@article {1563, title = {Separate and combined associations of body-mass index and abdominal adiposity with cardiovascular disease: collaborative analysis of 58 prospective studies.}, journal = {Lancet}, volume = {377}, year = {2011}, month = {2011 Mar 26}, pages = {1085-95}, abstract = {

BACKGROUND: Guidelines differ about the value of assessment of adiposity measures for cardiovascular disease risk prediction when information is available for other risk factors. We studied the separate and combined associations of body-mass index (BMI), waist circumference, and waist-to-hip ratio with risk of first-onset cardiovascular disease.

METHODS: We used individual records from 58 cohorts to calculate hazard ratios (HRs) per 1 SD higher baseline values (4.56 kg/m(2) higher BMI, 12.6 cm higher waist circumference, and 0.083 higher waist-to-hip ratio) and measures of risk discrimination and reclassification. Serial adiposity assessments were used to calculate regression dilution ratios.

RESULTS: Individual records were available for 221,934 people in 17 countries (14,297 incident cardiovascular disease outcomes; 1.87 million person-years at risk). Serial adiposity assessments were made in up to 63,821 people (mean interval 5.7 years [SD 3.9]). In people with BMI of 20 kg/m(2) or higher, HRs for cardiovascular disease were 1.23 (95\% CI 1.17-1.29) with BMI, 1.27 (1.20-1.33) with waist circumference, and 1.25 (1.19-1.31) with waist-to-hip ratio, after adjustment for age, sex, and smoking status. After further adjustment for baseline systolic blood pressure, history of diabetes, and total and HDL cholesterol, corresponding HRs were 1.07 (1.03-1.11) with BMI, 1.10 (1.05-1.14) with waist circumference, and 1.12 (1.08-1.15) with waist-to-hip ratio. Addition of information on BMI, waist circumference, or waist-to-hip ratio to a cardiovascular disease risk prediction model containing conventional risk factors did not importantly improve risk discrimination (C-index changes of -0.0001, -0.0001, and 0.0008, respectively), nor classification of participants to categories of predicted 10-year risk (net reclassification improvement -0.19\%, -0.05\%, and -0.05\%, respectively). Findings were similar when adiposity measures were considered in combination. Reproducibility was greater for BMI (regression dilution ratio 0.95, 95\% CI 0.93-0.97) than for waist circumference (0.86, 0.83-0.89) or waist-to-hip ratio (0.63, 0.57-0.70).

INTERPRETATION: BMI, waist circumference, and waist-to-hip ratio, whether assessed singly or in combination, do not importantly improve cardiovascular disease risk prediction in people in developed countries when additional information is available for systolic blood pressure, history of diabetes, and lipids.

FUNDING: British Heart Foundation and UK Medical Research Council.

}, keywords = {Age Factors, Blood Pressure, Body Mass Index, Cardiovascular Diseases, Cholesterol, Cholesterol, HDL, Diabetes Mellitus, Female, Humans, Male, Middle Aged, Obesity, Abdominal, Proportional Hazards Models, Prospective Studies, Risk Assessment, Sex Factors, Smoking, Systole, Waist Circumference, Waist-Hip Ratio}, issn = {1474-547X}, doi = {10.1016/S0140-6736(11)60105-0}, author = {Wormser, David and Kaptoge, Stephen and Di Angelantonio, Emanuele and Wood, Angela M and Pennells, Lisa and Thompson, Alex and Sarwar, Nadeem and Kizer, Jorge R and Lawlor, Debbie A and Nordestgaard, B{\o}rge G and Ridker, Paul and Salomaa, Veikko and Stevens, June and Woodward, Mark and Sattar, Naveed and Collins, Rory and Thompson, Simon G and Whitlock, Gary and Danesh, John} } @article {1399, title = {Lipid-related markers and cardiovascular disease prediction.}, journal = {JAMA}, volume = {307}, year = {2012}, month = {2012 Jun 20}, pages = {2499-506}, abstract = {

CONTEXT: The value of assessing various emerging lipid-related markers for prediction of first cardiovascular events is debated.

OBJECTIVE: To determine whether adding information on apolipoprotein B and apolipoprotein A-I, lipoprotein(a), or lipoprotein-associated phospholipase A2 to total cholesterol and high-density lipoprotein cholesterol (HDL-C) improves cardiovascular disease (CVD) risk prediction.

DESIGN, SETTING, AND PARTICIPANTS: Individual records were available for 165,544 participants without baseline CVD in 37 prospective cohorts (calendar years of recruitment: 1968-2007) with up to 15,126 incident fatal or nonfatal CVD outcomes (10,132 CHD and 4994 stroke outcomes) during a median follow-up of 10.4 years (interquartile range, 7.6-14 years).

MAIN OUTCOME MEASURES: Discrimination of CVD outcomes and reclassification of participants across predicted 10-year risk categories of low (<10\%), intermediate (10\%-<20\%), and high (>=20\%) risk.

RESULTS: The addition of information on various lipid-related markers to total cholesterol, HDL-C, and other conventional risk factors yielded improvement in the model{\textquoteright}s discrimination: C-index change, 0.0006 (95\% CI, 0.0002-0.0009) for the combination of apolipoprotein B and A-I; 0.0016 (95\% CI, 0.0009-0.0023) for lipoprotein(a); and 0.0018 (95\% CI, 0.0010-0.0026) for lipoprotein-associated phospholipase A2 mass. Net reclassification improvements were less than 1\% with the addition of each of these markers to risk scores containing conventional risk factors. We estimated that for 100,000 adults aged 40 years or older, 15,436 would be initially classified at intermediate risk using conventional risk factors alone. Additional testing with a combination of apolipoprotein B and A-I would reclassify 1.1\%; lipoprotein(a), 4.1\%; and lipoprotein-associated phospholipase A2 mass, 2.7\% of people to a 20\% or higher predicted CVD risk category and, therefore, in need of statin treatment under Adult Treatment Panel III guidelines.

CONCLUSION: In a study of individuals without known CVD, the addition of information on the combination of apolipoprotein B and A-I, lipoprotein(a), or lipoprotein-associated phospholipase A2 mass to risk scores containing total cholesterol and HDL-C led to slight improvement in CVD prediction.

}, keywords = {Aged, Biomarkers, Cardiovascular Diseases, Cholesterol, HDL, Cohort Studies, Female, Humans, Lipoproteins, Male, Middle Aged, Risk Assessment}, issn = {1538-3598}, doi = {10.1001/jama.2012.6571}, author = {Di Angelantonio, Emanuele and Gao, Pei and Pennells, Lisa and Kaptoge, Stephen and Caslake, Muriel and Thompson, Alexander and Butterworth, Adam S and Sarwar, Nadeem and Wormser, David and Saleheen, Danish and Ballantyne, Christie M and Psaty, Bruce M and Sundstr{\"o}m, Johan and Ridker, Paul M and Nagel, Dorothea and Gillum, Richard F and Ford, Ian and Ducimetiere, Pierre and Kiechl, Stefan and Koenig, Wolfgang and Dullaart, Robin P F and Assmann, Gerd and D{\textquoteright}Agostino, Ralph B and Dagenais, Gilles R and Cooper, Jackie A and Kromhout, Daan and Onat, Altan and Tipping, Robert W and G{\'o}mez-de-la-C{\'a}mara, Agust{\'\i}n and Rosengren, Annika and Sutherland, Susan E and Gallacher, John and Fowkes, F Gerry R and Casiglia, Edoardo and Hofman, Albert and Salomaa, Veikko and Barrett-Connor, Elizabeth and Clarke, Robert and Brunner, Eric and Jukema, J Wouter and Simons, Leon A and Sandhu, Manjinder and Wareham, Nicholas J and Khaw, Kay-Tee and Kauhanen, Jussi and Salonen, Jukka T and Howard, William J and Nordestgaard, B{\o}rge G and Wood, Angela M and Thompson, Simon G and Boekholdt, S Matthijs and Sattar, Naveed and Packard, Chris and Gudnason, Vilmundur and Danesh, John} } @article {6811, title = {Association of Cardiometabolic Multimorbidity With Mortality.}, journal = {JAMA}, volume = {314}, year = {2015}, month = {2015 Jul 7}, pages = {52-60}, abstract = {

IMPORTANCE: The prevalence of cardiometabolic multimorbidity is increasing.

OBJECTIVE: To estimate reductions in life expectancy associated with cardiometabolic multimorbidity.

DESIGN, SETTING, AND PARTICIPANTS: Age- and sex-adjusted mortality rates and hazard ratios (HRs) were calculated using individual participant data from the Emerging Risk Factors Collaboration (689,300 participants; 91 cohorts; years of baseline surveys: 1960-2007; latest mortality follow-up: April 2013; 128,843 deaths). The HRs from the Emerging Risk Factors Collaboration were compared with those from the UK Biobank (499,808 participants; years of baseline surveys: 2006-2010; latest mortality follow-up: November 2013; 7995 deaths). Cumulative survival was estimated by applying calculated age-specific HRs for mortality to contemporary US age-specific death rates.

EXPOSURES: A history of 2 or more of the following: diabetes mellitus, stroke, myocardial infarction (MI).

MAIN OUTCOMES AND MEASURES: All-cause mortality and estimated reductions in life expectancy.

RESULTS: In participants in the Emerging Risk Factors Collaboration without a history of diabetes, stroke, or MI at baseline (reference group), the all-cause mortality rate adjusted to the age of 60 years was 6.8 per 1000 person-years. Mortality rates per 1000 person-years were 15.6 in participants with a history of diabetes, 16.1 in those with stroke, 16.8 in those with MI, 32.0 in those with both diabetes and MI, 32.5 in those with both diabetes and stroke, 32.8 in those with both stroke and MI, and 59.5 in those with diabetes, stroke, and MI. Compared with the reference group, the HRs for all-cause mortality were 1.9 (95\% CI, 1.8-2.0) in participants with a history of diabetes, 2.1 (95\% CI, 2.0-2.2) in those with stroke, 2.0 (95\% CI, 1.9-2.2) in those with MI, 3.7 (95\% CI, 3.3-4.1) in those with both diabetes and MI, 3.8 (95\% CI, 3.5-4.2) in those with both diabetes and stroke, 3.5 (95\% CI, 3.1-4.0) in those with both stroke and MI, and 6.9 (95\% CI, 5.7-8.3) in those with diabetes, stroke, and MI. The HRs from the Emerging Risk Factors Collaboration were similar to those from the more recently recruited UK Biobank. The HRs were little changed after further adjustment for markers of established intermediate pathways (eg, levels of lipids and blood pressure) and lifestyle factors (eg, smoking, diet). At the age of 60 years, a history of any 2 of these conditions was associated with 12 years of reduced life expectancy and a history of all 3 of these conditions was associated with 15 years of reduced life expectancy.

CONCLUSIONS AND RELEVANCE: Mortality associated with a history of diabetes, stroke, or MI was similar for each condition. Because any combination of these conditions was associated with multiplicative mortality risk, life expectancy was substantially lower in people with multimorbidity.

}, keywords = {Adult, Aged, Comorbidity, Diabetes Mellitus, Female, Humans, Life Expectancy, Male, Middle Aged, Mortality, Myocardial Infarction, Risk Factors, Stroke}, issn = {1538-3598}, doi = {10.1001/jama.2015.7008}, author = {Di Angelantonio, Emanuele and Kaptoge, Stephen and Wormser, David and Willeit, Peter and Butterworth, Adam S and Bansal, Narinder and O{\textquoteright}Keeffe, Linda M and Gao, Pei and Wood, Angela M and Burgess, Stephen and Freitag, Daniel F and Pennells, Lisa and Peters, Sanne A and Hart, Carole L and H{\r a}heim, Lise Lund and Gillum, Richard F and Nordestgaard, B{\o}rge G and Psaty, Bruce M and Yeap, Bu B and Knuiman, Matthew W and Nietert, Paul J and Kauhanen, Jussi and Salonen, Jukka T and Kuller, Lewis H and Simons, Leon A and van der Schouw, Yvonne T and Barrett-Connor, Elizabeth and Selmer, Randi and Crespo, Carlos J and Rodriguez, Beatriz and Verschuren, W M Monique and Salomaa, Veikko and Sv{\"a}rdsudd, Kurt and van der Harst, Pim and Bj{\"o}rkelund, Cecilia and Wilhelmsen, Lars and Wallace, Robert B and Brenner, Hermann and Amouyel, Philippe and Barr, Elizabeth L M and Iso, Hiroyasu and Onat, Altan and Trevisan, Maurizio and D{\textquoteright}Agostino, Ralph B and Cooper, Cyrus and Kavousi, Maryam and Welin, Lennart and Roussel, Ronan and Hu, Frank B and Sato, Shinichi and Davidson, Karina W and Howard, Barbara V and Leening, Maarten J G and Leening, Maarten and Rosengren, Annika and D{\"o}rr, Marcus and Deeg, Dorly J H and Kiechl, Stefan and Stehouwer, Coen D A and Nissinen, Aulikki and Giampaoli, Simona and Donfrancesco, Chiara and Kromhout, Daan and Price, Jackie F and Peters, Annette and Meade, Tom W and Casiglia, Edoardo and Lawlor, Debbie A and Gallacher, John and Nagel, Dorothea and Franco, Oscar H and Assmann, Gerd and Dagenais, Gilles R and Jukema, J Wouter and Sundstr{\"o}m, Johan and Woodward, Mark and Brunner, Eric J and Khaw, Kay-Tee and Wareham, Nicholas J and Whitsel, Eric A and Nj{\o}lstad, Inger and Hedblad, Bo and Wassertheil-Smoller, Sylvia and Engstr{\"o}m, Gunnar and Rosamond, Wayne D and Selvin, Elizabeth and Sattar, Naveed and Thompson, Simon G and Danesh, John} } @article {8567, title = {Natriuretic peptides and integrated risk assessment for cardiovascular disease: an individual-participant-data meta-analysis.}, journal = {Lancet Diabetes Endocrinol}, volume = {4}, year = {2016}, month = {2016 10}, pages = {840-9}, abstract = {

BACKGROUND: Guidelines for primary prevention of cardiovascular diseases focus on prediction of coronary heart disease and stroke. We assessed whether or not measurement of N-terminal-pro-B-type natriuretic peptide (NT-proBNP) concentration could enable a more integrated approach than at present by predicting heart failure and enhancing coronary heart disease and stroke risk assessment.

METHODS: In this individual-participant-data meta-analysis, we generated and harmonised individual-participant data from relevant prospective studies via both de-novo NT-proBNP concentration measurement of stored samples and collection of data from studies identified through a systematic search of the literature (PubMed, Scientific Citation Index Expanded, and Embase) for articles published up to Sept 4, 2014, using search terms related to natriuretic peptide family members and the primary outcomes, with no language restrictions. We calculated risk ratios and measures of risk discrimination and reclassification across predicted 10 year risk categories (ie, <5\%, 5\% to <7{\textperiodcentered}5\%, and >=7{\textperiodcentered}5\%), adding assessment of NT-proBNP concentration to that of conventional risk factors (ie, age, sex, smoking status, systolic blood pressure, history of diabetes, and total and HDL cholesterol concentrations). Primary outcomes were the combination of coronary heart disease and stroke, and the combination of coronary heart disease, stroke, and heart failure.

FINDINGS: We recorded 5500 coronary heart disease, 4002 stroke, and 2212 heart failure outcomes among 95 617 participants without a history of cardiovascular disease in 40 prospective studies. Risk ratios (for a comparison of the top third vs bottom third of NT-proBNP concentrations, adjusted for conventional risk factors) were 1{\textperiodcentered}76 (95\% CI 1{\textperiodcentered}56-1{\textperiodcentered}98) for the combination of coronary heart disease and stroke and 2{\textperiodcentered}00 (1{\textperiodcentered}77-2{\textperiodcentered}26) for the combination of coronary heart disease, stroke, and heart failure. Addition of information about NT-proBNP concentration to a model containing conventional risk factors was associated with a C-index increase of 0{\textperiodcentered}012 (0{\textperiodcentered}010-0{\textperiodcentered}014) and a net reclassification improvement of 0{\textperiodcentered}027 (0{\textperiodcentered}019-0{\textperiodcentered}036) for the combination of coronary heart disease and stroke and a C-index increase of 0{\textperiodcentered}019 (0{\textperiodcentered}016-0{\textperiodcentered}022) and a net reclassification improvement of 0{\textperiodcentered}028 (0{\textperiodcentered}019-0{\textperiodcentered}038) for the combination of coronary heart disease, stroke, and heart failure.

INTERPRETATION: In people without baseline cardiovascular disease, NT-proBNP concentration assessment strongly predicted first-onset heart failure and augmented coronary heart disease and stroke prediction, suggesting that NT-proBNP concentration assessment could be used to integrate heart failure into cardiovascular disease primary prevention.

FUNDING: British Heart Foundation, Austrian Science Fund, UK Medical Research Council, National Institute for Health Research, European Research Council, and European Commission Framework Programme 7.

}, keywords = {Aged, Biomarkers, Cardiovascular Diseases, Female, Humans, Male, Middle Aged, Natriuretic Peptide, Brain, Peptide Fragments, Prospective Studies, Risk Assessment}, issn = {2213-8595}, doi = {10.1016/S2213-8587(16)30196-6}, author = {Willeit, Peter and Kaptoge, Stephen and Welsh, Paul and Butterworth, Adam and Chowdhury, Rajiv and Spackman, Sarah and Pennells, Lisa and Gao, Pei and Burgess, Stephen and Freitag, Daniel and Sweeting, Michael and Wood, Angela and Cook, Nancy and Judd, Suzanne and Trompet, Stella and Nambi, Vijay and Olsen, Michael and Everett, Brendan and Kee, Frank and Arnl{\"o}v, Johan and Salomaa, Veikko and Levy, Daniel and Kauhanen, Jussi and Laukkanen, Jari and Kavousi, Maryam and Ninomiya, Toshiharu and Casas, Juan-Pablo and Daniels, Lori and Lind, Lars and Kistorp, Caroline and Rosenberg, Jens and Mueller, Thomas and Rubattu, Speranza and Panagiotakos, Demosthenes and Franco, Oscar and de Lemos, James and Luchner, Andreas and Kizer, Jorge and Kiechl, Stefan and Salonen, Jukka and Goya Wannamethee, S and de Boer, Rudolf and Nordestgaard, B{\o}rge and Andersson, Jonas and J{\o}rgensen, Torben and Melander, Olle and Ballantyne, Christie and DeFilippi, Christopher and Ridker, Paul and Cushman, Mary and Rosamond, Wayne and Thompson, Simon and Gudnason, Vilmundur and Sattar, Naveed and Danesh, John and Di Angelantonio, Emanuele} } @article {7451, title = {REPEATED MEASUREMENTS OF BLOOD PRESSURE AND CHOLESTEROL IMPROVES CARDIOVASCULAR DISEASE RISK PREDICTION: AN INDIVIDUAL-PARTICIPANT-DATA META-ANALYSIS.}, journal = {Am J Epidemiol}, year = {2017}, month = {2017 May 26}, abstract = {

The added value of incorporating information from repeated measurements of blood pressure and cholesterol for cardiovascular disease (CVD) risk prediction has not been rigorously assessed. We used data from the Emerging Risk Factors Collaboration on 191,445 adults (38 cohorts from across 17 countries with data from 1962-2014) with~>~1 million measurements of systolic blood pressure, total cholesterol and high-density lipoprotein cholesterol; over a median 12 years of follow-up, 21,170 CVD events occurred. Risk prediction models using cumulative means of repeated measurements and summary measures from longitudinal modelling of the repeated measurements were compared to models using measurements from a single time point. Risk discrimination (C-index) and net reclassification were calculated, and changes in C-indices were meta-analysed across studies. Compared to the single time point model, the cumulative means and the longitudinal models increased the C-index by 0.0040 (95\% CI: 0.0023, 0.0057) and 0.0023 (0.0005, 0.0042), respectively. Reclassification was also improved in both models; compared to the single time point model, overall net reclassification improvements were 0.0369 (0.0303, 0.0436) for the cumulative means model and 0.0177 (0.0110, 0.0243) for the longitudinal model. In conclusion, incorporating repeated measurements of blood pressure and cholesterol into CVD risk prediction models slightly improves risk prediction.

}, issn = {1476-6256}, doi = {10.1093/aje/kwx149}, author = {Paige, Ellie and Barrett, Jessica and Pennells, Lisa and Sweeting, Michael and Willeit, Peter and Di Angelantonio, Emanuele and Gudnason, Vilmundur and Nordestgaard, B{\o}rge G and Psaty, Bruce M and Goldbourt, Uri and Best, Lyle G and Assmann, Gerd and Salonen, Jukka T and Nietert, Paul J and Verschuren, Wm Monique and Brunner, Eric J and Kronmal, Richard A and Salomaa, Veikko and Bakker, Stephan Jl and Dagenais, Gilles R and Sato, Shinichi and Jansson, Jan-H{\r a}kan and Willeit, Johann and Onat, Altan and de la C{\'a}mara, Agustin G{\'o}mez and Roussel, Ronan and V{\"o}lzke, Henry and Dankner, Rachel and Tipping, Robert W and Meade, Tom W and Donfrancesco, Chiara and Kuller, Lewis H and Peters, Annette and Gallacher, John and Kromhout, Daan and Iso, Hiroyasu and Knuiman, Matthew and Casiglia, Edoardo and Kavousi, Maryam and Palmieri, Luigi and Sundstr{\"o}m, Johan and Davis, Barry R and Nj{\o}lstad, Inger and Couper, David and Danesh, John and Thompson, Simon G and Wood, Angela} } @article {7923, title = {Equalization of four cardiovascular risk algorithms after systematic recalibration: individual-participant meta-analysis of 86 prospective studies.}, journal = {Eur Heart J}, year = {2018}, month = {2018 Nov 22}, abstract = {

Aims: There is debate about the optimum algorithm for cardiovascular disease (CVD) risk estimation. We conducted head-to-head comparisons of four algorithms recommended by primary prevention guidelines, before and after {\textquoteright}recalibration{\textquoteright}, a method that adapts risk algorithms to take account of differences in the risk characteristics of the populations being studied.

Methods and results: Using individual-participant data on 360 737 participants without CVD at baseline in 86 prospective studies from 22 countries, we compared the Framingham risk score (FRS), Systematic COronary Risk Evaluation (SCORE), pooled cohort equations (PCE), and Reynolds risk score (RRS). We calculated measures of risk discrimination and calibration, and modelled clinical implications of initiating statin therapy in people judged to be at {\textquoteright}high{\textquoteright} 10 year CVD risk. Original risk algorithms were recalibrated using the risk factor profile and CVD incidence of target populations. The four algorithms had similar risk discrimination. Before recalibration, FRS, SCORE, and PCE over-predicted CVD risk on average by 10\%, 52\%, and 41\%, respectively, whereas RRS under-predicted by 10\%. Original versions of algorithms classified 29-39\% of individuals aged >=40 years as high risk. By contrast, recalibration reduced this proportion to 22-24\% for every algorithm. We estimated that to prevent one CVD event, it would be necessary to initiate statin therapy in 44-51 such individuals using original algorithms, in contrast to 37-39 individuals with recalibrated algorithms.

Conclusion: Before recalibration, the clinical performance of four widely used CVD risk algorithms varied substantially. By contrast, simple recalibration nearly equalized their performance and improved modelled targeting of preventive action to clinical need.

}, issn = {1522-9645}, doi = {10.1093/eurheartj/ehy653}, author = {Pennells, Lisa and Kaptoge, Stephen and Wood, Angela and Sweeting, Mike and Zhao, Xiaohui and White, Ian and Burgess, Stephen and Willeit, Peter and Bolton, Thomas and Moons, Karel G M and van der Schouw, Yvonne T and Selmer, Randi and Khaw, Kay-Tee and Gudnason, Vilmundur and Assmann, Gerd and Amouyel, Philippe and Salomaa, Veikko and Kivimaki, Mika and Nordestgaard, B{\o}rge G and Blaha, Michael J and Kuller, Lewis H and Brenner, Hermann and Gillum, Richard F and Meisinger, Christa and Ford, Ian and Knuiman, Matthew W and Rosengren, Annika and Lawlor, Debbie A and V{\"o}lzke, Henry and Cooper, Cyrus and Mar{\'\i}n Iba{\~n}ez, Alejandro and Casiglia, Edoardo and Kauhanen, Jussi and Cooper, Jackie A and Rodriguez, Beatriz and Sundstr{\"o}m, Johan and Barrett-Connor, Elizabeth and Dankner, Rachel and Nietert, Paul J and Davidson, Karina W and Wallace, Robert B and Blazer, Dan G and Bj{\"o}rkelund, Cecilia and Donfrancesco, Chiara and Krumholz, Harlan M and Nissinen, Aulikki and Davis, Barry R and Coady, Sean and Whincup, Peter H and J{\o}rgensen, Torben and Ducimetiere, Pierre and Trevisan, Maurizio and Engstr{\"o}m, Gunnar and Crespo, Carlos J and Meade, Tom W and Visser, Marjolein and Kromhout, Daan and Kiechl, Stefan and Daimon, Makoto and Price, Jackie F and G{\'o}mez de la C{\'a}mara, Agustin and Wouter Jukema, J and Lamarche, Beno{\^\i}t and Onat, Altan and Simons, Leon A and Kavousi, Maryam and Ben-Shlomo, Yoav and Gallacher, John and Dekker, Jacqueline M and Arima, Hisatomi and Shara, Nawar and Tipping, Robert W and Roussel, Ronan and Brunner, Eric J and Koenig, Wolfgang and Sakurai, Masaru and Pavlovic, Jelena and Gansevoort, Ron T and Nagel, Dorothea and Goldbourt, Uri and Barr, Elizabeth L M and Palmieri, Luigi and Nj{\o}lstad, Inger and Sato, Shinichi and Monique Verschuren, W M and Varghese, Cherian V and Graham, Ian and Onuma, Oyere and Greenland, Philip and Woodward, Mark and Ezzati, Majid and Psaty, Bruce M and Sattar, Naveed and Jackson, Rod and Ridker, Paul M and Cook, Nancy R and D{\textquoteright}Agostino, Ralph B and Thompson, Simon G and Danesh, John and Di Angelantonio, Emanuele} } @article {9941, title = {Cardiac Troponins and Cardiovascular Disease Risk Prediction: An Individual-Participant-Data Meta-Analysis.}, journal = {J Am Coll Cardiol}, volume = {85}, year = {2025}, month = {2025 Apr 15}, pages = {1471-1484}, 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, Cardiovascular Diseases, Female, Humans, Hydroxymethylglutaryl-CoA Reductase Inhibitors, Male, Middle Aged, Risk Assessment, Risk Factors, Troponin I, Troponin T}, issn = {1558-3597}, doi = {10.1016/j.jacc.2025.02.016}, author = {Shah, Anoop S V and Keene, Spencer J and Pennells, Lisa and Kaptoge, Stephen and Kimenai, Dorien M and Walker, Matthew and Halley, Julianne D and Rocha, Sara and Hoogeveen, Ron C and Gudnason, Vilmundur and Bakker, Stephan J L and Wannamethee, Sasiwarang G and Pareek, Manan and Eggers, Kai M and Jukema, J Wouter and Hankey, Graeme J and deLemos, James A and Ford, Ian and Omland, Torbj{\o}rn and Lyngbakken, Magnus Nakrem and Psaty, Bruce M and deFilippi, Christopher R and Wood, Angela M and Danesh, John and Welsh, Paul and Sattar, Naveed and Mills, Nicholas L and Di Angelantonio, Emanuele} }