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.

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. MethodsOne 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. ResultsThe mean age of participants was 59 6 7.5 years old, with a mean BMI of 33.1 6 6.6 kg/m 2 , and the mean time from diagnosis was 2.9 6 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). ConclusionBoth 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.

Aims/hypothesis We sought to evaluate if the cellular localisation and molecular species of diacylglycerol (DAG) were related to insulin sensitivity in human skeletal muscle. Methods Healthy sedentary obese controls (Ob; n=6; mean±SEM age 39.5±2.3 years; mean±SEM BMI 33.3±1.4 kg/m2), individuals with type 2 diabetes (T2D; n=6; age 44±1.8 years; BMI 30.1±2.3 kg/m2), and lean endurance-trained athletes (Ath; n=10; age 35.4±3.1 years; BMI 23.3±0.8 kg/m2) were studied. Insulin sensitivity was determined using an IVGTT. Muscle biopsy specimens were taken after an overnight fast, fractionated using ultracentrifugation, and DAG species measured using liquid chromatography/MS/MS. Results Total muscle DAG concentration was higher in the Ob (mean±SEM 13.3±1.0 pmol/μg protein) and T2D (15.2±1.0 pmol/μg protein) groups than the Ath group (10.0±0.78 pmol/μg protein, p=0.002). The majority (76-86%) DAG was localised in the membrane fraction for all groups, but was lowest in the Ath group (Ob, 86.2±0.98%; T2D, 84.2±1.2%; Ath, 75.9±2.7%; p=0.008). There were no differences in cytoplasmic DAG species (p>0.12). Membrane DAG species C18:0/C20:4, Di-C16:0 and Di-C18:0 were significantly more abundant in the T2D group. Cytosolic DAG species were negatively related to activation of protein kinase C (PKC)ε but not PKCθ, whereas membrane DAG species were positively related to activation of PKCε, but not PKCθ. Only total membrane DAG (r=−0.624, p=0.003) and Di-C18:0 (r=−0.595, p=0.004) correlated with insulin sensitivity. Disaturated DAG species were significantly lower in the Ath group (p=0.001), and significantly related to insulin sensitivity (r=−0.642, p=0.002). Conclusions/interpretation These data indicate that both cellular localisation and composition of DAG influence the relationship to insulin sensitivity. Our results suggest that only saturated DAG in skeletal muscle membranes are related to insulin resistance in humans.

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 AlphaTocopherol, Beta-Carotene Cancer Prevention Study cohort. Participants completed a validated food-frequency questionnaire and provided a fasting serum sample before study randomization (1985)(1986)(1987)(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: 20.30 to 0.20; P = 6 3 10 215 to 8 3 10 26 ). 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. Am J Clin Nutr 2017;105:450-65.

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. 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. 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 (correlation < 1.47 × 10) 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). 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). 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.

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.Am J Clin Nutr 2016;104:776-89.

BACKGROUND.Ceramides are sphingolipids that play causative roles in diabetes and heart disease, with their serum levels measured clinically as biomarkers of cardiovascular disease (CVD). METHODS.We performed targeted lipidomics on serum samples from individuals with familial coronary artery disease (CAD) (n = 462) and population-based controls (n = 212) to explore the relationship between serum sphingolipids and CAD, using unbiased machine learning to identify sphingolipid species positively associated with CAD. RESULTS.Nearly every sphingolipid measured (n = 30 of 32) was significantly elevated in subjects with CAD compared with measurements in population controls. We generated a novel sphingolipid-inclusive CAD risk score, termed SIC, that demarcates patients with CAD independently and more effectively than conventional clinical CVD biomarkers including serum LDL cholesterol and triglycerides. This new metric comprises several minor lipids that likely serve as measures of flux through the ceramide biosynthesis pathway rather than the abundant deleterious ceramide species that are included in other ceramide-based scores. CONCLUSION.This study validates serum ceramides as candidate biomarkers of CVD and suggests that comprehensive sphingolipid panels should be considered as measures of CVD.

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.