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Disparity in Metabolic Conditions among Hispanic/Latina Women with Breast Cancer

Joh D1, Botrus G2, Dwivedi AK3, Dongur L4 and Nahleh Z5*

1Foster School of Medicine, Texas Tech University Health Sciences Center, USA

2Department of Internal Medicine, Texas Tech University Health Sciences Center, USA

3Department of Biomedical Sciences, Texas Tech University Health Sciences Center, USA

4Department of Biomedical Sciences, Ross University, USA

5Department of Hematology/Oncology, Maroone Cancer Center, Cleveland Clinic Florida, USA

*Corresponding Author:
Nahleh Z
Department of Hematology/Oncology
Maroone Cancer Center, Cleveland Clinic Florida, USA
Tel: (954)-659-5840 Fax: +278615107002 E-mail: ombamalu@uwc.ac.za

Received date: November 19, 2018; Accepted date: November 28, 2018; Published date: December 04, 2018

Citation: Joh D, Botrus G, Dwivedi AK, Dongur L, Nahleh Z (2018) Disparity in Metabolic Conditions among Hispanic/Latina Women with Breast Cancer. Arch Can Res Vol.6 No.4:21. doi:10.21767/2254-6081.100187

 
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Abstract

Background: Breast cancer is the most common cancer in Hispanic/Latina women. Common metabolic conditions prevalent in American Hispanics include diabetes mellitus, dyslipidemia, hypertension, and obesity and have been associated with poor overall survival. The association of such coexisting conditions with breast cancer risk, treatment and breast cancer characteristics in this population is largely understudied. In this study, we sought to explore the prevalence of one or combination of these comorbid conditions with breast cancer and possible association with breast cancer characteristics and subtypes in a predominantly Hispanic patient population.

Methods: After IRB approval, we conducted a retrospective cross-sectional study of consecutive breast cancer patients treated in a University based tertiary medical center in the large border city of El Paso, TX. We evaluated the prevalence of 4 common metabolic conditions in a Hispanic patient population using the breast cancer center database of patients treated between 2005 and 2014. Adjusted association analyses were carried out using multiple Poisson regression analyses and results were presented with prevalence ratio (PR) and p-value.

Keywords

Breast cancer; Hypertension; Hyperlipidemia; Diabetes mellitus

Introduction

Breast cancer is the most common invasive cancer in women worldwide and the second most common cause of cancer death in women in the United States [1]. Individuals with breast cancer who also have common metabolic conditions or diseases such as diabetes mellitus (DM), dyslipidemia, hypertension, and obesity have been shown to have inferior survival overall [2]. Among women with early stage breast cancer, cardio-metabolic risk factors have been associated with cardiovascular and othercause mortality, but not breast cancer mortality [3]. It remains unclear whether the complex aetiology of these comorbidities can lead to increased risk for breast cancer and whether it affects the severity of disease presentation. The presence of these comorbidities, however, increases the complexity of the decisionmaking process due to their significant impact on treatment and outcome. In the era of personalized medicine, it would be important to understand how common these conditions are and whether they are associated with different breast cancer characteristics. The association between potential breast cancer risk factors and the mechanism of disease is an active area of research and a better understanding of these correlations would provide guidance for developing more preventive and treatment strategies. The prevalence of cardiovascular risk factors in American Hispanics and their associated morbidity and mortality have been reported [4-6]. However, there is a paucity of literature regarding the prevalence of these factors among Hispanic breast cancer patients, a growing minority population. We aimed in this study at exploring the prevalence of hypertension, Diabetes Mellitus, dyslipidaemia, and obesity in Hispanic women with breast cancer and assess the potential association of these factors, individually or in combination with any breast cancer subtype. The city of El Paso, TX at the US-Mexico border region has a majority Hispanic population and provided the ideal setting for this study.

Methods

After obtaining Institutional Review Board (IRB) approval, we conducted a retrospective cross-sectional study utilizing the electronic medical database at a tertiary university based medical center. We identified all Hispanic women diagnosed with primary breast cancer consecutively between 2005 to 2014. We completed any missing diagnostic and comorbidities information of the target population using individual records from the cancer research core facility database housed at Texas Tech University Health Sciences Center in El Paso, TX. Age, Body mass index (BMI), ethnicity, breast cancer diagnosis, subtype, type of surgery and treatment, comorbidities including diabetes mellitus (DM), dyslipidemia, hypertension(HTN), obesity defined using Body Mass Index ≥ 30 kg/m2, and coronary artery disease (CAD), as well as patient demographics and disease characteristics including menopausal status (by age older than 50 years ), stage, estrogen receptor (ER), progesterone receptor (PgR) and Human epidermal receptor 2 neu (HER2) status were extracted from the database.

The primary exposure variable was defined in one of three ways:

• Presence of at least one comorbidity.

• Number of comorbidities.

• Individual comorbidities.

The primary outcome variables were considered as HER2+, ER+ or PgR+, Triple Negative Breast Cancer (TNBC if ER- and PR- and HER2-), Hormonal Positive (ER+ or PgR+), and ER+/PgR+ and HER2-. The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

Statistical considerations

The aim of this study was to determine the association of individual and combined comorbidities specifically DM, HTN, obesity and dyslipidemia with breast cancer and tumor characteristics. The quantitative variables were described using mean and standard deviation (SD) while categorical data were described using frequency and percentage. The prevalence of each comorbidity along with 95% confidence interval (CI) was estimated using binomial distribution. Clinical and tumor characteristics of the patients were compared based on DM status (yes vs. no), HTN status (yes vs. no), obesity status (yes vs. no), and dyslipidemia status (yes vs. no) using either unpaired t-test or Fisher’s exact test. The adjusted effects of individual status of DM, HTN, obesity, and dyslipidemia on ER+, PgR+, HER2, HR, ER/PR+ and HER2-, and TNBC status were examined using multiple Poisson regression with robust variance analyses to obtain prevalence ratio (PR). Further, Poisson regression with robust variance analysis was carried out to determine adjusted effects of number of comorbidities and presence of any comorbidity on tumor characteristics. Variable were found to be statistically significant in the unadjusted analysis were considered in the multivariable models. The results of Poisson regression analysis were presented using Prevalence Ratio (PR) along with 95%CI and p-value. All statistical analyses were carried out using STATA 13.

Results

A total of 1,003 breast cancer patients were included in the analysis. Average age was 56 years (SD: 12) and average body mass index (BMI) was 30.7 Kg/m2 (SD: 6.3) (Table 1). displays the patients’ characteristics for the entire cohort and the presence of the metabolic cardiovascular risk factors of interest. Of total, 85% of the cohort were self-identified Hispanics. Pathological type and characteristics of breast cancer were distributed as follows: 86.7% invasive ductal carcinoma, 68% ER+ tumors, 57% PgR+, and 18.6% HER2+ tumors. One-third of the patients were pre-menopausal. Patients with presence of at least one metabolic condition were more likely to be older, post-menopausal, receive more lumpectomies compared to mastectomies, have more CAD, and have higher prevalence of ER+/PgR+ tumors. The highest prevalence was noted for obesity 49.75% (95% CI: 46.61%, 52.89%) followed by HTN at 37.59% (95% CI: 34.58%, 40.67%), DM at 27.31% (95% CI: 24.58%, 30.1%) and dyslipidemia at 24.23% (95% CI: 21.60%, 27.00%). The majority (more than two-third) of individuals had at least one comorbidity (71.98%, 95% CI: 69.09%-74.75%). The distribution of the 4 comorbidities was as follows: 1 comorbidity (32.4% of patients), 2 comorbidities (19%), 3 comorbidities (13.4%) and 4 comorbidities (7%). 28% of all patients had no identifiable comorbidity (28%) (Tables 2 and 3). provide distribution and association of considered clinical and tumor characteristics according to DM, HTN, Obesity, and dyslipidemia. Breast cancer patients with DM were more likely to have increased BMI, older age, dyslipidemia, CAD, HTN, post-menopausal status and ER+/ PgR+ tumors. Presence of dyslipidemia was similarly found to be associated with increased BMI, older age, presence of DM, obesity, CAD and postmenopausal status but was associated with both ER+/PgR+ as well as TNBC. HTN and Obesity were associated with all considered comorbidities and did not associate with any tumor characteristics Table 4 shows adjusted association of individual and combined comorbidities with ER, PgR and HER2 status. The presence of at least one comorbidity was associated with the prevalence of ER+/PgR+ breast cancer (PR=1.15, p=0.04) and expressed a trend association with HER2 negative status (PR=1.08, p=0.086) after adjusting for significant confounders. Among individual factor associations, hypertension was found to be more prevalent as an independent factor in HER2 negative tumors (PR=1.12, p=0.003). Patients with all four comorbidities were more likely to have ER+ tumors (PR=1.18, p=0.033) after adjusting for potential confounders. Presence of 3 comorbidities (PR=1.25 p=0.013) or 4 comorbidities (PR=1.34, p=0.003) was significantly prevalent among individuals with ER+/PgR+ tumors after controlling for significant variables. In the adjusted analysis, HER2 negative status was found to be associated with 2 or more comorbidities. Table 5 shows the association of comorbidities with combination of ER, PgR and HER2 status. This table clearly shows that the presence of 4 comorbidities was associated with HR+ status (PR=1.16, p=0.048) in adjusted models. ER+/ PgR+ and HER2- was highly associated with the presence of the 4 comorbidities (PR=1.35, p=0.018) and showed a trend association with 3 comorbidities (PR=1.23, p=0.078). TNBC status was not found to be associated with the number of comorbidities or presence of any individual comorbidity. It only showed a trend association with presence of DM (PR=1.07, p=0.11).

Variables All Data
N (%)
Any comorbidities
No
N (%)
Yes
N (%)
p value
BMI (Kg/m2): mean, SD 30.72 (6.28) -- -- --
Age (in years): mean, SD 56.36 (12.04) 52.72 (13) 57.78 (11.35) <0.0001
Ethnicity
Hispanics 849 (84.65) 237 (84.34) 612 (84.76) 0.8461
Non - Hispanics 154 (15.35) 44 (15.66) 110 (15.24)
Diagnosis
Ductal 867 (86.7) 235 (83.63) 632 (87.9) 0.1349
Lobular 61 (6.1) 18 (6.41) 43 (5.98)
Ductal and Lobular 8 (0.8) 2 (0.71) 6 (0.83)
Other 64 (6.4) 26 (9.25) 38 (5.29)
Menopausal
Pre - menopause 319 (31.8) 130 (46.43) 186 (25.83) <0.0001
Post-Menopause 684 (68.2) 150 (53.57) 534 (74.17)
Stage
Unknown 239 (23.83) 74 (26.33) 165 (22.85) 0.0813
Stage I/II 485 (48.35) 120 (57.97) 365 (65.53)
Stages III/IV 279 (27.82) 87 (42.03) 192 (34.47)
Type of Surgery
None 74 (7.46) 33 (12.04) 41 (5.71) 0.0001
Lumpectomy 527 (53.13) 120 (43.8) 407 (56.69)
Mastectomy 385 (38.81) 118 (43.07) 267 (37.19)
Unknown 6 (0.6) 3 (1.09) 3 (0.42)
CAD
No 962 (95.91) 281 (100) 681 (94.32) <0.0001
Yes 41 (4.09) 0 (0) 41 (5.68)
ER +
No 310 (31.86) 94 (35.07) 216 (30.64) 0.191
Yes 663 (68.14) 174 (64.93) 489 (69.36)
PgR +
No 413 (42.53) 128 (47.76) 285 (40.54) 0.0498
Yes 558 (57.47) 140 (52.24) 418 (59.46)
HER2 neu positive
No 676 (81.45) 167 (76.61) 509 (83.17) 0.042
Yes 154 (18.55) 51 (23.39) 103 (16.83)
ER+/PgR+ (HR+)
No 297 (29.61) 91 (32.38) 206 (28.53) 0.0967
Yes 674 (67.20) 177 (62.99) 497 (68.84)
unknown 32 (3.19) 13 (4.63) 19 (2.63)
TNBC
No 643 (64.11) 167 (59.43) 476 (65.93) 0.0343
Yes 185 (18.44) 51 (18.15) 134 (18.56)
unknown 175 (17.45) 63 (22.42) 112 (15.51)

Table 1: Gene expression profiles, prognostics and treatment options.

Variables Diabetes Hypertension
No
N (%)
Yes
N (%)
p value No
N (%)
Yes
N (%)
p value
BMI (Kg/m2): mean, SD 29.97 (5.85) 32.72 (6.9) <.0001 29.88 (5.86) 32.12 (6.68) <0.0001
Age (in years): mean, SD 54.92 (12.44) 60.19 (9.98) <.0001 53.7 (11.82) 60.77 (11.09) <0.0001
Ethnicity
Hispanics 612 (83.95) 237 (86.5) 0.3763 529 (84.5) 320 (84.88) 0.928
Non - Hispanics 117 (16.05) 37 (13.5) 97 (15.5) 57 (15.12)
Diagnosis
Ductal 621 (85.3) 246 (90.44) 0.1971 538 (86.08) 329 (87.73) 0.481
Lobular 49 (6.73) 12 (4.41) 36 (5.76) 25 (6.67)
Ductal and Lobular 6 (0.82) 2 (0.74) 6 (0.96) 2 (0.53)
Other 52 (7.14) 12 (4.41) 45 (7.2) 19 (5.07)
Menopausal
Pre - menopause 278 (38.13) 41 (14.96) <0.0001 254 (40.71) 62 (16.49) <0.0001
Post-menopause 451 (61.87) 233 (85.04) 370 (59.29) 314 (83.51)
Stage
unknown 171 (23.46) 68 (24.82) 0.3463 149 (23.8) 90 (23.87) 0.0709
Stage I/II 346 (47.46) 139 (50.73) 288 (60.38) 197 (68.64)
Stage III/IV 212 (29.08) 67 (24.45) 189 (39.62) 90 (31.36)
Type of Surgery
None 63 (8.76) 11 (4.03) 0.0054 55 (8.91) 19 (5.07) 0.0101
Lumpectomy 361 (50.21) 166 (60.81) 306 (49.59) 221 (58.93)
Mastectomy 290 (40.33) 95 (34.8) 251 (40.68) 134 (35.73)
Unknown 5 (0.7) 1 (0.37) 5 (0.81) 1 (0.27)
Hypertension
No 534 (73.25) 92 (33.58) <0.0001 -- -- --
Yes 195 (26.75) 182 (66.42)
Diabetes
No -- -- -- 534 (85.3) 195 (51.72) <0.0001
Yes 92 (14.7) 182 (48.28)
Obesity
No 404 (55.42) 100 (36.50) <0.0001 345 (55.11) 159 (42.18) <0.0001
Yes 325 (44.58) 174 (63.50) 281 (44.89) 218 (57.82)
Dyslipidemia
No 616 (84.5) 144 (52.55) <0.0001 558 (89.14) 202 (53.58) <0.0001
Yes 113 (15.5) 130 (47.45) 68 (10.86) 175 (46.42)
Coronary artery disease
No 713 (97.81) 249 (90.88) <0.0001 619 (98.88) 343 (90.98) <0.0001
Yes 16 (2.19) 25 (9.12) 7 (1.12) 34 (9.02)
ER +
No 239 (33.76) 71 (26.79) 0.0443 211 (34.99) 99 (26.76) 0.0087
Yes 469 (66.24) 194 (73.21) 392 (65.01) 271 (73.24)
PgR +
No 316 (44.7) 97 (36.74) 0.0286 276 (45.77) 137 (37.23) --
Yes 391 (55.3) 167 (63.26) 327 (54.23) 231 (62.77)
HER2-neu positive
No 483 (80.9) 193 (82.83) 0.5523 384 (77.73) 292 (86.9) 0.0092
Yes 114 (19.1) 40 (17.17) 110 (22.27) 44 (13.1)
HR+
No 229 (31.41) 68 (24.82) 0.1452 205 (32.75) 92 (24.4) 0.0067
Yes 478 (65.57) 196 (71.53) 398 (63.58) 276 (73.21)
unknown 22 (3.02) 10 (3.65) 23 (3.67) 9 (2.39)
TNBC
No 142 (19.48) 43 (15.69) 0.1119 119 (19.01) 66 (17.51) 0.0001
Yes 454 (62.28) 189 (68.98) 375 (59.9) 268 (71.09)
unknown 133 (18.24) 42 (15.33) 132 (21.09) 43 (11.41)

Table 2: Differences between dose-volume histogram parameters for IMRT and VMAT.

Variables Dyslipidemia Obesity
No
N (%)
Yes
N (%)
p value No
N (%)
Yes
N (%)
p value
BMI (Kg/m2): mean, SD 30.32 (6.1) 31.95 (6.65) 0.0004 -- -- --
Age (in years): mean, SD 54.85 (11.89) 61.07 (11.31) <.0001 56.14 (12.99) 56.58 (11.02) 0.566
Ethnicity
Hispanics 638 (83.95) 211 (86.83) 0.3074 416 (82.54) 433 (86.77) 0.0662
Non - Hispanics 122 (16.05) 32 (13.17) 88 (17.46) 66 (13.23)
Diagnosis
Ductal 658 (86.69) 209 (86.72) 0.2525 432 (86.06) 435 (87.35) 0.86
Lobular 45 (5.93) 16 (6.64) 31 (6.18) 30 (6.02)
Ductal and Lobular 4 (0.53) 4 (1.66) 5 (1) 3 (0.6)
Other 52 (6.85) 12 (4.98) 34 (6.77) 30 (6.02)
Menopausal
Pre - menopause 278 (36.68) 38 (15.7) <0.0001 175 (34.93) 141 (28.26) 0.0248
Post-menopause 480 (63.32) 204 (84.3) 326 (65.07) 358 (71.74)
Stage
unknown 177 (23.29) 62 (25.51) 0.4345 126 (25) 113 (22.65) 0.5718
Stage I/II 364 (62.44) 121 (66.85) 236 (62.43) 249 (64.51)
Stage III/IV 219 (37.56) 60 (33.15) 142 (37.57) 137 (35.49)
Type of Surgery
None 65 (8.66) 9 (3.73) 0.013 46 (9.27) 28 (5.65) 0.0637
Lumpectomy 384 (51.13) 143 (59.34) 248 (50) 279 (56.25)
Mastectomy 296 (39.41) 89 (36.93) 198 (39.92) 187 (37.7)
Unknown 6 (0.8) 0 (0) 4 (0.81) 2 (0.4)
Hypertension
No 558 (73.42) 68 (27.98) <0.0001 345 (68.45) 281 (56.31) <0.0001
Yes 202 (26.58) 175 (72.02) 159 (31.55) 218 (43.69)
Diabetes
No 616 (81.05) 113 (46.5) <0.0001 404 (80.16) 325 (65.13) <0.0001
Yes 144 (18.95) 130 (53.5) 100 (19.84) 174 (34.87)
Obesity
No 397 (52.24) 107 (44.03) 0.0272 -- -- --
Yes 363 (47.76) 136 (55.97)
Dyslipidemia
No -- -- -- 397 (78.77) 363 (72.75) 0.0272
Yes 107 (21.23) 136 (27.25)
Coronary artery disease
No 751 (98.82) 211 (86.83) <0.0001 493 (97.82) 469 (93.99) 0.0023
Yes 9 (1.18) 32 (13.17) 11 (2.18) 30 (6.01)
ER +
No 239 (32.61) 71 (29.58) 0.4249 162 (33.33) 148 (30.39) 0.3359
Yes 494 (67.39) 169 (70.42) 324 (66.67) 339 (69.61)
PgR +
No 322 (44.05) 91 (37.92) 0.0984 217 (44.83) 196 (40.25) 0.1537
Yes 409 (55.95) 149 (62.08) 267 (55.17) 291 (59.75)
HER2-neu positive
No 497 (80.42) 179 (84.43) 0.2195 332 (79.81) 344 (83.09) 0.2459
Yes 121 (19.58) 33 (15.57) 84 (20.19) 70 (16.91)
HR+
No 229 (30.13) 68 (27.98) 0.0852 154 (30.56) 143 (28.66) 0.2673
Yes 502 (66.05) 172 (70.78) 330 (65.48) 344 (68.94)
unknown 29 (3.82) 3 (1.23) 20 (3.97) 12 (2.4)
TNBC
No 137 (18.03) 48 (19.75) 0.0817 92 (18.25) 93 (18.64) 0.9361
Yes 479 (63.03) 164 (67.49) 322 (63.89) 321 (64.33)
unknown 144 (18.95) 31 (12.76) 90 (17.86) 85 (17.03)

Table 3: Unadjusted associations of cofactors with lipids and obesity.

Model ER+ PR+ HER 2 -
PR (95%CI) p-value PR (95%CI) p-value PR (95%CI) p-value
Model 1
1 comorbidity 1.02 (0.90, 1.14) 0.794 1.09 (0.94, 1.27) 0.240 1.04 (0.95, 1.15) 0.386
2 comorbidities 1.07 (0.94, 1.21) 0.334 1.11 (0.94, 1.32) 0.212 1.10 (0.99, 1.21) 0.067
3 comorbidities 1.10 (0.96, 1.25) 0.190 1.25 (1.05, 1.49) 0.013 1.14 (1.03, 1.27) 0.015
4 comorbidities 1.18 (1.01, 1.37) 0.033 1.34 (1.10, 1.63) 0.003 1.08 (0.95, 1.24) 0.251
Model 2
Any comorbidities 1.06 (0.96, 1.17) 0.284 1.15 (1.01, 1.30) 0.041 1.08 (0.99, 1.17) 0.086
Model 3
Diabetes 1.07 (0.97, 1.19) 0.175 1.10 (0.97, 1.25) 0.142 0.97 (0.90, 1.05) 0.434
Hypertension 1.08 (0.98, 1.19) 0.102 1.10 (0.97, 1.24) 0.129 1.12 (1.04, 1.20) 0.003
Obesity 1.03 (0.94, 1.12) 0.537 1.06 (0.95, 1.18) 0.285 1.03 (0.96, 1.10) 0.39
Dyslipidemia 0.96 (0.87, 1.07) 0.483 1.02 (0.90, 1.16) 0.749 1.00 (0.93, 1.08) 0.937

Table 4: Adjusted association of commodities with tumor characteristics.

Model HR+ TNBC ER+ and PR+ and HER2-
PR (95%CI) p-value PR (95%CI) p-value PR (95%CI) p-value
Model 1
1 comorbidity 1.02 (0.91, 1.14) 0.779 0.99 (0.90, 1.10) 0.915 1.09 (0.90, 1.33) 0.37
2 comorbidities 1.08 (0.96, 1.22) 0.217 1.01 (0.91, 1.13) 0.805 1.16 (0.93, 1.44) 0.19
3 comorbidities 1.10 (0.97, 1.26) 0.149 0.97 (0.86, 1.11) 0.674 1.23 (0.98, 1.56) 0.078
4 comorbidities 1.16 (1.00, 1.35) 0.048 1.08 (0.95, 1.22) 0.240 1.35 (1.05, 1.74) 0.018
Model 2
Any comorbidities 1.06 (0.96, 1.17) 0.239 1.00 (0.92, 1.10) 0.912 1.16 (0.97, 1.38) 0.098
Model 3
Diabetes 1.06 (0.97, 1.17) 0.208 1.07 (0.98, 1.17) 0.110 1.09 (0.92, 1.28) 0.326
Hypertension 1.10 (1.00, 1.21) 0.04 1.03 (0.95, 1.12) 0.473 1.14 (0.97, 1.33) 0.111
Obesity 1.02 (0.94, 1.11) 0.681 0.98 (0.91, 1.06) 0.588 1.09 (0.95, 1.26) 0.206
Dyslipidemia 0.96 (0.87, 1.06) 0.43 0.94 (0.86, 1.04) 0.227 0.97 (0.83, 1.15) 0.759

Table 5: Adjusted association of commodities with combination of tumor characteristics.

Discussion

This large study suggests a high prevalence of hypertension, DM, dyslipidemia and obesity in Hispanic women with breast cancer, especially postmenopausal women. The prevalence of obesity (BMI>30) was alarmingly high at around 50%, also DM in this study population (27.31%) was higher than the one reported for the general population both at the national level (10.9%) and at the U.S.-Mexico border in a similar population (15.7%) respectively [6,7]. Also, this study suggests that the combination of more than one of these metabolic conditions appear to be prevalent in our breast cancer study population, particularly in postmenopausal women. 72% of the individuals studied had at least one condition and over 20% had three or four comorbidities. In a National Center for Health Statistics (NCHS) study, about 13% of the U.S. population had two of the following chronic conditions: hyperlipidemia, HTN, or DM, and 3% of the population had all three conditions [4]. We found that the combined presence of more than one comorbidity was more prevalent in HR+ positive tumor in postmenopausal women but that could reflect the common presentation of this breast cancer subtype. No individual condition was found to be associated with any particular breast tumor sub-type except for DM more likely to be seen in women with ER+/PgR+ tumors. Rather, the number of comorbidities (presence of two or more comorbidities) had a more increased association with ER+/PR+ and HER2 - tumors.

Given the significant prevalence of metabolic risk factors in Hispanic women with breast cancer, it would be desirable to further evaluate these conditions as underlying risk factors for this disease which, in turn, could be contributing to the increased cancer disparity previously noted in this patient population including a diagnosis at a younger age compared to non-Hispanic white women, a higher prevalence of TNBC and more advanced stages of disease [8].

Our study is consistent with other studies suggesting a strong association between increased breast cancer risk with obesity, DM, hyperlipidemia, and HTN. Obesity has been associated with the development of cancer, particularly breast cancer and is likely one of the most known modifiable risk factors for the development of breast cancer to date [9]. Several epidemiologic studies have noted that obesity, causing the development of a chronic low-grade inflammatory environment, may be more strongly associated with ER + postmenopausal breast cancer as seen in our study [10-12]. However, in a combined analysis of data from the Women's Health Initiative observational cohort and randomized trial, obesity was shown to be similarly related to both ER+ (hazard ratio=1.35, 95% CI: 1.20, 1.51) and TNBC (hazard ratio=1.37, 95% CI: 0.98, 1.93) [13]. The association of other metabolic risk factors with breast cancer risk and its outcome have been also explored. Type 2 DM has been thought to increase the risk of developing breast cancer, although the underlying mechanism is still uncertain [14,15]. Other studies have suggested that hyperlipidemia [16] and hypertension [17] might increase also the risk of breast cancer. Hypertension was linked to a 15% increase risk of breast cancer in the postmenopausal population (combined RR: 1.13; 95% Cl, 1.01- 1.26) [17]. The effects of hyperlipidemia are less clear. Touvier et al. reported results of a meta-analysis confirming the evidence of a modest but statistically significant inverse association between hyperlipidemia and the risk of breast cancer, supported by mechanistic plausibility from experimental studies [18]. More recently a large study based on the Women’s Health Initiative [19] examined the association of metabolic phenotypes of obesity defined by presence of the metabolic syndrome using baseline measurements of blood glucose, triglycerides, highdensity lipoprotein(HDL) -cholesterol, blood pressure, waist circumference, and BMI (normal, overweight, obese) with risk of postmenopausal breast cancer in a prospective analysis of a cohort of postmenopausal women (n ∼ 21,000). Over 15 years of follow-up, 1,176 cases of invasive breast cancer were diagnosed.

Obesity, regardless of metabolic health, was associated with increased risk of breast cancer. Being obese and metabolically unhealthy was associated with the highest risk (Hazard Ratio, 1.62; 95% CI, 1.33–1.96). The study concluded that beyond BMI, metabolic health should be considered a clinically relevant and modifiable risk factor for breast cancer. Some studies have explored the utilization of certain tools such as the Charlson Comorbidity Index (CCI) to determine the impact of comorbidities including cardiovascular risk factors on breast cancer risk but no substantial association between morbidity measured with the CCI and breast cancer risk could be definitively identified, and the utility of these tools remain unclear [20]. However, studies have consistently identified metabolic syndrome, defined as at least three among abdominal obesity, high blood triglycerides, low HDL cholesterol, high blood glucose, and high blood pressure, to be an important risk factor for breast cancer in postmenopausal women suggesting that screening for and prevention of metabolic syndrome through lifestyle changes could confer protection against breast cancer [21]. Metabolic syndrome is characterized by a state of insulin resistance/hyperinsulinemia and subacute chronic inflammation and both conditions offer a plausible mechanistic link towards breast cancer. Thus, in addition to their increased risk of cardiovascular morbidity and mortality, women with this syndrome represent a group at elevated risk of developing breast cancer and poorer prognosis [22].

Conclusion

The strengths of our study include the focus on Hispanic/Latina women with breast cancer and is to our knowledge, the first study to determine the correlation of the combined metabolic comorbidities with breast cancer in this unique population. Also, the study adds to the body of evidence linking the metabolic conditions evaluated with ER + breast cancer (p=0.048). The study had several limitations including not applying the specific metabolic syndrome criteria due to the retrospective nature of the analysis and the non-availability of the required measures in the archived data. We used BMI as our marker for obesity, which reflects general adiposity and might not correctly with fat distribution measurements for abdominal obesity, hip and waist circumference and waist-to-hip ratio. Also, we did not include detailed information about the subtypes of the dyslipidemia due to the limitation in the database.

This study, nevertheless, adds to the body of evidence supporting a more focused approach to address obesity through lifestyle changes and screen for other metabolic conditions in the underserved Hispanic minority and others as potential modifiable risk factors against breast cancer. These findings should be confirmed in future larger studies but increasing awareness regarding the prevalence of these common conditions in Hispanic/ Latino patients with breast cancer would be a reasonable first step.

References

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