MetaboLights

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ftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS13136/m_MTBLS13136_LC-MS_alternating_reverse-phase_metabolite_profiling_v2_maf.tsvftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS13136/i_Investigation.txtftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS13136/s_MTBLS13136.txtftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS13136/a_MTBLS13136_LC-MS_alternating_reverse-phase_metabolite_profiling.txtprimaryOK200ftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS13136Mass spectrometry-based quantitative metabolomics refers to the determination of the concentration of a substance in an unknown sample by comparing the unknown to a set of standard samples of known concentration (i.e., calibration curve). The calibration curve is a plot of how the analytical signal changes with the concentration of the analyte (the substance to be measured). For most analyses a plot of instrument response vs. concentration will show a linear relationship. This yields a model described by the equation y = ax + b, where y is the instrument response e.g., peak height or area, a represents the slope/sensitivity, and b is a constant that describes the background. The analyte concentration (x) of unknown samples may be calculated from this equation.MetaboLightsPublicLiquid Chromatography MS - alternating - reverse phaseA ultra-performance liquid chromatography coupled to tandem mass spectrometry (UPLC-MS/MS) system (ACQUITY UPLC-Xevo TQ-S, Waters Corp., Milford, MA, USA) was used to quantitate all targeted metabolites in this project.The optimized instrument settings are briefly described below. The instrument performance optimization and routine maintenance were performedevery week. The settings of the UPLC-MS instrument are as follows: (1) Column: ACQUITY UPLC BEH C18 1.7 µM VanGuard pre-column (2.1×5 mm) and ACQUITY UPLC BEH C18 1.7 µM analytical column (2.1×100 mm); (2) Column Temp.: 40℃; (3) Sample Manager Temp.: 10℃; (4) Mobile Phases: A=water with 0.1% formic acid; and B=acetonitrile/IPA (70:30); (5) Gradient Conditions: 0-1 min (5% B), 1-11min (5-78% B), 11-13.5 min (78-95% B), 13.5-14 min (95-100% B), 14-16 min (100% B), 16-16.1min (100-5% B), 16.1-18 min (5% B); (6) Flow Rate (mL/min): 0.40; (7) Injection Vol. (µl): 5.0.Targeted metabolomic profiling and machine learning-based prediction models for persistent atrial fibrillation: a multi-center observational study.Zhihui ZhangDepartment of Cardiovascular Medicine, Center for Circadian Metabolism and Cardiovascular Disease, Southwest Hospital, Army Medical Universitybloodmass spectrometry assayAll of the standards of targeted metabolites were obtained from Sigma-Aldrich (St. Louis, MO, USA), Steraloids Inc. (Newport, RI, USA) and TRC Chemicals (Toronto, ON, Canada). All the standards were accurately weighed and prepared in water, methanol, sodium hydroxide solution, or hydrochloric acid solution to obtain individual stock solution at a concentration of 5.0 mg/mL. Appropriate amount of each stock solution was mixed to create stock calibration solutions. Formic acid was of (Optima grade and obtained from Sigma-Aldrich (St. Louis, MO, USA). Methanol (Optima LC-MS), acetonitrile (Optima LC-MS), and isopropanol (Optima LC-MS) were purchased from Thermo-Fisher Scientific (FairLawn, NJ, USA). Ultrapure water was produced by a Mill-Q Reference system equipped with a LC-MS Pak filter (Millipore, Billerica, MA, USA).Samples were thawed on ice-bath to diminish sample degradation. 20μL of plasma was added to a 96-well plate. Then the plate was transferred to the Eppendorf epMotion Workstation(Eppendorf Inc., Humburg, Germany). 120μL ice cold methanol with partial internal standards was automatically added to each sample and vortexed vigorously for 5 minutes. The plate was centrifuged at 4000g for 30 minutes (Allegra X-15R Beckman Coulter, Inc., Indianapolis, IN, USA). Then the plate was returned back to the workstation. 30μL of supernatant was transferred to a clean 96-well plate, and 20μL of freshly prepared derivative reagents was added to each well. The plate was sealed and the derivatization was carried out at 30℃ for 60 min. After derivatization, 330μL of ice-cold 50% methanol solution was added to dilute the sample. Then the plate was stored at -20℃ for 20 minutes and followed by 4000g centrifugation at 4 ℃ for 30 minutes.Homo sapienshttps://www.ebi.ac.uk/metabolights/MTBLS13136Zhihui Zhang. Southwest Hospital, Army Medical University. xyzpj@tmmu.edu.cn.The raw data files generated by UPLC-MS/MS were processed using the TMBQ software to perform peak integration, calibration, and quantitation for each metabolite. The platform iMAP as used for statistical analyses, including PCA, OPLS-DA, univariate analysis and pathway analysis, et al. Our proprietary software can perform a collection of data processing, interpretation, and visualization. For many metabolomics studies, two types of statistical analysis are extensively performed: 1) multivariate statistical analyses such as principal component analysis (PCA), partial least square discriminant analysis (PLS-DA), orthogonal partial least square discriminant analysis (OPLS-DA), random forest, support vector machine learning and so on; 2) univariate statistical analyses including student t-test, Mann-Whitney-Wilcoxon (U-test), ANOVA, correlation analysis, etc. The optimal choice of statistical methods is often driven by the data and the project goals.Diseaseli17734775759@163.comVenous blood samples were drawn from patients following the provision of informed consent. The samples were collected in ethylenediaminetetraacetic acid (EDTA) anticoagulant tubes and stored at 4℃. Blood samples transported to the laboratory within 5 hours of collection were centrifuged at 4℃ and 1000×g for 10 min to obtain plasma. The separated plasma samples were subsequently stored at -80℃ and maintained in a frozen state until further analysis via metabolomics.MetabolomicsMetabolomicsatrial fibrillationpersistent atrial fibrillationParoxysmal atrial fibrillationMachine LearningMetabolomicsatrial fibrillationpersistent atrial fibrillationParoxysmal atrial fibrillationMachine LearningA ultra-performance liquid chromatography coupled to tandem mass spectrometry (UPLC-MS/MS) system (ACQUITY UPLC-Xevo TQ-S, Waters Corp., Milford, MA, USA) was used to quantitate all targeted metabolites in this project.The optimized instrument settings are briefly described below. The instrument performance optimization and routine maintenance were performedevery week. The settings of the MASS SPECTROMETER instrument are as follows: (1) Capillary (Kv):1.5 (ESI+), 2.0 (ESI-); (2) Source Temp.: 150℃; (3) Desolvation Temp.: 550℃; Desolvation Gas Flow (L/Hr):1000.Benzoic acidPhenylpyruvic acidbUDCAPhenylacetic acidAdipoylcarnitineCaproic acidHydrocinnamic acid5Z-Dodecenoic acidPhenylacetylglutamineGlucaric acidMethylsuccinic acidUDCAArachidonic acidOctanoic acidGlycylprolineButyric acidHistidineIndole-3-methyl acetateN-Acetyaspartic acidbeta-AlanineKetoleucineMalonylcarnitineMyristoleic acidThreoninePropionic acidMethionineOxoglutaric acidalpha-Ketoisovaleric acidp-Hydroxyphenylacetic acidCitrulline3-HydroxylisovalerylcarnitinePropionylcarnitineValeric acidSebacic acidDPA2-MethylbutyroylcarnitineLysineMethylmalonylcarnitineDHA4-HydroxyprolineGlutamic acidRicinoleic acidSuberic acidMethylglutaric acidPyruvic acidGalactonic acid10Z-Heptadecenoic acidButyrylcarnitine3-(3-Hydroxyphenyl)-3-hydroxypropanoic acidGlycineGCDCAPalmitelaidic acid9E-tetradecenoic acidPicolinic acidArginineOleic acidGlycolic acidIndoleacetic acidGlutaric acidPyroglutamic acidXyluloseN-AcetylserineKynurenine10Z-Nonadecenoic acidPentadecanoic acidCAHydroxyphenyllactic acidCDCAAlanineN-MethylnicotinamideMaltose/LactosePhenylalaninePetroselinic acidLinoelaidic acidTryptophanMethylcysteine2,2-Dimethylsuccinic acidPimelic acidTCDCACitric acidDecanoylcarnitineValineAsparagineIndole-3-propionic acidHexanylcarnitineN-Acetylneuraminic acid3-Methyl-2-oxopentanoic acidErythronic acidValerylcarnitineIndolelactic acidIsovaleric acidNicotinic acidGlyceraldehydeAminocaproic acidCreatinePipecolic acidIsocaproic acid5-Aminolevulinic acidRibuloseMethylmalonic acidOctanoylcarnitineMaleic acidAcetylglycineMalic acidGlyceric acidTetradecanoylcarnitineEthylmethylacetic acidTyrosineDCAPhthalic acidOleylcarnitineGallic acidAMP2-Hydroxycaproic acidMandelic acid2-Hydroxy-3-methylbutyric acidPalmitoylcarnitinealpha-Hydroxyisobutyric acidIndole-3-pyruvic acidCarnitineTCAMyristic acidgamma-GlutamylalanineRhamnoseSuccinic acidGABAHydroxypropionic acid2-PhenylpropionatePhenyllactic acidAdipic acidAcetic acidOrnithineGluconolactoneAdrenic acid12-Hydroxystearic acidHomoserinegamma-Linolenic acidMalonic acidDecanoic acidHippuric acidIsovalerylcarnitinealpha-Linolenic acidSerine3-Hydroxyisovaleric acidProline1-MethylhistidineLeucineCitramalic acidDihomo-gamma-linolenic acidDPAn-6Isocitric acidGLCA-3SAzelaic acidTridecanoic acidHomovanillic acidGlutarylcarnitineGDCAAspartic acid2-Hydroxybutyric acidImidazolepropionic acidIsoleucine2-Butenoic acidGCAGUDCASalicyluric acid2-Methy-4-pentenoic acidHomocitrullineGlutaconic acidDodecanoylcarnitineGCDCA-3SThreonic acidTartaric acidOxoadipic acid2-Hydroxyglutaric acidGuanidoacetic acidFructoseLinoleic acidAconitic acidIsobutyric acidFumaric acidAcetylcarnitinePalmitoleic acidLinoleylcarnitineEPASarcosineLactic acidStearylcarnitineLCA-3S3-Hydroxybutyric acidDimethylglycinealpha-Aminobutyric acidGlutaminefalseTargeted metabolomic profiling and machine learning-based prediction models for persistent atrial fibrillation: a multi-center observational studyBackground. Atrial fibrillation (AF), the most common cardiac arrhythmia worldwide, carries a high risk of severe complications. Patients diagnosed with paroxysmal atrial fibrillation (PAF) may progress to persistent atrial fibrillation (PerAF) following a period. We aimed to identify metabolites associated with PerAF using machine learning (ML), and predict the probability of PAF progressing to PerAF, enabling the adjustment of subsequent treatments in clinical practice.Methods. We enrolled AF patients without any anticoagulant therapy within the last 7 days between July 2020 and July 2022 in two centers in China. Targeted metabolomic profiling was performed on patient’s plasma. Differential metabolites and clinical features were identified through univariate and multivariate analyses. Patients were divided into discovery and validation cohorts (70%:30%). Four ML models (logistic regression, random forest, XGBoost, and LightGBM) were developed for PerAF prediction. Model predictive performance was measured mainly by AUC.Results. One hundred patients (65 PerAF, 35 PAF) were enrolled. Eight metabolites (kynurenine, N-Acetylaspartic acid, glyceric acid, adipic acid, citramalic acid, malic acid, isocitric acid, and oxoglutaric acid) and two clinical features (NT-proBNP and uric acid) had significant associations with PerAF. Pathway enrichment analysis highlighted alterations in the citrate cycle and glyoxylate/dicarboxylate metabolism. XGBoost was chosen for establishing the final model, since its predictive performance outperformed that of other algorithms. The model based on clinical parameters, metabolites, and demographics achieved the highest AUC in discovery cohort (0.751 [95% CI 0.631~0.867]) and validation cohort (0.985 [95% CI 0.940~1.000]). A simplified model with three features (NT-proBNP, citramalic acid, and uric acid) retained robust performance.Conclusions. This study shows strengths in identifying eight PerAF-related metabolites via targeted metabolomics and ML, and developing accurate predictive models (including a simplified, clinically feasible model) to guide early PerAF intervention. Future directions include large-scale multi-center validation, incorporating more confounders, and using multiple platforms to deeply explore AF-related metabolic alterations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