Metabolomic Signatures Research Articles

Metabolomic Signatures of Treatment Response in Bladder Cancer.

Bladder cancer (BC) stands as one of the most prevalent urological malignancies, with over 500 thousand newly diagnosed cases annually. Treatment decisions in BC depend on factors like the risk of recurrence, the type of tumor, and the stage of the disease. While standard therapeutic approaches encompass transurethral resection of the bladder tumor, radical cystectomy, and chemo- or immunotherapy, these methods exhibit limited efficacy in mitigating the aggressive and recurrent nature of bladder tumors. To overcome this challenge, it is crucial to develop innovative methods for monitoring and predicting treatment responses among patients with BC. Metabolomics is gaining recognition as a promising approach for discovering biomarkers. It has the potential to reveal metabolic disruptions that precisely reflect how BC patients respond to particular treatments, providing a revolutionary method to improve accuracy in monitoring and predicting outcomes. In this article, we present a comprehensive review of studies employing metabolomics approaches to investigate the metabolic responses associated with different treatment modalities for BC. The review encompasses an exploration of various models, samples, and analytical techniques applied in this context. Special emphasis is placed on the reported changes in metabolite levels derived from these studies, highlighting their potential as biomarkers for personalized medicine in BC.

Olive oil consumption, plasma metabolites, and risk of type 2 diabetes and cardiovascular disease

BackgroundOlive oil consumption has been inversely associated with the risk of type 2 diabetes (T2D) and cardiovascular disease (CVD). However, the impact of olive oil consumption on plasma metabolites remains poorly understood. This study aims to identify plasma metabolites related to total and specific types of olive oil consumption, and to assess the prospective associations of the identified multi-metabolite profiles with the risk of T2D and CVD.MethodsThe discovery population included 1837 participants at high cardiovascular risk from the PREvención con DIeta MEDiterránea (PREDIMED) trial with available metabolomics data at baseline. Olive oil consumption was determined through food-frequency questionnaires (FFQ) and adjusted for total energy. A total of 1522 participants also had available metabolomics data at year 1 and were used as the internal validation sample. Plasma metabolomics analyses were performed using LC–MS. Cross-sectional associations between 385 known candidate metabolites and olive oil consumption were assessed using elastic net regression analysis. A 10-cross-validation (CV) procedure was used, and Pearson correlation coefficients were assessed between metabolite-weighted models and FFQ-derived olive oil consumption in each pair of training–validation data sets within the discovery sample. We further estimated the prospective associations of the identified plasma multi-metabolite profile with incident T2D and CVD using multivariable Cox regression models.ResultsWe identified a metabolomic signature for the consumption of total olive oil (with 74 metabolites), VOO (with 78 metabolites), and COO (with 17 metabolites), including several lipids, acylcarnitines, and amino acids. 10-CV Pearson correlation coefficients between total olive oil consumption derived from FFQs and the multi-metabolite profile were 0.40 (95% CI 0.37, 0.44) and 0.27 (95% CI 0.22, 0.31) for the discovery and validation sample, respectively. We identified several overlapping and distinct metabolites according to the type of olive oil consumed. The baseline metabolite profiles of total and extra virgin olive oil were inversely associated with CVD incidence (HR per 1SD: 0.79; 95% CI 0.67, 0.92 for total olive oil and 0.70; 0.59, 0.83 for extra virgin olive oil) after adjustment for confounders. However, no significant associations were observed between these metabolite profiles and T2D incidence.ConclusionsThis study reveals a panel of plasma metabolites linked to the consumption of total and specific types of olive oil. The metabolite profiles of total olive oil consumption and extra virgin olive oil were associated with a decreased risk of incident CVD in a high cardiovascular-risk Mediterranean population, though no associations were observed with T2D incidence.Trial registration: The PREDIMED trial was registered at ISRCTN (http://www.isrctn.com/, ISRCTN35739639).

A Plasma Metabolite Score Related to Psychological Distress and Diabetes Risk: A Nested Case-control Study in US Women.

Psychological distress has been linked to diabetes risk. Few population-based, epidemiologic studies have investigated the potential molecular mechanisms (eg, metabolic dysregulation) underlying this association. To evaluate the association between a metabolomic signature for psychological distress and diabetes risk. We conducted a nested case-control study of plasma metabolomics and diabetes risk in the Nurses' Health Study, including 728 women (mean age: 55.2 years) with incident diabetes and 728 matched controls. Blood samples were collected between 1989 and 1990 and incident diabetes was diagnosed between 1992 and 2008. Based on our prior work, we calculated a weighted plasma metabolite-based distress score (MDS) comprised of 19 metabolites. We used conditional logistic regression accounting for matching factors and other diabetes risk factors to estimate odds ratios (OR) and 95% confidence intervals (CI) for diabetes risk according to MDS. After adjusting for sociodemographic factors, family history of diabetes, and health behaviors, the OR (95% CI) for diabetes risk across quintiles of the MDS was 1.00 (reference) for Q1, 1.16 (0.77, 1.73) for Q2, 1.30 (0.88, 1.91) for Q3, 1.99 (1.36, 2.92) for Q4, and 2.47 (1.66, 3.67) for Q5. Each SD increase in MDS was associated with 36% higher diabetes risk (95% CI: 1.21, 1.54; P-trend <.0001). This association was moderately attenuated after additional adjustment for body mass index (comparable OR: 1.17; 95% CI: 1.02, 1.35; P-trend = .02). The MDS explained 17.6% of the association between self-reported psychological distress (defined as presence of depression or anxiety symptoms) and diabetes risk (P = .04). MDS was significantly associated with diabetes risk in women. These results suggest that differences in multiple lipid and amino acid metabolites may underlie the observed association between psychological distress and diabetes risk.

Proteomic and metabolomic signatures of U87 glioblastoma cells treated with cisplatin and/or paclitaxel.

Glioblastoma (GBM) is a primary malignancy of the central nervous system and is classified as a grade IV astrocytoma by the World Health Organization (WHO). Although GBM rarely metastasizes, its prognosis remains poor. Moreover, the standard treatment for GBM, temozolomide (TMZ), is associated with chemoresistance, which is a major factor behind GBM-related deaths. Investigating drugs with repurposing potential in the context of GBM is worthwhile to bypass lengthy bench-to-bedside research. The field of omics has garnered significant interest in scientific research because of its potential to delineate the intricate regulatory network underlying tumor development. In particular, proteomic and metabolomic analyses are powerful approaches for the investigation of metabolic enzymes and intermediate metabolites since they represent the functional end of the cancer phenotype. We chose two of the most widely prescribed anticancer drugs, cisplatin and paclitaxel. To our knowledge, the current literature lacks studies examining their effects on metabolic and proteomic alterations in GBM. We employed the mass spectrometry technological platform 'UHPLC-Q-TOF-MS/MS' to examine the changes in the proteome and metabolome profiles of the U87 cell line with defined concentrations of cisplatin and/or paclitaxel via an untargeted approach. A total of 1,419 distinct proteins and 90 metabolites were generated, and subsequent analysis was performed. We observed that upon treatment with cisplatin (9.5 μM), U87 cells exhibited apparent efforts to cope with this exogenous stressor, understanding the effect of paclitaxel (5.3 μM) on altering the transport machinery of the cell, and how the combination of cisplatin and/or paclitaxel suggests potential interactions with promising benefits in GBM therapeutics. Our research provides a detailed map of alterations in response to cisplatin and paclitaxel treatment, provides crucial insights into the molecular basis of their action, and paves the way for further research to identify molecular targets for this elusive malignancy.

Identifying the metabolomics signatures of genetic variants identified for Alzheimer’s disease

AbstractBackgroundIntegrating genomics and metabolomics data in humans has the potential to unravel mechanistic insights about and look for evidence of metabolite causality in complex disorders.MethodWe conducted metabolome‐wide association studies (MWAS) on Alzheimer’s disease (AD) associated loci using ultraperformance liquid chromatography–mass spectrometry (UPLC‐MS) in two cohorts of the Airwave Study (1). Expanding our previous study focusing on ABCA7 (2), current MWAS investigated genetic variants in all other loci based on the largest and most recent GWAS on AD (3) to identify metabolic features associated with AD. We performed genome‐wide association studies in the Airwave Study (N = 3,708) to obtain genetic instruments of those features to run Mendelian randomisation (MR) and colocalization analyses.ResultOur MWAS identified 104 features associated with AD loci at metabolome‐wide significance level. MR analysis was conducted on 29 out of 104 features with at least three genetic instruments, showing the potentially causal effects of four metabolic features associated with rs1800978 (ABCA1) and rs587709 (LILRB2). Our in‐house annotation pipeline mapped those features as sphingomyelin (SM)(18:1/20:1), putative sphingolipid, and N‐Acylphosphatidylethanolamines (NAPE). We conducted pairwise colocalisation and HyPrColoc analysis, supporting evidence of common causal variants between SM(18:1/20:1), putative sphingolipid, and NAPE with AD (posterior probability of shared causal variant &gt;0.8).ConclusionThis analysis has broadened our insight into the metabolic signatures of AD, with further study needed to dissect the underlying molecular mechanisms.

Metabolomic profile of neuroendocrine tumors identifies methionine, porphyrin, and tryptophan metabolisms as key dysregulated pathways associated with patient survival.

Metabolic profiling is a valuable tool to characterize tumor biology but remains largely unexplored in neuroendocrine tumors (NETs). Our aim was to comprehensively assess the metabolomic profile of NETs and identify novel prognostic biomarkers and dysregulated molecular pathways. Multiplatform untargeted metabolomic profiling (GC-MS, CE-MS, and LC-MS) was performed in plasma from 77 patients with G1-2 extra-pancreatic NETs enrolled in the AXINET trial (NCT01744249) (study cohort) and from 68 non-cancer individuals (control). The prognostic value of each differential metabolite (n = 155) in NET patients (P < .05) was analyzed by univariate and multivariate analyses adjusted for multiple testing and other confounding factors. Related pathways were explored by Metabolite Set Enrichment Analysis (MSEA) and Metabolite Pathway Analysis (MPA). Thirty-four metabolites were significantly associated with progression-free survival (PFS) (n = 16) and/or overall survival (OS) (n = 27). Thirteen metabolites remained significant independent prognostic factors in multivariate analysis, 3 of them with a significant impact on both PFS and OS. Unsupervised clustering of these 3 metabolites stratified patients in 3 distinct prognostic groups (1-year PFS of 71.1%, 47.7%, and 15.4% (P = .012); 5-year OS of 69.7%, 32.5%, and 27.7% (P = .003), respectively). The MSEA and MPA of the 13-metablolite signature identified methionine, porphyrin, and tryptophan metabolisms as the 3 most relevant dysregulated pathways associated with the prognosis of NETs. We identified a metabolomic signature that improves prognostic stratification of NET patients beyond classical prognostic factors for clinical decisions. The enriched metabolic pathways identified reveal novel tumor vulnerabilities that may foster the development of new therapeutic strategies for these patients.

Deciphering Metabolomic Signatures Associated with Sickle Cell Disease Using Mouse Models

Introduction: Sickle cell disease (SCD) is debilitating and affects millions of people worldwide. Although SCD is caused by a single-point mutation of the β-globin chain of hemoglobin, the clinical manifestations are heterogeneous, and there is a lack of well-characterized predictive biomarkers of disease progression. Metabolomics is a promising tool to interrogate disease pathophysiology and underlying molecular mechanisms. Although prior metabolomic studies have been performed on human and murine red blood cells (RBCs) in the context of SCD, no systematic investigation across commonly used mouse models of SCD has been published. In the present study, we examined the blood cell metabolome of 2 widely used SCD mouse models to study and compare the metabolomic changes in disease models. Methods: Blood samples were collected from 5 groups of mice (10-14 weeks old, n=6-7 per group): wild-type (WT) C57BL/6J mice; Townes AA, AS, and SS mice; and Berkeley SCD mice. Direct injection high-resolution mass spectrometry was used to generate untargeted metabolomics data. Three matrices (RBC, plasma, and whole blood) were isolated from each mouse group. The RBC fraction, containing peripheral blood mononuclear cells, was separated from plasma via centrifugation of whole blood. The current study focuses on the RBC dataset.Raw profile data were centroided, merged, and recalibrated using methods previously described (Fuhrer et al. Anal Chem 2011). Putative annotations were generated based on compounds contained in the Human Metabolome Database, KEGG, and ChEBI databases using accurate mass per charge (tolerance 0.001 m/z) and isotopic correlation patterns. Z-score normalization was applied across all samples for unsupervised clustering using principal component analysis (PCA) and to identify metabolites with significant changes between groups. Metabolites were mapped to central RBC metabolic pathways using MetaboAnalyst (Xia et al. Nat Protoc 2011) and manual curation. Results: Unsupervised PCA that used all detectable RBC metabolites resulted in clear separation of mouse groups by disease status (Figure 1), suggesting robust metabolomic changes in mouse models of SCD. Focusing on the top divergent metabolites across the mouse groups, several enriched pathways were identified, such as amino acid metabolism, pyrimidine and purine metabolism, and aminoacyl-tRNA biosynthesis, all potentially attributable to the buildup of RBC precursors. Metabolites characteristic of immature RBCs were further examined, with elevated hexokinase activity (hexose:hexose-6-phosphate ratio), ATP levels, and mitochondrial metabolites found in RBCs from SCD, in line with the significantly elevated reticulocyte content in SCD mice. Metabolites were curated from additional relevant RBC metabolic pathways; some showed significant differences among mouse groups, and others were significantly elevated in sickle mice (Townes SS and Berkeley SCD) compared with non-SCD controls (WT and Townes AA), including some glycolytic and pentose phosphate pathway (PPP) intermediates (Figure 2). The product-to-substrate ratios of metabolite levels from glycolytic and oxidative PPP (oxPPP) steps suggested glycolysis was favored and the later stage of the oxPPP was reduced in mouse models of SCD. There was also a higher metabolic turnover of glutathione, which is characteristic of SCD in humans, but we observed divergent metabolomic signatures between the disease models in our study (Figure 2). Conclusions: To our knowledge, this is the most comprehensive metabolomics study in mouse models of SCD. It provides a foundation to assess disease biology and response to treatment in future studies. Untargeted RBC metabolomics revealed metabolomic signatures that are consistent with human disease pathology in both murine SCD models, including reticulocytosis, hemolysis, and mitochondria retention in RBCs, and signatures that are unique to the murine models. Of the 2 main RBC metabolic pathways, glycolysis was favored over the oxPPP in SCD mice. This is the first step towards understanding the impact of SCD on RBC carbohydrate metabolism.

Plasma metabolite predictors of metabolic syndrome incidence and reversion

BackgroundMetabolic Syndrome (MetS) is a progressive pathophysiological state defined by a cluster of cardiometabolic traits. However, little is known about metabolites that may be predictors of MetS incidence or reversion. Our objective was to identify plasma metabolites associated with MetS incidence or MetS reversion. MethodsThe study included 1468 participants without cardiovascular disease (CVD) but at high CVD risk at enrollment from two case-cohort studies nested within the PREvención con DIeta MEDiterránea (PREDIMED) study with baseline metabolomics data. MetS was defined in accordance with the harmonized International Diabetes Federation and the American Heart Association/National Heart, Lung, and Blood Institute criteria, which include meeting 3 or more thresholds for waist circumference, triglyceride, HDL cholesterol, blood pressure, and fasting blood glucose. MetS incidence was defined by not having MetS at baseline but meeting the MetS criteria at a follow-up visit. MetS reversion was defined by MetS at baseline but not meeting MetS criteria at a follow-up visit. Plasma metabolome was profiled by LC-MS. Multivariable-adjusted Cox regression models and elastic net regularized regressions were used to assess the association of 385 annotated metabolites with MetS incidence and MetS reversion after adjusting for potential risk factors. ResultsOf the 603 participants without baseline MetS, 298 developed MetS over the median 4.8-year follow-up. Of the 865 participants with baseline MetS, 285 experienced MetS reversion. A total of 103 and 88 individual metabolites were associated with MetS incidence and MetS reversion, respectively, after adjusting for confounders and false discovery rate correction. A metabolomic signature comprised of 77 metabolites was robustly associated with MetS incidence (HR: 1.56 (95 % CI: 1.33–1.83)), and a metabolomic signature of 83 metabolites associated with MetS reversion (HR: 1.44 (95 % CI: 1.25–1.67)), both p < 0.001. The MetS incidence and reversion signatures included several lipids (mainly glycerolipids and glycerophospholipids) and branched-chain amino acids. ConclusionWe identified unique metabolomic signatures, primarily comprised of lipids (including glycolipids and glycerophospholipids) and branched-chain amino acids robustly associated with MetS incidence; and several amino acids and glycerophospholipids associated with MetS reversion. These signatures provide novel insights on potential distinct mechanisms underlying the conditions leading to the incidence or reversion of MetS.

Comparative secretome metabolic dysregulation by six engineered dietary nanoparticles (EDNs) on the simulated gut microbiota

The potential use of engineered dietary nanoparticles (EDNs) in diet has been increasing and poses a risk of exposure. The effect of EDNs on gut bacterial metabolism remains largely unknown. In this study, liquid chromatography–mass spectrometry (LC-MS) based metabolomics was used to reveal significantly altered metabolites and metabolic pathways in the secretome of simulated gut microbiome exposed to six different types of EDNs (Chitosan, cellulose nanocrystals (CNC), cellulose nanofibrils (CNF) and polylactic-co-glycolic acid (PLGA); two inorganic EDNs including TiO2 and SiO2) at two dietary doses. We demonstrated that all six EDNs can alter the composition in the secretome with distinct patterns. Chitosan, followed by PLGA and SiO2, has shown the highest potency in inducing the secretome change with major pathways in tryptophan and indole metabolism, bile acid metabolism, tyrosine and phenol metabolism. Metabolomic alterations with clear dose response were observed in most EDNs. Overall, phenylalanine has been shown as the most sensitive metabolites, followed by bile acids such as chenodeoxycholic acid and cholic acid. Those metabolites might be served as the representative metabolites for the EDNs-gut bacteria interaction. Collectively, our studies have demonstrated the sensitivity and feasibility of using metabolomic signatures to understand and predict EDNs-gut microbiome interaction.

Metabolomic and Lipidomic Signature of Skeletal Muscle with Constitutively Active Mechanistic Target of Rapamycin Complex 1

BackgroundRegulation of mechanistic target of rapamycin complex 1 (mTORC1) plays an important role in aging and nutrition. For example, caloric restriction reduces mTORC1 signaling and extends lifespan, whereas nutrient abundance and obesity increase mTORC1 signaling and reduce lifespan. Skeletal muscle-specific knockout (KO) of DEP domain-containing 5 protein (DEPDC5) results in constitutively active mTORC1 signaling, muscle hypertrophy and an increase in mitochondrial respiratory capacity. The metabolic profile of skeletal muscle, in the setting of hyperactive mTORC1 signaling, is not well known. ObjectivesTo determine the metabolomic and lipidomic signature in skeletal muscle from female and male wild-type (WT) and DEPDC5 KO mice. MethodsTibialis anterior (TA) muscles from WT and transgenic (conditional skeletal muscle-specific DEPDC5 KO) were obtained from female and male adult mice. Polar metabolites and lipids were extracted using a Bligh–Dyer extraction from 5 samples per group and identified and quantified by LC-MS/MS. Resulting analyte peak areas were analyzed with t-test, analysis of variance, and Volcano plots for group comparisons (e.g., WT compared with KO) and multivariate statistical analysis for genotype and sex comparisons. ResultsA total of 162 polar metabolites (organic acids, amino acids, and amines and acyl carnitines) and 1141 lipid metabolites were detected in TA samples by LC-MS/MS. Few polar metabolites showed significant differences in KO muscles compared with WT within the same sex group. P-aminobenzoic acid, β-alanine, and dopamine were significantly higher in KO male muscle whereas erythrose-4-phosphate and oxoglutaric acid were significantly reduced in KO females. The lipidomic profile of the KO groups revealed an increase of muscle phospholipids and reduced triacylglycerol and diacylglycerol compared with the WT groups. ConclusionsSex differences were detected in polar metabolome and lipids were dependent on genotype. The metabolomic profile of mice with hyperactive skeletal muscle mTORC1 is consistent with an upregulation of mitochondrial function and amino acid utilization for protein synthesis.

Metabolomic Biomarker Signatures for Bipolar and Unipolar Depression.

Bipolar disorder (BD) is frequently misdiagnosed as major depressive disorder (MDD) because of overlapping symptoms and the lack of objective diagnostic tools. To identify a reproducible metabolomic biomarker signature in patient dried blood spots (DBSs) that differentiates BD from MDD during depressive episodes and assess its added value when combined with self-reported patient information. This diagnostic analysis used samples and data from the Delta study, conducted in the UK between April 27, 2018, and February 6, 2020. The primary objective was to identify BD in patients with a recent (within the past 5 years) diagnosis of MDD and current depressive symptoms (Patient Health Questionnaire-9 score of 5 or more). Participants were recruited online through voluntary response sampling. The analysis was carried out between February 2022 and July 2023. Patient data were collected using a purpose-built online questionnaire (n = 635 questions). DBS metabolites (n = 630) were analyzed using a targeted mass spectrometry-based platform. Mood disorder diagnoses were established using the Composite International Diagnostic Interview. Of 241 patients in the discovery cohort, 170 (70.5%) were female; 67 (27.8%) were subsequently diagnosed with BD and 174 (72.2%) were confirmed as having MDD; and the mean (SD) age was 28.1 (7.1) years. Of 30 participants in the validation cohort, 16 (53%) were female; 9 (30%) were diagnosed with BD and 21 (70%) with MDD; and the mean (SD) age was 25.4 (6.3) years. DBS metabolite levels were assessed in 241 patients with depressive symptoms with a recent diagnosis of MDD, of whom 67 were subsequently diagnosed with BD by the Composite International Diagnostic Interview and 174 were confirmed as having MDD. The identified 17-biomarker panel provided a mean (SD) cross-validated area under the receiver operating characteristic curve (AUROC) of 0.71 (SD, 0.12; P < .001), with ceramide d18:0/24:1 emerging as the strongest biomarker. Combining biomarker data with patient-reported information significantly enhanced diagnostic performance of models based on extensive demographic data, PHQ-9 scores, and the outcomes from the Mood Disorder Questionnaire. The identified biomarkers were correlated primarily with lifetime manic symptoms and were validated in a separate group of patients who received a new clinical diagnosis of MDD (n = 21) or BD (n = 9) during the study's 1-year follow-up period, with a mean (SD) AUROC of 0.73 (0.06; P < .001). This study provides a proof of concept for developing an accessible biomarker test to facilitate the differential diagnosis of BD and MDD and highlights the potential involvement of ceramides in the pathophysiological mechanisms of mood disorders.

The metabolomic signature of weight loss and remission in the Diabetes Remission Clinical Trial (DiRECT)

Aims/hypothesisHigh-throughput metabolomics technologies in a variety of study designs have demonstrated a consistent metabolomic signature of overweight and type 2 diabetes. However, the extent to which these metabolomic patterns can be reversed with weight loss and diabetes remission has been weakly investigated. We aimed to characterise the metabolomic consequences of a weight-loss intervention in individuals with type 2 diabetes.MethodsWe analysed 574 fasted serum samples collected within an existing RCT (the Diabetes Remission Clinical Trial [DiRECT]) (N=298). In the trial, participating primary care practices were randomly assigned (1:1) to provide either a weight management programme (intervention) or best-practice care by guidelines (control) treatment to individuals with type 2 diabetes. Here, metabolomics analysis was performed on samples collected at baseline and 12 months using both untargeted MS and targeted 1H-NMR spectroscopy. Multivariable regression models were fitted to evaluate the effect of the intervention on metabolite levels.ResultsDecreases in branched-chain amino acids, sugars and LDL triglycerides, and increases in sphingolipids, plasmalogens and metabolites related to fatty acid metabolism were associated with the intervention (Holm-corrected p<0.05). In individuals who lost more than 9 kg between baseline and 12 months, those who achieved diabetes remission saw greater reductions in glucose, fructose and mannose, compared with those who did not achieve remission.Conclusions/interpretationWe have characterised the metabolomic effects of an integrated weight management programme previously shown to deliver weight loss and diabetes remission. A large proportion of the metabolome appears to be modifiable. Patterns of change were largely and strikingly opposite to perturbances previously documented with the development of type 2 diabetes.Data availabilityThe data used for analysis are available on a research data repository (https://researchdata.gla.ac.uk/) with access given to researchers subject to appropriate data sharing agreements. Metabolite data preparation, data pre-processing, statistical analyses and figure generation were performed in R Studio v.1.0.143 using R v.4.0.2. The R code for this study has been made publicly available on GitHub at: https://github.com/lauracorbin/metabolomics_of_direct.Graphical

Developing A Baseline Metabolomic Signature Associated with COVID-19 Severity: Insights from Prospective Trials Encompassing 13 U.S. Centers.

Metabolic disease is a significant risk factor for severe COVID-19 infection, but the contributing pathways are not yet fully elucidated. Using data from two randomized controlled trials across 13 U.S. academic centers, our goal was to characterize metabolic features that predict severe COVID-19 and define a novel baseline metabolomic signature. Individuals (n = 133) were dichotomized as having mild or moderate/severe COVID-19 disease based on the WHO ordinal scale. Blood samples were analyzed using the Biocrates platform, providing 630 targeted metabolites for analysis. Resampling techniques and machine learning models were used to determine metabolomic features associated with severe disease. Ingenuity Pathway Analysis (IPA) was used for functional enrichment analysis. To aid in clinical decision making, we created baseline metabolomics signatures of low-correlated molecules. Multivariable logistic regression models were fit to associate these signatures with severe disease on training data. A three-metabolite signature, lysophosphatidylcholine a C17:0, dihydroceramide (d18:0/24:1), and triacylglyceride (20:4_36:4), resulted in the best discrimination performance with an average test AUROC of 0.978 and F1 score of 0.942. Pathways related to amino acids were significantly enriched from the IPA analyses, and the mitogen-activated protein kinase kinase 5 (MAP2K5) was differentially activated between groups. In conclusion, metabolites related to lipid metabolism efficiently discriminated between mild vs. moderate/severe disease. SDMA and GABA demonstrated the potential to discriminate between these two groups as well. The mitogen-activated protein kinase kinase 5 (MAP2K5) regulator is differentially activated between groups, suggesting further investigation as a potential therapeutic pathway.

Divergent Skeletal Muscle Metabolomic Signatures of Different Exercise Training Modes Independently Predict Cardiometabolic Risk Factors.

We aimed to understand the link between skeletal muscle metabolites and cardiometabolic health after exercise training. Although aerobic, resistance, and combined exercise training each enhance muscle insulin sensitivity as well as other cardiometabolic parameters, they disparately alter amino and citric acid metabolites as well as the lipidome, linking these metabolomic changes independently to the improvement of cardiometabolic risks with each exercise training mode. These findings reveal an important layer of the unique exercise mode-dependent changes in muscle metabolism, which may eventually lead to more informed exercise prescription for improving SI.

Uncovering the relationship between gut microbial dysbiosis, metabolomics, and dietary intake in type 2 diabetes mellitus and in healthy volunteers: a multi-omics analysis

Type 2 Diabetes Mellitus has reached epidemic levels globally, and several studies have confirmed a link between gut microbial dysbiosis and aberrant glucose homeostasis among people with diabetes. While the assumption is that abnormal metabolomic signatures would often accompany microbial dysbiosis, the connection remains largely unknown. In this study, we investigated how diet changed the gut bacteriome, mycobiome and metabolome in people with and without type 2 Diabetes.1 Differential abundance testing determined that the metabolites Propionate, U8, and 2-Hydroxybutyrate were significantly lower, and 3-Hydroxyphenyl acetate was higher in the high fiber diet compared to low fiber diet in the healthy control group. Next, using multi-omics factor analysis (MOFA2), we attempted to uncover sources of variability that drive each of the different groups (bacterial, fungal, and metabolite) on all samples combined (control and DM II). Performing variance decomposition, ten latent factors were identified, and then each latent factor was tested for significant correlations with age, BMI, diet, and gender. Latent Factor1 was the most significantly correlated. Remarkably, the model revealed that the mycobiome explained most of the variance in the DM II group (12.5%) whereas bacteria explained most of the variance in the control group (64.2% vs. 10.4% in the DM II group). The latent Factor1 was significantly correlated with dietary intake (q < 0.01). Further analyses of the impact of bacterial and fungal genera on Factor1 determined that the nine bacterial genera (Phocaeicola, Ligilactobacillus, Mesosutterella, Acidaminococcus, Dorea A, CAG-317, Caecibacter, Prevotella and Gemmiger) and one fungal genus (Malassezia furfur) were found to have high factor weights (absolute weight > 0.6). Alternatively, a linear regression model was fitted per disease group for each genus to visualize the relationship between the factor values and feature abundances, showing Xylose with positive weights and Propionate, U8, and 2-Hydroxybutyrate with negative weights. This data provides new information on the microbially derived changes that influence metabolic phenotypes in response to different diets and disease conditions in humans.

Validation in type 2 diabetes of a metabolomic signature of all-cause mortality.

Mortality in type 2 diabetes is twice that of the normoglycemic population. Unravelling biomarkers that identify high-risk patients for referral to the most aggressive and costly prevention strategies is needed. To validate in type 2 diabetes the association with all-cause mortality of a 14-metabolite score (14-MS) previously reported in the general population and whether this score can be used to improve well-established mortality prediction models. This is a sub-study consisting of 600 patients from the "Sapienza University Mortality and Morbidity Event Rate" (SUMMER) study in diabetes, a prospective multicentre investigation on all-cause mortality in patients with type 2 diabetes. Metabolic biomarkers were quantified from serum samples using high-throughput proton nuclear magnetic resonance metabolomics. In type 2 diabetes, the 14-MS showed a significant (p<0.0001) association with mortality, which was lower (p<0.0001) than that reported in the general population. This difference was mainly due to two metabolites (histidine and ratio of polyunsaturated fatty acids to total fatty acids) with an effect size that was significantly (p=0.01) lower in diabetes than in the general population. A parsimonious 12-MS (i.e. lacking the 2 metabolites mentioned above) improved patient discrimination and classification of two well-established mortality prediction models (p<0.0001 for all measures). The metabolomic signature of mortality in the general population is only partially effective in type 2 diabetes. Prediction markers developed and validated in the general population must be revalidated if they are to be used in patients with diabetes.

The metabolic potential of inflammatory and insulinaemic dietary patterns and risk of type 2 diabetes.

Diets with higher inflammatory and insulinaemic potential have been associated with an increased risk of type 2 diabetes. However, it remains unknown whether plasma metabolomic profiles related to proinflammatory/hyperinsulinaemic diets and to inflammatory/insulin biomarkers are associated with type 2 diabetes risk. We analysed 6840 participants from the Nurses' Health Study and Health Professionals Follow-up Study to identify the plasma metabolome related to empirical dietary inflammatory pattern (EDIP), empirical dietary index for hyperinsulinemia (EDIH), four circulating inflammatory biomarkers and C-peptide. Dietary intakes were assessed using validated food frequency questionnaires. Plasma metabolomic profiling was conducted by LC-MS/MS. Metabolomic signatures were derived using elastic net regression. Multivariable Cox regression was used to examine associations of the metabolomic profiles with type 2 diabetes risk. We identified 27 metabolites commonly associated with both EDIP and inflammatory biomarker z score and 21 commonly associated with both EDIH and C-peptide. Higher metabolomic dietary inflammatory potential (MDIP), reflecting higher metabolic potential of both an inflammatory dietary pattern and circulating inflammatory biomarkers, was associated with higher type 2 diabetes risk. The HR comparing highest vs lowest quartiles of MDIP was 3.26 (95% CI 2.39, 4.44). We observed a strong positive association with type 2 diabetes risk for the metabolomic signature associated with EDIP-only (HR 3.75; 95% CI 2.71, 5.17) or inflammatory biomarkers-only (HR 4.07; 95% CI 2.91, 5.69). In addition, higher metabolomic dietary index for hyperinsulinaemia (MDIH), reflecting higher metabolic potential of both an insulinaemic dietary pattern and circulating C-peptide, was associated with greater type 2 diabetes risk (HR 3.00; 95% CI 2.22, 4.06); further associations with type 2 diabetes were HR 2.79 (95% CI 2.07, 3.76) for EDIH-only signature and HR 3.89 (95% CI 2.82, 5.35) for C-peptide-only signature. The diet scores were significantly associated with risk, although adjustment for the corresponding metabolomic signature scores attenuated the associations with type 2 diabetes, these remained significant. The metabolomic signatures reflecting proinflammatory or hyperinsulinaemic diets and related biomarkers were positively associated with type 2 diabetes risk, supporting that these dietary patterns may influence type 2 diabetes risk via the regulation of metabolism.

Metabolomic analysis for asymptomatic hyperuricemia and gout based on a combination of dried blood spot sampling and mass spectrometry technology

BackgroundGout is the most common inflammatory arthritis and closely related to metabolic syndrome, leading to excruciating pain and the decline in quality of patients’ life. However, the pathogenesis of gout is still unclear, and novel biomarkers are demanded for the early prediction and diagnosis of gout.ObjectiveThis study aimed at profiling the dysregulated metabolic pathways in asymptomatic hyperuricemia (AHU) and gout and elucidating the associations between AHU, gout and metabolomics, which may aid in performing gout screening.MethodsA total of 300 participants, including 114 healthy controls, 92 patients with AHU, and 94 patients with gout, were analyzed by using a combination of dried blood spot (DBS) sampling and mass spectrometry (MS) technology. Multiple algorithms were applied to characterize altered metabolic profiles in AHU and gout. The mainly altered metabolites were identified by random forest analysis.ResultsThere were significant differences in AHU and gout compared with control group. The altered metabolites were involved in oxidation of fatty acids, carnitine synthesis, urea cycle, and amino acid metabolism in AHU and gout. Random forest classification of 16 metabolites yielded 3 important features to distinguish gout from AHU.ConclusionsDistinct metabolomic signatures were observed in AHU and gout. The selected metabolites may have the potential to improve the early detection of gout.

Prediction of clinically significant prostate cancer through urine metabolomic signatures: A large-scale validated study

PurposeCurrently, there are no accurate markers for predicting potentially lethal prostate cancer (PC) before biopsy. This study aimed to develop urine tests to predict clinically significant PC (sPC) in men at risk.MethodsUrine samples from 928 men, namely, 660 PC patients and 268 benign subjects, were analyzed by gas chromatography/quadrupole time-of-flight mass spectrophotometry (GC/Q-TOF MS) metabolomic profiling to construct four predictive models. Model I discriminated between PC and benign cases. Models II, III, and GS, respectively, predicted sPC in those classified as having favorable intermediate risk or higher, unfavorable intermediate risk or higher (according to the National Comprehensive Cancer Network risk groupings), and a Gleason sum (GS) of ≥ 7. Multivariable logistic regression was used to evaluate the area under the receiver operating characteristic curves (AUC).ResultsIn Models I, II, III, and GS, the best AUCs (0.94, 0.85, 0.82, and 0.80, respectively; training cohort, N = 603) involved 26, 24, 26, and 22 metabolites, respectively. The addition of five clinical risk factors (serum prostate-specific antigen, patient age, previous negative biopsy, digital rectal examination, and family history) significantly improved the AUCs of the models (0.95, 0.92, 0.92, and 0.87, respectively). At 90% sensitivity, 48%, 47%, 50%, and 36% of unnecessary biopsies could be avoided. These models were successfully validated against an independent validation cohort (N = 325). Decision curve analysis showed a significant clinical net benefit with each combined model at low threshold probabilities. Models II and III were more robust and clinically relevant than Model GS.ConclusionThis urine test, which combines urine metabolic markers and clinical factors, may be used to predict sPC and thereby inform the necessity of biopsy in men with an elevated PC risk.

Gut microbiota and metabolomics profiles in patients with chronic stable angina and acute coronary syndrome.

Cardiovascular disease (CVD) is the leading cause of death worldwide. The gut microbiota and its associated metabolites may be involved in the development and progression of CVD, although the mechanisms and impact on clinical outcomes are not fully understood. This study investigated the gut microbiome profile and associated metabolites in patients with chronic stable angina (CSA) and acute coronary syndrome (ACS) compared with healthy controls. Bacterial alpha diversity in stool from patients with ACS or CSA was comparable to healthy controls at both baseline and follow-up visits. Differential abundance analysis identified operational taxonomic units (OTUs) assigned to commensal taxa differentiating patients with ACS from healthy controls at both baseline and follow-up. Patients with CSA and ACS had significantly higher levels of trimethylamine N-oxide compared with healthy controls (CSA: 0.032 ± 0.023 mmol/L, P < 0.01 vs. healthy, and ACS: 0.032 ± 0.023 mmol/L, P = 0.02 vs. healthy, respectively). Patients with ACS had reduced levels of propionate and butyrate (119 ± 4 vs. 139 ± 5.1 µM, P = 0.001, and 14 ± 4.3 vs. 23.5 ± 8.1 µM, P < 0.001, respectively), as well as elevated serum sCD14 (2245 ± 75.1 vs. 1834 ± 45.8 ng/mL, P < 0.0001) and sCD163 levels (457.3 ± 31.8 vs. 326.8 ± 20.7 ng/mL, P = 0.001), compared with healthy controls at baseline. Furthermore, a modified small molecule metabolomic and lipidomic signature was observed in patients with CSA and ACS compared with healthy controls. These findings provide evidence of a link between gut microbiome composition and gut bacterial metabolites with CVD. Future time course studies in patients to observe temporal changes and subsequent associations with gut microbiome composition are required to provide insight into how these are affected by transient changes following an acute coronary event.NEW & NOTEWORTHY The study found discriminative microorganisms differentiating patients with acute coronary syndrome (ACS) from healthy controls. In addition, reduced levels of certain bacterial metabolites and elevated sCD14 and sCD163 were observed in patients with ACS compared with healthy controls. Furthermore, modified small molecule metabolomic and lipidomic signatures were found in both patient groups. Although it is not known whether these differences in profiles are associated with disease development and/or progression, the findings provide exciting options for potential new disease-related mechanism(s) and associated therapeutic target(s).