Lipidomic Risk Score to Enhance Cardiovascular Risk Stratification for Primary Prevention

Jingqin Wu¹, Corey Giles², Aleksandar Dakic³, Habtamu B Beyene⁴, Kevin Huynh², Tingting Wang², Thomas Meikle¹, Gavriel Olshansky¹, Agus Salim⁵, Thy Duong³, Gerald F Watts⁶, Joseph Hung⁷, Jennie Hui⁸, Gemma Cadby⁹, John Beilby¹⁰, John Blangero¹¹, Eric K Moses¹², Jonathan E Shaw⁵, Dianna J Magliano⁵, Dantong Zhu¹³, Jean Y Yang¹⁴, Stuart M Grieve¹⁵, Andrew Wilson¹⁶, Clara K Chow¹⁷, Stephen T Vernon¹⁸, Michael P Gray¹⁸, Gemma A Figtree¹⁹, Melinda J Carrington³, Mike Inouye²⁰, Thomas H Marwick³, Peter J Meikle²¹

Affiliations

¹ Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia; Baker Department of Cardiometabolic Health, Melbourne University, Melbourne, Victoria, Australia.
² 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.
³ Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia.
⁴ 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; Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Victoria, Australia.
⁵ Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia; Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Victoria, Australia.
⁶ Medical School, University of Western Australia, Perth, Western Australia, Australia; Lipid Disorders Clinic, Department of Cardiology, Royal Perth Hospital, Perth, Western Australia, Australia.
⁷ Medical School, University of Western Australia, Perth, Western Australia, Australia.
⁸ 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, Australia.
⁹ School of Population and Global Health, University of Western Australia, Crawley, Western Australia, Australia.
¹⁰ School of Biomedical Sciences, University of Western Australia, Perth, Western Australia, Australia.
¹¹ South Texas Diabetes and Obesity Institute, The University of Texas Rio Grande Valley, Brownsville, Texas, USA.
¹² School of Biomedical Sciences, University of Western Australia, Crawley, Western Australia, Australia; Menzies Institute for Medical Research, University of Tasmania, Hobart, Tasmania, Australia.
¹³ 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.
¹⁴ 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.
¹⁵ 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.
¹⁶ 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.
¹⁷ 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.
¹⁸ Kolling Institute of Medical Research, The University of Sydney, St Leonards, New South Wales, Australia.
¹⁹ 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.
²⁰ Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia; Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, United Kingdom.
²¹ 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; Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Victoria, Australia. Electronic address: peter.meikle@baker.edu.au.

PMID: 39048275
DOI: 10.1016/j.jacc.2024.04.060

Free article

Lipidomic Risk Score to Enhance Cardiovascular Risk Stratification for Primary Prevention

Jingqin Wu et al. J Am Coll Cardiol. 2024.

Free article

. 2024 Jul 30;84(5):434-446.

doi: 10.1016/j.jacc.2024.04.060.

Authors

Affiliations

¹ Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia; Baker Department of Cardiometabolic Health, Melbourne University, Melbourne, Victoria, Australia.
² 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.
³ Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia.
⁴ 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; Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Victoria, Australia.
⁵ Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia; Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Victoria, Australia.
⁶ Medical School, University of Western Australia, Perth, Western Australia, Australia; Lipid Disorders Clinic, Department of Cardiology, Royal Perth Hospital, Perth, Western Australia, Australia.
⁷ Medical School, University of Western Australia, Perth, Western Australia, Australia.
⁸ 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, Australia.
⁹ School of Population and Global Health, University of Western Australia, Crawley, Western Australia, Australia.
¹⁰ School of Biomedical Sciences, University of Western Australia, Perth, Western Australia, Australia.
¹¹ South Texas Diabetes and Obesity Institute, The University of Texas Rio Grande Valley, Brownsville, Texas, USA.
¹² School of Biomedical Sciences, University of Western Australia, Crawley, Western Australia, Australia; Menzies Institute for Medical Research, University of Tasmania, Hobart, Tasmania, Australia.
¹³ 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.
¹⁴ 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.
¹⁵ 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.
¹⁶ 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.
¹⁷ 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.
¹⁸ Kolling Institute of Medical Research, The University of Sydney, St Leonards, New South Wales, Australia.
¹⁹ 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.
²⁰ Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia; Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, United Kingdom.
²¹ 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; Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Victoria, Australia. Electronic address: peter.meikle@baker.edu.au.

PMID: 39048275
DOI: 10.1016/j.jacc.2024.04.060

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.

Keywords: Framingham risk score; cardiovascular disease; coronary artery calcium score; intermediate-risk stratification; lipidomic risk score.

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Conflict of interest statement

Funding Support and Author Disclosures This work was supported by The Heart Foundation 2020 Predictive Modelling Strategic Grant #105511; Medical Research Future Fund (MRFCRI000210); an award from the Ernest Heine Family Foundation; the Victorian Government’s Operational Infrastructure Support Program; and the National Health and Medical Research Council of Australia (#1101320). Drs Huynh, Magliano, and Meikle have received investigator grants from the National Health and Medical Research Council of Australia. Dr Carrington has received an endowed fellowship in the Cardiology Centre of Excellence from Filippo and Maria Casella. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose.

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Lipidomic Risk Score to Enhance Cardiovascular Risk Stratification for Primary Prevention

Affiliations

Lipidomic Risk Score to Enhance Cardiovascular Risk Stratification for Primary Prevention

Authors

Affiliations

Abstract

Conflict of interest statement

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