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Lipidomic Risk Score to Enhance Cardiovascular Risk Stratification for Primary Prevention.
Wu, Jingqin; Giles, Corey; Dakic, Aleksandar; Beyene, Habtamu B; Huynh, Kevin; Wang, Tingting; Meikle, Thomas; Olshansky, Gavriel; Salim, Agus; Duong, Thy; Watts, Gerald F; Hung, Joseph; Hui, Jennie; Cadby, Gemma; Beilby, John; Blangero, John; Moses, Eric K; Shaw, Jonathan E; Magliano, Dianna J; Zhu, Dantong; Yang, Jean Y; Grieve, Stuart M; Wilson, Andrew; Chow, Clara K; Vernon, Stephen T; Gray, Michael P; Figtree, Gemma A; Carrington, Melinda J; Inouye, Mike; Marwick, Thomas H; Meikle, Peter J.
Afiliação
  • Wu J; Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia; Baker Department of Cardiometabolic Health, Melbourne University, Melbourne, Victoria, Australia.
  • Giles C; Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia; Baker Department of Cardiometabolic Health, Melbourne University, Melbourne, Victoria, Australia; Baker Department of Cardiovascular Research Translation and Implementation, La Trobe University, Melbourne, Victoria, Australia.
  • Dakic A; Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia.
  • Beyene HB; Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia; Baker Department of Cardiometabolic Health, Melbourne University, Melbourne, Victoria, Australia; Baker Department of Cardiovascular Research Translation and Implementation, La Trobe University, Melbourne, Victoria, Australia; Facul
  • Huynh K; Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia; Baker Department of Cardiometabolic Health, Melbourne University, Melbourne, Victoria, Australia; Baker Department of Cardiovascular Research Translation and Implementation, La Trobe University, Melbourne, Victoria, Australia.
  • Wang T; Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia; Baker Department of Cardiometabolic Health, Melbourne University, Melbourne, Victoria, Australia; Baker Department of Cardiovascular Research Translation and Implementation, La Trobe University, Melbourne, Victoria, Australia.
  • Meikle T; Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia; Baker Department of Cardiometabolic Health, Melbourne University, Melbourne, Victoria, Australia.
  • Olshansky G; Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia; Baker Department of Cardiometabolic Health, Melbourne University, Melbourne, Victoria, Australia.
  • Salim A; Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia; Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Victoria, Australia.
  • Duong T; Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia.
  • Watts GF; Medical School, University of Western Australia, Perth, Western Australia, Australia; Lipid Disorders Clinic, Department of Cardiology, Royal Perth Hospital, Perth, Western Australia, Australia.
  • Hung J; Medical School, University of Western Australia, Perth, Western Australia, Australia.
  • Hui J; PathWest Laboratory Medicine of Western Australia, Nedlands, Western Australia, Australia; School of Biomedical Sciences, University of Western Australia, Perth, Western Australia, Australia; School of Population and Global Health, University of Western Australia, Crawley, Western Australia, Austral
  • Cadby G; School of Population and Global Health, University of Western Australia, Crawley, Western Australia, Australia.
  • Beilby J; School of Biomedical Sciences, University of Western Australia, Perth, Western Australia, Australia.
  • Blangero J; South Texas Diabetes and Obesity Institute, The University of Texas Rio Grande Valley, Brownsville, Texas, USA.
  • Moses EK; School of Biomedical Sciences, University of Western Australia, Crawley, Western Australia, Australia; Menzies Institute for Medical Research, University of Tasmania, Hobart, Tasmania, Australia.
  • Shaw JE; Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia; Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Victoria, Australia.
  • Magliano DJ; Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia; Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Victoria, Australia.
  • Zhu D; School of Mathematics and Statistics, The University of Sydney, Camperdown, New South Wales, Australia; Kolling Institute of Medical Research, The University of Sydney, St Leonards, New South Wales, Australia.
  • Yang JY; School of Mathematics and Statistics, The University of Sydney, Camperdown, New South Wales, Australia; Charles Perkins Centre, The University of Sydney, Camperdown, New South Wales, Australia.
  • Grieve SM; Department of Radiology, Royal Prince Alfred Hospital, Camperdown, New South Wales, Australia; Imaging and Phenotyping Laboratory, Charles Perkins Centre, University of Sydney, Camperdown, New South Wales, Australia.
  • Wilson A; Menzies Centre for Health Policy and Economics, Sydney School of Public Health, Faculty of Medicine and Health, University of Sydney, Camperdown, New South Wales, Australia.
  • Chow CK; Westmead Applied Research Centre, Faculty of Medicine and Health, University of Sydney, Westmead, New South Wales, Australia; Department of Cardiology, Westmead Hospital, Westmead, New South Wales, Australia.
  • Vernon ST; Kolling Institute of Medical Research, The University of Sydney, St Leonards, New South Wales, Australia.
  • Gray MP; Kolling Institute of Medical Research, The University of Sydney, St Leonards, New South Wales, Australia.
  • Figtree GA; Kolling Institute of Medical Research, The University of Sydney, St Leonards, New South Wales, Australia; Charles Perkins Centre, The University of Sydney, Camperdown, New South Wales, Australia; Department of Cardiology, Royal North Shore Hospital, St Leonards, New South Wales, Australia.
  • Carrington MJ; Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia.
  • Inouye M; Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia; Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, United Kingdom.
  • Marwick TH; Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia.
  • Meikle PJ; Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia; Baker Department of Cardiometabolic Health, Melbourne University, Melbourne, Victoria, Australia; Baker Department of Cardiovascular Research Translation and Implementation, La Trobe University, Melbourne, Victoria, Australia; Facul
J Am Coll Cardiol ; 84(5): 434-446, 2024 Jul 30.
Article em En | MEDLINE | ID: mdl-39048275
ABSTRACT

BACKGROUND:

Accurate risk stratification is vital for primary prevention of cardiovascular disease (CVD). However, traditional tools such as the Framingham Risk Score (FRS) may underperform within the diverse intermediate-risk group, which includes individuals requiring distinct management strategies.

OBJECTIVES:

This study aimed to develop a lipidomic-enhanced risk score (LRS), specifically targeting risk prediction and reclassification within the intermediate group, benchmarked against the FRS.

METHODS:

The LRS was developed via a machine learning workflow using ridge regression on the Australian Diabetes, Obesity, and Lifestyle Study (AusDiab; n = 10,339). It was externally validated with the Busselton Health Study (n = 4,492), and its predictive utility for coronary artery calcium scoring (CACS)-based outcomes was independently validated in the BioHEART cohort (n = 994).

RESULTS:

LRS significantly improved discrimination metrics for the intermediate-risk group in both AusDiab and Busselton Health Study cohorts (all P < 0.001), increasing the area under the curve for CVD events by 0.114 (95% CI 0.1123-0.1157) and 0.077 (95% CI 0.0755-0.0785), with a net reclassification improvement of 0.36 (95% CI 0.21-0.51) and 0.33 (95% CI 0.15-0.49), respectively. For CACS-based outcomes in BioHEART, LRS achieved a significant area under the curve improvement of 0.02 over the FRS (0.76 vs 0.74; P < 1.0 × 10-5). A simplified, clinically applicable version of LRS was also created that had comparable performance to the original LRS.

CONCLUSIONS:

LRS, augmenting the FRS, presents potential to improve intermediate-risk stratification and to predict atherosclerotic markers using a simple blood test, suitable for clinical application. This could facilitate the triage of individuals for noninvasive imaging such as CACS, fostering precision medicine in CVD prevention and management.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Prevenção Primária / Doenças Cardiovasculares Limite: Adult / Aged / Female / Humans / Male / Middle aged País/Região como assunto: Oceania Idioma: En Ano de publicação: 2024 Tipo de documento: Article
Texto completo
Imprimir
XML
PubMed Links
Buscar no Google

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Prevenção Primária / Doenças Cardiovasculares Limite: Adult / Aged / Female / Humans / Male / Middle aged País/Região como assunto: Oceania Idioma: En Ano de publicação: 2024 Tipo de documento: Article