MetaboLights

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identification and annotation were performed using the metaX package in R (version 4.0). Feature annotation was carried out by matching the accurate mass (m/z) of detected features against the KEGG (Kyoto Encyclopedia of Genes and Genomes) and HMDB (Human Metabolome Database) databases with a mass tolerance of 10 ppm for MS1 level identification. Isotopic distribution patterns and adduct information (M+H, M+Na, M+K, M+NH4 for positive mode; M-H, M+Cl, M+CHO2 for negative mode) were considered during annotation. For MS/MS confirmation, features were matched against an in-house fragment spectrum library with a mass tolerance of 20 ppm for both MS1 and MS2, requiring an identification score above 70%. The final metabolite identifications were validated by manual inspection of extracted ion chromatograms and fragmentation spectra where applicable.MetaboLightsPublicLiquid Chromatography MS - negative - reverse-phaseLiquid Chromatography MS - positive - reverse-phaseChromatographic separation was performed on a Thermo Vanquish Flex UPLC system (Thermo Fisher Scientific, Germany) equipped with an ACQUITY UPLC HSS T3 column (100 mm × 2.1 mm, 1.8 µm; Waters, Ireland). The mobile phase consisted of (A) 5 mM ammonium acetate and 5 mM acetic acid in water and (B) acetonitrile. Gradient elution was applied at a flow rate of 0.35 mL/min. The injection volume was 2 µL, and the column oven temperature was maintained at 40°C.Oral-gut microbiome and plasma metabolome reveal incremental restratification of coronary artery disease from the clinical gray zone.The First Affiliated Hospital of Henan University of Chinese Medicineqilong Zhangblood plasmamass spectrometry assay100 μL of plasma sample was mixed with 400 μL of ice-cold methanol/acetonitrile (1:1, v/v). The mixture was incubated at -20°C for 1 hour to precipitate proteins, then centrifuged at 13,000 rpm for 15 minutes at 4°C. The supernatant was collected and concentrated to dryness under reduced pressure at 4°C. The residue was reconstituted in an appropriate volume of initial mobile phase (5 mM ammonium acetate + 5 mM acetic acid in water / acetonitrile = 1:1) for LC-MS analysis. Quality control (QC) samples were prepared by pooling equal aliquots of the supernatant from all samples.Homo sapienshttps://www.ebi.ac.uk/metabolights/MTBLS15425Zhang Qilong. The First Affiliated Hospital of Henan University of Chinese Medicine. zhangqilong0536@126.com.Wei Jingjing.Raw LC-MS data files (.raw) were converted to mzXML format using MSConvert (ProteoWizard). Data preprocessing, including peak picking, peak alignment, retention time correction, and feature annotation, was performed using XCMS (version 3.8) and CAMERA packages implemented in R (version 4.0). Metabolite annotation was carried out using the metaX package, matching accurate mass (m/z) with a mass tolerance of 10 ppm against the KEGG and HMDB databases, supplemented by an in-house library. Features were filtered by removing those with >50% missing values in QC samples or >80% missing values in study samples. Missing values were imputed using the K-nearest neighbor (KNN) method. Data normalization was performed using probabilistic quotient normalization (PQN). Principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), and differential metabolite screening were conducted using the metaX and ropls R packages.Groupzhangqilong0536@126.comHuman plasma samples were collected and immediately stored at -80°C until analysis. Samples were thawed on ice prior to extraction.MetabolomicsMetabolomicsblankuntargeted analysisbetween group comparison objectivequality control sampleHomo sapiensIschemic heart disease (disorder)Q-Exactive Plus Hybrid Quadrupole-Orbitrap Mass Spectrometer (Thermo Fisher Scientific)experimental sampleblood plasmaThermo Vanquish Flex UPLC system (Thermo Fisher Scientific)disease state designcase control designMetabolomicsblankuntargeted analysisbetween group comparison objectivequality control sampleHomo sapiensIschemic heart disease (disorder)Q-Exactive Plus Hybrid Quadrupole-Orbitrap Mass Spectrometer (Thermo Fisher Scientific)experimental sampleblood plasmaThermo Vanquish Flex UPLC system (Thermo Fisher Scientific)disease state designcase control designMass spectrometric detection was performed on a Q-Exactive Plus Hybrid Quadrupole-Orbitrap mass spectrometer (Thermo Fisher Scientific, Germany) equipped with an electrospray ionization (ESI) source operating in positive and negative switching mode (polarity switching) for full scan acquisition, with separate positive and negative DDA runs for QC samples. Full-scan mass spectra were acquired over the m/z range of 70–1050 Da at a resolution of 70,000 (at m/z 200) with an AGC target of 3E6 and a maximum injection time of 100 ms. Data-dependent acquisition (DDA) was employed for MS/MS fragmentation of the top 5 precursor ions with intensity above 100,000, using a resolution of 17,500, maximum injection time of 50 ms, and dynamic exclusion of 6 s. The ion source temperature was 350°C; capillary voltage was +3.8 kV in positive mode and –3.4 kV in negative mode; sheath gas flow was 50 Arb; auxiliary gas flow was 15 Arb; sweep gas flow was 0 Arb.Valeric acidIsovaleric acid2-Hydroxyisobutyric acid2-Hydroxybutyric acid2,4-Dihydroxybutanoic acidfalseOral-gut microbiome and plasma metabolome reveal incremental restratification of coronary artery disease from the clinical gray zoneClinical prediction models have been widely applied for risk stratification of coronary artery disease (CAD), yet approximately 20% to 40% of screened individuals fall into the intermediate-risk interval and face a decision gap. Here, we adopted a two-stage validation design. In the first stage, a clinical prediction model was developed in a retrospective cohort of 1,500 patients, and predicted probabilities of 0.3 to 0.7 were defined as the clinical gray zone. In the second stage, tongue surface and fecal samples from a prospective cohort of 105 patients were analyzed by shotgun metagenomic sequencing, and fasting plasma was profiled by untargeted metabolomics. The oral and gut microbiomes maintained independent community structures in CAD, jointly explaining only 0.4% of the variation in plasma metabolites. The incremental effects of multi-omics were concentrated in gray-zone patients, with the gray-zone AUC increment being 1.63-fold and 1.93-fold the whole-population increment for CHD detection and ACS warning, respectively, and 88.9% and 75.9% of patients being correctly reclassified. Omics requirements differed across disease stages, with the metabolome alone providing incremental value in chronic coronary syndrome and combined microbiome-metabolome analysis being required for ACS. The nomogram constructed from selected variables showed robust discriminative performance. These findings validate that the incremental clinical value of multi-omics is concentrated within the uncertain interval of the clinical model and provide an evidence base for precise restratification of patients in the clinical gray zone.2026-08-312026-08-22MTBLS15425HMDB0029738HMDB0000172HMDB0000159HMDB0013302HMDB0000991HMDB0000122HMDB0000696HMDB0000162HMDB0000892HMDB0000718HMDB0000360HMDB0000729HMDB0000008