Abstract
Background
Weight change after a breast cancer diagnosis has been linked to lower survival. To further understand effects of post-diagnostic weight variation on survival, we examined the relationship by comorbid status and initial BMI.
Methods
The current analysis included 12,915 breast cancer patients diagnosed between 1990 and 2006 with Stage I–III tumors from four prospective cohorts in the US and China Hazard ratios (HR) and 95% confidence intervals (CI) representing the associations of five weight change categories (within <5% [reference]; 5–<10% and ≥10% loss and gain) with mortality were estimated using Cox proportional hazards models.
Results
Mean weight change was 1.6 kg. 14.7% of women lost and 34.7% gained weight. Weight stability in the early years post-diagnosis was associated with the lowest overall mortality risk. Weight loss ≥10% was related to a 40% increased risk of death (HR=1.41; 95% CI: 1.14, 1.75) in the US and over three times the risk of death (HR=3.25; 95% CI: 2.24, 4.73) in Shanghai. This association varied by pre-diagnosis BMI, and in the US lower survival were seen for women who lost weight and had comorbid conditions. Weight gain ≥10% was associated with a non-significant increased risk of death.
Conclusions
Prevention of excessive weight gain is a valid public health goal for breast cancer survivors. Although intentionality of weight loss could not be determined, women with comorbid conditions may be particularly at risk of weight loss and mortality.
Impact
Weight control strategies for breast cancer survivors should be personalized to the individual’s medical history.
Introduction
Current data indicate that a body mass index (BMI) of ≥30 kg/m2 or more at the time of breast cancer diagnosis is linked to poorer prognosis (1–7). However, effects of weight change on survival after a breast cancer diagnosis are less consistent (8–13) with some studies suggesting a U-shaped relationship with increasing risk for both weight gain and loss. Furthermore, among those studies that have found adverse relationships between weight gain and survival (8, 11, 13), it is unclear what degree of weight gain poses an increased risk. Additionally, none of the studies were able to distinguish whether the weight loss associated with worse survival was intentional or unintentional, and whether it was related to more advanced disease. Women most likely to lose weight after a breast cancer diagnosis may be those already at higher risk of poor outcomes: those who are obese (10, 13, 14) and/or have serious comorbid conditions (13, 15).
Using the resources of the After Breast Cancer Pooling Project that includes follow-up of over 18,000 breast cancer patients, we conducted a comprehensive evaluation of the association of weight changes with mortality. The purpose of our study was to examine the effects of post-diagnostic weight change on survival by comorbid status and initial weight status.
Methods
The After Breast Cancer Pooling Project
The After Breast Cancer Pooling Project (ABCPP) is an international collaboration of prospective studies of breast cancer survivors established to examine the role of physical activity, adiposity, dietary factors, supplement use, and quality of life in breast cancer prognosis (16). Briefly, the ABCPP includes data on 18,333 breast cancer survivors from four population-based prospective cohort studies recruited from multiple US sites and Shanghai, China. Three of the cohorts specifically recruited breast cancer patients: the Shanghai Breast Cancer Survival Study (SBCSS) (17), the Life after Cancer Epidemiology (LACE) Study (18); and the Women’s Healthy Eating and Living (WHEL) Study (19). The fourth cohort included breast cancer patients diagnosed in the Nurses’ Health Study (NHS), a large prospective cohort study of female nurses (20).
ABCPP participants were diagnosed with invasive breast cancer (AJCC Version 6 stages I-IV) between ages 20 to 83 years. Each cohort collected data on clinical factors (tumor characteristics, treatment status), reproductive factors, family history of breast cancer, quality of life, medical history including comorbidities, anthropometry, smoking history, alcohol intake, supplement use, physical activity, and diet. As part of the ABCPP, these data have been harmonized into a common dataset. Investigators of each individual cohort received IRB approval from their respective institution(s) to participate in this collaboration.
Ascertainment of Weight Change and Covariates
Weight change
Pre-diagnosis weight was collected from self-report from all studies and was defined as weight around one year before diagnosis. Post-diagnosis weight was assessed from self-report (SBCSS, LACE, NHS) and measurement (WHEL) between 18 to 48 months (mean 2.1 years) after diagnosis depending on the study. The rationale for this post-diagnosis assessment window was to allow sufficient time after completion of treatment for women to return to their usual weight Change in weight from pre- to post-diagnosis was calculated by subtracting the weight measure pre-diagnosis from the weight measure post-diagnosis; a positive and negative value were indicative of weight gain and loss, respectively.
Sociodemographic and lifestyle factors
Data assessed at baseline/first post-diagnosis survey included race/ethnicity (Non-Hispanic White, Non-Hispanic Black, Asian, Hispanic, Other), education (<college graduate vs. college graduate or higher), menopausal status at diagnosis (premenopausal, postmenopausal, unknown), and smoking history (never vs. ever). Pre-diagnosis BMI was categorized as normal weight (<20.0 kg/m2), normal weight (>20.0 –25.0 kg/m2) or overweight (>25 kg/m2). Exercise participation in metabolic equivalents (MET-hours/week) was determined from semi-quantitative questionnaires.
Clinical characteristics
Data included age at diagnosis (years), AJCC stage (I, II, III, IV), joint ER/PR status (ER+/PR+, ER+/PR−, ER−/PR+, ER−/PR−), surgery (none, lumpectomy, mastectomy, unknown), joint adjuvant therapy (none, chemotherapy only, radiation therapy only, both), hormonal therapy (no, yes), and any comorbidity (diabetes, hypertension, myocardial infarction [MI], stroke). However, WHEL did not collect information on MI and stroke. For all studies clinical data and tumor characteristics were collected by medical record review or by self-report and verified by medical record.
Ascertainment of Breast Cancer Outcomes
Outcomes were death due to breast cancer and all-cause mortality. All studies ascertained outcome events by self-report and regular linkage to electronic medical records and vital statistics registries. Reported events were verified by medical record review except for self-report of recurrences in the NHS. Cause of death was determined from death certificates and supplemented with medical records if necessary. Details of outcome ascertainment have been published (16).
Statistical Analysis
A five-level weight change variable of weight stable, weight loss (moderate, large), and weight gain (moderate, large) was created. Weight-loss/weight-gain was defined as 5–<10% change for moderate and ≥10% change for large relative to the initial pre-diagnosis weight. A weight change of <5% of the pre-diagnosis weight was considered weight stable (reference group). These categories were chosen because they are commonly used for weight loss recommendations to reduce risk of obesity, heart disease, diabetes, and cancer. NHS women were excluded from the analysis if they were diagnosed before 1990 to ensure comparability of treatment standards (n=2,965 [16%]). In addition, we eliminated women with missing weight measurements (n=2,408 [13%]) and those with had stage IV breast cancer (n=45 [<1%]), thus leaving 12,915 breast cancer survivors as the final analytic sample size.
Sociodemographic, lifestyle, and clinical characteristics of the overall pooled cohort and by US cohorts and SBCSS were summarized by frequency distributions for categorical variables and means with standard deviations (SD) for continuous variables. Chi-square tests were used to determine if covariates varied across weight gain categories.
The multivariable analysis involved three steps. First, delayed entry Cox proportional hazards regression models with time since diagnosis as the time scale were used to estimate study-specific adjusted hazard ratios (HRs) and 95% confidence intervals (CIs). The entry date was the date of the post-diagnosis weight measurement. The exit date was the date of death or date of last contact (i.e., date of last follow-up survey or date of last registry linkage, whichever was most recent). We assessed whether there was heterogeneity in the association between weight change and mortality and time at post-diagnosis weight measurement via inclusion of appropriate cross product (interaction) terms in the regression model, and found no evidence of effect heterogeneity. Similarly, we assessed whether there was heterogeneity in the weight change effect over time (since diagnosis: less than 5 years, and > 5 years), and found no appreciable variation in effect.
Second, a meta-analysis was conducted with study-specific HRs using inverse-variance weights in random-effects models (21). The Q test statistic was used to test for heterogeneity in risk estimates across studies (22). Third, if no evidence for heterogeneity was observed, then individual data from the four cohorts were combined, and a pooled analysis was conducted for the weight change-outcome associations of interest using delayed entry Cox proportional hazards regression models stratified by study. If evidence for heterogeneity was observed (p<0.05), then results from the random-effects meta-analysis and study-specific analyses were presented. There was heterogeneity when all sites were pooled, which was eliminated when Shanghai data were removed. Therefore, we present pooled data for US sites and Shanghai separately.
We examined the possibly non-linear relation between weight change and mortality with restricted cubic splines. We a priori specified 4 knots, noting that in practice, 3 to 5 knots should adequately represent most phenomena likely to be observed in medical studies. We used the software default for knot location (5th, 35th, 65th, and 95th percentiles of weight change distribution), noting that results are generally insensitive to knot locations unless they are placed in an extremely non-uniform way over the covariate space (23, 24).
Covariates were selected based on a priori assumptions, and models were fully adjusted for age at diagnosis, AJCC stage, race/ethnicity, menopausal status, hormone receptor status, number of positive nodes, treatment, pre-diagnosis BMI, and smoking history. We evaluated possible effect modification in the associations between weight change and mortality outcomes by hormone receptor status (ER+ vs. ER−), comorbidity status (at least one comorbidity vs. none), pre-diagnosis BMI (normal vs. overweight), and smoking (ever vs. never). Heterogeneity in association between individual levels of weight change and survival by potential effect modifiers (e.g. comorbidity yes/no) was assessed via inclusion of cross product terms in the Cox regression models (p for contrast).
Results
Over a mean (SD) follow-up time of 8.1 (4.0) years, 1,603 deaths were confirmed (1,040 deaths due to breast cancer). Mean time (range) to death was 6.7 (1.5–17.2) years from diagnosis.
US sites and Shanghai differed significantly on several baseline characteristics (Table 1). As expected, mean body size, as measured by pre-diagnosis weight (71.1 kg US vs. 60.0 kg Shanghai) and BMI (26.4 kg/m2 US vs. 23.8 kg/m2 Shanghai), were significantly different (p<0.0001). However, there was no significant difference in pre-diagnosis to post- diagnosis weight change (1.7 kg US vs. 1.5 kg Shanghai; p=0.93). In both the US and China weight gain was more common than weight loss (33.7% gain vs. 15.0% loss US and 36.6% gain vs. 13.9% loss Shanghai).
Table 1.
Characteristics of the analytic sample in the ABCPP
| Overall | U.S. Sites Pooled |
Shanghai | |||||
|---|---|---|---|---|---|---|---|
| n=12,915 | n=8,429 | n=4,486 | |||||
| Mean | (SD) | Mean | (SD) | Mean | (SD) | p-value* | |
| Age at diagnosis (years) | 57.0 | (10.5) | 58.9 | (10.3) | 53.5 | (10.0) | <.0001 |
| Height (m) | 1.6 | (0.1) | 1.6 | (0.1) | 1.59 | (0.1) | <.0001 |
| Pre-diagnosis BMI (kg/m2) | 25.5 | (5.0) | 26.4 | (5.4) | 23.8 | (3.5) | <.0001 |
| Pre-diagnosis weight (kg) | 67.2 | (14.3) | 71.1 | (15.1) | 60.0 | (9.0) | <.0001 |
| Post-diagnosis weight (kg) | 68.8 | (14.7) | 72.8 | (15.6) | 61.5 | (9.0) | <.0001 |
| Weight change pre to post (kg) | 1.6 | (6.3) | 1.7 | (6.9) | 1.5 | (4.7) | 0.93 |
| Time from diagnosis to post (years) | 2.1 | (0.7) | 2.4 | (0.6) | 1.6 | (0.3) | <.0001 |
| n | (%) | n | (%) | n | (%) | p-value* | |
| Weight change | |||||||
| Stable (within 5%) | 6,539 | (50.6) | 4,318 | (51.2) | 2,221 | (49.5) | <0.0001 |
| Moderate loss (5–10%) | 1,197 | (9.3) | 770 | (9.1) | 427 | (9.5) | |
| Large loss (≥10%) | 700 | (5.4) | 501 | (5.9) | 199 | (4.4) | |
| Moderate gain (5–10%) | 2,316 | (17.9) | 1,421 | (16.9) | 895 | (20.0) | |
| Large gain (≥10%) | 2,163 | (16.8) | 1,419 | (16.8) | 744 | (16.6) | |
| Race/ethnicity | |||||||
| Non-Hispanic White | 7,470 | (57.8) | 7,470 | (88.6) | 0 | (0.0) | <0.0001 |
| Non-Hispanic Black | 221 | (1.7) | 221 | (2.6) | 0 | (0.0) | |
| Asian | 4,701 | (36.4) | 215 | (2.6) | 4,486 | (100.0) | |
| Hispanic | 272 | (2.1) | 272 | (3.2) | 0 | (0.0) | |
| Other | 251 | (1.9) | 251 | (3.0) | 0 | (0.0) | |
| Education | |||||||
| Less than college graduate | 6,473 | (50.1) | 2,293 | (27.2) | 4,180 | (93.2) | <0.0001 |
| College graduate or higher | 6,441 | (49.9) | 6,135 | (72.8) | 306 | (6.8) | |
| Menopausal status at diagnosis | |||||||
| Premenopausal | 4,155 | (32.2) | 1,965 | (23.3) | 2,190 | (48.8) | <0.0001 |
| Postmenopausal | 8,342 | (64.6) | 6,046 | (71.7) | 2,296 | (51.2) | |
| Unknown | 418 | (3.2) | 418 | (5.0) | 0 | (0.0) | |
| Stage of breast cancer | |||||||
| I | 5,835 | (46.6) | 4,248 | (51.4) | 1,587 | (37.1) | <0.0001 |
| II | 5,118 | (40.8) | 3,062 | (37.1) | 2,056 | (48.1) | |
| III | 1,581 | (12.6) | 947 | (11.5) | 634 | (14.8) | |
| Hormone receptor status | |||||||
| ER+, PR+ | 7,489 | (60.8) | 5,193 | (65.7) | 2,296 | (52.1) | <0.0001 |
| ER−, PR+ | 589 | (4.8) | 255 | (3.2) | 334 | (7.6) | |
| ER+, PR− | 1,719 | (14.0) | 1,138 | (14.4) | 581 | (13.2) | |
| ER−, PR− | 2,518 | (20.4) | 1,320 | (16.7) | 1,198 | (27.2) | |
| Adjuvant Treatment | |||||||
| None | 1,924 | (15.1) | 1,602 | (19.4) | 322 | (7.2) | <0.0001 |
| Chemotherapy only | 4,400 | (34.5) | 1,671 | (20.2) | 2,729 | (60.8) | |
| Radiation only | 2,422 | (19.0) | 2,393 | (28.9) | 29 | (0.6) | |
| Both | 4,006 | (31.4) | 2,600 | (31.5) | 1,406 | (31.3) | |
| Hormonal therapy | |||||||
| No | 4,242 | (33.2) | 2,145 | (25.9) | 2,097 | (46.9) | <0.0001 |
| Yes | 8,526 | (66.8) | 6,148 | (74.1) | 2,378 | (53.1) | |
| Non-sedentary physical activity | |||||||
| None | 1,752 | (14.7) | 598 | (7.9) | 1,154 | (26.4) | <0.0001 |
| <4.6 hours | 2,268 | (19.0) | 1,881 | (24.9) | 387 | (8.9) | |
| 4.6 to <15.6 hours | 3,500 | (29.4) | 2,264 | (30.0) | 1,236 | (28.3) | |
| ≥15.6 hours | 4,395 | (36.9) | 2,804 | (37.2) | 1,591 | (36.4) | |
| Smoking history | |||||||
| Never | 8,430 | (65.4) | 4,063 | (48.4) | 4,367 | (97.3) | <0.0001 |
| Ever | 4,458 | (34.6) | 4,339 | (51.6) | 119 | (2.7) | |
| Diabetes | |||||||
| No | 11,530 | (93.0) | 7,376 | (93.2) | 4,154 | (92.6) | 0.20 |
| Yes | 867 | (7.0) | 536 | (6.8) | 331 | (7.4) | |
| Hypertension | |||||||
| No | 8,520 | (68.6) | 5,096 | (64.3) | 3,424 | (76.3) | <0.0001 |
| Yes | 3,894 | (31.4) | 2,833 | (35.7) | 1,061 | (23.7) | |
| Myocardial infarction | |||||||
| No | 9,410 | (95.2) | 5,162 | (95.7) | 4,248 | (94.7) | 0.03 |
| Yes | 471 | (4.8) | 234 | (4.3) | 237 | (5.3) | |
| Stroke | |||||||
| No | 9,773 | (97.8) | 5,398 | (98.1) | 4,375 | (97.5) | 0.07 |
| Yes | 216 | (2.2) | 106 | (1.9) | 110 | (2.5) | |
| Any comorbidity | |||||||
| No | 7,930 | (64.4) | 4,733 | (60.5) | 3,197 | (71.3) | <0.0001 |
| Yes | 4,376 | (35.6) | 3,088 | (39.5) | 1,288 | (28.7) | |
from Kruskal-Wallis test for continuous variables and Pearson chi-square test for categorical variables
At approximately 2 years post-diagnosis, 50% of US women remained weight-stable, regardless of their pre-diagnosis BMI (Table 2). In both populations, post-diagnosis weight gain was more common in normal weight women than in overweight women while conversely weight loss was more common in overweight women than normal weight women (Table 2). Also in both populations, postmenopausal women were more likely to lose weight and less likely to gain weight compared to premenopausal women. In US sites only, women with comorbidities were more likely to lose weight (19%) after a breast cancer diagnosis and less likely to gain weight (27%) compared to women without comorbidities (13% lose and 37% gain) and women diagnosed with later stage (Stage II or III) cancer were more likely to have large weight gains compared to women with Stage I cancer. In Shanghai, weight loss and weight gain were both more common among women with Stage III cancers than those with Stage I and II cancers.
Table 2.
Percent weight change by selected cohort characteristics in the ABCPP
| U.S. Sites (n=8,429) | Shanghai (n=4,486) | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Weight stable |
Moderate weight loss |
Large weight loss |
Moderate weight gain |
Large weight gain |
Weight stable |
Moderate weight loss |
Large weight loss |
Moderate weight gain |
Large weight gain |
|||
| within 5% |
>5–10% | ≥10% | >5–10% | ≥10% | within 5% |
>5–10% | ≥10% | >5–10% | ≥10% | |||
| n=4,318 | n=770 | n=501 | n=1,421 | n=1,419 | n=2,221 | n=427 | n=199 | n=895 | n=744 | |||
| Row % | Row % | Row % | Row % | Row % | p- value* |
Row % | Row % | Row % | Row % | Row % | p- value* |
|
| Pre-diagnosis BMI | ||||||||||||
| Underweight | 52.9 | 5.6 | 2.1 | 19.8 | 19.8 | <.0001 | 38.6 | 3.2 | 1.0 | 23.6 | 33.7 | <.0001 |
| Normal | 52.3 | 7.0 | 3.3 | 17.9 | 19.5 | 46.0 | 8.2 | 3.4 | 22.9 | 19.6 | ||
| Overweight | 51.1 | 10.3 | 6.5 | 16.3 | 15.8 | 59.4 | 13.8 | 6.5 | 14.4 | 5.9 | ||
| Obese | 48.9 | 12.5 | 11.4 | 14.8 | 12.4 | 59.3 | 15.7 | 12.3 | 9.8 | 3.0 | ||
| Smoking categories | ||||||||||||
| Never | 51.7 | 9.1 | 6.3 | 16.9 | 16.1 | 0.36 | 50.0 | 9.5 | 4.4 | 19.9 | 16.1 | <.0001 |
| Ever | 50.8 | 9.2 | 5.7 | 16.8 | 17.6 | 31.1 | 10.1 | 5.0 | 21.0 | 32.8 | ||
| Any comorbidity | ||||||||||||
| No | 50.3 | 7.7 | 5.3 | 17.5 | 19.3 | <.0001 | 47.2 | 7.4 | 3.1 | 22.4 | 19.8 | <.0001 |
| Yes | 53.9 | 11.6 | 7.4 | 15.7 | 11.3 | 55.1 | 14.7 | 7.8 | 13.8 | 8.5 | ||
| Education categories | ||||||||||||
| Less than college | 40.3 | 8.9 | 5.7 | 18.3 | 26.9 | <.0001 | 49.0 | 9.5 | 4.4 | 20.1 | 16.9 | 0.15 |
| College grad or higher | 55.3 | 9.2 | 6.1 | 16.3 | 13.1 | 55.9 | 9.5 | 4.6 | 17.3 | 12.8 | ||
| ER status | ||||||||||||
| Positive | 51.7 | 9.3 | 5.9 | 16.5 | 16.5 | 0.04 | 50.2 | 9.9 | 4.5 | 19.4 | 16.0 | 0.31 |
| Negative | 48.5 | 8.7 | 5.7 | 17.9 | 19.2 | 48.4 | 8.9 | 4.2 | 21.3 | 17.3 | ||
| Unknown/Missing | 54.3 | 7.8 | 7.3 | 18.3 | 12.4 | 43.8 | 9.4 | 4.7 | 15.6 | 26.6 | ||
| Menopausal status at diagnosis | ||||||||||||
| Premenopausal | 40.1 | 6.9 | 4.0 | 20.1 | 28.9 | <.0001 | 44.3 | 5.9 | 2.3 | 23.7 | 23.7 | <.0001 |
| Postmenopausal | 55.5 | 10.2 | 6.4 | 15.8 | 12.2 | 54.5 | 12.9 | 6.5 | 16.3 | 9.8 | ||
| Unknown | 41.9 | 4.6 | 8.9 | 17.5 | 27.3 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | ||
| Stage of breast cancer | ||||||||||||
| I | 54.4 | 9.5 | 6.0 | 15.8 | 14.3 | <.0001 | 52.0 | 9.2 | 4.2 | 19.8 | 14.8 | 0.006 |
| II | 47.4 | 9.0 | 6.1 | 18.2 | 19.4 | 50.2 | 9.4 | 4.1 | 19.9 | 16.4 | ||
| III | 47.2 | 7.9 | 5.6 | 17.7 | 21.5 | 42.6 | 10.9 | 5.4 | 20.4 | 20.8 | ||
from Pearson chi-square test
For US sites and Shanghai, both weight loss and weight gain were associated with an increased risk of overall mortality, suggesting a U shaped relationship (Figure 1; p for non-linearity <0.0001). In both countries, remaining weight stable was associated with the lowest risk. The risk for mortality increased gradually with increasing weight gain. In contrast, risk increased markedly as weight loss increased.
Figure 1.
HR and 95% CI for percent weight change and overall mortality for U.S. sites and Shanghai, China using restricted cubic splines with knots at −10.7, 0, 4.7, and 18.2 percent change for US sites and −9.4, 0, 5.3, and 16.4 percent change for Shanghai with 0 percent change as the reference in the ABCPP. Models adjusted for age at diagnosis, AJCC stage, race/ethnicity, menopausal status, hormone receptor status, nodal positivity, chemotherapy, radiation therapy, pre-diagnosis BMI, and smoking.
Weight Loss and Breast Cancer-Specific Mortality, Non-Breast Cancer Mortality, and Overall Mortality
Weight loss ≥10% (mean=11.64 kg) was related to overall mortality in the US sites (HR=1.41; 95% CI: 1.14, 1.75) and in Shanghai (HR=3.25; 95% CI: 2.24, 4.73) (Table 3). For Shanghai, since 86% of deaths were due to breast cancer, effects for overall mortality were similar to those with breast cancer-specific mortality (HR=3.60; 95% CI: 2.39, 5.42). For US sites, ≥10% weight loss was associated only with non-breast cancer mortality (HR=1.62; 95% CI: 1.21, 2.19) (data not shown), and not with breast cancer mortality (HR=1.13; 95% CI: 0.83, 1.56). We further stratified effects of weight loss on overall mortality by ER status, baseline comorbid status, smoking status, and pre-diagnosis BMI. No differences in effects were seen by ER status (data not shown). Women who ever smoked and had ≥10% weight loss had an increased risk of death (HR=1.58; 95% CI: 1.20, 2.09) while women who never smoked had no increased risk (Table 4). When women were stratified by pre-diagnosis BMI, moderate weight loss (5–10%, mean =4.9 kg) was associated with an increased risk for normal-weight women, but not overweight women, in both the US sites and Shanghai (p for contrast=0.05 and 0.14, respectively). Large weight loss was associated with increased risk in both populations, regardless of pre-diagnosis weight. In sensitivity analyses, removing the underweight women (BMI<20 kg/m2) and removing all deaths that occurred in the first year post measurement did not alter results (data not shown).
Table 3.
Delayed-entry Cox proportional hazard models* for weight change and breast cancer specific and overall mortality in the ABCPP
| U.S. Sites | Shanghai | |||||||
|---|---|---|---|---|---|---|---|---|
| Breast Cancer Specific Mortality | ||||||||
| n=8,354 (757 events) | n=4,441 (279 events) | |||||||
| # Events | HR | 95% CI | p for trend | # Events | HR | 95% CI | p for trend | |
| Weight Stable | ||||||||
| Within 5% | 365 | Reference | 111 | Reference | ||||
| Weight Loss | ||||||||
| Moderate 5–10% | 68 | 1.09 | 0.84,1.42 | 0.29 | 34 | 1.54 | 1.05, 2.28 | <.0001 |
| Large 10% | 44 | 1.13 | 0.83,1.56 | 31 | 3.60 | 2.39, 5.42 | ||
| Weight Gain | ||||||||
| Moderate 5–10% | 130 | 0.97 | 0.79,1.19 | 0.90 | 50 | 1.00 | 0.71, 1.41 | 0.28 |
| Large 10% | 150 | 1.03 | 0.84, 1.26 | 53 | 1.25 | 0.88, 1.77 | ||
| Overall Mortality | ||||||||
| n=8,354 (1,271 events) | n=4,441 (326 events) | |||||||
| # Events | HR | 95% CI | p for trend | # Events | HR | 95% CI | p for trend | |
| Weight Stable | ||||||||
| Within 5% | 614 | Reference | 139 | Reference | ||||
| Weight Loss | ||||||||
| Moderate 5–10% | 134 | 1.20 | 0.99, 1.45 | 0.0003 | 38 | 1.35 | 0.94, 1.94 | <.0001 |
| Large >=10% | 101 | 1.41 | 1.14, 1.75 | 37 | 3.25 | 2.24, 4.73 | ||
| Weight Gain | ||||||||
| Moderate 5–10% | 201 | 0.98 | 0.83, 1.15 | 0.38 | 55 | 0.93 | 0.68, 1.28 | 0.52 |
| Large 10% | 221 | 1.15 | 0.98, 1.35 | 57 | 1.16 | 0.84, 1.62 | ||
Models adjusted for age at diagnosis, race, menopausal status, stage, hormone receptor status, positive nodes, treatment (chemotherapy, radiation therapy, both), pre-diagnosis BMI, and smoking.
Table 4.
Delayed-entry Cox proportional hazard models* for overall mortality, stratified by comorbid status, pre-diagnosis BMI, and smoking history
| U.S. Sites | Shanghai | |||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Comorbidity | No Comorbidity | Comorbidity | No Comorbidity | |||||||||||
| n=3,031 (584 events) | n=4,715 (589 events) | p for contrast† |
n=1,275 (97 events) | n=3,165 (229 events) | p for contrast † |
|||||||||
| # Events | HR | 95% CI | # Events | HR | 95% CI | # Events | HR | 95% CI | # Events | HR | 95% CI | |||
| Weight Stable | ||||||||||||||
| Stable | 290 | Reference | 276 | Reference | 40 | Reference | 99 | Reference | ||||||
| Weight Loss | ||||||||||||||
| Loss 5–10% | 75 | 1.24 | 0.95, 1.60 | 55 | 1.29 | 0.96, 1.73 | 0.80 | 15 | 1.34 | 0.73, 2.44 | 23 | 1.43 | 0.90, 2.28 | 0.84 |
| Loss ≥10% | 66 | 1.70 | 1.29, 2.23 | 32 | 1.13 | 0.77, 1.65 | 0.05 | 20 | 3.68 | 2.09, 6.47 | 17 | 2.89 | 1.71, 4.89 | 0.36 |
| Weight Gain | ||||||||||||||
| Gain 5–10% | 89 | 0.98 | 0.77, 1.25 | 97 | 1.00 | 0.79, 1.26 | 0.68 | 13 | 1.22 | 0.64, 2.33 | 42 | 0.82 | 0.57, 1.19 | 0.26 |
| Gain ≥10% | 64 | 1.10 | 0.83, 1.45 | 129 | 1.17 | 0.94, 1.46 | 0.17 | 9 | 1.46 | 0.67, 3.18 | 48 | 1.05 | 0.73, 1.51 | 0.53 |
| Underweight/Normal | Overweight/Obese | Underweight/Normal | Overweight/Obese | |||||||||||
| n=3,962 (550 events) | n=4,392 (721 events) |
p for contrast† |
n=2,984 (199 events) | n=1,457 (127 events) |
p for contrast † |
|||||||||
| # Events | HR | 95% CI | # Events | HR | 95% CI | # Events | HR | 95% CI | #Events | HR | 95% CI | |||
| Weight Stable | ||||||||||||||
| Stable | 256 | Reference | 358 | Reference | 72 | Reference | 67 | Reference | ||||||
| Weight Loss | ||||||||||||||
| Loss 5–10% | 52 | 1.59 | 1.17, 2.16 | 82 | 1.05 | 0.82, 1.33 | 0.05 | 22 | 1.74 | 1.07, 2.83 | 16 | 0.99 | 0.57, 1.72 | 0.14 |
| Loss ≥10% | 27 | 1.74 | 1.16, 2.60 | 74 | 1.40 | 1.09, 1.81 | 0.36 | 16 | 4.08 | 2.35, 7.11 | 21 | 2.62 | 1.58, 4.36 | 0.38 |
| Weight Gain | ||||||||||||||
| Gain 5–10% | 97 | 1.06 | 0.84, 1.35 | 104 | 0.88 | 0.70, 1.09 | 0.21 | 40 | 0.99 | 0.67, 1.47 | 15 | 0.91 | 0.51, 1.62 | 0.71 |
| Gain ≥10% | 118 | 1.24 | 0.98, 1.56 | 103 | 1.04 | 0.83, 1.31 | 0.12 | 49 | 1.20 | 0.83, 1.75 | 8 | 1.42 | 0.67, 3.00 | 0.90 |
| Ever Smoked | Never Smoked | Ever Smoked | Never Smoked | |||||||||||
| n=4,298 (737 events) | n=4,029 (527 events) |
p for contrast† |
||||||||||||
| # Events | HR | 95% CI | # Events | HR | 95% CI | |||||||||
| Weight Stable | ||||||||||||||
| Stable | 351 | Reference | 260 | Reference | Not calculable – too few smokers in Shanghai population | |||||||||
| Weight Loss | ||||||||||||||
| Loss 5–10% | 76 | 1.20 | 0.94, 1.54 | 58 | 1.23 | 0.92, 1.65 | 0.89 | |||||||
| Loss ≥10% | 61 | 1.58 | 1.20, 2.09 | 40 | 1.27 | 0.91, 1.79 | 0.22 | |||||||
| Weight Gain | ||||||||||||||
| Gain 5–10% | 115 | 1.00 | 0.80, 1.23 | 85 | 0.98 | 0.76, 1.25 | 0.98 | |||||||
| Gain ≥10% | 134 | 1.20 | 0.97, 1.48 | 84 | 1.03 | 0.80, 1.33 | 0.56 | |||||||
Models adjusted for age at diagnosis, race, menopausal status, stage, hormone receptor status, positive nodes, treatment (chemotherapy, radiation therapy, both), pre-diagnosis BMI, and smoking. Pre-diagnosis BMI and smoking are not included as covariates in the respective stratified models.
p for contrast derived from inclusion of cross product terms in the Cox proportional hazard models
For the US sites, large weight loss was associated with an increased risk of overall mortality in women with existing comorbidities (HR=1.70; 95% CI: 1.29, 2.23) but not in women without comorbidities (HR=1.13; 95% CI: 0.77, 1.65). We performed sensitivity analyses excluding WHEL participants who did not have data on MI or stroke, which may have caused some misclassification on comorbid status. After exclusion of WHEL data, results were similar for women with comorbidities (HR=1.65; 95% CI: 1.24, 2.20) and without comorbidities (HR=1.55; 95% CI: 1.02, 2.34).
We further explored multivariable adjusted effects of large weight loss on overall mortality by dividing women into four groups based on comorbidity status (Yes/No) and initial BMI status (Normal/Overweight) and comparing them to women in the same comorbid/BMI category who remained weight stable for the US sites (Figure 2). Overweight women with comorbid conditions and normal weight women without comorbid conditions who lost weight were at increased risk of overall mortality. Normal weight women with comorbid conditions who had large weight loss were also at increased risk of poorer survival, but the risk was non-significant. The only group without increased risk was women who did not have a comorbid condition and were initially overweight. In these women, large weight loss was associated with a non-significant decreased risk of overall mortality (HR=0.78; 95% CI: 0.46, 1.30).
Figure 2.
Adjusted survival curves for comorbidity status and pre-diagnosis BMI category among U.S. women with >=10% weight loss in the ABCPP
Weight Gain and Breast Cancer Specific and Overall Mortality
In the US sites, weight gain ≥10% (mean=10.5 kg) was marginally related to overall mortality (HR=1.15; 95% CI: 0.98, 1.35) but not breast cancer-specific mortality (HR=1.03; 95% CI: 0.84, 1.26) (Table 3). A similar magnitude of risk was observed in Shanghai for overall mortality (HR=1.16; 95% CI: 0.84, 1.62) and breast cancer-specific mortality (HR=1.25; 95% CI: 0.88, 1.77) but neither were significant. When we further examined effects of weight gain on overall mortality by pre-diagnosis BMI, comorbid status, ER status and smoking status, there were no interactions with weight gain in either population (Table 4, ER results not shown). Women who gained ≥10% and were normal weight had a trend towards higher risk of overall mortality (HR=1.24; 95% CI: 0.98, 1.56) compared to their overweight counterparts (HR=1.04; 95% CI: 0.83, 1.31), but the difference between the groups was not significant (p for contrast=0.12). Categorizing women into four groups by both pre-diagnosis BMI and comorbid status did not change these findings; only normal weight women were at increased risk regardless of comorbid status (data not shown). In sensitivity analyses, we excluded underweight women (BMI<20 kg/m2), and results were unchanged (data not shown).
Discussion
This study of nearly 13,000 women with breast cancer demonstrated a U-shaped relationship between post-diagnosis weight change and all-cause mortality. It is the largest to date and the first among US and China breast cancer study populations to suggest that weight maintenance in the first few years after diagnosis is associated with the most favorable outcomes. The majority of previous studies reporting on weight loss and breast cancer outcomes have been cautious in their interpretation but have all suggested, as this report does, that weight loss is also associated with poorer breast cancer outcomes (10–13, 25). One study reports more than five times the risk of overall mortality and more than seven times the risk of breast cancer mortality for women who lose > 5% of their pre-diagnosis weight compared to women who remain relatively stable (within 5%) (25). This study is the first to further explore results by both comorbid status and initial weight, enabling better identification of women at highest risk of poor outcomes due to weight loss.
The association of weight gain with poorer breast cancer outcomes has been reported previously (8, 11–13). However, our results suggest that compared to women who remain weight stable, a woman must experience substantial weight gain before an increased risk of death is observed. Several mechanisms have been postulated through which weight gain may influence survival, including enhanced conversion in the adipose tissue of androgens to estrogens (26–28), as well as decreased levels of sex hormone-binding globulin and increased insulin and insulin-like growth factors and inflammatory factors (29).
Similar to our findings, several other studies have also found that normal-weight women are the most susceptible to weight gain after a breast cancer diagnosis (10, 14, 30, 31). We also found that normal-weight women are at highest risk of experiencing the negative effects of weight gain on overall mortality outcomes, as previously reported in an NHS analysis (8). Thus, the prevailing recommendation that women should not gain excessive amounts of weight post-diagnosis is supported by our data, and prevention of weight gain appears to be an evidence-based public health goal for breast cancer survivors.
In our study, the association of weight loss with mortality differed slightly by site; in the pooled US cohorts, the increased risk in overall mortality was seen in women with existing comorbid conditions, whereas in Shanghai, the increased risk in mortality was seen regardless of comorbid status. This may partially explain why in the US we only observed an increased risk in overall mortality and not breast cancer-specific mortality, suggesting that women who have comorbid conditions and lose weight are dying of causes most likely related to their comorbidity rather than their breast cancer. Our hypothesis is further supported by removing the WHEL women from the stratified comorbidity analyses due to having no information on MI or stroke. In this sensitivity analysis, no differences in the effects of weight loss by comorbid status were observed, thus suggesting that MI and stroke are key conditions for which negative effects of weight loss are seen. Since most women survive breast cancer, risk of death due to causes other than breast cancer is of important prognostic value.
One potential explanation for our observed risk of higher mortality among breast cancer survivors who lose weight is that, as a result of both the breast cancer and its associated treatments, some women develop cachexia or pre-cachexia, resulting in not only weight loss but substantial loss of lean body mass (LBM). Exaggerated losses of LBM in breast cancer survivors are hypothesized to be related to chronic inflammation, insulin resistance and decreases in physical activity (32).
Additionally, low levels of LBM in cancer patients have been associated with increased toxicity to anticancer therapy (33, 34) and higher occurrences of metabolic syndrome-related comorbid conditions (35, 36), with both mechanisms potentially leading to reduced rates of survival (37). Recent data suggest that LBM, similar to proposed effects of fat mass in breast cancer progression, may exert a powerful endocrine, immune and hormonal influence within the body (38). Of note, the association of weight loss with increased mortality has also been reported in several recent observational studies that have not been restricted to only breast cancer survivors (39–42).
Additional explanations have been hypothesized as to why weight loss among breast cancer survivors with comorbid conditions is associated with poorer outcomes. Women with existing comorbid conditions are known to receive less extensive breast cancer treatment (43), and since chemotherapy is associated with weight gain (44–47), the lack of weight gain or weight loss may be an indicator for treatment that is not the standard of care (48). Additionally, women with comorbid conditions at the time of breast cancer diagnosis are more likely to be subsequently hospitalized for chemotherapy toxicity, infection and fever, neutropenia, anemia, all of which increase the risk of weight loss (49) and decrease survival. Unfortunately, data were unavailable on treatment adherence/effectiveness or treatment toxicity to explore this further. Lastly, comorbidity itself among breast cancer survivors increases risk of mortality (50–52) and certain comorbid conditions such as COPD (53) and kidney failure (54) are known to be associated with weight loss.
While the weight loss observed in this study could be non-volitional and may be an early marker of cancer cachexia, comorbid overweight women appear to be at risk for weight loss. These results raise questions regarding the safety of intentional weight loss in the early period post diagnosis for breast cancer survivors presenting with a comorbid condition or for women who already have low levels of lean body mass. While weight loss strategies are typically recommended to women who are overweight and have comorbid conditions but do not have breast cancer (55, 56), the success or safety in women who concurrently have comorbid conditions and breast cancer has yet to be demonstrated. In fact, several researchers have now documented a puzzling phenomenon, termed the “obesity paradox,” in which overweight or even obese individuals with established diseases such as CVD, heart failure, and stroke have a better prognosis compared with normal weight or underweight subjects despite the associations between obesity and these health conditions (57, 58). In one recent study among patients with type 2 diabetes and cardiovascular comorbidity, not only did overweight and obese patients have a lower mortality compared to patients with normal weight, but weight loss and weight stability were associated with increased mortality and morbidity (59).
Our data indicate that large weight loss in women who are leaner is associated with worse survival, regardless of co-morbid status suggesting that overweight may confer some protection. Others have noted that being overweight may be associated with improved survival during recovery from adverse conditions (60, 61) and with improved prognosis for other adverse events (62, 63). In one large cohort of over 41,000 surgical ICU patients, being overweight or mildly obese was associated with decreased risk of 60-day in-hospital mortality (60). Such findings may be due to greater nutritional reserves playing a beneficial compensatory role in these patients or protection due to higher lean body mass associated with overweight (64). More studies are needed to understand the underlying biological mechanisms of weight loss on mortality.
Limitations of this study are that we only evaluated weight change at one time point post-diagnosis (on average two years post diagnosis). There is evidence that women who initially lose weight may regain their weight (65–68), such that the increased risk in mortality we observed with weight loss may in fact be related to a yo-yo pattern of initial loss and subsequent regain. Further research should examine prognostic effects of long-term weight patterns in breast cancer survivors. We also were unable to disentangle effects of non-volitional versus intentional weight loss; however, studies of intentional weight loss among breast cancer survivors are currently underway (69, 70), and results should be forthcoming to shed light on this question. Furthermore, because this analysis was a pooled analysis, we only had information on the most common comorbidities: hypertension diabetes, and CVD. Thus, our analyses on comorbid status are limited to these comorbidities. Lastly, there is a possibility that our results were biased by illness-induced weight loss prior to weight change measurement (“reverse causation”). However, in a sensitivity analysis, we removed all deaths that occurred in the first year post measurement, and results were essentially unchanged. A major strength of this pooled study is its size and inclusion of women from both US and China, which allowed us to further explore and understand effects of weight change by comorbid status and pre-diagnosis BMI across different treatment settings.
In summary, both weight gain and weight loss are associated with poorer overall survival in the US and China. Although risks varied slightly across countries and across specific weight and comorbid status categories, the overall results suggest that remaining weight stable, at least in the early years post-diagnosis, is associated with better overall survival. At present, the prevention of weight gain should be recommended to all women, regardless of initial body size, especially in light of the data that show leaner women are most likely to gain weight after a breast cancer diagnosis. Clinicians should be aware that some women with breast cancer may be at risk of weight loss, especially those with comorbid conditions, and that there is an increase in mortality associated with weight loss in these women. Large weight change, as with big shifts in other medical indicators, should be monitored closely. Similar to strategies for chemotherapy, weight control strategies for breast cancer survivors are not universal to all women and should be personalized to the individual’s prognostic profile and medical history.
Acknowledgments
Funding
This work was supported by the National Institutes of Health (3R01CA118229-03S1, R01 CA118229, R01 CA129059, P01 CA87969); Susan G. Komen Foundation (KG100988); and the Department of Defense (DAMD 17-02-1-0607).
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