Dataset Information

Combining Semi-Targeted Metabolomics and Machine Learning to Identify Metabolic Alterations in the Serum and Urine of Hospitalized Patients with COVID-19.

ABSTRACT: Viral infections cause metabolic dysregulation in the infected organism. The present study used metabolomics techniques and machine learning algorithms to retrospectively analyze the alterations of a broad panel of metabolites in the serum and urine of a cohort of 126 patients hospitalized with COVID-19. Results were compared with those of 50 healthy subjects and 45 COVID-19-negative patients but with bacterial infectious diseases. Metabolites were analyzed by gas chromatography coupled to quadrupole time-of-flight mass spectrometry. The main metabolites altered in the sera of COVID-19 patients were those of pentose glucuronate interconversion, ascorbate and fructose metabolism, nucleotide sugars, and nucleotide and amino acid metabolism. Alterations in serum maltose, mannonic acid, xylitol, or glyceric acid metabolites segregated positive patients from the control group with high diagnostic accuracy, while succinic acid segregated positive patients from those with other disparate infectious diseases. Increased lauric acid concentrations were associated with the severity of infection and death. Urine analyses could not discriminate between groups. Targeted metabolomics and machine learning algorithms facilitated the exploration of the metabolic alterations underlying COVID-19 infection, and to identify the potential biomarkers for the diagnosis and prognosis of the disease.

SUBMITTER: Baiges-Gaya G

PROVIDER: S-EPMC9856035 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature

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Combining Semi-Targeted Metabolomics and Machine Learning to Identify Metabolic Alterations in the Serum and Urine of Hospitalized Patients with COVID-19.

Baiges-Gaya Gerard G Iftimie Simona S Castañé Helena H Rodríguez-Tomàs Elisabet E Jiménez-Franco Andrea A López-Azcona Ana F AF Castro Antoni A Camps Jordi J Joven Jorge J

Biomolecules 20230112 1

Viral infections cause metabolic dysregulation in the infected organism. The present study used metabolomics techniques and machine learning algorithms to retrospectively analyze the alterations of a broad panel of metabolites in the serum and urine of a cohort of 126 patients hospitalized with COVID-19. Results were compared with those of 50 healthy subjects and 45 COVID-19-negative patients but with bacterial infectious diseases. Metabolites were analyzed by gas chromatography coupled to quadr ...[more]

PMID: 36671548

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Project description:BackgroundLipids are involved in the interaction between viral infection and the host metabolic and immunological responses. Several studies comparing the lipidome of COVID-19-positive hospitalized patients vs. healthy subjects have already been reported. It is largely unknown, however, whether these differences are specific to this disease. The present study compared the lipidomic signature of hospitalized COVID-19-positive patients with that of healthy subjects, as well as with COVID-19-negative patients hospitalized for other infectious/inflammatory diseases.MethodsWe analyzed the lipidomic signature of 126 COVID-19-positive patients, 45 COVID-19-negative patients hospitalized with other infectious/inflammatory diseases and 50 healthy volunteers. A semi-targeted lipidomics analysis was performed using liquid chromatography coupled to mass spectrometry. Two-hundred and eighty-three lipid species were identified and quantified. Results were interpreted by machine learning tools.ResultsWe identified acylcarnitines, lysophosphatidylethanolamines, arachidonic acid and oxylipins as the most altered species in COVID-19-positive patients compared to healthy volunteers. However, we found similar alterations in COVID-19-negative patients who had other causes of inflammation. Conversely, lysophosphatidylcholine 22:6-sn2, phosphatidylcholine 36:1 and secondary bile acids were the parameters that had the greatest capacity to discriminate between COVID-19-positive and COVID-19-negative patients.ConclusionThis study shows that COVID-19 infection shares many lipid alterations with other infectious/inflammatory diseases, and which differentiate them from the healthy population. The most notable alterations were observed in oxylipins, while alterations in bile acids and glycerophospholipis best distinguished between COVID-19-positive and COVID-19-negative patients. Our results highlight the value of integrating lipidomics with machine learning algorithms to explore the pathophysiology of COVID-19 and, consequently, improve clinical decision making.

Project description:ObjectivePolycystic ovary syndrome (PCOS) is a condition of androgen excess and chronic anovulation frequently associated with insulin resistance. We combined a nontargeted and targeted metabolomics approach to identify pathways and metabolites that distinguished PCOS from metabolic syndrome (MetS).MethodsTwenty obese women with PCOS were compared with 18 obese women without PCOS. Both groups met criteria for MetS but could not have diabetes mellitus or take medications that treat PCOS or affect lipids or insulin sensitivity. Insulin sensitivity was derived from the frequently sampled intravenous glucose tolerance test. A nontargeted metabolomics approach was performed on fasting plasma samples to identify differentially expressed metabolites, which were further evaluated by principal component and pathway enrichment analysis. Quantitative targeted metabolomics was then applied on candidate metabolites. Measured metabolites were tested for associations with PCOS and clinical variables by logistic and linear regression analyses.ResultsThis multiethnic, obese sample was matched by age (PCOS, 37±6; MetS, 40±6years) and body mass index (BMI) (PCOS, 34.6±5.1; MetS, 33.7±5.2kg/m2). Principal component analysis of the nontargeted metabolomics data showed distinct group separation of PCOS from MetS controls. From the subset of 385 differentially expressed metabolites, 22% were identified by accurate mass, resulting in 19 canonical pathways significantly altered in PCOS, including amino acid, lipid, steroid, carbohydrate, and vitamin D metabolism. Targeted metabolomics identified many essential amino acids, including branched-chain amino acids (BCAA) that were elevated in PCOS compared with MetS. PCOS was most associated with BCAA (P=.02), essential amino acids (P=.03), the essential amino acid lysine (P=.02), and the lysine metabolite α-aminoadipic acid (P=.02) in models adjusted for surrogate variables representing technical variation in metabolites. No significant differences between groups were observed in concentrations of free fatty acids or vitamin D metabolites. Evaluation of the relationship of metabolites with clinical characteristics showed 1) negative associations of essential and BCAA with insulin sensitivity and sex hormone-binding globulin and 2) positive associations with homeostasis model of insulin resistance and free testosterone; metabolites were not associated with BMI or percent body fat.ConclusionsPCOS was associated with significant metabolic alterations not attributed exclusively to androgen-related pathways, obesity, or MetS. Concentrations of essential amino acids and BCAA are increased in PCOS, which might result from or contribute to their insulin resistance.

Project description:The metabolic and physiologic responses to exercise are increasingly interesting, given that regular physical activity enhances antioxidant capacity, improves cardiac function, and protects against type 2 diabetes. The metabolic interactions between tissues and the heart illustrate a critical cross-talk we know little about. To better understand the metabolic changes induced by exercise, we investigated skeletal muscle (plantaris, soleus), liver, serum, and heart from exercise trained (or sedentary control) animals in an established rat model of exercise-induced aerobic training via non-targeted GC-MS metabolomics. Exercise-induced alterations in metabolites varied across tissues, with the soleus and serum affected the least. The alterations in the plantaris muscle and liver were most alike, with two metabolites increased in each (citric acid/isocitric acid and linoleic acid). Exercise training additionally altered nine other metabolites in the plantaris (C13 hydrocarbon, inosine/adenosine, fructose-6-phosphate, glucose-6-phosphate, 2-aminoadipic acid, heptadecanoic acid, stearic acid, alpha-tocopherol, and oleic acid). In the serum, we identified significantly decreased alpha-tocopherol levels, paralleling the increases identified in plantaris muscle. Eleven unique metabolites were increased in the heart, which were not affected in the other compartments (malic acid, serine, aspartic acid, myoinositol, glutamine, gluconic acid-6-phosphate, glutamic acid, pyrophosphate, campesterol, phosphoric acid, creatinine). These findings complement prior studies using targeted metabolomics approaches to determine the metabolic changes in exercise-trained human skeletal muscle. Specifically, exercise trained vastus lateralus biopsies had significantly increased linoleic acid, oleic acid, and stearic acid compared to the inactive groups, which were significantly increased in plantaris muscle in the present study. While increases in alpha-tocopherol have not been identified in muscle after exercise to our knowledge, the benefits of vitamin E (alpha-tocopherol) supplementation in attenuating exercise-induced muscle damage has been studied extensively. Skeletal muscle, liver, and the heart have primarily different metabolic changes, with few similar alterations and rare complementary alterations (alpha-tocopherol), which may illustrate the complexity of understanding exercise at the organismal level.

Project description:Background: Cigarette smoking is an important environmental risk factor for cardiovascular events of hypertension (HTN). Existing studies have provided evidence supporting altered gut microbiota by cigarette smoking, especially in hypertensive patients. Metabolic biomarkers play a central role in the functional potentials of the gut microbiome but are poorly characterized in hypertensive smokers. To explore whether serum metabolomics signatures and compositions of HTN patients were varied in smokers, and investigate their connecting relationship to gut microbiota, the serum metabolites were examined in untreated hypertensive patients using untargeted liquid chromatography-mass spectrometry (LC/MS) analysis. Results: A dramatic difference and clear separation in community features of circulating metabolomics members were seen in smoking HTN patients compared with the non-smoking controls, according to partial least squares discrimination analysis (PLS-DA) and orthogonal partial least squares discrimination analysis (OPLS-DA). Serum metabolic profiles and compositions of smoking patients with HTN were significantly distinct from the controls, and were characterized by enrichment of 12-HETE, 7-Ketodeoxycholic acid, Serotonin, N-Stearoyl tyrosine and Deoxycholic acid glycine conjugate, and the depletion of Tetradecanedioic acid, Hippuric acid, Glyceric acid, 20-Hydroxyeicosatetraenoic acid, Phenylpyruvic acid and Capric acid. Additionally, the metabolome displayed prominent functional signatures, with a majority proportion of the metabolites identified to be discriminating between groups distributed in Starch and sucrose metabolism, Caffeine metabolism, Pyruvate metabolism, Glycine, serine and threonine metabolism, and Phenylalanine metabolic pathways. Furthermore, the observation of alterations in metabolites associated with intestinal microbial taxonomy indicated that these metabolic members might mediate the effects of gut microbiome on the smoking host. Indeed, the metabolites specific to smoking HTNs were strongly organized into co-abundance networks, interacting with an array of clinical parameters, including uric acid (UA), low-denstiy lipoprotein cholesterol (LDLC) and smoking index. Conclusions: In conclusion, we demonstrated disparate circulating blood metabolome composition and functional potentials in hypertensive smokers, showing a linkage between specific metabolites in blood and the gut microbiome.

Dataset Information

Combining Semi-Targeted Metabolomics and Machine Learning to Identify Metabolic Alterations in the Serum and Urine of Hospitalized Patients with COVID-19.

Publications

Combining Semi-Targeted Metabolomics and Machine Learning to Identify Metabolic Alterations in the Serum and Urine of Hospitalized Patients with COVID-19.

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