Untargeted Plasma Metabolomics Research Articles

Multi-omics machine learning to study host-microbiome interactions in early-onset colorectal cancer

The incidence of early-onset colorectal cancer (eoCRC) is rising, and its pathogenesis is not completely understood. We hypothesized that machine learning utilizing paired tissue microbiome and plasma metabolome features could uncover distinct host-microbiome associations between eoCRC and average-onset CRC (aoCRC). Individuals with stages I–IV CRC (n = 64) were categorized as eoCRC (age ≤ 50, n = 20) or aoCRC (age ≥ 60, n = 44). Untargeted plasma metabolomics and 16S rRNA amplicon sequencing (microbiome analysis) of tumor tissue were performed. We fit DIABLO (Data Integration Analysis for Biomarker Discovery using Latent variable approaches for Omics studies) to construct a supervised machine-learning classifier using paired multi-omics (microbiome and metabolomics) data and identify associations unique to eoCRC. A differential association network analysis was also performed. Distinct clustering patterns emerged in multi-omic dimension reduction analysis. The metabolomics classifier achieved an AUC of 0.98, compared to AUC 0.61 for microbiome-based classifier. Circular correlation technique highlighted several key associations. Metabolites glycerol and pseudouridine (higher abundance in individuals with aoCRC) had negative correlations with Parasutterella, and Ruminococcaceae (higher abundance in individuals with eoCRC). Cholesterol and xylitol correlated negatively with Erysipelatoclostridium and Eubacterium, and showed a positive correlation with Acidovorax with higher abundance in individuals with eoCRC. Network analysis revealed different clustering patterns and associations for several metabolites e.g.: urea cycle metabolites and microbes such as Akkermansia. We show that multi-omics analysis can be utilized to study host-microbiome correlations in eoCRC and demonstrates promising biomarker potential of a metabolomics classifier. The distinct host-microbiome correlations for urea cycle in eoCRC may offer opportunities for therapeutic interventions.

Plasma metabolomic and lipidomic profiles accurately classify mothers of children with congenital heart disease: an observational study

IntroductionCongenital heart disease (CHD) is the most common congenital anomaly, representing a significant global disease burden. Limitations exist in our understanding of aetiology, diagnostic methodology and screening, with metabolomics offering promise in addressing these.ObjectiveTo evaluate maternal metabolomics and lipidomics in prediction and risk factor identification for childhood CHD.MethodsWe performed an observational study in mothers of children with CHD following pregnancy, using untargeted plasma metabolomics and lipidomics by ultrahigh performance liquid chromatography-high resolution mass spectrometry (UHPLC-HRMS). 190 cases (157 mothers of children with structural CHD (sCHD); 33 mothers of children with genetic CHD (gCHD)) from the children OMACp cohort and 162 controls from the ALSPAC cohort were analysed. CHD diagnoses were stratified by severity and clinical classifications. Univariate, exploratory and supervised chemometric methods were used to identify metabolites and lipids distinguishing cases and controls, alongside predictive modelling.Results499 metabolites and lipids were annotated and used to build PLS-DA and SO-CovSel-LDA predictive models to accurately distinguish sCHD and control groups. The best performing model had an sCHD test set mean accuracy of 94.74% (sCHD test group sensitivity 93.33%; specificity 96.00%) utilising only 11 analytes. Similar test performances were seen for gCHD. Across best performing models, 37 analytes contributed to performance including amino acids, lipids, and nucleotides.ConclusionsHere, maternal metabolomic and lipidomic analysis has facilitated the development of sensitive risk prediction models classifying mothers of children with CHD. Metabolites and lipids identified offer promise for maternal risk factor profiling, and understanding of CHD pathogenesis in the future.

Plasma Metabolome Predicts Aortic Stiffness and Future Risk of Coronary Artery Disease and Mortality After 23 Years of Follow-Up in the General Population.

Increased aortic stiffness (arteriosclerosis) is associated with early vascular aging independent of age and sex. The underlying mechanisms of early vascular aging remain largely unexplored in the general population. We aimed to investigate the plasma metabolomic profile in aortic stiffness (vascular aging) and associated risk of incident cardiovascular disease and mortality. We included 6865 individuals from 2 Swedish population-based cohorts. Untargeted plasma metabolomics was performed by liquid-chromatography mass spectrometry. Aortic stiffness was assessed directly by carotid-femoral pulse wave velocity (PWV) and indirectly by augmentation index (AIx@75). A least absolute shrinkage and selection operator (LASSO) regression model was created on plasma metabolites in order to predict aortic stiffness. Associations between metabolite-predicted aortic stiffness and risk of new-onset cardiovascular disease, cardiovascular mortality, and all-cause mortality were calculated. Metabolite-predicted aortic stiffness (PWV and AIx@75) was positively associated particularly with acylcarnitines, dimethylguanidino valeric acid, glutamate, and cystine. The plasma metabolome predicted aortic stiffness (PWV and AIx@75) with good accuracy (R2=0.27 and R2=0.39, respectively). Metabolite-predicted aortic stiffness (PWV and AIx@75) was significantly correlated with age, sex, systolic blood pressure, body mass index, and low-density lipoprotein. After 23 years of follow-up, metabolite-predicted aortic stiffness (PWV and AIx@75) was significantly associated with increased risk of new-onset coronary artery disease, cardiovascular mortality, and all-cause mortality. Aortic stiffness is associated particularly with altered metabolism of acylcarnitines, cystine, and dimethylguanidino valeric acid. These metabolic disturbances predict increased risk of new-onset coronary artery disease, cardiovascular mortality, and all-cause mortality after more than 23 years of follow-up in the general population.

A nested case-control study of untargeted plasma metabolomics and lung cancer among never-smoking women within the prospective Shanghai Women's Health Study.

The etiology of lung cancer in never-smokers remains elusive, despite 15% of lung cancer cases in men and 53% in women worldwide being unrelated to smoking. Here, we aimed to enhance our understanding of lung cancer pathogenesis among never-smokers using untargeted metabolomics. This nested case-control study included 395 never-smoking women who developed lung cancer and 395 matched never-smoking cancer-free women from the prospective Shanghai Women's Health Study with 15,353 metabolic features quantified in pre-diagnostic plasma using liquid chromatography high-resolution mass spectrometry. Recognizing that metabolites often correlate and seldom act independently in biological processes, we utilized a weighted correlation network analysis to agnostically construct 28 network modules of correlated metabolites. Using conditional logistic regression models, we assessed the associations for both metabolic network modules and individual metabolic features with lung cancer, accounting for multiple testing using a false discovery rate (FDR) < 0.20. We identified a network module of 121 features inversely associated with all lung cancer (p = .001, FDR = 0.028) and lung adenocarcinoma (p = .002, FDR = 0.056), where lyso-glycerophospholipids played a key role driving these associations. Another module of 440 features was inversely associated with lung adenocarcinoma (p = .014, FDR = 0.196). Individual metabolites within these network modules were enriched in biological pathways linked to oxidative stress, and energy metabolism. These pathways have been implicated in previous metabolomics studies involving populations exposed to known lung cancer risk factors such as traffic-related air pollution and polycyclic aromatic hydrocarbons. Our results suggest that untargeted plasma metabolomics could provide novel insights into the etiology and risk factors of lung cancer among never-smokers.

Abstract P277: Adherence to Time-Restricted Eating, Plasma Metabolomic Signatures, and Risk of Coronary Heart Disease

Introduction: Early time-restricted eating (TRE) has been recently suggested as an effective dietary intervention strategy to improve risk factors of coronary heart disease (CHD), whereas late TRE may be related to poor cardiometabolic health. Metabolomic pathways linking the adherence to habitual TREs depending on the time of the day and CHD risk are unknown. Hypothesis: We tested whether adherence to early or late TRE was related to distinct metabolomic signatures (including those in pathways of energy/purine pyrimidine metabolism, lipids, carbohydrates, and acylcarnitines). We then tested whether TRE-related plasma metabolites were associated with the risk of incident CHD among women without prior CHD. Methods: This study included 674 women without cardiovascular disease who completed 7-day food records and measurements of untargeted plasma metabolomics in a sub-study of the Nurses’ Health Studies (NHS/NHSII). TRE was indicated by 4-12 hours of daily eating window. The adherence to TRE was assessed by summing up the TRE days in a week (0-7 days). We analyzed two TRE habits: early TRE (starting before 9 a.m./ending no later than 9 p.m.) and late TRE (beginning after 9 a.m.). Separately, plasma metabolites were measured in a prospective nested case-control of 762 incident cases of CHD and 762 matched controls identified over 10-14 years of follow-up in the NHS. A metabolomic risk score for predicting adherence to late TRE (MRS-TRE late ) was created based on metabolites selected through the elastic net procedure with a 10-fold cross-validation. Results: Adherence to late TRE was positively associated with greater adiposity, insulin/proinsulin, uric acid, and medium-chain fatty acids/acylcarnitines; the late TRE habit was inversely related to metabolites in the TCA cycle/pyruvate metabolism and specific PC species ( P FDR &lt;0.05). On the other hand, metabolites in lipid pathways (including diglyceride DG 38:5) and glucose were inversely related to the adherence to early TRE. Individual levels of late-TRE-related metabolites in energy/phosphatidylcholine /acylcarnitine pathways were significantly associated with an increased risk of CHD. Every 1 SD increment of MRS-TRE late , based on 21 metabolites in various metabolic pathways, was associated with a relative risk (RR) of 1.24 (95% CI: 1.11, 1.38) for CHD in a model adjusted for matching factors, and a RR of 1.15 (1.02, 1.29) after adjusting for covariates, such as demographic factors, diet quality, alcohol, lifestyle factors, and BMI. Women in the top tertile of MRS-TRE late had a 1.44-fold (1.10, 1.89) increased risk of CHD as compared to those in the lowest tertile after adjusting for the covariates. Conclusions: TRE habit, depending on the timing, was related to distinct metabolomic pathways, and adherence to late TRE may be associated with metabolic reliance linked to the risk of incident CHD among women.

Exploration of differential responses to FODMAPs and gluten in people with irritable bowel syndrome- a double-blind randomized cross-over challenge study

IntroductionThere is large variation in response to diet in irritable bowel syndrome (IBS) and determinants for differential response are poorly understood.ObjectivesOur aim was to investigate differential clinical and molecular responses to provocation with fermentable oligo-, di-, monosaccharides, and polyols (FODMAPs) and gluten in individuals with IBS.MethodsData were used from a crossover study with week-long interventions with either FODMAPs, gluten or placebo. The study also included a rapid provocation test. Molecular data consisted of fecal microbiota, short chain fatty acids, and untargeted plasma metabolomics. IBS symptoms were evaluated with the IBS severity scoring system. IBS symptoms were modelled against molecular and baseline questionnaire data, using Random Forest (RF; regression and clustering), Parallel Factor Analysis (PARAFAC), and univariate methods.ResultsRegression and classification RF models were in general of low predictive power (Q2 ≤ 0.22, classification rate < 0.73). Out of 864 clustering models, only 2 had significant associations to clusters (0.69 < CR < 0.73, p < 0.05), but with no associations to baseline clinical measures. Similarly, PARAFAC revealed no clear association between metabolome data and IBS symptoms.ConclusionDifferential IBS responses to FODMAPs or gluten exposures could not be explained from clinical and molecular data despite extensive exploration with different data analytical approaches. The trial is registered at www.clinicaltrials.gov as NCT03653689 31/08/2018.

Metabolite Signature of Life's Essential 8 and Risk of Coronary Heart Disease Among Low-Income Black and White Americans.

Life's essential 8 (LE8) is a comprehensive construct of cardiovascular health. Yet, little is known about the LE8 score, its metabolic correlates, and their predictive implications among Black Americans and low-income individuals. In a nested case-control study of coronary heart disease (CHD) among 299 pairs of Black and 298 pairs of White low-income Americans from the Southern Community Cohort Study, we estimated LE8 score and applied untargeted plasma metabolomics and elastic net with leave-one-out cross-validation to identify metabolite signature (MetaSig) of LE8. Associations of LE8 score and MetaSig with incident CHD were examined using conditional logistic regression. The mediation effect of MetaSig on the LE8-CHD association was also examined. The external validity of MetaSig was evaluated in another nested CHD case-control study among 299 pairs of Chinese adults. Higher LE8 score was associated with lower CHD risk (standardized odds ratio, 0.61 [95% CI, 0.53-0.69]). The MetaSig, consisting of 133 metabolites, showed significant correlation with LE8 score (r=0.61) and inverse association with CHD (odds ratio, 0.57 [0.49-0.65]), robust to adjustment for LE8 score and across participants with different sociodemographic and health status ([odds ratios, 0.42-0.69]; all P<0.05). MetaSig mediated a large portion of the LE8-CHD association: 53% (32%-80%). Significant associations of MetaSig with LE8 score and CHD risk were found in validation cohort (r=0.49; odds ratio, 0.57 [0.46-0.69]). Higher LE8 score and its MetaSig were associated with lower CHD risk among low-income Black and White Americans. Metabolomics may offer an objective measure of LE8 and its metabolic phenotype relevant to CHD prevention among diverse populations.

Genetic and nongenetic drivers of platelet reactivity in healthy Tanzanian individuals

BackgroundPlatelets play a key role in hemostasis, inflammation, and cardiovascular diseases. Platelet reactivity is highly variable between individuals. The drivers of this variability in populations from Sub-Saharan Africa remain largely unknown. ObjectivesWe aimed to investigate the nongenetic and genetic determinants of platelet reactivity in healthy adults living in a rapidly urbanizing area in Northern Tanzania. MethodsPlatelet activation and reactivity were measured by platelet P-selectin expression and the binding of fibrinogen in unstimulated blood and after ex vivo stimulation with adenosine diphosphate and PAR-1 and PAR-4 ligands. We then analyzed the associations of platelet parameters with host genetic and nongenetic factors, environmental factors, plasma inflammatory markers, and plasma metabolites. ResultsOnly a few associations were found between platelet reactivity parameters and plasma inflammatory markers and nongenetic host and environmental factors. In contrast, untargeted plasma metabolomics revealed a large number of associations with food-derived metabolites, including phytochemicals that were previously reported to inhibit platelet reactivity. Genome-wide single-nucleotide polymorphism genotyping identified 2 novel single-nucleotide polymorphisms (rs903650 and rs4789332) that were associated with platelet reactivity at the genome-wide level (P < 5 × 10−8) as well as a number of variants in the PAR4 gene (F2RL3) that were associated with PAR4-induced reactivity. ConclusionOur study uncovered factors that determine variation in platelet reactivity in a population in East Africa that is rapidly transitioning to an urban lifestyle, including the importance of genetic ancestry and the gradual abandoning of the traditional East African diet.

Untargeted plasma metabolomics and risk of colorectal cancer—an analysis nested within a large-scale prospective cohort

BackgroundColorectal cancer (CRC) is a leading cause of cancer-related death worldwide, but if discovered at an early stage, the survival rate is high. The aim of this study was to identify novel markers predictive of future CRC risk using untargeted metabolomics.MethodsThis study included prospectively collected plasma samples from 902 CRC cases and 902 matched cancer-free control participants from the population-based Northern Sweden Health and Disease Study (NSHDS), which were obtained up to 26 years prior to CRC diagnosis. Using reverse-phase liquid chromatography–mass spectrometry (LC–MS), data comprising 5015 metabolic features were obtained. Conditional logistic regression was applied to identify potentially important metabolic features associated with CRC risk. In addition, we investigated if previously reported metabolite biomarkers of CRC risk could be validated in this study population.ResultsIn the univariable analysis, seven metabolic features were associated with CRC risk (using a false discovery rate cutoff of 0.25). Two of these could be annotated, one as pyroglutamic acid (odds ratio per one standard deviation increase = 0.79, 95% confidence interval, 0.70–0.89) and another as hydroxytigecycline (odds ratio per one standard deviation increase = 0.77, 95% confidence interval, 0.67–0.89). Associations with CRC risk were also found for six previously reported metabolic biomarkers of prevalent and/or incident CRC: sebacic acid (inverse association) and L-tryptophan, 3-hydroxybutyric acid, 9,12,13-TriHOME, valine, and 13-OxoODE (positive associations).ConclusionsThese findings suggest that although the circulating metabolome may provide new etiological insights into the underlying causes of CRC development, its potential application for the identification of individuals at higher risk of developing CRC is limited.

Plasma untargeted metabolomics with proteinase K discloses phospholipid signature associated with pulmonary arterial hypertension

Pulmonary arterial hypertension is a rare but life-threatening and clinically heterogeneous disease. The diagnostic schedule of this disorder is complex, and no specific indicator of the arterial etiology has been explored. In this study, untargeted plasma metabolomics was applied to evaluate the metabolic fingerprints of pulmonary arterial hypertension patients. Plasma samples were prepared using a new approach, which applies proteinase K during the sample preparation procedure to increase the metabolite coverage. The metabolic fingerprints were determined via LC–MS and subsequently analyzed with the use of both uni- and multivariate statistics. A total of 21 metabolites were discovered to be significantly altered in pulmonary arterial hypertensive patients. The metabolites were mainly related to the phospholipid metabolic pathways. In this study, decreases were found in the phosphatidylcholines (PCs) [PC(32:0), PC(40:7), PC(42:7)], phosphatidylethanolamine PE(18:0/18:2), lysophosphatidylethanolamines (LPEs) [LPE(22:6), LPE(18:2), LPE(18:0), LPE(20:4), LPE(20:1), LPE(20:0)], lysophosphatidylcholine LPC(20:4) and lysophosphatidylserine LPS(19:0), as well as increase of sphingomyelin SM(36:2), in the plasma samples of pulmonary arterial hypertensive patients in comparison to the control group. Besides their function as components of the biological membranes, these metabolites are also involved in the intracellular signaling pathways that are related to cell proliferation and apoptosis. The results obtained during this study confirm the potential of (untargeted) metabolomics to identify the molecular characteristics of the pathophysiology of pulmonary arterial hypertension. The clinical relevance of this study constitutes the selection of a metabolic panel that can potentially detect and properly diagnose the disease.

Mechanisms of Yangxin Tongmai Formula for blood stasis syndrome in coronary heart disease rats based on untargeted plasma metabolomics and intestinal flora 16S rRNA sequencing

ObjectiveTo investigate the correlations between intestinal flora, plasma metabolites, and blood stasis syndrome in coronary heart disease (CHD), and the mechanisms of Yangxin Tongmai Formula (养心通脉方, YXTMF) for blood stasis syndrome in CHD rats. MethodsA total of 18 specific pathogen free (SPF) male Sqrague-Dawley (SD) rats were used to establish CHD rat models with blood stasis syndrome, which were then randomized into model, YXTMF, and atorvastatin calcium (AVT) groups, with six rats in each group, and were intervened through gavage for two weeks. Subsequently, additional six rats that received normal diet were included as normal group. The pathological changes in the CHD rat models were identified by hematoxylin-eosin (HE) staining. The electrocardiogram, hemodynamics, and lipid profiles of the rats were detected as well. The untargeted plasma metabolomics of rats were analyzed by liquid chromotography-tandem mass spectrometry (LC-MS/MS), their ileal mucosal flora by 16S rRNA sequencing, and the correlation between the two results were also analyzed. ResultsThe whole blood viscosity, total cholesterol (TC), and low-density lipoprotein cholesterol (LDL-C) of rats in the model group increased compared with those in the control group (P < 0.05). In the model group, the proliferation of endothelial cells in the coronary artery of rats was damaged, with quite a few vacuolated pathological changes observed. However, the endothelial lesions in the coronary artery of rats were alleviated in the intervention groups (YXTMF and AVT groups). With the use of LC-MS/MS, a total of 33 potential endogenous metabolites were identified in plasma, among which 1-methylhistidine, N-acetylhistamine, progesterone, and deoxycorticosterone were expected to be the differential metabolites in CHD rats with blood stasis syndrome. The 16S rRNA sequencing results showed that improved diversity and abundance of intestinal flora were observed in the YXTMF group. The correlation analysis suggested that Hydrogenophaga, Limnohabitans, and Polaromonas, which were highly related to the formation of blood stasis syndrome in CHD patients, were positively correlated with plasma metabolites such as 5-hydroxyindole, N-acetylhistamine, and progesterone (P < 0.01), but were negatively correlated with plasma metabolites such as L-arginine, homoarginine, and Boc-beta-cyano-L-alanine (P < 0.01). After YXTMF intervention, Lactobacillus, Corynebacterium, and Candidatus Nitrososphaera were positively correlated with plasma metabolites such as Boc-β-cyano-L-alanine, stachydrine, and naringenin (P < 0.05), while negatively correlated with 5-hydroxyindole, N-acetylhistamine, and oleoylethanolamide (P < 0.05). ConclusionYXTMF could alleviate blood stasis syndrome in CHD rats through improving their plasma metabolisms achieved by regulating the intestinal flora.

Abstract 6056: A nested case-control study of untargeted plasma metabolomics and lung cancer risk among never-smoking women in the prospective Shanghai Women’s Health Study

Abstract Background: The etiology of lung cancer among never-smokers is unclear despite 15% of cases in men and 53% in women worldwide are not smoking-related. Metabolomics provides a snapshot of dynamic biochemical activities, including those found to be driving tumor formation and progression. This study used untargeted metabolomics with network analysis to agnostically identify network modules and independent metabolites in pre-diagnostic blood samples among never-smokers to further understand the pathogenesis of lung cancer. Methods: Within the prospective Shanghai Women’s Health Study, we conducted a nested case-control study of 395 never-smoking incident lung cancer cases and 395 never-smoking controls matched on age. We performed liquid chromatography high-resolution mass spectrometry to quantify 20,348 unique metabolic features in plasma. Because metabolic features are expected to be highly correlated and more likely to be involved in biological processes as a network of intertwined features than individually, we agnostically constructed 28 network modules using a weighted correlation network analysis approach. The associations between metabolite network modules and individual metabolites with lung cancer were assessed using conditional logistic regression models, adjusting for age, body mass index, and exposure to environmental tobacco smoke. We accounted for multiple testing using a false discovery rate (FDR) &amp;lt; 0.20. Results: We identified a network module of 122 metabolic features enriched in lysophosphatidylethanolamines that was associated with all lung cancer combined (p = 0.001, FDR = 0.028) and lung adenocarcinoma (p = 0.002, FDR = 0.056) and another network module of 440 metabolic features that was associated with lung adenocarcinoma (p = 0.014, FDR = 0.196). Metabolic features were enriched in pathways associated with cell growth and proliferation, including oxidative stress, bile acid biosynthesis, and metabolism of nucleic acids, carbohydrates, and amino acids, including 1-carbon compounds. Conclusions: Our prospective study suggests that untargeted plasma metabolomics in pre-diagnostic samples could provide new insights into the etiology of lung cancer in never-smokers. Replication and further characterization of these associations are warranted. Citation Format: Mohammad L. Rahman, Xiao-Ou Shu, Douglas Walker, Dean P. Jones, Wei Hu, Bu-tian Ji, Batel Blechter, Jason YY Wong, Qiuyin Cai, Gong Yang, Tu-Tang Gao, Wei Zheng, Nathaniel Rothman, Qing Lan. A nested case-control study of untargeted plasma metabolomics and lung cancer risk among never-smoking women in the prospective Shanghai Women’s Health Study [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 6056.

Metformin Monotherapy Alters the Human Plasma Lipidome Independent of Clinical Markers of Glycemic Control and Cardiovascular Disease Risk in a Type 2 Diabetes Clinical Cohort.

Type 2 diabetes (T2D) is a rising pandemic worldwide. Diet and lifestyle changes are typically the first intervention for T2D. When this intervention fails, the biguanide, metformin, is the most common pharmaceutical therapy. Yet, it's full mechanisms of action remain unknown. In this work, we applied an ultrahigh resolution, mass spectrometry-based platform for untargeted plasma metabolomics to human plasma samples from a case-control observational study of non-diabetic and well-controlled T2D subjects, the latter treated conservatively with metformin or diet and lifestyle changes only. No statistically significant differences existed in baseline demographic parameters, glucose control, or clinical markers of cardiovascular disease risk between the two T2D groups, which we hypothesized would allow the identification of circulating metabolites independently associated with treatment modality. Over 3000 blank-reduced metabolic features were detected, with the majority of annotated features being lipids or lipid-like molecules. Altered abundance of multiple fatty acids and phospholipids were found in T2D subjects treated with diet and lifestyle changes as compared to non-diabetic subjects: changes that were often reversed by metformin. Our findings provide direct evidence that metformin monotherapy alters the human plasma lipidome independent of T2D disease control and support a potential cardioprotective effect of metformin worthy of future study. Significance Statement This work provides important new information on the systemic effects of metformin in type 2 diabetic subjects. We observed significant changes in the plasma lipidome with metformin therapy, with metabolite classes previously associated with cardiovascular disease risk significantly reduced as compared to diet and lifestyle changes. While cardiovascular disease risk was not a primary outcome of our study, our results provide a jumping-off point for future work into the cardioprotective effects of metformin, even in well-controlled type 2 diabetes.

New Insights into the Identification of Metabolites and Cytokines Predictive of Outcome for Patients with Severe SARS-CoV-2 Infection Showed Similarity with Cancer

SARS-CoV-2 infection is characterized by several clinical manifestations, ranging from the absence of symptoms to severe forms that necessitate intensive care treatment. It is known that the patients with the highest rate of mortality develop increased levels of proinflammatory cytokines, called the "cytokine storm", which is similar to inflammatory processes that occur in cancer. Additionally, SARS-CoV-2 infection induces modifications in host metabolism leading to metabolic reprogramming, which is closely linked to metabolic changes in cancer. A better understanding of the correlation between perturbed metabolism and inflammatory responses is necessary. We evaluated untargeted plasma metabolomics and cytokine profiling via 1H-NMR (proton nuclear magnetic resonance) and multiplex Luminex assay, respectively, in a training set of a limited number of patients with severe SARS-CoV-2 infection classified on the basis of their outcome. Univariate analysis and Kaplan-Meier curves related to hospitalization time showed that lower levels of several metabolites and cytokines/growth factors, correlated with a good outcome in these patients and these data were confirmed in a validation set of patients with similar characteristics. However, after the multivariate analysis, only the growth factor HGF, lactate and phenylalanine retained a significant prediction of survival. Finally, the combined analysis of lactate and phenylalanine levels correctly predicted the outcome of 83.3% of patients in both the training and the validation set. We highlighted that the cytokines and metabolites involved in COVID-19 patients' poor outcomes are similar to those responsible for cancer development and progression, suggesting the possibility of targeting them by repurposing anticancer drugs as a therapeutic strategy against severe SARS-CoV-2 infection.

Abstract 41: Comprehensive Plasma Metabolomic Risk Scores to Predict Incident Coronary Heart Disease Events

Introduction: Integrating multiple metabolomics signatures related to coronary heart disease (CHD) into a metabolomic risk score (MRS) may contribute to improving the risk prediction beyond traditional risk factors, although applications of the MRS remain underdetermined, especially in people at usual risk. Hypothesis: We analyzed associations of plasma metabolites with the risk of CHD to develop MRS for predicting risk of incident CHD by incorporating comprehensive metabolic pathways among women at usual risk from the Nurses’ Health Study (NHS). We also tested whether changes in CHD-related plasma metabolites were associated with significant reductions in atherosclerotic cardiovascular disease (ASCVD) risk scores among participants in the POUNDS Lost trial. Methods: Untargeted metabolomics profiling was performed to measure plasma metabolites at baseline in a prospective nested case-control of 762 incident cases of CHD (fatal CHD and nonfatal myocardial infarction) and 762 matched controls identified over 10-14 years of follow-up time in NHS. An MRS of CHD risk was created based on metabolites that were selected through conditional logistic regression with the elastic net procedure. The POUNDS Lost trial included adults with overweight/obesity who consumed weight-loss diets with different macronutrient intakes. We measured untargeted plasma metabolomics signatures at baseline, 6 months, and 2 years to calculate changes in response to the dietary interventions. The primary outcome in the POUNDS Lost trial was the reduction of 10-year ASCVD risk scores calculated by the validated pooled cohort equations. Results: Various plasma metabolites (such as gut-microbiota-related and others in the pathways of ceramides, phospholipids, and polyamine metabolism) were associated with the risk of incident CHD ( P FDR &lt;0.05). We developed MRS for CHD combining 122 metabolites and found that the highest quartile (Q4) was associated with 11.3 times (95% CI: 6.88, 18.6) higher risk of CHD, as compared to the lowest quartile (Q1), even after adjusting for covariates including common CHD risk factors (such as a family history of MI, lifestyle factors, obesity, diabetes, hypertension, and dyslipidemia). Every 1 SD increment of MRS was associated with an adjusted RR of 3.23 (95% CI 2.62, 3.97) for CHD events. In the POUNDS Lost, diet-intervention-induced changes in CHD-related metabolites, particularly those in ceramide pathways, were significantly associated with reductions in the ASCVD risk score at 6 months and 2 years. Conclusions: Comprehensive metabolomic scores were strongly related to the risk of incident CHD among women without prior CHD. Dietary interventions may modify the unfavorable relations between CHD-risk-related metabolites and ASCVD risk. Further studies in other populations are warranted to validate the performance of MRS in predicting ASCVD events.

Untargeted, High-Resolution Metabolomics in Pediatric Eosinophilic Esophagitis.

Eosinophilic esophagitis (EoE) is an inflammatory disease of unclear etiology. The aim of this study was to use untargeted plasma metabolomics to identify metabolic pathway alterations associated with EoE to better understand the pathophysiology. This prospective, case-control study included 72 children, aged 1-17 years, undergoing clinically indicated upper endoscopy (14 diagnosed with EoE and 58 controls). Fasting plasma samples were analyzed for metabolomics by high-resolution dual-chromatography mass spectrometry. Analysis was performed on sex-matched groups at a 2:1 ratio. Significant differences among the plasma metabolite features between children with and without EoE were determined using multivariate regression analysis and were annotated with a network-based algorithm. Subsequent pathway enrichment analysis was performed. Patients with EoE had a higher proportion of atopic disease (85.7% vs 50%, P = 0.019) and any allergies (100% vs 57.1%, P = 0.0005). Analysis of the dual chromatography features resulted in a total of 918 metabolites that differentiated EoE and controls. Glycerophospholipid metabolism was significantly enriched with the greatest number of differentiating metabolites and overall pathway enrichment ( P < 0.01). Multiple amino and fatty acid pathways including linoleic acid were also enriched, as well as pyridoxine metabolism ( P < 0.01). In this pilot study, we found differences in metabolites involved in glycerophospholipid and inflammation pathways in pediatric patients with EoE using untargeted metabolomics, as well as overlap with amino acid metabolome alterations found in atopic disease.

Abstract 12505: Patients With Heart Failure Living in Socioeconomically Deprived Neighborhoods Have Unique Metabolomic Profiles

Introduction: Patients living in neighborhoods with lower socioeconomic status have a greater risk for incident heart failure (HF), more severe symptoms, and increased risk of adverse clinical outcomes. We used high resolution metabolomics profiling to identify novel biomarkers associated with neighborhood socioeconomic status (nSES) in patients with HFrEF. Methods: The Area Deprivation Index (ADI) was used to characterize nSES in a discovery (n=170) and a validation (n=166) cohort of patients with HFrEF. Targeted and untargeted high-resolution plasma metabolomics profiling coupled with partial least-squares discriminant analysis (PLS-DA) was used to identify metabolites and metabolic pathways uniquely perturbed in patients in the highest tertile of the ADI (indicating lower nSES). Metabolites with a variable importance in projection (VIP) &gt; 1 entered pathway enrichment analysis. Results: Compared to patients in the lower and middle ADI tertiles, patients with HFrEF living in the highest ADI tertile were more likely to be Black, and had higher body mass index and lower ejection fraction (all P &lt; 0.01). PLS-DA confirmed metabolites that may be associated with worse diet quality in patients in both the discovery and validation cohorts who lived in neighborhoods in the highest ADI tertile, including amino acids and their metabolic products (carnitine, methionine, tryptophan, phenylalanine, valine, phenethylamine, indole), and urate. Moreover, metabolites associated with the human exposome (5-valerolactone, 1-naphthylamine) were also identified. Pathway enrichment analysis revealed branched-chain amino acid metabolism, fatty acid metabolism, and purine metabolism as part of this distinct metabolic signature ( Figure ). Conclusions: Patients with HFrEF living in neighborhoods with lower SES have a distinct metabolic signature that may, in part, be related to worse diet quality and more severe HF.

The Beneficial Effect of Cinnamon and Red Capsicum Intake on Postprandial Changes in Plasma Metabolites Evoked by a High-Carbohydrate Meal in Men with Overweight/Obesity.

The relationship of high-carbohydrate (HC) meal intake to metabolic syndrome is still not fully explained. Metabolomics has the potential to indicate metabolic pathways altered by HC meals, which may improve our knowledge regarding the mechanisms by which HC meals may contribute to metabolic syndrome development. The fasting and postprandial metabolic response to HC or normo-carbohydrate (NC) meals with/without cinnamon + capsicum intake was evaluated using untargeted metabolomics and compared between normal-weight (NW) and overweight/obese (OW/OB) healthy men. Healthy male participants (age-matched) were divided into two groups (12 subjects per group). One was composed of men with normal weight (NW) and the other of men with overweight/obesity (OW/OB). On separate visits (with 2–3 week intervals), the participants received standardized HC or NC meals (89% or 45% carbohydrates, respectively). Fasting (0 min) and postprandial (30, 60, 120, 180 min) blood were collected for untargeted plasma metabolomics. Based on each metabolic feature’s intensity change in time, the area under the curve (AUC) was calculated. Obtained AUCs were analyzed using multivariate statistics. Several metabolic pathways were found dysregulated after an HC meal in people from the OW/OB group but not the NW group. The consumption of HC meals by people with overweight/obesity led to a substantial increase in AUC, mainly for metabolites belonging to phospholipids and fatty acid amides. The opposite was observed for selected sphingolipids. The intake of cinnamon and capsicum normalized the concentration of selected altered metabolites induced by the intake of HC meals. A HC meal may induce an unfavourable postprandial metabolic response in individuals with overweight/obesity, and such persons should avoid HC meals.

Metabolomics links lifestyle variables to breast cancer in the Long Island Breast Cancer Study Project

Background/Aim: Healthy lifestyle has been associated with decreased risk of developing breast cancer. Using untargeted metabolomics profiling, we aim to identify the molecular mechanisms connecting lifestyle and breast cancer through network analysis. Methods: A total of 50 post-menopausal women with breast cancer and 50 cancer-free controls were selected from the Long Island Breast Cancer Study Project (LIBCSP). We measured untargeted plasma metabolomics using liquid chromatography- high resolution mass spectrometry (LC-HRMS). Using ‘enet’, we retained metabolites found in active molecular network (AMN) clusters for analysis. LASSO was used to explore associations between cancer status and metabolites; 14 lifestyle related variables including smoking, physical activity, alcohol consumption, meat consumption, fruit and vegetables consumption, supplemental vitamin use; and 20 covariates including various reproductive factors. LASSO was repeated to explore associations between selected lifestyle factors and metabolites. Results were displayed as a network to uncover biological pathways linking lifestyle factors to breast cancer. Results: After filtering, 1797 metabolomics peaks in the plasma samples remained. Of these, 851 &amp;#x22;active” metabolites were retained in 197 correlation AMN clusters. Through LASSO, breast cancer status was associated with 66 &amp;#x22;active” metabolites. Several of these metabolites were also associated with lifestyle variables including meat consumption, alcohol consumption, and supplemental β-carotene, B12 and folate use. No individual lifestyle factors were directly associated with breast cancer status through LASSO, suggesting that metabolites may act as biological intermediaries between healthy lifestyle factors and breast cancer. In particular, 13-HPODE, a metabolite linked with inflammation, was associated with breast cancer status and connected to β-carotene supplement usage through an AMN. Conclusions: We found several plasma metabolites associated with both lifestyle factors and breast cancer status. These results suggest that inflammation may play a mechanistic role linking supplement usage to breast cancer status. Keywords: breast cancer, lifestyle factors, metabolomics, network analysis

Untargeted plasma metabolomics for risk prediction of hepatocellular carcinoma: A prospective study in two Chinese cohorts.

Characterization of metabolic perturbation prior to hepatocellular carcinoma (HCC) may deepen the understanding of causal pathways and identify novel biomarkers for early prevention. We conducted two 1:1 matched nested case-control studies (108 and 55 pairs) to examine the association of plasma metabolome (profiled using LC-MS) with the risk of HCC based on two prospective cohorts in China. Differential metabolites were identified by paired t tests and orthogonal partial least-squares discriminant analysis (OPLS-DA). Weighted gene coexpression network analysis (WGCNA) was performed to classify metabolites into modules for identifying biological pathways involved in hepatocarcinogenesis. We assessed the risk predictivity of metabolites using multivariable logistic regression models. Among 612 named metabolites, 44 differential metabolites were identified between cases and controls, including 12 androgenic/progestin steroid hormones, 8 bile acids, 10 amino acids, 6 phospholipids, and 8 others. These metabolites were associated with HCC in the multivariable logistic regression analyses, with odds ratios ranging from 0.19 (95% confidence interval [CI]: 0.11-0.35) to 5.09 (95% CI: 2.73-9.50). WGCNA including 612 metabolites showed 8 significant modules related to HCC risk, including those representing metabolic pathways of androgen and progestin, primary and secondary bile acids, and amino acids. A combination of 18 metabolites of independent effects showed the potential to predict HCC risk, with an AUC of 0.87 (95% CI: 0.82-0.92) and 0.86 (95% CI: 0.80-0.93) in the training and validation sets, respectively. In conclusion, we identified a panel of plasma metabolites that could be implicated in hepatocellular carcinogenesis and have the potential to predict HCC risk.