For the more than 2 million female breast cancer survivors in the United States,1 it is important to identify modifiable factors that may improve survival. Of particular interest is the effect on survival of changes in body size after diagnosis because weight gain among women with breast cancer is often observed in response to treatment with chemotherapy, as well as among women of younger age, women with lower prediagnosis body size, women who are premenopausal, and women who present with later disease stage at diagnosis.2 – 7 Higher levels of adipose tissue are associated with greater circulating levels of hormones such as estrogen, insulin, and related growth factors, which increase proliferation of mammary cells and are associated with breast carcinogenesis.8 – 10
Body size at or before diagnosis4 has been related to survival, but whether weight change after diagnosis affects survival has been studied less frequently and with inconsistent results.4,11 – 14 Furthermore, most previous studies have not used longitudinal measures of postdiagnosis weight change and, thus, have been unable to examine whether the effect may vary over time. The effect of changes in body size around the time of diagnosis is a critically underaddressed issue in survivorship research. Of the few studies that have addressed this issue, most limit their approach in several key ways. First, most previous investigations have assessed weight change only once after diagnosis; multiple measurements after the times of diagnosis would help to elucidate the critical window of exposure in which weight change might affect survival. Second, the study populations in previous analyses have been limited to survivors 2 or more years after diagnosis (often much more); this approach creates cohorts biased in their tendency to survive, which again fails to account for the entire survivorship experience.
A recent report from our group showed that weight gain after diagnosis was associated with greater all-cause and breast cancer–specific mortality,15 among a cohort of women interviewed shortly after diagnosis and followed for approximately 9 years. The primary purpose of that manuscript was to describe an analytic method developed to account for missing covariate data in survival analysis. That report did not address several important issues regarding how the effect of weight change on survival may vary over time or how the effect may be confounded by, or vary by, prediagnosis body size characteristics. Here, we apply that method to examine the effect of postdiagnosis weight change over time on survival after breast cancer diagnosis in a large, population-based cohort of women diagnosed with breast cancer in 1996–1997 on Long Island, NY.
We used data from the Long Island Breast Cancer Study Project, which was initiated as a population-based case-control study16 and continued as a follow-up of the cohort of case women. The study was approved by the institutional review board of participating institutions.
Cases were English-speaking adult women with a first primary in situ or invasive breast cancer diagnosed between 1 August 1996 and 31 July 1997, from Nassau and Suffolk counties in New York. Potentially eligible subjects were identified through participating hospitals, and their physicians were contacted to confirm the diagnosis and obtain permission to contact the patients for participation in the study. A total of 1508 eligible cases (82%) signed informed consent in person and completed the baseline questionnaires an average of 3 months after diagnosis.16
At baseline, 94 case women declined to be contacted at a later date for participation in the follow-up. For the remaining cases, they or their proxy were contacted approximately 5 years after diagnosis of the first primary breast cancer. Of the 1414 women who initially agreed to participate, 60 subsequently declined when contacted by mail, 65 declined when contacted by telephone, 18 declined due to illness, 22 were unable to complete the interview, 55 were lost to follow-up, and 96 were deceased with no identifiable proxy to complete the interview. Of the remaining 1098 subjects for whom informed consent was obtained by telephone, 65 were omitted because they provided information only about their first course of treatment for their first primary breast cancer. Ultimately, 1033 case subjects completed the follow-up interview,17 of which about 8% were with the proxy. The follow-up questionnaire included ascertainment of information similar to that gathered in the baseline questionnaire,16 but relevant to the time period since diagnosis.
Date and cause of death were determined through the National Death Index (NDI),18 a centralized database of death records maintained by the National Center for Health Statistics, which is considered the standard source of mortality data for epidemiologic research.19 For deceased women, we constructed 2 indicators: (1) breast cancer–specific mortality (any breast cancer–related death, International Classification of Disease code 174.9 or C-50.9), and (2) all-cause mortality (death from any cause). Among the 1508 cases from the parent study, there were 308 deaths as of 31 December 2005, with just over half (n = 164) attributed to breast cancer.
Body Size Assessment
The baseline questionnaire16 assessed self-reported height in inches and body weight in pounds at 20 years of age and at 1 year before the date of diagnosis. It also included assessments of weight in pounds according to decade of life from 20 years through 70 years of age. The follow-up questionnaire included assessments of self-reported height and body weight in pounds at diagnosis, 1 year after diagnosis, and at the time of the follow-up interview (or 1 year before death for deceased subjects for whom the questionnaire was completed by proxy). This yielded 3 assessments of body size at and after diagnosis.
Percent change in body weight was calculated from the year before diagnosis to diagnosis, 1 year after diagnosis, and the time of follow-up interview as
We categorized weight change as >5% loss, maintained within (plus or minus) 5%, 5%–10% gain, and greater than 10% weight gain. These categories were selected for comparison with other reports,11 and they correspond to weight management recommendations aimed at cancer patients.20 To avoid small counts within strata when assessing effect modification, we combined the upper 2 categories of weight gain. Other body size variables included body mass index (BMI) 1 year before diagnosis (<25 kg/m2, 25–30 kg/m2, and ≥30 kg/m2) and adult weight change, from 20 years of age to 1 year before diagnosis (>3 kg loss, maintenance within 3 kg, and >3 kg gain). These categorizations reflect those used in previous analyses of the Long Island Breast Cancer Project.21
Structured questionnaires were administered by trained interviewers at baseline16 and at follow-up. Information collected included sociodemographic characteristics, select clinical characteristics (including first course of treatment for the first primary breast cancer), and known and suspected risk and prognostic factors for breast cancer.
For case women who signed a medical record release form at baseline (97.7%), tumor stage and estrogen and progesterone receptor (ER/PR) status of the first primary breast cancer were ascertained from the medical records. At the follow-up, signed medical record release forms were obtained again, and medical records were abstracted for 598 women to obtain the details regarding complete course of treatment for the first primary breast cancer diagnosis. These data were then compared with the self-reported information obtained during the telephone follow-up interview. Kappa coefficients comparing self-report and medical records were high for all 3 treatment modalities examined: radiation therapy (κ = 0.97), chemotherapy (κ = 0.96), and hormone therapy (κ = 0.92)21; thus, the self-reported data were included in these analyses. Data on tumor size were obtained from the New York State Cancer Registry.
Nonresponse to specific questions among subjects alive at each time, coupled with nonparticipation in the follow-up interview, yielded missing data on weight (48%, 49%, and 34% at time of diagnosis, 1-year after diagnosis, and final follow-up, respectively). Given such substantial missing data, and the sensitivity of assessing body size, there was concern that body size data may be not missing at random, a condition that arises when the probability that data are missing is dependent on the unobserved values.22,23 In this study, the potential issue was that nonresponse to the follow-up questionnaire may be more likely among heavier women, who may be more psychologically sensitive to questions regarding bodyweight or who may be in poor health caused by being overweight. To address this issue, we used a selection model for proportional hazards regression with nonignorably missing time-varying covariates.15 The selection model describes the joint distribution of the probability that covariate data is missing (indicated by the vector of binary variables R), the outcome (failure time, denoted by T) and the covariates with missing data (denoted by the vector Z), some of which may vary over time, with fully observed covariates (denoted by X). This joint distribution is expressed as a sequence of conditional distributions:
which is used to derive the likelihood.
The probability that data were missing for weight change at each observation, p(R | T, Z, X), was modeled as a logistic regression with age, weight change at the corresponding observation, and missingness indicators for previous observations as predictors. Note that the inclusion of the value of weight change in this model accounts for the fact that data are potentially not missing at random.
For the distribution of the outcome, p(T | Z, X), a proportional hazards regression with time-varying covariates was specified to estimate the effect of postdiagnosis weight change on time to death. Values for postdiagnosis weight change between assessed time points (eg, years 2, 3, and 4) were approximated by linear interpolation.15 This model included postdiagnosis change in body size and was adjusted for age (continuous), chemotherapy (yes, no), tumor size (≥2 cm vs. <2cm), estrogen-receptor status (ER status; yes, no), and progesterone-receptor status (PR status; yes, no)—all of which are confounders that were identified through use of a directed acyclic graph. We present models with and without adjusting for prediagnosis body size, BMI 1 year before diagnosis (<25 kg/m2 [referent category], ≥30 kg/m2), and adult weight change from 20 years of age (>3 kg loss, maintained within 3 kg [referent category], >3 kg gain).
The distribution of change in body size at each time, p(Z | X), was modeled as a linear regression dependent on previous change in body size variables, as well as age, chemotherapy, menopausal status, and BMI before diagnosis variables, established in the literature to be consistently associated with postdiagnosis weight change.2 – 7 In addition to weight change, other covariates with relatively high levels of missing data included chemotherapy, tumor size, ER status, and PR status, with 32.2%, 31.6%, 34.0%, and 34.3% of data missing, respectively. Chemotherapy and tumor size were modeled as logistic regressions against age, income (<$20,000; $20,000–49,999; $50,000–89,999, and ≥$90,000), and education (high school or less, some college, college graduate, and postcollege education), whereas hormone receptor status indicators were modeled as logistic regressions as a function of age. Treatment and tumor characteristics were unlikely to be nonignorably missing and, therefore, did not require specification of models for their missing-data mechanisms.
To assess effect modification, the product of postdiagnosis weight change and menopausal status (premenopausal, postmenopausal), time (<2 years, ≥2 years), BMI 1 year before diagnosis (categorized as <25 kg/m2 and ≥25 kg/m2), and adult weight gain from 20 years of age to 1 year before diagnosis (any loss or maintain within 3 kg, gain ≥3 kg) were included. The time interaction of 2 years was chosen because recent studies of postdiagnosis weight change assessed women at approximately this time11,12 and to distinguish the possible effects of weight gain shortly after diagnosis.
The ancillary models (p(R | T, Z, X) and p(Z | X)) are not of inferential interest and are needed only to provide unbiased estimates of the survival model. Subjects with minor amounts of missing data (menopausal status, 2% missing; prediagnosis BMI, 1% missing; adult weight change, 1.2% missing; education, <1% missing; and income, <1% missing) were excluded from the analysis, as these data were unlikely to influence our results. This analysis ultimately included 1436 women, 292 of whom died during our follow-up, with 156 of these deaths attributed to breast cancer.
We used a fully Bayesian approach to parameter estimation using the Gibbs sampler in WinBUGS 1.424 to sample from the joint posterior distribution of the parameters. We specified vague prior distributions: normal with mean 0 and variance 106 for regression coefficients, gamma with shape and inverse scale parameters of 0.001 for baseline hazards and variance parameters to generate posterior estimates that are similar to those from frequentist analysis. The sampler was run for 200,000 iterations, discarding the first 100,000 as a burn-in sample, retaining every fifth iteration to reduce serial correlation. Posterior hazard ratios (HR) and 95% posterior credible intervals were calculated by taking the antilogarithm of the mean of the samples for the beta coefficients (log-hazard ratios) from the proportional hazards model and the 2.5th and 97.5th percentiles of these samples, respectively.
Median follow-up time was 8.8 years after diagnosis (range, 0.2–9.4 years). At the time of diagnosis, the age range of the women was between 25 and 98 years, with an average of 59 years, and most were postmenopausal (Table 1). Fewer than half of the women received chemotherapy treatment, and most tumors were hormone-receptor positive. Fewer than 20% of the tumors were 2 cm in size or larger. Among women with complete data on at least 1 follow-up measure of body size, 55% maintained their prediagnosis body size.
Mortality was increased for women who either lost or gained weight after diagnosis (Table 2). The association of moderate weight gain with mortality (all-cause posterior HR = 1.09 [95% credible interval = 0.51–2.18]) was somewhat greater after adjustment for prediagnostic BMI and adult weight gain, and similar to the minimally adjusted model we reported previously.15 These additional adjustments did not meaningfully change the observation, from our previous model, that mortality risk was more than doubled for large postdiagnosis weight gain (all-cause posterior HR = 2.67 [1.37–5.05]; breast cancer–specific posterior HR = 2.84 [1.15–6.65]; Table 2). Among women who lost weight after diagnosis, the effect on survival was adverse; the estimates for all-cause (5.29 [3.48–8.09]) and breast cancer–specific mortality (7.09 [3.93–13.4]) were similarly pronounced, even after adjusting for weight changes throughout adulthood but before diagnosis.
As shown in Table 3, the association between weight gain and survival appeared somewhat stronger among women who were premenopausal before diagnosis (all-cause posterior HR = 2.29 [0.84–6.73]) compared with postmenopausal women (1.51 [0.77–2.87]). The deleterious effects of postdiagnosis weight gain appear stronger when weight is gained shortly after diagnosis (within the first 2 years after diagnosis, all-cause posterior HR = 5.87 [0.89–47.8] and breast cancer–specific HR = 3.75 [0.56–31.2]), although the wide credible intervals indicate imprecise estimates resulting from the low number of deaths (52 all cause, 36 breast cancer specific) within the first 2 years. Weight gain after 2 years showed similar effects for all-cause and breast cancer–specific mortality, with moderate increases in risk of death, compared with women who maintained their weight. The effect of weight gain is greater among women who were overweight and obese (BMI ≥25 kg/m2) before diagnosis than among women of ideal weight (BMI <25 kg/m2) before diagnosis, with the difference being greatest for breast-cancer–related deaths (BMI <25 kg/m2, posterior HR = 1.08 [0.34–3.21] and BMI ≥25 kg/m2, 2.76 [0.94–8.47]). The effect of weight gain on all-cause mortality may be limited to those women who gained weight before diagnosis (<3 kg gain before diagnosis, posterior HR = 1.07 [0.30–3.37] and ≥3 kg gain before diagnosis, 1.80 [0.99–3.26]). Due to the small number of breast-cancer–related deaths among women without prediagnosis weight gain (n = 21), we were unable to calculate stable estimates of risk of breast-cancer–related mortality associated with postdiagnosis weight change in this subgroup. However, on limiting the analysis to women who gained at least 3 kg before diagnosis, we observed a strong effect of postdiagnosis weight gain (HR = 2.24 [0.97–5.26]), which was similar in magnitude to that observed for all-cause mortality.
Previously reported findings on weight change after diagnosis in this cohort of 1436 women diagnosed with in situ or invasive breast cancer focused on the methodologic approach for considering missing covariate data in survival analysis.15 That previous analysis did not include adjustments for prediagnosis levels of BMI or adult weight change, nor did it consider effect measure modification, including how the effect may vary over time. In the analysis presented here, even after adjusting for prediagnosis body size, moderate increases in risk of all-cause mortality were still observed among women who gained between 5% and 10% of their prediagnosis weight at any time after diagnosis. Large increases in mortality risk were found among women who gained more than 10% of their prediagnosis weight after diagnosis. When prediagnosis anthropometric measures of adiposity were included in the models, the effects were essentially unchanged. The effect of weight gain on mortality appeared stronger among premenopausal women compared with postmenopausal women and for women within the first 2 years after diagnosis compared with after 2 years, although analytic precision was limited to draw firm conclusions regarding these differences. The effect of postdiagnosis weight gain on breast-cancer–specific mortality was also stronger among women who had gained weight as an adult before diagnosis.
Fat mass may promote the development of postmenopausal breast cancer because visceral adipose tissue is metabolically active25 and affects a number of pathways that are involved in carcinogenesis. Visceral adipose tissue is associated with increased estradiol and decreased sex hormone–binding globulin (SHBG),26 as well as increased insulin and insulin-like growth factors,10,27 which can promote a hormonal environment that encourages proliferation of both normal and cancerous mammary cells.8 – 10 Weight gain after adolescence is associated with accumulation of visceral adipose tissue; therefore, changes in body size may be relevant.28 Similar biologic effects of increasing levels of adipose tissue on prognosis are of particular concern, as weight gain from approximately 1 kg to more than 10 kg within the first 2 years after diagnosis of breast cancer is well documented.4
The relationship between breast cancer survival and postdiagnosis weight change is far less studied than the relationship with prediagnostic body size.11 – 14,29 – 33 Although the methodology varied considerably among previous studies, findings in general for postdiagnostic weight gain have been null when modest increases in weight have been considered, with mean weight gain of less than 6 kg in the upper category.31 – 33 However, greater increases in mortality have been noted with larger changes in weight or BMI, particularly in subgroups,12,29,30 with mean weight change among subjects in the upper category typically more than 10 kg. Recent findings for postdiagnostic weight gain remain inconsistent; Caan and colleagues11 failed to find an association between postdiagnosis weight change and survival; however, Nichols et al13 found results more similar to those reported here. Most recently, in a Chinese cohort, Chen et al14 reported that weight gain of 5 kg or more during the 18 months after diagnosis was associated with poor survival, and the effect was slightly stronger for breast cancer–specific mortality.
Differences in findings related to postdiagnostic weight gain across studies are therefore likely due to variations across studies in the study cohort (years since the first primary diagnosis, ethnicity, age/menopausal status of the patients, sample size, and length of follow-up) and in the exposure measure (number of weight assessments, and the average weight gained postdiagnosis). For example, studies with null findings have been limited by small sample sizes31 – 33 and short follow-up.31,33 Timing of the postdiagnostic weight change measures is also likely to contribute to varied findings. Unlike previous work, our study was able to use multiple anthropometric measures from date of diagnosis, allowing us to estimate the effect of this time-varying exposure, as well as to estimate differences in effect across relevant time periods. Our findings of a possible stronger association of postdiagnosis weight gain among premenopausal women are supported by some other studies12,29 but not all.13,14 The very different study populations in the latter reports (a Chinese cohort in one,14 only nonmetastatic cases interviewed 1–2 years after diagnosis in the other13) could explain this discrepancy. We found the effect of weight gain was more pronounced closer to diagnosis, which was not assessed in other studies that instead used body size measures at least 2 years after diagnosis.11,12 The only previous study that did assess weight change near and after diagnosis14 analyzed weight change only as a series of fixed time exposures, which fails to account for the longitudinal aspect of this variable.
Our findings of greater mortality associated with postdiagnostic weight loss are consistent with recent reports,11,13,14 which all report hazard ratios more than 2 in the greatest category of weight loss for death from any cause. Data from the Nurses' Health Study also suggested a greater risk of death among those who lost weight, although this finding was not statistically significant.12 It is unclear if this observed association between weight loss and survival is due to a distinct effect of the weight loss or to the fact that those who are near death are likely to be losing weight. This issue could possibly be clarified by studies with larger sample size that assessed intent of weight loss. Recent recommendations regarding weight loss for breast cancer patients34 have been made based on the observation that greater BMI at diagnosis is associated with poor survival. However, these recommendations may be premature, given that they are not consistent with the currently available epidemiologic evidence, including the study reported here.
Obesity at diagnosis and adult weight gain before diagnosis are established indicators of poorer prognosis,4,34 – 36 although most of the studies conducted to date have not accounted for postdiagnosis changes in body size. Similarly, a recent report that used the Long Island breast cancer cohort observed hazard ratios ranging from 1.63 to 2.85 for women who were obese before diagnosis.21 Additionally, our observation of a more pronounced adverse effect for postdiagnosis weight gain among women who also gained weight as an adult before diagnosis underscores the importance of avoiding increases in adiposity at any point in a woman's life trajectory.
Strengths of this study include its population-based study design and relatively large sample size. Also, an innovative analytic approach to the treatment of missing exposure and confounder data was used. A commonly used alternative approach is the complete case analysis, which is automatically carried out by most software packages; however, it usually reduces statistical efficiency and can yield biased effect estimates in all but the most rigid conditions. Ad hoc adjustment for missing data is still common in epidemiology, such as some variant of the “missing indicator” method or improper imputation, which can perform even worse than complete case analysis.22 Formal treatment of missing data is crucial to accurate inference, and the selection model approach used here can account for potentially nonignorably missing covariates, which is a situation where bias and statistical efficiency are of greatest concern.23 Although the methodology used here is theoretically sound, it is important to keep in mind that even the most rigorous statistical model is no substitute for having the data that were unobserved. Missing-data models rely on untestable assumptions and can be quite sensitive to changes in specification.37
An important strength of this analysis is that data from multiple assessments of change in body size over the follow-up experience were used, starting at and near diagnosis, which allowed for determination of differential effects based on timing of weight change—an assessment that until now has not been addressed. A limitation of this analysis is the use of self-reported body size measures, which leaves open the potential for measurement error. However, self-reported anthropometric measures have been shown to be highly correlated with measurements taken in a clinic setting.38 Data from the NHANES III study showed that self-reported and measured weight are highly correlated and that older women, who make up most of this cohort, tend to report their weight accurately.39 Also, self- and interviewer-obtained measurements have shown nearly identical associations in a recent analysis of prediagnosis BMI and survival after breast cancer diagnosis in a similar cohort.40 The use of proxy interviews could also be a source of bias, although these accounted for only a small portion of our study sample (<8%), and a recent detailed report comparing the use of proxy and case assessments illustrated that proxy assessments of anthropometric measures yielded associations that were nearly identical to those completed by the case subject.41 To address this concern, we conducted a sensitivity analysis omitting those subjects with data from proxy interviews with similar results (data not shown).
The results from our stratified models should be interpreted as exploratory given that statistical precision was reduced for subgroup analysis. The categorization of prediagnosis BMI and prediagnosis weight gain yielded a different percentage of cases in each subgroup. Notably, 80% of the cohort appeared in the upper category of prediagnosis weight gain, which may have contributed to the differences noted, as BMI and weight gain are highly correlated.
In summary, these findings suggest that weight maintenance after breast cancer diagnosis should be encouraged, especially among women who have gained weight as an adult before diagnosis. The time period immediately after diagnosis may be especially relevant for weight maintenance, when treatment-related weight gain is common.
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