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Changes in diet quality in a randomized weight loss trial in breast cancer survivors: the lifestyle, exercise, and nutrition (LEAN) study
research-article
Author(s):
Chelsea Anderson
1 ,
Maura Harrigan
1 ,
Stephanie M George
2 ,
Leah M Ferrucci
1
,
3 ,
Tara Sanft
3
,
4 ,
Melinda L Irwin
1
,
3 ,
Brenda Cartmel
1
,
3
,
*
Publication date (Electronic):
24 August 2016
Journal:
NPJ Breast Cancer
Publisher:
Nature Publishing Group
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Abstract
Obesity is associated with increased breast cancer recurrence and mortality. Though some post-diagnosis weight loss interventions have achieved weight loss outcomes, it is unclear whether they also improve diet quality. In the Lifestyle, Exercise, and Nutrition (LEAN) study, overweight or obese breast cancer survivors were randomized to either usual care group ( n=33) or the 6-month lifestyle intervention ( n=67). Dietary intake was assessed at baseline and 6 months using a validated food frequency questionnaire, and overall diet quality was calculated using the Healthy Eating Index (HEI)-2010 (range 0–100). Intervention effects on diet were evaluated with generalized linear models. Among the 81 participants (51 intervention, 30 usual care) with dietary data, the mean baseline HEI score was 70.5 (s.d.=8.8) and was improved at 6 months (intervention group=6.8 point increase vs usual care=3.1, P=0.09). Intervention group participants achieved greater reductions in percent of energy from total fat (−4.2% vs −1.2%; P=0.013) and saturated fat (−2.2% vs −1.1%; P=0.003), and greater increases in fiber (4.8 g per 1000 kcal vs 1.3 g per 1000 kcal; P=0.007) and fruit (0.5 servings vs 0.0 servings; P=0.006) intake. Intervention group participants who lost ⩾5% body weight ( n=27) demonstrated significantly greater improvements in HEI score (10.4 vs 2.8) than those who lost <5% ( n=23). The intervention increased fruit and fiber intake and decreased percent energy from fat, and those with greater weight loss achieved greater increases in overall diet quality. These findings support the ability of a weight loss intervention to improve diet among breast cancer survivors.
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Most cited references13
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Body mass index and survival in women with breast cancer—systematic literature review and meta-analysis of 82 follow-up studies
D S M Chan, A. R. Vieira, D. Aune … (2014)
introduction The number of female breast cancer survivors is growing because of longer survival as a consequence of advances in treatment and early diagnosis. There were ∼2.6 million female breast cancer survivors in US in 2008 [1], and in the UK, breast cancer accounted for ∼28% of the 2 million cancer survivors in 2008 [2]. Obesity is a pandemic health concern, with over 500 million adults worldwide estimated to be obese and 958 million were overweight in 2008 [3]. One of the established risk factors for breast cancer development in post-menopausal women is obesity [4], which has further been linked to breast cancer recurrence [5] and poorer survival in pre- and post-menopausal breast cancer [6, 7]. Preliminary findings from randomised, controlled trials suggest that lifestyle modifications improved biomarkers associated with breast cancer progression and overall survival [8]. The biological mechanisms underlying the association between obesity and breast cancer survival are not established, and could involve interacting mediators of hormones, adipocytokines, and inflammatory cytokines which link to cell survival or apoptosis, migration, and proliferation [9]. Higher level of oestradiol produced in postmenopausal women through aromatisation of androgens in the adipose tissues [10], and higher level of insulin [11], a condition common in obese women, are linked to poorer prognosis in breast cancer. A possible interaction between leptin and insulin [12], and obesity-related markers of inflammation [13] have also been linked to breast cancer outcomes. Non-biological mechanisms could include chemotherapy under-dosing in obese women, suboptimal treatment, and obesity-related complications [14]. Numerous studies have examined the relationship between obesity and breast cancer outcomes, and past reviews have concluded that obesity is linked to a lower survival; however, when investigated in a meta-analysis of published data, only the results of obese compared with non-obese or lighter women were summarised [6, 7, 15]. We carried out a systematic literature review and meta-analysis of published studies to explore the magnitude and the shape of the association between body fatness, as measured by body mass index (BMI), and the risk of total and cause-specific mortality, overall and in women with pre- and post-menopausal breast cancer. As body weight may change close to diagnosis and during primary treatment of breast cancer [16], we examined BMI in three periods: before diagnosis, <12 months after diagnosis, and ≥12 months after breast cancer diagnosis. materials and methods data sources and search We carried out a systematic literature search, limited to publications in English, for articles on BMI and survival in women with breast cancer in OVID MEDLINE and EMBASE from inception to 30 June 2013 using the search strategy implemented for the WCRF/AICR Continuous Update Project on breast cancer survival. The search strategy contained medical subject headings and text words that covered a broad range of factors on diet, physical activity, and anthropometry. The protocol for the review is available at http://www.dietandcancerreport.org/index.php [17]. In addition, we hand-searched the reference lists of relevant articles, reviews, and meta-analysis papers. study selection Included were follow-up studies of breast cancer survivors, which reported estimates of the associations of BMI ascertained before and after breast cancer diagnosis with total or cause-specific mortality risks. Studies that investigated BMI after diagnosis were divided into two groups: BMI <12 months after diagnosis (BMI <12 months) and BMI 12 months or more after diagnosis (BMI ≥12 months). Outcomes included total mortality, breast cancer mortality, death from cardiovascular disease, and death from causes other than breast cancer. When multiple publications on the same study population were found, results based on longer follow-up and more outcomes were selected for the meta-analysis. data extraction DSMC, TN, and DA conducted the search. DSMC, ARV, and DNR extracted the study characteristics, tumour-related information, cancer treatment, timing and method of weight and height assessment, BMI levels, number of outcomes and population at-risk, outcome type, estimates of association and their measure of variance [95% confidence interval (CI) or P value], and adjustment factors in the analysis. statistical analysis Categorical and dose–response meta-analyses were conducted using random-effects models to account for between-study heterogeneity [18]. Summary relative risks (RRs) were estimated using the average of the natural logarithm of the RRs of each study weighted by the inverse of the variance and then unweighted by applying a random-effects variance component which is derived from the extent of variability of the effect sizes of the studies. The maximally adjusted RR estimates were used for the meta-analysis except for the follow-up of randomised, controlled trials [19, 20] where unadjusted results were also included, as these studies mostly involved a more homogeneous study population. BMI or Quetelet's Index (QI) measured in units of kg/m2 was used. We conducted categorical meta-analyses by pooling the categorical results reported in the studies. The studies used different BMI categories. In some studies, underweight (BMI <18.5 kg/m2 according to WHO international classification) and normal weight women (BMI 18.5–<25.0 kg/m2) were classified together but, in some studies, they were classified separately. Similarly, most studies classified overweight (BMI 25.0–<30.0 kg/m2) and obese (BMI ≥30.0 kg/m2) women separately but, in some studies, overweight and obese women were combined. The reference category was normal weight or underweight together with normal weight, depending on the studies. For convenience, the BMI categories are referred to as underweight, normal weight, overweight, and obese in the present review. We derived the RRs for overweight and obese women compared with normal weight women in two studies [19, 21] that had more than four BMI categories using the method of Hamling et al. [22]. Studies that reported results for obese compared with non-obese women were analysed separately. The non-linear dose–response relationship between BMI and mortality was examined using the best-fitting second-order fractional polynomial regression model [23], defined as the one with the lowest deviance. Non-linearity was tested using the likelihood ratio test [24]. In the non-linear meta-analysis, the reference category was the lowest BMI category in each study and RRs were recalculated using the method of Hamling et al. [22] when the reference category was not the lowest BMI category in the study. We also conducted linear dose–response meta-analyses, excluding the category underweight when reported separately in the studies, by pooling estimates of RR per unit increase (with its standard error) provided by the studies, or derived by us from categorical data using generalised least-squares for trend estimation [25]. To estimate the trend, the numbers of outcomes and population at-risk for at least three BMI categories, or the information required to derive them using standard methods [26], and means or medians of the BMI categories, or if not reported in the studies, the estimated midpoints of the categories had to be available. When the extreme BMI categories were open-ended, we used the width of the adjacent close-ended category to estimate the midpoints. Where the RRs were presented by subgroups (age group [27], menopausal status [28, 29], stage [30] or subtype [31] of breast cancer, or others [32–34]), an overall estimate for the study was obtained by a fixed-effect model before pooling in the meta-analysis. We estimated the risk increase of death for an increment of 5 kg/m2 of BMI. To assess heterogeneity, we computed the Cochran Q test and I 2 statistic [35]. The cut points of 30% and 50% were used for low, moderate, and substantial level of heterogeneity. Sources of heterogeneity were explored by meta-regression and subgroup analyses using pre-defined factors, including indicators of study quality (menopausal status, hormone receptor status, number of outcomes, length of follow-up, study design, geographic location, BMI assessment, adjustment for confounders, and others). Small study or publication bias was examined by Egger's test [36] and visual inspection of the funnel plots. The influence of each individual study on the summary RR was examined by excluding the study in turn [37]. A P value of <0.05 was considered statistically significant. All analyses were conducted using Stata version 12.1 (Stata Statistical Software: Release 12, StataCorp LP, College Station, TX). results A total of 124 publications investigating the relationship of body fatness and mortality in women with breast cancer were identified. We excluded 31 publications, including four publications on other obesity indices [38–41], 12 publications without a measure of association [42–53], and 15 publications superseded by publications of the same study with more outcomes [54–68]. A further 14 publications were excluded because of insufficient data for the meta-analysis (five publications [69–73]) or unadjusted results (nine publications [74–82]), from which nine publications reported statistically significant increased risk of total, breast cancer or non-breast cancer mortality in obese women (before or <12 months after diagnosis) compared with the reference BMI [69, 71–74, 76, 77, 79, 82], two publications reported non-significant inverse associations [75, 80] and three publications reported no association [70, 78, 81] of BMI with survival after breast cancer. Hence, 79 publications from 82 follow-up studies with 41 477 deaths (23 182 from breast cancer) in 213 075 breast cancer survivors were included in the meta-analyses (Figure 1). Supplementary Table S1, available at Annals of Oncology online shows the characteristics of the studies included in the meta-analyses and details of the excluded studies are in supplementary Table S2, available at Annals of Oncology online. Results of the meta-analyses are summarised in Table 1. Table 1. Summary of meta-analyses of BMI and survival in women with breast cancera BMI before diagnosis BMI <12 months after diagnosis BMI ≥12 months after diagnosis N RR (95% CI) I 2 (%) P h N RR (95% CI) I 2 (%) P h N RR (95% CI) I 2 (%) P h Total mortality Under versus normal weight 10 1.10 (0.92–1.31) 48%0.04 11 1.25 (0.99–0.57) 63%<0.01 3 1.29 (1.02–1.63) 0%0.39 Over versus normal weight 19 1.07 (1.02–1.12) 0%0.88 22 1.07 (1.02–1.12) 21%0.18 4 0.98 (0.86–1.11) 0%0.72 Obese versus normal weight 21 1.41 (1.29–1.53) 38%0.04 24 1.23 (1.12–1.33) 69%<0.01 5 1.21 (1.06–1.38) 0%0.70 Obese versus non-obese – – – 12 1.26 (1.07–1.47) 80%<0.01 – – – Per 5 kg/m2 increase 15 1.17 (1.13–1.21) 7%0.38 12 1.11 (1.06–1.16) 55%0.01 4 1.08 (1.01–1.15) 0%0.52 Breast cancer mortality Under versus normal weight 8 1.02 (0.85–1.21) 31%0.18 5 1.53 (1.27–1.83) 0%0.59 1 1.10 (0.15–8.08) – Over versus normal weight 21 1.11 (1.06–1.17) 0%0.66 12 1.11 (1.03–1.20) 14%0.31 2 1.37 (0.96–1.95) 0%0.90 Obese versus normal weight 22 1.35 (1.24–1.47) 36%0.05 12 1.25 (1.10–1.42) 53%0.02 2 1.68 (0.90–3.15) 67%0.08 Obese versus non-obese – – – 6 1.26 (1.05–1.51) 64%0.02 – – – Per 5 kg/m2 increase 18 1.18 (1.12–1.25) 47%0.01 8 1.14 (1.05–1.24) 66%0.01 2 1.29 (0.97–1.72) 64%0.10 Cardiovascular disease related mortality Over versus normal weight 2 1.01 (0.80–1.29) 0%0.87 – – – – – – Obese versus normal weight 2 1.60 (0.66–3.87) 78%0.03 – – – – – – Per 5 kg/m2 increase 2 1.21 (0.83–1.77) 80%0.03 – – – – – – Non-breast cancer mortality Over versus normal weight – – – 5 0.96 (0.83–1.11) 26%0.25 – – – Obese versus normal weight – – – 5 1.29 (0.99–1.68) 72%0.01 – – – aBMI before and after diagnosis (<12 months after, or ≥12 months after diagnosis) was classified according to the exposure period which the studies referred to in the BMI assessment; the BMI categories were included in the categorical meta-analyses as defined by the studies. P h, P for heterogeneity between studies. Figure 1. Flowchart of search. Studies were follow-up of women with breast cancer identified in prospective aetiologic cohort studies (women were free of cancer at enrolment), or cohorts of breast cancer survivors whose participants were identified in hospitals or through cancer registries, or follow-up of breast cancer patients enrolled in case–control studies or randomised clinical trials. Some studies included only premenopausal women [83–85] or postmenopausal women [21, 27, 86–94], but most studies included both. Menopausal status was usually determined at time of diagnosis. Year of diagnosis was from 1957–1965 [70] to 2002–2009 [74]. Patient tumour characteristics and stage of disease at diagnosis varied across studies, and some studies included carcinoma in situ. No all studies provided clinical information on the tumour, treatment, and co-morbidities. Most of the studies were based in North America or Europe. There were three studies from each of Australia [79, 95, 96], Korea [97, 98] and China [99–101]; two studies from Japan [71, 102]; one study from Tunisia [103] and four international studies [19, 104–106]. Study size ranged from 96 [107] to 24 698 patients [97]. Total number of deaths ranged from 56 [93] to 7397 [108], and the proportion of deaths from breast cancer ranged from 22% [27] to 98% [84] when reported. All but eight studies [30, 93, 94, 98, 99, 109–111] had an average follow-up of more than 5 years. BMI and total mortality categorical meta-analysis For BMI before diagnosis, compared with normal weight women, the summary RRs were 1.41 (95% CI 1.29–1.53, 21 studies) for obese women, 1.07 (95% CI 1.02–1.12, 19 studies) for overweight women, and 1.10 (95% CI 0.92–1.31, 10 studies) for underweight women (Figure 2). For BMI <12 months after diagnosis and the same comparisons, the summary RRs were 1.23 (95% CI 1.12–1.33, 24 studies) for obese women, 1.07 (95% CI 1.02–1.12, 22 studies) for overweight women, and 1.25 (95% CI 0.99–1.57, 11 studies) for underweight women (supplementary Figure S1, available at Annals of Oncology online). Substantial heterogeneities were observed between studies of obese women and underweight women (I 2 = 69%, P < 0.01; I 2 = 63%, P < 0.01, respectively). For BMI ≥12 months after diagnosis, the summary RRs were 1.21 (95% CI 1.06–1.38, 5 studies) for obese women, 0.98 (95% CI 0.86–1.11, 4 studies) for overweight women, and 1.29 (95% CI 1.02–1.63, 3 studies) for underweight women (supplementary Figure S2, available at Annals of Oncology online). Twelve additional studies reported results for obese versus non-obese women <12 months after diagnosis, and the summary RR was 1.26 (95% CI 1.07–1.47, I 2 = 80%, P < 0.01). Figure 2. Categorical meta-analysis of pre-diagnosis BMI and total mortality. dose–response meta-analysis There was evidence of a J-shaped association in the non-linear dose–response meta-analyses of BMI before and after diagnosis with total mortality (all P < 0.01; Figure 3), suggesting that underweight women may be at slightly increased risk compared with normal weight women. The curves show linear increasing trends from 20 kg/m2 for BMI before diagnosis and <12 months after diagnosis, and from 25 kg/m2 for BMI ≥12 months after diagnosis. When linear models were fitted excluding the underweight category, the summary RRs of total mortality for each 5 kg/m2 increase in BMI were 1.17 (95% CI 1.13–1.21, 15 studies, 6358 deaths), 1.11 (95% CI 1.06–1.16, 12 studies, 6020 deaths), and 1.08 (95% CI 1.01–1.15, 4 studies, 1703 deaths) for BMI before, <12 months after, and ≥12 months after diagnosis, respectively (Figure 4). Substantial heterogeneity was observed between studies on BMI <12 months after diagnosis (I 2 = 55%, P = 0.01). Figure 3. Non-linear dose–response curves of BMI and mortality. Figure 4. Linear dose–response meta-analysis of BMI and total mortality. BMI and breast cancer mortality categorical meta-analysis BMI was significantly associated with breast cancer mortality. Compared with normal weight women, for BMI before diagnosis, the summary RRs were 1.35 (95% CI 1.24–1.47, 22 studies) for obese women, 1.11 (95% CI 1.06–1.17, 21 studies) for overweight women, and 1.02 (95% CI 0.85–1.21, 8 studies) for underweight women (Figure 5). For BMI <12 months after diagnosis, the summary RRs were 1.25 (95% CI 1.10–1.42, 12 studies) for obese women, 1.11 (95% CI 1.03–1.20, 12 studies) for overweight women, and 1.53 (95% CI 1.27–1.83, 5 studies) for underweight women (supplementary Figure S3, available at Annals of Oncology online). Substantial heterogeneity was observed between studies of obese women (I 2 = 53%, P = 0.02). For BMI ≥12 months after diagnosis, the summary RRs of the two studies identified were 1.68 (95% CI 0.90–3.15) for obese women and 1.37 (95% CI 0.96–1.95) for overweight women (supplementary Figure S4, available at Annals of Oncology online). The summary of another six studies that reported RRs for obese versus non-obese <12 months after diagnosis was 1.26 (95% CI 1.05–1.51, I 2 = 64%, P = 0.02). Figure 5. Categorical meta-analysis of pre-diagnosis BMI and breast cancer mortality. dose–response meta-analysis There was no significant evidence of a non-linear relationship between BMI before, <12 months after, and ≥12 months after diagnosis and breast cancer mortality (P = 0.21, P = 1.00, P = 0.86, respectively) (Figure 3). When linear models were fitted excluding data from the underweight category, statistically significant increased risks of breast cancer mortality with BMI before and <12 months after diagnosis were observed (Figure 6). The summary RRs for each 5 kg/m2 increase were 1.18 (95% CI 1.12–1.25, 18 studies, 5262 breast cancer deaths) for BMI before diagnosis and 1.14 (95% CI 1.05–1.24, 8 studies, 3857 breast cancer deaths) for BMI <12 months after diagnosis, with moderate (I 2 = 47%, P = 0.01) and substantial (I 2 = 66%, P = 0.01) heterogeneities between studies, respectively. Only two studies on BMI ≥12 months after diagnosis and breast cancer mortality (N = 220 deaths) were identified. The summary RR was 1.29 (95% CI 0.97–1.72). Figure 6. Linear dose–response meta-analysis of BMI and breast cancer mortality. BMI and other mortality outcomes Only two studies reported results for death from cardiovascular disease (N = 151 deaths) [27, 112]. The summary RR for obese versus normal weight before diagnosis was 1.60 (95% CI 0.66–3.87). No association was observed for overweight versus normal weight (summary RR = 1.01, 95% CI 0.80–1.29). For each 5 kg/m2 increase in BMI, the summary RR was 1.21 (95% CI 0.83–1.77). Five studies reported results for deaths from any cause other than breast cancer (N = 2704 deaths) [21, 34, 108, 113, 114]. The summary RRs were 1.29 (95% CI 0.99–1.68, I 2 = 72%, P = 0.01) for obese women, and 0.96 (95% CI 0.83–1.11, I 2 = 26%, P = 0.25) for overweight women compared with normal weight women. subgroup, meta-regression, and sensitivity analyses The results of the subgroup and meta-regression analyses are in supplementary Tables S3 and S4, available at Annals of Oncology online. Subgroup analysis was not carried out for BMI ≥12 months after diagnosis as the limited number of studies would hinder any meaningful comparisons. Increased risks of mortality were observed in the meta-analyses by menopausal status. While the summary risk estimates seem stronger with premenopausal breast cancer, there was no significant heterogeneity between pre- and post-menopausal breast cancer as shown in the meta-regression analyses (P = 0.28–0.89) (supplementary Tables S3 and S4, available at Annals of Oncology online). For BMI before diagnosis and total mortality, the summary RRs for obese versus normal weight were 1.75 (95% CI 1.26–2.41, I 2 = 70%, P < 0.01, 7 studies) in women with pre-menopausal breast cancer and 1.34 (95% CI 1.18–1.53, I 2 = 27%, P = 0.20, 9 studies) in women with post-menopausal breast cancer. Studies with larger number of deaths [105, 115], conducted in Europe [28, 115], or with weight and height assessed through medical records [28, 104, 115, 116] tended to report weaker associations for BMI <12 months after diagnosis and total mortality compared with other studies (meta-regression P = 0.01, 0.02, 0.01, respectively) (supplementary Table S3, available at Annals of Oncology online); while studies with larger number of deaths [101], conducted in Asia [101, 102], or adjusted for co-morbidity [101, 102] reported weaker associations for BMI <12 months after diagnosis and breast cancer mortality (meta-regression P = 0.01, 0.02, 0.01, respectively) (supplementary Table S4, available at Annals of Oncology online). Analyses stratified by study designs, or restricted to studies with invasive cases only, early-stage non-metastatic cases only, or mammography screening detected cases only, or controlled for previous diseases did not produce results that were materially different from those obtained in the overall analyses (results not shown). Summary risk estimates remained statistically significant when each study was omitted in turn, except for BMI ≥12 months after diagnosis and total mortality. The summary RR was 1.06 (95% CI 0.98–1.15) per 5 kg/m2 increase when Flatt et al. [117] which contributed 315 deaths was omitted. small studies or publication bias Asymmetry was only detected in the funnel plots of BMI <12 months after diagnosis and total mortality, and breast cancer mortality, which suggests that small studies with an inverse association are missing (plots not shown). Egger's tests were borderline significant (P = 0.05) or statistically significant (P = 0.03), respectively. discussion The present systematic literature review and meta-analysis of follow-up studies clearly supports that, in breast cancer survivors, higher BMI is consistently associated with lower overall and breast cancer survival, regardless of when BMI is ascertained. The limited number of studies on death from cardiovascular disease is also consistent with a positive association. For before, <12 months after, and 12 months or more after breast cancer diagnosis, compared with normal weight women, obese women had 41%, 23%, and 21% higher risk for total mortality, and 35%, 25%, and 68% increased risk for breast cancer mortality, respectively. The findings were supported by the positive associations observed in the linear dose–response meta-analysis. All associations were statistically significant, apart from the relationship between BMI ≥12 months after diagnosis and breast cancer mortality. This may be due to limited statistical power, with only 220 breast cancer deaths from two follow-up studies. Positive associations, in some cases statistically significant, were also observed in overweight, and underweight women compared with normal weight women. Women with BMI of 20 kg/m2 before, or <12 months after diagnosis, and of 25 kg/m2 12 months or more after diagnosis appeared to have the lowest mortality risk in the non-linear dose–response analysis. Co-morbid conditions may cause the observed increased risk in underweight women. Thorough investigation within the group and on their contribution to the shape of the association is hindered, as not all studies in this review reported results for this group. The increased risk associated with obesity was similar in pre- or post-menopausal breast cancer. We did not find any evidence of a protective effect of obesity on survival after pre-menopausal breast cancer, contrary to what has been observed for the development of breast cancer in pre-menopausal women [4]. A large body of evidence with 41 477 deaths (23 182 from breast cancer) in over 210 000 breast cancer survivors was systematically reviewed in the present study. We carried out categorical, linear, and non-linear dose–response meta-analyses to examine the magnitude and the shape of the associations for total and cause-specific mortality in underweight, overweight, and obese women by time periods before and after diagnosis that is important in relation to the population-at-risk and breast cancer survivors. Our findings agree with and further extend the results from previous meta-analyses. A review published in 2010 reported statistically significant increased risks of 33% of both total and breast cancer mortality for obesity versus non-obesity around diagnosis [7]. These estimates are slightly higher than ours, which may be explained by the different search periods and inclusion criteria for the articles (33 studies and 15 studies included in the analyses, respectively). Another review published in 2012 further reported consistent positive associations of total and breast cancer mortality with higher versus lower BMI around diagnosis [6]. No significant differences were observed by menopausal status or hormone receptor status. The After Breast Cancer Pooling Project of four prospective cohort studies found differential effects of levels of pre-diagnosis obesity on survival [118]. Compared with normal weight women, significant or borderline significant increased risks of 81% of total and 40% of breast cancer mortality were only observed for morbidly obese (≥40 kg/m2) women and not for women in other obesity categories. We observed statistically significant increased risks also for overweight women, probably because of a larger number of studies. We were unable to investigate the associations with severely and morbidly obese women because only two studies included in this review reported such results [19, 113]. Overall, our findings are consistent with previous meta-analyses in showing elevated total and breast cancer mortality associated with higher BMI and support the current guidelines for breast cancer survivors to stay as lean as possible within the normal range of body weight [4], for overweight women to avoid weight gain during treatment and for obese women to lose weight after treatment [119]. The present review is limited by the challenges and flaws encountered by the individual epidemiological studies evaluating the body fatness–mortality relationship in breast cancer survivors. Most studies did not adjust for co-morbidities and assess intentional weight loss. Women with more serious health issues, and especially smokers, may lose weight but are at an increased risk of mortality, and this might cause an apparent increased risk in underweight women. Body weight information through the natural history of the disease and treatment information were usually not complete or available. Increase of body weight post-diagnosis is common in women with breast cancer, particularly during chemotherapy [16]. Chemotherapy under-dosing is a common problem in obese women and may contribute to their increased mortality [120]. Although several studies with pre-diagnosis BMI adjusted for underlying illnesses or excluded the first few years of follow-up, reverse causation may have affected the results in studies that assessed BMI in women with cancer and other illnesses. However, in these studies, the associations were similar to other studies. Possible survival benefit (subjects with better prognostic factors survive) may be present in the survival cohorts, in which the range of BMI could be narrower, and may cause an underestimation of the association. Follow-up studies with variable characteristics were pooled in the meta-analysis. Women identified in clinical trials may have had specific tumour subtypes, with fewer co-morbidities, and were more likely to receive protocol treatments with high treatment completion rates. Women who were recruited through mammography screening programmes may have had healthier lifestyles or access to medical facilities, and more likely to be diagnosed with in situ or early-stage breast cancer. Cancer detection methods, tumour classifications and treatment regimens change over time, and may vary within (if follow-up is long) and between studies, and could not be simply examined by using the diagnosis or treatment date. We cannot rule out the effect of unmeasured or residual confounding in our analysis. Nevertheless, most results were adjusted for multiple confounding factors, including tumour stage or other-related variables and stratified analyses by several key factors showed similar summary risk estimates. Small study or publication bias was observed in the analyses of BMI <12 months after diagnosis. However, the overall evidence is supported by large, well-designed studies and is unlikely to be changed. We did not conduct analyses by race/ethnicity and treatment types as only limited studies had published results. Future studies of body fatness and breast cancer outcomes should aim to account for co-morbidities, separate intended and unintended changes of body weight, and collect complete treatment information during study follow-up. Randomised clinical trials are needed to test interventions for weight loss and maintenance on survival in women with breast cancer. In conclusion, the present systematic literature review and meta-analysis extends and confirms the associations of obesity with an unfavourable overall and breast cancer survival in pre- and post-menopausal breast cancer, regardless of when BMI is ascertained. Increased risks of mortality in underweight and overweight women were also observed. Given the comparable elevated risks with obesity in the development (for post-menopausal women) and prognosis of breast cancer, and the complications with cancer treatment and other obesity-related co-morbidities, it is prudent to maintain a healthy body weight (BMI 18.5–<25.0 kg/m2) throughout life. funding This work was supported by the World Cancer Research Fund International (grant number: 2007/SP01) (http://www.wcrf-uk.org/). The funder of this study had no role in the decisions about the design and conduct of the study; collection, management, analysis, or interpretation of the data; or the preparation, review, or approval of the manuscript. The views expressed in this review are the opinions of the authors. They may not represent the views of the World Cancer Research Fund International/American Institute for Cancer Research and may differ from those in future updates of the evidence related to food, nutrition, physical activity, and cancer survival. disclosure DCG reports personal fees from World Cancer Research Fund/American Institute for Cancer Research, during the conduct of the study; grants from Danone, and grants from Kelloggs, outside the submitted work. AM reports personal fees from Metagenics/Metaproteomics, personal fees from Pfizer, outside the submitted work. All remaining authors have declared no conflicts of interest. Supplementary Material Supplementary Data
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Measurement characteristics of the Women's Health Initiative food frequency questionnaire.
R. Patterson, A R Kristal, L. Tinker … (1999)
The Women's Health Initiative (WHI) is the largest research program ever initiated in the United States with a focus on diet and health. Therefore, it is important to understand and document the measurement characteristics of the key dietary assessment instrument: the WHI food frequency questionnaire (FFQ). Data are from 113 women screened for participation in the WHI in 1995. We assessed bias and precision of the FFQ by comparing the intake of 30 nutrients estimated from the FFQ with means from four 24-hour dietary recalls and a 4-day food record. For most nutrients, means estimated by the FFQ were within 10% of the records or recalls. Precision, defined as the correlation between the FFQ and the records and recalls, was similar to other FFQs. Energy adjusted correlation coefficients ranged from 0.2 (vitamin B12) to 0.7 (magnesium) with a mean of 0.5. The correlation for percentage energy from fat (a key measure in WHI) was 0.6. Vitamin supplement use was common. For example, almost half of total vitamin E intake was obtained from supplements. Including supplemental vitamins and minerals increased micronutrient correlation coefficients, which ranged from 0.2 (thiamin) to 0.8 (vitamin E) with a mean of 0.6. The WHI FFQ produced nutrient estimate, that were similar to those obtained from short-term dietary recall and recording methods. Comparison of WHI FFQ nutrient intake measures to independent and unbiased measures, such as doubly labeled water estimates of energy expenditure, are needed to help address the validity of the FFQ in this population.
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The Diabetes Prevention Program. Design and methods for a clinical trial in the prevention of type 2 diabetes.
L Dawson, B Eberhardt, PG Watson … (1999)
The Diabetes Prevention Program is a randomized clinical trial testing strategies to prevent or delay the development of type 2 diabetes in high-risk individuals with elevated fasting plasma glucose concentrations and impaired glucose tolerance. The 27 clinical centers in the U.S. are recruiting at least 3,000 participants of both sexes, approximately 50% of whom are minority patients and 20% of whom are > or = 65 years old, to be assigned at random to one of three intervention groups: an intensive lifestyle intervention focusing on a healthy diet and exercise and two masked medication treatment groups--metformin or placebo--combined with standard diet and exercise recommendations. Participants are being recruited during a 2 2/3-year period, and all will be followed for an additional 3 1/3 to 5 years after the close of recruitment to a common closing date in 2002. The primary outcome is the development of diabetes, diagnosed by fasting or post-challenge plasma glucose concentrations meeting the 1997 American Diabetes Association criteria. The 3,000 participants will provide 90% power to detect a 33% reduction in an expected diabetes incidence rate of at least 6.5% per year in the placebo group. Secondary outcomes include cardiovascular disease and its risk factors; changes in glycemia, beta-cell function, insulin sensitivity, obesity, diet, physical activity, and health-related quality of life; and occurrence of adverse events. A fourth treatment group--troglitazone combined with standard diet and exercise recommendations--was included initially but discontinued because of the liver toxicity of the drug. This randomized clinical trial will test the possibility of preventing or delaying the onset of type 2 diabetes in individuals at high risk.
Author and article information
Journal
Journal ID (nlm-ta): NPJ Breast Cancer
Journal ID (iso-abbrev): NPJ Breast Cancer
Title:
NPJ Breast Cancer
Publisher:
Nature Publishing Group
ISSN
(Electronic):
2374-4677
Publication date
(Electronic):
24
August
2016
Publication date Collection: 2016
Volume: 2
Page: 16026
Affiliations
Author notes
M.L.I., B.C., M.H., and L.M.F. conceived and designed the study. M.H. recruited the subjects, conducted data collection, and delivered the intervention. C.A. conducted the data analysis. S.M.G. and L.M.F. assisted in the data analysis. C.A., B.C., M.L.I., S.M.G., M.H., L.M.F., T.S., and C.A. participated in interpreting the data and writing the manuscript.
Article
Publisher Item ID: npjbcancer201626DOI: 10.1038/npjbcancer.2016.26
PMC ID: 5515327
PubMed ID: 28721384
SO-VID: 1147e233-a662-4c2c-a990-94252a7e7772
Copyright © Copyright © 2016 The Author(s)
License:
This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/
History
Date
received
: 11
February
2016
Date
revision received
: 10
June
2016
Date
accepted
: 21
July
2016
Categories
Subject:
Article
Data availability: