There is growing interest in the use of molecular features as predictors of age, age-related disease risk and mortality. A major shortcoming of this field, however, is the lack of suitable translational research models to identify and understand the underlying mechanisms of these predictive biomarkers in human populations. In particular, we lack a system which, like humans, is genetically variable, lives in diverse environments, and experiences age-related chronic conditions treated in the context of a sophisticated health care system. Here, we present results from our analysis of data from the Dog Aging Project (DAP), a long-term longitudinal study of aging in companion dogs. Using longitudinal survival models on data from 937 dogs of the deeply phenotyped Precision Cohort within the DAP, we present the striking finding of a strong, highly significant positive correlation between the effect of individual metabolites on all-cause mortality in humans, and the association of those same metabolites on all-cause mortality in dogs. We also find that across these independent human studies, the biomarkers identified are also highly correlated, strongly suggesting a general signature of mortality within the plasma metabolome across humans, and now in dogs as well. Given the many similarities between dogs and humans with respect to genetics, environment, disease, and disease treatment, and the fact that dogs are so much shorter lived than humans, we argue that dogs represent an extremely valuable translational model in our ongoing effort to understand the underlying molecular causes and consequences of age-related morbidity and mortality in humans.

Metabolomic indices summarizing diet-related metabolic responses are instrumental for examining and replicating diet–disease associations. Here we aim to identify metabolomic signatures characterizing the amounts and types of dietary carbohydrate and assess their associations with type 2 diabetes (T2D) risk. Nutritional metabolomics indices were developed using data from 1,196 healthy participants in the Lifestyle Validation Study with 7-day diet records (7DDRs). Elastic net regression within cross-validation was used to derive metabolomic indices of total carbohydrates and primary food sources. Replication was conducted using feeding menu data among 153 women from the Nutrition and Physical Activity Assessment Study. Associations with incident T2D were examined using multivariable Cox regression in 11,454 participants from the Nurses’ Health Study, Nurses’ Health Study II and Health Professionals Follow-up Study. Metabolites positively associated with total carbohydrates and added sugars mainly included glycerolipids (diacylglycerols and triglycerides), whereas glycerophospholipids (phosphatidylethanolamines and phosphatidylcholines) were inversely associated. Whole grains were linked to betaine, 3-indolepropionic acid (IPA) and hippuric acid; vegetables and legumes to IPA, N-acetylornithine and pipecolic acid; and fruits to proline-betaine and IPA. Identified metabolomic signatures showed significant correlations with a 7-day diet record-assessed diet in the Lifestyle Validation Study (Pearson r 0.33–0.65). In the Nutrition and Physical Activity Assessment Study, the metabolomic index of total carbohydrates was also significantly correlated with intake (r = 0.40). Signatures for total carbohydrates, added sugars, refined grains and potatoes were associated with higher T2D risk (HR per s.d. (95

We developed calibration equations using metabolomics from fasting blood and 24-hour urine for Healthy Eating Index 2010 (HEI-2010) and Alternative Healthy Eating Index 2010 to address measurement error from self-reported diet. We examined associations between metabolomic-calibrated dietary patterns and cancer risk in the Women's Health Initiative (WHI) (n = 108 522). Metabolomic signatures were created from a WHI Feeding (n = 153; 2010-2014) and WHI Observational Study (n = 450; 2006-2009). Dietary patterns were regressed on metabolites using the feeding study food intake records. Metabolomic-based dietary patterns were estimated from 24-hour dietary recalls, food frequency questionnaire and 4-day food record in the Observational Study using a stepwise approach. Cox regression estimated cancer risk of metabolomic-calibrated dietary patterns with a median follow-up of 15.8 years. Adjusted R2 for HEI-2010 and Alternative Healthy Eating Index 2010 calibration equations were 57.5% and 48.8% for food frequency questionnaire, 61.6% and 62.6% for 4-day food record, and 52.5% and 53.2% for dietary recalls. Without calibration, a 20% increment in HEI-2010 was associated with lower risk of colorectal (HR, 0.94; 95% CI, 0.90-0.99), lung (HR, 0.90; 95% CI, 0.86-0.94), bladder (HR, 0.86; 95% CI, 0.75-0.99), and total invasive cancers (HR, 0.98; 95% CI, 0.96-0.99). With metabolomic calibration, higher HEI-2010 was associated with lower risk of lung (HR, 0.79; 95% CI, 0.71-0.88) and total invasive cancers (HR, 0.96; 95% CI, 0.92-1.00). Metabolomic-calibrated dietary patterns might mitigate measurement errors and strengthen diet-cancer associations. Trial registration:  www.whi.org.

BACKGROUND:Associations between dietary macronutrient composition and risk of major chronic diseases remain uncertain, partly because of reliance on self-reported dietary intake. OBJECTIVES:To make comparisons among biomarker-calibrated dietary macronutrient densities and risks of cardiovascular diseases (CVDs), cancers, and type-2 diabetes (T2D) over long-term follow-up in Women's Health Initiative (WHI) cohorts of postmenopausal United States females. METHODS:Biomarker-based intake estimates from serum and 24-h urine metabolomic profiles were calculated for macronutrient component densities in a WHI Nutrition and Physical Activity Assessment Study (n = 436). These values were regressed linearly on corresponding food frequency questionnaire (FFQ) density estimates and participant characteristics to produce calibration equations that adjust FFQ estimates for random and systematic measurement error. Biomarker-calibrated dietary density estimates were calculated in larger WHI cohorts (n = 82,121). Hazard ratio (HR) methods were used to prospectively relate both biomarker-calibrated intake assessments and, separately, FFQ assessments to chronic disease risk. RESULTS:Calibration equations meeting an adjusted R2 criterion could be developed for protein, carbohydrate, saturated fatty acid (SFA), and polyunsaturated fatty acid (PUFA) densities, and for several specific SFA and PUFA densities, but not for monounsaturated fatty acid density. HR estimates (95% confidence intervals) for 20% increments in biomarker-calibrated SFA density and PUFA density, compared with other macronutrient sources, were 1.13 (1.04, 1.23) and 0.95 (0.87, 1.04) for coronary artery disease (CAD), 1.02 (0.96, 1.08) and 1.10 (1.02, 1.17) for breast cancer, and 1.07 (1.04, 1.11) and 1.07 (1.03, 1.12) for T2D. Protein density was also directly associated with T2D risk. Positive SFA density associations with CVD and T2D risk may be attributable to dietary palmitic acid. CONCLUSIONS:Compared with other macronutrient sources, risk may be elevated for CAD at higher SFA density, for breast cancer at higher PUFA density, and for T2D risk at higher SFA, PUFA, and protein densities, among postmenopausal United States females. This study was registered with clinicaltrials.gov identifier as NCT00000611 (https://clinicaltrials.gov/study/NCT00000611).

Plant-based dietary strategies may offer a tractable approach to mitigating microbiome disruption and improving outcomes in patients undergoing autologous hematopoietic cell transplantation (auto-HCT) for multiple myeloma, a population in whom intestinal dysbiosis has been linked to infectious complications and inferior survival. We conducted a single-arm study to test the feasibility and biological activity of a high-fiber, plant-based, whole-food meal delivery intervention during the peri-transplant period. Adults with multiple myeloma (n = 22) received fully prepared, plant-based meals for 5 weeks spanning conditioning, neutropenia, and early recovery, with the goal of supporting consumption of nutrient-dense, high-fiber foods despite transplant-related symptoms that often limit oral intake. The primary endpoints were feasibility and tolerability, defined by successful enrollment, adherence to study procedures, and patient-reported intake of study meals; diet was quantified using prospective food diaries and 24-hour dietary recall surveys. Secondary endpoints included changes in gut microbiome composition and function assessed by shotgun metagenomic sequencing and stool short-chain fatty acid (SCFA) measurements. The intervention was feasible and generally well tolerated, with all participants consuming at least some proportion of delivered meals and with adherence sufficient to support planned dietary and correlative analyses. Greater intake of study meals was associated with more pronounced shifts in gut microbial communities, including enrichment of SCFA-producing taxa and compositional changes consistent with a fiber-responsive microbiome. Stool SCFA concentrations increased from baseline to the end of the intervention, suggesting a functional impact of the dietary strategy on microbial metabolite production during the peri-transplant period. These findings demonstrate that a plant-based meal delivery intervention is implementable during auto-HCT and suggest dose-dependent modulation of the gut microbiome and its metabolic output. Larger randomized trials are warranted to determine whether microbiome-targeted nutrition can reduce transplant-related toxicities, enhance immune recovery, and improve disease control in multiple myeloma. The trial is registered at ClinicalTrials.gov ( NCT06559709 ).

BACKGROUND:Although measures of blood and tissue fatty acid (FA) concentrations are available, objective measures of dietary FA densities (grams per kilocalories) are generally lacking. OBJECTIVES:We aimed to explore the development of biomarkers for specific and composite dietary FA densities, not including contributions from dietary supplements, using metabolite profiles from serum and 24-h urine, along with separately measured serum phospholipid FA concentrations in the Women's Health Initiative. METHODS:Potential biomarker equations were based on linear regression of feeding study dietary FA densities on metabolite concentrations, each log-transformed, among participants in a habitual-diet human feeding study (n = 153) within the Women's Health Initiative. Corresponding biomarker equations were also considered for total SFA, MUFA, and PUFA densities and for total n-3 and n-6 PUFA densities. Dietary FA density estimates derived from these equations were evaluated by correlation with feeding study intake densities, and by other important biomarker criteria. RESULTS:Regression cross-validated R2 values >30% for specific SFAs were 64.7 butyric, 60.9 caprioc, 48.7 caprylic, 53.0 capric, 39.9 lauric, 61.0 myristic, 42.2 palmitic, 34.2 stearic, 34.8 arachidic, 49.9 decosanoic; for specific MUFAs were 31.3 oleic; and for specific PUFAs were 51.7 linoleic, 50.1 α-linolenic, 39.7 arachidonic, 40.2 EPA, 53.5 decosapentaenoic acid, and 47.9 DHA. Corresponding values were 46.4, 52.8, 46.1, and 52.4 for total SFA, total PUFA, total n-3, and total n-6 densities. Many FA density equations had contributions from multiple metabolites, mostly serum metabolites, and from total energy expenditure. Sensitivity and specificity criteria are plausibly satisfied for proposed biomarkers, based on the feeding study design and on the sets of selected metabolites. CONCLUSIONS:Combinations of log-transformed metabolite concentrations can lead to objective intake density estimates for multiple FAs in the diets of United States postmenopausal females, with relevance to the reliable study of dietary FA densities and chronic disease risk. This study was registered at clinicaltrials.gov as NCT00000611 https://clinicaltrials.gov/study/NCT00000611).

The aim of metabolic phenotyping (metabotyping) is to discover and identify metabolites (including lipids) that can be used to characterize biological samples and differentiate between different physiological states. The identification of the metabolites responsible for this differentiation is essential if mechanistic understanding is to be obtained. Confident metabolite identification arguably represents the most important outcome of untargeted metabolomics studies but currently the standards used for metabolite identification reported in many publications do not strictly follow the various published guidelines and thus these identifications lack sufficient proof. In this perspective we define problems that currently plague the field of metabolite identification using MS-based techniques, particularly LC-MS, in untargeted metabolic phenotyping. Despite considerable efforts by the community (researchers, instrument manufacturers, software, and database developers) this continues to be a contentious and error-prone step in the metabolomics workflow. The majority of publications provide only sparse data on the evidence for metabolic markers “identified” and we have observed an alarming increase in the frequency of erroneous metabolite identifications. Here, we describe the problem and provide several illustrative case studies. Our goal is to raise awareness and highlight the issue of poor metabolite identification, since it is also increasingly apparent that these errors are not always recognised during the reviewing process, such that papers with potentially erroneous metabolite identities reach publication. Poor metabolite identification potentially represents an existential threat to the credibility of untargeted “discovery” metabolomics and can pollute the literature. Here we describe the aetiology of the problem and explain how and why this issue affects the field. We argue that coordinated action is required by researchers, database managers, scientific societies and the reviewers, editors and publishers of scientific journals to both acknowledge and address this important problem.

Abstract Plant-based whole food diets may represent a tractable approach to mitigating microbiome disruption and improving outcomes in patients undergoing autologous hematopoietic cell transplantation (auto-HCT) for multiple myeloma, a population in whom intestinal dysbiosis has been linked with inferior survival. We conducted a single-arm clinical trial at our center, in which participants undergoing auto-HCT (n = 22) received fresh, pre-prepared, plant-based meals for 5 weeks spanning conditioning, neutropenia, and early recovery, with the goal of supporting the consumption of nutrient-dense, high-fiber foods. The primary end points were feasibility and tolerability, defined by successful enrollment and patient-reported intake of study meals. Dietary intake was quantified using prospective food diaries and 24-hour dietary recall surveys. Secondary end points included changes in gut microbiome composition and function assessed by shotgun metagenomic sequencing and stool short-chain fatty acid (SCFA) measurements. The intervention was feasible and generally well tolerated, with all participants consuming delivered meals to some degree, with adherence sufficient to support planned dietary and correlative analyses. Greater intake of study meals was associated with more pronounced shifts in gut microbial communities, including enrichment of SCFA-producing taxa and compositional changes consistent with a fiber-responsive microbiome. Stool SCFA concentrations increased from baseline to the end of the intervention, suggesting a potential influence of the dietary strategy on microbial metabolite production during the peritransplant period. These findings demonstrate that a plant-based meal delivery intervention is implementable during auto-HCT and suggest dose-dependent modulation of the gut microbiome and its metabolic output. The trial was registered at ClinicalTrials.gov as NCT06559709.

Multiple therapeutic strategies are being developed to slow Huntington's disease (HD) progression through targeted reduction of huntingtin (HTT) protein or mRNA. Despite HTT's discovery over 30 years ago, its cellular functions remain incompletely understood, and the long-term consequences of HTT-lowering therapies remain unclear. We previously demonstrated that hepatic HTT loss in mice disrupts hepatocyte zonation and metabolism. Here, we investigate the physiological consequences of hepatic Htt loss. Across multiple models of Htt loss--including ubiquitous and hepatocyte-specific genetic knockouts and a therapeutically relevant Htt-targeting siRNA--there was elevated expression of IL-6/STAT3-driven acute phase response genes. Single-nucleus RNA sequencing reveals a zonal pattern of hepatocyte stress, most highly upregulated in pericentral hepatocytes, and identifies a distinct pericentral cluster of stressed hepatocytes that was enriched ~9.6-fold following Htt knockout. Histological examination reveals that Htt loss results in increased hepatic pathology, including hepatic intranuclear inclusions, apoptosis, and necrosis, as well as prevalence of granulomas. Transcriptomic analysis reveals significant upregulation of metallothionein genes following Htt loss, as confirmed by elevated plasma metallothionein-1 (MT1) levels in knockout mice. These findings underscore important safety considerations for HTT-lowering therapies and suggest candidate biomarkers for monitoring hepatic off-target effects in clinical trials.

Background Associations of the macronutrient composition of the diet with risks of postmenopausal breast and colorectal cancer (CRC) are uncertain, partly because of reliance on self-reported dietary data. Objectives We aimed to study biomarker development for several macronutrient component densities using serum and spot urine metabolomics and, when appropriate, to assess their associations with breast cancer and CRC risks in a Women’s Health Initiative (WHI) cohort of postmenopausal U.S. females. Design and Methods We explored linear biomarker equations for log-transformed macronutrient component densities using fasting serum metabolomic profiles, with and without spot urine, in a WHI feeding study (n=153). We used equations satisfying a cross-validated regression R2 (CV-R2) criterion to calculate potential biomarker values for 577 breast cancer cases and 181 colorectal cancer cases and their 1-1 matched controls. We used Cox regression with baseline stratification on matched pairs to examine dietary composition associations with cancer risk. Results Serum-based biomarker equations for macronutrient component densities had CV-R2 values as follows: polyunsaturated fatty acids (PUFA) 46.7%, monounsaturated fatty acids (MUFA) 36.3%, saturated fatty acids (SFA) 33.6%, carbohydrate 32.3%, and protein 29.4%. The inclusion of spot urine metabolites did not materially improve these values. In analyses including serum-based PUFA, MUFA, SFA and carbohydrate densities the breast cancer hazard ratios (95% CIs) for 20% increments in dietary densities were 0.98 (0.89, 1.07) for PUFA and 1.14 (0.97, 1.33) for MUFA. Corresponding CRC hazard ratios were 0.98 (0.81, 1.17) and 1.46 (1.10, 1.93). Analyses based on food frequency questionnaires differed from these estimates for breast cancer, but tended to agree for CRC. Intakes of dairy and meat products may help to explain observed associations. Conclusions In a population of U.S. postmenopausal females dietary MUFA density was associated with an elevation in CRC risk. Breast cancer associations with biomarker-based macronutrient densities require further developmentThis study is registered with clinicaltrials.gov identifier: NCT00000611 https://clinicaltrials.gov/study/NCT00000611.

Ergothioneine (ERG), a unique, naturally occurring antioxidant of dietary origin, is gaining increasing attention due to its crucial roles in human health and diseases. Despite its significance, ERG is rarely detected in biospecimens by mass spectrometry (MS) and, to date, has not been characterized by nuclear magnetic resonance (NMR) spectroscopy, two widely used analytical techniques in metabolomics. In this study, we investigated human plasma, whole blood (WB), and red blood cells (RBC), as well as mouse blood and tissues, combining NMR, LC-MS, and ratio analysis techniques. The results demonstrate the ability of simple 1D 1H NMR to routinely identify and quantify ERG in various biological samples. The levels of ERG vary widely and depend on the type of biological sample, with human RBC exhibiting remarkably high concentrations, often exceeding 1.5 mM. The average levels of ERG in human plasma, WB, and RBC were in ratios of 1:70:140, respectively. Moreover, ERG levels showed a linear correlation between plasma and WB (R 2 = 0.59), plasma and RBC (R 2 = 0.75), and WB and RBC (R 2 = 0.98). In mice, ERG levels exhibit a distinct whole-body distribution, with average levels in the mouse skeletal muscle, brain, heart, kidney, and liver in ratios of 0:1:10:35:45, respectively. The demonstration of surprisingly high levels of ERG in biological samples using 1H NMR opens new avenues for its analysis using both NMR and MS methods to explore its roles in human health and diseases, as part of routine global or targeted metabolomics studies.

BACKGROUND:Concurrent consumption of dietary fiber and n-3 polyunsaturated fatty acids reduces colon tumor formation. However, their combined effects on colorectal cancer risk remain unexplored in human trials. OBJECTIVES:This study investigated the synergistic effects of fish oil (FO) and fermentable fiber on the gut transcriptional profiles and microbiome composition in older adults. METHODS:In a randomized controlled crossover pilot study, 30 adults (ages 50-75 y), received fermentable fiber (33 g/d soluble corn fiber; SCF) plus eicosapentaenoic acid and docosahexaenoic acid (EPA + DHA as FO, 7.7 g/d) or a comparator (similar doses of maltodextrin plus corn oil; MD + CO) for 30 d, followed by a 60-d washout period before crossing over to the alternate intervention. Serum phospholipid fatty acids, stool exfoliome [ribonucleic acid sequencing (RNAseq)], microbiome (16S ribosomal ribonucleic acid gene sequencing), butyrate kinase (but) gene abundance (digital droplet polymerase chain reaction), and fecal short-chain fatty acids were analyzed. Linear mixed models were used for the majority of outcome analyses. Differential expression and pathway enrichment analyses were applied to RNAseq data, whereas microbiome diversity was assessed using α and β diversity. RESULTS:Serum EPA and DHA concentrations were higher after SCF + FO than MD + CO supplementation [EPA: β = 0.51; 95% confidence interval: 0.31, 0.72; DHA: β = 0.18; 95% confidence interval: 0.10, 0.27; P < 0.0001]. Analysis of host gut transcriptional networks revealed that SCF + FO supplementation inhibited the glucose-insulin receptor-phosphatidylinositol-3 kinase-signaling axis. Microbiome analysis revealed significant intervention differences in β-diversity (F = 4.4, R2 = 0.08, P = 0.001), and 27 of 73 genera analyzed, several known short-chain fatty acid producers, differed between the 2 interventions (false discovery rate <0.05). Abundance of the but gene from Roseburia sp (P < 0.001) and the genera Roseburia (P = 0.006) were lower in the SCF + FO compared to MD + CO intervention, although fecal butyrate concentrations did not differ. CONCLUSIONS:Thirty-day supplementation of SCF + FO compared with MD + CO showed significant shifts in intestinal cell pathways relevant to colorectal cancer with concomitant differences in gut microbial community structure and butyrate-producing taxa.

Studies in laboratory organisms typically minimize all environmental and genetic variation other than the intervention of interest. In aging studies, these highly controlled conditions have yielded profound insights into aging. But even within isogenic cohorts of lab animals in controlled environments, we observe substantial variation in lifespan. Here we exploited the climbing behavior of Drosophila to study variation in mortality among isogenic populations in a controlled environment. We show that fractionating large cohorts of relatively young isogenic flies by climbing behavior predicts future mortality risk and stress sensitivity. Using metabolomics to dissect this variation, we found metabolites whose abundances differ among the fractions. We also took advantage of the large number of individuals in each fraction, and the ease with which they can be collected, to explore the covariance structure of metabolites in flies that are genetically identical, but divisible into short-lived and long-lived fractions. In doing so, we identified metabolites and metabolic pathways as candidate biomarkers of intrinsic mortality risk.

BACKGROUND:Associations of the macronutrient composition of the diet with total energy intake (EI) are uncertain, as are associations of macronutrient composition with self-reported energy underreporting. OBJECTIVES:We aimed to estimate the associations of biomarker-assessed EI with both biomarker-assessed and self-reported macronutrient component densities in a Women's Health Initiative (WHI) subcohort of postmenopausal females in the United States. Secondarily, we examined energy underreporting using food records, recalls, and frequencies, for association with macronutrient densities. METHODS:We used a previously proposed EI biomarker equation based on doubly labeled water (DLW) and updated biomarker equations for several macronutrient component densities, to estimate EI and macronutrient component densities in a WHI nutritional biomarkers subcohort (n = 436; 2007-2009). We used linear regression of EI biomarker values on biomarker and self-reported macronutrient component densities, and of EI underreporting values on biomarker densities, to examine targeted associations. RESULTS:Using biomarker assessments, the geometric mean (95% CI) for EI corresponding to a 20% increment in carbohydrate density was 2.0% (0.1%, 3.9%) higher, and for a 20% protein density increment was 2.1% (0.5%, 3.7%) lower. The former was attributable to added sugars. Similarly, EI values for 20% increments in polyunsaturated (PUFA), and monounsaturated (MUFA) fatty acid densities were 1.4% (0.3%, 2.6%) higher and 1.5% (0.1%, 2.9%) lower, respectively. Pertinent associations were either not detected or were substantially attenuated if instead self-reported macronutrient densities were used. Also, EI underreporting was strongly related to self-reported macronutrient densities using food records, recalls, or frequencies. CONCLUSIONS:Among postmenopausal females in the United States lower EI was associated with diets relatively high in protein or MUFA, and higher EI was associated with diets relatively high in PUFA or added sugars. These associations are of public health importance but are mostly missed using self-reported dietary density assessments. Self-reported energy underestimation is substantially associated with self-reported macronutrient densities. CLINICAL TRIAL REGISTRY:This study is registered with clinicaltrials.gov identifier: NCT00000611.

Transplanted human pluripotent stem cell-derived cardiomyocytes (hPSC-CMs) improve ventricular performance when delivered acutely post-myocardial infarction but are ineffective in chronic myocardial infarction/heart failure. 2’-deoxy-ATP (dATP) activates cardiac myosin and potently increases contractility. Here we engineered hPSC-CMs to overexpress ribonucleotide reductase, the enzyme controlling dATP production. In vivo, dATP-producing CMs formed new myocardium that transferred dATP to host cardiomyocytes via gap junctions, increasing their dATP levels. Strikingly, when transplanted into chronically infarcted hearts, dATP-producing grafts increased left ventricular function, whereas heart failure worsened with wild-type grafts or vehicle injections. dATP-donor cells recipients had greater voluntary exercise, improved cardiac metabolism, reduced pulmonary congestion and pathological cardiac hypertrophy, and improved survival. This combination of remuscularization plus enhanced host contractility offers a novel approach to treating the chronically failing heart. One Sentence Summary Transplanting gene-edited dATP-donor cardiomyocytes in chronically infarcted heart restores their cardiac function, improving both exercise tolerance and survival.

The rapidly expanding field of metabolomics presents an invaluable resource for understanding the associations between metabolites and various diseases. However, the high dimensionality, presence of missing values, and measurement errors associated with metabolomics data can present challenges in developing reliable and reproducible methodologies for disease association studies. Therefore, there is a compelling need to develop robust statistical methods that can navigate these complexities to achieve reliable and reproducible disease association studies. In this paper, we focus on developing such a methodology with an emphasis on controlling the False Discovery Rate during the screening of mutual metabolomic signals for multiple disease outcomes. We illustrate the versatility and performance of this procedure in a variety of scenarios, dealing with missing data and measurement errors. As a specific application of this novel methodology, we target two of the most prevalent cancers among US women: breast cancer and colorectal cancer. By applying our method to the Wome's Health Initiative data, we successfully identify metabolites that are associated with either or both of these cancers, demonstrating the practical utility and potential of our method in identifying consistent risk factors and understanding shared mechanisms between diseases.

Objectives:Metabolic demands of the developing conceptus are highly dynamic during pregnancy. While placental metabolism has been well described at term and in cell lines, changes in the placental metabolome during development remains understudied. We investigated the placental metabolome, metabolite trajectories, and altered pathways across trimesters in normal human pregnancy by integrating metabolomic and transcriptomic data. Methods:Targeted aqueous metabolomic profiling of 372 metabolites was conducted on placental biopsies from samples collected in the first (n=12), second (n=13), and third (n=11) trimesters of normal pregnancy using liquid chromatography-tandem mass spectrometry. Robust linear models identified differentially abundant metabolites across trimesters in models adjusted for fetal sex and total protein. We conducted pathway analysis using a human metabolic reconstruction. To further aid in biological interpretation, we leveraged publicly available transcriptomics data to conduct pathway-level multi-omic integration throughout gestation. Results:Samples clustered by trimester in principal component analysis and we identified 5 metabolite trajectories. Out of 193 detectable metabolites, 149 (77%) differed by trimester (FDR<0.05). Using pathway-level multi-omic integration, pathways involved in extracellular transport, and pyruvate, amino acid, NAD, and membrane lipid metabolism are up-regulated in the second trimester compared to the first. In the late third trimester, pathways involved in amino acid metabolism, redox balance, mitochondrial transport, and biomolecule synthesis were down-regulated compared to second trimester. Conclusions:Placental metabolite abundances change substantially across gestation and integration with metabolic gene expression provides insight into dynamic metabolic function during pregnancy. Observed pathway-level changes potentially reflect the metabolic response to invading maternal circulation in the first-to-second trimester transition, as well as changing maternal and fetal metabolic requirements. Differences observed at term may reflect placental senescence and preparation for parturition. These data can inform other molecular analyses of the placenta by providing enhanced resolution of metabolic changes across pregnancy.