{"doi":"10.1371/journal.pone.0280657","title":"Prediction of venous thromboembolism incidence in the general adult population using two published genetic risk scores","abstract":"INTRODUCTION: Most strategies for prevention of venous thromboembolism focus on preventing recurrent events. Yet, primary prevention might be possible through approaches targeting the whole population or high-risk patients. To inform possible prevention strategies, population-based information on the ability of genetic risk scores to identify risk of incident venous thromboembolism is needed. MATERIALS AND METHODS: We used proportional hazards regression to relate two published genetic risk scores (273-variants versus 5-variants) with venous thromboembolism incidence in the Atherosclerosis Risk in Communities Study (ARIC) cohort (n = 11,292), aged 45-64 at baseline, drawn from 4 US communities. RESULTS: Over a median of 28 years, ARIC identified 788 incident venous thromboembolism events. Incidence rates rose more than two-fold across quartiles of the 273-variant genetic risk score: 1.7, 2.7, 3.4 and 4.0 per 1,000 person-years. For White participants, age, sex, and ancestry-adjusted hazard ratios (95% confidence intervals) across quartiles were strong [1 (reference), 1.30 (0.99,1.70), 1.85 (1.43,2.40), and 2.58 (2.04,3.28)] but weaker for Black participants [1, 1.05 (0.63,1.75), 1.37 (0.84,2.22), and 1.32 (0.80,2.20)]. The 5-variant genetic risk score showed a less steep gradient, with hazard ratios in Whites of 1, 1.17 (0.89,1.54), 1.48 (1.14,1.92), and 2.18 (1.71,2.79). Models including the 273-variant genetic risk score plus lifestyle and clinical factors had a c-statistic of 0.67. CONCLUSIONS: In the general population, middle-aged adults in the highest quartile of either genetic risk score studied have approximately two-fold higher risk of an incident venous thromboembolism compared with the lowest quartile. The genetic risk scores show a weaker association with venous thromboembolism for Black people.","journal":"PLoS ONE","year":2023,"id":361640,"datarank":0.32958368660043297,"base_score":2.1972245773362196,"endowment":2.1972245773362196,"self_citation_contribution":0.32958368660043297,"citation_network_contribution":0.0,"self_endowment_contribution":0.32958368660043297,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":8,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9542,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2023-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":414002,"name":"Weihong Tang","orcid":"0000-0003-1200-0270","position":1,"is_corresponding":false},{"id":1114898,"name":"Ching-Ping Hong","orcid":null,"position":2,"is_corresponding":false},{"id":91402,"name":"Wayne D. Rosamond","orcid":"0000-0001-5271-074X","position":3,"is_corresponding":false},{"id":256102,"name":"John Lane","orcid":"0000-0002-3582-5825","position":4,"is_corresponding":false},{"id":51219,"name":"Mary Cushman","orcid":"0000-0002-7871-6143","position":5,"is_corresponding":false},{"id":40739,"name":"Nathan Pankratz","orcid":"0000-0001-5958-693X","position":6,"is_corresponding":false},{"id":245397,"name":"Aaron R. Folsom","orcid":"0000-0003-2635-2699","position":0,"is_corresponding":true}],"reference_count":29,"raw_metadata":{"citation_network_status":"fetched"},"created_at":"2026-07-19T01:14:15.063476Z","pmid":"36716319","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":[]}