{"doi":"10.1371/journal.pone.0237579","title":"Untargeted high-resolution plasma metabolomic profiling predicts outcomes in patients with coronary artery disease","abstract":"OBJECTIVE: Patients with CAD have substantial residual risk of mortality, and whether hitherto unknown small-molecule metabolites and metabolic pathways contribute to this risk is unclear. We sought to determine the predictive value of plasma metabolomic profiling in patients with CAD. APPROACH AND RESULTS: Untargeted high-resolution plasma metabolomic profiling of subjects undergoing coronary angiography was performed using liquid chromatography/mass spectrometry. Metabolic features and pathways associated with mortality were identified in 454 subjects using metabolome-wide association studies and Mummichog, respectively, and validated in 322 subjects. A metabolomic risk score comprising of log-transformed HR estimates of metabolites that associated with mortality and passed LASSO regression was created and its performance validated. In 776 subjects (66.8 years, 64% male, 17% Black), 433 and 357 features associated with mortality (FDR-adjusted q<0.20); and clustered into 21 and 9 metabolic pathways in first and second cohorts, respectively. Six pathways (urea cycle/amino group, tryptophan, aspartate/asparagine, lysine, tyrosine, and carnitine shuttle) were common. A metabolomic risk score comprising of 7 metabolites independently predicted mortality in the second cohort (HR per 1-unit increase 2.14, 95%CI 1.62, 2.83). Adding the score to a model of clinical predictors improved risk discrimination (delta C-statistic 0.039, 95%CI -0.006, 0.086; and Integrated Discrimination Index 0.084, 95%CI 0.030, 0.151) and reclassification (continuous Net Reclassification Index 23.3%, 95%CI 7.9%, 38.2%). CONCLUSIONS: Differential regulation of six metabolic pathways involved in myocardial energetics and systemic inflammation is independently associated with mortality in patients with CAD. A novel risk score consisting of representative metabolites is highly predictive of mortality.","journal":"PLoS ONE","year":2020,"id":96663,"datarank":1.1372720988436686,"base_score":3.5553480614894135,"endowment":3.5553480614894135,"self_citation_contribution":0.5333022092234121,"citation_network_contribution":0.6039698896202567,"self_endowment_contribution":0.5333022092234121,"citer_contribution":0.6039698896202567,"corpus_percentile":null,"corpus_rank":null,"citation_count":34,"citer_count":29,"citers_with_citation_signal":23,"citers_with_endowment":23,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9539,"is_data_producer":true,"deposit_databanks":{"Dryad":["10.5061/dryad.866t1g1mt"]},"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2020-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":236695,"name":"Chang Liu","orcid":"0000-0002-8918-7224","position":1,"is_corresponding":false},{"id":338685,"name":"Aditi Nayak","orcid":"0000-0002-6986-0987","position":2,"is_corresponding":false},{"id":423070,"name":"Ayman Samman Tahhan","orcid":null,"position":3,"is_corresponding":false},{"id":304836,"name":"Yi‐An Ko","orcid":"0000-0002-6543-9765","position":4,"is_corresponding":false},{"id":445437,"name":"Devinder S. Dhindsa","orcid":"0000-0003-0497-0349","position":5,"is_corresponding":false},{"id":328668,"name":"Jeong Hwan Kim","orcid":"0000-0001-7186-1033","position":6,"is_corresponding":false},{"id":263299,"name":"Salim S. Hayek","orcid":"0000-0003-0180-349X","position":7,"is_corresponding":false},{"id":307077,"name":"Laurence Sperling","orcid":"0000-0001-9417-6370","position":8,"is_corresponding":false},{"id":328672,"name":"Puja K. Mehta","orcid":"0000-0002-5678-812X","position":9,"is_corresponding":false},{"id":22017,"name":"Yan V. Sun","orcid":"0000-0002-2838-1824","position":10,"is_corresponding":false},{"id":246042,"name":"Karan Uppal","orcid":"0000-0001-5985-1668","position":11,"is_corresponding":false},{"id":231483,"name":"Dean P. Jones","orcid":"0000-0002-2090-0677","position":12,"is_corresponding":false},{"id":236696,"name":"Arshed A. Quyyumi","orcid":"0000-0002-8166-679X","position":13,"is_corresponding":false},{"id":445436,"name":"Anurag Mehta","orcid":"0000-0002-6910-5551","position":0,"is_corresponding":true}],"reference_count":88,"raw_metadata":null,"created_at":"2026-07-18T22:35:06.014648Z","pmid":"32810196","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}