Abstract
Background
Obesity is associated with poorer breast cancer-specific survival. The purpose of this study was to investigate the relationships between obesity and the presence of angiolymphatic invasion as well as other features of invasive breast cancer, including stage at presentation, estrogen receptor (ER) status, the triple-negative phenotype, and tumor grade.
Methods
Detailed clinical and pathologic data were abstracted from the medical records of all 1,312 patients with Stage I – III primary breast cancer who had breast surgery at the University of Michigan Comprehensive Cancer Center between January 1, 2000 and December 31, 2006. Bivariate and multivariate analyses were conducted to investigate the relationships between body mass index and tumor biologic features controlling for menopausal status, diabetes and hypertension, hormone replacement therapy before diagnosis, race, and ethnicity.
Results
In multivariate analyses, severe obesity was independently associated with the presence of angiolymphatic invasion (odds ratio, OR 1.80, 95% C.I. 1.08 to 2.99, joint test of significance, p = 0.03). Severe obesity was associated with a lower likelihood of triple-negative breast cancer (OR 0.39, 95% C.I. 0.16 to 0.96). Among premenopausal women with diabetes, ER-negative (OR 5.22, 95% C.I. 1.22 to 24.29) and triple-negative (OR 14.8, 95% C.I. 1.92 to 113.91) disease was significantly more common.
Discussion
In this large sample of invasive breast cancers, obesity was independently associated with the presence of angiolymphatic invasion. Higher rates of angiolymphatic invasion among obese women may account in part for the poorer outcomes among obese women with breast cancer.
Keywords: Obesity, breast cancer, triple-negative breast cancer, angiolymphatic invasion, diabetes
Introduction
Obesity is associated both with higher rates of breast cancer1–4 and with unfavorable breast cancer outcomes.5–9 Hazard ratios for long-term (ten or more years) breast cancer-specific mortality among obese women compared with healthy weight women range from 1.34 (95% confidence intervals, C.I. 1.09 to 1.65)8 to 2.1 (95% C.I. 1.5 to 2.9).10
Explanations for the poorer survival rates among obese women with breast cancer include more advanced disease at presentation11–13 (in part related to lower rates of screening mammography),14 systematic underdosing of adjuvant chemotherapy,15,16 and higher rates of diabetes and hypertension, each of which has been associated with unfavorable breast cancer outcomes.17,18
Unfavorable tumor biology in obese women may also contribute to poorer outcomes. In a population-based study of 1177 women, obese women under the age of 45 were found to have higher histologic grade and a higher likelihood of estrogen receptor (ER)-negative tumors.10 Triple-negative breast cancers—negative for expression of ER, progesterone receptors (PR), and human epidermal growth factor-2 receptor (HER2)—are associated with a worse prognosis and have been shown in some19–21 but not all 22 studies to be more prevalent among obese women. Finally, diabetes has been associated with a higher likelihood of ER-negative breast cancer; the higher rate of diabetes among obese women may thus contribute to differences in tumor biology among obese women compared with lean women.23
Angiolymphatic invasion, defined as presence of tumor cells in peritumoral lymphatics or blood vessels and associated with a higher risk of breast cancer recurrence,24–27 may also be more common in the breast cancers of obese women. The adipocytokines, cytokines produced by adipose tissue, may have proangiogenic effects, promoting vascular proliferation.28 In one study of 393 patients, the presence of angiolymphatic invasion was more often identified among women weighing over 80 kilograms. In logistic regression, angiolymphatic invasion was independently associated with nodal status, histologic grade, weight, and height.29 The investigators did not, however, control for other factors associated with tumor biology, such as estrogen receptor status or diabetes. Information on the presence or absence of angiolymphatic invasion is largely missing from studies of breast cancer prognosis in obese women. In a recent study of 26 patients, body mass index was similarly associated with angiolymphatic invasion.30
The purpose of this study was to investigate the relationship between obesity status, measured as body mass index (BMI), and tumor biologic features among women with breast cancer. We sought to characterize the independent association of obesity with the presence of angiolymphatic invasion, hormone receptor (ER and PR) status, HER2 status, stage, and tumor grade, after controlling for age, menopausal status, use of hormone replacement therapy (HRT), and diabetes and hypertension. We were particularly interested in the relationship between obesity and the prevalence of angiolymphatic invasion in primary breast cancers.
Materials and Methods
Patient selection
All adult female patients 21 years of age and older who had surgery for a primary Stage I, II, or III breast cancer at the University of Michigan Comprehensive Cancer Center between January 1, 2000 and December 31, 2006 were eligible for inclusion. Pregnant women and women with an occult breast primary tumor, metaplastic carcinoma, or inflammatory breast cancer were excluded. After the sample was selected, we further excluded women who were on tamoxifen for breast cancer prevention at the time of diagnosis (n = 2), patients who had had more than one primary within the breast (n = 35), patients treated on an institutional protocol of cryoablation (n = 7), and patients treated with primary endocrine therapy (n = 5).
Data collection
Detailed medical record review was conducted to obtain information regarding age at diagnosis, race, ethnicity, menopausal status, the use of systemic hormone replacement therapy in the year(s) before the diagnosis of breast cancer, height and weight at the time of diagnosis, the presence of diabetes or hypertension, and tumor pathologic characteristics (tumor size, lymph node status, histologic grade, ER status, PR status, HER2 status, and presence of angiolymphatic invasion). For patients in whom menopausal status was unknown, age over 50 was used as a proxy for postmenopausal status. Height and weight, collected by a second abstractor who was blinded to tumor and clinical characteristics, was obtained from the electronic medical record. BMI was calculated using the Quetelet Index and categorized according to World Health Organization criteria as shown in Table 1.31 Continuous quality checks of the data were performed.
Table 1.
Sample Characteristics
| All | Healthy Weight (BMI <25) | Overweight (BMI 25 – 29.9) | Obese (BMI 30 – 34.9) | Severely Obese (BMI ≥ 35 ) | |||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
|
| |||||||||||
| N = 1,312 | N = 502 | N = 404 | N = 232 | N = 174 | p-value | ||||||
| Mean age, years (SD) | 55.3 (12.5) | 52.8 (12.9) | 55.9 (12.7) | 58.5 (11.5) | 56.4 (10.8) | <0.001 | |||||
| N | % | N | % | N | % | N | % | N | % | ||
| Race | <0.001 | ||||||||||
| White | 1,158 | 88.3 | 443 | 88.3 | 365 | 90.4 | 210 | 90.5 | 140 | 80.5 | |
| Black | 77 | 5.9 | 14 | 2.8 | 21 | 5.2 | 18 | 7.8 | 24 | 13.8 | |
| Asian/Pacific Islander | 45 | 3.4 | 34 | 6.8 | 8 | 2.0 | 1 | 0.4 | 2.0 | 1.2 | |
| Multiracial | 3 | 0.2 | 1 | 0.2 | 1 | 0.3 | 0 | 0 | 1 | 0.6 | |
| Unknown | 29 | 2.2 | 10 | 2.0 | 9 | 2.2 | 3 | 1.3 | 7 | 4.0 | |
| Ethnicity | 0.22 | ||||||||||
| Hispanic | 25 | 1.9 | 7 | 1.4 | 11 | 2.7 | 6 | 2.6 | 1 | 0.6 | |
| Non-Hispanic | 1,287 | 98.1 | 495 | 98.6 | 393 | 97.3 | 226 | 97.4 | 173 | 99.4 | |
| Menopausal status* | <0.001 | ||||||||||
| Pre/perimenopausal | 533 | 40.6 | 257 | 51.2 | 161 | 39.9 | 64 | 27.6 | 51 | 29.3 | |
| Postmenopausal | 779 | 59.4 | 245 | 48.8 | 243 | 60.2 | 168 | 72.4 | 123 | 70.7 | |
| Comorbidity | |||||||||||
| Hypertension | 425 | 32.4 | 87 | 17.3 | 121 | 30.0 | 108 | 46.6 | 109 | 62.6 | <0.001 |
| Diabetes mellitus | 101 | 7.7 | 11 | 2.2 | 21 | 5.2 | 29 | 12.5 | 40 | 23.0 | <0.001 |
| Type 1 | 9 | 8.9 | 2 | 18.2 | 1 | 4.8 | 2 | 6.9 | 4 | 10.0 | 0.61 |
| Type 2 | 92 | 91.1 | 9 | 81.8 | 20 | 95.2 | 27 | 93.1 | 36 | 90.0 | |
| Insulin | 20 | 21.7 | 1 | 11.1 | 2 | 10.0 | 5 | 18.5 | 12 | 33.3 | 0.16 |
| HRT | 457 | 34.8 | 162 | 32.3 | 141 | 34.9 | 88 | 37.9 | 66 | 37.9 | 0.37 |
| Primary systemic therapy | |||||||||||
| Chemotherapy | 199 | 15.2 | 70 | 13.9 | 73 | 18.1 | 35 | 15.1 | 21 | 12.1 | 0.15 |
44 patients menopausal status unknown; age used for assignment of menopausal status (see text)
p values are chi-square tests except for weight, which is an F-test from ANOVA
HRT, hormone replacement therapy
Analyses
Descriptive statistics were generated for each of the independent and dependent variables. Tumor size, and thus pathologic stage, was not determined for patients who received primary systemic chemotherapy therapy. Clinical and pathologic tumor features were compared between BMI categories using chi-squared tests. In patients who received primary systemic chemotherapy (n = 199), we report the tumor characteristics of the core biopsy specimen.
Separate multivariate logistic analyses were performed to identify predictors of ER-negative disease, the triple-negative (ER-, PR-, and HER2-negative) phenotype, grade 3 histology, the presence of angiolymphatic invasion, and Stage III disease. The independent variables for each of these analyses were menopausal status, race, ethnicity, obesity status, diabetes, hypertension, and use of hormone replacement therapy. Age was collinear with menopausal status in preliminary analyses and was thus dropped in the multivariate analyses. Analyses were repeated according to menopausal status (pre/perimenopausal vs. postmenopausal). For obesity, joint tests of significance were performed across categories. If the value of the dependent variable was unknown, those tumors were dropped from that analysis but retained for other analyses. Thus, the sample size for each multivariate analysis did not always sum to 1,312.
The Institutional Review Board at the University of Michigan approved all study procedures.
Results
Sample characteristics
Our sample included 1,312 patients. Table 1 shows the sample characteristics for the entire sample and by obesity status. Most (88.3%) of the patients were non-Hispanic whites. The mean age was 55.3 years (SD 12.5). Diabetes was present in 7.7% of the sample, and 32.4% of the tumors developed in patients who carried a diagnosis of hypertension. Hypertension (p < 0.001) and diabetes (p < 0.001) were significantly associated with increasing BMI. Insulin use did not differ according to BMI category. For 44 tumors, patient menopausal status was unknown, and in those patients we used age over 50 years as a proxy for menopausal status as described above. Hormone replacement therapy (HRT) at the time of the diagnosis was documented in 34.8%, with no difference in rates of HRT use according to BMI category.
Obesity and Stage of Disease
The majority (79.0%) of the cancers were pathologic Stage I or II. As described above, patients who received primary systemic chemotherapy (n = 199) were excluded from this portion of the analysis because a pathologic tumor measurement before resection could not be determined. There was no association between stage and obesity status.
Obesity and Tumor Biology
In bivariate analyses (Table 2), obesity was not associated with hormone receptor status, HER2 status, triple-negative phenotype, or histologic grade. There was a significant association between obesity category and the presence of angiolymphatic invasion (p = 0.03), severely obese women being the most likely to have angiolymphatic invasion. When the analysis was repeated by menopausal status, the likelihood of angiolymphatic invasion in the tumors varied by obesity category (p = 0.05) with angiolymphatic invasion more likely in severely obese (21.6%) compared with the tumors of healthy weight (14.8%) women (data not shown) among the pre- and perimenopausal women.
Table 2.
Stage and Tumor Biologic Characteristics, N = 1312
| Normal Weight N = 502 |
Overweight N = 404 |
Obese N = 232 |
Severely Obese N = 174 |
p value | |||||
|---|---|---|---|---|---|---|---|---|---|
|
|
|||||||||
| N | % | N | % | N | % | N | % | ||
|
|
|||||||||
| AJCC 2003 Stage | 0.58 | ||||||||
| I | 268 | 53.4 | 206 | 51.0 | 128 | 55.2 | 90 | 51.7 | |
| II | 138 | 27.5 | 104 | 25.7 | 56 | 24.1 | 47 | 27.0 | |
| III | 21 | 4.2 | 16 | 4.0 | 12 | 5.2 | 13 | 7.5 | |
| Unknown | 10 | 2.0 | 10 | 2.5 | 3 | 1.3 | 5 | 2.9 | |
| Clinical Stage II–III† | 65 | 13.0 | 68 | 16.8 | 33 | 14.2 | 19 | 10.9 | |
| Mean tumor size (SD)†† | 1.5 cm | (1.1) | 1.6 cm | (1.1) | 1.5 cm | (1.3) | 1.7 cm | (1.5) | 0.35 |
| ER status | 0.70 | ||||||||
| Positive | 351 | 69.9 | 291 | 72.0 | 165 | 71.1 | 132 | 75.9 | |
| Negative | 132 | 26.3 | 102 | 25.3 | 62 | 26.7 | 37 | 21.3 | |
| Unknown | 19 | 3.8 | 11 | 2.7 | 5 | 2.2 | 5 | 2.9 | |
| PR status | 0.24 | ||||||||
| Positive | 263 | 52.4 | 236 | 58.4 | 129 | 55.6 | 110 | 63.2 | |
| Negative | 219 | 43.6 | 157 | 38.9 | 96 | 41.4 | 60 | 34.5 | |
| Unknown | 20 | 4.0 | 11 | 2.7 | 7 | 3.0 | 4 | 2.3 | |
| HER2 status | 0.48 | ||||||||
| Positive | 87 | 17.3 | 76 | 18.8 | 36 | 15.5 | 21 | 12.1 | |
| Negative | 392 | 78.1 | 312 | 77.2 | 189 | 81.5 | 145 | 83.3 | |
| Unknown | 23 | 4.6 | 16 | 4.0 | 7 | 3.0 | 8 | 4.6 | |
| Triple negative | 0.20 | ||||||||
| Yes | 42 | 8.4 | 24 | 5.9 | 12 | 5.2 | 7 | 4.0 | |
| Unknown | 28 | 5.6 | 18 | 4.5 | 7 | 3.0 | 10 | 5.8 | |
| Angiolymphatic invasion | |||||||||
| Yes | 69 | 13.8 | 71 | 17.6 | 26 | 11.2 | 36 | 20.7 | 0.03 |
| No | 433 | 86.3 | 333 | 82.4 | 206 | 88.8 | 138 | 79.3 | |
| Grade | 0.75 | ||||||||
| Grade I | 110 | 21.9 | 66 | 16.3 | 43 | 18.5 | 37 | 21.3 | |
| Grade II | 215 | 42.8 | 194 | 48 | 101 | 43.5 | 76 | 43.7 | |
| Grade III | 130 | 25.9 | 106 | 26.2 | 65 | 28 | 46 | 26.4 | |
| Unknown | 47 | 9.4 | 38 | 9.4 | 23 | 9.9 | 15 | 8.6 | |
Treated with primary systemic chemotherapy, n = 199
Excludes women treated women primary systemic chemotherapy, n = 199
In multivariate analyses (Table 3), obesity status was independently associated with the presence of angiolymphatic invasion (joint test of significance, p = 0.03) after controlling for menopausal status, use of hormone replacement therapy at the time of diagnosis, diabetes and hypertension, and tumor features, including grade, stage, estrogen receptor status, and HER2 status. Grade was also associated with angiolymphatic invasion (odds ratio, OR, for grade 2 was 5.12, 95% C.I. 2.61 to 10.04; OR for grade 3 8.98, 95% C.I. 4.38 to 18.43). Obesity was associated with lower odds of triple-negative disease (OR among severely obese women 0.39, 95% C.I. 0.16 to 0.96). Obesity was not associated with other tumor features, including HER2 status (not shown in table).
Table 3.
Multivariate analyses
| Stage III n = 1247 |
Grade III n = 1189 |
ER-negative 1272 |
Triple-negative n = 979 |
Angiolymphatic Invasion n = 1312 |
|
|---|---|---|---|---|---|
|
| |||||
| OR (95% C.I.) | OR (95% C.I.) | OR (95% C.I.) | OR (95% C.I.) | OR (95% C.I.) | |
|
| |||||
| Menopausal status | |||||
| Pre/peri | Referent | Referent | Referent | Referent | Referent |
| Post | 0.84 (0.44 – 1.60) | 0.80 (0.55 – 1.17) | 0.97 (0.67 – 1.41) | 1.24 (0.70 – 2.22) | 0.80 (0.54 – 1.18) |
| Race | |||||
| White | Referent | Referent | Referent | Referent | Referent |
| Black | 0.91 (0.31 – 2.69) | 2.55 (1.40 – 4.66) | 0.90 (0.49 – 1.65) | 0.46 (0.14 – 1.57) | 1.20 (0.64 – 2.25) |
| Asian/Pacific Islander | 1.43 (0.42 – 4.93) | 0.94 (0.40 – 2.19) | 0.62 (0.25 – 1.52) | 0.82 (0.23 – 2.86) | 1.23 (0.54 – 2.78) |
| Unknown | 2.62 (0.86 – 7.94) | 1.14 (0.46 – 2.82) | 1.31 (0.54 – 3.22) | 2.22 (0.76 – 6.46) | 2.18 (0.97 – 4.88) |
| Ethnicity | |||||
| Hispanic | (Dropped) | 0.96 (0.31 – 2.94) | 1.24 (0.44 – 3.52) | (Dropped) | 0.52 (0.12 – 2.31) |
| Non-Hispanic | Referent | Referent | Referent | Referent | Referent |
| Obesity status | |||||
| Normal weight | Referent | Referent | Referent | Referent | Referent |
| Overweight | 0.99 (0.50 – 1.95) | 1.12 (.77 – 1.61) | 0.89 (0.62 – 1.27) | 0.62 (0.36 – 1.08) | 1.36 (0.93 – 1.99) |
| Obese | 1.36 (0.63 – 2.93) | 1.28 (0.82 – 1.99) | 0.97 (0.63 – 1.50) | 0.51 (0.25 – 1.03) | 0.84 (0.51 – 1.40) |
| Severely obese | 1.85 (0.83 – 4.11) | 1.26 (0.76 – 2.09) | 0.75 (0.44 – 1.26) | 0.39 (0.16 – 0.96) | 1.80 (1.08 – 2.99) |
| Joint test for significance for obesity category test for trend | p = 0.40 | p = 0.69 | p = 0.70 | p = 0.08 | p = 0.03 |
| p = 0.12 | p = 0.25 | p = 0.39 | p = 0.02 | p = 0.14 | |
| Diabetes | 1.46 (0.59 – 3.61) | 0.67 (0.36 – 1.23) | 1.16 (0.63 – 2.12) | 2.03 (0.85 – 4.86) | 1.27 (0.69 – 2.32) |
| Hypertension | 0.85 (0.44 – 1.65) | 1.01 (0.70 – 1.46) | 0.68 (0.47 – 0.98) | 0.82 (0.45 – 1.50) | 0.75 (0.50 – 1.12) |
| HRT | 0.73 (0.37 – 1.43) | 0.82 (0.56 – 1.18) | 1.12 (0.78 – 1.62) | 0.96 (0.54 – 1.69) | 0.98 (0.66 – 1.45) |
| ER status | |||||
| Positive | Referent | Referent | Referent | ||
| Negative | 0.66 (0.33 – 1.31) | 13.15 (9.63 – 17.96) | 0.80 (0.54 – 1.29) | ||
| Unknown | (Dropped) | 2.09 (0.54 – 8.13) | 0.26 (0.03 – 2.06) | ||
| Grade | |||||
| Grade 1 | Referent | Referent | (Dropped) | Referent | |
| Grade 2 | 1.37 (0.63 – 2.97) | 4.89 (2.41 – 9.92) | Referent | 5.12 (2.61 – 10.04) | |
| Grade 3 | 2.39 (1.00 – 5.68) | 47.15 23.28 – 95.50 | 3.64 (2.25 – 5.88) | 8.98 (4.38 – 18.43) | |
| Unknown | 0.32 (0.04 – 2.59) | 8.88 (3.91 – 20.13) | 0.22 (0.03 – 1.61) | 1.02 (0.31 – 3.37) | |
HRT, hormone replacement therapy
There were significant associations between diabetes and tumor biology in multivariate analyses restricted to tumors in pre- and perimenopausal women (Table 4). Diabetes was associated with ER-negative tumor status (OR 5.22, 95% C.I. 1.12 to 24.29) and with the triple-negative phenotype (OR 14.80, 95% C.I. 1.92 to 113.91).
Table 4.
Multivariate analyses, premenopausal only
| Stage III N=463 |
Grade III N=478 |
ER-negative N=515 |
Triple-negative N=418 |
Angiolymphatic Invasion N=521 |
|
|---|---|---|---|---|---|
|
| |||||
| OR (95% C.I.) | OR (95% C.I.) | OR (95% C.I.) | OR (95% C.I.) | OR (95% C.I.) | |
|
| |||||
| Race | |||||
| White | Referent | Referent | Referent | Referent | Referent |
| Black | 0.52 (0.06 – 4.45) | 2.17 (0.76 – 6.25) | 1.44 (0.49 – 4.24) | 0.33 (0.03 – 3.49) | 2.33 (0.89 – 6.13) |
| Asian/Pacific Islander | 0.70 (0.09 – 5.60) | 0.22 (0.06 – 0.86) | 1.49 (0.49 – 4.52) | 1.11 (0.21 – 5.87) | 0.96 (0.31 – 3.03) |
| Unknown | 2.28 (0.42 – 12.52) | 1.20 (0.27 – 5.30) | 2.58 (0.57 – 11.64) | 5.29 (0.82 – 33.94) | 3.11 (0.83 – 11.59) |
| Ethnicity | |||||
| Hispanic | Dropped | 0.80 (0.14 – 4.39) | 2.80 (0.67 – 11.61) | Dropped | Dropped |
| Non-Hispanic | Referent | Referent | |||
| Obesity Status | |||||
| Normal weight | Referent | Referent | Referent | Referent | Referent |
| Overweight | 0.80 (0.30 – 2.09) | 0.88 (0.52 – 1.51) | 1.16 (0.69 – 1.95) | 0.63 (0.27 – 1.43) | 1.76 (1.04 – 2.99) |
| Obese | 0.56 (0.12 – 2.63) | 1.27 (0.61 – 2.62) | 0.94 (0.45 – 1.98) | 0.38 (0.10 – 1.50) | 0.73 (0.31 – 1.75) |
| Severely obese | 2.68 (0.88 – 8.12) | 0.99 (0.41 – 2.38) | 0.53 (0.20 – 1.37) | 0.09 (0.01 – 0.97) | 1.61 (0.69 – 3.76) |
| Joint test for significance for obesity category test for trend | p=0.15 | p=0.84 | p=0.46 | p=0.15 | p=0.08 |
| p=0.27 | p=0.82 | p=0.36 | p=0.02 | p=0.45 | |
| Diabetes | 1.29 (0.12 – 13.65) | 0.08 (0.01 – 0.95) | 5.22 (1.12 – 24.29) | 14.80 (1.92 – 113.91) | 0.54 (0.06 – 5.14) |
| Hypertension | 1.29 (0.40 – 4.16) | 1.00 (0.47 – 2.14) | 0.46 (0.20 – 1.05) | 1.44 (0.42 – 4.97) | 0.70 (0.31 – 1.57) |
| HRT | 1.42 (0.30 – 6.67) | 2.40 (0.87 – 6.64) | 0.83 (0.31 – 2.21) | 0.45 (0.06 – 3.58) | 0.54 (0.15 – 1.90) |
| ER status | |||||
| Positive | Referent | Referent | Referent | ||
| Negative | 1.23 (0.47 – 3.23) | 13.65 (8.32 – 22.39) | 1.18 (0.66 – 2.10) | ||
| Unknown | Dropped | 2.53 (0.45 – 14.30) | 0.65 (0.07 – 5.75) | ||
| Grade | |||||
| Grade 1 | Referent | Referent | Dropped | Referent | |
| Grade 2 | 1.09 (0.33 – 3.55) | 5.06 (1.48 – 17.31) | Referent | 6.43 (1.93 – 21.49) | |
| Grade 3 | 1.50 (0.41 – 5.59) | 51.71 (15.19 – 176.02) | 3.64 (1.67 – 7.95) | 8.43 (2.38 – 29.84) | |
| Unknown | Dropped | 13.66 (3.56 – 52.41) | 0.45 (0.05 – 3.84) | 0.53 (0.05 – 5.45) | |
HRT, hormone replacement therapy
Discussion
In summary, in this sample of 1,312 patients, severe obesity was associated with a higher likelihood of angiolymphatic invasion after controlling for stage, grade, menopausal status, diabetes and hypertension, and use of HRT at the time of diagnosis. In pre- and perimenopausal women, diabetes was also independently associated with ER-negative status.23 Triple-negative tumors were less common among the severely obese women in our sample.
Our results are consistent with those of other investigators demonstrating higher rates of angiolymphatic invasion in the tumors of heavy women.29,30 In contrast to other studies demonstrating higher rates of triple-negative breast cancer,19–21 the heavy patients in our sample were less likely to have triple-negative tumors. Obese women in our sample, as in others,32 are more likely to have diabetes. The association we identified between diabetes and triple-negative breast cancer raises the possibility that it is not obesity but rather diabetes that contributes to higher rates of triple-negative breast cancer in obese women.
Our sample is limited by low numbers of minority women and the lack of data on socioeconomic status (SES). Black race33 and lower SES34 have all been associated with ER-negative disease. Furthermore, black race22,35,36 and Hispanic ethnicity35 have been associated with a higher likelihood of triple-negative breast cancer regardless of obesity status. It remains unknown whether there is a relationship between race, ethnicity, and SES and angiolymphatic invasion. Investigations of these relationships should include consideration of comorbid diabetes. In addition, our study is limited by the absence of information on exercise and dietary patterns and by the absence of information on duration of obesity and history of obesity among the non-obese.
Both estrogen-dependent and estrogen-independent mechanisms have been proposed as mechanisms for the association between obesity and poorer outcome.37 Higher body mass index is associated with higher levels of bioavailable estradiol, which in turn facilitates tumor growth.38,39 Although not a finding in our study, obesity has been associated with a higher likelihood of ER-negative disease by some authors.10 In addition, higher fasting insulin levels among obese people may lead to higher proliferative rates due to the mitogenic effects of insulin.40 Finally, adipocytokines (also referred to as adipokines), such as leptin, tumor necrosis factor-alpha and interleukin-6, are increased in obesity and are associated with increased cell proliferation and angiogenesis in animal models28,41,42 and in cell lines.43 The independent association of severe obesity with the presence of angiolymphatic invasion in our sample supports the relationship between adipocytokines and angiolymphatic invasion in primary breast cancer.
Reductions in adiposity, whether achieved through diet,44 exercise,45 or gastric reduction surgery,46,47 have been shown to reduce leptin and other adipocytokines. Such interventions may reduce the risk of breast cancer overall and, specifically, breast cancer exhibiting angiolymphatic invasion. Although a direct relationship between these interventions and rates of angiolymphatic invasion has not been identified, it is possible that the reduction in cancer mortality among obese patients having bariatric surgery48,49 can be attributed in part to reductions in adipocytokines.
In conclusion, this study has identified the independent association between severe obesity and the presence of angiolymphatic invasion. These findings may help explain poorer outcomes among obese women with breast cancer. Furthermore, diabetes was identified as being independently associated with triple-negative breast cancer. Further study of the relationships between tumor biology, obesity, diabetes, race, ethnicity, and SES are warranted.
Synopsis.
This study demonstrates that severe obesity is independently associated with the presence of angiolymphatic invasion after controlling for other clinical and pathologic factors.
Acknowledgments
Financial support: Supported in part by the University of Michigan Medical School 2008 Student Biomedical Research Program (EFG) and by NIH/NCI R01 CA922444-01 (JJG).
Contributor Information
Erin F. Gillespie, University of Michigan Medical School
Melony E. Sorbero, Health Policy Researcher, RAND Corporation.
David A. Hanauer, Department of Pediatrics, Bioinformatics Core, University of Michigan Comprehensive Cancer Center.
Michael S. Sabel, Department of General Surgery, Division of Surgical Oncology, University of Michigan Comprehensive Cancer Center.
Emily J. Herrmann, University of Michigan
Laura J. Weiser, University of Michigan
Christina H. Jagielski, Department of Internal Medicine, Division of Hematology/Oncology, University of Michigan Medical School.
Jennifer J. Griggs, Associate Professor, Department of Internal Medicine, Division of Hematology/Oncology, University of Michigan Comprehensive Cancer Center.
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