{"doi":"10.1016/j.jlr.2021.100064","title":"Genetic evidence for independent causal relationships between metabolic biomarkers and risk of coronary artery diseases","abstract":"Decades of epidemiological research have identified numerous risk factors and biomarkers that are associated with risk of coronary artery disease (CAD) and myocardial infarction. The most well recognized of these are circulating levels of total cholesterol, LDL-cholesterol, HDL-cholesterol, and triglycerides, as well as metabolic syndrome-related traits, such as obesity, hypertension, and T2D (1Arnett D.K. Blumenthal R.S. Albert M.A. Buroker A.B. Goldberger Z.D. Hahn E.J. Himmelfarb C.D. Khera A. Lloyd-Jones D. McEvoy J.W. Michos E.D. Miedema M.D. Munoz D. Smith Jr., S.C. Virani S.S. et al.2019 ACC/AHA Guideline on the Primary Prevention of Cardiovascular Disease: executive summary: a report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines.Circulation. 2019; 140: e563-e595Crossref PubMed Scopus (209) Google Scholar). However, the association of a biomarker with CAD in observational studies does not necessarily prove a causal relationship. Furthermore, inferring causality based on epidemiology alone can be confounded by the biomarkers themselves often being associated with each other (i.e., obesity and blood triglyceride levels). In this regard, the gold standard approach for establishing causality is through rigorous and appropriately designed randomized clinical trials. For example, therapeutic interventions that lower LDL levels or blood pressure have demonstrated clear protective effects on risk of CAD, thus validating their causal roles in development and progression of atherosclerosis (2Ford E.S. Ajani U.A. Croft J.B. Critchley J.A. Labarthe D.R. Kottke T.E. Giles W.H. Capewell S. Explaining the decrease in U.S. deaths from coronary disease, 1980-2000.N. Engl. J. Med. 2007; 356: 2388-2398Crossref PubMed Scopus (2110) Google Scholar). By comparison, clinical trials aimed at raising HDL levels have failed to demonstrate therapeutic benefits for CAD outcomes (3Barter P.J. Caulfield M. Eriksson M. Grundy S.M. Kastelein J.J. Komajda M. Lopez-Sendon J. Mosca L. Tardif J.C. Waters D.D. Shear C.L. Revkin J.H. Buhr K.A. Fisher M.R. Tall A.R. et al.Effects of torcetrapib in patients at high risk for coronary events.N. Engl. J. Med. 2007; 357: 2109-2122Crossref PubMed Scopus (2554) Google Scholar, 4Schwartz G.G. Olsson A.G. Abt M. Ballantyne C.M. Barter P.J. Brumm J. Chaitman B.R. Holme I.M. Kallend D. Leiter L.A. Leitersdorf E. McMurray J.J. Mundl H. Nicholls S.J. Shah P.K. et al.Effects of dalcetrapib in patients with a recent acute coronary syndrome.N. Engl. J. Med. 2012; 367: 2089-2099Crossref PubMed Scopus (1503) Google Scholar, 5Lincoff A.M. Nicholls S.J. Riesmeyer J.S. Barter P.J. Brewer H.B. Fox K.A.A. Gibson C.M. Granger C. Menon V. Montalescot G. Rader D. Tall A.R. McErlean E. Wolski K. Ruotolo G. et al.Evacetrapib and cardiovascular outcomes in high-risk vascular disease.N. Engl. J. Med. 2017; 376: 1933-1942Crossref PubMed Scopus (404) Google Scholar, 6Boden W.E. Probstfield J.L. Anderson T. Chaitman B.R. Desvignes-Nickens P. Koprowicz K. McBride R. Teo K. Weintraub W. Aim-High InvestigatorsNiacin in patients with low HDL cholesterol levels receiving intensive statin therapy.N. Engl. J. Med. 2011; 365: 2255-2267Crossref PubMed Scopus (2193) Google Scholar, 7Landray M.J. Haynes R. Hopewell J.C. Parish S. Aung T. Tomson J. Wallendszus K. Craig M. Jiang L. Collins R. Armitage J. HPS2-THRIVE Collaborative GroupEffects of extended-release niacin with laropiprant in high-risk patients.N. Engl. J. Med. 2014; 371: 203-212Crossref PubMed Scopus (1106) Google Scholar). These latter observations cast doubt on the causal role of HDL in CAD despite its strong inverse clinical association with CAD. Another complementary and efficient strategy for inferring causality relies on human genetics and is termed Mendelian randomization (MR) (8Smith G.D. Timpson N. Ebrahim S. Strengthening causal inference in cardiovascular epidemiology through Mendelian randomization.Ann. Med. 2008; 40","journal":"Journal of Lipid Research","year":2021,"id":211700,"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":1,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9486,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2021-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":236718,"name":"Hooman Allayee","orcid":"0000-0002-2384-5239","position":0,"is_corresponding":true}],"reference_count":18,"raw_metadata":null,"created_at":"2026-07-18T23:52:20.112538Z","pmid":"33705740","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":[]}