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Article: Age-Biomarkers-Clinical Risk Factors for Prediction of Cardiovascular Events in Patients With Coronary Artery Disease.

TitleAge-Biomarkers-Clinical Risk Factors for Prediction of Cardiovascular Events in Patients With Coronary Artery Disease.
Authors
KeywordsAdipocyte
Coronary artery disease
Fibroblast growth factor
Lipocalin-2
Risk factor
Issue Date2018
PublisherLippincott Williams & Wilkins. The Journal's web site is located at http://www.lww.com/product/?1079-5642
Citation
Arteriosclerosis, Thrombosis, and Vascular Biology, 2018, v. 38 n. 10, p. 2519-2527 How to Cite?
AbstractObjective- In patients with stable coronary artery disease, conventional risk factors provide limited incremental predictive value for cardiovascular events. We sought to investigate whether a panel of cardiometabolic biomarkers alone or combined with conventional risk factors would exhibit incremental value in the prediction of cardiovascular events. Approach and Results- In the discovery cohort, we measured serum adiponectin, A-FABP (adipocyte fatty acid-binding protein), lipocalin-2, FGF (fibroblast growth factor)-19 and 21, plasminogen activator inhibitor-1, and retinol-binding protein-4 in 1166 Chinese coronary artery disease patients. After a median follow-up of 35 months, 170 patients developed new-onset major adverse cardiovascular events (MACE). In the model with age ≥65 years and conventional risk factors, area under the curve for predicting MACE was 0.68. Addition of lipocalin-2 to the age-clinical risk factor model improved predictive accuracy (area under the curve=0.73). Area under the curve further increased to 0.75 when a combination of lipocalin-2, A-FABP, and FGF-19 was added to yield age-biomarkers-clinical risk factor model. The adjusted hazard ratio on MACEs for lipocalin-2, A-FABP, and FGF-19 levels above optimal cutoffs were 2.23 (95% CI, 1.62-3.08), 1.99 (95% CI, 1.43-2.76), and 1.65 (95% CI, 1.15-2.35), respectively. In the validation cohort of 1262 coronary artery disease patients with type 2 diabetes mellitus, the age-biomarkers-clinical risk factor model was confirmed to provide good discrimination and calibration over the conventional risk factor alone for prediction of MACE. Conclusions- A combination of the 3 biomarkers, lipocalin-2, A-FABP, and FGF-19, with clinical risk factors to yield the age-biomarkers-clinical risk factor model provides an optimal and validated prediction of new-onset MACE in patients with stable coronary artery disease.
Persistent Identifierhttp://hdl.handle.net/10722/260529
ISSN
2017 Impact Factor: 6.086
2015 SCImago Journal Rankings: 3.356

 

DC FieldValueLanguage
dc.contributor.authorWong, YK-
dc.contributor.authorCheung, YY-
dc.contributor.authorTang, SM-
dc.contributor.authorAu, KW-
dc.contributor.authorHai, SHJJ-
dc.contributor.authorLee, CHP-
dc.contributor.authorLau, GKK-
dc.contributor.authorCheung, BMY-
dc.contributor.authorSham, PC-
dc.contributor.authorXu, A-
dc.contributor.authorLam, KSL-
dc.contributor.authorTse, HF-
dc.date.accessioned2018-09-14T08:43:10Z-
dc.date.available2018-09-14T08:43:10Z-
dc.date.issued2018-
dc.identifier.citationArteriosclerosis, Thrombosis, and Vascular Biology, 2018, v. 38 n. 10, p. 2519-2527-
dc.identifier.issn1079-5642-
dc.identifier.urihttp://hdl.handle.net/10722/260529-
dc.description.abstractObjective- In patients with stable coronary artery disease, conventional risk factors provide limited incremental predictive value for cardiovascular events. We sought to investigate whether a panel of cardiometabolic biomarkers alone or combined with conventional risk factors would exhibit incremental value in the prediction of cardiovascular events. Approach and Results- In the discovery cohort, we measured serum adiponectin, A-FABP (adipocyte fatty acid-binding protein), lipocalin-2, FGF (fibroblast growth factor)-19 and 21, plasminogen activator inhibitor-1, and retinol-binding protein-4 in 1166 Chinese coronary artery disease patients. After a median follow-up of 35 months, 170 patients developed new-onset major adverse cardiovascular events (MACE). In the model with age ≥65 years and conventional risk factors, area under the curve for predicting MACE was 0.68. Addition of lipocalin-2 to the age-clinical risk factor model improved predictive accuracy (area under the curve=0.73). Area under the curve further increased to 0.75 when a combination of lipocalin-2, A-FABP, and FGF-19 was added to yield age-biomarkers-clinical risk factor model. The adjusted hazard ratio on MACEs for lipocalin-2, A-FABP, and FGF-19 levels above optimal cutoffs were 2.23 (95% CI, 1.62-3.08), 1.99 (95% CI, 1.43-2.76), and 1.65 (95% CI, 1.15-2.35), respectively. In the validation cohort of 1262 coronary artery disease patients with type 2 diabetes mellitus, the age-biomarkers-clinical risk factor model was confirmed to provide good discrimination and calibration over the conventional risk factor alone for prediction of MACE. Conclusions- A combination of the 3 biomarkers, lipocalin-2, A-FABP, and FGF-19, with clinical risk factors to yield the age-biomarkers-clinical risk factor model provides an optimal and validated prediction of new-onset MACE in patients with stable coronary artery disease.-
dc.languageeng-
dc.publisherLippincott Williams & Wilkins. The Journal's web site is located at http://www.lww.com/product/?1079-5642-
dc.relation.ispartofArteriosclerosis, Thrombosis, and Vascular Biology-
dc.rightsThis is a non-final version of an article published in final form in (provide complete journal citation)-
dc.subjectAdipocyte-
dc.subjectCoronary artery disease-
dc.subjectFibroblast growth factor-
dc.subjectLipocalin-2-
dc.subjectRisk factor-
dc.titleAge-Biomarkers-Clinical Risk Factors for Prediction of Cardiovascular Events in Patients With Coronary Artery Disease.-
dc.typeArticle-
dc.identifier.emailWong, YK: debbieyk@hku.hk-
dc.identifier.emailCheung, YY: cyy0219@hku.hk-
dc.identifier.emailTang, SM: claratang@hku.hk-
dc.identifier.emailAu, KW: aukawing@hku.hk-
dc.identifier.emailHai, SHJJ: haishjj@hku.hk-
dc.identifier.emailLee, CHP: pchlee@hku.hk-
dc.identifier.emailLau, GKK: gkklau@hku.hk-
dc.identifier.emailCheung, BMY: mycheung@hkucc.hku.hk-
dc.identifier.emailSham, PC: pcsham@hku.hk-
dc.identifier.emailXu, A: amxu@hkucc.hku.hk-
dc.identifier.emailLam, KSL: ksllam@hku.hk-
dc.identifier.emailTse, HF: hftse@hkucc.hku.hk-
dc.identifier.authorityCheung, YY=rp02243-
dc.identifier.authorityTang, SM=rp02105-
dc.identifier.authorityHai, SHJJ=rp02047-
dc.identifier.authorityLee, CHP=rp02043-
dc.identifier.authorityLau, GKK=rp01499-
dc.identifier.authorityCheung, BMY=rp01321-
dc.identifier.authoritySham, PC=rp00459-
dc.identifier.authorityXu, A=rp00485-
dc.identifier.authorityLam, KSL=rp00343-
dc.identifier.authorityTse, HF=rp00428-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1161/ATVBAHA.118.311726-
dc.identifier.pmid30354221-
dc.identifier.hkuros290905-
dc.identifier.volume38-
dc.identifier.issue10-
dc.identifier.spage2519-
dc.identifier.epage2527-
dc.publisher.placeUnited States-

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