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Association of Obesity and Diabetes With the Incidence of Breast Cancer in Louisiana

      Introduction

      Breast cancer is a heterogeneous disease, consisting of multiple molecular subtypes. Obesity has been associated with an increased risk for postmenopausal breast cancer, but few studies have examined breast cancer subtypes separately. Obesity is often complicated by type 2 diabetes, but the possible association of diabetes with specific breast cancer subtypes remains poorly understood.

      Methods

      In this retrospective case-control study, Louisiana Tumor Registry records of primary invasive breast cancer diagnosed in 2010–2015 were linked to electronic health records in the Louisiana Public Health Institute's Research Action for Health Network. Controls were selected from Research Action for Health Network and matched to cases by age and race. Conditional logistic regression was used to identify metabolic risk factors. Data analysis was conducted in 2020‒2021.

      Results

      There was a significant association between diabetes and breast cancer for Luminal A, Triple-Negative Breast Cancer, and human epidermal growth factor 2‒positive subtypes. In multiple logistic regression, including both obesity status and diabetes as independent risk factors, Luminal A breast cancer was also associated with overweight status. Diabetes was associated with increased risk for Luminal A and Triple-Negative Breast Cancer in subgroup analyses, including women aged ≥50 years, Black women, and White women.

      Conclusions

      Although research has identified obesity and diabetes as risk factors for breast cancer, these results underscore that comorbid risk is complex and may differ by molecular subtype. There was a significant association between diabetes and the incidence of Luminal A, Triple-Negative Breast Cancer, and human epidermal growth factor 2‒positive breast cancer in Louisiana.

      INTRODUCTION

      Breast cancer is the most common form of cancer and the second cause of cancer mortality in women of the U.S.

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      In summary, previous studies have produced inconsistent findings on the relationship between obesity and breast cancer development, particularly as it pertains to HR-negative tumors. Most previous studies did not include all breast cancer molecular subtypes and/or had inconclusive results on the association of obesity with breast cancer risk among Black women. Furthermore, limiting the definition of obesity to BMI does not capture the range of dysmetabolism among patients with obesity. T2D is a frequent metabolic complication of obesity and a diagnosis that can be readily established from electronic health records (EHRs). Louisiana has a high prevalence of obesity, T2D, and breast cancer as well as a diverse population that allows stratification by self-reported race. Therefore, we investigated the association between obesity, diabetes, and the most common breast cancer subtypes by race in Louisiana.

      METHODS

      Study Population

      Data were collected by the Louisiana Tumor Registry (LTR), a participant of the National Cancer Institute's Surveillance, Epidemiology and End Results Program, and the Centers for Disease Control and Prevention's National Program of Cancer Registries. The study included women aged ≥20 years with primary invasive breast cancer (International Classification of Diseases [ICD] for Oncology, third edition site codes C50.0-C50.9) diagnosed from 2010 to 2015. Breast cancers with ICD-O-3 histology codes 9050–9055, 9140, and 9590–9989 diagnosed by autopsy or death certificate were excluded. Eligible case records from LTR were linked to records from the Louisiana Public Health Institute's Research Action for Health Network (REACHnet) to obtain BMI and diagnoses and chronic conditions recorded during patient encounters. REACHnet is an EHR-based clinical data repository that uses the National Patient-Centered Clinical Research Network Common Data Model.
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      All research activities were approved by Louisiana State University Health Sciences Center, New Orleans IRB.

      Measures

      The molecular subtype was classified on the basis of joint HR status (estrogen/progesterone) and HER2 status. Joint HR status was considered negative if the tumor lacked both estrogen and progesterone reactivity. Borderline hormonal receptor was considered positive. Briefly, the 4 molecular breast cancer subtypes were defined as Luminal A (HR+/HER‒), Luminal B (HR+/HER+), Triple Negative (HR‒/HER2‒), and HER2 (HR‒/HER2+).
      Patient age was categorized into age groups of 10 years. Self-reported race by patients was categorized as Black or African American, White or European American, or other. BMI was classified as lean (18.5≤BMI<25), overweight (25≤ BMI<30), mild obesity (30≤ BMI<35), and high obesity (BMI≥35). Records with extreme BMI values (>65) were excluded from data summarization to control for potential data entry errors. Diabetes and pregnancy were indicated by ICD, Ninth Revision and ICD-10 diagnoses codes (Appendix Table 2, available online).

      Study Design

      The study has a retrospective case-control design. Controls were matched to cases on the basis of age and race at a rate of up to 5:1. Matching was performed separately by molecular subtype. Patients who were missing BMI data, underweight (BMI<18.5), or pregnant during the study period were excluded. The stepwise schematic diagram of case and control eligibility for the study is presented in Appendix Figure 1 (available online).
      Figure 1
      Figure 1AORs and 95% CIs from multivariable conditional logistic regression model.
      HER2, human epidermal growth factor 2; TNBC, Triple-Negative Breast Cancer.
      For cases, date of diagnosis, age at diagnosis, race, and molecular subtype were obtained from the LTR database. Obesity status for each patient was determined as the mode or most prevalent BMI category obtained from REACHnet patient records spanning 12 months before diagnosis through 3 months (90 days) after diagnosis. Diabetes and pregnancy status were identified by condition and diagnosis codes from patient encounters in REACHnet that occurred no later than 3 months after the date of diagnosis. Patient information from the EHR was restricted to 3 months after the date of diagnosis to include patients who may have been referred into the health systems shortly after their breast cancer diagnosis while limiting cause-effect bias in the study results.
      Controls were selected from a random sample of breast cancer‒free patients in REACHnet with at least 1 encounter or visit during the study period. Age and self-reported race were obtained from the medical records, with age observed 12 months after the first encounter. Obesity status for each patient was determined as the mode or most prevalent BMI category across a period of 15 months after the first encounter. Diabetes and pregnancy status were identified by condition and diagnosis codes from patient encounters during the same period (15 months from the first encounter).

      Statistical Analysis

      Conditional logistic regression was performed to evaluate the risk factors associated with breast cancer in the case-control sample. This was conducted through effect stratification, with each case and its matched controls constituting 1 stratum.

      SAS/STAT(R) 9.2 user's guide, second edition. SAS. https://support.sas.com/documentation/cdl/en/statug/63033/HTML/default/viewer.htm#statug_logistic_sect062.htm. Accessed December 13, 2021.

      Case-control matching and all data analyses were performed in SAS (SAS Institute Inc, Cary, NC), version 9.4, during 2020‒2021. Analyses were executed for each subtype. The primary analysis included 2 logistic regression models with obesity status and diabetes as a single risk factor and a multiple logistic regression with both obesity and diabetes as risk factors (multivariable model). Secondary analyses to confirm the consistency of results from the final model included subanalyses for women aged >50 years and among White/European women and Black/African American women.

      RESULTS

      Patient characteristics for the cases and controls for each molecular subtype are presented in Table 1. Most breast cancer cases in the study were Luminal A (n=1,584). TNBC was the next most common subtype with 364 cases. Luminal B and HER2+ were the least common subtypes with 232 and 115 cases, respectively. Among cases, obesity (mild and high) prevalence ranged from 46.3% (Luminal A) to 51.7% (TNBC). The prevalence of diabetes in cases ranged from 19.8% (Luminal B) to 28.9% (TNBC). Among age- and race-matched controls, the prevalence of obesity ranged from 44.1% to 48.9%, and the prevalence of diabetes ranged from 15.4% to 19%.
      Table 1Patient Characteristics for Breast Cancer Cases and Matched Controls
      Luminal ALuminal BTNBCHER2
      CharacteristicsCases (n=1,584)Controls (n=5,436)Cases (n=232)Controls (n=1,095)Cases (n=364)Controls (n=1,686)Cases (n=115)Controls (n=562)
      Age, years
       20‒290.3 (5)0.5 (25)0.4 (1)0.5 (5)0.6 (2)0.6 (10)0.9 (1)0.9 (5)
       30‒391.9 (30)2.7 (149)7.8 (18)8.1 (89)6 (22)6.5 (110)6.1 (7)6.2 (35)
       40‒4910.9 (172)13.8 (748)19.4 (45)19.8 (217)14.6 (53)14.8 (250)7.8 (9)8 (45)
       50‒5924.4 (387)27.1 (1,471)23.7 (55)23.8 (261)26.7 (97)26.3 (444)31.3 (36)31.7 (178)
       60‒6931.3 (495)29.3 (1,591)26.7 (62)26.7 (292)28 (102)28.1 (473)31.3 (36)31.1 (175)
       ≥7031.3 (495)26.7 (1,452)22 (51)21.1 (231)24.2 (88)23.7 (399)22.6 (26)22.1 (124)
      Race
       White69.6 (1,103)66.6 (3,620)62.1 (144)61.4 (672)50 (182)51.1 (861)54.8 (63)54.6 (307)
       Black28.1 (445)30.6 (1,662)35.8 (83)36.4 (399)48.9 (178)47.8 (805)41.7 (48)41.8 (235)
       Other2.3 (36)2.8 (154)2.2 (5)2.2 (24)1.1 (4)1.2 (20)3.5 (4)3.6 (20)
      Obesity
       Lean22.4 (354)25.8 (1,400)22.4 (52)23.1 (253)21.2 (77)21.8 (368)23.5 (27)21.4 (120)
       Overweight31.4 (497)30.2 (1,641)29.7 (69)29.5 (323)27.2 (99)30.5 (514)27 (31)29.7 (167)
       Mild obesity23.4 (371)21.8 (1,183)24.6 (57)22.3 (244)24.5 (89)22.4 (377)19.1 (22)22.2 (125)
       High obesity22.9 (362)22.3 (1,212)23.3 (54)25.1 (275)27.2 (99)25.3 (427)30.4 (35)26.7 (150)
      Type II diabetes
       No76.8 (1,217)82.3 (4,476)80.2 (186)84.6 (926)71.2 (259)81 (1,366)73 (84)81.9 (460)
       Yes23.2 (367)17.7 (960)19.8 (46)15.4 (169)28.9 (105)19 (320)27 (31)18.2 (102)
      HER2, human epidermal growth factor 2; TNBC, Triple-Negative Breast Cancer.
      The association of obesity and diabetes with breast cancer was assessed with conditional logistic regression models, controlling for age and race matching (Table 2). In the first model, there was an increased risk of obesity among cases compared with that among the controls for Luminal A breast cancer, where the odds of overweight, mild obesity, and high obesity among cases were 17%–24% greater than those among the controls. There were no significant associations between elevated BMI groups and Luminal B, TNBC, or HER2+ breast cancer subtypes. The second model indicated a significant association between diabetes and breast cancer for Luminal A (OR [95% CI]=1.37 [1.19, 1.58]), TNBC (OR [95% CI]=1.82 [1.38, 2.39]), and HER2+ (OR [95% CI]=1.68 [1.05, 2.71]) subtypes. In multiple logistic regression, including both obesity status and diabetes as independent risk factors, the association between Luminal A breast cancer and overweight (OR [95% CI]=1.20 [1.01, 1.42]) and diabetes (OR [95% CI]=1.35 [1.16, 1.56]) remained significant. Similarly, there were significant associations between diabetes and TNBC (OR [95% CI]=1.8 [1.36, 2.37]) and HER2+ (OR [95% CI]=1.69 [1.04, 2.76]) (Table 2 and Figure 1).
      Table 2Results From Case-Control Analyses of Metabolic Risk for Breast Cancer, by Molecular Subtype
      Risk factorsObesity model,Diabetes model,Multivariable model,
      OR (95% CI)OR (95% CI)OR (95% CI)
      Luminal A (n=1,584)
       Overweight1.17 (1.00, 1.38)1.16 (0.99, 1.36)
       Mild obesity1.24 (1.05, 1.47)1.20 (1.01, 1.42)
       High obesity1.22 (1.02, 1.45)1.13 (0.95, 1.35)
       Diabetes1.37 (1.19, 1.58)1.35 (1.16, 1.56)
      Luminal B (n=232)
       Overweight1.04 (0.70, 1.54)1.01 (0.68, 1.50)
       Mild obesity1.14 (0.74, 1.74)1.10 (0.71, 1.69)
       High obesity0.96 (0.62, 1.48)0.89 (0.57, 1.38)
       Diabetes1.39 (0.95, 2.04)1.43 (0.97, 2.11)
      TNBC (n=364)
       Overweight0.92 (0.67, 1.28)0.90 (0.65, 1.25)
       Mild obesity1.13 (0.81, 1.60)1.05 (0.74, 1.49)
       High obesity1.12 (0.79, 1.57)0.99 (0.70, 1.40)
       Diabetes1.82 (1.38, 2.39)1.80 (1.36, 2.37)
      HER2 (n=115)
       Overweight0.82 (0.46, 1.46)0.78 (0.43, 1.39)
       Mild obesity0.77 (0.40, 1.48)0.74 (0.38, 1.43)
       High obesity1.03 (0.58, 1.85)0.92 (0.51, 1.68)
       Diabetes1.68 (1.05, 2.71)1.69 (1.04, 2.76)
      Note: Boldface indicates statistical significance (p<0.05).
      Results are provided as ORs and 95% CIs from conditional logistic regression models. Obesity and diabetes models include a single fixed effect for the risk factor of interest. The multivariable model includes both obesity status and diabetes as fixed effects. All models were conditioned on case-control matching (age group 10 years and race).
      HER2, human epidermal growth factor 2; TNBC, Triple-Negative Breast Cancer.
      Table 3 contains the conditional OR estimates and 95% CIs from the full case-control sample and subgroup analyses. For Luminal A breast cancer, the association between overweight, mild obesity, and high obesity and breast cancer incidence was most pronounced for women aged ≥50 years, a common proxy for menopausal status, with OR (95% CI) ranging from 1.21 (1.01, 1.43) to 1.33 (1.10, 1.60), and among Black women, with OR (95% CI) ranging from 1.39 (0.96, 2.02) to 1.52 (1.05, 2.20). The association between diabetes and breast cancer was greatest among Black women for Luminal A (OR [95% CI]=1.48 [1.17, 1.87]) and TNBC (OR [95% CI]=1.96 [1.35, 2.85]).
      Table 3Results From Subgroup Case-Control Analyses of Metabolic Risk for Breast Cancer, by Molecular Subtype
      Risk factorsAll,Age >50 years,White,Black,
      OR (95% CI)OR (95% CI)OR (95% CI)OR (95% CI)
      Luminal A (n=1,584)
       Overweight1.16 (0.99, 1.36)1.21 (1.01, 1.43)1.10 (0.92, 1.32)1.52 (1.05, 2.20)
       Mild obesity1.20 (1.01, 1.42)1.33 (1.10, 1.60)1.17 (0.96, 1.43)1.39 (0.96, 2.02)
       High obesity1.13 (0.95, 1.35)1.25 (1.03, 1.52)1.05 (0.85, 1.30)1.47 (1.02, 2.13)
       Diabetes1.35 (1.16, 1.56)1.31 (1.13, 1.53)1.33 (1.10, 1.61)1.48 (1.17, 1.87)
      Luminal B (n=232)
       Overweight1.01 (0.68, 1.50)0.92 (0.57, 1.50)1.15 (0.73, 1.81)0.60 (0.26, 1.38)
       Mild obesity1.10 (0.71, 1.69)1.24 (0.75, 2.05)1.01 (0.59, 1.72)0.94 (0.42, 2.11)
       High obesity0.89 (0.57, 1.38)0.95 (0.56, 1.61)0.90 (0.52, 1.57)0.68 (0.30, 1.53)
       Diabetes1.43 (0.97, 2.11)1.39 (0.91, 2.12)1.33 (0.76, 2.34)1.53 (0.88, 2.66)
      TNBC (n=364)
       Overweight0.90 (0.65, 1.25)0.91 (0.63, 1.31)0.95 (0.63, 1.43)0.80 (0.45, 1.43)
       Mild obesity1.05 (0.74, 1.49)1.09 (0.74, 1.61)1.06 (0.67, 1.69)1.06 (0.61, 1.86)
       High obesity0.99 (0.70, 1.40)1.03 (0.69, 1.53)0.78 (0.47, 1.28)1.15 (0.67, 1.98)
       Diabetes1.80 (1.36, 2.37)1.87 (1.39, 2.51)1.68 (1.10, 2.57)1.96 (1.35, 2.85)
      HER2 (n=115)
       Overweight0.78 (0.43, 1.39)0.77 (0.40, 1.50)0.60 (0.29, 1.24)1.03 (0.33, 3.23)
       Mild obesity0.74 (0.38, 1.43)0.90 (0.44, 1.82)1.01 (0.46, 2.21)0.45 (0.12, 1.64)
       High obesity0.92 (0.51, 1.68)1.17 (0.60, 2.28)0.62 (0.27, 1.42)1.17 (0.40, 3.47)
       Diabetes1.69 (1.04, 2.76)1.66 (1.00, 2.75)1.93 (0.95, 3.92)1.39 (0.68, 2.85)
      Note: Boldface indicates statistical significance (p<0.05).
      Results are provided as ORs and 95% CIs from conditional logistic regression models. All models include both obesity status and diabetes as fixed effects and were conditioned on case-control matching (age group of 10 years and race).
      HER2, human epidermal growth factor 2; TNBC, Triple-Negative Breast Cancer.

      DISCUSSION

      In this retrospective case-control study, metabolic risk factors were significantly associated with breast cancer. This study also highlights the heterogeneity of risk factors across breast cancer molecular subtypes. Results indicated that elevated BMI was a risk factor for Luminal A breast cancer, especially in postmenopausal women. In women aged ≥50 years, cases of Luminal A breast cancer had 21%‒33% greater odds of presenting with BMI ≥25, which includes the categories of overweight, mild obesity, and high obesity, than controls. These results are in congruence with a body of literature that indicates a positive relationship between body mass and the risk of HR+ breast cancers in postmenopausal women, including several meta-analyses,
      • Bandera EV
      • Chandran U
      • Hong CC
      • et al.
      Obesity, body fat distribution, and risk of breast cancer subtypes in African American women participating in the AMBER Consortium.
      ,
      • Dietze EC
      • Chavez TA
      • Seewaldt VL
      Obesity and triple-negative breast cancer: disparities, controversies, and biology.
      ,
      • 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.
      ,
      • 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.
      and although not addressed in this study, literature further suggests that this particular relationship is strongest when there is significant weight gain in adulthood.
      • Bandera EV
      • Chandran U
      • Hong CC
      • et al.
      Obesity, body fat distribution, and risk of breast cancer subtypes in African American women participating in the AMBER Consortium.
      ,
      • Hao Y
      • Jiang M
      • Miao Y
      • et al.
      Effect of long-term weight gain on the risk of breast cancer across women's whole adulthood as well as hormone-changed menopause stages: a systematic review and dose–response meta-analysis.
      In this study, the relationship between elevated BMI and HR‒ breast cancers was not significant. A consensus on the role of body mass in these breast cancer subtypes has yet to be reached, with contradictory findings in past studies.
      • Dietze EC
      • Chavez TA
      • Seewaldt VL
      Obesity and triple-negative breast cancer: disparities, controversies, and biology.
      For instance, a meta-analysis of case-control TNBC studies suggested that body mass is a significant risk factor, but several studies included did not control for race or other covariates.
      • Pierobon M
      • Frankenfeld CL
      Obesity as a risk factor for triple-negative breast cancers: a systematic review and meta-analysis.
      Other meta-analyses of HR‒ breast cancers found no significant association.
      • 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.
      ,
      • 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.
      Conversely, a rigorous study by the African American Breast Cancer Epidemiology and Risk consortium, which aimed to specify the relationship between body type and breast cancer among Black women, found that recently elevated BMI correlated with decreased risk of TNBC.
      • Bandera EV
      • Chandran U
      • Hong CC
      • et al.
      Obesity, body fat distribution, and risk of breast cancer subtypes in African American women participating in the AMBER Consortium.
      Additional studies have also suggested a protective effect of elevated BMI on HR‒ breast cancer tumors.
      • Berstad P
      • Coates RJ
      • Bernstein L
      • et al.
      A case-control study of body mass index and breast cancer risk in white and African-American women.
      • Bandera EV
      • Chandran U
      • Zirpoli G
      • et al.
      Body fatness and breast cancer risk in women of African ancestry.
      • Siddharth S
      • Sharma D
      Racial disparity and triple-negative breast cancer in African-American women: a multifaceted affair between obesity, biology, and socioeconomic determinants.
      • Agurs-Collins T
      • Ross SA
      • Dunn BK
      The many faces of obesity and its influence on breast cancer risk.
      Diabetes and prediabetes have been reported as significant risk factors for breast cancer in many populations, with excess risk in the range of 10%‒30%.
      • Maskarinec G
      • Jacobs S
      • Park SY
      • et al.
      Type II diabetes, obesity, and breast cancer risk: the multiethnic cohort.
      • Boyle P
      • Boniol M
      • Koechlin A
      • et al.
      Diabetes and breast cancer risk: a meta-analysis.
      • Larsson SC
      • Mantzoros CS
      • Wolk A
      Diabetes mellitus and risk of breast cancer: a meta-analysis.
      ,
      • Hardefeldt PJ
      • Edirimanne S
      • Eslick GD
      Diabetes increases the risk of breast cancer: a meta-analysis.
      • Hu Y
      • Zhang X
      • Ma Y
      • et al.
      Incident type 2 diabetes duration and cancer risk: a prospective study in two U.S. cohorts.
      • Tsilidis KK
      • Kasimis JC
      • Lopez DS
      • Ntzani EE
      • Ioannidis JP
      Type 2 diabetes and cancer: umbrella review of meta-analyses of observational studies.
      • Redaniel MT
      • Jeffreys M
      • May MT
      • Ben-Shlomo Y
      • Martin RM
      Associations of type 2 diabetes and diabetes treatment with breast cancer risk and mortality: a population-based cohort study among British women.
      There was a significant association between diabetes and three molecular subtypes of breast cancer in Louisiana―Luminal A, TNBC, and HER2+―in this study. Diabetes exhibited the greatest risk in TNBC, where cases had 82% greater odds of diabetes than controls. After controlling for obesity status, the OR was nearly unchanged, at 80%. There were similar results for Luminal A and HER2+ subtypes, where the risk associated with diabetes was not attenuated after adjusting for BMI. These results support diabetes as a strong independent risk factor for breast cancer. This contrasts with studies that have reported that the associations between diabetes and breast cancer were attenuated after adjusting for BMI.
      • Maskarinec G
      • Jacobs S
      • Park SY
      • et al.
      Type II diabetes, obesity, and breast cancer risk: the multiethnic cohort.
      ,
      • Boyle P
      • Boniol M
      • Koechlin A
      • et al.
      Diabetes and breast cancer risk: a meta-analysis.
      ,
      • Redaniel MT
      • Jeffreys M
      • May MT
      • Ben-Shlomo Y
      • Martin RM
      Associations of type 2 diabetes and diabetes treatment with breast cancer risk and mortality: a population-based cohort study among British women.

      Limitations

      Limitations of this study include the retrospective observational study design, which only allows for the determination of association, not causation. The strength of the control population in the study is that it originates from large health systems in the greater New Orleans area, which not only maintain hospitals and emergency departments but also maintain walk-in clinics and urgent-care facilities. However, system-based controls do have the potential for selection bias because this population is more likely to include individuals who receive routine medical care. In the event that the selection bias favors individuals with poorer health status, those who require more frequent care, this would bias results toward the null. The linkage of the EHR records to LTR to identify breast cancer cases ensured that there was no loss to follow-up among controls. The external validity of the sample is fair because cancer cases were identified through the state cancer registry (LTR) and linked to a large regional EHR network (REACHnet). However, REACHnet does not have complete regional coverage; thus, the sample may not be fully representative.
      A distinct objective and strength of this study were to assess metabolic risk factors by molecular subtype. However, low case counts for Luminal B and HER2 molecular subtypes in the study population likely lead to diminished statistical power for these models. The limited sample also precluded the ability to assess risk factors among premenopausal women or postmenopausal women by race. Molecular subtypes were assigned on the basis of IHC commonly used in clinical practice, as opposed to whole-transcriptome gene expression profiling. This prevented an investigation of the molecular subsubtypes of TNBC that are defined strictly through gene expression profiling.
      Although the classification of BMI in this study was based on the WHO's classification, it did not differentiate between Class II (35≤ BMI<39.9) and Class III (BMI≥40) obesity owing to low case counts. Instead, these categories were combined into a single category (high obesity), which is consistent with previous literature in the field.
      • Bandera EV
      • Chandran U
      • Hong CC
      • et al.
      Obesity, body fat distribution, and risk of breast cancer subtypes in African American women participating in the AMBER Consortium.
      ,
      • Kwan ML
      • Kroenke CH
      • Sweeney C
      • et al.
      Association of high obesity with PAM50 breast cancer intrinsic subtypes and gene expression.
      The diabetes exposure was defined by an indication of diabetes in the medical record. This did not include duration or control of disease, which may have the potential to modify risk. Finally, this analysis was limited to data in the National Patient-Centered Clinical Research Network Common Data Model for EHRs and thus did not control for other known risk factors regarding health behaviors (e.g., diet, physical activity), reproductive history, or social determinants of health (e.g., SES, environmental exposures) that were not available in the database.
      There are known shortcomings of using BMI as an indicator of obesity in cancer research, with several reviews highlighting the inability of BMI to accurately characterize body composition across races.
      • Dietze EC
      • Chavez TA
      • Seewaldt VL
      Obesity and triple-negative breast cancer: disparities, controversies, and biology.
      ,
      • Caan BJ
      • Cespedes Feliciano EM
      • Kroenke CH
      The importance of body composition in explaining the overweight paradox in cancer-counterpoint.
      ,
      • Brown JC
      • Carson TL
      • Thompson HJ
      • Agurs-Collins T
      The triple health threat of diabetes, obesity, and cancer-epidemiology, disparities, mechanisms, and interventions.
      The Carolina Breast Cancer Study found that waist‒hip ratio was associated with breast cancer incidence in both White and Black women, whereas there was no risk associated with BMI.
      • 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.
      Unfortunately, other measures of body composition are often not as readily available as BMI, especially in EHR-derived data. Results from this study support the notion that future studies should use multiple or composite measures of metabolic risk that include other comorbid conditions or laboratory profiles, such as Edmonton Obesity staging or metabolic syndrome criteria.
      • Sharma AM
      • Kushner RF
      A proposed clinical staging system for obesity.
      ,
      • Zhao P
      • Xia N
      • Zhang H
      • Deng T
      The metabolic syndrome is a risk factor for breast cancer: a systematic review and meta-analysis.
      For example, a meta-analysis found that 3 components of metabolic syndrome―obesity, diabetes, and hypertension―posed a significant breast cancer risk, and the presence of metabolic syndrome was associated with a twofold increase in breast cancer risk in postmenopausal women.
      • Zhao P
      • Xia N
      • Zhang H
      • Deng T
      The metabolic syndrome is a risk factor for breast cancer: a systematic review and meta-analysis.

      CONCLUSIONS

      There was a significant association between diabetes and the incidence of Luminal A, TNBC, and HER2+ breast cancer in Louisiana. These results suggest that the severity of metabolic sequelae of obesity may be better indicators of breast cancer risk than elevated BMI alone. Future breast cancer research should also consider additional factors that may modify risk in patients with diabetes, such as duration and control of disease, social determinants, and genomic risks.

      ACKNOWLEDGMENTS

      The authors also thank the Louisiana Tumor Registry for data collections. The authors acknowledge the participation of REACHnet partner health systems Ochsner Health, Tulane University, and Louisiana State University Health Sciences Center in this project. FMH and DMD are cofirst authors.
      The content is solely the responsibility of the authors and does not necessarily represent the official views of the funding agencies or data collection authorities.
      This research was supported in part by U54 GM104940 from the National Institute of General Medical Sciences of the NIH, which funds the Louisiana Clinical and Translational Science Center; P20 CA233374 from the National Cancer Institute; and Obesity Health Disparities Research Center pilot grant supported by National Institute on Minority Health and Health Disparities U54MD000502. The research reported in this article was conducted in partnership with Research Action for Health Network (REACHnet), funded by the Patient-Centered Outcomes Research Institute Award RI-CRN-2020-008. REACHnet is a partner network in the National Patient-Centered Clinical Research Network, which was developed with funding from the Patient-Centered Outcomes Research Institute.
      No financial disclosures were reported by the authors of this paper.
      This article is part of a supplement entitled Obesity-Related Health Disparities: Addressing the Complex Contributors, which is sponsored by the National Institute on Minority Health and Health Disparities (NIMHD), National Institutes of Health (NIH), U.S. Department of Health and Human Services (HHS). The findings and conclusions in this article are those of the author(s) and do not necessarily represent the official position of NIMHD, NIH, or HHS.

      CRediT AUTHOR STATEMENT

      Fokhrul M. Hossain: Conceptualization, Writing - original Draft. Denise M. Danos: Formal analysis, Methodology, Writing - original draft. Qiufan Fu: Formal analysis, Writing - review and editing. Xinnan Wang: Formal analysis. Richard A. Scribner: Conceptualization, Methodology. San T. Chu: Data curation, Writing - review and editing. Ronald L. Horswell: Data curation. Eboni G. Price-Haywood: Supervision. Bridgette M. Collins-Burow: Supervision. Xiao-Cheng Wu: Data curation, Supervision, Writing - review and editing. Augusto C. Ochoa: Funding acquisition, Supervision. Lucio Miele: Conceptualization, Supervision, Writing - review and editing.

      Appendix. SUPPLEMENTAL MATERIAL

      SUPPLEMENT NOTE

      This article is part of a supplement entitled Obesity-Related Health Disparities: Addressing the Complex Contributors, which is sponsored by the National Institute on Minority Health and Health Disparities (NIMHD), National Institutes of Health (NIH), U.S. Department of Health and Human Services (HHS). The findings and conclusions in this article are those of the authors and do not necessarily represent the official position of NIMHD, NIH, or HHS.

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