{"doi":"10.1016/j.ajpc.2022.100373","title":"GlycA, hsCRP differentially associated with MI, ischemic stroke: In the Dallas Heart Study and Multi-Ethnic Study of Atherosclerosis","abstract":"Objective: Inflammatory markers are associated with cardiovascular disease (CVD); however, the ability to specifically predict myocardial infarction (MI) as well as ischemic stroke remains unknown. There has not been a direct comparison of the associations between GlycA and hsCRP and MI and ischemic stroke in a multi-ethnic pooled cohort. Methods: Multi-center, multi-ethnic, population-based community prospective pooled cohort of the Dallas Heart Study (DHS) and Multi-Ethnic Study of Atherosclerosis (MESA). 9,785 participants without baseline CVD enrolled with median follow-up of 13.4 years. Fatal/nonfatal MI and fatal/nonfatal ischemic stroke were assessed separately and then combined. Results: GlycA was moderately associated with hsCRP (R=0.58 in DHS and R=0.55 in MESA). In adjusted Cox proportional hazards models with competing risk adjusted for both inflammatory markers, GlycA was directly associated with MI (HR Q4 vs. Q1 1.90, 95% CI 1.39 to 2.58), whereas hsCRP was not (HR Q4 vs. Q1 0.92, 95% CI 0.70 to 1.21). Conversely, hsCRP was directly associated with ischemic stroke (HR Q4 vs. Q1 1.73, 95% CI 1.15 to 2.59), but GlycA was not (HR Q4 vs. Q1 1.21, 95% CI 0.77 to 1.90). GlycA improved net reclassification for MI and hsCRP did so for ischemic stroke. Conclusions: Although both GlycA and hsCRP were associated with incident CVD, GlycA more strongly predicted incident MI, and hsCRP more strongly predicted ischemic stroke.","journal":"American Journal of Preventive Cardiology","year":2022,"id":261221,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":15,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.8191,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2022-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":293937,"name":"Parag H. Joshi","orcid":"0000-0001-7863-2794","position":1,"is_corresponding":false},{"id":259507,"name":"Amit Khera","orcid":"0000-0001-7255-6874","position":2,"is_corresponding":false},{"id":293938,"name":"James D. Otvos","orcid":"0000-0001-5686-7103","position":3,"is_corresponding":false},{"id":328232,"name":"Philip Greenland","orcid":"0000-0002-6327-2439","position":4,"is_corresponding":false},{"id":293942,"name":"Colby Ayers","orcid":"0000-0003-2060-2263","position":5,"is_corresponding":false},{"id":293943,"name":"Anand Rohatgi","orcid":"0000-0003-0164-2382","position":6,"is_corresponding":false},{"id":872459,"name":"Kayla A. Riggs","orcid":"0000-0003-2871-5983","position":0,"is_corresponding":true}],"reference_count":48,"raw_metadata":null,"created_at":"2026-07-19T00:26:12.158801Z","pmid":"36061365","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":[]}