Data for: Comparative urinary metabolomics distinguishes Yiqi Huoxue Formula from isosorbide mononitrate in a rat model of permanent LAD ligation-induced myocardial infarction

Published: 10 November 2026| Version 1 | DOI: 10.17632/4vhp89w8d4.1
Contributor:
moye tan

Description

Processed metabolomic data underlying the manuscript. Files contain (1) the plasma LC-MS/MS feature table (317 annotated features; molecular formula, adduct, m/z, retention time, mass error, and peak areas for 40 samples), (2) the urine LC-MS/MS feature table (842 annotated features with KEGG and HMDB identifiers and chemical class, plus peak areas for 48 samples and 12 pooled QC injections), and (3) 1H-NMR quantified metabolite concentrations for plasma (39 metabolites) and urine (37 metabolites). Sample codes A1–A10, B1–B10, C1–C10 and D1–D10 correspond to YQHXF, ISMN, model and sham groups, respectively.

Files

Steps to reproduce

1. Files.plasma_LCMS_317_features.xlsx holds 317 annotated plasma features (molecular formula, adduct, precursor m/z, retention time, mass error, peak area). urine_LCMS_features.xlsx holds 842 annotated urinary features with KEGG/HMDB identifiers and chemical class. NMR_concentrations.xlsx holds quantified 1H-NMR metabolite concentrations for plasma and urine as delivered by the vendor. 2. Group assignment. Sample codes A1–A10 = Yiqi Huoxue Formula (YQHXF); B1–B10 = isosorbide mononitrate (ISMN); C1–C10 = model; D1–D10 = sham. In the urine file, columns prefixed NY-QC are pooled quality-control injections. 3. Differential analysis. Peak areas were log2-transformed. Welch t tests were run for C vs D, A vs C, B vs C and A vs B, followed by Benjamini–Hochberg correction; features with FDR<0.05 and PLS-DA VIP>1 were retained. 4. Discrimination criterion. A feature was accepted as a discriminator only if it was quantified (above 1% of that feature's median) in all 12 samples of both drug groups. 5. Pathway analysis. NMR metabolite concentrations were submitted to MetaboAnalyst 5.0 for KEGG enrichment and topology analysis.

Categories

Medicine, Biochemistry, Pharmacology

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