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Meta-Analysis
. 2025 Apr 15;85(14):1471-1484.
doi: 10.1016/j.jacc.2025.02.016.

Cardiac Troponins and Cardiovascular Disease Risk Prediction: An Individual-Participant-Data Meta-Analysis

Anoop S V Shah  1 , Spencer J Keene  2 , Lisa Pennells  3 , Stephen Kaptoge  3 , Dorien M Kimenai  4 , Matthew Walker  3 , Julianne D Halley  3 , Sara Rocha  3 , Ron C Hoogeveen  5 , Vilmundur Gudnason  6 , Stephan J L Bakker  7 , Sasiwarang G Wannamethee  8 , Manan Pareek  9 , Kai M Eggers  10 , J Wouter Jukema  11 , Graeme J Hankey  12 , James A deLemos  13 , Ian Ford  14 , Torbjørn Omland  15 , Magnus Nakrem Lyngbakken  15 , Bruce M Psaty  16 , Christopher R deFilippi  17 , Angela M Wood  18 , John Danesh  19 , Paul Welsh  20 , Naveed Sattar  20 , Nicholas L Mills  21 , Emanuele Di Angelantonio  22 ; CAPRICE Co-Investigators
Collaborators, Affiliations
Meta-Analysis

Cardiac Troponins and Cardiovascular Disease Risk Prediction: An Individual-Participant-Data Meta-Analysis

Anoop S V Shah et al. J Am Coll Cardiol. .

Abstract

Background: The extent to which high-sensitivity cardiac troponin can predict cardiovascular disease (CVD) is uncertain.

Objectives: We aimed to quantify the potential advantage of adding information on cardiac troponins to conventional risk factors in the prevention of CVD.

Methods: We meta-analyzed individual-participant data from 15 cohorts, comprising 62,150 participants without prior CVD. We calculated HRs, measures of risk discrimination, and reclassification after adding cardiac troponin T (cTnT) or I (cTnI) to conventional risk factors. The primary outcome was first-onset CVD (ie, coronary heart disease or stroke). We then modeled the implications of initiating statin therapy using incidence rates from 2.1 million individuals from the United Kingdom.

Results: Among participants with cTnT or cTnI measurements, 8,133 and 3,749 incident CVD events occurred during a median follow-up of 11.8 and 9.8 years, respectively. HRs for CVD per 1-SD higher concentration were 1.31 (95% CI: 1.25-1.37) for cTnT and 1.26 (95% CI: 1.19-1.33) for cTnI. Addition of cTnT or cTnI to conventional risk factors was associated with C-index increases of 0.015 (95% CI: 0.012-0.018) and 0.012 (95% CI: 0.009-0.015) and continuous net reclassification improvements of 6% and 5% in cases and 22% and 17% in noncases. One additional CVD event would be prevented for every 408 and 473 individuals screened based on statin therapy in those whose CVD risk is reclassified from intermediate to high risk after cTnT or cTnI measurement, respectively.

Conclusions: Measurement of cardiac troponin results in a modest improvement in the prediction of first-onset CVD that may translate into population health benefits if used at scale.

Keywords: biomarkers; cardiac troponin; primary prevention; risk stratification.

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Conflict of interest statement

Funding Support and Author Disclosures This work was funded by the BHF Intermediate Clinical Fellowship (FS/19/17/34172) held by Dr Shah. The work of the coordinating center was funded by the British Heart Foundation (RG/18/13/33946: RG/F/23/110103), NIHR Cambridge Biomedical Research Centre (NIHR203312), BHF Chair Award (CH/12/2/29428), Cambridge BHF Centre of Research Excellence (RE/24/130011, RE/18/1/34212), and by Health Data Research UK, which is funded by the UK Medical Research Council, Engineering and Physical Sciences Research Council, Economic and Social Research Council, Department of Health and Social Care (England), Chief Scientist Office of the Scottish Government Health and Social Care Directorates, Health and Social Care Research and Development Division (Welsh Government), Public Health Agency (Northern Ireland), British Heart Foundation, and the Wellcome Trust. Dr Mills is supported by a Research Excellence Award (RE/24/130012), Programme Grant (RG/20/10/34966) and Chair Award (CH/F/21/90010) from the British Heart Foundation. Supplemental Text 3 lists some of the funders of the component studies in this analysis provided by investigators. Dr Mills has received honoraria from Abbott Diagnostics, Siemens Healthineers, and Roche Diagnostics over the past 3 years. Dr Sattar has received consulting fees and/or received speaker honoraria from Abbott Laboratories, AbbVie, Amgen, AstraZeneca, Boehringer Ingelheim, Eli Lilly, Hanmi Pharmaceuticals, Janssen, Menarini-Ricerche, Novartis, Novo Nordisk, Pfizer, Roche Diagnostics, and Sanofi; and has received grant support paid to his University from AstraZeneca, Boehringer Ingelheim, Novartis, and Roche Diagnostics outside the submitted work. Dr Kimenai is supported by a British Heart Foundation Intermediate Basic Science Research Fellowship (FS/IBSRF/23/25161); and has received honoraria from Roche Diagnostics outside the submitted work. Dr Omland has received research support via institution from Abbott Laboratories, CardiNor, ChromaDex, Novartis, and Roche Diagnostics; and has received honoraria from Abbott Laboratories, CardiNor, Novo Nordisk, Roche Diagnostics, and SpinChip Diagnostics. Dr deFilippi has received grant funding or contracts from NIH, Abbott Diagnostics, FujiRebio, Quidel:Ortho, Randox, Roche Diagnostics, Siemens Healthineers; has consulted or served on advisory Boards for Abbott Diagnostics, FujiRebio, Quidel:Ortho, and Roche Diagnostics; has performed endpoint adjudication for Siemens Healthineers, and Tosoh; has been a speaker for PathFast/PolyMedco, and Siemens Heathineers; and has been named a co-owner on a patent awarded to the University of Maryland (US Patent Application Number: 15/ 309,754) entitled: “Methods for Assessing Differential Risk for Developing Heart Failure.” Dr Danesh serves on scientific advisory boards for AstraZeneca, Novartis, and UK Biobank; and has received multiple grants from academic, charitable and industry sources outside of the submitted work. Dr Danesh holds a British Heart Foundation Professorship and an NIHR Senior Investigator Award. Dr Wood is supported by the BHF Data Science Centre (HDRUK2023.0239), Health Data Research UK (Big Data for Complex Disease-HDR-23012), and as an NIHR Research Professor (NIHR303137). Dr Psaty serves on the Steering Committee of the Yale Open Data Access Project funded by Johnson & Johnson. Dr deLemos has received grant support from Abbott Diagnostics; has received consulting fees from Quidel Cardiovascular, Inc; has received honoraria for participation in endpoint committees from Beckman Coulter and Siemen’s Health Care Diagnostics; and has received fees for participation in Data Monitoring Committees from AstraZeneca, Novo Nordisk, Eli Lilly, Merck, Regeneron, Amgen, and Verve Therapeutics. Dr de Lemos has been named a co-owner on a patent awarded to the University of Maryland (US Patent Application Number: 15/309,754) entitled: “Methods for Assessing Differential Risk for Developing Heart Failure.” Dr Pareek has been on the advisory boards of AstraZeneca, Janssen-Cilag, and Novo Nordisk; has received grant support from the Danish Cardiovascular Academy funded by the Novo Nordisk Foundation and the Danish Heart Foundation (grant number: CPD5Y-2022004-HF) and the Danish Heart Foundation (grant number: 2024-12587); and has received speaker honoraria from AstraZeneca, Bayer, Boehringer Ingelheim, and Janssen-Cilag. Dr Welsh has received grant income from AstraZeneca, Boehringer Ingelheim, Novartis, and Roche Diagnostics; and has received speaker fees from Novo Nordisk and Raisio. Dr Eggers has received consultancy fees from Roche Diagnostics. Dr Shah has received honoraria from Abbott Diagnostics. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose.

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