim Byers for his contribution to the 4-Corners Breast Cancer Study, and Dr. Josh Galanter for assistance in selection of AIMs markers.

Financial Support

The Breast Cancer Health Disparities Study was funded by grant CA14002 (M.L. Slattery) from the National Cancer Institute. The San Francisco Bay Area Breast Cancer Study was supported by grants CA063446 (E.M. John) and CA07730 (E.M. John) from the National Cancer Institute, grant DAMD17-96-1-6071 (E.M. John) from the U.S. Department of Defense and grant 7PB-0068 (E.M. John) from the California Breast Cancer Research Program. The collection of cancer incidence data used in this study was supported by the California Department of Public Health as part of the statewide cancer reporting program mandated by California Health and Safety Code Section 103885; the National Cancer Institute's Surveillance, Epidemiology and End Results Program under contract HHSN261201000036C awarded to the Cancer Prevention Institute of California; and the Centers for Disease Control and Prevention's National Program of Cancer Registries, under agreement #1U58 DP000807-01 awarded to the Public Health Institute. The 4-Corners Breast Cancer Study was funded by grants CA078682 (M.L. Slattery), CA078762 (K.B. Baumgartner), CA078552 (T. Byers), and CA078802 (A.R. Giuliano) from the National Cancer Institute. The research also was supported by the Utah Cancer Registry, which is funded by contract N01-PC-67000 from the National Cancer Institute, with additional support from the State of Utah Department of Health, the New Mexico Tumor Registry, and the Arizona and Colorado cancer registries, funded by the Centers for Disease Control and Prevention National Program of Cancer Registries and additional state support. The contents of this manuscript are solely the responsibility of the authors and do not necessarily represent the official view of the National Cancer Institute or endorsement by the State of California Department of Public Health, the National Cancer Institute, and the Centers for Disease Control and Prevention or their Contractors and Subcontractors.

Footnotes

Conflict of interest

None of the authors have any conflict of interest to report.

References

  • 1.World Cancer Research Fund / American Institute for Cancer Research. Food, Nutrition, Physical Activty, and Prevention of Cancer: A Global Perspective. Washington, D.C: AICR; 2007. [Google Scholar]
  • 2.Amadou A, Hainaut P, Romieu I. Role of obesity in the risk of breast cancer: lessons from anthropometry. J Oncol. 2013;2013:906495. doi: 10.1155/2013/906495. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3.Wenten M, Gilliland FD, Baumgartner K, Samet JM. Associations of weight, weight change, and body mass with breast cancer risk in Hispanic and non-Hispanic white women. Ann Epidemiol. 2002;12:435–434. doi: 10.1016/s1047-2797(01)00293-9. [DOI] [PubMed] [Google Scholar]
  • 4.Slattery ML, Sweeney C, Edwards S, Herrick J, Baumgartner K, Wolff R, et al. Body size, weight change, fat distribution and breast cancer risk in Hispanic and non-Hispanic white women. Breast Cancer Res Treat. 2007;102:85–101. doi: 10.1007/s10549-006-9292-y. [DOI] [PubMed] [Google Scholar]
  • 5.Sangaramoorthy M, Phipps AI, Horn-Ross PL, Koo J, John EM. Early-life factors and breast cancer risk in Hispanic women: the role of adolescent body size. Cancer Epidemiol Biomarkers Prev. 2011;20:2572–2582. doi: 10.1158/1055-9965.EPI-11-0848. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6.White KK, Park SY, Kolonel LN, Henderson BE, Wilkens LR. Body size and breast cancer risk: The multiethnic cohort. Int J Cancer. 2012;131:E705–E716. doi: 10.1002/ijc.27373. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7.John EM, Phipps AI, Sangaramoorthy M. Body size, modifying factors, and postmenopausal breast cancer risk in a multiethnic population: the San Francisco Bay Area Breast Cancer Study. Springerplus. 2013;2:239. doi: 10.1186/2193-1801-2-239. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Amadou A, Torres Mejia G, Fagherazzi G, Ortega C, Angeles-Llerenas A, Chajes V, et al. Anthropometry, silhouette trajectory, and risk of breast cancer in Mexican women. Am J Prev Med. 2014;46:S52–S64. doi: 10.1016/j.amepre.2013.10.024. [DOI] [PubMed] [Google Scholar]
  • 9.Flegal KM, Carroll MD, Kit BK, Ogden CL. Prevalence of obesity and trends in the distribution of body mass index among US adults, 1999–2010. JAMA. 2012;307:491–497. doi: 10.1001/jama.2012.39. [DOI] [PubMed] [Google Scholar]
  • 10.Suzuki R, Orsini N, Saji S, Key TJ, Wolk A. Body weight and incidence of breast cancer defined by estrogen and progesterone receptor status--a meta-analysis. Int J Cancer. 2009;124:698–712. doi: 10.1002/ijc.23943. [DOI] [PubMed] [Google Scholar]
  • 11.Friedenreich CM. Review of anthropometric factors and breast cancer risk. Eur J Cancer Prev. 2001;10:15–32. doi: 10.1097/00008469-200102000-00003. [DOI] [PubMed] [Google Scholar]
  • 12.Morimoto LM, White E, Chen Z, Chlebowski RT, Hays J, Kuller L, et al. Obesity, body size, and risk of postmenopausal breast cancer: the Women's Health Initiative (United States) Cancer Causes Control. 2002;13:741–751. doi: 10.1023/a:1020239211145. [DOI] [PubMed] [Google Scholar]
  • 13.Feigelson HS, Jonas CR, Teras LR, Thun MJ, Calle EE. Weight gain, body mass index, hormone replacement therapy, and postmenopausal breast cancer in a large prospective study. Cancer Epidemiol Biomarkers Prev. 2004;13:220–224. doi: 10.1158/1055-9965.epi-03-0301. [DOI] [PubMed] [Google Scholar]
  • 14.Chu SY, Lee NC, Wingo PA, Senie RT, Greenberg RS, Peterson HB. The relationship between body mass and breast cancer among women enrolled in the Cancer and Steroid Hormone Study. J Clin Epidemiol. 1991;44:1197–1206. doi: 10.1016/0895-4356(91)90152-y. [DOI] [PubMed] [Google Scholar]
  • 15.Canchola AJ, Anton-Culver H, Bernstein L, Clarke CA, Henderson K, Ma H, et al. Body size and the risk of postmenopausal breast cancer subtypes in the California Teachers Study cohort. Cancer Causes Control. 2012 doi: 10.1007/s10552-012-9897-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16.Ziv E, John EM, Choudhry S, Kho J, Lorizio W, Perez-Stable EJ, et al. Genetic ancestry and risk factors for breast cancer among Latinas in the San Francisco Bay Area. Cancer Epidemiol Biomarkers Prev. 2006;15:1878–1885. doi: 10.1158/1055-9965.EPI-06-0092. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17.Slattery ML, John EM, Torres-Mejia G, Lundgreen A, Herrick JS, Baumgartner KB, et al. Genetic variation in genes involved in hormones, inflammation and energetic factors and breast cancer risk in an admixed population. Carcinogenesis. 2012;33:1512–1521. doi: 10.1093/carcin/bgs163. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.John EM, Horn-Ross PL, Koo J. Lifetime physical activity and breast cancer risk in a multiethnic population: the San Francisco Bay area breast cancer study. Cancer Epidemiol Biomarkers Prev. 2003;12:1143–1152. [PubMed] [Google Scholar]
  • 19.John EM, Phipps AI, Davis A, Koo J. Migration history, acculturation, and breast cancer risk in Hispanic women. Cancer Epidemiol Biomarkers Prev. 2005;14:2905–2913. doi: 10.1158/1055-9965.EPI-05-0483. [DOI] [PubMed] [Google Scholar]
  • 20.John EM, Sangaramoorthy M, Phipps AI, Koo J, Horn-Ross PL. Adult body size, hormone receptor status, and premenopausal breast cancer risk in a multiethnic population: the San Francisco Bay Area breast cancer study. Am J Epidemiol. 2011;173:201–216. doi: 10.1093/aje/kwq345. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21.Molarius A, Seidell JC. Selection of anthropometric indicators for classification of abdominal fatness--a critical review. Int J Obes Relat Metab Disord. 1998;22:719–727. doi: 10.1038/sj.ijo.0800660. [DOI] [PubMed] [Google Scholar]
  • 22.Huang Z, Hankinson SE, Colditz GA, Stampfer MJ, Hunter DJ, Manson JE, et al. Dual effects of weight and weight gain on breast cancer risk. Jama. 1997;278:1407–1411. [PubMed] [Google Scholar]
  • 23.Friedenreich CM, Courneya KS, Bryant HE. Case-control study of anthropometric measures and breast cancer risk. Int J Cancer. 2002;99:445–452. doi: 10.1002/ijc.10389. [DOI] [PubMed] [Google Scholar]
  • 24.van den Brandt PA, Spiegelman D, Yaun SS, Adami HO, Beeson L, Folsom AR, et al. Pooled analysis of prospective cohort studies on height, weight, and breast cancer risk. Am J Epidemiol. 2000;152:514–527. doi: 10.1093/aje/152.6.514. [DOI] [PubMed] [Google Scholar]
  • 25.Kabat GC, Anderson ML, Heo M, Hosgood HD, 3rd, Kamensky V, Bea JW, et al. Adult stature and risk of cancer at different anatomic sites in a cohort of postmenopausal women. Cancer Epidemiol Biomarkers Prev. 2013;22:1353–1363. doi: 10.1158/1055-9965.EPI-13-0305. [DOI] [PubMed] [Google Scholar]
  • 26.Ritte R, Lukanova A, Tjonneland A, Olsen A, Overvad K, Mesrine S, et al. Height, age at menarche and risk of hormone receptor-positive and -negative breast cancer: a cohort study. Int J Cancer. 2013;132:2619–2629. doi: 10.1002/ijc.27913. [DOI] [PubMed] [Google Scholar]
  • 27.Rosner B, Glynn RJ, Tamimi RM, Chen WY, Colditz GA, Willett WC, et al. Breast cancer risk prediction with heterogeneous risk profiles according to breast cancer tumor markers. Am J Epidemiol. 2013;178:296–308. doi: 10.1093/aje/kws457. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28.Ahn J, Schatzkin A, Lacey JV, Jr, Albanes D, Ballard-Barbash R, Adams KF, et al. Adiposity, adult weight change, and postmenopausal breast cancer risk. Arch Intern Med. 2007;167:2091–2102. doi: 10.1001/archinte.167.19.2091. [DOI] [PubMed] [Google Scholar]
  • 29.Vrieling A, Buck K, Kaaks R, Chang-Claude J. Adult weight gain in relation to breast cancer risk by estrogen and progesterone receptor status: a meta-analysis. Breast Cancer Res Treat. 2010;123:641–649. doi: 10.1007/s10549-010-1116-4. [DOI] [PubMed] [Google Scholar]
  • 30.Munsell MF, Sprague BL, Berry DA, Chisholm G, Trentham-Dietz A. Body mass index and breast cancer risk according to postmenopausal estrogen-progestin use and hormone receptor status. Epidemiol Rev. 2014;36:114–136. doi: 10.1093/epirev/mxt010. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31.Colditz GA, Rosner BA, Chen WY, Holmes MD, Hankinson SE. Risk factors for breast cancer according to estrogen and progesterone receptor status. J Natl Cancer Inst. 2004;96:218–228. doi: 10.1093/jnci/djh025. [DOI] [PubMed] [Google Scholar]
  • 32.Ballard-Barbash R. Anthropometry and breast cancer. Body size--a moving target. Cancer. 1994;74:1090–1100. doi: 10.1002/1097-0142(19940801)74:3+<1090::aid-cncr2820741518>3.0.co;2-x. [DOI] [PubMed] [Google Scholar]
  • 33.Siiteri PK. Adipose tissue as a source of hormones. Am J Clin Nutr. 1987;45:277–282. doi: 10.1093/ajcn/45.1.277. [DOI] [PubMed] [Google Scholar]
  • 34.Hankinson SE. Endogenous hormones and risk of breast cancer in postmenopausal women. Breast Dis. 2005;24:3–15. doi: 10.3233/bd-2006-24102. [DOI] [PubMed] [Google Scholar]
  • 35.Kaaks R. Nutrition, hormones, and breast cancer: is insulin the missing link? Cancer Causes Control. 1996;7:605–625. doi: 10.1007/BF00051703. [DOI] [PubMed] [Google Scholar]
  • 36.Barnes-Josiah D, Potter JD, Sellers TA, Himes JH. Early body size and subsequent weight gain as predictors of breast cancer incidence (Iowa, United States) Cancer Causes Control. 1995;6:112–118. doi: 10.1007/BF00052771. [DOI] [PubMed] [Google Scholar]
  • 37.Brinton LA, Swanson CA. Height and weight at various ages and risk of breast cancer. Ann Epidemiol. 1992;2:597–609. doi: 10.1016/1047-2797(92)90004-a. [DOI] [PubMed] [Google Scholar]
  • 38.Magnusson C, Baron J, Persson I, Wolk A, Bergstrom R, Trichopoulos D, et al. Body size in different periods of life and breast cancer risk in post-menopausal women. Int J Cancer. 1998;76:29–34. doi: 10.1002/(sici)1097-0215(19980330)76:1<29::aid-ijc6>3.0.co;2-#. [DOI] [PubMed] [Google Scholar]
  • 39.Berstad P, Coates RJ, Bernstein L, Folger SG, Malone KE, Marchbanks PA, et al. A case-control study of body mass index and breast cancer risk in white and African-American women. Cancer Epidemiol Biomarkers Prev. 2010;19:1532–1544. doi: 10.1158/1055-9965.EPI-10-0025. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 40.Bardia A, Vachon CM, Olson JE, Vierkant RA, Wang AH, Hartmann LC, et al. Relative weight at age 12 and risk of postmenopausal breast cancer. Cancer Epidemiol Biomarkers Prev. 2008;17:374–378. doi: 10.1158/1055-9965.EPI-07-0389. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 41.Baer HJ, Tworoger SS, Hankinson SE, Willett WC. Body fatness at young ages and risk of breast cancer throughout life. Am J Epidemiol. 2010;171:1183–1194. doi: 10.1093/aje/kwq045. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 42.Setiawan VW, Monroe KR, Wilkens LR, Kolonel LN, Pike MC, Henderson BE. Breast cancer risk factors defined by estrogen and progesterone receptor status: the multiethnic cohort study. Am J Epidemiol. 2009;169:1251–1259. doi: 10.1093/aje/kwp036. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 43.Feigelson HS, Patel AV, Teras LR, Gansler T, Thun MJ, Calle EE. Adult weight gain and histopathologic characteristics of breast cancer among postmenopausal women. Cancer. 2006;107:12–21. doi: 10.1002/cncr.21965. [DOI] [PubMed] [Google Scholar]
  • 44.Banegas MP, Li CI. Breast cancer characteristics and outcomes among Hispanic Black and Hispanic White women. Breast Cancer Res Treat. 2012;134:1297–1304. doi: 10.1007/s10549-012-2142-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 45.Saquib N, Flatt SW, Natarajan L, Thomson CA, Bardwell WA, Caan B, et al. Weight gain and recovery of pre-cancer weight after breast cancer treatments: evidence from the women's healthy eating and living (WHEL) study. Breast Cancer Res Treat. 2007;105:177–186. doi: 10.1007/s10549-006-9442-2. [DOI] [PubMed] [Google Scholar]
  • 46.Davis SR, Castelo-Branco C, Chedraui P, Lumsden MA, Nappi RE, Shah D, et al. Understanding weight gain at menopause. Climacteric. 2012;15:419–429. doi: 10.3109/13697137.2012.707385. [DOI] [PubMed] [Google Scholar]
  • 47.Macinnis RJ, English DR, Gertig DM, Hopper JL, Giles GG. Body size and composition and risk of postmenopausal breast cancer. Cancer Epidemiol Biomarkers Prev. 2004;13:2117–2125. [PubMed] [Google Scholar]
  • 48.Ritte R, Lukanova A, Berrino F, Dossus L, Tjonneland A, Olsen A, et al. Adiposity, hormone replacement therapy use and breast cancer risk by age and hormone receptor status: a large prospective cohort study. Breast Cancer Res. 2012;14:R76. doi: 10.1186/bcr3186. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 49.Fagherazzi G, Chabbert-Buffet N, Fabre A, Guillas G, Boutron-Ruault MC, Mesrine S, et al. Hip circumference is associated with the risk of premenopausal ER-/PR- breast cancer. Int J Obes (Lond) 2012;36:431–439. doi: 10.1038/ijo.2011.66. [DOI] [PubMed] [Google Scholar]
  • 50.Mannisto S, Pietinen P, Pyy M, Palmgren J, Eskelinen M, Uusitupa M. Body-size indicators and risk of breast cancer according to menopause and estrogen-receptor status. Int J Cancer. 1996;68:8–13. doi: 10.1002/(SICI)1097-0215(19960927)68:1<8::AID-IJC2>3.0.CO;2-V. [DOI] [PubMed] [Google Scholar]
  • 51.Hall IJ, Newman B, Millikan RC, Moorman PG. Body size and breast cancer risk in black women and white women: the Carolina Breast Cancer Study. Am J Epidemiol. 2000;151:754–764. doi: 10.1093/oxfordjournals.aje.a010275. [DOI] [PubMed] [Google Scholar]
  • 52.Folsom AR, Kaye SA, Prineas RJ, Potter JD, Gapstur SM, Wallace RB. Increased incidence of carcinoma of the breast associated with abdominal adiposity in postmenopausal women. Am J Epidemiol. 1990;131:794–803. doi: 10.1093/oxfordjournals.aje.a115570. [DOI] [PubMed] [Google Scholar]
  • 53.Kaaks R, Van Noord PA, Den Tonkelaar I, Peeters PH, Riboli E, Grobbee DE. Breast-cancer incidence in relation to height, weight and body-fat distribution in the Dutch "DOM" cohort. Int J Cancer. 1998;76:647–651. doi: 10.1002/(sici)1097-0215(19980529)76:5<647::aid-ijc6>3.0.co;2-q. [DOI] [PubMed] [Google Scholar]
  • 54.Huang Z, Willett WC, Colditz GA, Hunter DJ, Manson JE, Rosner B, et al. Waist circumference, waist:hip ratio, and risk of breast cancer in the Nurses' Health Study. Am J Epidemiol. 1999;150:1316–1324. doi: 10.1093/oxfordjournals.aje.a009963. [DOI] [PubMed] [Google Scholar]
  • 55.Connolly BS, Barnett C, Vogt KN, Li T, Stone J, Boyd NF. A meta-analysis of published literature on waist-to-hip ratio and risk of breast cancer. Nutr Cancer. 2002;44:127–138. doi: 10.1207/S15327914NC4402_02. [DOI] [PubMed] [Google Scholar]
  • 56.Huang WY, Newman B, Millikan RC, Schell MJ, Hulka BS, Moorman PG. Hormone-related factors and risk of breast cancer in relation to estrogen receptor and progesterone receptor status. Am J Epidemiol. 2000;151:703–714. doi: 10.1093/oxfordjournals.aje.a010265. [DOI] [PubMed] [Google Scholar]
  • 57.Sonnenschein E, Toniolo P, Terry MB, Bruning PF, Kato I, Koenig KL, et al. Body fat distribution and obesity in pre- and postmenopausal breast cancer. Int J Epidemiol. 1999;28:1026–1031. doi: 10.1093/ije/28.6.1026. [DOI] [PubMed] [Google Scholar]
  • 58.Lahmann PH, Hoffmann K, Allen N, van Gils CH, Khaw KT, Tehard B, et al. Body size and breast cancer risk: findings from the European Prospective Investigation into Cancer And Nutrition (EPIC) Int J Cancer. 2004;111:762–771. doi: 10.1002/ijc.20315. [DOI] [PubMed] [Google Scholar]
  • 59.Tehard B, Clavel-Chapelon F. Several anthropometric measurements and breast cancer risk: results of the E3N cohort study. Int J Obes (Lond) 2006;30:156–163. doi: 10.1038/sj.ijo.0803133. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 60.Borgquist S, Jirstrom K, Anagnostaki L, Manjer J, Landberg G. Anthropometric factors in relation to different tumor biological subgroups of postmenopausal breast cancer. Int J Cancer. 2009;124:402–411. doi: 10.1002/ijc.23850. [DOI] [PubMed] [Google Scholar]
  • 61.Potter JD, Cerhan JR, Sellers TA, McGovern PG, Drinkard C, Kushi LR, et al. Progesterone and estrogen receptors and mammary neoplasia in the Iowa Women's Health Study: how many kinds of breast cancer are there? Cancer Epidemiol Biomarkers Prev. 1995;4:319–326. [PubMed] [Google Scholar]
  • 62.Sellers TA, Davis J, Cerhan JR, Vierkant RA, Olson JE, Pankratz VS, et al. Interaction of waist/hip ratio and family history on the risk of hormone receptor-defined breast cancer in a prospective study of postmenopausal women. Am J Epidemiol. 2002;155:225–233. doi: 10.1093/aje/155.3.225. [DOI] [PubMed] [Google Scholar]
  • 63.Liedtke S, Schmidt ME, Vrieling A, Lukanova A, Becker S, Kaaks R, et al. Postmenopausal sex hormones in relation to body fat distribution. Obesity (Silver Spring) 2012;20:1088–1095. doi: 10.1038/oby.2011.383. [DOI] [PubMed] [Google Scholar]
  • 64.Haffner SM, Katz MS, Dunn JF. Increased upper body and overall adiposity is associated with decreased sex hormone binding globulin in postmenopausal women. Int J Obes. 1991;15:471–478. [PubMed] [Google Scholar]
  • 65.Rinaldi S, Key TJ, Peeters PH, Lahmann PH, Lukanova A, Dossus L, et al. Anthropometric measures, endogenous sex steroids and breast cancer risk in postmenopausal women: a study within the EPIC cohort. Int J Cancer. 2006;118:2832–2839. doi: 10.1002/ijc.21730. [DOI] [PubMed] [Google Scholar]
  • 66.Stoll BA. Upper abdominal obesity, insulin resistance and breast cancer risk. Int J Obes Relat Metab Disord. 2002;26:747–753. doi: 10.1038/sj.ijo.0801998. [DOI] [PubMed] [Google Scholar]
  • 67.Pichard C, Plu-Bureau G, Neves ECM, Gompel A. Insulin resistance, obesity and breast cancer risk. Maturitas. 2008;60:19–30. doi: 10.1016/j.maturitas.2008.03.002. [DOI] [PubMed] [Google Scholar]
  • 68.Okorodudu DO, Jumean MF, Montori VM, Romero-Corral A, Somers VK, Erwin PJ, et al. Diagnostic performance of body mass index to identify obesity as defined by body adiposity: a systematic review and meta-analysis. Int J Obes (Lond) 2010;34:791–799. doi: 10.1038/ijo.2010.5. [DOI] [PubMed] [Google Scholar]
  • 69.Blew RM, Sardinha LB, Milliken LA, Teixeira PJ, Going SB, Ferreira DL, et al. Assessing the validity of body mass index standards in early postmenopausal women. Obes Res. 2002;10:799–808. doi: 10.1038/oby.2002.108. [DOI] [PubMed] [Google Scholar]
  • 70.Gallagher D, Visser M, Sepulveda D, Pierson RN, Harris T, Heymsfield SB. How useful is body mass index for comparison of body fatness across age, sex, and ethnic groups? Am J Epidemiol. 1996;143:228–239. doi: 10.1093/oxfordjournals.aje.a008733. [DOI] [PubMed] [Google Scholar]
  • 71.Li C, Ford ES, Zhao G, Balluz LS, Giles WH. Estimates of body composition with dual-energy X-ray absorptiometry in adults. Am J Clin Nutr. 2009;90:1457–1465. doi: 10.3945/ajcn.2009.28141. [DOI] [PubMed] [Google Scholar]
  • 72.Flegal KM, Shepherd JA, Looker AC, Graubard BI, Borrud LG, Ogden CL, et al. Comparisons of percentage body fat, body mass index, waist circumference, and waist-stature ratio in adults. Am J Clin Nutr. 2009;89:500–508. doi: 10.3945/ajcn.2008.26847. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 73.Rohan TE, Heo M, Choi L, Datta M, Freudenheim JL, Kamensky V, et al. Body fat and breast cancer risk in postmenopausal women: a longitudinal study. J Cancer Epidemiol. 2013;2013:754815. doi: 10.1155/2013/754815. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

1

RESOURCES