Mary Playdon

Weight Gain After Breast Cancer Diagnosis and All-Cause Mortality: Systematic Review and Meta-Analysis

BACKGROUND Overweight and obesity are associated with breast cancer mortality. However, the relationship between postdiagnosis weight gain and mortality is unclear. We conducted a systematic review and meta-analysis of weight gain after breast cancer diagnosis and breast cancer-specific, all-cause mortality and recurrence outcomes. METHODS Electronic databases identified articles up through December 2014, including: PubMed (1966-present), EMBASE (1974-present), CINAHL (1982-present), and Web of Science. Language and publication status were unrestricted. Cohort studies and clinical trials measuring weight change after diagnosis and all-cause/breast cancer-specific mortality or recurrence were considered. Participants were women age 18 years or older with stage I-IIIC breast cancer. Fixed effects analysis summarized the association between weight gain (≥5.0% body weight) and all-cause mortality; all tests were two-sided. RESULTS Twelve studies (n = 23 832) were included. Weight gain (≥5.0%) compared with maintenance (<±5.0%) was associated with increased all-cause mortality (hazard ratio [HR] = 1.12, 95% confidence interval [CI] = 1.03 to 1.22, P = .01, I(2) = 55.0%). Higher risk of mortality was apparent for weight gain ≥10.0% (HR = 1.23, 95% CI = 1.09 to 1.39, P < .001); 5% to 10.0% weight gain was not associated with all-cause mortality (P = .40). The association was not statistically significant for those with a prediagnosis body mass index (BMI) of less than 25 kg/m(2) (HR = 1.14, 95% CI = 0.99 to 1.31, P = .07) or with a BMI of 25 kg/m(2) or higher (HR = 1.00, 95% CI = 0.86 to 1.16, P = .19). Weight gain of 10.0% or more was not associated with hazard of breast cancer-specific mortality (HR = 1.17, 95% CI = 1.00 to 1.38, P = .05). CONCLUSIONS Weight gain after diagnosis of breast cancer is associated with higher all-cause mortality rates compared with maintaining body weight. Adverse effects are greater for weight gains of 10.0% or higher.

Diet, nutrition, and cancer: past, present and future

Despite the potentially important roles of diet and nutrition in cancer prevention, the evidence to support these roles is widely perceived by the public and health professionals as being inconsistent. In this Review, we present the issues and challenges in conducting and interpreting diet–cancer research, including those relating to the design of epidemiological studies, dietary data collection methods, and factors that affect the outcome of intervention trials. Approaches to improve effect estimates, such as the use of biomarkers to improve the accuracy of characterizing dietary exposures, are also discussed. Nutritional and dietary patterns are complex; therefore, the use of a reductionist approach to investigations, by focusing on specific nutrients, can produce misleading information. The effects of tumour heterogeneity and the failure to appreciate the nonlinear, U-shaped relationship between micronutrients and cancer in both observational studies and clinical trials are discussed. New technologies and investigational approaches are enabling the exploration of complex interactions between genetic, epigenetic, metabolic, and gut-microbial processes that will inform our knowledge of the diet–cancer relationship. Communicating the status of the evolving science in the context of the overall scientific evidence base, and evidence-based dietary recommendations for cancer prevention, should be emphasized in guidance for the public and for individual patients.

Randomized Trial Comparing Telephone Versus In-Person Weight Loss Counseling on Body Composition and Circulating Biomarkers in Women Treated for Breast Cancer: The Lifestyle, Exercise, and Nutrition (LEAN) Study

PURPOSE Obesity is associated with a higher risk of breast cancer mortality. The gold standard approach to weight loss is in-person counseling, but telephone counseling may be more feasible. We examined the effect of in-person versus telephone weight loss counseling versus usual care on 6-month changes in body composition, physical activity, diet, and serum biomarkers. METHODS One hundred breast cancer survivors with a body mass index ≥ 25 kg/m(2) were randomly assigned to in-person counseling (n = 33), telephone counseling (n = 34), or usual care (UC) (n = 33). In-person and telephone counseling included 11 30-minute counseling sessions over 6 months. These focused on reducing caloric intake, increasing physical activity, and behavioral therapy. Body composition, physical activity, diet, and serum biomarkers were measured at baseline and 6 months. RESULTS The mean age of participants was 59 ± 7.5 years old, with a mean BMI of 33.1 ± 6.6 kg/m(2), and the mean time from diagnosis was 2.9 ± 2.1 years. Fifty-one percent of the participants had stage I breast cancer. Average 6-month weight loss was 6.4%, 5.4%, and 2.0% for in-person, telephone, and UC groups, respectively (P = .004, P = .009, and P = .46 comparing in-person with UC, telephone with UC, and in-person with telephone, respectively). A significant 30% decrease in C-reactive protein levels was observed among women randomly assigned to the combined weight loss intervention groups compared with a 1% decrease among women randomly assigned to UC (P = .05). CONCLUSION Both in-person and telephone counseling were effective weight loss strategies, with favorable effects on C-reactive protein levels. Our findings may help guide the incorporation of weight loss counseling into breast cancer treatment and care.

Identifying biomarkers of dietary patterns by using metabolomics

BACKGROUND Healthy dietary patterns that conform to national dietary guidelines are related to lower chronic disease incidence and longer life span. However, the precise mechanisms involved are unclear. Identifying biomarkers of dietary patterns may provide tools to validate diet quality measurement and determine underlying metabolic pathways influenced by diet quality. OBJECTIVE The objective of this study was to examine the correlation of 4 diet quality indexes [the Healthy Eating Index (HEI) 2010, the Alternate Mediterranean Diet Score (aMED), the WHO Healthy Diet Indicator (HDI), and the Baltic Sea Diet (BSD)] with serum metabolites. DESIGN We evaluated dietary patterns and metabolites in male Finnish smokers (n = 1336) from 5 nested case-control studies within the Alpha-Tocopherol, Beta-Carotene Cancer Prevention Study cohort. Participants completed a validated food-frequency questionnaire and provided a fasting serum sample before study randomization (1985-1988). Metabolites were measured with the use of mass spectrometry. We analyzed cross-sectional partial correlations of 1316 metabolites with 4 diet quality indexes, adjusting for age, body mass index, smoking, energy intake, education, and physical activity. We pooled estimates across studies with the use of fixed-effects meta-analysis with Bonferroni correction for multiple comparisons, and conducted metabolic pathway analyses. RESULTS The HEI-2010, aMED, HDI, and BSD were associated with 23, 46, 23, and 33 metabolites, respectively (17, 21, 11, and 10 metabolites, respectively, were chemically identified; r-range: -0.30 to 0.20; P = 6 × 10-15 to 8 × 10-6). Food-based diet indexes (HEI-2010, aMED, and BSD) were associated with metabolites correlated with most components used to score adherence (e.g., fruit, vegetables, whole grains, fish, and unsaturated fat). HDI correlated with metabolites related to polyunsaturated fat and fiber components, but not other macro- or micronutrients (e.g., percentages of protein and cholesterol). The lysolipid and food and plant xenobiotic pathways were most strongly associated with diet quality. CONCLUSIONS Diet quality, measured by healthy diet indexes, is associated with serum metabolites, with the specific metabolite profile of each diet index related to the diet components used to score adherence. This trial was registered at clinicaltrials.gov as NCT00342992.

Comparing metabolite profiles of habitual diet in serum and urine

BACKGROUND Diet plays an important role in chronic disease etiology, but some diet-disease associations remain inconclusive because of methodologic limitations in dietary assessment. Metabolomics is a novel method for identifying objective dietary biomarkers, although it is unclear what dietary information is captured from metabolites found in serum compared with urine. OBJECTIVE We compared metabolite profiles of habitual diet measured from serum with those measured from urine. DESIGN We first estimated correlations between consumption of 56 foods, beverages, and supplements assessed by a food-frequency questionnaire, with 676 serum and 848 urine metabolites identified by untargeted liquid chromatography mass spectrometry, ultra-high performance liquid chromatography tandem mass spectrometry, and gas chromatography mass spectrometry in a colon adenoma case-control study (n = 125 cases and 128 controls) while adjusting for age, sex, smoking, fasting, case-control status, body mass index, physical activity, education, and caloric intake. We controlled for multiple comparisons with the use of a false discovery rate of <0.1. Next, we created serum and urine multiple-metabolite models to predict food intake with the use of 10-fold crossvalidation least absolute shrinkage and selection operator regression for 80% of the data; predicted values were created in the remaining 20%. Finally, we compared predicted values with estimates obtained from self-reported intake for metabolites measured in serum and urine. RESULTS We identified metabolites associated with 46 of 56 dietary items; 417 urine and 105 serum metabolites were correlated with ≥1 food, beverage, or supplement. More metabolites in urine (n = 154) than in serum (n = 39) were associated uniquely with one food. We found previously unreported metabolite associations with leafy green vegetables, sugar-sweetened beverages, citrus, added sugar, red meat, shellfish, desserts, and wine. Prediction of dietary intake from multiple-metabolite profiles was similar between biofluids. CONCLUSIONS Candidate metabolite biomarkers of habitual diet are identifiable in both serum and urine. Urine samples offer a valid alternative or complement to serum for metabolite biomarkers of diet in large-scale clinical or epidemiologic studies.

Effect of a low fat versus a low carbohydrate weight loss dietary intervention on biomarkers of long term survival in breast cancer patients ('CHOICE'): study protocol

BackgroundWeight loss in overweight or obese breast cancer patients is associated with an improved prognosis for long term survival. However, it is not clear whether the macronutrient composition of the chosen weight loss dietary plan imparts further prognostic benefit. A study protocol is presented for a dietary intervention to investigate the effects of weight loss dietary patterns that vary markedly in fat and carbohydrate contents on biomarkers of exposure to metabolic processes that may promote tumorigenesis and that are predictive of long term survival. The study will also determine how much weight must be lost for biomarkers to change in a favorable direction.Methods/DesignApproximately 370 overweight or obese postmenopausal breast cancer survivors (body mass index: 25.0 to 34.9 kg/m2) will be accrued and assigned to one of two weight loss intervention programs or a non-intervention control group. The dietary intervention is implemented in a free living population to test the two extremes of popular weight loss dietary patterns: a high carbohydrate, low fat diet versus a low carbohydrate, high fat diet. The effects of these dietary patterns on biomarkers for glucose homeostasis, chronic inflammation, cellular oxidation, and steroid sex hormone metabolism will be measured. Participants will attend 3 screening and dietary education visits, and 7 monthly one-on-one dietary counseling and clinical data measurement visits in addition to 5 group visits in the intervention arms. Participants in the control arm will attend two clinical data measurement visits at baseline and 6 months. The primary outcome is high sensitivity C-reactive protein. Secondary outcomes include interleukin-6, tumor necrosis factor-α, insulin-like growth factor-1 (IGF), IGF binding protein-3, 8-isoprostane-F2-alpha, estrone, estradiol, progesterone, sex hormone binding globulin, adiponectin, and leptin.DiscussionWhile clinical data indicate that excess weight for height is associated with poor prognosis for long term survival, little attention is paid to weight control in the clinical management of breast cancer. This study will provide information that can be used to answer important patient questions about the effects of dietary pattern and magnitude of weight loss on long term survival following breast cancer treatment.Clinical Trial RegistrationCA125243

Effect of dietary patterns differing in carbohydrate and fat content on blood lipid and glucose profiles based on weight-loss success of breast-cancer survivors

IntroductionHealthy body weight is an important factor for prevention of breast cancerrecurrence. Yet, weight loss and weight gain are not currently included inclinical-practice guidelines for posttreatment of breast cancer. The work reportedaddresses one of the questions that must be considered in recommending weight lossto patients: does it matter what diet plan is used, a question of particularimportance because breast cancer treatment can increase risk for cardiovasculardisease.MethodsWomen who completed treatment for breast cancer were enrolled in a nonrandomized,controlled study investigating effects of weight loss achieved by using twodietary patterns at the extremes of macronutrient composition, although both dietarms were equivalent in protein: high fat, low carbohydrate versus low fat, highcarbohydrate. A nonintervention group served as the control arm; women wereassigned to intervention arms based on dietary preferences. During the 6-monthweight-loss program, which was menu and recipe defined, participants had monthlyclinical visits at which anthropometric data were collected and fasting blood wasobtained for safety monitoring for plasma lipid profiles and fasting glucose.Results from 142 participants are reported.ResultsAdverse effects on fasting blood lipids or glucose were not observed in eitherdietary arm. A decrease in fasting glucose was observed with progressive weightloss and was greater in participants who lost more weight, but the effect was notstatistically significant, even though it was observed across both diet groups(P = 0.21). Beneficial effects of weight loss on cholesterol (4.7%;P = 0.001), triglycerides (21.8%; P = 0.01), and low-densitylipoprotein (LDL) cholesterol (5.8%; P = 0.06) were observed in bothgroups. For cholesterol (P = 0.07) and LDL cholesterol (P =0.13), greater reduction trends were seen on the low-fat diet pattern; whereas,for triglycerides (P = 0.01) and high-density lipoprotein (HDL)cholesterol (P = 0.08), a decrease or increase, respectively, was greateron the low-carbohydrate diet pattern.ConclusionsBecause an individuals dietary preferences can affect dietary adherence andweight-loss success, the lack of evidence of a negative effect of dietary patternon biomarkers associated with cardiovascular risk is an important consideration inthe development of breast cancer practice guidelines for physicians who recommendthat their patients lose weight. Whether dietary pattern affects biomarkers thatpredict long-term survival is a primary question in this ongoing clinicaltrial.

Impact of Weight Loss on Plasma Leptin and Adiponectin in Overweight-to-Obese Post Menopausal Breast Cancer Survivors.

Women who are obese at the time of breast cancer diagnosis have higher overall mortality than normal weight women and some evidence implicates adiponectin and leptin as contributing to prognostic disadvantage. While intentional weight loss is thought to improve prognosis, its impact on these adipokines is unclear. This study compared the pattern of change in plasma leptin and adiponectin in overweight-to-obese post-menopausal breast cancer survivors during weight loss. Given the controversies about what dietary pattern is most appropriate for breast cancer control and regulation of adipokine metabolism, the effect of a low fat versus a low carbohydrate pattern was evaluated using a non-randomized, controlled study design. Anthropometric data and fasted plasma were obtained monthly during the six-month weight loss intervention. While leptin was associated with fat mass, adiponectin was not, and the lack of correlation between leptin and adiponectin concentrations throughout weight loss implies independent mechanisms of regulation. The temporal pattern of change in leptin but not adiponectin was affected by magnitude of weight loss. Dietary pattern was without effect on either adipokine. Mechanisms not directly related to dietary pattern, weight loss, or fat mass appear to play dominant roles in the regulation of circulating levels of these adipokines.

Nutritional metabolomics and breast cancer risk in a prospective study

Background: The epidemiologic evidence for associations between dietary factors and breast cancer is weak and etiologic mechanisms are often unclear. Exploring the role of dietary biomarkers with metabolomics can potentially facilitate objective dietary characterization, mitigate errors related to self-reported diet, agnostically test metabolic pathways, and identify mechanistic mediators.Objective: The aim of this study was to evaluate associations of diet-related metabolites with the risk of breast cancer in the Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer Screening Trial.Design: We examined prediagnostic serum concentrations of diet-related metabolites in a nested case-control study in 621 postmenopausal invasive breast cancer cases and 621 matched controls in the multicenter PLCO cohort. We calculated partial Pearson correlations between 617 metabolites and 55 foods, food groups, and vitamin supplements on the basis of the 2015 Dietary Guidelines for Americans and derived from a 137-item self-administered food-frequency questionnaire. Diet-related metabolites (P-correlation < 1.47 × 10-6) were evaluated in breast cancer analyses. ORs for the 90th compared with the 10th percentile were calculated by using conditional logistic regression, with body mass index, physical inactivity, other breast cancer risk factors, and caloric intake controlled for (false discovery rate <0.2).Results: Of 113 diet-related metabolites, 3 were associated with overall breast cancer risk (621 cases): caprate (10:0), a saturated fatty acid (OR: 1.77; 95% CI = 1.28, 2.43); γ-carboxyethyl hydrochroman (γ-CEHC), a vitamin E (γ-tocopherol) derivative (OR: 1.64; 95% CI: 1.18, 2.28); and 4-androsten-3β,17β-diol-monosulfate (1), an androgen (OR: 1.61; 95% CI: 1.20, 2.16). Nineteen metabolites were significantly associated with estrogen receptor (ER)-positive (ER+) breast cancer (418 cases): 12 alcohol-associated metabolites, including 7 androgens and α-hydroxyisovalerate (OR: 2.23; 95% CI: 1.50, 3.32); 3 vitamin E (tocopherol) derivatives (e.g., γ-CEHC; OR: 1.80; 95% CI: 1.20, 2.70); butter-associated caprate (10:0) (OR: 1.81; 95% CI: 1.23, 2.67); and fried food-associated 2-hydroxyoctanoate (OR: 1.46; 95% CI: 1.03, 2.07). No metabolites were significantly associated with ER-negative breast cancer (144 cases).Conclusions: Prediagnostic serum concentrations of metabolites related to alcohol, vitamin E, and animal fats were moderately strongly associated with ER+ breast cancer risk. Our findings show how nutritional metabolomics might identify diet-related exposures that modulate cancer risk. This trial was registered at clinicaltrials.gov as NCT00339495.

Metabolites Associated With Lean Mass and Adiposity in Older Black Men

To identify biomarkers of body mass index, body fat, trunk fat, and appendicular lean mass, nontargeted metabolomics was performed in plasma from 319 black men in the Health, Aging and Body Composition study (median age 72 years, median body mass index 26.8 kg/m2). Body mass index was calculated from measured height and weight; percent fat, percent trunk fat, and appendicular lean mass were measured with dual-energy x-ray absorptiometry. Pearson partial correlations between body composition measures and metabolites were adjusted for age, study site, and smoking. Out of 350 metabolites, body mass index, percent fat, percent trunk fat, and appendicular lean mass were significantly correlated with 92, 48, 96, and 43 metabolites at p less than .0014. Metabolites most strongly correlated with body composition included carnitine, a marker of fatty acid oxidation (positively correlated), triacylglycerols (positively correlated), and amino acids including branched-chain amino acids (positively correlated except for acetylglycine and serine). Gaussian Graphical Models of metabolites found that 25 lipid metabolites clustered into a single network. Groups of five amino acids, three plasmalogens, and two carnitines were also observed. Findings confirm prior reports of associations between amino acids, lean mass, and fat mass in addition to associations not previously reported. Future studies should consider whether these metabolites are relevant for metabolic disease processes.