Abstract
Accumulating evidence suggests that the aggregation of common metabolic conditions (high blood pressure, diabetes, dyslipidemia) is a risk factor for breast cancer. Breast cancer incidence has risen steadily in Asian American women, and whether these metabolic conditions contribute to breast cancer risk in certain Asian American subgroups is unknown. We investigated the role of physician-diagnosed hypertension, high cholesterol, and diabetes separately, and in combination, in relation to the risk of breast cancer in a population-based case-control study of 2,167 Asian Americans diagnosed with breast cancer and 2,035 age and ethnicity matched control women in Los Angeles County. Compared to Asian American women who did not have any of the metabolic conditions, those with 1, 2, or 3 conditions showed a steady increase in risk (respective odds ratios (ORs) were 1.12, 1.42, 1.62; P trend=0.001) with adjustment for covariates including body mass index. Similar significant trends were observed in Filipina Americans (P trend=0.021), postmenopausal women (P trend =0.001), Asian women who were born in the United States (US) (P trend=0.052) and migrants who have lived in the US for at least 20 years (P trend=0.004), but not migrants who lived in the US for <20 years (P trend=0.64). These results suggest that westernization in lifestyle (diet, physical inactivity) and corresponding increase in adiposity have contributed to the rising prevalence of these metabolic conditions, which in turn, are associated with an increase in breast cancer.
Keywords: Filipina, Japanese, Chinese, metabolic factors, diabetes, hypertension, high cholesterol, body size
Introduction
There is accumulating epidemiologic evidence that metabolic syndrome, namely, the clustering of conditions including abdominal adiposity, high blood pressure, diabetes, and dyslipidemia may have a significant influence on breast cancer risk. In a meta-analysis of 9 studies (3 US (largely whites and African Americans), 4 Europe, 1 Uruguay, I Japan), history of metabolic syndrome was associated with an elevated risk (OR=1.52, 95% CI 1.20–1.93) of breast cancer in postmenopausal women1. However, the evidence is less consistent in Asians. These metabolic factors, separately or in combination, were not associated with breast cancer in a prospective cohort study from Japan in which relevant covariates including parity, age at menarche, and body mass index (BMI) were adjusted for in the analysis.2 In a case-control study from Korea, postmenopausal women with a history of metabolic conditions had a significant 2-fold greater risk of breast cancer that was due largely to the increased risk associated with high BMI but risk was unrelated to the individual metabolic conditions (i.e., elevated levels of triglycerides or high-density lipoprotein cholesterol (HDL-C), high blood pressure (HBP), or fasting serum glucose) after adjustment for various cofactors3. These metabolic conditions individually and in combination were associated with higher breast cancer risk in a cohort analysis from Japan4 and a case-control study from Taiwan5, but neither study had information on parity, BMI, and other relevant cofactors. Lack of information on potential confounders in some studies and differences in distribution of menopausal status in the study population may have contributed to inconsistencies in study findings. As we have previously reported, Filipina women have one of the highest breast cancer incidence rates in Asia and among Asian Americans for poorly understood reasons. Breast cancer risk factors varied in their magnitude in Asian ethnic subgroups and body size risk factors were more prominently associated with risk in postmenopausal Filipina and Japanese Americans than in Chinese Americans.6
We conducted a comprehensive analysis of breast cancer risk and physician-diagnosed metabolic conditions (HBP, diabetes, and high cholesterol) separately and in combination, among Filipina, Japanese, and Chinese Americans in a population-based case-control study in Los Angeles (LA) County. We investigated risk patterns by menopausal status, Asian ethnicity, and migration status, with adjustment for BMI and other potential confounders. We also considered risk associations in relation to timing of diagnosis of the metabolic conditions as well as treatment for metabolic conditions.
Subjects and methods
Study design and population
The study population and methods used in this population-based case-control study have been described previously 7, 8. In brief, breast cancer patients were identified by the LA County Cancer Surveillance Program, the population-based cancer registry covering LA County, a member of the National Cancer Institute’s Surveillance, Epidemiology, and End Results (SEER) program, and the statewide California Cancer Registry. This analysis included women who were identified as Chinese, Japanese, or Filipina between the ages of 25 and 74 inclusive at the time of diagnosis of an incident breast cancer including in situ breast cancer. Case patients were diagnosed between 1995 and 2001 or between 2003 and 2006. In total, we identified 3,797 eligible case patients (1,496 Chinese, 865 Japanese, 1,436 Filipina) and interviewed 2,303 cases (929 Chinese, 547 Japanese, and 827 Filipina) (response rate of 61%). Among those who did not participate, 869 declined to be interviewed (375 Chinese, 222 Japanese, 272 Filipina), 77 had died (17 Chinese, 24 Japanese, 36 Filipina), and 548 could not be located (175 Chinese, 72 Japanese, 301 Filipina). The 2,035 control subjects (923 Chinese, 518 Japanese, and 594 Filipina) were selected from the neighborhoods where the case patients resided at the time of diagnosis. A well-established algorithm was used to identify neighborhood controls for population-based case-control studies conducted in LA County as this provides a mechanism of matching on socioeconomic status which is likely to influence various lifestyle habits 9, 10. We initially defined a specified sequence of houses to be visited in the neighborhoods where index cases lived at the time of diagnosis. We then sought to interview the first eligible resident in the sequence (not necessarily the first willing control subject). If the first eligible control subject refused to participate, the second eligible one in the sequence was asked, and so on. Letters were left when no one was home, and follow-up was by mail and telephone. Controls were sought to frequency-match to the cases on specific Asian ethnicities and 5-year age groups. On average, a suitable control was identified after visiting a mean of 60 households (48.1 for Chinese, 58.0 for Japanese, and 74.6 for Filipina). Of the controls interviewed, 64% were the first identified eligible control (range was 64% for Filipina and 67% for Chinese), 18% were the second-identified eligible control, and 18% were the third or later eligible control.
Data collection
Cases and controls were interviewed using a standardized, structured questionnaire. Almost all Filipina and Japanese American women were interviewed in English; only 5 Japanese (4 cases, 1 control) and 67 Filipina (52 cases, 15 controls) were interviewed with a mix of English and respective Asian language with the help of interpreters who were either family members of the study participants or study staff. A Chinese-translated questionnaire was used for subjects when appropriate. Interviews were in Mandarin or Cantonese for 62.5% (337 Mandarin, 234 Cantonese) of Chinese cases and 51.2% (301 Mandarin, 162 Cantonese) of Chinese controls. To the extent possible, each case-control pair was interviewed by the same interviewer. The questionnaire covered demographic characteristics and migration history, menstrual and reproductive history, body size, physical activity, family history of breast cancer, and diet history11, 12. Subjects were asked about height and usual weight history at age 18 years, at age 30 years and each decade thereafter when they were not pregnant. Trained interviewers measured the circumferences of the waist and hips of study participants. Waist circumference was measured at the narrowest torso circumference and hip circumference was measured at the widest hip circumference. Relative body weight was evaluated by body mass index (BMI), calculated as the weight in kilograms divided by the square of height in meters (kg/m2). We examined BMI using the recommended 5 category cut points (<22.9, 23 −24.9, 25–27.4, 27.5–29.9, ≥30 kg/m2) for studies in Asian Americans13, which incorporated the standard WHO definition (<25, 25–29.9, and ≥30 kg/m2) of normal, overweight, and obese as well as the corresponding WHO Asian BMI definition (<22.9, 23–27.5, and ≥27.5 kg/m2). Subjects were asked about history of specific conditions, including HBP, diabetes, and high cholesterol that were diagnosed by a physician at least one year before diagnosis (for cases) and interview (for controls). Participants who responded positively were then asked a series of additional questions including the age they were first diagnosed of the condition, and if they were treated for the condition by medication, lifestyle intervention or other means.
Statistical Analysis
The results presented below are based on 2,167 cases and 1,967 controls without any history of previous cancers and for whom we have information on these metabolic conditions, body size, menstrual and reproductive factors as well as the covariates included for adjustment. We calculated Asian ethnic-specific odds ratios (ORs) and their corresponding 95% confidence intervals (CIs) and P values by conditional logistic regression methods, with matched sets defined jointly by Asian ethnicity (Filipina, Japanese, Chinese), and reference age (<39, 40–44, 45–49, 50–54, 55–59, 60–64, 65–69, 70+ years)11. All regression models included the following covariates: years of residence in the US (US born, >20 years, 11–20 years, ≤10 years), education (less than high school, high school, some college, college graduate), income, interviewer, family history of breast cancer (no, yes any first degree relative with breast cancer), history of benign breast disease (no, yes), age at menarche (≤11, 12, 13, 14, 15+ years) and parity (0, 1, 2, 3, 4+ births). In addition, current (i.e., before diagnosis or interview) BMI (<22.9, 23 −24.9, 25–27.4, 27.5–29.9, ≥30 kg/m2) was included in the analysis13. P values less than 0.05 were considered statistically significant and all P values quoted are 2-sided. All analyses were performed by using EPILOG Windows (version 1.01s) statistical software system (Pasadena, CA) and the SAS statistical software system (version 9.3; SAS Institute, Cary, NC).
Results
Breast cancer cases compared to control women had significantly fewer births, and were more likely to have a family history of breast cancer and a personal history of benign breast diseases but they did not differ significantly in age at menarche, history of alcohol intake or energy intake (Supplemental table 1). Table 1 shows distribution of select lifestyle factors by history of three metabolic conditions (HBP, high cholesterol, and diabetes) combined (none, 1, 2+ conditions or any conditions) for Asian American control women and breast cancer cases. History of these conditions was higher in Filipina (46.8%) and Japanese (46.3%) than Chinese (34.1%) control women, among Asia Americans who were US born or were long-term (>20 years) residents in the US and those who were older, postmenopausal, parous, and had higher BMI or higher waist/hip ratios (WHR). Any history of metabolic conditions was weakly associated with age at menarche and family history of breast cancer but was unrelated to education level and physical activity. These patterns in control women were similarly observed among breast cancer cases (data not shown).
Table 1.
History of three conditions (hypertension, high cholesterol, and diabetes) by demographic and select breast cancer risk factors among control women in Los Angeles County
| Combined metabolic condition | |||||||||
|---|---|---|---|---|---|---|---|---|---|
| Variable | None N=1161 |
% | One n=595 |
% | Two+ n=211 |
% | p value1 |
% Any |
P value2 |
| Race | |||||||||
| Chinese | 583 | 65.9 | 239 | 26.9 | 64 | 7.2 | <0.001 | 34.1 | <0.001 |
| Japanese | 270 | 53.8 | 168 | 33.5 | 64 | 12.8 | 46.3 | ||
| Filipino | 308 | 53.2 | 188 | 32.5 | 83 | 14.3 | 46.8 | ||
| Age | |||||||||
| <40 | 202 | 81.8 | 39 | 15.8 | 6 | 2.4 | <0.001 | 18.2 | <0.001 |
| 40–49 | 525 | 71.6 | 180 | 24.6 | 28 | 3.8 | 28.4 | ||
| 50–59 | 292 | 51.6 | 205 | 36.2 | 69 | 12.2 | 48.4 | ||
| ≥60 | 142 | 33.7 | 171 | 40.6 | 108 | 25.7 | 66.3 | ||
| Migration | |||||||||
| US born (USB) | 276 | 53.0 | 174 | 33.4 | 71 | 13.6 | <0.001 | 47.0 | <0.001 |
| Non USB>20yrs | 415 | 57.7 | 220 | 30.6 | 84 | 11.7 | 42.3 | ||
| Non USB≤20yrs | 470 | 64.7 | 201 | 27.7 | 56 | 7.7 | 35.4 | ||
| Education | |||||||||
| ≤high school | 188 | 57.9 | 98 | 30.2 | 39 | 12.0 | 0.84 | 42.2 | 0.52 |
| some college | 249 | 56.6 | 142 | 32.3 | 49 | 11.1 | 43.4 | ||
| college | 509 | 59.7 | 255 | 29.9 | 88 | 10.3 | 40.2 | ||
| Graduate | 215 | 61.4 | 100 | 28.6 | 35 | 10.0 | 38.6 | ||
| Menopause | |||||||||
| Premenopause | 753 | 72.5 | 245 | 23.6 | 41 | 4.0 | <0.001 | 27.6 | <0.001 |
| Postmenopause | 408 | 44.0 | 350 | 37.7 | 170 | 18.3 | 56.0 | ||
| Body mass index (BMI) | |||||||||
| ≤22.9 | 773 | 67.5 | 294 | 25.7 | 79 | 6.9 | <0.001 | 32.6 | <0.001 |
| >22.9 to ≤24.9 | 204 | 53.0 | 130 | 33.8 | 51 | 13.3 | 47.1 | ||
| >24.9 to ≤27.5 | 130 | 47.5 | 99 | 36.1 | 45 | 16.4 | 52.5 | ||
| >27.5 to ≤29.9 | 27 | 32.5 | 39 | 47.0 | 17 | 20.5 | 67.5 | ||
| >29.9 | 27 | 34.2 | 33 | 41.8 | 19 | 24.1 | 65.9 | ||
| Waist hip ratio (WHR) | |||||||||
| ≤0.76 | 298 | 76.4 | 76 | 19.5 | 16 | 4.1 | <0.001 | 23.6 | <0.001 |
| ≤0.80 | 311 | 64.5 | 139 | 28.8 | 32 | 6.6 | 35.4 | ||
| ≤0.845 | 295 | 57.0 | 168 | 32.4 | 55 | 10.6 | 43.0 | ||
| >0.845 | 239 | 44.2 | 198 | 36.6 | 104 | 19.2 | 55.8 | ||
| DK | 18 | 50.0 | 14 | 38.9 | 4 | 11.1 | 50.0 | ||
| Age at menarche | |||||||||
| ≤11 | 174 | 52.7 | 113 | 34.2 | 43 | 13.0 | 0.28 | 47.2 | 0.05 |
| 12 | 297 | 57.0 | 156 | 29.9 | 56 | 10.8 | 40.7 | ||
| 13 | 298 | 58.0 | 151 | 30.1 | 52 | 10.4 | 40.5 | ||
| 14 | 203 | 61.5 | 84 | 26.6 | 29 | 9.2 | 35.8 | ||
| ≥15 | 189 | 60.8 | 91 | 29.3 | 31 | 10.0 | 39.3 | ||
| Parity | |||||||||
| 0 | 210 | 63.4 | 95 | 28.7 | 26 | 7.9 | <0.001 | 36.6 | <0.001 |
| 1 | 223 | 67.4 | 81 | 24.5 | 26 | 7.9 | 32.4 | ||
| 2 | 423 | 61.9 | 201 | 29.4 | 57 | 8.4 | 37.8 | ||
| ≥3 | 305 | 48.8 | 218 | 34.9 | 102 | 16.3 | 51.2 | ||
| Physical Activity | |||||||||
| 0–4 yrs | 148 | 55.0 | 93 | 34.6 | 28 | 10.4 | 0.19 | 45.0 | 0.13 |
| 5–9 | 259 | 62.0 | 112 | 26.8 | 47 | 11.2 | 38.0 | ||
| 10–19 | 392 | 61.0 | 180 | 28.0 | 71 | 11.0 | 39.0 | ||
| ≥20 | 361 | 56.8 | 210 | 33.0 | 65 | 10.2 | 43.2 | ||
| Family history of breast cancer (first degree relatives) | |||||||||
| No | 1063 | 60.0 | 525 | 29.6 | 184 | 10.4 | 0.11 | 40.0 | 0.03 |
| yes | 81 | 49.7 | 60 | 36.8 | 22 | 13.5 | 50.3 | ||
| Dk | 17 | 53.1 | 10 | 31.3 | 5 | 15.6 | 46.9 | ||
P value- chi square test (2 df) comparing between none, one, two or more conditions
P value- chi square test (1 df) comparing between none vs any conditions
Table 2 shows risk associations between the individual metabolic conditions and breast cancer risk after adjustment for BMI and other established risk factors. In Filipina, Chinese, and Japanese Americans combined, risk of breast cancer increased with having a history of any one (OR 1.19, 95% CI 1.03–1.37) or two or more (OR 1.45, 95% CI 1.16–1.80) metabolic conditions. Specifically, Asian American women with a history of high cholesterol (OR 1.26, 95% CI 1.08–1.47), and long-standing (>10 years) diabetes (OR 1.63, 95% CI 1.10–2.41) experienced significantly elevated risk. Results by Asian ethnicity showed that among Filipina-Americans, risk was higher among those with a history of HBP (OR 1.34, 95 % CI=1.03–1.76) and diabetes for 10 years of more (OR 1.95, 95% CI 0.97–3.93). There was a trend of increasing risk with increasing number of conditions so that compared to Filipina women who did not have any of the three conditions, the ORs were 1.20,1.42, 1.88, respectively (p trend =0.021) for Filipina who had 1, 2, or 3 conditions. Risk of breast cancer among Japanese Americans was higher (OR=1.39, 95% CI 1.02–1.90) for those with a history of high cholesterol. Japanese Americans who reported 2 metabolic conditions had a significantly increased risk (OR 1.60, 95% CI 1.01–2.54). Although there was no further increase in risk among those with all 3 conditions, this was based on small numbers. Breast cancer risk of Chinese Americans was not significantly related to history of these conditions, separately or in combination.
Table 2.
Risk of breast cancer and history of hypertension (HBP), high cholesterol, and diabetes in Chinese, Japanese, and Filipina Americans in Los Angeles County
| Chinese | Japanese | Filipina | All | |||||||
|---|---|---|---|---|---|---|---|---|---|---|
| Ca | Co | OR1 (95%CI) | Ca | Co | OR1 (95%CI) | Ca | Co | OR1 (95%CI) | OR2 (95%CI) | |
| HBP | ||||||||||
| No | 689 | 724 | 1.00 | 349 | 362 | 1.00 | 468 | 402 | 1.00 | 1.00 |
| Yes | 191 | 161 | 0.87 (0.66, 1.14) | 167 | 140 | 0.99 (0.71, 1.37) | 303 | 177 | 1.34 (1.03, 1.76) | 1.11 (0.94, 1.30) |
| Lagtime4 | ||||||||||
| ≤10yrs | 120 | 106 | 0.86 (0.63, 1.18) | 98 | 83 | 0.96 (0.66, 1.41) | 199 | 97 | 1.66 (1.21, 2.28) | 1.18 (0.99, 1.42) |
| >10yrs | 71 | 55 | 0.89 (0.58, 1.35) | 69 | 57 | 1.03 (0.66, 1.62) | 104 | 80 | 0.94 (0.64, 1.36) | 0.98 (0.78, 1.23) |
| P trend5 | 0.39 | 0.95 | 0.43 | 0.61 | ||||||
| High Cholesterol | ||||||||||
| No | 668 | 723 | 1.00 | 330 | 368 | 1.00 | 544 | 441 | 1.00 | 1.00 |
| Yes | 212 | 162 | 1.21 (0.94, 1.56) | 185 | 134 | 1.39 (1.02, 1.90) | 226 | 138 | 1.21 (0.91, 1.60) | 1.26 (1.08, 1.47) |
| Lagtime4 | ||||||||||
| ≤10yrs | 178 | 143 | 1.15 (0.88, 1.50) | 146 | 108 | 1.42 (1.02, 1.99) | 203 | 117 | 1.28 (0.96, 1.72) | 1.26 (1.07, 1.48) |
| >10yrs | 34 | 19 | 1.69 (0.91, 3.14) | 39 | 26 | 1.28 (0.71, 2.28) | 23 | 21 | 0.79 (0.41, 1.53) | 1.26 (0.89, 1.78) |
| P trend5 | 0.076 | 0.075 | 0.43 | 0.006 | ||||||
| Diabetes | ||||||||||
| No | 801 | 821 | 1.00 | 463 | 458 | 1.00 | 678 | 519 | 1.00 | 1.00 |
| Yes | 78 | 65 | 1.11 (0.77, 1.61) | 53 | 44 | 1.10 (0.68, 1.77) | 93 | 60 | 1.18 (0.81, 1.73) | 1.20 (0.95, 1.50) |
| Lagtime4 | ||||||||||
| ≤10yrs | 53 | 50 | 0.98 (0.64, 1.50) | 31 | 28 | 0.99 (0.55, 1.77) | 64 | 45 | 0.97 (0.63, 1.51) | 1.04 (0.80, 1.36) |
| >10yrs | 25 | 15 | 1.55 (0.78, 3.06) | 22 | 16 | 1.32 (0.63, 2.78) | 29 | 15 | 1.95 (0.97, 3.93) | 1.63 (1.10, 2.41) |
| P trend5 | 0.36 | 0.55 | 0.16 | 0.033 | ||||||
| 3 conditions3 | ||||||||||
| None | 513 | 583 | 1.00 | 241 | 270 | 1.00 | 352 | 308 | 1.00 | 1.006 |
| 1 | 282 | 239 | 1.13 (0.89, 1.42) | 169 | 168 | 1.02 (0.73, 1.41) | 258 | 188 | 1.20 (0.91, 1.58) | 1.12 (0.96, 1.30) |
| 2 | 69 | 57 | 0.99 (0.65, 1.49) | 92 | 51 | 1.60 (1.01, 2.54) | 130 | 70 | 1.42 (0.97, 2.07) | 1.42 (1.13, 1.78) |
| 3 | 16 | 7 | 1.64 (0.62, 4.33) | 14 | 13 | 1.02 (0.42, 2.45) | 31 | 13 | 1.88 (0.90, 3.93) | 1.62 (1.02, 2.57) |
| P trend5 | 0.41 | 0.17 | 0.021 | 0.001 | ||||||
| Any condition | 367 | 303 | 1.11 (0.89, 1.39) | 275 | 232 | 1.12 (0.83, 1.53) | 419 | 271 | 1.28 (0.99, 1.64) | 1.19 (1.03, 1.37) |
| 2 or 3 conditions | 85 | 64 | 1.06 (0.71, 1.56) | 106 | 64 | 1.49 (0.96, 2.30) | 161 | 83 | 1.48 (1.04, 2.13) | 1.45 (1.16, 1.80) |
| Yes treatment | 239 | 191 | 1.04 (0.80, 1.37) | 183 | 165 | 1.01 (0.71, 1.43 | 330 | 203 | 1.32 (1.00, 1.74) | 1.15 (0.98, 1.36) |
| No treatment | 128 | 112 | 1.21 (0.90, 1.63) | 92 | 67 | 1.32 (0.89, 1.97) | 89 | 68 | 1.19 (0.81, 1.74) | 1.24 (1.02, 1.51) |
Adjusted for age, education, income, years of residence in the US among non-US born, interviewer, family history of breast cancer, benign breast diseases, parity, age at menarche, age at and type of menopause, education and body mass index.
As above and also adjusted for Asian ethnicity,
Included HBP, high cholesterol, and diabetes. Yes treatment means for any 1 of the 3 conditions; no treatment means no for all 3 conditions.
Lagtime is defined as years between first diagnosis of metabolic condition and breast cancer diagnosis.
For the p trend analyses, the baseline group comprised those with no metabolic condition.
There were no changes in ORs with further adjustment for alcohol intake (never, former and current) for none, 1, 2, 3 conditions combined (ORs were 1.00, 1.12 (0.96, 1.30), 1.42 (1.13, 1.78) and 1.62 (1.02, 2.57), respectively).
Breast cancer risk among premenopausal Asian American women was not significantly associated with history of HBP, high cholesterol, or diabetes or the three conditions combined (Table 3). In contrast, history of high cholesterol and long-history (10+years) of diabetes were significantly associated with risk in postmenopausal Asian American women; risks increased with increasing duration of these conditions. For example, risk of breast cancer doubled in postmenopausal women who had diabetes for 10+ years (OR 2.03, 95% CI 1.24–3.34). Compared to postmenopausal women who had none of the conditions, the adjusted ORs were 1.27, 1.43, and 1.87, respectively in association with 1, 2 or 3 conditions (P trend=0.001) after adjustment for BMI and other risk factors. The increase in risk was less marked for postmenopausal women who reported they received treatment (OR 1.21, 95% 0.99–1.49) compared to those who were not treated (OR 1.80, 95% 1.33–2.42) for these metabolic conditions.
Table 3.
Risk of breast cancer and history of hypertension (HBP), high cholesterol, and diabetes in pre- and postmenopausal Asian American women in Los Angeles County
| Premenopausal | Postmenopausal | |||||
|---|---|---|---|---|---|---|
| Ca | Co | OR1 (95%CI) | Ca | Co | OR2 (95%CI) | |
| HBP | ||||||
| No | 811 | 906 | 1 | 695 | 582 | 1 |
| Yes | 141 | 132 | 1.09 (0.81, 1.45) | 520 | 346 | 1.11 (0.92, 1.35) |
| Lagtime | ||||||
| ≤10yrs | 106 | 88 | 1.20 (0.86, 1.66) | 311 | 198 | 1.19 (0.95, 1.49) |
| >10yrs | 35 | 44 | 0.85 (0.52, 1.40) | 209 | 148 | 1.00 (0.77, 1.30) |
| P trend | 0.94 | 0.65 | ||||
| High cholesterol | ||||||
| No | 804 | 905 | 1 | 738 | 627 | 1 |
| Yes | 147 | 134 | 1.15 (0.87, 1.51) | 476 | 300 | 1.35 (1.11, 1.63) |
| Lagtime | ||||||
| ≤10yrs | 128 | 115 | 1.15 (0.86, 1.53) | 399 | 253 | 1.34 (1.09, 1.64) |
| >10yrs | 19 | 19 | 1.16 (0.59, 2.30) | 77 | 47 | 1.40 (0.93, 2.09) |
| P trend | 0.34 | 0.004 | ||||
| Diabetes | ||||||
| No | 889 | 966 | 1 | 1053 | 832 | 1 |
| Yes | 62 | 73 | 1.01 (0.69, 1.48) | 162 | 96 | 1.30 (0.97, 1.73) |
| Lagtime | ||||||
| ≤10yrs | 47 | 54 | 1.00 (0.65, 1.56) | 101 | 69 | 1.05 (0.74, 1.47) |
| >10yrs | 15 | 19 | 1.05 (0.51, 2.14) | 61 | 27 | 2.03 (1.24, 3.34) |
| P trend | 0.92 | 0.014 | ||||
| 3 conditions3 | ||||||
| None | 669 | 753 | 1 | 437 | 408 | 1 |
| 1 | 227 | 245 | 0.96 (0.78, 1.23) | 482 | 350 | 1.27 (1.03, 1.56) |
| 2 | 50 | 34 | 1.69 (1.03, 2.77) | 241 | 144 | 1.43 (1.09, 1.88) |
| 3 | 8 | 7 | 0.87 (0.27, 2.84) | 55 | 26 | 1.87 (1.11, 3.15) |
| P trend | 0.31 | 0.001 | ||||
| Any condition | 283 | 286 | 1.05 (0.85, 1.30) | 778 | 520 | 1.34 (1.11, 1.62) 4 |
| 2 or 3 conditions | 56 | 41 | 1.56 (0.97, 2.46) | 296 | 170 | 1.50 (1.16, 1.93) |
| Yes treatment | 152 | 137 | 1.15 (0.87, 1.54) | 600 | 422 | 1.21 (0.99, 1.49)5 |
| No treatment | 131 | 149 | 0.97 (0.73, 1.27) | 178 | 98 | 1.80 (1.33, 2.42) |
Adjusted for Asian ethnicity, age, education, income, years of residence in the US among non-US born, interviewer, family history of breast cancer, benign breast diseases, parity, age at menarche, education and body mass index.
As above, also adjusted for age at menopause and type of menopause
Included HBP, high cholesterol, and diabetes. Yes treatment means for any 1 of the 3 conditions; no treatment means no for all 3 conditions
P interaction- any of 3 metabolic condition for premenopausal vs postmenopausal women was 0.034
P interaction- yes treatment to any metabolic condition for premenopausal vs postmenopausal women was 0.01
Prevalence of these conditions and the corresponding risk patterns differed by migration status (Table 4). History of HBP and high cholesterol were most prevalent in US born Asian control women (28.4% and 26.9%, respectively), intermediate in long-term migrants who lived >20 years in the US (25.6% and 23.6%, respectively), and less prevalent in recent migrants who lived <20 years in the US (20.1% and 17.1%, respectively). History of diabetes was comparable among long-term (7.8%) and recent (8.4%) migrants and was higher in US born Asian American control women (10.0%). Breast cancer risks among long-term migrants increased in association with history of diabetes (OR 1.41, 95% CI 0.97–2.06, p=0.075), high cholesterol (OR 1.29, 95% CI 1.02–1.65, p=0.037), and HBP (OR 1.24, 95% CI 0.95–1.60, p=0.096). Although elevated risks were also observed among US born Asian Americans, results were statistically significant only for high cholesterol (OR 1.45, 95% CI 1.06–1.99) while no significant associations were observed among recent migrants. Risk of breast cancer increased with increasing number of these conditions among long-term migrants (P trend=0.004) and US born Asian American women (P trend=0.052) but not among more recent migrants (P trend=0.64).
Table 4.
Risk of breast cancer and history of hypertension (HBP), high cholesterol, and diabetes for US born and Non US born Asian American women combined in Los Angeles County
| US born | Non-US born - ≥20 yrs in US | Non-US born- <20 yrs in US | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Ca | Co | OR1 (95%CI) | Ca | Co | OR1 (95%CI) | Ca | Co | OR1 (95%CI) | |
| HBP | |||||||||
| No | 291 | 373 | 1 | 572 | 534 | 1 | 643 | 581 | 1 |
| Yes | 163 | 148 | 1.03 (0.74, 1.43) | 296 | 184 | 1.24 (0.96, 1.60) | 202 | 146 | 0.93 (0.70, 1.24) |
| Lagtime | |||||||||
| ≤10yrs | 93 | 84 | 1.04 (0.71, 1.53) | 185 | 102 | 1.42 (1.06, 1.91) | 139 | 100 | 0.95 (0.69, 1.30) |
| >10yrs | 70 | 64 | 1.02 (0.66, 1.58) | 111 | 82 | 0.99 (0.69, 1.41) | 63 | 46 | 0.90 (0.57, 1.42) |
| P trend | 0.89 | 0.44 | 0.60 | ||||||
| High Cholesterol | |||||||||
| No | 281 | 381 | 1 | 586 | 549 | 1 | 675 | 602 | 1 |
| Yes2 | 172 | 140 | 1.45 (1.06, 1.99) | 282 | 170 | 1.29 (1.02, 1.65) | 169 | 124 | 0.95 (0.72, 1.27) |
| Lagtime | |||||||||
| ≤10yrs | 136 | 117 | 1.43 (1.02, 2.00) | 237 | 137 | 1.38 (1.06, 1.78) | 154 | 114 | 0.93 (0.69, 1.24) |
| >10yrs | 36 | 23 | 1.54 (0.83, 2.83) | 45 | 33 | 0.96 (0.58, 1.58) | 15 | 10 | 1.29 (0.53, 3.16) |
| P trend | 0.027 | 0.14 | 0.90 | ||||||
| Diabetes | |||||||||
| No | 398 | 469 | 1 | 776 | 663 | 1 | 768 | 666 | 1 |
| Yes | 56 | 52 | 1.22 (0.77, 1.94) | 91 | 56 | 1.41 (0.97, 2.06) | 77 | 61 | 0.88 (0.59, 1.30) |
| Lagtime | |||||||||
| ≤10yrs | 32 | 32 | 1.10 (0.62, 1.94) | 54 | 42 | 1.05 (0.67, 1.66) | 62 | 49 | 0.83 (0.54, 1.27) |
| >10yrs | 24 | 20 | 1.44 (0.71, 2.92) | 37 | 14 | 2.54 (1.31, 4.92) | 15 | 12 | 1.11 (0.49, 2.53) |
| P trend | 0.31 | 0.016 | 0.69 | ||||||
| 3 Conditions3 | |||||||||
| None | 186 | 276 | 1 | 410 | 415 | 1 | 510 | 470 | 1 |
| 1 | 167 | 174 | 1.24 (0.89, 1.72) | 299 | 220 | 1.25 (0.98, 1.59) | 243 | 201 | 0.87 (0.68, 1.13) |
| 2 | 87 | 57 | 1.67 (1.07, 2.63) | 128 | 75 | 1.39 (0.97, 1.99) | 76 | 46 | 1.02 (0.66, 1.60) |
| 3 | 14 | 14 | 1.18 (0.48, 2.86) | 31 | 9 | 2.83 (1.25, 6.39) | 16 | 10 | 0.94 (0.40, 2.25) |
| P trend | 0.052 | 0.004 | 0.64 | ||||||
| Any condition | 268 | 245 | 1.33 (0.98, 1.81) | 458 | 304 | 1.32 (1.05, 1.65) | 335 | 257 | 0.90 (0.71, 1.14) |
| 2 or 3 conditions | 101 | 74 | 1.59 (1.03, 2.46) | 159 | 84 | 1.54 (1.09, 2.16) | 92 | 56 | 1.01 (0.67, 1.53) |
| Yes treatment | 180 | 177 | 1.13 (0.80, 1.61) | 343 | 219 | 1.28 (0.99, 1.66) | 229 | 163 | 0.96 (0.72, 1.29) |
| No treatment | 88 | 68 | 1.71 (1.14, 2.57) | 115 | 85 | 1.39 (1.00, 1.93) | 106 | 94 | 0.82 (0.59, 1.14) |
Adjusted for Asian ethnicity, age, education, income, interviewer, family history of breast cancer, benign breast diseases, parity, age at menarche, education, body mass index, and age and type of menopause.
P interaction: High cholesterol: US born vs Non-US born <20 yrs was 0.04; Non-US born 20+ yrs vs Non US born <20 yrs was 0.089
Included HBP, high cholesterol, and diabetes; Yes treatment means for any 1 of the 3 conditions; no treatment means no for all 3 conditions
We investigated the joint effects of history of the three conditions separately and combined by categories of normal BMI (≤24.9 kg/m2) and high BMI (>24.9 kg/m2) as well as by low (≤86 cm) and high (>86 cm) waist circumference (Table 5). No significant associations were observed in premenopausal women (data not shown). Among postmenopausal women, breast cancer risk increased among Asian American women with both high BMI and a history of HBP (OR 1.42, 95% CI 0.98–2.06, p=0.065) whereas the increased risk was more modest among women with normal BMI (OR 1.08). Elevated risk with high cholesterol was observed among those with normal BMI and high BMI. Interestingly, the increased risk associated with diabetes was higher among women with normal BMI (OR 1.65, 95% 1.09–2.41) whereas those with high BMI showed no increased risk (OR 0.89, 95% CI 0.56–1.40). This difference was borderline statistically significant (p=0.097). Results were essentially identical when we considered low (≤86 cm) vs high (>86 cm) waist circumference in relation to metabolic conditions (Table 5). We also investigated history of these three conditions stratified by a combined index of BMI and WHR. History of any 3 conditions was associated with a statistically significant increased risk of breast cancer among those with both low BMI and low waist circumference (OR 1.39, 95% 1.09–1.77) or low BMI and high waist circumference (OR 2.22, 95% CI 1.02–4.81), but there were no significant increased risks among those with high BMI and low waist circumference (OR 1.44, 95% 0.56–3.78) or high BMI and high waist circumference (OR 1.29, 95% 0.78–2.12) (data not shown).
Table 5.
Risk of breast cancer and history of hypertension (HBP), high cholesterol, and diabetes by body mass index (BMI, kg/m2) and waist circumference (cm) in postmenopausal Asian American women in Los Angeles County
| BMI ≤ 24.91 | BMI > 24.91 | Waist ≤ 86 cm | Waist > 86 cm | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Ca | Co | OR2 (95%CI) | Ca | Co | OR2(95%CI) | Ca | Co | OR2 (95%CI) | Ca | Co | OR2 (95%CI) | |
| HPB | ||||||||||||
| No | 525 | 457 | 1 | 149 | 120 | 1 | 510 | 450 | 1 | 164 | 127 | 1 |
| Yes | 287 | 214 | 1.08 (0.86, 1.37) | 217 | 124 | 1.42 (0.98, 2.06) | 277 | 205 | 1.12 (0.88, 1.42) | 227 | 133 | 1.31 (0.92, 1.86 |
| P interaction | 0.28 | 0.38 | ||||||||||
| High Cholesterol | ||||||||||||
| No | 501 | 456 | 1.00 | 237 | 171 | 1 | 486 | 446 | 1.00 | 229 | 174 | 1 |
| Yes | 335 | 222 | 1.37 (1.09, 1.73) | 141 | 78 | 1.21 (0.82, 1.78) | 300 | 208 | 1.35 (1.07, 1.71) | 162 | 86 | 1.42 (0.98, 2.05) |
| P interaction | 0.82 | 0.88 | ||||||||||
| Diabetes | ||||||||||||
| No | 727 | 625 | 1.00 | 293 | 195 | 1 | 707 | 611 | 1.00 | 313 | 209 | 1 |
| Yes | 85 | 46 | 1.62 (1.09, 2.41) | 73 | 49 | 0.89 (0.56,1.40) | 73 | 44 | 1.61 (1.07, 2.42) | 78 | 51 | 1.10 (0.71, 1.70) |
| P interaction | 0.097 | 0.18 | ||||||||||
| Any 3 Conditions | ||||||||||||
| No | 325 | 323 | 1.00 | 98 | 81 | 1 | 322 | 319 | 1.00 | 101 | 85 | 1.00 |
| Yes | 487 | 348 | 1.40 (1.12, 1.75) | 268 | 163 | 1.30 (0.87, 1.95) | 465 | 336 | 1.39 (1.10, 1.74) | 290 | 175 | 1.43 (0.97, 2.10) |
| P interaction | 0.72 | 0.87 | ||||||||||
We excluded 37 cases and 13 control women who had data on BMI but did not have information on waist circumferences in the above analyses. All the above stratified results by BMI remained largely the same when we included these 37 cases and 13 control women in the analyses.
Adjusted for Asian ethnicity, age, education, income, years of residence in the US among non-US born, interviewer, family history of breast cancer, benign breast diseases, parity, age at menarche, education, and age and type of menopause.
Compared to women with no reported metabolic conditions, those with any metabolic conditions showed elevated risk of both in situ (adjusted OR=1.26, 95% CI 0.98–1.61, p=0.067) and invasive (adjusted OR=1.17, 95% CI 1.01–1.36) breast cancer (data not shown). History of any metabolic conditions was associated with an increased risk for hormone receptor positive (estrogen receptor positive (ER+) and progesterone receptor positive (PR+)) tumors (OR 1.22, 95% CI 1.03–1.45, p=0.023). This was observed separately for history of high cholesterol (OR 1.27, 95% CI 1.05–1.53, P=0.012), diabetes (OR 1.28, 95% CI 0.98–1.67, P=0.065) and HBP (OR 1.18, 95% CI 0.98–1.43, P=0.083). There was also a borderline statistically significant increased risk of ER-PR+ tumors (OR 1.77, 95% CI 0.97–3.22, p=0.063) with any metabolic condition which was due, primarily, to an increased risk among those with a history of high cholesterol (Supplemental Table 2).
Discussion
Previous studies conducted in mostly western (non-Asian) populations support a significant positive association between metabolic conditions and breast cancer risk primarily in postmenopausal women.1 To our knowledge, this is one of the first population-based studies of breast cancer among Asian Americans to examine risk patterns in association with three common metabolic conditions (HBP, high cholesterol, diabetes) separately and in combination, with consideration of duration as well as any treatment for these conditions. We explored risk patterns separately in Filipina, Chinese, and Japanese Americans, by migration history, menopausal status, and body size, and adjusted for relevant potential confounders. While there is consensus that high BMI14 is a significant risk factor for postmenopausal breast cancer in western and Asian populations15, there is less consistency whether breast cancer risk is also increased in relation to diabetes and other metabolic conditions such as high cholesterol, HBP, or a combination of these factors in Asians.2,3, 5, 15 Recent national studies show that history of diabetes and dyslipidemia patterns are more prevalent in Asian Americans than in non-Hispanic whites16,17 despite lower BMI in Asians, suggesting that Asians may be more susceptible to metabolic conditions at a given BMI.
Before discussing the significance of our findings, some limitations and strengths of our study should be mentioned. First, our results are limited to self-reported metabolic conditions that were diagnosed by a physician. Although we asked about age at diagnosis as well as treatment for these metabolic conditions, our questions on treatment were not detailed precluding analysis by type of treatment (e.g., specific medications). Thus our results may not be directly comparable to previous studies which have included a range of definitions for single conditions including hypertension (HBP values or self-reported hypertension diagnosed by a physician), reported diabetes or fasting glucose values, reported history of high cholesterol or reported blood values of triglyceride, HDL-C, and total cholesterol as well as combination of these factors.1, 18 In our question on high cholesterol, we did not ask specifically about cholesterol fraction. In addition, the overall participation rate of this study was modest (61% among cases and 64% among controls). While we cannot rule out the possibility of recall bias in case-control studies, there was internal consistency in our data such as higher prevalence of metabolic conditions in relation to increasing age and BMI among control women and case patients. The study strengths include its large sample size allowing comparison of risk association by hormone receptor status of breast cancer and separately by Asian ethnicity, migration and menopausal status, and body size measures and the ability to adjust for potential confounders.
Among Filipina American women, breast cancer risk was significantly higher among those with a history of hypertension and nearly doubled for those with a long-standing (10 or more years) history of diabetes (Table 2). Compared to Filipina women who did not have any metabolic conditions, risk increased in a stepwise manner for Filipina women with 1, 2, and 3 metabolic conditions. Although the prevalence of any metabolic condition was comparable between Filipina and Japanese-American control women, the risk associations were weaker in Japanese Americans than in Filipina women. For reasons that are not apparent, studies on metabolic conditions and breast cancer risk in Japan have generally found no or a weak association 2,19. We are not aware of previous studies on metabolic conditions and breast cancer risk in Filipina women in the US or in the Philippines. However, Filipino Americans have been found to have higher prevalence of various metabolic conditions,20, 21 including dyslipidemia,16 hypertension,22–24 and diabetes16, 22 than Japanese and Chinese Americans and to experience high risks of cardiovascular disease and mortality.25 The high prevalence of these metabolic conditions and risk of cardiovascular diseases are observed in the Philippines as well.26–28
In all Asian American women combined, history of any one metabolic condition conferred a significant 19% increased risk and those with 2 or 3 metabolic conditions showed a significant 45% increased risk. Somewhat stronger associations were observed for postmenopausal Asian American women (Table 3). These results in postmenopausal Asian American women are compatible with findings from a meta-analysis of 9 studies of primarily Caucasian postmenopausal women in which breast cancer risk was significantly increased 50% in relation to history of metabolic syndrome (obesity, hypertension, diabetes, dyslipidemia)1.
Our results by single metabolic factors showed a significant effect of high cholesterol that was diagnosed by a physician; the increased risk was found in all three Asian groups, in pre- and postmenopausal women, and among those who had the condition for less than 10 years or for a longer time. However, a significant increased risk (29% to 45%) was observed among US born Asians and those who had lived in the US for at least 20 years but there was no increased risk among more recent migrants. Prevalence of high cholesterol was higher in US born (27%) and long-term migrants (24%) than recent migrants (17%). Although foreign-born and US born Asians in the National Health and Nutrition Examination Survey (NHANES) 2011–2012 did not show significant differences in prevalence of high total and LDL-cholesterol, these results were presented for all Asian Americans and in men and women combined29. Self-reported history of high cholesterol30 and hyperlipidemia31 were not associated with breast cancer risk in case-control studies conducted in the US and Italy, but disorder of lipid metabolism was a significant risk factor in a population-based study in Taiwan5. The relationship between serum cholesterol and breast cancer risk also differed in previous studies. A meta-analysis of 14 prospective studies found that total blood cholesterol and more specifically HDL-C was significantly inversely associated with risk of breast cancer after excluding cancer cases diagnosed during the first years of follow-up18. In two separate prospective studies in Korea, total serum cholesterol was positively associated with breast cancer after adjustment for BMI.32, 33 In another Korean study that investigated risk associations separately by BMI, the positive association with high cholesterol was stronger among women with lower BMI (<25 kg/m2) (1.13, 95% CI 1.02–1.25) than those with higher BMI (1.01, 95% CI 0.90–1.14).32 Abnormal levels of lipids and lipoproteins may increase breast cancer risk by having adverse influence on endogenous sex steroid hormone profiles and promoting low-grade inflammation34.
Breast cancer risk was nonsignificantly higher by 20% among Asian American women with a history of diabetes; this risk was 63% higher and was statistically significant among women who were diabetic for at least 10 years of more (Table 2). Similar to our initial report based on a subset of study participants,12 the diabetes-breast cancer association was stronger among postmenopausal women who were diabetic for 10 years or more (Table 3). Larger numbers in this analyses allowed investigation of risk patterns by migration status, showing an increased risk among long-term migrants (Table 4) and those with normal BMI or low waist circumference (Table 5). In a comprehensive meta-analysis of 40 independent risk estimates from largely western populations, the risk of breast cancer in women with type 2 diabetes was increased by 27%, which was lowered to 16% after adjustment for BMI.15 Nevertheless, the role of diabetes and risk of breast cancer is less consistent in studies in Asian and in Asian Americans. Diabetes was unrelated to breast cancer risk in a pooled analysis of cohort studies from Japan19 and among Japanese Americans in the Multiethnic Cohort study35. However, diabetes was significantly associated with breast cancer mortality in a prospective cohort study from Korea36 and in a recent Asia Cohort Consortium pooled analysis in which BMI was considered37. Hyperinsulinemia, impaired secretion of adipokines, and chronic inflammation have been implicated to link diabetes to breast cancer risk.38
Breast cancer risk in Asian Americans was significantly associated with history of hypertension among Filipina women, particularly among those who had HBP within the 10 years before diagnosis of breast cancer/interview. Prevalence of HBP was also highest among Filipina (30.6%), intermediate among Japanese (27.9%) and lowest (18.2%) among Chinese American control women. High prevalence of hypertension and other components of metabolic syndrome has been reported in other studies of Filipino in the US.21, 23, 39 In a meta-analysis of 30 studies on hypertension and breast cancer, a significant positive association was found in postmenopausal women (RR 1.20, 95% CI 1.09–1.31) but not in premenopausal women (RR 0.97, 95% CI 0.84–1.12) and was more consistent in studies conducted in western than in Asian populations.40 Hypertension may increase risk of breast cancer by mediating effects via adipose tissues, inflammation, or other pathways. These results in Filipina American women emphasize the need to further understand the mechanism of breast cancer pathogenesis affected by hypertension and the need for better monitoring and treatment of these common metabolic conditions as breast cancer risk factors.
It is of interest that our findings on metabolic conditions and breast cancer risks were observed in US born Asians and long-term migrants but not among recent migrants (Table 4). Long-term Asian control migrants in this study have lived an average of 30.1 years in the US compared to 12.2 years for short-term migrants. The prevalence of each of the metabolic conditions, separately and in combination increased steadily and was highest in US born, intermediate in long-term migrant, and lowest in short-term migrants. Thus, while it has been difficult to pinpoint the specific individual factors, westernization in lifestyle (diet, physical inactivity) have undoubtedly contributed to the rising prevalence of these metabolic conditions, independent of obesity, which in turn, increase the risk of breast cancer. It is concerning that these metabolic conditions have now emerged to be significant breast cancer risk factors for the three groups of Asian American women combined and particularly for Filipina Americans, which may have contributed, in part, to the high breast cancer incidence rates among women in Asia, and specifically among Filipinas residing in Asia or in the US. It is a priority to develop strategies to prevent these modifiable metabolic conditions and to treat these conditions by lifestyle intervention and/or medication whenever possible.
Novelty & Impact.
It is not known whether metabolic conditions (high blood pressure, diabetes, dyslipidemia) have contributed to the rising breast cancer incidence in Asian Americans. We found a significant trend of increasing breast cancer risk with history of these metabolic conditions in a large study of Asian Americans. Subgroup analyses showed statistically significant associations among Filipina Americans, postmenopausal women, and those who were US born or were long-term residents in the US.
Acknowledgments
Grant Support: The Asian American Breast Cancer Study was supported by the California Breast Research Program grants 1RB-0287, 3PB-0120, 5PB-0018, 10PB-0098 (Wu AH, Tseng CC). This work is also supported by USC Norris Comprehensive Cancer Center Core Support grant (P30 CA14089) (Wu) and a NIEHS grant (2P30 ES007048021) (Wu).
Footnotes
Disclosure of Potential Conflicts of interest: None
References
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