Background: Body mass index is an established risk factor for postmenopausal breast cancer. Epidemiologic studies have also reported a positive association between type 2 diabetes (T2D) and breast cancer risk.

Methods: To investigate a genetic basis linking these common phenotypes with breast cancer, we tested 31 common variants for T2D and obesity in a case–control study of 1,915 breast cancer cases and 2,884 controls nested within the Multiethnic Cohort (MEC) study.

Results: Following adjustment for multiple tests, we found no significant association between any variant and breast cancer risk. Summary scores comprising the numbers of risk alleles for T2D and/or obesity were also not found to be significantly associated with breast cancer risk.

Conclusions: Our findings provide no evidence for association between established T2D and/or obesity risk variants and breast cancer risk among women of various ethnicities.

Impact: These results suggest that the potential for a shared biology between T2D/obesity and breast cancer is not due to pleiotropic effects of these risk variants. Cancer Epidemiol Biomarkers Prev; 20(5); 1039–42. ©2011 AACR.

Obesity is a risk factor for many common chronic diseases, including breast cancer in postmenopausal women (1, 2) and type 2 diabetes (T2D). Many epidemiologic studies have also reported diabetics to have a greater risk of breast cancer than nondiabetics, independent of body weight (3, 4). Biological markers of obesity and diabetes, such as insulin and insulin-like growth factors (IGFs), have been associated with breast cancer risk (5–7), which suggests that there may be shared biological processes in the etiology of these common phenotypes. We further explored the hypothesis of shared etiologic pathways for obesity, T2D, and breast cancer, by testing for pleiotropic effects of 31 established risk variants for T2D (n = 18) and obesity (n = 13) in a study of 1,915 breast cancer cases and 2,884 breast cancer controls from the Multiethnic Cohort (MEC).

Study subjects

The MEC is a prospective cohort study consisting of 215,251 adult men and women living in Hawaii and California (8) predominantly of 5 populations: European Americans, African Americans, Native Hawaiians, Japanese, and Latinos. Through 2005, the breast cancer case–control study in the MEC included 1,915 invasive cases and 2,014 controls. Cases were identified through cohort linkage to population-based cancer Surveillance, Epidemiology, and End Results (SEER) registries in California and Hawaii. We also included an additional 870 controls with no history of breast cancer from a colorectal cancer (CRC) case–control study in the MEC.

Genotyping

Genotyping of the 31 SNPs was performed using the allelic discrimination assay. The genotype completion rate for each SNP was more than 95.0% for cases and controls (average 99.1%). Hardy–Weinberg Equilibrium (HWE) was assessed for each allele in each racial/ethnic group, and of the 155 tests 7 were significant, whereas 8 were expected.

Statistical analysis

We tested for log–additive effects of the 31 variants with ORs estimated using unconditional logistic regression adjusted for age (quartiles), body mass index (quartiles), self-reported diabetes, and race/ethnicity (in pooled analysis). To account for multiple hypothesis testing, an α 0.0016 or less (0.05/31 tests) was used. To examine the combined contribution of all variants on breast cancer risk, we constructed summary risk scores, taken as the number of risk alleles for the 18 validated T2D SNPs, for the 13 validated obesity SNPs, and for the total of 31 T2D/obesity SNPs, respectively. Individuals missing genotypes were given the mean score for that variant within each population of the same race/ethnicity. We excluded from the analysis 32 (1.7%) cases and 78 (2.7%) controls with missing genotypes for 10 or more SNPs. Analysis for overall breast cancer risk was conducted on 1,883 cases and 2,806 controls. We also conducted analyses stratified by ER status (ER+ cases, n = 1,217; ER− cases, n = 299). The statistical analysis was performed using the SAS 9.2 package, SAS Institute Inc.

The mean age of the breast cancer cases (65.3 years at diagnosis) was only slightly higher than that of controls (64.9 years at the time of blood draw).

We observed a nominally significant association (P < 0.05) with only 1 variant in the pooled analysis (Table 1), whereas 1.6 were expected. The most significant findings included inverse associations with rs5219 (KCNJ11) among all cases, (OR = 0.89, P = 0.012) and ER− cases (OR = 0.73, P = 0.0031), as well as with rs864745 (JAZF) in ER− cases (OR = 0.75, P = 0.0020). However, these associations were no longer significant after adjusting for multiple comparisons (Table 1). Results were similar when limiting the analysis to postmenopausal women (n = 1,197 cases and 1,731 controls).

Table 1.

Association of known T2D and obesity risk alleles with breast cancer risk by race/ethnicity

OR (95%CI)a
Risk allele frequency in controls
SNP/allele testedbChr./nearest geneEuropean AmericansAfrican AmericansNative HawaiiansJapanese AmericansLatinosPooledP for trend pooledPhetc
(503 cases, 633 controls)(381 cases, 542 controls)(135 cases, 344 controls)(509 cases, 782 controls)(355 cases, 505 controls)(1,883 cases, 2,806 controls)
Type 2 diabetes SNPs 
rs10923931 1 1.13 (0.85–1.49) 0.87 (0.71–1.07) 0.96 (0.49–1.86) 0.97 (0.58–1.63) 1.28 (0.93–1.76) 1.01 (0.88–1.15) 0.94 0.32 
T NOTCH2 0.097 0.33 0.055 0.025 0.095 0.12   
rs7578597 2 1.05 (0.88–1.48) 1.13 (0.90–1.41) 1.04 (0.54–2.00) 1.47 (0.69–3.15) 0.71 (0.48–1.03) 1.03 (0.89–1.20) 0.69 0.27 
T THADA 0.89 0.75 0.95 0.99 0.94 0.90   
rs1801282 3 1.14 (0.88–1.48) 0.70 (0.39–1.26) 1.06 (0.55–2.04) 0.83 (0.54–1.28) 1.15 (0.84–1.57) 1.04 (0.88–1.23) 0.63 0.41 
C PPARG 0.87 0.98 0.94 0.97 0.89 0.93   
rs4607103 3 0.86 (0.71–1.04) 0.99 (0.81–1.22) 0.89 (0.64–1.22) 0.95 (0.80–1.13) 0.99 (0.80–1.22) 0.94 (0.86–1.03) 0.20 0.81 
C ADAMTS9 0.74 0.71 0.74 0.65 0.68 0.70   
rs4402960 3 0.91 (0.76–1.08) 1.07 (0.88–1.30) 0.89 (0.64–1.23) 0.91 (0.76–1.08) 1.16 (0.92–1.45) 0.98 (0.89–1.07) 0.61 0.30 
T IGF2BP2 0.34 0.49 0.28 0.33 0.26 0.34   
rs10010131 4 1.01 (0.86–1.20) 0.91 (0.74–1.12) 0.86 (0.60–1.23) 1.14 (0.68–1.89) 0.96 (0.78–1.19) 0.97 (0.87–1.07) 0.55 0.82 
G WFS1 0.59 0.67 0.80 0.98 0.70 0.76   
rs7754840 6 1.02 (0.85–1.20) 1.10 (0.91–1.34) 1.04 (0.78–1.38) 1.06 (0.90–1.25) 1.00 (0.81–1.23) 1.05 (0.96–1.14) 0.29 0.97 
C CDKAL1 0.31 0.54 0.52 0.43 0.32 0.42   
rs864745 7 0.93 (0.78–1.09) 0.87 (0.71–1.07) 0.81 (0.59–1.10) 1.02 (0.84–1.23) 1.01 (0.83–1.24) 0.94 (0.86–1.03) 0.17 0.68 
A JAZF1 0.53 0.74 0.72 0.78 0.62 0.68   
rs13266634 8 1.00 (0.83–1.20) 0.78 (0.57–1.07) 0.95 (0.70–1.28) 1.05 (0.89–1.24) 1.04 (0.82–1.31) 1.00 (0.90–1.10) 0.91 0.60 
C SLC30A8 0.71 0.90 0.63 0.63 0.75 0.72   
rs2383208 9 1.01 (0.81–1.25) 1.06 (0.83–1.34) 0.98 (0.71–1.35) 1.07 (0.91–1.26) 0.96 (0.73–1.27) 1.03 (0.93–1.13) 0.62 0.92 
A CDKN2B 0.81 0.80 0.74 0.57 0.86 0.74   
rs1111875 10 1.01 (0.85–1.20) 1.16 (0.93–1.46) 1.09 (0.79–1.52) 1.20 (1.00–1.44) 0.94 (0.76–1.16) 1.07 (0.98–1.18) 0.15 0.48 
G HHEX 0.60 0.75 0.30 0.26 0.63 0.50   
rs7903146 10 1.06 (0.89–1.28) 1.00 (0.81–1.23) 1.16 (0.77–1.73) 1.05 (0.72–1.55) 1.00 (0.79–1.27) 1.03 (0.92–1.15) 0.59 0.98 
T TCF7L2 0.29 0.27 0.14 0.045 0.22 0.19   
rs12779790 10 0.88 (0.71–1.10) 0.97 (0.74–1.26) 1.15 (0.80–1.64) 1.21 (0.97–1.49) 0.98 (0.76–1.27) 1.03 (0.92–1.15) 0.63 0.33 
C CDC123 0.17 0.14 0.18 0.16 0.17 0.16   
rs2237895d 11 0.99 (0.83–1.18) 0.98 (0.77–1.25) 0.94 (0.68–1.31) 1.06 (0.88–1.32) 1.00 (0.80–1.27) 1.00 (0.90–1.10) 0.93 0.96 
C KCNQ1 0.42 0.21 0.36 0.35 0.42 0.35   
rs2237897d 11 1.01 (0.68–1.49) 1.13 (0.80–1.59) 1.13 (0.77–1.67) 0.92 (0.75–1.13) 0.97 (0.74–1.26) 1.00 (0.89–1.13) 1.00 0.016 
C KCNQ1 0.95 0.92 0.78 0.62 0.78 0.80   
rs5219 11 1.07 (0.90–1.28) 0.77 (0.55–1.08) 1.01 (0.75–1.37) 0.81 (0.68–0.95) 0.77 (0.63–0.96) 0.89 (0.81–0.97) 0.012 0.082 
T KCNJ11 0.35 0.10 0.38 0.39 0.39 0.32   
rs7961581 12 0.96 (0.79–1.17) 0.95 (0.75–1.20) 1.10 (0.80–1.51) 1.01 (0.82–1.24) 1.27 (1.00–1.60) 0.94 (0.76–1.15) 0.60 0.39 
C TSPAN8 0.27 0.21 0.28 0.20 0.20 0.23   
rs8050136 16 0.86 (0.72–1.02) 0.89 (0.73–1.08) 0.63 (0.43–0.93) 1.07 (0.88–1.30) 0.96 (0.77–1.19) 0.91 (0.83–1.00) 0.055 0.21 
A FTO 0.42 0.44 0.24 0.21 0.28 0.32   
Obesity SNPs 
rs2815752 1 1.00 (0.85–1.19) 1.10 (0.90–1.34) 1.04 (0.70–1.56) 0.85 (0.64–1.14) 1.06 (0.85–1.31) 1.02 (0.92–1.13) 0.70 0.63 
A NEGR1 0.63 0.54 0.83 0.93 0.71 0.73   
rs10913469 1 0.99 (0.80–1.23) 0.87 (0.70–1.08) 0.57 (0.38–0.85) 1.20 (1.00–1.44) 0.89 (0.70–1.14) 0.96 (0.86–1.06) 0.38 0.016 
C SEC16B 0.18 0.28 0.21 0.23 0.20 0.22   
rs6548238 2 1.08 (0.87–1.34) 1.07 (0.80–1.44) 0.59 (0.31–1.13) 1.15 (0.89–1.49) 1.17 (0.88–1.56) 1.08 (0.95–1.22) 0.23 0.40 
T TMEM18 0.17 0.11 0.079 0.10 0.13 0.12   
rs10938397 4 0.93 (0.78–1.10) 0.81 (0.64–1.02) 1.14 (0.83–1.55) 0.97 (0.81–1.16) 1.08 (0.88–1.32) 0.97 (0.89–1.06) 0.50 0.34 
G GNPDA2 0.42 0.25 0.28 0.29 0.35 0.32   
rs925946e 11 0.92 (0.76–1.12) 1.01 (0.81–1.26) 1.73 (1.19–2.53) 1.12 (0.70–1.77) 0.90 (0.70–1.15) 1.02 (0.91–1.14) 0.78 0.24 
T BDNF 0.29 0.28 0.18 0.030 0.23 0.19   
rs6265e 11 1.33 (1.06–1.68) 1.32 (0.85–2.05) 0.70 (0.51–0.97) 0.92 (0.78–1.09) 0.91 (0.70–1.19) 1.01 (0.91–1.12) 0.88 0.049 
C BDNF 0.79 0.94 0.68 0.60 0.85 0.76   
rs10838738 11 0.99 (0.83–1.19) 1.16 (0.86–1.56) 0.96 (0.70–1.33) 0.99 (0.83–1.17) 1.17 (0.96–1.43) 1.05 (0.95–1.15) 0.36 0.58 
G MTCH2 0.34 0.11 0.32 0.34 0.35 0.29   
rs7138803 12 0.93 (0.78–1.10) 0.97 (0.77–1.23) 0.97 (0.68–1.38) 1.06 (0.90–1.26) 0.97 (0.78–1.21) 0.99 (0.90–1.09) 0.83 0.90 
A BCDIN3D 0.39 0.19 0.19 0.34 0.26 0.29   
rs7498665 16 0.82 (0.69–0.97) 1.03 (0.84–1.26) 1.03 (0.75–1.41) 1.00 (0.79–1.27) 0.87 (0.72–1.06) 0.92 (0.83–1.01) 0.073 0.47 
G SH2B1 0.40 0.27 0.28 0.13 0.45 0.30   
rs8050136 16 0.87 (0.74–1.03) 0.91 (0.75–1.10) 0.60 (0.41–0.86) 1.04 (0.86–1.26) 0.96 (0.78–1.19) 0.92 (0.84–1.00) 0.055 0.21 
A FTO 0.42 0.44 0.24 0.21 0.28 0.32   
rs17782313 18 0.97 (0.80–1.19) 0.98 (0.80–1.20) 1.00 (0.65–1.52) 0.95 (0.79–1.15) 0.92 (0.68–1.23) 0.98 (0.88–1.08) 0.63 0.98 
C MC4R 0.22 0.29 0.13 0.23 0.13 0.21   
rs11084753 19 0.89 (0.75–1.07) 1.12 (0.92–1.36) 1.04 (0.78–1.39) 1.03 (0.87–1.22) 0.96 (0.79–1.18) 1.00 (0.91–1.09) 0.97 0.53 
G KCTD15 0.68 0.64 0.42 0.29 0.67 0.53   
rs29941 19 0.90 (0.75–1.08) 1.11 (0.86–1.42) 0.87 (0.65–1.17) 1.05 (0.87–1.27) 0.81 (0.66–1.00) 0.94 (0.85–1.03) 0.18 0.26 
G KCTD15 0.69 0.82 0.40 0.21 0.66 0.54   
OR (95%CI)a
Risk allele frequency in controls
SNP/allele testedbChr./nearest geneEuropean AmericansAfrican AmericansNative HawaiiansJapanese AmericansLatinosPooledP for trend pooledPhetc
(503 cases, 633 controls)(381 cases, 542 controls)(135 cases, 344 controls)(509 cases, 782 controls)(355 cases, 505 controls)(1,883 cases, 2,806 controls)
Type 2 diabetes SNPs 
rs10923931 1 1.13 (0.85–1.49) 0.87 (0.71–1.07) 0.96 (0.49–1.86) 0.97 (0.58–1.63) 1.28 (0.93–1.76) 1.01 (0.88–1.15) 0.94 0.32 
T NOTCH2 0.097 0.33 0.055 0.025 0.095 0.12   
rs7578597 2 1.05 (0.88–1.48) 1.13 (0.90–1.41) 1.04 (0.54–2.00) 1.47 (0.69–3.15) 0.71 (0.48–1.03) 1.03 (0.89–1.20) 0.69 0.27 
T THADA 0.89 0.75 0.95 0.99 0.94 0.90   
rs1801282 3 1.14 (0.88–1.48) 0.70 (0.39–1.26) 1.06 (0.55–2.04) 0.83 (0.54–1.28) 1.15 (0.84–1.57) 1.04 (0.88–1.23) 0.63 0.41 
C PPARG 0.87 0.98 0.94 0.97 0.89 0.93   
rs4607103 3 0.86 (0.71–1.04) 0.99 (0.81–1.22) 0.89 (0.64–1.22) 0.95 (0.80–1.13) 0.99 (0.80–1.22) 0.94 (0.86–1.03) 0.20 0.81 
C ADAMTS9 0.74 0.71 0.74 0.65 0.68 0.70   
rs4402960 3 0.91 (0.76–1.08) 1.07 (0.88–1.30) 0.89 (0.64–1.23) 0.91 (0.76–1.08) 1.16 (0.92–1.45) 0.98 (0.89–1.07) 0.61 0.30 
T IGF2BP2 0.34 0.49 0.28 0.33 0.26 0.34   
rs10010131 4 1.01 (0.86–1.20) 0.91 (0.74–1.12) 0.86 (0.60–1.23) 1.14 (0.68–1.89) 0.96 (0.78–1.19) 0.97 (0.87–1.07) 0.55 0.82 
G WFS1 0.59 0.67 0.80 0.98 0.70 0.76   
rs7754840 6 1.02 (0.85–1.20) 1.10 (0.91–1.34) 1.04 (0.78–1.38) 1.06 (0.90–1.25) 1.00 (0.81–1.23) 1.05 (0.96–1.14) 0.29 0.97 
C CDKAL1 0.31 0.54 0.52 0.43 0.32 0.42   
rs864745 7 0.93 (0.78–1.09) 0.87 (0.71–1.07) 0.81 (0.59–1.10) 1.02 (0.84–1.23) 1.01 (0.83–1.24) 0.94 (0.86–1.03) 0.17 0.68 
A JAZF1 0.53 0.74 0.72 0.78 0.62 0.68   
rs13266634 8 1.00 (0.83–1.20) 0.78 (0.57–1.07) 0.95 (0.70–1.28) 1.05 (0.89–1.24) 1.04 (0.82–1.31) 1.00 (0.90–1.10) 0.91 0.60 
C SLC30A8 0.71 0.90 0.63 0.63 0.75 0.72   
rs2383208 9 1.01 (0.81–1.25) 1.06 (0.83–1.34) 0.98 (0.71–1.35) 1.07 (0.91–1.26) 0.96 (0.73–1.27) 1.03 (0.93–1.13) 0.62 0.92 
A CDKN2B 0.81 0.80 0.74 0.57 0.86 0.74   
rs1111875 10 1.01 (0.85–1.20) 1.16 (0.93–1.46) 1.09 (0.79–1.52) 1.20 (1.00–1.44) 0.94 (0.76–1.16) 1.07 (0.98–1.18) 0.15 0.48 
G HHEX 0.60 0.75 0.30 0.26 0.63 0.50   
rs7903146 10 1.06 (0.89–1.28) 1.00 (0.81–1.23) 1.16 (0.77–1.73) 1.05 (0.72–1.55) 1.00 (0.79–1.27) 1.03 (0.92–1.15) 0.59 0.98 
T TCF7L2 0.29 0.27 0.14 0.045 0.22 0.19   
rs12779790 10 0.88 (0.71–1.10) 0.97 (0.74–1.26) 1.15 (0.80–1.64) 1.21 (0.97–1.49) 0.98 (0.76–1.27) 1.03 (0.92–1.15) 0.63 0.33 
C CDC123 0.17 0.14 0.18 0.16 0.17 0.16   
rs2237895d 11 0.99 (0.83–1.18) 0.98 (0.77–1.25) 0.94 (0.68–1.31) 1.06 (0.88–1.32) 1.00 (0.80–1.27) 1.00 (0.90–1.10) 0.93 0.96 
C KCNQ1 0.42 0.21 0.36 0.35 0.42 0.35   
rs2237897d 11 1.01 (0.68–1.49) 1.13 (0.80–1.59) 1.13 (0.77–1.67) 0.92 (0.75–1.13) 0.97 (0.74–1.26) 1.00 (0.89–1.13) 1.00 0.016 
C KCNQ1 0.95 0.92 0.78 0.62 0.78 0.80   
rs5219 11 1.07 (0.90–1.28) 0.77 (0.55–1.08) 1.01 (0.75–1.37) 0.81 (0.68–0.95) 0.77 (0.63–0.96) 0.89 (0.81–0.97) 0.012 0.082 
T KCNJ11 0.35 0.10 0.38 0.39 0.39 0.32   
rs7961581 12 0.96 (0.79–1.17) 0.95 (0.75–1.20) 1.10 (0.80–1.51) 1.01 (0.82–1.24) 1.27 (1.00–1.60) 0.94 (0.76–1.15) 0.60 0.39 
C TSPAN8 0.27 0.21 0.28 0.20 0.20 0.23   
rs8050136 16 0.86 (0.72–1.02) 0.89 (0.73–1.08) 0.63 (0.43–0.93) 1.07 (0.88–1.30) 0.96 (0.77–1.19) 0.91 (0.83–1.00) 0.055 0.21 
A FTO 0.42 0.44 0.24 0.21 0.28 0.32   
Obesity SNPs 
rs2815752 1 1.00 (0.85–1.19) 1.10 (0.90–1.34) 1.04 (0.70–1.56) 0.85 (0.64–1.14) 1.06 (0.85–1.31) 1.02 (0.92–1.13) 0.70 0.63 
A NEGR1 0.63 0.54 0.83 0.93 0.71 0.73   
rs10913469 1 0.99 (0.80–1.23) 0.87 (0.70–1.08) 0.57 (0.38–0.85) 1.20 (1.00–1.44) 0.89 (0.70–1.14) 0.96 (0.86–1.06) 0.38 0.016 
C SEC16B 0.18 0.28 0.21 0.23 0.20 0.22   
rs6548238 2 1.08 (0.87–1.34) 1.07 (0.80–1.44) 0.59 (0.31–1.13) 1.15 (0.89–1.49) 1.17 (0.88–1.56) 1.08 (0.95–1.22) 0.23 0.40 
T TMEM18 0.17 0.11 0.079 0.10 0.13 0.12   
rs10938397 4 0.93 (0.78–1.10) 0.81 (0.64–1.02) 1.14 (0.83–1.55) 0.97 (0.81–1.16) 1.08 (0.88–1.32) 0.97 (0.89–1.06) 0.50 0.34 
G GNPDA2 0.42 0.25 0.28 0.29 0.35 0.32   
rs925946e 11 0.92 (0.76–1.12) 1.01 (0.81–1.26) 1.73 (1.19–2.53) 1.12 (0.70–1.77) 0.90 (0.70–1.15) 1.02 (0.91–1.14) 0.78 0.24 
T BDNF 0.29 0.28 0.18 0.030 0.23 0.19   
rs6265e 11 1.33 (1.06–1.68) 1.32 (0.85–2.05) 0.70 (0.51–0.97) 0.92 (0.78–1.09) 0.91 (0.70–1.19) 1.01 (0.91–1.12) 0.88 0.049 
C BDNF 0.79 0.94 0.68 0.60 0.85 0.76   
rs10838738 11 0.99 (0.83–1.19) 1.16 (0.86–1.56) 0.96 (0.70–1.33) 0.99 (0.83–1.17) 1.17 (0.96–1.43) 1.05 (0.95–1.15) 0.36 0.58 
G MTCH2 0.34 0.11 0.32 0.34 0.35 0.29   
rs7138803 12 0.93 (0.78–1.10) 0.97 (0.77–1.23) 0.97 (0.68–1.38) 1.06 (0.90–1.26) 0.97 (0.78–1.21) 0.99 (0.90–1.09) 0.83 0.90 
A BCDIN3D 0.39 0.19 0.19 0.34 0.26 0.29   
rs7498665 16 0.82 (0.69–0.97) 1.03 (0.84–1.26) 1.03 (0.75–1.41) 1.00 (0.79–1.27) 0.87 (0.72–1.06) 0.92 (0.83–1.01) 0.073 0.47 
G SH2B1 0.40 0.27 0.28 0.13 0.45 0.30   
rs8050136 16 0.87 (0.74–1.03) 0.91 (0.75–1.10) 0.60 (0.41–0.86) 1.04 (0.86–1.26) 0.96 (0.78–1.19) 0.92 (0.84–1.00) 0.055 0.21 
A FTO 0.42 0.44 0.24 0.21 0.28 0.32   
rs17782313 18 0.97 (0.80–1.19) 0.98 (0.80–1.20) 1.00 (0.65–1.52) 0.95 (0.79–1.15) 0.92 (0.68–1.23) 0.98 (0.88–1.08) 0.63 0.98 
C MC4R 0.22 0.29 0.13 0.23 0.13 0.21   
rs11084753 19 0.89 (0.75–1.07) 1.12 (0.92–1.36) 1.04 (0.78–1.39) 1.03 (0.87–1.22) 0.96 (0.79–1.18) 1.00 (0.91–1.09) 0.97 0.53 
G KCTD15 0.68 0.64 0.42 0.29 0.67 0.53   
rs29941 19 0.90 (0.75–1.08) 1.11 (0.86–1.42) 0.87 (0.65–1.17) 1.05 (0.87–1.27) 0.81 (0.66–1.00) 0.94 (0.85–1.03) 0.18 0.26 
G KCTD15 0.69 0.82 0.40 0.21 0.66 0.54   

aOR adjusted for age (quartiles), body mass index (quartiles), diabetes status (self-report), and ethnicity (in pooled analysis).

bNCBI build 36 (forward strand).

cP value for interaction between risk allele and ethnic groups (4-df).

dRs2237895 and rs2237897 adjusted for each other.

eRs925946 and rs6265 adjusted for each other.

View Large

We also did not find any significant association of the aggregate risk scores comprising the T2D, obesity, or T2D/obesity risk alleles with breast cancer risk (T2D SNPs: OR = 1.01, P = 0.31; obesity SNPs: OR = 1.02, P = 0.24; and all SNPs: OR = 1.01, P = 0.14).

We found no strong evidence that the validated risk variants for T2D and obesity are associated with breast cancer risk among women of various ethnicities. Neither did we find any significant association between a summary risk score comprising the risk alleles for these variants and breast cancer risk. We had adequate statistical power (80%) to detect an OR of 1.21 for SNPs with a MAF of 0.10, and an OR of 1.15 for SNPs with a MAF of 0.20. However, power may be lower as most of these markers of T2D and obesity risk were identified in GWAS among men and women of European ancestry and may not be strongly correlated with the functional alleles in all populations.

In conclusion, while obesity and, to a lesser extent, T2D are risk factors for breast cancer, we found no evidence that the known risk variants for T2D or obesity are associated with breast cancer risk in a multiethnic population. These data suggest that the potential for a shared biology between T2D/obesity and breast cancer is not due to pleiotropic effects of these risk variants.

The contents of this article are solely the responsibility of the authors and do not necessarily represent the official views of the NIH.

The authors thank the participants of the MEC who have contributed to a better understanding of the genetic contributions to breast cancer. Assistance with phenotype harmonization, SNP selection and annotation, data cleaning, data management, integration and dissemination, and general study coordination was provided by the PAGE Coordinating Center (U01HG004801-01). The National Institutes of Mental Health also contributes to the support for the Coordinating Center.

The Population Architecture using Genomics and Epidemiology (PAGE) program is funded by the National Human Genome Research Institute (NHGRI), supported by U01HG004803 (CALiCo), U01HG004798 (EAGLE), U01HG004802 (MEC), U01HG004790 (WHI), and U01HG004801 (Coordinating Center). The complete list of PAGE members can be found at http://www.pagestudy.org. The Multiethnic Cohort study (MEC) characterization of epidemiological architecture is funded through the NHGRI PAGE program (U01HG004802). The MEC study is funded through the National Cancer Institute (R37CA54281, R01 CA63464, P01CA33619, U01CA136792, and U01CA98758).

1.
Morimoto
 
LM
,
White
 
E
,
Chen
 
Z
,
Chlebowske
 
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
 
2001
;
13
:
741
–
51
.
Google Scholar
Crossref
Search ADS
 
2.
Petrelli
 
JM
,
Calle
 
EE
,
Rodriguez
 
C
,
Thun
 
MJ
. 
Body mass index, height, and postmenopausal breast cancer mortality in a prospective cohort of US women
.
Cancer Causes Control
 
2002
;
13
:
325
–
32
.
Google Scholar
Crossref
Search ADS
PubMed
 
3.
Novosyadlyy
 
R
,
Lann
 
DE
,
Vijayakumar
 
A
,
Rowzee
 
A
,
Lazzarino
 
DA
,
Fierz
 
Y
, et al 
Insulin-mediated acceleration of breast cancer development and progression in a nonobese model of type 2 diabetes
.
Cancer Res
 
2010
;
70
:
741
–
51
.
Google Scholar
Crossref
Search ADS
PubMed
 
4.
Grote
 
V
,
Becker
 
S
,
Kaaks
 
R
. 
Diabetes mellitus type 2—an independent risk factor for cancer?
Exp Clin Endocrinol Diabetes
 
2010
;
118
:
4
–
8
.
Google Scholar
Crossref
Search ADS
PubMed
 
5.
Kaaks
 
R
. 
Nutrition, insulin, IGF-1 metabolism and cancer risk: a summary of epidemiological evidence
.
Novartis Found Symp
 
2004
;
262
:
247
–
60
.
Google Scholar
PubMed
 
6.
Endogenous Hormones and Breast Cancer Collaborative Group
. 
Insulin-like growth factor 1 (IGF1), IGF binding protein 3 (IGFBP3), and breast cancer risk: pooled individual data analysis of 17 prospective studies
.
Lancet Oncol
 
2010
;
11
:
530
–
42
.
Crossref
Search ADS
PubMed
 
7.
Verheus
 
M
,
Peeters
 
PHM
,
Rinaldi
 
S
,
Dossus
 
L
,
Biessy
 
C
,
Olsen
 
A
, et al 
Serum C-peptide levels and breast cancer risk: results from the European prospective investigation into cancer and nutrition (EPIC)
.
Int J Cancer
 
2006
;
119
:
659
–
67
.
Google Scholar
Crossref
Search ADS
PubMed
 
8.
Kolonel
 
LN
,
Henderson
 
BE
,
Hankin
 
JH
,
Nomura
 
AM
,
Wilkens
 
LR
,
Pike
 
MC
, et al 
A multiethnic cohort in Hawaii and Los Angeles: baseline characteristics
.
Am J Epidemiol
 
2000
;
151
:
346
–
57
.
Google Scholar
Crossref
Search ADS
PubMed
 
©2011 American Association for Cancer Research.
2011