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BACKGROUND: Circadian rhythms play an important role in the regulation of eating and fasting, and mistimed dietary intakes may be detrimental to metabolic health. Extended overnight fasting has been proposed as a strategy to better align the eating-fasting cycle with the internal circadian clock, and both observational and experimental studies have linked longer overnight fasting with lower body weight. However, it remains unclear if the timing of overnight fasting modifies the relationship between fasting duration and weight outcomes. METHODS: The current study included 495 men and 499 women age 50–74 years. Dietary intake over 12 months was assessed by 24-hour dietary recalls every two months, and body-mass index was measured at the beginning, middle and end of the study. Logistic regression was used to estimate the relationship between overnight fasting duration and the likelihood of being overweight or obesity adjusted for multiple confounders, and assessed whether the relationship was modified by the timing of overnight fasting, measured as the midpoint of the fasting period. RESULTS: Among participants with early overnight fasting (midpoint < 02:19 am), a longer fasting duration was associated with lower odds of overweight and obesity; while among those with late fasting (≥ 02:19 am), longer fasting was associated with higher odds of overweight and obesity. Specifically, when compared to the shortest quintile of overnight fasting duration, the longest quintile was associated with a 53% reduction in the odds of overweight and obesity in the early fasting group (OR=0.47, 95% CI=0.23, 0.97), but a 2.36-fold increase in the late fasting group (OR=3.36, 95% CI=1.48, 7.62). Additionally adjusting for dietary intakes during morning and late evening periods did not affect the observed associations. CONCLUSIONS: Longer overnight fasting was associated with a reduced likelihood of being overweight or obese, but only among those with an early timing of fasting.

B vitamins involved in one-carbon metabolism have been implicated in the development of inflammation- and angiogenesis-related chronic diseases, such as colorectal cancer (CRC). Yet, the role of one-carbon metabolism in inflammation and angiogenesis among CRC patients remains unclear. The objective of this study was to investigate associations of components of one-carbon metabolism with inflammation and angiogenesis biomarkers among newly diagnosed CRC patients (n 238) in the prospective ColoCare Study, Heidelberg. We cross-sectionally analysed associations between twelve B vitamins and one-carbon metabolites and ten inflammation and angiogenesis biomarkers from pre-surgery serum samples using multivariable linear regression models. We further explored associations among novel biomarkers in these pathways with Spearman partial correlation analyses. We hypothesised that pyridoxal-5’-phosphate (PLP) is inversely associated with inflammatory biomarkers. We observed that PLP was inversely associated with C-reactive protein (CRP) (r –0·33, Plinear < 0·0001), serum amyloid A (SAA) (r –0·23, Plinear = 0·003), IL-6 (r –0·39, Plinear < 0·0001), IL-8 (r –0·20, Plinear = 0·02) and TNFα (r –0·12, Plinear = 0·045). Similar findings were observed for 5-methyl-tetrahydrofolate and CRP (r –0·14), SAA (r –0·14) and TNFα (r –0·15) among CRC patients. Folate catabolite acetyl-para-aminobenzoylglutamic acid (pABG) was positively correlated with IL-6 (r 0·27, Plinear < 0·0001), and pABG was positively correlated with IL-8 (r 0·21, Plinear < 0·0001), indicating higher folate utilisation during inflammation. Our data support the hypothesis of inverse associations between PLP and inflammatory biomarkers among CRC patients. A better understanding of the role and inter-relation of PLP and other one-carbon metabolites with inflammatory processes among colorectal carcinogenesis and prognosis could identify targets for future dietary guidance for CRC patients.

Background Metabolomics has proven useful for detecting objective biomarkers of diet that may help to improve dietary measurement. Studies to date, however, have focused on a relatively narrow set of lipid classes. Objective The aim of this study was to uncover candidate dietary biomarkers by identifying serum metabolites correlated with self-reported diet, particularly metabolites in underinvestigated lipid classes, e.g. triglycerides and plasmalogens. Methods We assessed dietary questionnaire data and serum metabolite correlations from 491 male and female participants aged 55-75 y in an exploratory cross-sectional study within the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial (PLCO). Self-reported intake was categorized into 50 foods, food groups, beverages, and supplements. We examined 522 identified metabolites using 2 metabolomics platforms (Broad Institute and Massachusetts General Hospital). Correlations were identified using partial Pearson's correlations adjusted for age, sex, BMI, smoking status, study site, and total energy intake [Bonferroni-corrected level of 0.05/(50 × 522) = 1.9 × 10-6]. We assessed prediction of dietary intake by multiple-metabolite linear models with the use of 10-fold crossvalidation least absolute shrinkage and selection operator (LASSO) regression. Results Eighteen foods, beverages, and supplements were correlated with ≥1 serum metabolite at the Bonferroni-corrected significance threshold, for a total of 102 correlations. Of these, only 5 have been reported previously, to our knowledge. Our strongest correlations were between citrus and proline betaine (r = 0.55), supplements and pantothenic acid (r = 0.46), and fish and C40:9 phosphatidylcholine (PC) (r = 0.35). The multivariate analysis similarly found reasonably large correlations between metabolite profiles and citrus (r = 0.59), supplements (r = 0.57), and fish (r = 0.44). Conclusions Our study of PLCO participants identified many novel food-metabolite associations and replicated 5 previous associations. These candidate biomarkers of diet may help to complement measures of self-reported diet in nutritional epidemiology studies, though further validation work is still needed.

While conventional nutrition research has yielded biomarkers such as doubly labeled water for energy metabolism and 24-h urinary nitrogen for protein intake, a critical need exists for additional, equally robust biomarkers that allow for objective assessment of specific food intake and dietary exposure. Recent advances in high-throughput MS combined with improved metabolomics techniques and bioinformatic tools provide new opportunities for dietary biomarker development. In September 2018, the NIH organized a 2-d workshop to engage nutrition and omics researchers and explore the potential of multiomics approaches in nutritional biomarker research. The current Perspective summarizes key gaps and challenges identified, as well as the recommendations from the workshop that could serve as a guide for scientists interested in dietary biomarkers research. Topics addressed included study designs for biomarker development, analytical and bioinformatic considerations, and integration of dietary biomarkers with other omics techniques. Several clear needs were identified, including larger controlled feeding studies, testing a variety of foods and dietary patterns across diverse populations, improved reporting standards to support study replication, more chemical standards covering a broader range of food constituents and human metabolites, standardized approaches for biomarker validation, comprehensive and accessible food composition databases, a common ontology for dietary biomarker literature, and methodologic work on statistical procedures for intake biomarker discovery. Multidisciplinary research teams with appropriate expertise are critical to moving forward the field of dietary biomarkers and producing robust, reproducible biomarkers that can be used in public health and clinical research.

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. FUNDING: The NIH (DK112826, DK108833, DK115824, DK116888, and DK116450); the Juvenile Diabetes Research Foundation (JDRF 3-SRA-2019-768-A-B); the American Diabetes Association; the American Heart Association; the Margolis Foundation; the National Cancer Institute, NIH (5R00CA218694-03); and the Huntsman Cancer Institute Cancer Center Support Grant (P30CA040214).

Metabolomics can detect metabolic shifts resulting from lifestyle behaviors and may provide insight on the relevance of changes to carcinogenesis. We used non-targeted nuclear magnetic resonance to examine associations between metabolic measures and cancer preventive behaviors in 1319 participants (50% male, mean age 54 years) from the BC Generations Project. Behaviors were dichotomized: BMI 2, ≥ 5 servings of fruits or vegetables/day, ≤ 2 alcoholic drinks/day for men or 1 drink/day for women and ≥ 30 min of moderate or vigorous physical activity/day. Linear regression was used to estimate coefficients and 95% confidence intervals with a false discovery rate (FDR) of 0.10. Of the 218 metabolic measures, 173, 103, 71 and 6 were associated with BMI, fruits and vegetables, alcohol consumption and physical activity. Notable findings included negative associations between glycoprotein acetyls, an inflammation-related metabolite with lower BMI and greater fruit and vegetable consumption, a positive association between polyunsaturated fatty acids and fruit and vegetable consumption and positive associations between high-density lipoprotein subclasses with lower BMI. These findings provide insight into metabolic alterations in the context of cancer prevention and the diverse biological pathways they are involved in. In particular, behaviors related to BMI, fruit and vegetable and alcohol consumption had a large metabolic impact.

Introduction: Multiple case studies have reported diabetic ketoacidosis (DKA) in recently-diagnosed cancer patients, especially those taking immune checkpoint inhibitors. The prevalence of DKA and other acute diabetes complications (ADC), including hypoglycemia and hyperosmolarity, and their relationship with the onset of systemic cancer therapy is unclear. Methods: This study utilized data from n=9,588 cancer patients from the Huntsman Cancer Institute, UT. Data on clinicodemographic characteristics, biomarkers, and cancer diagnoses were extracted from electronic medical records (EMR) and tumor registry. Patients included in this analysis received an ICD diagnosis of hypoglycemia, hyperosmolarity with or without coma, and/or DKA with or without coma. Systemic therapy was defined as any chemotherapy, immunotherapy or hormone therapy resulting from a specific first primary cancer diagnosis. Results: There were 243 instances of ADC within this cohort, occurring in 211 (2.2%) unique patients. Of these 211 unique patients with an ADC, 106 (50.0%) experienced their first ADC within one year after cancer diagnosis, with a median time to ADC diagnosis of 88 days. Forty-seven (44%) of these patients experienced the ADC following the onset of systemic therapy, with a median time from systemic therapy onset to ADC diagnosis of 120 days. In these 47 patients who received an ADC diagnosis following the onset of systemic therapy, 40 (85.1%) received chemotherapy (n=20 as monotherapy), 20 (42.6%) received immunotherapy (n=4 as monotherapy) and 12 (25.5%) received hormone therapy (n=2 as monotherapy). Only 54% of patients experiencing an ADC after cancer diagnosis had a history of diabetes prior to cancer, and on average their first diabetes diagnosis was 5.4 ± 4.4 years prior to their cancer diagnosis. Discussion: ADCs may be common following a cancer diagnosis, however a substantial number occurred prior to the onset of systemic cancer therapy and in patients with no prior history of diabetes. Disclosure R. Viskochil: None. S. Hardikar: None. S. Pauleck: None. M. Winn: None. K. Funai: None. M. L. Litchman: Research Support; Self; Abbott Diabetes. W. Akerley: None. H. Colman: Advisory Panel; Self; Adastra Pharmaceuticals, Orbus Therapeutics, Consultant; Self; Best Doctors/Teladoc. C. M. Ulrich: None. M. C. Playdon: None. Funding University of Utah Health/Larry H. Miller Family Wellness Initiative

Introduction: Cancer patients are more likely to develop hyperglycemia (HG) compared to cancer-free adults, and this increased risk is highest in the year following cancer diagnosis. The persistence of post-diagnosis HG and degree of glycemic control in this population is unclear. Methods: This study utilized data from n=9,588 cancer patients from the Huntsman Cancer Institute, UT. Clinicodemographic characteristics, biomarkers, and cancer diagnoses were extracted from electronic medical records (EMR) and tumor registry. Patients with a new HG diagnosis (ICD) of any kind between 30 days and 1 year after cancer diagnosis were included. Persistence of HG was determined through elevated Hemoglobin A1c (HbA1c) and/or new anti-HG medication prescription. Patients who did not survive >1 year after cancer diagnosis or were diagnosed with cancer Results: Of the 2,964 (30.9%) cancer patients with a HG diagnosis after cancer, 26.2% (n=777) were diagnosed with HG between 30 days and 1 year after cancer diagnosis, of these n=357 met criteria for HG evaluation. Data on HbA1c and/or anti-HG medication >1 but Discussion: Taken together, 82.5% of patients diagnosed with HG in the year following a cancer diagnosis still presented with HG between one and two years post-cancer diagnosis, and there was no difference by BMI among those that did and did not receive anti-HG medications. This suggests that HG post-cancer diagnosis is not a transient effect of cancer therapy (e.g., corticosteroids), and may have long-term health implications in cancer survivors. Disclosure R. Viskochil: None. S. Hardikar: None. S. Pauleck: None. M. Winn: None. K. Funai: None. M. L. Litchman: Research Support; Self; Abbott Diabetes. W. Akerley: None. H. Colman: Advisory Panel; Self; Adastra Pharmaceuticals, Orbus Therapeutics, Consultant; Self; Best Doctors/Teladoc. C. M. Ulrich: None. M. C. Playdon: None. Funding University of Utah Health/Larry H. Miller Family Wellness Initiative

Metabolomics offers several opportunities for advancement in nutritional cancer epidemiology; however, numerous research gaps and challenges remain. This narrative review summarizes current research, challenges, and future directions for epidemiologic studies of nutritional metabolomics and cancer. Although many studies have used metabolomics to investigate either dietary exposures or cancer, few studies have explicitly investigated diet-cancer relationships using metabolomics. Most studies have been relatively small (≤ ~ 250 cases) or have assessed a limited number of nutritional metabolites (e.g., coffee or alcohol-related metabolites). Nutritional metabolomic investigations of cancer face several challenges in study design; biospecimen selection, handling, and processing; diet and metabolite measurement; statistical analyses; and data sharing and synthesis. More metabolomics studies linking dietary exposures to cancer risk, prognosis, and survival are needed, as are biomarker validation studies, longitudinal analyses, and methodological studies. Despite the remaining challenges, metabolomics offers a promising avenue for future dietary cancer research.

Breast cancer is the most common cancer in women, but its incidence can only be partially explained through established risk factors. Our aim was to use metabolomics to identify novel risk factors for breast cancer and to validate recently reported metabolite-breast cancer findings. We measured levels of 1275 metabolites in prediagnostic serum in a nested case-control study of 782 postmenopausal breast cancer cases and 782 matched controls. Metabolomics analysis was performed by Metabolon Inc using ultra-performance liquid chromatography and a Q-Exactive high resolution/accurate mass spectrometer. Controls were matched by birth date, date of blood draw, and race/ethnicity. Odds ratios (ORs) and 95% confidence intervals (CIs) of breast cancer at the 90th versus 10th percentile (modeled on a continuous basis) of metabolite levels were estimated using conditional logistic regression, with adjustment for age. Twenty-four metabolites were significantly associated with breast cancer risk at a false discovery rate &lt, 0.20. For the nine metabolites positively associated with risk, the ORs ranged from 1.75 (95% CI: 1.29–2.36) to 1.45 (95% CI: 1.13–1.85), and for the 15 metabolites inversely associated with risk, ORs ranged from 0.59 (95% CI: 0.43–0.79) to 0.69 (95% CI: 0.55–0.87). These metabolites largely comprised carnitines, glycerolipids, and sex steroid metabolites. Associations for three sex steroid metabolites validated findings from recent studies and the remainder were novel. These findings contribute to growing data on metabolite-breast cancer associations by confirming prior findings and identifying novel leads for future validation efforts.

Objective To assess the relationship between time-of-day of exercise training and changes in relevant cancer health outcomes among cancer survivors. Methods Retrospective analysis of data collected from 2016–2019 from a hospital-based exercise oncology program. Descriptive statistics were calculated for demographic, clinical, and exercise timing characteristics (e.g. AM, PM, or mix) among survivors with available data for exercise training time (n = 233). For the total sample and a breast cancer sub-analysis, univariate analysis of covariance, adjusted for age, was carried out by exercise training time, for change in the following outcomes collected during the program’s assessment sessions: cardiorespiratory fitness and muscular endurance (human performance variables), physical function, anthropometrics, self-reported fatigue, and quality of life (QoL). Change in body mass index (BMI) and body weight was included in the breast cancer analysis. Results Overall, 37.3% of survivors habitually engaged in AM exercise (e.g. ≥ 75% AM training), 34.3% in PM exercise, and 28.3% in a mix of AM and PM exercise training throughout the program. Median time in the program was 17 weeks. Significant improvements in most human performance and physical function variables were observed in the total sample regardless of exercise training time-of-day. Among breast cancer survivors, PM but not AM or mixed was associated with improvements in fitness, and lower-body muscular endurance and function. Mixed exercise timing was linked with greater increase in waist circumference (total sample: 3.02cm, 95%CI 1.55, 4.49; breast cancer: 3.57cm 95%CI 0.96, 6.18), body weight (breast cancer: 1.6kg, 95%CI 0.3, 2.8) and BMI (breast cancer: 0.6kg/m2, 95%CI 0.1, 1.0). AM and PM exercise, but not mixed, was associated with improvements in fatigue and QoL. Conclusion Time-of-day of exercise training may differentially impact changes in human performance and physical function variables. Mixed exercise training time may result in less favorable outcomes related of weight management variables among cancer survivors.

ContextGenome-wide association studies have identified associations between a common single nucleotide polymorphism (SNP; rs267738) in CERS2, a gene that encodes a (dihydro)ceramide synthase that is involved in the biosynthesis of very-long-chain sphingolipids (eg, C20-C26) and indices of metabolic dysfunction (eg, impaired glucose homeostasis). However, the biological consequences of this mutation on enzyme activity and its causal roles in metabolic disease are unresolved.ObjectiveThe studies described herein aimed to characterize the effects of rs267738 on CERS2 enzyme activity, sphingolipid profiles, and metabolic outcomes.DesignWe performed in-depth lipidomic and metabolic characterization of a novel CRISPR knock-in mouse modeling the rs267738 variant. In parallel, we conducted mass spectrometry-based, targeted lipidomics on 567 serum samples collected through the Utah Coronary Artery Disease study, which included 185 patients harboring 1 (n = 163) or both (n = 22) rs267738 alleles.ResultsIn-silico analysis of the amino acid substitution within CERS2 caused by the rs267738 mutation suggested that rs267738 is deleterious for enzyme function. Homozygous knock-in mice had reduced liver CERS2 activity and enhanced diet-induced glucose intolerance and hepatic steatosis. However, human serum sphingolipids and a ceramide-based cardiac event risk test 1 score of cardiovascular disease were not significantly affected by rs267738 allele count.ConclusionsThe rs267738 SNP leads to a partial loss-of-function of CERS2, which worsened metabolic parameters in knock-in mice. However, rs267738 was insufficient to effect changes in serum sphingolipid profiles in subjects from the Utah Coronary Artery Disease Study.

Objective The majority of endometrial cancer patients are overweight or obese at cancer diagnosis. Obesity is a shared risk factor for both endometrial cancer and diabetes, but it is unknown whether endometrial cancer patients have increased diabetes risks. The aim of our study was to investigate diabetes risk among endometrial cancer patients. Methods Endometrial cancer patients diagnosed between 1997 and 2012 in Utah (n = 2,314) were identified. Women from the general population (n = 8,583) were matched to the cancer patients on birth year and birth state. Diabetes diagnoses were identified from electronic medical records and statewide healthcare facility databases. Cox proportional hazards models were used to estimate hazard ratios for diabetes after cancer diagnosis. Results Endometrial cancer survivors had a significantly higher risk of type II diabetes when compared to women from the general population in the first year after cancer diagnosis (HR = 5.22, 95% CI = 4.05, 6.71), >1–5 years after cancer diagnosis (HR = 1.67, 95% CI = 1.31, 2.12), and >5 years after cancer diagnosis (HR = 1.65, 95% CI = 1.29, 2.11). Endometrial cancer patients who were obese at cancer diagnosis had a three-fold increase in type II diabetes risk (HR = 2.99, 95%CI = 2.59, 3.45). Although endometrial cancer patients diagnosed at distant stage had a higher risk of diabetes, cancer treatment did not appear to contribute to any diabetes risks. Conclusions In conclusion, endometrial cancer survivors had a higher risk of diabetes than women in the general population. These results suggest that long term monitoring for diabetes is indicated for endometrial cancer survivors.

Background: Alcohol consumption is associated with an increased risk of several cancers. Potential mechanisms include altered oestrogen metabolism. Parent oestrogens metabolise into alternate pathways of oestrogen metabolites that may have variable effects on cancer pathogenesis. We examined associations of alcohol consumption with circulating oestrogen/oestrogen metabolites in postmenopausal women in the Women’s Health Initiative (WHI)-Observational Study (OS). Methods: We conducted a cross-sectional analysis of prediagnosis ovarian/endometrial cancer case-control data within WHI-OS (N=1864). Alcohol consumption was measured by validated food frequency questionnaire. Fasting serum parent oestrogens/oestrogen metabolites were assayed using liquid chromatography tandem mass-spectrometry. Geometric mean analyte concentrations (GM, pmol l−1) were calculated by alcohol category using inverse-probability weighted linear regression, adjusting for venepuncture age/year, race, smoking, body mass index, years since menopause, oral contraceptive duration, caffeine intake, and physical activity. Results: There was evidence for a positive association between alcohol consumption and oestrone, oestradiol and 2-hydroxylation oestrogen metabolite concentrations among menopausal hormone therapy (MHT) users. We observed an association between liquor consumption and parent oestrogens among non-MHT users, who consumed larger doses of liquor than MHT users. Conclusions: Among postmenopausal women, the association between alcohol intake and parent oestrogen, but not oestrogen metabolite concentrations, may be influenced by MHT and type of alcohol.

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

Background: The relationship between diet and survival after ovarian cancer diagnosis is unclear as a result of a limited number of studies and inconsistent findings. Methods: We examined the association between pre-diagnostic diet and overall survival in a population-based cohort (n=811) of Australian women diagnosed with invasive epithelial ovarian cancer between 2002 and 2005. Diet was measured by validated food frequency questionnaire. Deaths were ascertained up to 31 August 2014 via medical record review and Australian National Death Index linkage. We conducted Cox proportional hazards regression analysis, controlling for diagnosis age, tumour stage, grade and subtype, residual disease, smoking status, body mass index, physical activity, marital status, and energy intake. Results: We observed improved survival with highest compared with lowest quartile of fibre intake (hazard ratio (HR)=0.69, 95% CI: 0.53–0.90, P-trend=0.002). There was a suggestion of better survival for women with highest compared with lowest intake category of green leafy vegetables (HR=0.79, 95% CI: 0.62–0.99), fish (HR=0.74, 95% CI: 0.57–0.95), poly- to mono-unsaturated fat ratio (HR=0.76, 95% CI: 0.59–0.98), and worse survival with higher glycaemic index (HR=1.28, 95% CI: 1.01–1.65, P-trend=0.03). Conclusions: The associations we observed between healthy components of diet pre-diagnosis and ovarian cancer survival raise the possibility that dietary choices after diagnosis may improve survival.

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.

The application of metabolomics technology to epidemiological studies is emerging as a new approach to elucidate disease etiology and for biomarker discovery. However, analysis of metabolomics data is complex and there is an urgent need for the standardization of analysis workflow and reporting of study findings. To inform the development of such guidelines, we conducted a survey of 47 cohort representatives from the Consortium of Metabolomics Studies (COMETS) to gain insights into the current strategies and procedures used for analyzing metabolomics data in epidemiological studies worldwide. The results indicated a variety of applied analytical strategies, from biospecimen and data pre-processing and quality control to statistical analysis and reporting of study findings. These strategies included methods commonly used within the metabolomics community and applied in epidemiological research, as well as novel approaches to pre-processing pipelines and data analysis. To help with these discrepancies, we propose use of open-source initiatives such as the online web-based tool COMETS Analytics, which includes helpful tools to guide analytical workflow and the standardized reporting of findings from metabolomics analyses within epidemiological studies. Ultimately, this will improve the quality of statistical analyses, research findings, and study reproducibility.

The Consortium of Metabolomics Studies (COMETS) was established in 2014 to facilitate large-scale collaborative research on the human metabolome and its relationship with disease etiology, diagnosis, and prognosis. COMETS comprises 47 cohorts from Asia, Europe, North America, and South America that together include more than 136,000 participants with blood metabolomics data on samples collected from 1985 to 2017. Metabolomics data were provided by 17 different platforms, with the most frequently used labs being Metabolon, Inc. (14 cohorts), the Broad Institute (15 cohorts), and Nightingale Health (11 cohorts). Participants have been followed for a median of 23 years for health outcomes including death, cancer, cardiovascular disease, diabetes, and others; many of the studies are ongoing. Available exposure-related data include common clinical measurements and behavioral factors, as well as genome-wide genotype data. Two feasibility studies were conducted to evaluate the comparability of metabolomics platforms used by COMETS cohorts. The first study showed that the overlap between any 2 different laboratories ranged from 6 to 121 metabolites at 5 leading laboratories. The second study showed that the median Spearman correlation comparing 111 overlapping metabolites captured by Metabolon and the Broad Institute was 0.79 (interquartile range, 0.56-0.89).

Importance Several meta-analyses have summarized evidence for the association between dietary factors and the incidence of colorectal cancer (CRC). However, to date, there has been little synthesis of the strength, precision, and quality of this evidence in aggregate. Objective To grade the evidence from published meta-analyses of prospective observational studies that assessed the association of dietary patterns, specific foods, food groups, beverages (including alcohol), macronutrients, and micronutrients with the incidence of CRC. Data Sources MEDLINE, Embase, and the Cochrane Library were searched from database inception to September 2019. Evidence Review Only meta-analyses of prospective observational studies with a cohort study design were eligible. Evidence of association was graded according to established criteria as follows: convincing, highly suggestive, suggestive, weak, or not significant. Results From 9954 publications, 222 full-text articles (2.2%) were evaluated for eligibility, and 45 meta-analyses (20.3%) that described 109 associations between dietary factors and CRC incidence were selected. Overall, 35 of the 109 associations (32.1%) were nominally statistically significant using random-effects meta-analysis models; 17 associations (15.6%) demonstrated large heterogeneity between studies (I2 > 50%), whereas small-study effects were found for 11 associations (10.1%). Excess significance bias was not detected for any association between diet and CRC. The primary analysis identified 5 (4.6%) convincing, 2 (1.8%) highly suggestive, 10 (9.2%) suggestive, and 18 (16.5%) weak associations between diet and CRC, while there was no evidence for 74 (67.9%) associations. There was convincing evidence of an association of intake of red meat (high vs low) and alcohol (≥4 drinks/d vs 0 or occasional drinks) with the incidence of CRC and an inverse association of higher vs lower intakes of dietary fiber, calcium, and yogurt with CRC risk. The evidence for convincing associations remained robust following sensitivity analyses. Conclusions and Relevance This umbrella review found convincing evidence of an association between lower CRC risk and higher intakes of dietary fiber, dietary calcium, and yogurt and lower intakes of alcohol and red meat. More research is needed on specific foods for which evidence remains suggestive, including other dairy products, whole grains, processed meat, and specific dietary patterns.

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

Survivorship care plans (SCPs) provide cancer patients and health care providers with a treatment summary and outline of recommended medical follow-up. Few studies have investigated the information needs and preferred sources among long-term cancer survivors. Cancer survivors of the ten most common cancers enrolled in the longitudinal Study of Cancer Survivors-I (SCS-I) completed a survey 9 years post-diagnosis (n = 3138); at time of diagnosis of the SCS-I cohort, SCPs were not considered usual care. We assessed participants’ current desire and preferred sources for information across ten SCP items and evaluated factors associated with information need 9 years after diagnosis. The proportion of long-term cancer survivors endorsing a need for cancer and health information 9 years post-diagnosis ranged from 43 % (cancer screening) to 9 % (consequences of cancer on ability to work). Print media and personalized reading materials were the most preferred information sources. Younger age, higher education, race other than non-Hispanic white, later cancer stage, having breast cancer, having ≥2 comorbidities, and self-reporting poor health were associated with greater informational need (p

Objective. In the United States, ovarian cancer is the eleventh most common female cancer and the fifth most common cause of cancer death among women. Although incidence and death rates have been declining over the last 10 years, the 5-year survival rate is 47.6%. Diabetes was the seventh leading cause of death in the United States and is associated with an increased mortality rate among cancer patients. While previous studies have examined the association between diabetes and the risk of ovarian cancer, there are very few studies investigating the risk of diabetes after an ovarian cancer diagnosis. We previously reported increased risks of type I and II diabetes among endometrial cancer survivors, but there is no previous study, to our knowledge, that examined the risk of diabetes among ovarian cancer survivors. The aim of this study was to investigate long-term diabetes risk among ovarian cancer survivors compared with a general population of women. Methods. Ovarian cancer patients diagnosed from 1996 to 2012 in the Utah Cancer Registry were matched to up to 5 cancer-free women on birth state and birth year using the Utah Population Database. Diabetes diagnoses were identified with ICD-9 codes from statewide ambulatory surgery records, statewide inpatient databases and electronic medical records. We estimated hazard ratios with Cox Proportional Hazards models to: (1) investigate risk for diabetes after cancer diagnosis among ovarian cancer survivors compared with the general population, and (2) examine risk factors for type II diabetes among ovarian cancer survivors. Results. A total 1,520 ovarian cancer patients and 5,709 women from the general population were included in the final cohort. A slightly higher proportion of ovarian cancer patients were obese at baseline (16.3%) than the general population cohort (14.9%). Ovarian cancer survivors had a higher risk of type II diabetes in the first year after cancer diagnosis (HR=3.09, 95%CI=2.09, 4.56) and overall (HR=1.52, 95%CI=1.23, 1.88) compared with the general population. However, associations between ovarian cancer and type II diabetes were not observed in the later follow up periods. High BMI was a significant risk factor for type II diabetes among ovarian cancer survivors, with a dose-response relation (overweight; HR=1.81, 95%CI=1.21, 2.71, obese; HR=2.48, 95%CI=1.61, 3.83, P-trend Conclusion. Ovarian cancer survivors had an increased risk of type II diabetes compared with women in the general population. Risk was elevated only within the first year of cancer diagnosis, thus surveillance bias cannot be ruled out. Our findings suggest that the increased risk of type II diabetes does not persist beyond the first year after cancer diagnosis. Citation Format: Seungmin Kim, Kerry Rowe, John Snyder, Alison Fraser, Ken Smith, Vikrant G. Deshmukh, Michael Newman, Kimberley Herget, Chun-Pin Chang, Dominik Ose, Mary C. Playdon, David Gaffney, Mia Hashibe. Long-term diabetes risk among ovarian cancer survivors in a population-based cohort study [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 5795.

Demographic, lifestyle and biospecimen-related factors at the time of blood collection can influence metabolite levels in epidemiological studies. Identifying the major influences on metabolite concentrations is critical to designing appropriate sample collection protocols and considering covariate adjustment in metabolomics analyses. We examined the association of age, sex, and other short-term pre-blood collection factors (time of day, season, fasting duration, physical activity, NSAID use, smoking and alcohol consumption in the days prior to collection) with 133 targeted plasma metabolites (acylcarnitines, amino acids, biogenic amines, sphingolipids, glycerophospholipids, and hexoses) among 108 individuals that reported exposures within 48 h before collection. The differences in mean metabolite concentrations were assessed between groups based on pre-collection factors using two-sided t-tests and ANOVA with FDR correction. Percent differences in metabolite concentrations were negligible across season, time of day of collection, fasting status or lifestyle behaviors at the time of collection, including physical activity or the use of tobacco, alcohol or NSAIDs. The metabolites differed in concentration between the age and sex categories for 21.8% and 14.3% metabolites, respectively. In conclusion, extrinsic factors in the short period prior to collection were not meaningfully associated with concentrations of selected endogenous metabolites in a cross-sectional sample, though metabolite concentrations differed by age and sex. Larger studies with more coverage of the human metabolome are warranted.

Background Elevated body mass index (BMI) is associated with increased risk of postmenopausal breast cancer. The underlying mechanisms, however, remain elusive. Methods In a nested case-control study of 621 postmenopausal breast cancer case participants and 621 matched control participants, we measured 617 metabolites in prediagnostic serum. We calculated partial Pearson correlations between metabolites and BMI, and then evaluated BMI-associated metabolites (Bonferroni-corrected α level for 617 statistical tests = P < 8.10 × 10-5) in relation to invasive breast cancer. Odds ratios (ORs) of breast cancer comparing the 90th vs 10th percentile (modeled on a continuous basis) were estimated using conditional logistic regression while controlling for breast cancer risk factors, including BMI. Metabolites with the lowest P values (false discovery rate < 0.2) were mutually adjusted for one another to determine those independently associated with breast cancer risk. Results Of 67 BMI-associated metabolites, two were independently associated with invasive breast cancer risk: 16a-hydroxy-DHEA-3-sulfate (OR = 1.65, 95% confidence interval [CI] = 1.22 to 2.22) and 3-methylglutarylcarnitine (OR = 1.67, 95% CI = 1.21 to 2.30). Four metabolites were independently associated with estrogen receptor-positive (ER+) breast cancer risk: 16a-hydroxy-DHEA-3-sulfate (OR = 1.84, 95% CI = 1.27 to 2.67), 3-methylglutarylcarnitine (OR = 1.91, 95% CI = 1.23 to 2.96), allo-isoleucine (OR = 1.76, 95% CI = 1.23 to 2.51), and 2-methylbutyrylcarnitine (OR = 1.89, 95% CI = 1.22 to 2.91). In a model without metabolites, each 5 kg/m2 increase in BMI was associated with a 14% higher risk of breast cancer (OR = 1.14, 95% CI = 1.01 to 1.28), but adding 16a-hydroxy-DHEA-3-sulfate and 3-methylglutarylcarnitine weakened this association (OR = 1.06, 95% CI = 0.93 to 1.20), with the logOR attenuating by 57.6% (95% CI = 21.8% to 100.0+%). Conclusion These four metabolites may signal metabolic pathways that contribute to breast carcinogenesis and that underlie the association of BMI with increased postmenopausal breast cancer risk. These findings warrant further replication efforts.

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.

Background Metabolic pathways that give rise to functional decline and mobility disability in older adults are incompletely understood. Methods To identify metabolic perturbations that may affect functional decline, nontargeted metabolomics was used to measure 350 metabolites in baseline plasma from 313 black men in the Health ABC Study (median age 74 years). Usual gait speed was measured over 20 m. Cross-sectional relationships between gait speed and metabolites were explored with partial correlations adjusted for age, study site, and smoking status. Risk of incident mobility disability (two consecutive reports of severe difficulty walking quarter mile or climb 10 stairs) over 13 years of follow-up was explored with Cox regression models among 307 men who were initially free of mobility disability. Significance was determined at p ≤ .01 and q (false discovery rate) ≤ 0.30. Results Two metabolites were correlated with gait speed: salicylurate (r = -.19) and 2-hydroxyglutarate (r = -.18). Metabolites of amino acids and amino acid degradation (indoxy sulfate; hazard ratio [HR] = 1.48, 95% confidence interval [CI] = 1.09-2.03, symmetric dimethylarginine; HR = 3.58, 95% CI = 1.57-8.15, N-carbamoyl beta-alanine; HR = 1.91, 95% CI = 1.16-3.14, quinolinate; HR = 2.56, 95% CI = 1.65-3.96) and metabolites related to kidney function (aforementioned symmetric dimethylarginine and indoxy sulfate as well as creatinine; HR = 5.91, 95% CI = 2.06-16.9, inositol; HR = 2.70, 95% CI = 1.47-4.97) were among the 23 metabolites associated with incident mobility disability. Conclusions This study highlights the potential role of amino acid derivatives and products and kidney function early in the development of mobility disability and suggests metabolic profiles could help identify individuals at risk of functional decline.

The original version of this article was revised: "The article was published with errors on text as the author's corrections were misinterpreted."

Body weight change is defined as one or more periods of weight gain or weight loss that can vary in terms of magnitude, timeframe over which the change(s) occurs, and the number of times the pattern changes. Epidemiological and clinical data provide evidence of increased lifetime risk for breast cancer due to adult weight gain and a reduction of risk with weight loss. These findings parallel the majority of preclinical carcinogenesis experiments in which caloric intake in excess of basal metabolic requirements in rodents permits the development of cancer in proportion to the level of caloric intake. Dieting has been unsuccessful in reducing cancer risk unless a lower body weight was maintained at the end of weight change. Based on this evidence, it is recommended that consideration be given to the inclusion of the following recommendations in clinical practice guidelines for managing lifetime risk for breast cancer: (1) maintain adult body mass index in the desirable range (18.5-24.9 kg/m2) by preventing adult weight gain in both pre- and postmenopausal women, and (2) actively monitor BMI and, when BMI is above the defined ideal range, prescribe corrective lifestyle changes until body weight returns to the target range.

Background: High sodium intake is known to increase blood pressure and is difficult to measure in epidemiologic studies.Objective: We examined the effect of sodium intake on metabolites within the DASH (Dietary Approaches to Stop Hypertension Trial)-Sodium Trial to further our understanding of the biological effects of sodium intake beyond blood pressure.Design: The DASH-Sodium Trial randomly assigned individuals to either the DASH diet (low in fat and high in protein, low-fat dairy, and fruits and vegetables) or a control diet for 12 wk. Participants within each diet arm received, in random order, diets containing high (150 nmol or 3450 mg), medium (100 nmol or 2300 mg), and low (50 nmol or 1150 mg) amounts of sodium for 30 d (crossover design). Fasting blood samples were collected at the end of each sodium intervention. We measured 531 identified plasma metabolites in 73 participants at the end of their high- and low-sodium interventions and in 46 participants at the end of their high- and medium-sodium interventions (N = 119). We used linear mixed-effects regression to model the relation between each log-transformed metabolite and sodium intake. We also combined the resulting P values with Fisher's method to estimate the association between sodium intake and 38 metabolic pathways or groups.Results: Six pathways were associated with sodium intake at a Bonferroni-corrected threshold of 0.0013 (e.g., fatty acid, food component or plant, benzoate, γ-glutamyl amino acid, methionine, and tryptophan). Although 82 metabolites were associated with sodium intake at a false discovery rate ≤0.10, only 4-ethylphenylsufate, a xenobiotic related to benzoate metabolism, was significant at a Bonferroni-corrected threshold (P < 10-5). Adjustment for coinciding change in blood pressure did not substantively alter the association for the top-ranked metabolites.Conclusion: Sodium intake is associated with changes in circulating metabolites, including gut microbial, tryptophan, plant component, and γ-glutamyl amino acid-related metabolites. This trial was registered at clinicaltrials.gov as NCT00000608.

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.

Our objective was to determine whether defects underlying impaired fasting glucose (IFG) are maintained and additive when combined with impaired glucose tolerance (IGT) (representing a progressive form of prediabetes) or are distinct in IFG/IGT (reflecting a parallel form of prediabetes). Volunteers with IFG ( n = 10), IFG/IGT ( n = 14), or normal glucose tolerance (NGT; n = 15) were matched for demographics and anthropometry. Insulin secretion was assessed using the glucose step-up protocol and insulin action through the use of a two-stage hyperinsulinemic euglycemic clamp with infusion of [6,6-2H2]glucose. Modeling of insulin secretory parameters revealed similar basal (Φb) but diminished dynamic (Φd) components in both IFG and IFG/IGT ( P = 0.05 vs. NGT for both). Basal glucose rate of appearance (Ra) was higher in IFG compared with NGT ( P < 0.01) and also, surprisingly, with IFG/IGT ( P < 0.04). Moreover, glucose Ra suppressed more during the low-dose insulin clamp in IFG ( P < 0.01 vs. NGT, P = 0.08 vs. IFG/IGT). Insulin-stimulated glucose uptake [glucose rate of disappearance (Rd)] was similar in IFG, IFG/IGT, and NGT throughout the clamp. We conclude that nuances of β-cell dysfunction observed in IFG were also noted in IFG/IGT. A trend for additional insulin secretory defects was observed in IFG/IGT, possibly suggesting progression in β-cell failure in this group. In contrast, basal glucose Ra and its suppressability with insulin were higher in IFG, but not IFG/IGT, compared with NGT. Together, these data indicate that IFG/IGT may be a distinct prediabetic syndrome rather than progression from IFG.

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/m2 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/m2, 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.

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 (

Body weight management is not emphasized in clinical practice guidelines for breast cancer survivors, reflecting the lack of evidence that weight loss improves prognosis. Even if this situation changes, the optimal design for weight loss interventions is unclear. We conducted a 6-month non-randomized, controlled weight loss intervention in 249 post-menopausal breast cancer survivors. This paper reports effects on two secondary endpoints, change in body weight and composition. Participants were predominantly non-Hispanic whites (89%) with a mean age of 54.9 ± 9.2 years, a mean BMI of 29.0 ± 2.6 kg/m: 2 and an average of 43 ± 5% body fat. Two dietary interventions, low fat or low carbohydrate, were investigated and consisted of a 42 day cycle of menus and recipes. Weight loss counseling and anthropometric assessment were provided at monthly clinic visits. One hundred ninety-two women completed the trial (77% retention). In comparison to the nonintervention control, both intervention arms achieved significant decreases in body weight (12.5%), body fat (27.5%), waist circumference (9.5%), and hip circumference (7.8%) (all p < 0.001) with minimal effects on lean mass (1.3% decrease). Median time to 5 and 10% weight loss was 2 (95% confidence interval = 1 to 3) and 4 (95% confidence interval = 3 to 5) months, respectively, and 23% of participants experienced ≥ 15% weight loss. Loss of body weight and fat mass was rapid and substantial irrespective of dietary approach when a structured program was provided with monthly anthropometric assessment and weight loss counseling. Trial Registration ClinicalTrials.gov {"type":"clinical-trial","attrs":{"text":"NCT01315483","term_id":"NCT01315483"}}NCT01315483

Background: While there is a growing body of literature on metabolomics and body mass index (BMI), there are limited studies in relation to weight change in overweight and obese individuals, and to our knowledge, no such studies in breast cancer survivors. Methods: The Lifestyle, Exercise and Nutrition (LEAN) randomized diet- and exercise-induced weight loss trial in breast cancer survivors enrolled overweight or obese breast cancer survivors (Stage 0-III) identified via the Yale-New Haven Hospital tumor registry or self-referral. Participants were randomized to either usual care group (n=33) or the 6-month lifestyle intervention (11 sessions with a registered dietitian) (n=67). Height and weight were measured by study staff at baseline and 6 months, and a fasting (≥ 12 hours) blood draw was also performed at these time points. Paired (baseline and 6-month) serum metabolomics data were available for 83 women. Only those metabolites consistently detected in >80% of quality control samples with a coefficient of variation Results: Overall, a total of 307 metabolites were analyzed; 168 were quantitated by LC-MS and 139 were semi-quantitated by GC-TOF/MS. For BMI change from baseline to 6-months, we observed correlations with 16 metabolites (isocitric acid, 3-hydroxybutyric acid, serine, L-cysteine, petroselinic acid, aminomalonic acid, palmitoleic acid, linoleic acid, 2-hydroxybutyric acid, and seven unidentified metabolites, p Conclusions: There were no clear associations between metabolites and change in BMI or randomization group among overweight and obese breast cancer survivors enrolled in a weight loss trial. Larger sample sizes would help to clarify if any of the potential signals observed in this population may be associated with BMI or other lifestyle changes, such as diet quality. Citation Format: Leah M. Ferrucci, Brenda Cartmel, Maura Harrigan, Tara Sanft, Mary Playdon, Wei Jia, Herbert Yu, Caroline H. Johnson, Lajos Pusztai, Anees B. Chagpar, Melinda L. Irwin. Metabolomics and body mass index among breast cancer survivors in The Lifestyle, Exercise, and Nutrition (LEAN) Study [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 5321. doi:10.1158/1538-7445.AM2017-5321

To determine the effectiveness of weight loss intervention for breast cancer survivors. From October 2012 until March 2013, Pubmed was searched for weight loss intervention trials that reported body weight or weight loss as a primary outcome. Fifteen of these studies are included in this review. Of the 15 studies included, 14 resulted in statistically significant weight loss and 10 obtained clinically meaningful weight loss of ≥5 % from baseline. Evidence was provided of the feasibility of using several methods of weight loss intervention (telephone, in person, individual, group). Successful intervention used a comprehensive approach, with dietary, physical activity, and behavior modification components. Weight loss improved cardiovascular risk factors and markers of glucose homeostasis. However, there is insufficient evidence to identify the components of this intervention that led to successful weight loss, or to determine the weight loss necessary to affect biomarkers linked to breast cancer prognosis. The small number of randomized controlled trials shared several limitations, including small study sample sizes and lack of follow-up beyond 6 months. Intervention with longer follow-up revealed weight regain, showing the importance of considering strategies to promote long-term weight maintenance. Weight loss intervention for breast cancer survivors can lead to statistically significant and clinically meaningful weight loss, but the limited number of interventional studies, small sample sizes, and short duration of follow-up in many studies limit our ability to draw conclusions regarding the most efficacious weight-loss intervention after a breast cancer diagnosis. The findings to date are encouraging, but research on the effect of weight loss on breast cancer recurrence and mortality, and on prevention of weight gain for women newly diagnosed with breast cancer, is needed.

Metabolomics, an emerging area of interest in the field of epidemiology, has significant potential to evaluate the effects of nutritional, environmental, and pharmaceutical exposures; conduct risk assessments; predict disease development; and diagnose diseases. To understand current funding and potential gaps for population-based metabolomics research, we cross-sectionally evaluated the National Institutes of Health (NIH)-supported research grant awards related to metabolomics and epidemiology that were active on February 9, 2015. The grant portfolio was analyzed using NIH's Query View Report (QVR) software, using relevant search terms, which included metabolome, metabonome, metabolomic(s), metabonomic(s), metabolic profile, metabolite profile, metabolic signature, glycomic(s), and lipidomic(s). Basic descriptive statistics on technology platform, approach, disease phenotype, biospecimen type, exposure, study design, and population were examined. The search terms initially identified 473 grants. After exclusion of grants with Citation Format: Krista A. Zanetti, Mukesh Verma, Scott Rogers, Majda Haznadar, Padma Maruvada, Holly L. Nicastro, Mary C. Playdon, Sharon A. Ross. Evaluation of NIH funding for metabolomics research in epidemiologic studies: opportunities and challenges. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 3447.

10103Background: Obesity and physical inactivity are associated with breast cancer recurrence and mortality. The mechanisms explaining these associations are not fully understood. Vascular Endothel...

Smoking is the most common cause of preventable morbidity and mortality in the United States, in part because it is an independent risk factor for the development of insulin resistance and type 2 diabetes. However, mechanisms responsible for smoking-induced insulin resistance are unclear. In this study, we found smokers were less insulin sensitive compared with controls, which increased after either 1 or 2 weeks of smoking cessation. Improvements in insulin sensitivity after smoking cessation occurred with normalization of IRS-1ser636 phosphorylation. In muscle cell culture, nicotine exposure significantly increased IRS-1ser636 phosphorylation and decreased insulin sensitivity, recapitulating the phenotype of smoking-induced insulin resistance in humans. The two pathways known to stimulate IRS-1ser636 phosphorylation (p44/42 mitogen-activated protein kinase [MAPK] and mammalian target of rapamycin [mTOR]) were both stimulated by nicotine in culture. Inhibition of mTOR, but not p44/42 MAPK, during nicotine exposure prevented IRS-1ser636 phosphorylation and normalized insulin sensitivity. These data indicate nicotine induces insulin resistance in skeletal muscle by activating mTOR. Therapeutic agents designed to oppose skeletal muscle mTOR activation may prevent insulin resistance in humans who are unable to stop smoking or are chronically exposed to secondhand smoke.

We sought to evaluate if the cellular localisation and molecular species of diacylglycerol (DAG) were related to insulin sensitivity in human skeletal muscle.Healthy sedentary obese controls (Ob; n = 6; mean±SEM age 39.5 ± 2.3 years; mean ± SEM BMI 33.3 ± 1.4 kg/m(2)), individuals with type 2 diabetes (T2D; n = 6; age 44 ± 1.8 years; BMI 30.1 ± 2.3 kg/m(2)), and lean endurance-trained athletes (Ath; n = 10; age 35.4 ± 3.1 years; BMI 23.3 ± 0.8 kg/m(2)) 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.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).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.

Introduction: Healthy body weight is an important factor for prevention of breast cancer recurrence. Yet, weight loss and weight gain are not currently included in clinical-practice guidelines for posttreatment of breast cancer. The work reported addresses one of the questions that must be considered in recommending weight loss to patients: does it matter what diet plan is used, a question of particular importance because breast cancer treatment can increase risk for cardiovascular disease. Methods: Women who completed treatment for breast cancer were enrolled in a nonrandomized, controlled study investigating effects of weight loss achieved by using two dietary patterns at the extremes of macronutrient composition, although both diet arms were equivalent in protein: high fat, low carbohydrate versus low fat, high carbohydrate. A nonintervention group served as the control arm; women were assigned to intervention arms based on dietary preferences. During the 6-month weight-loss program, which was menu and recipe defined, participants had monthly clinical visits at which anthropometric data were collected and fasting blood was obtained for safety monitoring for plasma lipid profiles and fasting glucose. Results from 142 participants are reported. Results: Adverse effects on fasting blood lipids or glucose were not observed in either dietary arm. A decrease in fasting glucose was observed with progressive weight loss and was greater in participants who lost more weight, but the effect was not statistically 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-density lipoprotein (LDL) cholesterol (5.8%; P = 0.06) were observed in both groups. 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 greater on the low-carbohydrate diet pattern. Conclusions: Because an individual’s dietary preferences can affect dietary adherence and weight-loss success, the lack of evidence of a negative effect of dietary pattern on biomarkers associated with cardiovascular risk is an important consideration in the development of breast cancer practice guidelines for physicians who recommend that their patients lose weight. Whether dietary pattern affects biomarkers that predict long-term survival is a primary question in this ongoing clinical trial.

Background Weight 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/Design Approximately 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. Discussion While 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 Registration CA125243

Whether intramuscular triglyceride (IMTG) concentration or flux is more important in the progression to type 2 diabetes is controversial. Therefore, this study examined IMTG concentration, as well as its fractional synthesis rate (FSR), in obese people with normal glucose tolerance (NGT; n = 20) vs. obese people with prediabetes (PD; n = 19), at rest and during exercise. Insulin action and secretion were assessed using an intravenous glucose tolerance test. [U-(13)C]palmitate was infused for 4 h before and throughout 1.5 h of treadmill walking at 50% VO(2(max)). IMTG concentration was measured by gas chromatograph/mass spectrometer, and FSR by gas chromatography-combustion isotope ratio mass spectrometer, from muscle biopsies taken immediately before and after exercise. Basal IMTG concentration was higher (43 +/- 5.7 vs. 27 +/- 3.9 mg/mg dry weight, P = 0.03) and FSR trended lower (0.23 +/- 0.04 vs. 0.32 +/- 0.05/h, P = 0.075), as did insulin action (S(i); 2.9 +/- 0.43 vs. 3.3 +/- 0.35 x 10(-4)/mU/ml, P = 0.07), in PD vs. NGT. IMTG concentration did not change significantly during exercise, but was no longer different in PD vs. NGT (45 +/- 7.7 vs. 37 +/- 5.8 mg/mg dry weight, P = 0.41). IMTG FSR suppressed during exercise in NGT (-81% to 0.06 +/- 0.13/h, P = 0.02), but not PD (+4% to 0.24 +/- 0.13%/h, P = 0.95). Palmitate oxidation was similar during rest (P = 0.92) and exercise (P = 0.94) between groups, but its source appeared different with more coming from muscle at rest and plasma during exercise in NGT, whereas the converse was true in PD. Altogether, higher basal IMTG concentration that is metabolically inflexible distinguishes obese people with PD from those with NGT.

1505 Background: Obesity and physical inactivity are associated with poorer prognosis among breast cancer survivors. Inflammatory and metabolic pathways may explain these associations. We hypothesi...

174 Background: Because obesity portends a higher risk of breast cancer mortality, achieving a healthy weight is recommended for breast cancer survivors. The impact of weight history on the ability to lose weight is unclear. We previously reported a 6.2 + 0.7% vs. 2.1 + 0.9% weight loss (p = .0003) in 100 breast cancer survivors randomized to a 6-month weight loss intervention vs. usual care. We examined whether weight history modified the effect of the intervention on body weight changes. Methods: Breast cancer survivors with a BMI > 25 kg/m2were randomized to usual care or 6-month, 11-session diet and exercise-counseling intervention. Baseline and 6 month weight and height were measured; weight at ages 18 and 35, 5 years and 1 year before and at diagnosis were self-reported. We defined weight history as: 1) change in weight between each time point and baseline; and 2) duration of obesity (i.e., number of years of having a BMI > 30 between age 18 and baseline). Generalized linear models were used to evaluate mean changes at 6 months between the intervention and usual care groups, adjusted and stratified by weight history variables. Results: Mean age and time since diagnosis were 59 + 7 years and 2.9 + 2.1 years, respectively. BMI increased over time (age 18 BMI = 21.8 + 2.9; baseline BMI = 32.4 + 6.5). Number of years being obese was 5.3 + 8.2 years (range 0-40 years). BMI at baseline, change in BMI from various time points to baseline, and years of obesity did not modify weight loss results. After adjusting for weight history, women randomized to intervention vs. usual care lost 6.1 + 0.7% vs. 2.0 + 0.9%, p = .0006, respectively. Conclusions: Participants reported a history of steady weight gain over time. The duration of obesity did not modify weight loss results. Weight history did not hinder survivors’ ability to lose clinically meaningful weight via a structured intervention.