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oai:figshare.com:article/33771073 2026-09-15T04:29:58Z category_696 portal_316 item_type_3 month_year_09_2026

Table 1_Discovery of plasma metabolomic biomarkers and a predictive model for immune checkpoint inhibitor-associated myocarditis: a pilot case-control study.docx Zhuoling Zheng (19946499) Qingling Gu (24912031) Yiting Wang (169115) Lihong Huang (1346928) Jingwen Xie (6792485) Chunmei Dai (750193) Jiali Li (436886) Xiaoyan Li (117291) Genetic Immunology biomarkers gut microbiota immune checkpoint inhibitors metabolomics myocarditis oxidative stress Background<p>Immune checkpoint inhibitor (ICI)-associated myocarditis is a rare but potentially fatal immune-related adverse event lacking reliable biomarkers for risk stratification. This exploratory pilot case-control study aimed to characterize baseline plasma metabolomic profiles in patients who developed ICI-associated myocarditis and to evaluate a metabolite-based predictive model.</p>Methods<p>Baseline plasma samples from 15 patients with ICI-associated myocarditis and 15 matched controls were analyzed by untargeted metabolomics using UHPLC-Q/Orbitrap HRMS. Differential metabolites were identified by OPLS-DA and univariate analysis. A metabolite-derived PC1 was used in logistic regression models (clinical-only, metabolite-only, combined), with performance assessed by AUC.</p>Results<p>Ten differential metabolites were identified, implicating glutathione metabolism, phenylalanine metabolism, and histidine metabolism pathways. Key alterations included elevated phenylpyruvic acid, 1-methylpseudouridine, cinnamoylglycine, pyroglutamic acid and spermine, with decreased ceramide, cyclo-prolylglycine, propionic acid, glycolic acid, and histamine. A clinical model incorporating three baseline laboratory variables (lymphocyte ratio, monocyte ratio, and eosinophil ratio) achieved an AUC of 0.618. The metabolite PC1 model alone attained an AUC of 0.964, outperforming the clinical model. The combined clinical-metabolite model reached a fitted AUC of 0.973 (cross-validated AUC 0.925). Decision curve analysis indicated that the combined model provided potential net benefit within the 20%-40% threshold range, marginally exceeding the metabolite-only model.</p>Conclusion<p>This exploratory study identified baseline plasma metabolites potentially associated with ICI-associated myocarditis risk, implicating oxidative stress, immune modulation, and gut microbiota-host interactions. The metabolite-based model showed preliminary discriminative ability, but prospective validation in independent cohorts is required.</p> 2026-09-15T04:29:58Z Dataset Dataset 10.3389/fimmu.2026.1915039.s004 https://figshare.com/articles/dataset/Table_1_Discovery_of_plasma_metabolomic_biomarkers_and_a_predictive_model_for_immune_checkpoint_inhibitor-associated_myocarditis_a_pilot_case-control_study_docx/33771073 CC BY 4.0