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Obesity is a chronic, relapsing, progressive disease of excess adiposity that increases the risk of developing at least 13 types of cancer. This report provides a concise review of the current state of the science regarding metabolic and bariatric surgery and obesity pharmacotherapy related to cancer risk. Meta-analyses of cohort studies report that metabolic and bariatric surgery is independently associated with a lower risk of incident cancer than nonsurgical obesity care. Less is known regarding the cancer-preventive effects of obesity pharmacotherapy. The recent approval and promising pipeline of obesity drugs will provide the opportunity to understand the potential for obesity therapy to emerge as an evidence-based cancer prevention strategy. There are myriad research opportunities to advance our understanding of how metabolic and bariatric surgery and obesity pharmacotherapy may be used for cancer prevention.

The effects of vitamin E supplementation on cancer and other chronic diseases are not clear. We compared the serum metabolomic profile of differing vitamin E dosages in order to re-examine the previously observed changes in a novel C22 lactone sulfate compound, androgenic steroids, and other metabolites. A total of 3409 women and men previously selected for metabolomics studies in the PLCO Cancer Screening Trial were included in this investigation. Serum metabolites were profiled using ultrahigh-performance liquid and gas chromatography/tandem mass spectrometry. Seventy known metabolites including C22 lactone sulfate and androgens were significantly associated with vitamin E supplementation. In the sex-stratified analysis, 10 cofactors and vitamins (e.g., alpha-CEHC sulfate and alpha-CEHC glucuronide), two carbohydrates (glyceric and oxalic acids), and one lipid (glycocholenate sulfate) were significantly associated with vitamin E dose in both males and females (FDR-adjusted p-value < 0.01). However, the inverse association between C22 lactone sulfate and daily vitamin E supplementation was evident in females only, as were two androgenic steroids, 5-androstenediol and androsterone glucuronide. Our study provides evidence of distinct steroid hormone pathway responses based on vitamin E dosages. Further studies are needed to gain biological insights into vitamin E biochemical effects relevant to cancer and other chronic diseases.

No evidence-based prevention strategies currently exist for cancer-related cognitive decline (CRCD). Although patients are often advised to engage in healthy lifestyle activities (e.g., nutritious diet), little is known about the impact of diet on preventing CRCD. This secondary analysis evaluated the association of pre-treatment diet quality indices on change in self-reported cognition during chemotherapy.Study participants (n = 96) completed the Block Brief Food Frequency Questionnaire (FFQ) before receiving their first infusion and the PROMIS cognitive function and cognitive abilities questionnaires before infusion and again 5 days later (i.e., when symptoms were expected to be their worst). Diet quality indices included the Dietary Approaches to Stop Hypertension (DASH), Alternate Mediterranean Diet (aMED), and a low carbohydrate diet index and their components. Descriptive statistics were generated for demographic and clinical variables and diet indices. Residualized change models were computed to examine whether diet was associated with change in cognitive function and cognitive abilities, controlling for age, sex, cancer type, treatment type, depression, and fatigue.Study participants had a mean age of 59 ± 10.8 years and 69% were female. Although total diet index scores did not predict change in cognitive function or cognitive abilities, higher pre-treatment ratio of aMED monounsaturated/saturated fat was associated with less decline in cognitive function and cognitive abilities at 5-day post-infusion (P ≤ .001).Higher pre-treatment ratio of monounsaturated/saturated fat intake was associated with less CRCD early in chemotherapy. Results suggest greater monounsaturated fat and less saturated fat intake could be protective against CRCD during chemotherapy.

ContextPolycystic ovarian syndrome (PCOS) is a complex disorder in which metabolic abnormalities are associated with reproductive dysfunction. Women with PCOS have increased ceramide concentrations. Previous studies demonstrated that treating metabolic abnormalities of PCOS with metformin improved glucose effectiveness after 12 weeks.ObjectiveWe evaluated whether, in women with PCOS, lower baseline ceramide, diacylglycerol (DAG), and triacylglycerol (TAG) concentrations were associated with improved metabolic response to metformin.MethodsWomen (n = 29), aged 29 ± 5 years and diagnosed with PCOS by the NIH criteria underwent an intravenous glucose tolerance test (IVGTT) before and after 12-week treatment with metformin (1500 mg per day). Metabolic responders were defined by improved glucose effectiveness, specifically, the ability of glucose to stimulate uptake and suppress production, after metformin treatment.ResultsTwelve weeks of metformin resulted in weight loss (−1.7 ± 2.6 kg, P ConclusionLower total Cer(16:0) and DHSM concentrations are associated with a beneficial metabolic response to metformin in women with PCOS. Based on the known association between higher ceramide levels and type 2 diabetes, the data suggest that metformin improves metabolic parameters in women with mild metabolic derangements.

Background Although unopposed estrogen exposure is considered a major driver of endometrial carcinogenesis, chronic inflammation and insulin resistance and hyperinsulinemia are also major endometrial cancer risk factors. However, it is unclear whether diets with inflammatory or insulinemic potential are associated with risk of endometrial cancer. Methods We followed 48 330 women from the Nurses’ Health Study (1984-2016) and 85 426 women from the Nurses’ Health Study II (1989-2017). Using food frequency questionnaires, we calculated repeated measures of empirical dietary inflammatory pattern (EDIP) and empirical dietary index for hyperinsulinemia (EDIH) scores, which characterize the potential of the whole diet to modulate circulating biomarkers of inflammation or C-peptide, respectively. We used multivariable-adjusted Cox regression to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for type I endometrial cancer risk. Results We documented 1462 type I endometrial cancer cases over 2 823 221 person-years of follow-up. In the pooled multivariable-adjusted analyses, women in the highest compared with lowest quintiles were at higher risk of type I endometrial cancer (EDIP HRQ5vsQ1 = 1.46, 95% CI = 1.24 to 1.73; Ptrend < .001; EDIH HRQ5vsQ1 = 1.58, 95% CI = 1.34 to 1.87; Ptrend < .001). Additional adjustment for body mass index attenuated the associations (EDIP HR = 1.03, 95% CI = 0.87 to 1.22; EDIH HR = 1.01, 95% CI = 0.85 to 1.21), and mediation analyses showed that body mass index may explain 60.4% (95% CI = 37.4% to 79.6%; P < .001) and 71.8% (95% CI = 41.0% to 90.4%; P < .001) of the association of endometrial cancer with EDIP and EDIH, respectively. Conclusions In this large cohort study, higher dietary inflammatory and insulinemic potential were each associated with increased endometrial cancer incidence, and this association may be almost entirely mediated by adiposity.

Background: Per- and polyfluoroalkyl substances (PFAS) are environmentally persistent synthetic chemicals detectable in the blood of most Americans. Elevated exposures to perfluorooctane sulfonate (PFOS) and perfluorooctanoate (PFOA), the two most prevalent PFAS, have been associated with several health outcomes including metabolic outcomes and some cancers. To evaluate possible mechanisms related to the biological effects of these chemicals, we conducted a pooled metabolome-wide association study (MWAS) of circulating PFOS and PFOA among 3,647 participants included in one of eight nested case-control serum metabolomic profiling studies conducted in the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial (PLCO) population. Methods: All metabolomic profiling was conducted by Metabolon Inc, using liquid-phase or gas chromatography coupled with mass spectrometry. All metabolites including PFOS and PFOA were centered (mean=0, standard deviation=1) and log-transformed. We conducted multivariable linear regression analyses to estimate study-specific associations between metabolite levels and PFOS and PFOA, adjusted for age at specimen collection, race/ethnicity, body mass index, smoking, case-control status, study center, and year of specimen collection. Sex was additionally adjusted for studies including both men and women. We then conducted combined the study-specific findings through meta-analysis using random effects models. Results: The meta-analysis results of 1,040 metabolites for PFOS and 1,103 for PFOA identified 83 and 67 metabolites, respectively, associated at Bonferroni-corrected significance thresholds (P=4.81 × 10−5 for PFOS; P=4.53 × 10−5 for PFOA). For both PFOS and PFOA, the strongest positive associations were observed for the xenobiotics 3,5-dichloro-2,6-dihydroxybenzoic acid [PFOS, summary beta coefficient =0.74, 95% confidence interval=0.67,0.80; PFOA, 0.48 (0.35,0.61)] and 3-bromo-5-chloro-2,6-dihydroxybenzoic acid [0.57 (0.43,0.72) and 0.39 (0.23,0.55)]. We also observed strong positive associations with PFOS and PFOA for metabolites related to the sphingomyelin (SM) pathway, such as hydroxypalmitoyl sphingomyelin (d18:1/16:0(OH)) [PFOS, 0.44 (0.24,0.64)] and SM (d18:1/18:0) [PFOS, 0.35 (0.24,0.46); PFOA, 0.25 (0.16,0.34)], and SM (d18:2/24:2) [PFOS, 0.32 (0.17,0.47)]. The PFOS metabolite associations remained after model adjustment for PFOA; however, the PFOA associations were greatly attenuated after controlling for PFOS. Conclusions: In this MWAS of PFAS, to our knowledge the largest to date, we observed robust positive associations with serum PFOS for several xenobiotic compounds and for metabolites related to the sphingomyelin pathway. Further investigation of these metabolites may offer insight into PFOS-related biologic effects contributing to disease development. Citation Format: Jongeun Rhee, Steven C. Moore, Demetrius Albanes, Tracy M. Layne, Erikka Loftfield, Rachael Stolzenberg-Solomon, Linda M. Liao, Mary C. Playdon, Mark P. Purdue. A metabolomic investigation of serum perfluorooctane sulfonate and perfluorooctanoate. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 4223.

Background: Obesity is a risk factor for endometrial cancer but whether metabolic dysfunction is associated with endometrial cancer independent of body size is not known. Methods: The association of metabolically defined body size phenotypes with endometrial cancer risk was investigated in a nested case–control study (817 cases/ 817 controls) within the European Prospective Investigation into Cancer and Nutrition (EPIC). Concentrations of C-peptide were used to define metabolically healthy (MH, World Health Organization, Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, Danish Cancer Society, Ligue nationale contre le cancer, Institut Gustave Roussy, Mutuelle Generale de l'Education Nationale, Institut National de la Sante et de la Recherche Medicale (Inserm), Deutsche Krebshilfe, Helmholtz Association, German Institute of Human Nutrition PotsdamRehbruecke (DIfE), Federal Ministry of Education & Research (BMBF), Fondazione AIRC per la ricerca sul cancro, Compagnia di San Paolo, Consiglio Nazionale delle Ricerche (CNR), Dutch Ministry of Public Health, Welfare and Sports (VWS), Netherlands Cancer Registry (NKR), LK Research Funds, Dutch Prevention Funds Netherlands Organization for Scientific Research (NWO), World Cancer Research Fund (WCRF-ERC) 232997, Netherlands Government, Health Research Fund (FIS)-Instituto de Salud Carlos III (ISCIII), Junta de Andalucia, Principality of Asturias, Basque Government, Regional Government of Murcia, Regional Government of Navarra, Catalan Institute of OncologyICO (Spain), Swedish Cancer Society Swedish Research Council, European Commission, County Council of Skane, County Council of Vasterbotten (Sweden), Cancer Research UK 14136 C8221/A29017 C19335/A21351, UK Research & Innovation (UKRI), Medical Research Council UK (MRC), European Commission 1000143 MR/M012190/1

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).

A major gap impeding development of new treatments for cancer-related fatigue is an inadequate understanding of the complex biological, clinical, demographic, and lifestyle mechanisms underlying fatigue. In this paper, we describe a new application of a comprehensive model for cancer-related fatigue: the predisposing, precipitating, and perpetuating (3P) factors model. This model framework outlined herein, which incorporates the emerging field of metabolomics, may help to frame a more in-depth analysis of the etiology of cancer-related fatigue as well as a broader and more personalized set of approaches to the clinical treatment of fatigue in oncology care. Included within this review paper is an in-depth description of the proposed biological mechanisms of cancer-related fatigue, as well as a presentation of the 3P model’s application to this phenomenon. We conclude that a clinical focus on organization risk stratification and treatment around the 3P model may be warranted, and future research may benefit from expanding the 3P model to understand fatigue not only in oncology, but also across a variety of chronic conditions.

The metabolic dysfunction driven by obesity, including hyperglycemia and dyslipidemia, increases risk for developing at least 13 cancer types. The concept of "metabolic dysfunction" is often defined by meeting various combinations of criteria for metabolic syndrome. However, the lack of a unified definition of metabolic dysfunction makes it difficult to compare findings across studies. This review summarizes 129 studies that evaluated variable definitions of metabolic dysfunction in relation to obesity-related cancer risk and mortality after a cancer diagnosis. Strategies for metabolic dysfunction management are also discussed.A comprehensive search of relevant publications in MEDLINE (PubMed) and Google Scholar with review of references was conducted.Metabolic dysfunction, defined as metabolic syndrome diagnosis or any number of metabolic syndrome criteria out of clinical range, inflammatory biomarkers, or markers of metabolic organ function, has been associated with risk for, and mortality from, colorectal, pancreatic, postmenopausal breast, and bladder cancers. Metabolic dysfunction associations with breast and colorectal cancer risk have been observed independently of BMI, with increased risk in individuals with metabolically unhealthy normal weight or overweight/obesity compared with metabolically healthy normal weight.Metabolic dysfunction is a key risk factor for obesity-related cancer, regardless of obesity status. Nonetheless, a harmonized definition of metabolic dysfunction will further clarify the magnitude of the relationship across cancer types, enable better comparisons across studies, and further guide criteria for obesity-related cancer risk stratification.

Background: The etiology of colorectal cancer is not fully understood. Methods: Using genetic variants and metabolomics data including 217 metabolites from the Framingham Heart Study (n = 1,357), we built genetic prediction models for circulating metabolites. Models with prediction R2 > 0.01 (Nmetabolite = 58) were applied to predict levels of metabolites in two large consortia with a combined sample size of approximately 46,300 cases and 59,200 controls of European and approximately 21,700 cases and 47,400 controls of East Asian (EA) descent. Genetically predicted levels of metabolites were evaluated for their associations with colorectal cancer risk in logistic regressions within each racial group, after which the results were combined by meta-analysis. Results: Of the 58 metabolites tested, 24 metabolites were significantly associated with colorectal cancer risk [Benjamini–Hochberg FDR (BH-FDR) < 0.05] in the European population (ORs ranged from 0.91 to 1.06; P values ranged from 0.02 to 6.4 × 10−8). Twenty one of the 24 associations were replicated in the EA population (ORs ranged from 0.26 to 1.69, BH-FDR < 0.05). In addition, the genetically predicted levels of C16:0 cholesteryl ester was significantly associated with colorectal cancer risk in the EA population only (OREA: 1.94, 95% CI, 1.60−2.36, P = 2.6 × 10−11; OREUR: 1.01, 95% CI, 0.99−1.04, P = 0.3). Nineteen of the 25 metabolites were glycerophospholipids and triacylglycerols (TAG). Eighteen associations exhibited significant heterogeneity between the two racial groups (PEUR-EA-Het < 0.005), which were more strongly associated in the EA population. This integrative study suggested a potential role of lipids, especially certain glycerophospholipids and TAGs, in the etiology of colorectal cancer. Conclusions: This study identified potential novel risk biomarkers for colorectal cancer by integrating genetics and circulating metabolomics data. Impact: The identified metabolites could be developed into new tools for risk assessment of colorectal cancer in both European and EA populations.

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.

Introduction: Obesity-related cancer (ORC) diagnosed individuals are at an elevated risk of new-onset diabetes compared with cancer-free individuals. However, less is known about mortality among those with incident type 2 diabetes after cancer. Methods: In the Women’s Health Initiative (N=14,455 ORC cases), we measured the association of incident type 2 diabetes diagnosed 0-1 years, >1-3, >3-5, >5-7 and >7-10 years after ORC diagnosis with cancer-specific and overall mortality using stratified Cox proportional hazards regression analysis. Results: The participants were post-menopausal females with ORC and predominantly white. The number of WHI participants with ORC who developed diabetes after cancer diagnosis were: 0-1 years - 205/12992 (1.58%); 1-3 years - 326/11322 (2.88%); 3-5 years - 276/9784 (2.82%); 5-7 years - 268/8420 (3.18%); and 7-10 years - 510/6419 (7.95%). The median survival after the first year of cancer diagnosis for participants without diabetes was 16.6 years (95% CI: 16.17-17.0 years) and for those with diabetes was 10.3 years (95% CI: 7.97-14.9 years). New-onset diabetes after ORC diagnosis showed elevated overall mortality and cancer-specific mortality when compared with no diabetes diagnosis. Conclusions: Incident type 2 diabetes is associated with worse cancer-specific and overall survival, particularly in the year after diagnosis, among women with ORC. Type 2 Diabetes from time since cancer diagnosis Overall mortality HR (95% CI)a Cancer-specific mortality HR (95% CI)a 0-1 years 1.79 (1.41-2.29) 1.56 (1.04-2.34) >1-3 years 1.22 (0.99-1.51) 1.05 (0.72-1.55) >3-5 years 1.21 (0.95-1.55) 1.37 (0.89-2.12) >5-7 years 1.02 (0.78-1.33) 0.80 (0.47-1.36) >7-10 years 1.10 (0.87-1.40) 0.97 (0.59-1.60) aModel adjusted for demographics (age, education, race/ethnicity, marital status), body mass index (BMI)) and cancer characteristics (cancer type, stage, grade). CI = confidence interval; HR = Hazard ratio. Citation Format: Prasoona Karra, Maci Winn, Benjamin Haaland, Garnet Anderson, Cynthia A. Thomson, Aladdin H. Shadyab, Juhua Luo, Nazmus Saquib, Howard Strickler, Rowan Chlebowski, Rhonda Arthur, Sheetal Hardikar, Mary C. Playdon. Diabetes incidence after obesity-related cancer diagnosis and survival in the women’s health initiative [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5880.

The study of natural plant molecules and their medicinal properties, pharmacognosy, provides a taxonomy for botanical families that represent diverse chemical groupings with potentially distinct functions in relation to human health. Yet, this reservoir of knowledge has not been systematically applied to elucidating the role of patterns of plant food consumption on gut microbial ecology and function. All chemical classes of dietary phytochemicals can affect the composition of the microbes that colonize the gut and their function. In turn, the gut microbiome affects the host via multiple mechanisms including gut barrier function, immune function, satiety and taste regulation and the activity of biological signaling pathways that influence health and disease. Herein, we report the development of a botanical diversity index (BDI) to evaluate plant food consumption as a novel metric for identifying and quantifying phytochemicals to which an individual is exposed. A rationale is advanced for using the BDI to investigate how plant food diversity impacts gut microbial ecology and functionality.

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.

Introduction: The metabolomics quality assurance and quality control consortium (mQACC) evolved from the recognized need for a community-wide consensus on improving and systematizing quality assurance (QA) and quality control (QC) practices for untargeted metabolomics. Objectives: In this work, we sought to identify and share the common and divergent QA and QC practices amongst mQACC members and collaborators who use liquid chromatography-mass spectrometry (LC–MS) in untargeted metabolomics. Methods: All authors voluntarily participated in this collaborative research project by providing the details of and insights into the QA and QC practices used in their laboratories. This sharing was enabled via a six-page questionnaire composed of over 120 questions and comment fields which was developed as part of this work and has proved the basis for ongoing mQACC outreach. Results: For QA, many laboratories reported documenting maintenance, calibration and tuning (82%); having established data storage and archival processes (71%); depositing data in public repositories (55%); having standard operating procedures (SOPs) in place for all laboratory processes (68%) and training staff on laboratory processes (55%). For QC, universal practices included using system suitability procedures (100%) and using a robust system of identification (Metabolomics Standards Initiative level 1 identification standards) for at least some of the detected compounds. Most laboratories used QC samples (>86%); used internal standards (91%); used a designated analytical acquisition template with randomized experimental samples (91%); and manually reviewed peak integration following data acquisition (86%). A minority of laboratories included technical replicates of experimental samples in their workflows (36%). Conclusions: Although the 23 contributors were researchers with diverse and international backgrounds from academia, industry and government, they are not necessarily representative of the worldwide pool of practitioners due to the recruitment method for participants and its voluntary nature. However, both questionnaire and the findings presented here have already informed and led other data gathering efforts by mQACC at conferences and other outreach activities and will continue to evolve in order to guide discussions for recommendations of best practices within the community and to establish internationally agreed upon reporting standards. We very much welcome further feedback from readers of this article. Fil: Evans, Anne M.. No especifíca; Fil: ODonovan, Claire. European Bioinformatics Institute; Reino Unido Fil: Playdon, Mary. University of Utah; Estados Unidos Fil: Beecher, Chris. No especifíca; Fil: Beger, Richard D.. National Center For Toxicological Research; Estados Unidos Fil: Bowden, John A.. University of Florida; Estados Unidos Fil: Broadhurst, David. Edith Cowan University; Australia Fil: Clish, Clary B.. Broad Institute; Estados Unidos Fil: Dasari, Surendra. Mayo Clinic; Estados Unidos Fil: Dunn, Warwick B.. University Of Birmingham; Reino Unido Fil: Griffin, Julian L.. University of Cambridge; Estados Unidos. Imperial College London; Reino Unido Fil: Hartung, Thomas. University Johns Hopkins; Estados Unidos Fil: Hsu, Ping Ching. University of Arkansas for Medical Sciences; Estados Unidos Fil: Huan, Tao. University of British Columbia; Canadá Fil: Jans, Judith. University of Utrecht; Países Bajos Fil: Jones, Christina M.. National Institute Of Standards And Technology; Estados Unidos Fil: Kachman, Maureen. Michigan State University; Estados Unidos Fil: Kleensang, Andre. University Johns Hopkins; Estados Unidos Fil: Lewis, Matthew R.. Imperial College London; Reino Unido Fil: Monge, Maria Eugenia. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Centro de Investigaciones en Bionanociencias "Elizabeth Jares Erijman"; Argentina Fil: Mosley, Jonathan D.. Environmental Protection Agency; Estados Unidos Fil: Taylor, Eric. University of Iowa; Estados Unidos Fil: Tayyari, Fariba. University of Iowa; Estados Unidos Fil: Theodoridis, Georgios. Aristotle University; Grecia Fil: Torta, Federico. No especifíca; Fil: Ubhi, Baljit K.. No especifíca; Fil: Vuckovic, Dajana. Concordia University; Canadá

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).

We describe here the agreed upon first development steps and priority objectives of a community engagement effort to address current challenges in quality assurance (QA) and quality control (QC) in untargeted metabolomic studies. This has included (1) a QA and QC questionnaire responded to by the metabolomics community in 2015 which recommended education of the metabolomics community, development of appropriate standard reference materials and providing incentives for laboratories to apply QA and QC; (2) a 2-day 'Think Tank on Quality Assurance and Quality Control for Untargeted Metabolomic Studies' held at the National Cancer Institute's Shady Grove Campus and (3) establishment of the Metabolomics Quality Assurance and Quality Control Consortium (mQACC) to drive forward developments in a coordinated manner.

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.