Circulating Plasma Metabolites Research Articles

Causal Metabolomic and Lipidomic Analysis of Circulating Plasma Metabolites in Autism: A Comprehensive Mendelian Randomization Study with Independent Cohort Validation.

The increasing prevalence of autism spectrum disorder (ASD) highlights the need for objective diagnostic markers and a better understanding of its pathogenesis. Metabolic differences have been observed between individuals with and without ASD, but their causal relevance remains unclear. Bidirectional two-sample Mendelian randomization (MR) was used to assess causal associations between circulating plasma metabolites and ASD using large-scale genome-wide association study (GWAS) datasets-comprising 1091 metabolites, 309 ratios, and 179 lipids-and three European autism datasets (PGC 2015: n = 10,610 and 10,263; 2017: n = 46,351). Inverse-variance weighted (IVW) and weighted median methods were employed, along with robust sensitivity and power analyses followed by independent cohort validation. Higher genetically predicted levels of sphingomyelin (SM) (d17:1/16:0) (OR, 1.129; 95% CI, 1.024-1.245; p = 0.015) were causally linked to increased ASD risk. Additionally, ASD children had higher plasma creatine/carnitine ratios. These MR findings were validated in an independent US autism cohort using machine learning analysis. Utilizing large datasets, two MR approaches, robust sensitivity analyses, and independent validation, our novel findings provide evidence for the potential roles of metabolomics and circulating metabolites in ASD diagnosis and etiology.

Long-term air pollution exposure and the blood metabolome: The rotterdam study

BackgroundAir pollution is a well-established risk factor for several adverse health outcomes, but the specific molecular mechanisms, particularly those involving metabolic processes, remain incompletely understood. ObjectiveTo evaluate associations between long-term air pollutant exposure and circulating plasma metabolites in two sub-cohorts of the population-based Rotterdam Study. MethodsWe analyzed data from 1455 participants of sub-cohort I (mean age 76.9 years, 58% female, 2002–2004) and 1061 participants from sub-cohort III (mean age 62.6 years, 56% female, 2012–2014). Mean annual exposure to fine particulate matter (PM2.5), black carbon, nitrogen dioxide, and ozone (measured both annually and in warm seasons only) were estimated at residential addresses using land use regression models. Plasma metabolites were measured by Metabolon Inc., using ultra-high-performance liquid chromatography coupled with tandem mass spectrometry. Cross-sectional associations between each air pollutant and 940 metabolites were determined using linear regression models. Benjamini-Hochberg false discovery rate (FDR) was utilized to control for multiple testing. Enrichment analysis was performed on statistically significant associated metabolites to identify significant metabolic pathways (p-value <0.05). ResultsIn sub-cohort I, PM2.5, black carbon, nitrogen dioxide, annual ozone and ozone in warm season were statistically significantly associated with, respectively, 63, 30, 20, 31, and 41 metabolites (FDR <0.05) mostly belonging to lipid and amino acid sub-classes, and unannotated metabolites. Sphinganine, X – 16576 and 2-pyrrolidinone displayed statistically significant associations across all five air pollutants. In sub-cohort III, black carbon, nitrogen dioxide and ozone in warm seasons were associated with a single unannotated metabolite (X – 24970), and annual ozone with two unannotated metabolites (X – 24970 and X – 24306). Enriched pathways identified in sub-cohort I included pyrimidine metabolism and steroid hormone biosynthesis. ConclusionsOur study revealed associations of long-term air pollutant exposure with several metabolites and enrichment of two pathways, which are known to be involved in the adrenal and reproductive system and cell metabolism.

Association of Ultraprocessed Foods Intake with Untargeted Metabolomics Profiles in Adolescents and Young Adults in the DONALD Cohort Study

BackgroundHigh consumption of ultraprocessed foods (UPFs) continues to draw significant public health interest because of the associated negative health outcomes. Metabolomics can contribute to the understanding of the biological mechanisms through which UPFs may influence health. ObjectivesTo investigate urine and plasma metabolomic biomarkers of UPF intake in adolescents and young adults. MethodsWe used data from the Dortmund Nutritional and Anthropometric Longitudinally Designed study to investigate cross-sectional associations of UPF intake with concentrations of urine metabolites in adolescents using 3d weighed dietary records (3d-WDR) and 24-h urine samples (n = 339), and associations of repeatedly assessed UPF intake with concentrations of circulating plasma metabolites in young adults with 3–6 3d-WDRs within 5 y preceding blood measurement (n = 195). Urine and plasma samples were analyzed using mass spectrometry-based metabolomics. Biosample-specific metabolite patterns (MPs) were determined using robust sparse principal components analysis. Multivariable linear regression models were applied to assess the associations of UPF consumption (as a percentage of total food intake in g/d) with concentrations of individual metabolites and MP scores. ResultsThe median proportion of UPF intake was 22.0% [interquartile range (IQR): 12.3, 32.9] in adolescents and 23.2% (IQR: 16.0, 31.6) in young adults. We identified 42 and 6 UPF intake-associated metabolites in urine and plasma samples, respectively. One urinary MP, “xenobiotics and amino acids” [β = 0.042, 95% confidence interval (CI): 0.014, 0.070] and 1 plasma MP, “lipids, xenobiotics, and amino acids” (β = 0.074, 95% CI: 0.031, 0.117) showed positive association with UPF intake. Both patterns shared 29 metabolites, mostly of xenobiotic metabolism. ConclusionsWe identified urine and plasma metabolites associated with UPF intake in adolescents and young adults, which may represent some of the biological mechanisms through which UPFs may influence metabolism and health.

Plasma metabolomic profiling reveals a novel circulating biomarker signature in chronic pruritus of unknown origin

Chronic pruritus of unknown origin (CPUO) is characterized by chronic itch for 6 weeks or greater without an identifiable primary cause. Studies are needed to investigate circulating blood biomarkers to elucidate disease pathogenesis. The objective of this study was to investigate changes in circulating blood metabolites in CPUO patients and to identify potential therapeutic targets. Our cross-sectional study collected plasma from 11 CPUO patients and 11 matched control patients for mass-spectrometry based metabolite data analysis. 15 metabolites differed significantly in the blood of CPUO patients compared to controls, including nine amino acids (isoleucine, L-tyrosine, threonine, DL-tryptophan, L-valine, methionine, glycine, lysine, and L-phenylalanine), four amino acid derivatives (creatinine, DL-carnitine, acetyl-L-carnitine, and indole-3-acrylic acid), and two aromatic and fatty acid derivatives (2-hydroxycinnamic acid and oleamide). These metabolites were also correlated with itch severity. Metabolic set enrichment analysis (MSEA) identified downregulation of several pathways in CPUO: phenylalanine, tyrosine, tryptophan biosynthesis; catecholamine biosynthesis; and glycine, serine, and threonine metabolism. Our study identified decreases in several circulating plasma metabolites in CPUO patients and downregulation of pathways related to catecholamine biosynthesis and tryptophan biosynthesis, providing insight into the pathogenesis of CPUO.

Integrated plasma metabolomic and cytokine analysis reveals a distinct immunometabolic signature in atopic dermatitis.

Disease models for atopic dermatitis (AD) have primarily focused on understanding underlying environmental, immunologic, and genetic etiologies. However, the role of metabolic mechanisms in AD remains understudied. To investigate the circulating blood metabolomic and cytokine profile of AD as compared to healthy control patients. This study collected plasma from 20 atopic dermatitis with moderate-to-severe itch (score of ≥5 on the itch Numeric Rating Scale and IGA score ≥3) and 24 healthy control patients. Mass-spectrometry based metabolite data were compared between AD and healthy controls. Unsupervised and supervised machine learning algorithms and univariate analysis analyzed metabolic concentrations. Metabolite enrichment and pathway analyses were performed on metabolites with significant fold change between AD and healthy control patients. To investigate the correlation between metabolites levels and cytokines, Spearman's rank correlation coefficients were calculated between metabolites and cytokines. Patients were recruited from the Johns Hopkins Itch Center and dermatology outpatient clinics in the Johns Hopkins Outpatient Center. The study included 20 atopic dermatitis patients and 24 healthy control patients. Fold changes of metabolites in AD vs healthy control plasma. In patients with AD, amino acids isoleucine, tyrosine, threonine, tryptophan, valine, methionine, and phenylalanine, the amino acid derivatives creatinine, indole-3-acrylic acid, acetyl-L-carnitine, L-carnitine, 2-hydroxycinnamic acid, N-acetylaspartic acid, and the fatty amide oleamide had greater than 2-fold decrease (all P-values<0.0001) compared to healthy controls. Enriched metabolites were involved in branched-chain amino acid (valine, leucine, and isoleucine) degradation, catecholamine biosynthesis, thyroid hormone synthesis, threonine metabolism, and branched and long-chain fatty acid metabolism. Dysregulated metabolites in AD were positively correlated cytokines TARC and MCP-4 and negatively correlated with IL-1a and CCL20. Our study characterized novel dysregulated circulating plasma metabolites and metabolic pathways that may be involved in the pathogenesis of AD. These metabolic pathways serve as potential future biomarkers and therapeutic targets in the treatment of AD.

Abstract 15734: Local Admixture Mapping of Metabolites in African Americans

Introduction: There is increasing evidence that metabolite levels in African Americans (AA) are influenced by population differentiated genetic variation. We investigated how local genetic ancestry may be associated with levels of circulating plasma metabolites using metabolomic profiling in the Jackson Heart Study, an AA cohort, in an attempt to identify additional genetic determinants of metabolite levels. Hypothesis: Certain metabolites will be associated with local African genetic ancestry. Methods: We performed admixture mapping using local ancestry estimates (probabilities of whether an individual has 0, 1 or 2 alleles of African ancestry at each site in the genome from RFMix, based on similarity to 1000G reference panel) from TOPMed sequencing data for autosomal SNPs. Associations were then tested for 302 targeted metabolites measured using LC-MS. Each local ancestry regression model was adjusted for age, sex, and estimated global ancestry. We used a previously established significance threshold for local African ancestry analysis, p&lt;2.1X10 -5 . We further adjusted findings for previously reported GWAS variants in conditional analysis. Results: There were 38 local admixture mapping signals for 32 metabolites. Fourteen metabolite signals survived conditional analysis and represent signals distinct from those identified in single variant GWAS. Metabolites of note include the branched chain amino acids, whose association with cardiometabolic disease has been shown to differ based on self-identified race. Conclusions: We present local admixture mapping results providing further evidence that population differentiated genetic variants influence circulating levels of plasma metabolites. For a large proportion of our findings, admixture associations were not attenuated by conditioning on previously reported SNPs from GWAS studies. These analyses suggest that admixture mapping may identify association signals that are missed by GWAS, and may help to elucidate population differences affecting key biologic pathways.

Metabolic fingerprinting on retinal pigment epithelium thickness for individualized risk stratification of type 2 diabetes mellitus

The retina is an important target organ of diabetes mellitus, with increasing evidence from patients and animal models suggesting that retinal pigment epithelium (RPE) may serve as an early marker for diabetes-related damages. However, their longitudinal relationship and the biological underpinnings remain less well understood. Here, we demonstrate that reduced in vivo measurements of RPE thickness (RPET) represents a significant risk factor for future type 2 diabetes mellitus (T2DM) and its microvascular phenotypes. After performing systematic analyses of circulating plasma metabolites using two complementary approaches, we identify a wide range of RPET metabolic fingerprints that are independently associated with reduced RPET. These fingerprints hold their potential to improve predictability and clinical utility for stratifying future T2DM and related microvascular phenotypes beyond traditional clinical indicators, providing insights into the promising role of retinas as a window to systemic health.

Analysis of Plasma Metabolic Profile on Ganglion Cell–Inner Plexiform Layer Thickness With Mortality and Common Diseases

The neural retina is considered a unique window to systemic health, but its biological link with systemic health remains unknown. To investigate the independent associations of retinal ganglion cell-inner plexiform layer thickness (GCIPLT) metabolic profiles with rates of mortality and morbidity of common diseases. This cohort study evaluated UK Biobank participants enrolled between 2006 and 2010, and prospectively followed them up for multidisease diagnosis and mortality. Additional participants from the Guangzhou Diabetes Eye Study (GDES) underwent optical coherence tomography scanning and metabolomic profiling and were included for validation. Systematic analysis of circulating plasma metabolites to identify GCIPLT metabolic profiles; prospective associations of these profiles with mortality and morbidity of 6 common diseases with their incremental discriminative value and clinical utility. Among 93 838 community-based participants (51 182 [54.5%] women), the mean (SD) age was 56.7 (8.1) years and mean (SD) follow-up was 12.3 (0.8) years. Of 249 metabolic metrics, 37 were independently associated with GCIPLT, including 8 positive and 29 negative associations, and most were associated with the rates of future mortality and common diseases. These metabolic profiles significantly improved the models for discriminating type 2 diabetes over clinical indicators (C statistic: 0.862; 95% CI, 0.852-0.872 vs clinical indicators only, 0.803; 95% CI, 0.792-0.814; P < .001), myocardial infarction (0.792; 95% CI, 0.775-0.808 vs 0.768; 95% CI, 0.751-0.786; P < .001), heart failure (0.803; 95% CI, 0.786-0.820 vs 0.790; 95% CI, 0.773-0.807; P < .001), stroke (0.739; 95% CI, 0.714-0.764 vs 0.719; 95% CI, 0.693-0.745; P < .001), all-cause mortality (0.747; 95% CI, 0.734-0.760 vs 0.724; 95% CI, 0.711-0.738; P < .001), and cardiovascular disease mortality (0.790; 95% CI, 0.767-0.812 vs 0.763; 95% CI, 0.739-0.788; P < .001). Additionally, the potential of GCIPLT metabolic profiles for risk stratification of cardiovascular diseases were further confirmed in the GDES cohort using a different metabolomic approach. In this prospective study of multinational participants, GCIPLT-associated metabolites demonstrated the potential to inform mortality and morbidity risks. Incorporating information on these profiles may facilitate individualized risk stratification for these health outcomes.

Abstract 745: Circulating plasma metabolites and breast cancer risk

Abstract Metabolite profiles reflect the integrated impact of the genome and exogenous exposures on the metabolic state and may provide insight into biologic mechanisms contributing to breast cancer development. To better understand this, we explored the association between pre-diagnostic plasma metabolites (N=307) and breast cancer among women in the Nurses' Health Study. Plasma metabolites were profiled for cases and controls via liquid chromatography tandem mass spectrometry (LC-MS) using samples taken ≥10 years prior to diagnosis (distant, N=939 cases), and &amp;lt;10 years prior to diagnosis (proximate, N=592 cases). Multivariable conditional logistic regression adjusting for standard breast cancer risk factors was used to estimate odds ratios (ORs) and 95% confidence intervals (CIs) for a one standard deviation change in metabolite level, using the number of effective tests to account for testing multiple correlated hypotheses. Metabolite set enrichment analysis (MSEA) was used to group molecularly-similar metabolites based on pre-defined classes; triacylglycerides (TAGs) were further separated by double bond content (≥3 or &amp;lt;3 double bonds) (N classes=17). Weighted gene co-expression network analysis (WGCNA) identified data-driven metabolite modules based on their interconnectedness; associations between resulting module scores and breast cancer risk were examined. Multiple testing in GSEA and WGCNA was accounted for using the false discovery rate.Though no individual metabolites were significantly associated with breast cancer risk, several patterns emerged. At the distant timepoint, cholesterol esters were inversely associated with risk [e.g.: C22:5 CE OR = 0.82, 95% CI=0.75-0.9]. TAGs with ≥3 double bonds were inversely associated with breast cancer at the proximate timepoint [e.g.: C58:7 TAG OR=0.81, 95% CI=0.72-0.92]. MSEA showed negative enrichment (corresponding to an inverse association with breast cancer) of cholesterol esters at both distant (normalized enrichment score (NES)=-2.38, padj=0.02) and proximate (NES=-2.0, padj=0.02) timepoints. Though positive enrichment of TAGs with &amp;lt;3 double bonds was observed at both time points, TAGs with ≥3 double bonds were significantly inversely associated with breast cancer at the proximate time point (NES=-2.92, padj=0.02). At the proximate time point, a module characterized by essential amino acids (positively weighted) and TAGs with high number of double bonds (inversely weighted) was significantly associated with breast cancer risk (OR=1.21, 95% CI=1.06-1.37, padj=0.04). Overall, the results suggest that cholesterol esters may be inversely related to early development of breast cancer, while TAGs with many double bonds appear inversely associated with development of breast cancer closer to diagnosis. Analyses of metabolite differences between each time point and by ER status are ongoing to better understand the biologic mechanisms contributing to breast cancer risk. Citation Format: Kristen D. Brantley, Oana A. Zeleznik, Rulla Tamimi, Bernard Rosner, Julian Avila-Pacheco, Clary B. Clish, A Heather Eliassen. Circulating plasma metabolites and breast cancer risk [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 745.

41-LB: Changes in Circulating Metabolites, Including Butyrate, Points to Underlying Mechanism of a Probiotic Intervention That Improves Postprandial Hyperglycemia in Subjects with Type 2 Diabetes

Studies in type 2 diabetes (T2D) rodent models suggest the potential for individual microbial taxa in the human gut to improve or worsen metabolic disease via specific metabolites (signals) in hepatic or general circulation. We previously reported significant improvement in postprandial glucose control in T2D subjects following ingestion of a 5-strain novel probiotic formulation (INT) in a placebo (PBO) controlled setting. (DOI:10.1136/bmjdrc-2020-001319) Here we report measurements of fecal and fasting circulating plasma metabolites, implicated as mediators of microbiome effects, collected before and after the 12-week intervention. Consistent with the hypothesized benefits of increased colonic production of butyrate, plasma butyrate increased in INT but not PBO (within group, concentration increase = 0.15 µM, p = 0.007; between group, p = 0.11), but not acetate nor propionate, paralleling changes in subject stool and production by in vitro culture. We detected changes consistent with enhanced fatty acid oxidation in INT relative to placebo, especially a decrease in medium chain fatty acids, their carnitinylates, and a decrease in bilirubin degradation byproducts. We also detected increases in certain bile acids, especially ursodeoxycholic acid (UDCA). In vitro monoculture supernatant showed that the formulation’s C. butyricum strain specifically and efficiently synthesizes UDCA during butyrogenic growth in rich media amended with the primary bile acid chenodeoxycholic acid. Untargeted data also uncovered previously undocumented use of sulfonylurea (SFU), accounting for 6 out of 7 subjects with A1c non-response in the INT group. To our knowledge, this is the first description of an increase in circulating butyrate and UDCA due solely to a bacterial probiotic intervention in humans with T2D, and may help explain the observed improvement in postprandial glucose control. Disclosure P. Mcmurdie: Employee; Self; Pendulum Therapeutics. M. K. Stoeva: Employee; Self; Pendulum Therapeutics. M. Nemchek: Employee; Self; Pendulum Therapeutics. S. Tyagi: None. J. Gines: Employee; Self; Pendulum Therapeutics. J. Eid: Stock/Shareholder; Self; Pendulum Therapeutics. O. G. Kolterman: Board Member; Self; GlySens Incorporated, Consultant; Self; ADOCIA, Cirius Therapeutics, Nanoprecision Medical, Viacyte, Inc., Employee; Self; Pendulum Therapeutics.

Distinct Basal Metabolism in Three Mouse Models of Neurodevelopmental Disorders.

Prevalence of metabolic disturbances is higher among individuals with neurodevelopmental disorders (NDDs), yet this association has been largely overlooked. Investigation into human disease remains challenging, as a complete pathophysiological understanding relies on accurate modeling and highly controlled variables. Genetically engineered mouse models are widely used to gain insight into the biology of human NDDs, but research focus has been on behavioral and neurophysiological abnormalities. Such models not only allow for evaluating usefulness in reproducing human features, including similarities and discrepancies with rodent phenotypes, but they also represent a unique opportunity to observe and quantify novel anomalies. Here, we present the first characterization and comparison of basal metabolism in three mouse models of NDDs, namely, Down syndrome (DS; Dp(16)Yey/+ mice), 16p11.2 deletion syndrome (16pDel; 16p11.2df/+ mice), and fragile X syndrome [FXS; Fmr1 knock-out (KO) mice] and their wild-type (WT) counterparts. Using the Comprehensive Lab Animal Monitoring System (CLAMS) coupled to EchoMRI, as well as quantification of key plasma metabolites by liquid chromatography mass spectrometry (LC-MS), our in vivo study reveals that each mouse model expresses a unique metabolic signature that is sex-specific, independent of the amount of food consumed and minimally influenced by physical activity. In particular, we identify striking differences in body composition, respiratory exchange ratio (RER), caloric expenditure (CE), and concentrations of circulating plasma metabolites related to mitochondrial function. Providing novel insight into NDD-associated metabolic alterations is an essential prerequisite for future preclinical and clinical interventions.

Charged metabolite biomarkers of food intake assessed via plasma metabolomics in a population-based observational study in Japan.

Food intake biomarkers can be critical tools that can be used to objectively assess dietary exposure for both epidemiological and clinical nutrition studies. While an accurate estimation of food intake is essential to unravel associations between the intake and specific health conditions, random and systematic errors affect self-reported assessments. This study aimed to clarify how habitual food intake influences the circulating plasma metabolome in a free-living Japanese regional population and to identify potential food intake biomarkers. To achieve this aim, we conducted a cross-sectional analysis as part of a large cohort study. From a baseline survey of the Tsuruoka Metabolome Cohort Study, 7,012 eligible male and female participants aged 40-69 years were chosen for this study. All data on patients' health status and dietary intake were assessed via a food frequency questionnaire, and plasma samples were obtained during an annual physical examination. Ninety-four charged plasma metabolites were measured using capillary electrophoresis mass spectrometry, by a non-targeted approach. Statistical analysis was performed using partial-least-square regression. A total of 21 plasma metabolites were likely to be associated with long-term food intake of nine food groups. In particular, the influential compounds in each food group were hydroxyproline for meat, trimethylamine-N-oxide for fish, choline for eggs, galactarate for dairy, cystine and betaine for soy products, threonate and galactarate for carotenoid-rich vegetables, proline betaine for fruits, quinate and trigonelline for coffee, and pipecolate for alcohol, and these were considered as prominent food intake markers in Japanese eating habits. A set of circulating plasma metabolites was identified as potential food intake biomarkers in the Japanese community-dwelling population. These results will open the way for the application of new reliable dietary assessment tools not by self-reported measurements but through objective quantification of biofluids.

The association between plasma metabolites and sleep quality in the Southall and Brent Revisited (SABRE) Study: A cross-sectional analysis.

We examined the association between plasma metabolites and abnormal sleep patterns using data from the Southall and Brent REvisited (SABRE) cohort. Nuclear magnetic resonance spectroscopy provided 146 circulating plasma metabolites. Sleep questionnaires identified the presence or absence of: difficulty falling asleep, early morning waking, waking up tired, and snoring. Metabolites were compared between the sleep quality categories using the t test, and then filtered using a false discovery rate of 0.05. Generalised linear models with logit‐link assessed the associations between filtered metabolites and sleep phenotypes. Adjustment was made for important demographic and health‐related covariates. In all, 2,718 participants were included in the analysis. After correcting for multiple testing, three metabolites remained for difficulty falling asleep, 59 for snoring, and none for early morning waking and waking up tired. After adjusting for sex, age, ethnicity and years of education, 1 standard deviation increase in serum histidine and valine associated with lower odds of difficulty falling asleep by 0.89–0.90 (95% confidence intervals [CIs] 0.80–0.99). Branched‐chain and aromatic amino acids (odds ratios [ORs] 1.19–1.25, 95% CIs 1.09–1.36) were positively associated with snoring. Total cholesterol in low‐density lipoprotein (OR 0.90, 95% CI 0.83–0.97) and high‐density lipoprotein (OR 0.88, 95% CI 0.81–0.95) associated with lower odds of snoring. In the fully adjusted model, most associations persisted. To conclude, histidine and valine associated with lower odds of difficulty falling asleep, while docosahexaenoic acid and cholesterol in low‐density lipoprotein and high‐density lipoprotein subfractions associated with lower odds of snoring. Identified metabolites could provide guidance on the metabolic pathways associated with adverse sleep quality.

Circulating Plasma Metabolites and Cognitive Function in a Puerto Rican Cohort.

Minorities, including mainland Puerto Ricans, are impacted disproportionally by Alzheimer's disease (AD), dementia, and cognitive decline. Studying blood metabolomics in this population has the potential to probe the biological underpinnings of this health disparity. We performed a comprehensive analysis of circulating plasma metabolites in relation to cognitive function in 736 participants from the Boston Puerto Rican Health Study (BPRHS) who underwent untargeted mass-spectrometry based metabolomics analysis and had undergone a battery of in-person cognitive testing at baseline. After relevant exclusions, 621 metabolites were examined. We used multivariable regression, adjusted for age, sex, education, apolipoprotein E genotype, smoking, and Mediterranean dietary pattern, to identify metabolites related to global cognitive function in our cohort. LASSO machine learning was used in a complementary analysis to identify metabolites that could discriminate good from poor extremes of cognition. We also conducted sensitivity analyses: restricted to participants without diabetes, and to participants with good adherence to Mediterranean diet. Of 621 metabolites, FDR corrected (p < 0.05) multivariable linear regression identified 3 metabolites positively, and 10 negatively, associated with cognitive function in the BPRHS. In a combination of FDR-corrected linear regression, logistic regression regularized via LASSO, and sensitivity analyses restricted to participants without diabetes, and with good adherence to the Mediterranean diet, β-cryptoxanthin plasma concentration was consistently associated with better cognitive function and N-acetylisoleucine and tyramine O-sulfate concentrations were consistently associated with worse cognitive function. This untargeted metabolomics study identified potential biomarkers for cognitive function in a cohort of Puerto Rican older adults.

Circulating plasma metabolites and risk of rheumatoid arthritis in the Nurses' Health Study.

RA develops slowly over years. We tested for metabolic changes prior to RA onset using a large non-targeted metabolomics platform to identify novel pathways and advance understanding of RA development. Two hundred and fifty-four incident RA cases with plasma samples drawn pre-RA onset in the Nurses' Health Study (NHS) cohorts were matched 1:2 to 501 controls on age, race, menopause/post-menopausal hormone use and blood collection features. Relative abundances of 360 unique, known metabolites were measured. Conditional logistic regression analyses assessed associations between metabolites and incidence of RA, adjusted for age, smoking and BMI, accounting for multiple comparisons. Subgroup analyses investigated seropositive (sero+) RA and RA within 5 years of sample collection. Significant metabolites were then tested in a female military pre-RA case-control study (n = 290). In the NHS, metabolites associated with RA and sero+RA in multivariable models included 4-acetamidobutanoate (odds ratio (OR) = 0.80/S.d., 95% CI: 0.66, 0.95), N-acetylputrescine (OR = 0.82, 95% CI: 0.69, 0.96), C5 carnitine (OR = 0.84, 95% CI: 0.71, 0.99) and C5:1 carnitine (OR = 0.81, 95% CI: 0.68, 0.95). These were involved primarily in polyamine and leucine, isoleucine and valine metabolism. Several metabolites associated with sero+RA within 5 years of diagnosis were replicated in the independent military cohort: C5 carnitine (OR = 0.55, 95% CI: 0.33, 0.92), C5:1 carnitine (OR = 0.62, 95% CI: 0.39, 0.99) and C3 carnitine (OR = 0.57, 95% CI: 0.36, 0.91). Several metabolites were inversely associated with incidence of RA among women. Three short-chain acylcarnitines replicated in a smaller dataset and may reflect inflammation in the 5-year period prior to sero+RA diagnosis.

A Prospective Analysis of Circulating Plasma Metabolites Associated with Ovarian Cancer Risk.

Ovarian cancer has few known risk factors, hampering identification of high-risk women. We assessed the association of prediagnostic plasma metabolites (N = 420) with risk of epithelial ovarian cancer, including both borderline and invasive tumors. A total of 252 cases and 252 matched controls from the Nurses' Health Studies were included. Multivariable logistic regression was used to estimate ORs and 95% confidence intervals (CI), comparing the 90th-10th percentile in metabolite levels, using the permutation-based Westfall and Young approach to account for testing multiple correlated hypotheses. Weighted gene coexpression network analysis (WGCNA; n = 10 metabolite modules) and metabolite set enrichment analysis (n = 23 metabolite classes) were also evaluated. An increase in pseudouridine levels from the 10th to the 90th percentile was associated with a 2.5-fold increased risk of overall ovarian cancer (OR = 2.56; 95% CI, 1.48-4.45; P = 0.001/adjusted P = 0.15); a similar risk estimate was observed for serous/poorly differentiated tumors (n = 176 cases; comparable OR = 2.38; 95% CI, 1.33-4.32; P = 0.004/adjusted P = 0.55). For nonserous tumors (n = 34 cases), pseudouridine and C36:2 phosphatidylcholine plasmalogen had the strongest statistical associations (OR = 9.84; 95% CI, 2.89-37.82; P < 0.001/adjusted P = 0.07; and OR = 0.11; 95% CI, 0.03-0.35; P < 0.001/adjusted P = 0.06, respectively). Five WGCNA modules and 9 classes were associated with risk overall at FDR ≤ 0.20. Triacylglycerols (TAG) showed heterogeneity by tumor aggressiveness (case-only heterogeneity P < 0.0001). The TAG association with risk overall and serous tumors differed by acyl carbon content and saturation. In summary, this study suggests that pseudouridine may be a novel risk factor for ovarian cancer and that TAGs may also be important, particularly for rapidly fatal tumors, with associations differing by structural features. SIGNIFICANCE: Pseudouridine represents a potential novel risk factor for ovarian cancer and triglycerides may be important particularly in rapidly fatal ovarian tumors.

The metabolomic signature of extreme longevity: naked mole rats versus mice.

The naked mole-rat (Heterocephalus glaber) is characterized by a more than tenfold higher life expectancy compared to another rodent species of the same size, namely, the laboratory mouse (Mus musculus). We used mass spectrometric metabolomics to analyze circulating plasma metabolites in both species at different ages. Interspecies differences were much more pronounced than age-associated alterations in the metabolome. Such interspecies divergences affected multiple metabolic pathways involving amino, bile and fatty acids as well as monosaccharides and nucleotides. The most intriguing metabolites were those that had previously been linked to pro-health and antiaging effects in mice and that were significantly increased in the long-lived rodent compared to its short-lived counterpart. This pattern applies to α-tocopherol (also known as vitamin E) and polyamines (in particular cadaverine, N8-acetylspermidine and N1,N8-diacetylspermidine), all of which were more abundant in naked mole-rats than in mice. Moreover, the age-associated decline in spermidine and N1-acetylspermidine levels observed in mice did not occur, or is even reversed (in the case of N1-acetylspermidine) in naked mole-rats. In short, the present metabolomics analysis provides a series of testable hypotheses to explain the exceptional longevity of naked mole-rats.

Plasma metabolites associated with colorectal cancer: A discovery-replication strategy.

Colorectal cancer is known to arise from multiple tumorigenic pathways; however, the underlying mechanisms remain not completely understood. Metabolomics is becoming an increasingly popular tool in assessing biological processes. Previous metabolomics research focusing on colorectal cancer is limited by sample size and did not replicate findings in independent study populations to verify robustness of reported findings. Here, we performed a ultrahigh performance liquid chromatography‐quadrupole time‐of‐flight mass spectrometry (UHPLC‐QTOF‐MS) screening on EDTA plasma from 268 colorectal cancer patients and 353 controls using independent discovery and replication sets from two European cohorts (ColoCare Study: n = 180 patients/n = 153 controls; the Colorectal Cancer Study of Austria (CORSA) n = 88 patients/n = 200 controls), aiming to identify circulating plasma metabolites associated with colorectal cancer and to improve knowledge regarding colorectal cancer etiology. Multiple logistic regression models were used to test the association between disease state and metabolic features. Statistically significant associated features in the discovery set were taken forward and tested in the replication set to assure robustness of our findings. All models were adjusted for sex, age, BMI and smoking status and corrected for multiple testing using False Discovery Rate. Demographic and clinical data were abstracted from questionnaires and medical records.

Identifying the metabolomic fingerprint of high and low flavonoid consumers

High flavonoid consumption can improve vascular health. Exploring flavonoid-metabolome relationships in population-based settings is challenging, as: (i) there are numerous confounders of the flavonoid-metabolome relationship; and (ii) the set of dependent metabolite variables are inter-related, highly variable and multidimensional. The Metabolite Fingerprint Score has been developed as a means of approaching such data. This study aims to compare its performance with that of more traditional methods, in identifying the metabolomic fingerprint of high and low flavonoid consumers. This study did not aim to identify biomarkers of intake, but rather to explore how systemic metabolism differs in high and low flavonoid consumers. Using liquid chromatography-tandem MS, 174 circulating plasma metabolites were profiled in 584 men and women who had complete flavonoid intake assessment. Participants were randomised to one of two datasets: (a) training dataset, to determine the models for the discrimination variables (n 399); and (b) validation dataset, to test the capacity of the variables to differentiate higher from lower total flavonoid consumers (n 185). The stepwise and full canonical variables did not discriminate in the validation dataset. The Metabolite Fingerprint Score successfully identified a unique pattern of metabolites that discriminated high from low flavonoid consumers in the validation dataset in a multivariate-adjusted setting, and provides insight into the relationship of flavonoids with systemic lipid metabolism. Given increasing use of metabolomics data in dietary association studies, and the difficulty in validating findings using untargeted metabolomics, this paper is of timely importance to the field of nutrition. However, further validation studies are required.

Roles of UGT, P450, and Gut Microbiota in the Metabolism of Epacadostat in Humans.

Epacadostat (EPA, INCB024360) is a first-in-class, orally active, investigational drug targeting the enzyme indoleamine 2,3-dioxygenase 1 (IDO1). In Phase I studies, EPA has demonstrated promising clinical activity when used in combination with checkpoint modulators. When the metabolism of EPA was investigated in humans, three major, IDO1-inactive, circulating plasma metabolites were detected and characterized: M9, a direct O-glucuronide of EPA; M11, an amidine; and M12, N-dealkylated M11. Glucuronidation of EPA to form M9 is the dominant metabolic pathway, and in vitro, this metabolite is formed by UGT1A9. However, negligible quantities of M11 and M12 were detected when EPA was incubated with a panel of human microsomes from multiple tissues, hepatocytes, recombinant human cytochrome P450s (P450s), and non-P450 enzymatic systems. Given the reductive nature of M11 formation and the inability to define its source, the role of gut microbiota was investigated. Analysis of plasma from mice dosed with EPA following pretreatment with either antibiotic (ciprofloxacin) to inhibit gut bacteria or 1-aminobenzotriazole (ABT) to systemically inhibit P450s demonstrated that gut microbiota is responsible for the formation of M11. Incubations of EPA in human feces confirmed the role of gut bacteria in the formation of M11. Further, incubations of M11 with recombinant P450s showed that M12 is formed via N-dealkylation of M11 by CYP3A4, CYP2C19, and CYP1A2. Thus, in humans three major plasma metabolites of EPA were characterized: two primary metabolites, M9 and M11, formed directly from EPA via UGT1A9 and gut microbiota, respectively, and M12 formed as a secondary metabolite via P450s from M11.