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31 results on '"Emerging Risk Factors Collaboration"'

1. Separate and combined associations of body-mass index and abdominal adiposity with cardiovascular disease: collaborative analysis of 58 prospective studies

Author: The Emerging Risk Factors Collaboration
Published: 2011
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2. The Emerging Risk Factors Collaboration: analysis of individual data on lipid, inflammatory and other markers in over 1.1 million participants in 104 prospective studies of cardiovascular diseases

Author: The Emerging Risk Factors Collaboration
Published: 2007

3. Diabetes mellitus, fasting blood glucose concentration, and risk of vascular disease: a collaborative meta-analysis of 102 prospective studies

Author: The Emerging Risk Factors Collaboration
Published: 2010
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4. Equalization of four cardiovascular risk algorithms after systematic recalibration : individual-participant meta-analysis of 86 prospective studies

Author: Emerging Risk Factors Collaboration
Subjects: Risk algorithms, Research Support, Non-U.S. Gov't, Calibration, Discrimination, Journal Article, Cardiovascular disease, Cardiology and Cardiovascular Medicine, Risk prediction
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 'recalibration', 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 'high' 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.
Published: 2019

5. Meta-analysis of non-linear exposure-outcome relationships using individual participant data: A comparison of two methods

Author: White, Ian R, Kaptoge, Stephen, Royston, Patrick, Sauerbrei, Willi, Emerging Risk Factors Collaboration, White, Ian R [0000-0002-6718-7661], and Apollo - University of Cambridge Repository
Subjects: Male, prognostic research, Models, Statistical, Coronary Disease, fractional polynomials, multivariate meta-analysis, Middle Aged, random effects models, Body Mass Index, meta-analysis, Meta-Analysis as Topic, Nonlinear Dynamics, Risk Factors, Humans, Female, Mortality
Abstract: Non-linear exposure-outcome relationships such as between body mass index (BMI) and mortality are common. They are best explored as continuous functions using individual participant data from multiple studies. We explore two two-stage methods for meta-analysis of such relationships, where the confounder-adjusted relationship is first estimated in a non-linear regression model in each study, then combined across studies. The "metacurve" approach combines the estimated curves using multiple meta-analyses of the relative effect between a given exposure level and a reference level. The "mvmeta" approach combines the estimated model parameters in a single multivariate meta-analysis. Both methods allow the exposure-outcome relationship to differ across studies. Using theoretical arguments, we show that the methods differ most when covariate distributions differ across studies; using simulated data, we show that mvmeta gains precision but metacurve is more robust to model mis-specification. We then compare the two methods using data from the Emerging Risk Factors Collaboration on BMI, coronary heart disease events, and all-cause mortality (>80 cohorts, >18 000 events). For each outcome, we model BMI using fractional polynomials of degree 2 in each study, with adjustment for confounders. For metacurve, the powers defining the fractional polynomials may be study-specific or common across studies. For coronary heart disease, metacurve with common powers and mvmeta correctly identify a small increase in risk in the lowest levels of BMI, but metacurve with study-specific powers does not. For all-cause mortality, all methods identify a steep U-shape. The metacurve and mvmeta methods perform well in combining complex exposure-disease relationships across studies.
Published: 2019

6. Cardiovascular Risk Factors Associated with Venous Thromboembolism

Author: Emerging Risk Factors Collaboration
Subjects: Journal Article, Cardiology and Cardiovascular Medicine
Abstract: Importance: It is uncertain to what extent established cardiovascular risk factors are associated with venous thromboembolism (VTE). Objective: To estimate the associations of major cardiovascular risk factors with VTE, ie, deep vein thrombosis and pulmonary embolism. Design, Setting, and Participants: This study included individual participant data mostly from essentially population-based cohort studies from the Emerging Risk Factors Collaboration (ERFC; 731728 participants; 75 cohorts; years of baseline surveys, February 1960 to June 2008; latest date of follow-up, December 2015) and the UK Biobank (421537 participants; years of baseline surveys, March 2006 to September 2010; latest date of follow-up, February 2016). Participants without cardiovascular disease at baseline were included. Data were analyzed from June 2017 to September 2018. Exposures: A panel of several established cardiovascular risk factors. Main Outcomes and Measures: Hazard ratios (HRs) per 1-SD higher usual risk factor levels (or presence/absence). Incident fatal outcomes in ERFC (VTE, 1041; coronary heart disease [CHD], 25131) and incident fatal/nonfatal outcomes in UK Biobank (VTE, 2321; CHD, 3385). Hazard ratios were adjusted for age, sex, smoking status, diabetes, and body mass index (BMI). Results: Of the 731728 participants from the ERFC, 403396 (55.1%) were female, and the mean (SD) age at the time of the survey was 51.9 (9.0) years; of the 421537 participants from the UK Biobank, 233699 (55.4%) were female, and the mean (SD) age at the time of the survey was 56.4 (8.1) years. Risk factors for VTE included older age (ERFC: HR per decade, 2.67; 95% CI, 2.45-2.91; UK Biobank: HR, 1.81; 95% CI, 1.71-1.92), current smoking (ERFC: HR, 1.38; 95% CI, 1.20-1.58; UK Biobank: HR, 1.23; 95% CI, 1.08-1.40), and BMI (ERFC: HR per 1-SD higher BMI, 1.43; 95% CI, 1.35-1.50; UK Biobank: HR, 1.37; 95% CI, 1.32-1.41). For these factors, there were similar HRs for pulmonary embolism and deep vein thrombosis in UK Biobank (except adiposity was more strongly associated with pulmonary embolism) and similar HRs for unprovoked vs provoked VTE. Apart from adiposity, these risk factors were less strongly associated with VTE than CHD. There were inconsistent associations of VTEs with diabetes and blood pressure across ERFC and UK Biobank, and there was limited ability to study lipid and inflammation markers. Conclusions and Relevance: Older age, smoking, and adiposity were consistently associated with higher VTE risk..
Published: 2019

7. Equalization of four cardiovascular risk algorithms after systematic recalibration: individual-participant meta-analysis of 86 prospective studies

Author: Epi Methoden, Cancer, Circulatory Health, JC onderzoeksprogramma Methodologie, Cardiovasculaire Epidemiologie, JC onderzoeksprogramma Cardiovasculaire Epidemiologie, Pathologie Pathologen staf, Public Health Epidemiologie, Emerging Risk Factors Collaboration, Epi Methoden, Cancer, Circulatory Health, JC onderzoeksprogramma Methodologie, Cardiovasculaire Epidemiologie, JC onderzoeksprogramma Cardiovasculaire Epidemiologie, Pathologie Pathologen staf, Public Health Epidemiologie, and Emerging Risk Factors Collaboration
Published: 2019

8. Major lipids, apolipoproteins, and risk of vascular disease

Author: Emerging Risk Factors Collaboration, Di Angelantonio E, Sarwar N, Perry P, Kaptoge S, Ray KK, Thompson A, Wood AM, Lewington S, Sattar N, Packard CJ, Collins R, Thompson SG, Tipping RW, Ford CE, Pressel SL, Walldius G, Jungner I, Folsom AR, Chambless LE, Panagiotakos DB, Pitsavos C, Chrysohoou C, Stefanadis C, Knuiman M, Goldbourt U, Benderly M, Tanne D, Whincup PH, Wannamethee SG, Morris RW, Kiechl S, Willeit J, Santer P, Mayr A, Wald N, Ebrahim S, Lawlor DA, Yarnell JW, Gallacher J, Casiglia E, Tikhonoff V, Nietert PJ, Sutherland SE, Bachman DL, Keil JE, Cushman M, Psaty BM, Tracy RP, Tybjaerg Hansen A, Nordestgaard BG, Benn M, Frikke Schmidt R, Giampaoli S, Palmieri L, Vanuzzo D, Pilotto L, Gómez de la Cámara A, Gómez Gerique JA, Simons L, McCallum J, Friedlander Y, Fowkes FG, Lee AJ, Smith FB, Taylor J, Guralnik JM, Phillips CL, Wallace R, Guralnik J, Blazer DG, Khaw KT, Brenner H, Raum E, Müller H, Rothenbacher D, Jansson JH, Wennberg P, Nissinen A, Donfrancesco C, Salomaa V, Harald K, Jousilahti P, Vartiainen E, Woodward M, D'Agostino RB, Wolf PA, Vasan RS, Pencina MJ, Bladbjerg EM, Jørgensen T, Møller L, Jespersen J, Dankner R, Chetrit A, Lubin F, Rosengren A, Wilhelmsen L, Lappas G, Eriksson H, Björkelund C, Lissner L, Bengtsson C, Cremer P, Nagel D, Tilvis RS, Strandberg TE, Rodriguez B, Dekker JM, Nijpels G, Stehouwer CD, Rimm E, Pai JK, Sato S, Iso H, Kitamura A, Noda H, Salonen JT, Nyyssönen K, Tuomainen TP, Deeg DJ, Poppelaars JL, Meade TW, Cooper JA, Hedblad B, Berglund G, Engstrom G, Verschuren WM, Blokstra A, Döring A, Koenig W, Meisinger C, Mraz W, Verschure WM, Bas Bueno de Mesquita H, Kuller LH, Grandits G, Selmer R, Tverdal A, Nystad W, Gillum R, Mussolino M, Hankinson S, Manson J, Knottenbelt C, Bauer KA, Naito Y, Holme I, Nakagawa H, Miura K, Ducimetiere P, Jouven X, Crespo CJ, Garcia Palmieri MR, Amouyel P, Arveiler D, Evans A, Ferrieres J, Schulte H, Assmann G, Shepherd J, Ford I, Cantin B, Lamarche B, Després JP, Dagenais GR, Barrett Connor E, Wingard DL, Bettencourt R, Gudnason V, Aspelund T, Sigurdsson G, Thorsson B, Trevisan M, Witteman J, Kardys I, Breteler M, Hofman A, Tunstall Pedoe H, Tavendale R, Lowe GD, Howard BV, Zhang Y, Best L, Umans J, Ben Shlomo Y, Davey Smith G, Onat A, Njølstad I, Mathiesen EB, Løchen ML, Wilsgaard T, Ingelsson E, Lind L, Giedraitis V, Lannfelt L, Gaziano JM, Stampfer M, Ridker P, Ulmer H, Diem G, Concin H, Tosetto A, Rodeghiero F, Marmot M, Clarke R, Fletcher A, Brunner E, Shipley M, Buring J, Cobbe SM, Robertson M, He Y, Marin Ibañez A, Feskens EJ, Kromhout D, Walker M, Watson S, Erqou S, Orfei L, Pennells L, Perry PL, Alexander M, Wensley F, White IR, Danesh J., PANICO, SALVATORE, Developmental Genetics, EMGO+ - Lifestyle, Overweight and Diabetes, Epidemiology and Data Science, General practice, Psychiatry, EMGO - Lifestyle, overweight and diabetes, Emerging Risk Factors, Collaboration, Di Angelantonio, E, Sarwar, N, Perry, P, Kaptoge, S, Ray, Kk, Thompson, A, Wood, Am, Lewington, S, Sattar, N, Packard, Cj, Collins, R, Thompson, Sg, Tipping, Rw, Ford, Ce, Pressel, Sl, Walldius, G, Jungner, I, Folsom, Ar, Chambless, Le, Panagiotakos, Db, Pitsavos, C, Chrysohoou, C, Stefanadis, C, Knuiman, M, Goldbourt, U, Benderly, M, Tanne, D, Whincup, Ph, Wannamethee, Sg, Morris, Rw, Kiechl, S, Willeit, J, Santer, P, Mayr, A, Wald, N, Ebrahim, S, Lawlor, Da, Yarnell, Jw, Gallacher, J, Casiglia, E, Tikhonoff, V, Nietert, Pj, Sutherland, Se, Bachman, Dl, Keil, Je, Cushman, M, Psaty, Bm, Tracy, Rp, Tybjaerg Hansen, A, Nordestgaard, Bg, Benn, M, Frikke Schmidt, R, Giampaoli, S, Palmieri, L, Panico, Salvatore, Vanuzzo, D, Pilotto, L, Gómez de la Cámara, A, Gómez Gerique, Ja, Simons, L, Mccallum, J, Friedlander, Y, Fowkes, Fg, Lee, Aj, Smith, Fb, Taylor, J, Guralnik, Jm, Phillips, Cl, Wallace, R, Guralnik, J, Blazer, Dg, Khaw, Kt, Brenner, H, Raum, E, Müller, H, Rothenbacher, D, Jansson, Jh, Wennberg, P, Nissinen, A, Donfrancesco, C, Salomaa, V, Harald, K, Jousilahti, P, Vartiainen, E, Woodward, M, D'Agostino, Rb, Wolf, Pa, Vasan, R, Pencina, Mj, Bladbjerg, Em, Jørgensen, T, Møller, L, Jespersen, J, Dankner, R, Chetrit, A, Lubin, F, Rosengren, A, Wilhelmsen, L, Lappas, G, Eriksson, H, Björkelund, C, Lissner, L, Bengtsson, C, Cremer, P, Nagel, D, Tilvis, R, Strandberg, Te, Rodriguez, B, Dekker, Jm, Nijpels, G, Stehouwer, Cd, Rimm, E, Pai, Jk, Sato, S, Iso, H, Kitamura, A, Noda, H, Salonen, Jt, Nyyssönen, K, Tuomainen, Tp, Deeg, Dj, Poppelaars, Jl, Meade, Tw, Cooper, Ja, Hedblad, B, Berglund, G, Engstrom, G, Verschuren, Wm, Blokstra, A, Döring, A, Koenig, W, Meisinger, C, Mraz, W, Verschure, Wm, Bas Bueno de Mesquita, H, Kuller, Lh, Grandits, G, Selmer, R, Tverdal, A, Nystad, W, Gillum, R, Mussolino, M, Hankinson, S, Manson, J, Knottenbelt, C, Bauer, Ka, Naito, Y, Holme, I, Nakagawa, H, Miura, K, Ducimetiere, P, Jouven, X, Crespo, Cj, Garcia Palmieri, Mr, Amouyel, P, Arveiler, D, Evans, A, Ferrieres, J, Schulte, H, Assmann, G, Shepherd, J, Ford, I, Cantin, B, Lamarche, B, Després, Jp, Dagenais, Gr, Barrett Connor, E, Wingard, Dl, Bettencourt, R, Gudnason, V, Aspelund, T, Sigurdsson, G, Thorsson, B, Trevisan, M, Witteman, J, Kardys, I, Breteler, M, Hofman, A, Tunstall Pedoe, H, Tavendale, R, Lowe, Gd, Howard, Bv, Zhang, Y, Best, L, Umans, J, Ben Shlomo, Y, Davey Smith, G, Onat, A, Njølstad, I, Mathiesen, Eb, Løchen, Ml, Wilsgaard, T, Ingelsson, E, Lind, L, Giedraitis, V, Lannfelt, L, Gaziano, Jm, Stampfer, M, Ridker, P, Ulmer, H, Diem, G, Concin, H, Tosetto, A, Rodeghiero, F, Marmot, M, Clarke, R, Fletcher, A, Brunner, E, Shipley, M, Buring, J, Cobbe, Sm, Robertson, M, He, Y, Marin Ibañez, A, Feskens, Ej, Kromhout, D, Walker, M, Watson, S, Erqou, S, Orfei, L, Pennells, L, Perry, Pl, Alexander, M, Wensley, F, White, Ir, and Danesh, J.
Subjects: medicine.medical_specialty, Apolipoprotein B, biology, Triglyceride, business.industry, Vascular disease, Cholesterol, Proportional hazards model, Hazard ratio, Context (language use), General Medicine, 11 Medical And Health Sciences, medicine.disease, Gastroenterology, Article, chemistry.chemical_compound, Endocrinology, chemistry, Internal medicine, General & Internal Medicine, biology.protein, Medicine, lipids (amino acids, peptides, and proteins), Myocardial infarction, business
Abstract: Udgivelsesdato: 2009-Nov-11 CONTEXT: Associations of major lipids and apolipoproteins with the risk of vascular disease have not been reliably quantified. OBJECTIVE: To assess major lipids and apolipoproteins in vascular risk. DESIGN, SETTING, AND PARTICIPANTS: Individual records were supplied on 302,430 people without initial vascular disease from 68 long-term prospective studies, mostly in Europe and North America. During 2.79 million person-years of follow-up, there were 8857 nonfatal myocardial infarctions, 3928 coronary heart disease [CHD] deaths, 2534 ischemic strokes, 513 hemorrhagic strokes, and 2536 unclassified strokes. MAIN OUTCOME MEASURES: Hazard ratios (HRs), adjusted for several conventional factors, were calculated for 1-SD higher values: 0.52 log(e) triglyceride, 15 mg/dL high-density lipoprotein cholesterol (HDL-C), 43 mg/dL non-HDL-C, 29 mg/dL apolipoprotein AI, 29 mg/dL apolipoprotein B, and 33 mg/dL directly measured low-density lipoprotein cholesterol (LDL-C). Within-study regression analyses were adjusted for within-person variation and combined using meta-analysis. RESULTS: The rates of CHD per 1000 person-years in the bottom and top thirds of baseline lipid distributions, respectively, were 2.6 and 6.2 with triglyceride, 6.4 and 2.4 with HDL-C, and 2.3 and 6.7 with non-HDL-C. Adjusted HRs for CHD were 0.99 (95% CI, 0.94-1.05) with triglyceride, 0.78 (95% CI, 0.74-0.82) with HDL-C, and 1.50 (95% CI, 1.39-1.61) with non-HDL-C. Hazard ratios were at least as strong in participants who did not fast as in those who did. The HR for CHD was 0.35 (95% CI, 0.30-0.42) with a combination of 80 mg/dL lower non-HDL-C and 15 mg/dL higher HDL-C. For the subset with apolipoproteins or directly measured LDL-C, HRs were 1.50 (95% CI, 1.38-1.62) with the ratio non-HDL-C/HDL-C, 1.49 (95% CI, 1.39-1.60) with the ratio apo B/apo AI, 1.42 (95% CI, 1.06-1.91) with non-HDL-C, and 1.38 (95% CI, 1.09-1.73) with directly measured LDL-C. Hazard ratios for ischemic stroke were 1.02 (95% CI, 0.94-1.11) with triglyceride, 0.93 (95% CI, 0.84-1.02) with HDL-C, and 1.12 (95% CI, 1.04-1.20) with non-HDL-C. CONCLUSION: Lipid assessment in vascular disease can be simplified by measurement of either total and HDL cholesterol levels or apolipoproteins without the need to fast and without regard to triglyceride.
Published: 2016
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9. Glycated hemoglobin measurement and prediction of cardiovascular disease

Author: Emerging Risk Factors Collaboration, Di Angelantonio E, Gao P, Khan H, Butterworth AS, Wormser D, Kaptoge S, Kondapally Seshasai SR, Thompson A, Sarwar N, Willeit P, Ridker PM, Barr EL, Khaw KT, Psaty BM, Brenner H, Balkau B, Dekker JM, Lawlor DA, Daimon M, Willeit J, Njølstad I, Nissinen A, Brunner EJ, Kuller LH, Price JF, Sundström J, Knuiman MW, Feskens EJ, Verschuren WM, Wald N, Bakker SJ, Whincup PH, Ford I, Goldbourt U, Gómez de la Cámara A, Gallacher J, Simons LA, Rosengren A, Sutherland SE, Björkelund C, Blazer DG, Wassertheil Smoller S, Onat A, Marín Ibañez A, Casiglia E, Jukema JW, Simpson LM, Giampaoli S, Nordestgaard BG, Selmer R, Wennberg P, Kauhanen J, Salonen JT, Dankner R, Barrett Connor E, Kavousi M, Gudnason V, Evans D, Wallace RB, Cushman M, D'Agostino RB Sr, Umans JG, Kiyohara Y, Nakagawa H, Sato S, Gillum RF, Folsom AR, van der Schouw YT, Moons KG, Griffin SJ, Sattar N, Wareham NJ, Selvin E, Thompson SG, Danesh J. Collaborators Simpson LM, Coresh J, Wagenknecht L, Shaw JE, Zimmet PZ, Magliano D, Wannamethee SG, Morris RW, Kiechl S, Santer P, Bonora E, Casas JP, Ebrahim S, Ben Shlomo Y, Yarnell JW, Elwood P, Bachman DL, Nietert PJ, Håheim LL, Søgaard AJ, Tybjaerg Hansen A, Frikke Schmidt R, Benn M, Palmieri L, Vanuzzo D, Bonnet F, Copin N, Roussel R, Gómez Gerique JA, Rubio Herrera MA, Gutiérrez Fuentes JA, Friedlander Y, McCallum J, Simons J, Lee AJ, McLachlan S, Taylor JO, Guralnik JM, Phillips CL, Evans DA, Kohout F, Cohen H, George L, Fillenbaum G, McGloin JM, Khaw K., Schöttker B, Müller H, Rothenbacher D, Jansson J., Hallmans G, Tuomilehto J, Donfrancesco C, Woodward M, Oizumi T, Kayama T, Kato T, Danker R, Chetrit A, Wilhelmsen L, Eriksson H, Lappas G, Bengtsson C, Lissner L, Skoog I, Cremer P, Arima H, Ninomiya T, Hata J, Nijpels G, Stehouwer CD, Tuomainen T., Voutilainen S, Kurl S, de Boer IH, Bertoni AG, Veschuren WM, Dullaart RP, Lambers Heerspink HJ, Hilege HL, Trompet S, Stott DJ, Dagenais GR, Cantin B, Dehghan D, Hofman A, Franco OH, Tunstall Pedoe H, Lee E, Best L, Howard BV, Can G, Ademoğlu E, Sakurai M, Nakamura K, Morikawa Y, Løchen M., Mathiesen EB, Wilsgaard T, Byberg L, Cederholm T, Olsson E, Pradhan AD, Cook NR, Kromhout D, Walker M, Watson S, Burgess S, Gregson J, Harshfield E, Pennells L, Spackman S, Warnakula S, Wood AM, Danesh J., PANICO, SALVATORE, Emerging Risk Factors, Collaboration, Di Angelantonio, E, Gao, P, Khan, H, Butterworth, A, Wormser, D, Kaptoge, S, Kondapally Seshasai, Sr, Thompson, A, Sarwar, N, Willeit, P, Ridker, Pm, Barr, El, Khaw, Kt, Psaty, Bm, Brenner, H, Balkau, B, Dekker, Jm, Lawlor, Da, Daimon, M, Willeit, J, Njølstad, I, Nissinen, A, Brunner, Ej, Kuller, Lh, Price, Jf, Sundström, J, Knuiman, Mw, Feskens, Ej, Verschuren, Wm, Wald, N, Bakker, Sj, Whincup, Ph, Ford, I, Goldbourt, U, Gómez de la Cámara, A, Gallacher, J, Simons, La, Rosengren, A, Sutherland, Se, Björkelund, C, Blazer, Dg, Wassertheil Smoller, S, Onat, A, Marín Ibañez, A, Casiglia, E, Jukema, Jw, Simpson, Lm, Giampaoli, S, Nordestgaard, Bg, Selmer, R, Wennberg, P, Kauhanen, J, Salonen, Jt, Dankner, R, Barrett Connor, E, Kavousi, M, Gudnason, V, Evans, D, Wallace, Rb, Cushman, M, D'Agostino RB, Sr, Umans, Jg, Kiyohara, Y, Nakagawa, H, Sato, S, Gillum, Rf, Folsom, Ar, van der Schouw, Yt, Moons, Kg, Griffin, Sj, Sattar, N, Wareham, Nj, Selvin, E, Thompson, Sg, Danesh J., Collaborators Simpson LM, Coresh, J, Wagenknecht, L, Shaw, Je, Zimmet, Pz, Magliano, D, Wannamethee, Sg, Morris, Rw, Kiechl, S, Santer, P, Bonora, E, Casas, Jp, Ebrahim, S, Ben Shlomo, Y, Yarnell, Jw, Elwood, P, Bachman, Dl, Nietert, Pj, Håheim, Ll, Søgaard, Aj, Tybjaerg Hansen, A, Frikke Schmidt, R, Benn, M, Palmieri, L, Vanuzzo, D, Panico, Salvatore, Bonnet, F, Copin, N, Roussel, R, Gómez Gerique, Ja, Rubio Herrera, Ma, Gutiérrez Fuentes, Ja, Friedlander, Y, Mccallum, J, Simons, J, Lee, Aj, Mclachlan, S, Taylor, Jo, Guralnik, Jm, Phillips, Cl, Evans, Da, Kohout, F, Cohen, H, George, L, Fillenbaum, G, Mcgloin, Jm, Khaw, K., Schöttker, B, Müller, H, Rothenbacher, D, Jansson, J., Hallmans, G, Tuomilehto, J, Donfrancesco, C, Woodward, M, Oizumi, T, Kayama, T, Kato, T, Danker, R, Chetrit, A, Wilhelmsen, L, Eriksson, H, Lappas, G, Bengtsson, C, Lissner, L, Skoog, I, Cremer, P, Arima, H, Ninomiya, T, Hata, J, Nijpels, G, Stehouwer, Cd, Tuomainen, T., Voutilainen, S, Kurl, S, de Boer, Ih, Bertoni, Ag, Veschuren, Wm, Dullaart, Rp, Lambers Heerspink, Hj, Hilege, Hl, Trompet, S, Stott, Dj, Dagenais, Gr, Cantin, B, Dehghan, D, Hofman, A, Franco, Oh, Tunstall Pedoe, H, Lee, E, Best, L, Howard, Bv, Can, G, Ademoğlu, E, Sakurai, M, Nakamura, K, Morikawa, Y, Løchen, M., Mathiesen, Eb, Wilsgaard, T, Byberg, L, Cederholm, T, Olsson, E, Pradhan, Ad, Cook, Nr, Kromhout, D, Walker, M, Watson, S, Burgess, S, Gregson, J, Harshfield, E, Pennells, L, Spackman, S, Warnakula, S, Wood, Am, and Danesh, J.
Published: 2014

10. Association of Cardiometabolic Multimorbidity With Mortality

Author: Di Angelantonio, Emanuele, Kaptoge, Stephen, Wormser, David, Willeit, Peter, Butterworth, Adam S, Bansal, Narinder, O'Keeffe, Linda M, Gao, Pei, Wood, Angela M, Burgess, Stephen, Freitag, Daniel F, Pennells, Lisa, Peters, Sanne A, Hart, Carole L, Håheim, Lise Lund, Gillum, Richard F, Nordestgaard, Børge G, Psaty, Bruce M, Yeap, Bu B, Knuiman, Matthew W, Nietert, Paul J, Kauhanen, Jussi, Salonen, Jukka T, Kuller, Lewis H, Simons, Leon A, van der Schouw, Yvonne T, Barrett-Connor, Elizabeth, Selmer, Randi, Crespo, Carlos J, Rodriguez, Beatriz, Verschuren, W M Monique, Salomaa, Veikko, Svärdsudd, Kurt, van der Harst, Pim, Björkelund, Cecilia, Wilhelmsen, Lars, Wallace, Robert B, Brenner, Hermann, Amouyel, Philippe, Barr, Elizabeth L M, Iso, Hiroyasu, Onat, Altan, Trevisan, Maurizio, D'Agostino, Ralph B, Cooper, Cyrus, Kavousi, Maryam, Welin, Lennart, Roussel, Ronan, Hu, Frank B, Sato, Shinichi, and Emerging Risk Factors Collaboration
Subjects: Adult, Male, Research Support, Non-U.S. Gov't, Myocardial Infarction, Comorbidity, Middle Aged, Stroke, Life Expectancy, Risk Factors, Diabetes Mellitus, Journal Article, Humans, Female, Mortality, Aged
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.
Published: 2015

11. Separate and combined associations of body-mass index andabdominal adiposity with cardiovascular disease: collaborative analysis of 58prospective studies

Author: Emerging Risk Factors Collaboration, Wormser D, Kaptoge S, Di Angelantonio E, Wood AM, Pennells L, Thompson A, Sarwar N, Kizer JR, Lawlor DA, Nordestgaard BG, Ridker P, Salomaa V, Stevens J, Woodward M, Sattar N, Collins R, Thompson SG, Whitlock G, Danesh J., PANICO, SALVATORE, Emerging Risk Factors, Collaboration, Wormser, D, Kaptoge, S, Di Angelantonio, E, Wood, Am, Pennells, L, Thompson, A, Sarwar, N, Kizer, Jr, Lawlor, Da, Nordestgaard, Bg, Ridker, P, Salomaa, V, Stevens, J, Woodward, M, Sattar, N, Collins, R, Thompson, Sg, Whitlock, G, Danesh, J., and Panico, Salvatore
Published: 2011

12. C-reactive protein concentration and risk of coronary heart disease, stroke, and mortality: an individual participant meta-analysis

Author: Emerging Risk Factors Collaboration, Kaptoge S, Di Angelantonio E, Lowe G, Pepys MB, Thompson SG, Collins R, Danesh JTipping RW, Ford CE, Pressel SL, Walldius G, Jungner I, Folsom AR, Chambless L, Ballantyne CM, Panagiotakos D, Pitsavos C, Chrysohoou C, Stefanadis C, Knuiman MW, Goldbourt U, Benderly M, Tanne D, Whincup P, Wannamethee SG, Morris RW, Kiechl S, Willeit J, Mayr A, Schett G, Wald N, Ebrahim S, Lawlor D, Yarnell J, Gallacher J, Casiglia E, Tikhonoff V, Nietert PJ, Sutherland SE, Bachman DL, Keil JE, Cushman M, Psaty BM, Tracy R, Tybjaerg Hansen A, Nordestgaard BG, Zacho J, Frikke Schmidt R, Giampaoli S, Palmieri L, Vanuzzo D, Pilotto L, de la Cámara AG, Gerique JA, Simons L, McCallum J, Friedlander Y, Fowkes FG, Lee A, Taylor J, Guralnik JM, Phillips CL, Wallace RB, Blazer DG, Khaw KT, Brenner H, Raum E, Müller H, Rothenbacher D, Jansson JH, Wennberg P, Nissinen A, Donfrancesco C, Salomaa V, Harald K, Jousilahti P, Vartiainen E, Woodward M, D'Agostino RB, Wolf PA, Vasan RS, Benjamin EJ, Bladbjerg EM, Jørgensen T, Møller L, Jespersen J, Dankner R, Chetrit A, Lubin F, Rosengren A, Wilhelmsen L, Lappas G, Eriksson H, Björkelund C, Lissner L, Bengtsson C, Cremer P, Nagel D, Tilvis RS, Strandberg TE, Kiyohara Y, Arima H, Doi Y, Ninomiya T, Rodriguez B, Dekker J, Nijpels G, Stehouwer CD, Rimm E, Pai JK, Sato S, Iso H, Kitamura A, Noda H, Salonen JT, Nyyssönen K, Tuomainen TP, Laukkanen JA, Deeg DJ, Bremmer MA, Meade TW, Cooper JA, Hedblad B, Berglund G, Engström G, Verschuren WM, Blokstra A, Shea S, Döring A, Koenig W, Meisinger C, Bueno de Mesquita HB, Kuller LH, Grandits G, Selmer R, Tverdal A, Nystad W, Gillum RF, Mussolino M, Hankinson S, Manson JE, Knottenbelt C, Bauer KA, Davidson K, Kirkland S, Shaffer J, Korin MR, Naito Y, Holme I, Nakagawa H, Miura K, Ducimetiere P, Jouven X, Luc G, Crespo CJ, Garcia Palmieri MR, Amouyel P, Arveiler D, Evans A, Ferrieres J, Schulte H, Assmann G, Packard CJ, Sattar N, Westendorp RG, Buckley BM, Cantin B, Lamarche B, Després JP, Dagenais GR, Barrett Connor E, Wingard DL, Bettencourt RR, Gudnason V, Aspelund T, Sigurdsson G, Thorsson B, Trevisan M, Witteman J, Kardys I, Breteler MM, Hofman A, Tunstall Pedoe H, Tavendale R, Howard BV, Zhang Y, Best L, Umans J, Ben Shlomo Y, Davey Smith G, Onat A, Njølstad I, Mathiesen EB, Løchen ML, Wilsgaard T, Ingelsson E, Basu S, Cederholm T, Byberg L, Gaziano JM, Stampfer M, Ridker PM, Ulmer H, Diem G, Concin H, Tosetto A, Rodeghiero F, Wassertheil Smoller S, Marmot IM, Clarke R, Fletcher A, Brunner E, Shipley M, Buring J, Shepherd J, Cobbe S, Ford I, Robertson M, He Y, Ibañez AM, Feskens EJ, Walker M, Watson S, Erqou S, Lewington S, Pennells L, Perry PL, Ray KK, Sarwar N, Alexander M, Thompson A, White IR, Wood AM, Danesh J., PANICO, SALVATORE, Interne Geneeskunde, MUMC+: MA Interne Geneeskunde (3), RS: CARIM School for Cardiovascular Diseases, General practice, EMGO - Lifestyle, overweight and diabetes, Psychiatry, EMGO - Mental health, Emerging Risk Factors, Collaboration, Kaptoge, S, Di Angelantonio, E, Lowe, G, Pepys, Mb, Thompson, Sg, Collins, R, Danesh JTipping, Rw, Ford, Ce, Pressel, Sl, Walldius, G, Jungner, I, Folsom, Ar, Chambless, L, Ballantyne, Cm, Panagiotakos, D, Pitsavos, C, Chrysohoou, C, Stefanadis, C, Knuiman, Mw, Goldbourt, U, Benderly, M, Tanne, D, Whincup, P, Wannamethee, Sg, Morris, Rw, Kiechl, S, Willeit, J, Mayr, A, Schett, G, Wald, N, Ebrahim, S, Lawlor, D, Yarnell, J, Gallacher, J, Casiglia, E, Tikhonoff, V, Nietert, Pj, Sutherland, Se, Bachman, Dl, Keil, Je, Cushman, M, Psaty, Bm, Tracy, R, Tybjaerg Hansen, A, Nordestgaard, Bg, Zacho, J, Frikke Schmidt, R, Giampaoli, S, Palmieri, L, Panico, Salvatore, Vanuzzo, D, Pilotto, L, de la Cámara, Ag, Gerique, Ja, Simons, L, Mccallum, J, Friedlander, Y, Fowkes, Fg, Lee, A, Taylor, J, Guralnik, Jm, Phillips, Cl, Wallace, Rb, Blazer, Dg, Khaw, Kt, Brenner, H, Raum, E, Müller, H, Rothenbacher, D, Jansson, Jh, Wennberg, P, Nissinen, A, Donfrancesco, C, Salomaa, V, Harald, K, Jousilahti, P, Vartiainen, E, Woodward, M, D'Agostino, Rb, Wolf, Pa, Vasan, R, Benjamin, Ej, Bladbjerg, Em, Jørgensen, T, Møller, L, Jespersen, J, Dankner, R, Chetrit, A, Lubin, F, Rosengren, A, Wilhelmsen, L, Lappas, G, Eriksson, H, Björkelund, C, Lissner, L, Bengtsson, C, Cremer, P, Nagel, D, Tilvis, R, Strandberg, Te, Kiyohara, Y, Arima, H, Doi, Y, Ninomiya, T, Rodriguez, B, Dekker, J, Nijpels, G, Stehouwer, Cd, Rimm, E, Pai, Jk, Sato, S, Iso, H, Kitamura, A, Noda, H, Salonen, Jt, Nyyssönen, K, Tuomainen, Tp, Laukkanen, Ja, Deeg, Dj, Bremmer, Ma, Meade, Tw, Cooper, Ja, Hedblad, B, Berglund, G, Engström, G, Verschuren, Wm, Blokstra, A, Shea, S, Döring, A, Koenig, W, Meisinger, C, Bueno de Mesquita, Hb, Kuller, Lh, Grandits, G, Selmer, R, Tverdal, A, Nystad, W, Gillum, Rf, Mussolino, M, Hankinson, S, Manson, Je, Knottenbelt, C, Bauer, Ka, Davidson, K, Kirkland, S, Shaffer, J, Korin, Mr, Naito, Y, Holme, I, Nakagawa, H, Miura, K, Ducimetiere, P, Jouven, X, Luc, G, Crespo, Cj, Garcia Palmieri, Mr, Amouyel, P, Arveiler, D, Evans, A, Ferrieres, J, Schulte, H, Assmann, G, Packard, Cj, Sattar, N, Westendorp, Rg, Buckley, Bm, Cantin, B, Lamarche, B, Després, Jp, Dagenais, Gr, Barrett Connor, E, Wingard, Dl, Bettencourt, Rr, Gudnason, V, Aspelund, T, Sigurdsson, G, Thorsson, B, Trevisan, M, Witteman, J, Kardys, I, Breteler, Mm, Hofman, A, Tunstall Pedoe, H, Tavendale, R, Howard, Bv, Zhang, Y, Best, L, Umans, J, Ben Shlomo, Y, Davey Smith, G, Onat, A, Njølstad, I, Mathiesen, Eb, Løchen, Ml, Wilsgaard, T, Ingelsson, E, Basu, S, Cederholm, T, Byberg, L, Gaziano, Jm, Stampfer, M, Ridker, Pm, Ulmer, H, Diem, G, Concin, H, Tosetto, A, Rodeghiero, F, Wassertheil Smoller, S, Marmot, Im, Clarke, R, Fletcher, A, Brunner, E, Shipley, M, Buring, J, Shepherd, J, Cobbe, S, Ford, I, Robertson, M, He, Y, Ibañez, Am, Feskens, Ej, Walker, M, Watson, S, Erqou, S, Lewington, S, Pennells, L, Perry, Pl, Ray, Kk, Sarwar, N, Alexander, M, Thompson, A, White, Ir, Wood, Am, and Danesh, J.
Subjects: Lung Diseases, Male, Databases, Factual, Plasma-fibrinogen, Blood Pressure, Coronary Disease, 030204 cardiovascular system & hematology, Associations, Body Mass Index, Low-density-lipoprotein, Leukocyte Count, 0302 clinical medicine, Risk Factors, Neoplasms, 030212 general & internal medicine, Stroke, Framingham Risk Score, biology, Smoking, 11 Medical And Health Sciences, General Medicine, Articles, Middle Aged, 3. Good health, C-Reactive Protein, Cholesterol, Regression Analysis, low-density lipoprotein cardiovascular-disease nonvascular mortality regression dilution plasma-fibrinogen mendelian randomization independent predictor prospective cohorts vascular-disease inflammation, Female, Risk assessment, medicine.medical_specialty, Alcohol Drinking, Regression dilution, Motor Activity, Risk Assessment, 03 medical and health sciences, Sex Factors, Cardiovascular-disease, General & Internal Medicine, Internal medicine, Diabetes Mellitus, medicine, Humans, Women, Risk factor, Serum Albumin, Triglycerides, Inflammation, Markers, Independent predictor, Interleukin-6, business.industry, Vascular disease, C-reactive protein, Fibrinogen, medicine.disease, Surgery, Relative risk, biology.protein, Nonvascular mortality, business, Biomarkers
Abstract: Udgivelsesdato: 2010-Jan-9 BACKGROUND: Associations of C-reactive protein (CRP) concentration with risk of major diseases can best be assessed by long-term prospective follow-up of large numbers of people. We assessed the associations of CRP concentration with risk of vascular and non-vascular outcomes under different circumstances. METHODS: We meta-analysed individual records of 160 309 people without a history of vascular disease (ie, 1.31 million person-years at risk, 27 769 fatal or non-fatal disease outcomes) from 54 long-term prospective studies. Within-study regression analyses were adjusted for within-person variation in risk factor levels. RESULTS: Log(e) CRP concentration was linearly associated with several conventional risk factors and inflammatory markers, and nearly log-linearly with the risk of ischaemic vascular disease and non-vascular mortality. Risk ratios (RRs) for coronary heart disease per 1-SD higher log(e) CRP concentration (three-fold higher) were 1.63 (95% CI 1.51-1.76) when initially adjusted for age and sex only, and 1.37 (1.27-1.48) when adjusted further for conventional risk factors; 1.44 (1.32-1.57) and 1.27 (1.15-1.40) for ischaemic stroke; 1.71 (1.53-1.91) and 1.55 (1.37-1.76) for vascular mortality; and 1.55 (1.41-1.69) and 1.54 (1.40-1.68) for non-vascular mortality. RRs were largely unchanged after exclusion of smokers or initial follow-up. After further adjustment for fibrinogen, the corresponding RRs were 1.23 (1.07-1.42) for coronary heart disease; 1.32 (1.18-1.49) for ischaemic stroke; 1.34 (1.18-1.52) for vascular mortality; and 1.34 (1.20-1.50) for non-vascular mortality. INTERPRETATION: CRP concentration has continuous associations with the risk of coronary heart disease, ischaemic stroke, vascular mortality, and death from several cancers and lung disease that are each of broadly similar size. The relevance of CRP to such a range of disorders is unclear. Associations with ischaemic vascular disease depend considerably on conventional risk factors and other markers of inflammation. FUNDING: British Heart Foundation, UK Medical Research Council, BUPA Foundation, and GlaxoSmithKline.
Published: 2010
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13. Lipoprotein(a) concentration and the risk of coronary heart disease, stroke, and nonvascular mortality

Author: Emerging Risk Factors Collaboration, Erqou S, Kaptoge S, Perry PL, Di Angelantonio E, Thompson A, White IR, Marcovina SM, Collins R, Thompson SG, Tipping RW, Ford CE, Simpson LM, Walldius G, Jungner I, Folsom AR, Chambless L, Panagiotakos D, Pitsavos C, Chrysohoou C, Stefanadis C, Goldbourt U, Benderly M, Tanne D, Whincup P, Wannamethee SG, Morris RW, Kiechl S, Willeit J, Santer P, Mayr A, Wald N, Ebrahim S, Lawlor D, Yarnell J, Gallacher J, Casiglia E, Tikhonoff V, Nietert PJ, Sutherland SE, Bachman DL, Cushman M, Psaty BM, Tracy R, Tybjaerg Hansen A, Nordestgaard BG, Frikke Schmidt R, Kamstrup PR, Giampaoli S, Palmieri L, Vanuzzo D, Pilotto L, Gómez de la Cámara A, Gómez Gerique JA, Simons L, McCallum J, Friedlander Y, Fowkes FG, Lee A, Smith FB, Taylor J, Guralnik JM, Phillips CL, Wallace RB, Blazer DG, Brenner H, Raum E, Müller H, Rothenbacher D, Jansson JH, Wennberg P, Nissinen A, Donfrancesco C, Salomaa V, Harald K, Jousilahti P, Vartiainen E, Woodward M, D'Agostino RB, Wolf PA, Vasan RS, Pencina MJ, Bladbjerg EM, Jørgensen T, Møller L, Jespersen J, Dankner R, Chetrit A, Lubin F, Rosengren A, Wilhelmsen L, Lappas G, Eriksson H, Björkelund C, Lissner L, Bengtsson C, Cremer P, Nagel D, Tilvis RS, Strandberg TE, Rodriguez B, Dekker J, Nijpels G, Stehouwer CD, Rimm E, Pai JK, Sato S, Iso H, Kitamura A, Noda H, Salonen JT, Nyyssönen K, Tuomainen TP, Deeg DJ, Poppelaars JL, Hedblad B, Berglund G, Engström G, Verschuren WM, Blokstra A, Döring A, Koenig W, Meisinger C, Mraz W, Bueno de Mesquita HB, Kuller LH, Grandits G, Selmer R, Tverdal A, Nystad W, Gillum RF, Mussolino M, Hankinson S, Manson JE, Cooper JA, Bauer KA, Naito Y, Holme I, Nakagawa H, Miura K, Ducimetiere P, Jouven X, Luc G, Crespo CJ, Garcia Palmieri MR, Amouyel P, Arveiler D, Evans A, Ferrieres J, Schulte H, Assmann G, Shepherd J, Packard CJ, Sattar N, Ford I, Cantin B, Lamarche B, Després JP, Dagenais GR, Barrett Connor E, Daniels LB, Laughlin GA, Gudnason V, Aspelund T, Sigurdsson G, Thorsson B, Trevisan M, Witteman J, Kardys I, Breteler MM, Hofman A, Tunstall Pedoe H, Tavendale R, Lowe G, Ben Shlomo Y, Davey Smith G, Howard BV, Zhang Y, Best L, Umans J, Onat A, Njølstad I, Mathiesen EB, Løchen ML, Wilsgaard T, Ingelsson E, Sundström J, Lind L, Lannfelt L, Gaziano JM, Stampfer M, Ridker PM, Ulmer H, Diem G, Concin H, Tosetto A, Rodeghiero F, Marmot M, Clarke R, Fletcher A, Brunner E, Shipley M, Buring J, Cobbe S, Robertson M, He Y, Marin Ibanñez A, Feskens E, Kromhout D, Walker M, Watson S, Lewington S, Orfei L, Pennells L, Ray KK, Sarwar N, Alexander M, Wensley F, Wood AM, Danesh J., PANICO, SALVATORE, Developmental Genetics, EMGO+ - Lifestyle, Overweight and Diabetes, Emerging Risk Factors, Collaboration, Erqou, S, Kaptoge, S, Perry, Pl, Di Angelantonio, E, Thompson, A, White, Ir, Marcovina, Sm, Collins, R, Thompson, Sg, Tipping, Rw, Ford, Ce, Simpson, Lm, Walldius, G, Jungner, I, Folsom, Ar, Chambless, L, Panagiotakos, D, Pitsavos, C, Chrysohoou, C, Stefanadis, C, Goldbourt, U, Benderly, M, Tanne, D, Whincup, P, Wannamethee, Sg, Morris, Rw, Kiechl, S, Willeit, J, Santer, P, Mayr, A, Wald, N, Ebrahim, S, Lawlor, D, Yarnell, J, Gallacher, J, Casiglia, E, Tikhonoff, V, Nietert, Pj, Sutherland, Se, Bachman, Dl, Cushman, M, Psaty, Bm, Tracy, R, Tybjaerg Hansen, A, Nordestgaard, Bg, Frikke Schmidt, R, Kamstrup, Pr, Giampaoli, S, Palmieri, L, Panico, Salvatore, Vanuzzo, D, Pilotto, L, Gómez de la Cámara, A, Gómez Gerique, Ja, Simons, L, Mccallum, J, Friedlander, Y, Fowkes, Fg, Lee, A, Smith, Fb, Taylor, J, Guralnik, Jm, Phillips, Cl, Wallace, Rb, Blazer, Dg, Brenner, H, Raum, E, Müller, H, Rothenbacher, D, Jansson, Jh, Wennberg, P, Nissinen, A, Donfrancesco, C, Salomaa, V, Harald, K, Jousilahti, P, Vartiainen, E, Woodward, M, D'Agostino, Rb, Wolf, Pa, Vasan, R, Pencina, Mj, Bladbjerg, Em, Jørgensen, T, Møller, L, Jespersen, J, Dankner, R, Chetrit, A, Lubin, F, Rosengren, A, Wilhelmsen, L, Lappas, G, Eriksson, H, Björkelund, C, Lissner, L, Bengtsson, C, Cremer, P, Nagel, D, Tilvis, R, Strandberg, Te, Rodriguez, B, Dekker, J, Nijpels, G, Stehouwer, Cd, Rimm, E, Pai, Jk, Sato, S, Iso, H, Kitamura, A, Noda, H, Salonen, Jt, Nyyssönen, K, Tuomainen, Tp, Deeg, Dj, Poppelaars, Jl, Hedblad, B, Berglund, G, Engström, G, Verschuren, Wm, Blokstra, A, Döring, A, Koenig, W, Meisinger, C, Mraz, W, Bueno de Mesquita, Hb, Kuller, Lh, Grandits, G, Selmer, R, Tverdal, A, Nystad, W, Gillum, Rf, Mussolino, M, Hankinson, S, Manson, Je, Cooper, Ja, Bauer, Ka, Naito, Y, Holme, I, Nakagawa, H, Miura, K, Ducimetiere, P, Jouven, X, Luc, G, Crespo, Cj, Garcia Palmieri, Mr, Amouyel, P, Arveiler, D, Evans, A, Ferrieres, J, Schulte, H, Assmann, G, Shepherd, J, Packard, Cj, Sattar, N, Ford, I, Cantin, B, Lamarche, B, Després, Jp, Dagenais, Gr, Barrett Connor, E, Daniels, Lb, Laughlin, Ga, Gudnason, V, Aspelund, T, Sigurdsson, G, Thorsson, B, Trevisan, M, Witteman, J, Kardys, I, Breteler, Mm, Hofman, A, Tunstall Pedoe, H, Tavendale, R, Lowe, G, Ben Shlomo, Y, Davey Smith, G, Howard, Bv, Zhang, Y, Best, L, Umans, J, Onat, A, Njølstad, I, Mathiesen, Eb, Løchen, Ml, Wilsgaard, T, Ingelsson, E, Sundström, J, Lind, L, Lannfelt, L, Gaziano, Jm, Stampfer, M, Ridker, Pm, Ulmer, H, Diem, G, Concin, H, Tosetto, A, Rodeghiero, F, Marmot, M, Clarke, R, Fletcher, A, Brunner, E, Shipley, M, Buring, J, Cobbe, S, Robertson, M, He, Y, Marin Ibanñez, A, Feskens, E, Kromhout, D, Walker, M, Watson, S, Lewington, S, Orfei, L, Pennells, L, Ray, Kk, Sarwar, N, Alexander, M, Wensley, F, Wood, Am, and Danesh, J.
Subjects: medicine.medical_specialty, Context (language use), Coronary Disease, Article, Risk Factors, General & Internal Medicine, Internal medicine, Cause of Death, medicine, Humans, Myocardial infarction, Prospective cohort study, Stroke, biology, business.industry, 11 Medical And Health Sciences, General Medicine, Lipoprotein(a), medicine.disease, Surgery, Relative risk, Nested case-control study, biology.protein, business, Cohort study
Abstract: Udgivelsesdato: 2009-Jul-22 CONTEXT: Circulating concentration of lipoprotein(a) (Lp[a]), a large glycoprotein attached to a low-density lipoprotein-like particle, may be associated with risk of coronary heart disease (CHD) and stroke. OBJECTIVE: To assess the relationship of Lp(a) concentration with risk of major vascular and nonvascular outcomes. STUDY SELECTION: Long-term prospective studies that recorded Lp(a) concentration and subsequent major vascular morbidity and/or cause-specific mortality published between January 1970 and March 2009 were identified through electronic searches of MEDLINE and other databases, manual searches of reference lists, and discussion with collaborators. DATA EXTRACTION: Individual records were provided for each of 126,634 participants in 36 prospective studies. During 1.3 million person-years of follow-up, 22,076 first-ever fatal or nonfatal vascular disease outcomes or nonvascular deaths were recorded, including 9336 CHD outcomes, 1903 ischemic strokes, 338 hemorrhagic strokes, 751 unclassified strokes, 1091 other vascular deaths, 8114 nonvascular deaths, and 242 deaths of unknown cause. Within-study regression analyses were adjusted for within-person variation and combined using meta-analysis. Analyses excluded participants with known preexisting CHD or stroke at baseline. DATA SYNTHESIS: Lipoprotein(a) concentration was weakly correlated with several conventional vascular risk factors and it was highly consistent within individuals over several years. Associations of Lp(a) with CHD risk were broadly continuous in shape. In the 24 cohort studies, the rates of CHD in the top and bottom thirds of baseline Lp(a) distributions, respectively, were 5.6 (95% confidence interval [CI], 5.4-5.9) per 1000 person-years and 4.4 (95% CI, 4.2-4.6) per 1000 person-years. The risk ratio for CHD, adjusted for age and sex only, was 1.16 (95% CI, 1.11-1.22) per 3.5-fold higher usual Lp(a) concentration (ie, per 1 SD), and it was 1.13 (95% CI, 1.09-1.18) following further adjustment for lipids and other conventional risk factors. The corresponding adjusted risk ratios were 1.10 (95% CI, 1.02-1.18) for ischemic stroke, 1.01 (95% CI, 0.98-1.05) for the aggregate of nonvascular mortality, 1.00 (95% CI, 0.97-1.04) for cancer deaths, and 1.00 (95% CI, 0.95-1.06) for nonvascular deaths other than cancer. CONCLUSION: Under a wide range of circumstances, there are continuous, independent, and modest associations of Lp(a) concentration with risk of CHD and stroke that appear exclusive to vascular outcomes.
Published: 2009
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14. Glycated hemoglobin measurement and prediction of cardiovascular disease

Author: Di Angelantonio, Emanuele, Gao, Pei, Khan, Hassan, Butterworth, Adam S., Wormser, David, Kaptoge, Stephen, Seshasai, Sreenivasa Rao Kondapally, Thompson, Alex, Sarwar, Nadeem, Willeit, Peter, Ridker, Paul M., Barr, Elizabeth L. M., Khaw, Kay-Tee, Psaty, Bruce M., Brenner, Hermann, Balkau, Beverley, Dekker, Jacqueline M., Lawlor, Debbie A., Daimon, Makoto, Willeit, Johann, Njolstad, Inger, Nissinen, Aulikki, Brunner, Eric J., Kuller, Lewis H., Price, Jackie F., Sundstrom, Johan, Knuiman, Matthew W., Feskens, Edith J. M., Verschuren, W. M. M., Wald, Nicholas, Bakker, Stephan J. L., Whincup, Peter H., Ford, Ian, Goldbourt, Uri, Gomez-de-la-Camara, Agustin, Gallacher, John, Simons, Leon A., Rosengren, Annika, Sutherland, Susan E., Bjorkelund, Cecilia, Blazer, Dan G., Wassertheil-Smoller, Sylvia, Onat, Altan, Ibanez, Alejandro Marin, Casiglia, Edoardo, Jukema, J. Wouter, Simpson, Lara M., Giampaoli, Simona, Nordestgaard, Borge G., Selmer, Randi, Wennberg, Patrik, Kauhanen, Jussi, Salonen, Jukka T., Dankner, Rachel, Barrett-Connor, Elizabeth, Kavousi, Maryam, Gudnason, Vilmundur, Evans, Denis, Wallace, Robert B., Cushman, Mary, D'Agostino, Ralph B., Sr., Umans, Jason G., Kiyohara, Yutaka, Nakagawa, Hidaeki, Sato, Shinichi, Gillum, Richard F., Folsom, Aaron R., van der Schouw, Yvonne T., Moons, Karel G., Griffin, Simon J., Sattar, Naveed, Wareham, Nicholas J., Selvin, Elizabeth, Thompson, Simon G., Emerging Risk Factors Collaboration, Stehouwer, Coen, Danesh, John, Di Angelantonio, Emanuele [0000-0001-8776-6719], Butterworth, Adam [0000-0002-6915-9015], Kaptoge, Stephen [0000-0002-1155-4872], Khaw, Kay-Tee [0000-0002-8802-2903], Griffin, Simon [0000-0002-2157-4797], Wareham, Nicholas [0000-0003-1422-2993], Thompson, Simon [0000-0002-5274-7814], Danesh, John [0000-0003-1158-6791], Apollo - University of Cambridge Repository, Groningen Institute for Organ Transplantation (GIOT), Lifestyle Medicine (LM), Groningen Kidney Center (GKC), Epidemiology, Epidemiology and Data Science, EMGO - Lifestyle, overweight and diabetes, Epidemiologie, MUMC+: HVC Pieken Maastricht Studie (9), Interne Geneeskunde, MUMC+: MA Interne Geneeskunde (3), and RS: CARIM - R3 - Vascular biology
Subjects: Male, task-force, endocrine system diseases, Nutrition and Disease, Coronary Disease, GUIDELINES, GLUCOSE, chemistry.chemical_compound, Voeding en Ziekte, adults, guidelines, Prospective Studies, glucose, Prospective cohort study, Framingham Risk Score, biology, General Medicine, Middle Aged, 3. Good health, Stroke, C-Reactive Protein, Predictive value of tests, Cardiology, Female, hormones, hormone substitutes, and hormone antagonists, diabetes-mellitus, metaanalysis, medicine.medical_specialty, risk score, Risk Assessment, Article, SDG 3 - Good Health and Well-being, Predictive Value of Tests, Internal medicine, Diabetes mellitus, medicine, Diabetes Mellitus, Humans, METAANALYSIS, VLAG, Aged, Glycated Hemoglobin, Cholesterol, business.industry, MORTALITY, C-reactive protein, Cholesterol, HDL, nutritional and metabolic diseases, DIABETES-MELLITUS, ADULTS, medicine.disease, RISK SCORE, mortality, Endocrinology, chemistry, statistical-methods, biology.protein, STATISTICAL-METHODS, Hemoglobin, Glycated hemoglobin, business, TASK-FORCE
Abstract: IMPORTANCE The value of measuring levels of glycated hemoglobin (HbA(1c)) for the prediction of first cardiovascular events is uncertain. OBJECTIVE To determine whether adding information on HbA(1c) values to conventional cardiovascular risk factors is associated with improvement in prediction of cardiovascular disease (CVD) risk. DESIGN, SETTING, AND PARTICIPANTS Analysis of individual-participant data available from 73 prospective studies involving 294 998 participants without a known history of diabetes mellitus or CVD at the baseline assessment. MAIN OUTCOMES AND MEASURES Measures of risk discrimination for CVD outcomes (eg, C-index) and reclassification (eg, net reclassification improvement) of participants across predicted 10-year risk categories of low (= 7.5%) risk. RESULTS During a median follow-up of 9.9 (interquartile range, 7.6-13.2) years, 20 840 incident fatal and nonfatal CVD outcomes (13 237 coronary heart disease and 7603 stroke outcomes) were recorded. In analyses adjusted for several conventional cardiovascular risk factors, there was an approximately J-shaped association between HbA(1c) values and CVD risk. The association between HbA(1c) values and CVD risk changed only slightly after adjustment for total cholesterol and triglyceride concentrations or estimated glomerular filtration rate, but this association attenuated somewhat after adjustment for concentrations of high-density lipoprotein cholesterol and C-reactive protein. The C-index for a CVD risk prediction model containing conventional cardiovascular risk factors alone was 0.7434 (95% CI, 0.7350 to 0.7517). The addition of information on HbA(1c) was associated with a C-index change of 0.0018 (0.0003 to 0.0033) and a net reclassification improvement of 0.42 (-0.63 to 1.48) for the categories of predicted 10-year CVD risk. The improvement provided by HbA(1c) assessment in prediction of CVD risk was equal to or better than estimated improvements for measurement of fasting, random, or postload plasma glucose levels. CONCLUSIONS AND RELEVANCE In a study of individuals without known CVD or diabetes, additional assessment of HbA(1c) values in the context of CVD risk assessment provided little incremental benefit for prediction of CVD risk.
Published: 2014
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15. Assessing risk prediction models using individual participant data from multiple studies

Author: Pennells, Lisa, Kaptoge, Stephen, White, Ian R, Thompson, Simon G, Wood, Angela M, Emerging Risk Factors Collaboration, Kaptoge, Stephen [0000-0002-1155-4872], and Apollo - University of Cambridge Repository
Subjects: Male, Models, Statistical, C index, inverse variance, Coronary Disease, individual participant data, Middle Aged, D measure, Risk Assessment, meta-analysis, risk prediction, C-Reactive Protein, Meta-Analysis as Topic, Risk Factors, Data Interpretation, Statistical, Humans, Female, weighting, Prospective Studies, coronary heart disease, Proportional Hazards Models
Abstract: Individual participant time-to-event data from multiple prospective epidemiologic studies enable detailed investigation into the predictive ability of risk models. Here we address the challenges in appropriately combining such information across studies. Methods are exemplified by analyses of log C-reactive protein and conventional risk factors for coronary heart disease in the Emerging Risk Factors Collaboration, a collation of individual data from multiple prospective studies with an average follow-up duration of 9.8 years (dates varied). We derive risk prediction models using Cox proportional hazards regression analysis stratified by study and obtain estimates of risk discrimination, Harrell's concordance index, and Royston's discrimination measure within each study; we then combine the estimates across studies using a weighted meta-analysis. Various weighting approaches are compared and lead us to recommend using the number of events in each study. We also discuss the calculation of measures of reclassification for multiple studies. We further show that comparison of differences in predictive ability across subgroups should be based only on within-study information and that combining measures of risk discrimination from case-control studies and prospective studies is problematic. The concordance index and discrimination measure gave qualitatively similar results throughout. While the concordance index was very heterogeneous between studies, principally because of differing age ranges, the increments in the concordance index from adding log C-reactive protein to conventional risk factors were more homogeneous.
Published: 2014

16. Meta-analysis of non-linear exposure-outcome relationships using individual participant data: A comparison of two methods.

Author: White, Ian R., Kaptoge, Stephen, Royston, Patrick, Sauerbrei, Willi, and Emerging Risk Factors Collaboration
Subjects: CHAOS theory, CORONARY disease, META-analysis, MORTALITY, BODY mass index, STATISTICAL models
Abstract: Non-linear exposure-outcome relationships such as between body mass index (BMI) and mortality are common. They are best explored as continuous functions using individual participant data from multiple studies. We explore two two-stage methods for meta-analysis of such relationships, where the confounder-adjusted relationship is first estimated in a non-linear regression model in each study, then combined across studies. The "metacurve" approach combines the estimated curves using multiple meta-analyses of the relative effect between a given exposure level and a reference level. The "mvmeta" approach combines the estimated model parameters in a single multivariate meta-analysis. Both methods allow the exposure-outcome relationship to differ across studies. Using theoretical arguments, we show that the methods differ most when covariate distributions differ across studies; using simulated data, we show that mvmeta gains precision but metacurve is more robust to model mis-specification. We then compare the two methods using data from the Emerging Risk Factors Collaboration on BMI, coronary heart disease events, and all-cause mortality (>80 cohorts, >18 000 events). For each outcome, we model BMI using fractional polynomials of degree 2 in each study, with adjustment for confounders. For metacurve, the powers defining the fractional polynomials may be study-specific or common across studies. For coronary heart disease, metacurve with common powers and mvmeta correctly identify a small increase in risk in the lowest levels of BMI, but metacurve with study-specific powers does not. For all-cause mortality, all methods identify a steep U-shape. The metacurve and mvmeta methods perform well in combining complex exposure-disease relationships across studies. [ABSTRACT FROM AUTHOR]
Published: 2019
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17. C-Reactive Protein, Fibrinogen, and Cardiovascular Disease Prediction

Author: Emerging Risk Factors Collaboration, Kaptoge, Stephen, Di Angelantonio, Emanuele, Pennells, Lisa, Wood, Angela M, White, Ian R, Gao, Pei, Walker, Matthew, Thompson, Alexander, Sarwar, Nadeem, Caslake, Muriel, Butterworth, Adam S, Amouyel, Philippe, Assmann, Gerd, Bakker, Stephan JL, Barr, Elizabeth LM, Barrett-Connor, Elizabeth, Benjamin, Emelia J, Björkelund, Cecilia, Brenner, Hermann, Brunner, Eric, Clarke, Robert, Cooper, Jackie A, Cremer, Peter, Cushman, Mary, Dagenais, Gilles R, D'Agostino, Ralph B, Dankner, Rachel, Davey-Smith, George, Deeg, Dorly, Dekker, Jacqueline M, Engström, Gunnar, Folsom, Aaron R, Fowkes, F Gerry R, Gallacher, John, Gaziano, J Michael, Giampaoli, Simona, Gillum, Richard F, Hofman, Albert, Howard, Barbara V, Ingelsson, Erik, Iso, Hiroyasu, Jørgensen, Torben, Kiechl, Stefan, Kitamura, Akihiko, Kiyohara, Yutaka, Koenig, Wolfgang, Kromhout, Daan, Kuller, Lewis H, Lawlor, Debbie A, Meade, Tom W, Nissinen, Aulikki, Nordestgaard, Børge G, Onat, Altan, Panagiotakos, Demosthenes B, Psaty, Bruce M, Rodriguez, Beatriz, Rosengren, Annika, Salomaa, Veikko, Kauhanen, Jussi, Salonen, Jukka T, Shaffer, Jonathan A, Shea, Steven, Ford, Ian, Stehouwer, Coen DA, Strandberg, Timo E, Tipping, Robert W, Tosetto, Alberto, Wassertheil-Smoller, Sylvia, Wennberg, Patrik, Westendorp, Rudi G, Whincup, Peter H, Wilhelmsen, Lars, Woodward, Mark, Lowe, Gordon DO, Wareham, Nicholas J, Khaw, Kay-Tee, Sattar, Naveed, Packard, Chris J, Gudnason, Vilmundur, Ridker, Paul M, Pepys, Mark B, Thompson, Simon G, and Danesh, John
Abstract: BACKGROUND: There is debate about the value of assessing levels of C-reactive protein (CRP) and other biomarkers of inflammation for the prediction of first cardiovascular events. METHODS: We analyzed data from 52 prospective studies that included 246,669 participants without a history of cardiovascular disease to investigate the value of adding CRP or fibrinogen levels to conventional risk factors for the prediction of cardiovascular risk. We calculated measures of discrimination and reclassification during follow-up and modeled the clinical implications of initiation of statin therapy after the assessment of CRP or fibrinogen. RESULTS: The addition of information on high-density lipoprotein cholesterol to a prognostic model for cardiovascular disease that included age, sex, smoking status, blood pressure, history of diabetes, and total cholesterol level increased the C-index, a measure of risk discrimination, by 0.0050. The further addition to this model of information on CRP or fibrinogen increased the C-index by 0.0039 and 0.0027, respectively (P
Published: 2012

18. Separate and combined associations of body-mass index and abdominal adiposity with cardiovascular disease: collaborative analysis of 58 prospective studies

Author: Emerging Risk Factors Collaboration, Wormser, David, Kaptoge, Stephen, Di Angelantonio, Emanuele, Wood, Angela M, Pennells, Lisa, Thompson, Alex, Sarwar, Nadeem, Kizer, Jorge R, Lawlor, Debbie A, Nordestgaard, Børge G, Ridker, Paul, Salomaa, Veikko, Stevens, June, Woodward, Mark, Sattar, Naveed, Collins, Rory, Thompson, Simon G, Whitlock, Gary, and Danesh, John
Subjects: nutritional and metabolic diseases
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.
Published: 2011

19. Association of Cardiometabolic Multimorbidity With Mortality

Author: Cardiovasculaire Epi Team 5, JC onderzoeksprogramma Cardiovasculaire Epidemiologie, Cardiovasculaire Epidemiologie, Public Health Epidemiologie, Di Angelantonio, Emanuele, Kaptoge, Stephen, Wormser, David, Willeit, Peter, Butterworth, Adam S, Bansal, Narinder, O'Keeffe, Linda M, Gao, Pei, Wood, Angela M, Burgess, Stephen, Freitag, Daniel F, Pennells, Lisa, Peters, Sanne A, Hart, Carole L, Håheim, Lise Lund, Gillum, Richard F, Nordestgaard, Børge G, Psaty, Bruce M, Yeap, Bu B, Knuiman, Matthew W, Nietert, Paul J, Kauhanen, Jussi, Salonen, Jukka T, Kuller, Lewis H, Simons, Leon A, van der Schouw, Yvonne T, Barrett-Connor, Elizabeth, Selmer, Randi, Crespo, Carlos J, Rodriguez, Beatriz, Verschuren, W M Monique, Salomaa, Veikko, Svärdsudd, Kurt, van der Harst, Pim, Björkelund, Cecilia, Wilhelmsen, Lars, Wallace, Robert B, Brenner, Hermann, Amouyel, Philippe, Barr, Elizabeth L M, Iso, Hiroyasu, Onat, Altan, Trevisan, Maurizio, D'Agostino, Ralph B, Cooper, Cyrus, Kavousi, Maryam, Welin, Lennart, Roussel, Ronan, Hu, Frank B, Sato, Shinichi, Emerging Risk Factors Collaboration, Cardiovasculaire Epi Team 5, JC onderzoeksprogramma Cardiovasculaire Epidemiologie, Cardiovasculaire Epidemiologie, Public Health Epidemiologie, Di Angelantonio, Emanuele, Kaptoge, Stephen, Wormser, David, Willeit, Peter, Butterworth, Adam S, Bansal, Narinder, O'Keeffe, Linda M, Gao, Pei, Wood, Angela M, Burgess, Stephen, Freitag, Daniel F, Pennells, Lisa, Peters, Sanne A, Hart, Carole L, Håheim, Lise Lund, Gillum, Richard F, Nordestgaard, Børge G, Psaty, Bruce M, Yeap, Bu B, Knuiman, Matthew W, Nietert, Paul J, Kauhanen, Jussi, Salonen, Jukka T, Kuller, Lewis H, Simons, Leon A, van der Schouw, Yvonne T, Barrett-Connor, Elizabeth, Selmer, Randi, Crespo, Carlos J, Rodriguez, Beatriz, Verschuren, W M Monique, Salomaa, Veikko, Svärdsudd, Kurt, van der Harst, Pim, Björkelund, Cecilia, Wilhelmsen, Lars, Wallace, Robert B, Brenner, Hermann, Amouyel, Philippe, Barr, Elizabeth L M, Iso, Hiroyasu, Onat, Altan, Trevisan, Maurizio, D'Agostino, Ralph B, Cooper, Cyrus, Kavousi, Maryam, Welin, Lennart, Roussel, Ronan, Hu, Frank B, Sato, Shinichi, and Emerging Risk Factors Collaboration
Published: 2015

20. Statistical methods for the time-to-event analysis of individual participant data from multiple epidemiological studies

Author: Thompson, S. Kaptoge, S. White, I. Wood, A. Perry, P. Danesh, J. The Emerging Risk Factors Collaboration Thompson, S.G. Kaptoge, S. White, I.R. Wood, A.M. Perry, P.L. Tipping, R.W. Ford, C.E. Simpson, L.M. Walldius, G. Jungner, I. Chambless, L.E. Panagiotakos, D.B. Pitsavos, C. Chrysohoou, C. Stefanadis, C. Knuiman, M. Goldbourt, U. Benderly, M. Tanne, D. Whincup, P.H. Wannamethee, S.G. Morris, R.W. Willeit, J. Kiechl, S. Santer, P. Mayr, A. Lawlor, D.A. Yarnell, J.W.G. Gallacher, J. Casiglia, E. Tikhonoff, V. Nietert, P.J. Sutherland, S.E. Bachman, D.L. Keil, J.E. Cushman, M. Tracy, R.P. Tybjærg-Hansen, A. Nordestgaard, B.G. Benn, M. Frikke- Schmidt, R. Giampaoli, S. Palmieri, L. Panico, S. Vanuzzo, D. Gómez de la Cámara, A. Gómez- Gerique, J.A. Simons, L. McCallum, J. Friedlander, Y. Fowkes, F.G.R. Lee, A.J. Taylor, J. Guralnik, J.M. Wallace, R. Guralnik, J. Blazer, D.G. Guralnik, J.M. Guralnik, J.M. Khaw, K.-T. Brenner, H. Raum, E. Müller, H. Rothenbacher, D. Jansson, J.H. Wennberg, P. Nissinen, A. Donfrancesco, C. Salomaa, V. Harald, K. Jousilahti, P. Vartiainen, E. Woodward, M. D'Agostino, R.B. Vasan, R.S. Pencina, M.J. Bladbjerg, E.M. Jørgensen, T. Jespersen, J. Møller, L. Dankner, R. Chetrit, A. Lubin, F. Rosengren, A. Lappas, G. Björkelund, C. Lissner, L. Bengtsson, C. Cremer, P. Nagel, D. Tilvis, R.S. Strandberg, T.E. Kiyohara, Y. Arima, H. Doi, Y. Ninomiya, T. Rodriguez, B. Dekker, J.M. Nijpels, G. Stehouwer, C.D.A. Rimm, E. Pai, J.K. Sato, S. Kitamura, A. Iso, H. Goldbourt, U. Noda, H. Harald, K. Jousilahti, P. Vartiainen, E. Salonen, J.T. Tuomainen, T.-P. Deeg, D.J.H. Poppelaars, J.L. Meade, T.W. Cooper, J.A. Hedblad, B. Berglund, G. Engstrom, G. Verschuren, W.M.M. Blokstra, A. Cushman, M. Shea, S. Döring, A. Koenig, W. Meisinger, C. Mraz, W. Bas Bueno-de-Mesquita, H. Kuller, L.H. Grandits, G. Selmer, R. Tverdal, A. Nystad, W. Gillum, R. Mussolino, M. Rimm, E. Manson, J.E. Pai, J.K. Meade, T.W. Cooper, J.A. Cooper, J.A. Knottenbelt, C. Bauer, K.A. Naito, Y. Holme, I. Hankinson, S. Tverdal, A. Nystad, W. Nakagawa, H. Miura, K. Ducimetiere, P. Jouven, X. Crespo, C.J. Garcia Palmieri, M.R. Amouyel, P. Arveiler, D. Evans, A. Ferrieres, J. Schulte, H. Assmann, G. Shepherd, J. Packard, C.J. Sattar, N. Ford, I. Cantin, B. Després, J.-P. Dagenais, G.R. Barrett-Connor, E. Wingard, D.L. Bettencourt, R. Gudnason, V. Aspelund, T. Sigurdsson, G. Thorsson, B. Trevisan, M. Witteman, J. Kardys, I. Breteler, M. Hofman, A. Tunstall-Pedoe, H. Tavendale, R. Lowe, G.D.O. Ben-Shlomo, Y. Howard, B.V. Zhang, Y. Umans, J. Onat, A. Davey-Smith, G. Wilsgaard, T. Ingelsson, E. Lind, L. Giedraitis, V. Lannfelt, L. Gaziano, J.M. Ridker, P. Gaziano, J.M. Ridker, P. Ulmer, H. Diem, G. Concin, H. Tosetto, A. Rodeghiero, F. Wassertheil-Smoller, S. Manson, J.E. Marmot, M. Clarke, R. Collins, R. Brunner, E. Shipley, M. Ridker, P. Buring, J. Shepherd, J. Cobbe, S.M. Robertson, M. He, Y. Marín Ibañez, A. Feskens, E.J.M. Kromhout, D. Collins, R. Di Angelantonio, E. Erqou, S. Kaptoge, S. Lewington, S. Orfei, L. Pennells, L. Perry, P.L. Ray, K.K. Sarwar, N. Alexander, M. Thompson, A. Thompson, S.G. Walker, M. Watson, S. Wensley, F. White, I.R. Wood, A.M.
Abstract: Background Meta-analysis of individual participant time-to-event data from multiple prospective epidemiological studies enables detailed investigation of exposure-risk relationships, but involves a number of analytical challenges. Methods This article describes statistical approaches adopted in the Emerging Risk Factors Collaboration, in which primary data from more than 1 million participants in more than 100 prospective studies have been collated to enable detailed analyses of various risk markers in relation to incident cardiovascular disease outcomes. Results Analyses have been principally based on Cox proportional hazards regression models stratified by sex, undertaken in each study separately. Estimates of exposure-risk relationships, initially unadjusted and then adjusted for several confounders, have been combined over studies using meta-analysis. Methods for assessing the shape of exposure-risk associations and the proportional hazards assumption have been developed. Estimates of interactions have also been combined using meta-analysis, keeping separate within-and between-study information. Regression dilution bias caused by measurement error and within-person variation in exposures and confounders has been addressed through the analysis of repeat measurements to estimate corrected regression coefficients. These methods are exemplified by analysis of plasma fibrinogen and risk of coronary heart disease, and Stata code is made available. Conclusion Increasing numbers of meta-analyses of individual participant data from observational data are being conducted to enhance the statistical power and detail of epidemiological studies. The statistical methods developed here can be used to address the needs of such analyses. © The Author 2010; all rights reserved.
Published: 2010

21. Statistical methods for the time-to-event analysis of individual participant data from multiple epidemiological studies

Author: Thompson, S. Kaptoge, S. White, I. Wood, A. Perry, P. Danesh, J. The Emerging Risk Factors Collaboration Thompson, S.G. Kaptoge, S. White, I.R. Wood, A.M. Perry, P.L. Tipping, R.W. Ford, C.E. Simpson, L.M. Walldius, G. Jungner, I. Chambless, L.E. Panagiotakos, D.B. Pitsavos, C. Chrysohoou, C. Stefanadis, C. Knuiman, M. Goldbourt, U. Benderly, M. Tanne, D. Whincup, P.H. Wannamethee, S.G. Morris, R.W. Willeit, J. Kiechl, S. Santer, P. Mayr, A. Lawlor, D.A. Yarnell, J.W.G. Gallacher, J. Casiglia, E. Tikhonoff, V. Nietert, P.J. Sutherland, S.E. Bachman, D.L. Keil, J.E. Cushman, M. Tracy, R.P. Tybjærg-Hansen, A. Nordestgaard, B.G. Benn, M. Frikke- Schmidt, R. Giampaoli, S. Palmieri, L. Panico, S. Vanuzzo, D. Gómez de la Cámara, A. Gómez- Gerique, J.A. Simons, L. McCallum, J. Friedlander, Y. Fowkes, F.G.R. Lee, A.J. Taylor, J. Guralnik, J.M. Wallace, R. Guralnik, J. Blazer, D.G. Guralnik, J.M. Guralnik, J.M. Khaw, K.-T. Brenner, H. Raum, E. Müller, H. Rothenbacher, D. Jansson, J.H. Wennberg, P. Nissinen, A. Donfrancesco, C. Salomaa, V. Harald, K. Jousilahti, P. Vartiainen, E. Woodward, M. D'Agostino, R.B. Vasan, R.S. Pencina, M.J. Bladbjerg, E.M. Jørgensen, T. Jespersen, J. Møller, L. Dankner, R. Chetrit, A. Lubin, F. Rosengren, A. Lappas, G. Björkelund, C. Lissner, L. Bengtsson, C. Cremer, P. Nagel, D. Tilvis, R.S. Strandberg, T.E. Kiyohara, Y. Arima, H. Doi, Y. Ninomiya, T. Rodriguez, B. Dekker, J.M. Nijpels, G. Stehouwer, C.D.A. Rimm, E. Pai, J.K. Sato, S. Kitamura, A. Iso, H. Goldbourt, U. Noda, H. Harald, K. Jousilahti, P. Vartiainen, E. Salonen, J.T. Tuomainen, T.-P. Deeg, D.J.H. Poppelaars, J.L. Meade, T.W. Cooper, J.A. Hedblad, B. Berglund, G. Engstrom, G. Verschuren, W.M.M. Blokstra, A. Cushman, M. Shea, S. Döring, A. Koenig, W. Meisinger, C. Mraz, W. Bas Bueno-de-Mesquita, H. Kuller, L.H. Grandits, G. Selmer, R. Tverdal, A. Nystad, W. Gillum, R. Mussolino, M. Rimm, E. Manson, J.E. Pai, J.K. Meade, T.W. Cooper, J.A. Cooper, J.A. Knottenbelt, C. Bauer, K.A. N and Thompson, S. Kaptoge, S. White, I. Wood, A. Perry, P. Danesh, J. The Emerging Risk Factors Collaboration Thompson, S.G. Kaptoge, S. White, I.R. Wood, A.M. Perry, P.L. Tipping, R.W. Ford, C.E. Simpson, L.M. Walldius, G. Jungner, I. Chambless, L.E. Panagiotakos, D.B. Pitsavos, C. Chrysohoou, C. Stefanadis, C. Knuiman, M. Goldbourt, U. Benderly, M. Tanne, D. Whincup, P.H. Wannamethee, S.G. Morris, R.W. Willeit, J. Kiechl, S. Santer, P. Mayr, A. Lawlor, D.A. Yarnell, J.W.G. Gallacher, J. Casiglia, E. Tikhonoff, V. Nietert, P.J. Sutherland, S.E. Bachman, D.L. Keil, J.E. Cushman, M. Tracy, R.P. Tybjærg-Hansen, A. Nordestgaard, B.G. Benn, M. Frikke- Schmidt, R. Giampaoli, S. Palmieri, L. Panico, S. Vanuzzo, D. Gómez de la Cámara, A. Gómez- Gerique, J.A. Simons, L. McCallum, J. Friedlander, Y. Fowkes, F.G.R. Lee, A.J. Taylor, J. Guralnik, J.M. Wallace, R. Guralnik, J. Blazer, D.G. Guralnik, J.M. Guralnik, J.M. Khaw, K.-T. Brenner, H. Raum, E. Müller, H. Rothenbacher, D. Jansson, J.H. Wennberg, P. Nissinen, A. Donfrancesco, C. Salomaa, V. Harald, K. Jousilahti, P. Vartiainen, E. Woodward, M. D'Agostino, R.B. Vasan, R.S. Pencina, M.J. Bladbjerg, E.M. Jørgensen, T. Jespersen, J. Møller, L. Dankner, R. Chetrit, A. Lubin, F. Rosengren, A. Lappas, G. Björkelund, C. Lissner, L. Bengtsson, C. Cremer, P. Nagel, D. Tilvis, R.S. Strandberg, T.E. Kiyohara, Y. Arima, H. Doi, Y. Ninomiya, T. Rodriguez, B. Dekker, J.M. Nijpels, G. Stehouwer, C.D.A. Rimm, E. Pai, J.K. Sato, S. Kitamura, A. Iso, H. Goldbourt, U. Noda, H. Harald, K. Jousilahti, P. Vartiainen, E. Salonen, J.T. Tuomainen, T.-P. Deeg, D.J.H. Poppelaars, J.L. Meade, T.W. Cooper, J.A. Hedblad, B. Berglund, G. Engstrom, G. Verschuren, W.M.M. Blokstra, A. Cushman, M. Shea, S. Döring, A. Koenig, W. Meisinger, C. Mraz, W. Bas Bueno-de-Mesquita, H. Kuller, L.H. Grandits, G. Selmer, R. Tverdal, A. Nystad, W. Gillum, R. Mussolino, M. Rimm, E. Manson, J.E. Pai, J.K. Meade, T.W. Cooper, J.A. Cooper, J.A. Knottenbelt, C. Bauer, K.A. N
Abstract: Background Meta-analysis of individual participant time-to-event data from multiple prospective epidemiological studies enables detailed investigation of exposure-risk relationships, but involves a number of analytical challenges. Methods This article describes statistical approaches adopted in the Emerging Risk Factors Collaboration, in which primary data from more than 1 million participants in more than 100 prospective studies have been collated to enable detailed analyses of various risk markers in relation to incident cardiovascular disease outcomes. Results Analyses have been principally based on Cox proportional hazards regression models stratified by sex, undertaken in each study separately. Estimates of exposure-risk relationships, initially unadjusted and then adjusted for several confounders, have been combined over studies using meta-analysis. Methods for assessing the shape of exposure-risk associations and the proportional hazards assumption have been developed. Estimates of interactions have also been combined using meta-analysis, keeping separate within-and between-study information. Regression dilution bias caused by measurement error and within-person variation in exposures and confounders has been addressed through the analysis of repeat measurements to estimate corrected regression coefficients. These methods are exemplified by analysis of plasma fibrinogen and risk of coronary heart disease, and Stata code is made available. Conclusion Increasing numbers of meta-analyses of individual participant data from observational data are being conducted to enhance the statistical power and detail of epidemiological studies. The statistical methods developed here can be used to address the needs of such analyses. © The Author 2010; all rights reserved.
Published: 2010

22. Cardiovascular Disease Risk Prediction Factors—Reply

Author: Di Angelantonio, Emanuele, primary, Gao, Pei, additional, Danesh, John, additional, and Emerging Risk Factors Collaboration, for the, additional
Published: 2012
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23. Cardiovascular Disease Risk Prediction Factors—Reply

Author: Emanuele Di Angelantonio, Pei Gao, John Danesh, and for the Emerging Risk Factors Collaboration
Subjects: medicine.medical_specialty, chemistry.chemical_compound, Apolipoprotein B, biology, chemistry, business.industry, Cholesterol, Internal medicine, Disease risk, biology.protein, Medicine, General Medicine, business
Published: 2012
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24. Statistical methods for the time-to-event analysis of individual participant data from multiple epidemiological studies.

Author: Thompson, Simon, Kaptoge, Stephen, White, Ian, Wood, Angela, Perry, Philip, Danesh, John, and Emerging Risk Factors Collaboration
Subjects: META-analysis, EPIDEMIOLOGY, RISK exposure, DISEASE risk factors, SURVIVAL analysis (Biometry), AGE distribution, COMPARATIVE studies, CORONARY disease, EPIDEMIOLOGICAL research, FIBRINOGEN, RESEARCH methodology, MEDICAL cooperation, RESEARCH, SEX distribution, SMOKING, STATISTICS, TIME, DATA analysis, EVALUATION research, PROPORTIONAL hazards models
Abstract: Background: Meta-analysis of individual participant time-to-event data from multiple prospective epidemiological studies enables detailed investigation of exposure-risk relationships, but involves a number of analytical challenges.Methods: This article describes statistical approaches adopted in the Emerging Risk Factors Collaboration, in which primary data from more than 1 million participants in more than 100 prospective studies have been collated to enable detailed analyses of various risk markers in relation to incident cardiovascular disease outcomes.Results: Analyses have been principally based on Cox proportional hazards regression models stratified by sex, undertaken in each study separately. Estimates of exposure-risk relationships, initially unadjusted and then adjusted for several confounders, have been combined over studies using meta-analysis. Methods for assessing the shape of exposure-risk associations and the proportional hazards assumption have been developed. Estimates of interactions have also been combined using meta-analysis, keeping separate within- and between-study information. Regression dilution bias caused by measurement error and within-person variation in exposures and confounders has been addressed through the analysis of repeat measurements to estimate corrected regression coefficients. These methods are exemplified by analysis of plasma fibrinogen and risk of coronary heart disease, and Stata code is made available.Conclusion: Increasing numbers of meta-analyses of individual participant data from observational data are being conducted to enhance the statistical power and detail of epidemiological studies. The statistical methods developed here can be used to address the needs of such analyses. [ABSTRACT FROM AUTHOR]
Published: 2010
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25. Triglyceride-mediated pathways and coronary disease: collaborative analysis of 101 studies

Author: Triglyceride Coronary Disease Genetics Consortium, Emerging Risk Factors Collaboration, Sarwar, N, Sandhu, Ms, Ricketts, Sl, Butterworth, As, Di Angelantonio, E, Boekholdt, Sm, Ouwehand, W, Watkins, H, Samani, Nj, Saleheen, D, Lawlor, D, Reilly, Mp, Hingorani, Ad, Talmud, Pj, Collaborators: Braund PS, Danesh J., Hall, As, Thompson, J, März, W, Sivapalaratnam, S, Soranzo, N, Trip, M, Lawlor, Da, Casas, Jp, Ebrahim, S, Arsenault, Bj, Khaw, Kt, Wareham, Nj, Grallert, H, Illig, T, Humphries, Se, Talmud, T, Rader, Dj, He, J, Clarke, R, Hamsten, A, Hopewell, Jc, Frossard, P, Deloukas, P, Danesh, J, Ye, S, Simpson, Ia, Onat, A, Kömürcü Bayrak, E, Martinelli, Nicola, Olivieri, Oliviero, Girelli, Domenico, Kivimäki, M, Kumari, M, Aouizerat, Be, Baum, L, Campos, H, Chaaba, R, Chen, Bs, Cho, Ey, Evans, D, Hill, J, Hsu, La, Hubacek, Ja, Lai, Cq, Lee, Jh, Klos, K, Liu, H, Masana, L, Melegh, B, Nabika, T, Ribalta, J, Ruiz Narvaez, E, Thomas, Gn, Tomlinson, B, Szalai, C, Vaverkova, H, Yamada, Y, Yang, Y, Tipping, Rw, Ford, Ce, Pressel, Sl, Ballantyne, C, Brautbar, A, Knuiman, M, Whincup, Ph, Wannamethee, Sg, Morris, Rw, Kiechl, S, Willeit, J, Santer, P, Mayr, A, Wald, N, Yarnell, Jw, Gallacher, J, Casiglia, E, Tikhonoff, V, Cushman, M, Psaty, Bm, Tracy, Rp, Tybjaerg Hansen, A, Nordestgaard, Bg, Benn, M, Frikke Schmidt, R, Giampaoli, S, Palmieri, L, Panico, S, Vanuzzo, D, Pilotto, L, de la Cámara AG, Gómez Gerique JA, Simons, L, Mccallum, J, Friedlander, Y, Fowkes, Fg, Lee, Aj, Taylor, J, Guralnik, Jm, Phillips, Cl, Wallace, R, Blazer, Dg, Brenner, H, Raum, E, Müller, H, Rothenbacher, D, Jansson, Jh, Wennberg, P, Nissinen, A, Donfrancesco, C, Salomaa, V, Harald, K, Jousilahti, P, Vartiainen, E, D'Agostino, Rb, Vasan, Rs, Pencina, Mj, Bladbjerg, Em, Jørgensen, T, Møller, L, Jespersen, J, Dankner, R, Chetrit, A, Lubin, F, Björkelund, C, Lissner, L, Bengtsson, C, Cremer, P, Nagel, D, Rodriguez, B, Dekker, Jm, Nijpels, G, Stehouwer, Cd, Sato, S, Iso, H, Kitamura, A, Noda, H, Salonen, Jt, Nyyssönen, K, Tuomainen, Tp, Voutilainen, S, Meade, Tw, Cooper, Ja, Kuller, Lh, Grandits, G, Gillum, R, Mussolino, M, Rimm, E, Hankinson, S, Manson, Ja, Pai, Jk, Bauer, Ka, Naito, Y, Amouyel, P, Arveiler, D, Evans, A, Ferrières, J, Schulte, H, Assmann, G, Packard, Cj, Sattar, N, Westendorp, Rg, Buckley, Bm, Cantin, B, Lamarche, B, Després, Jp, Dagenais, Gr, Barrett Connor, E, Wingard, Dl, Bettencourt, R, Gudnason, V, Aspelund, T, Sigurdsson, G, Thorsson, B, Trevisan, M, Tunstall Pedoe, H, Tavendale, R, Lowe, Gd, Woodward, M, Howard, Bv, Zhang, Y, Best, L, Umans, J, Ben Shlomo, Y, Davey Smith, G, Njølstad, I, Mathiesen, Eb, Løchen, Ml, Wilsgaard, T, Ingelsson, E, Lind, L, Giedraitis, V, Michaëlsson, K, Brunner, E, Shipley, M, Ridker, P, Buring, J, Shepherd, J, Cobbe, Sm, Ford, I, Robertson, M, Ibañez, Am, Feskens, Ej, Kromhout, D, Walker, M, Watson, S, Collins, R, Kaptoge, S, Perry, Pl, Thompson, A, Thompson, Sg, White, Ir, Wood, Am, Danesh, J., ACS - Amsterdam Cardiovascular Sciences, Cardiology, Vascular Medicine, Interne Geneeskunde, MUMC+: MA Interne Geneeskunde (3), RS: CARIM School for Cardiovascular Diseases, Sarwar, N, Sandhu, M, Ricketts, Sl, Butterworth, A, Braund, P, Hall, A, Samani, Nj, Thompson, J, März, W, Ouwehand, W, Sivapalaratnam, S, Soranzo, N, Trip, M, Lawlor, Da, Casas, Jp, Ebrahim, S, Arsenault, Bj, Boekholdt, Sm, Khaw, Kt, Wareham, Nj, Grallert, H, Illig, T, Humphries, Se, Talmud, T, Rader, Dj, He, J, Reilly, Mp, Clarke, R, Hamsten, A, Hopewell, Jc, Watkins, H, Saleheen, D, Frossard, P, Deloukas, P, Danesh, J, Ye, S, Simpson, Ia, Onat, A, Kömürcü Bayrak, E, Martinelli, N, Olivieri, O, Girelli, D, Hingorani, Ad, Kivimäki, M, Kumari, M, Aouizerat, Be, Baum, L, Campos, H, Chaaba, R, Chen, B, Cho, Ey, Evans, D, Hill, J, Hsu, La, Hubacek, Ja, Lai, Cq, Lee, Jh, Klos, K, Liu, H, Masana, L, Melegh, B, Nabika, T, Ribalta, J, Ruiz Narvaez, E, Thomas, Gn, Tomlinson, B, Szalai, C, Vaverkova, H, Yamada, Y, Yang, Y, Kastelein, Jj, Tipping, Rw, Ford, Ce, Pressel, Sl, Ballantyne, C, Brautbar, A, Knuiman, M, Whincup, Ph, Wannamethee, Sg, Morris, Rw, Kiechl, S, Willeit, J, Santer, P, Mayr, A, Wald, N, Yarnell, Jw, Gallacher, J, Casiglia, E, Tikhonoff, V, Cushman, M, Psaty, Bm, Tracy, Rp, Tybjaerg Hansen, A, Nordestgaard, Bg, Benn, M, Frikke Schmidt, R, Giampaoli, S, Palmieri, L, Panico, Salvatore, Vanuzzo, D, Pilotto, L, de la Cámara, Ag, Gómez Gerique, Ja, Simons, L, Mccallum, J, Friedlander, Y, Fowkes, Fg, Lee, Aj, Taylor, J, Guralnik, Jm, Phillips, Cl, Wallace, R, Blazer, Dg, Brenner, H, Raum, E, Müller, H, Rothenbacher, D, Jansson, Jh, Wennberg, P, Nissinen, A, Donfrancesco, C, Salomaa, V, Harald, K, Jousilahti, P, Vartiainen, E, D'Agostino, Rb, Vasan, R, Pencina, Mj, Bladbjerg, Em, Jørgensen, T, Møller, L, Jespersen, J, Dankner, R, Chetrit, A, Lubin, F, Björkelund, C, Lissner, L, Bengtsson, C, Cremer, P, Nagel, D, Rodriguez, B, Dekker, Jm, Nijpels, G, Stehouwer, Cd, Sato, S, Iso, H, Kitamura, A, Noda, H, Salonen, Jt, Nyyssönen, K, Tuomainen, Tp, Voutilainen, S, Meade, Tw, Cooper, Ja, Kuller, Lh, Grandits, G, Gillum, R, Mussolino, M, Rimm, E, Hankinson, S, Manson, Ja, Pai, Jk, Bauer, Ka, Naito, Y, Amouyel, P, Arveiler, D, Evans, A, Ferrières, J, Schulte, H, Assmann, G, Packard, Cj, Sattar, N, Westendorp, Rg, Buckley, Bm, Cantin, B, Lamarche, B, Després, Jp, Dagenais, Gr, Barrett Connor, E, Wingard, Dl, Bettencourt, R, Gudnason, V, Aspelund, T, Sigurdsson, G, Thorsson, B, Trevisan, M, Tunstall Pedoe, H, Tavendale, R, Lowe, Gd, Woodward, M, Howard, Bv, Zhang, Y, Best, L, Umans, J, Ben Shlomo, Y, Davey Smith, G, Njølstad, I, Mathiesen, Eb, Løchen, Ml, Wilsgaard, T, Ingelsson, E, Lind, L, Giedraitis, V, Michaëlsson, K, Brunner, E, Shipley, M, Ridker, P, Buring, J, Shepherd, J, Cobbe, Sm, Ford, I, Robertson, M, Ibañez, Am, Feskens, Ej, Kromhout, D, Walker, M, Watson, S, Collins, R, Di Angelantonio, E, Kaptoge, S, Perry, Pl, Thompson, A, Thompson, Sg, White, Ir, Wood, Am, Lawlor, D, Talmud, Pj, Danesh, J., Epidemiology and Data Science, General practice, and EMGO - Lifestyle, overweight and diabetes
Subjects: Very low-density lipoprotein, Nutrition and Disease, Heart disease, Coronary Disease, Lipoproteins, VLDL, 030204 cardiovascular system & hematology, low-density-lipoprotein apolipoprotein-a-v transfer protein heart-disease myocardial-infarction metabolic syndrome rich lipoproteins risk dyslipidemia association, Bioinformatics, chemistry.chemical_compound, 0302 clinical medicine, triglyceride, APOA5 gene polymorphysm, coronary heart disease, Gene Frequency, Risk Factors, Voeding en Ziekte, Medicine, Myocardial infarction, Promoter Regions, Genetic, risk, 0303 health sciences, Men, Mendelian Randomization Analysis, Articles, General Medicine, Lipids, myocardial-infarction, 3. Good health, Lipoproteins, LDL, Low-density lipoprotein, Lipoproteins, HDL, apolipoprotein-a-v, Receptor, medicine.medical_specialty, Genotype, transfer protein, Snp, Polymorphism, Single Nucleotide, metabolic syndrome, 03 medical and health sciences, Internal medicine, Humans, Particle Size, Apolipoproteins A, Triglycerides, VLAG, 030304 developmental biology, low-density-lipoprotein, Triglyceride, business.industry, dyslipidemia, association, rich lipoproteins, medicine.disease, heart-disease, Apolipoproteins, Endocrinology, chemistry, Apolipoprotein A-V, Metabolic syndrome, business, Dyslipidemia
Abstract: Udgivelsesdato: May-8 BACKGROUND: Whether triglyceride-mediated pathways are causally relevant to coronary heart disease is uncertain. We studied a genetic variant that regulates triglyceride concentration to help judge likelihood of causality. METHODS: We assessed the -1131T>C (rs662799) promoter polymorphism of the apolipoprotein A5 (APOA5) gene in relation to triglyceride concentration, several other risk factors, and risk of coronary heart disease. We compared disease risk for genetically-raised triglyceride concentration (20,842 patients with coronary heart disease, 35,206 controls) with that recorded for equivalent differences in circulating triglyceride concentration in prospective studies (302 430 participants with no history of cardiovascular disease; 12,785 incident cases of coronary heart disease during 2.79 million person-years at risk). We analysed -1131T>C in 1795 people without a history of cardiovascular disease who had information about lipoprotein concentration and diameter obtained by nuclear magnetic resonance spectroscopy. FINDINGS: The minor allele frequency of -1131T>C was 8% (95% CI 7-9). -1131T>C was not significantly associated with several non-lipid risk factors or LDL cholesterol, and it was modestly associated with lower HDL cholesterol (mean difference per C allele 3.5% [95% CI 2.6-4.6]; 0.053 mmol/L [0.039-0.068]), lower apolipoprotein AI (1.3% [0.3-2.3]; 0.023 g/L [0.005-0.041]), and higher apolipoprotein B (3.2% [1.3-5.1]; 0.027 g/L [0.011-0.043]). By contrast, for every C allele inherited, mean triglyceride concentration was 16.0% (95% CI 12.9-18.7), or 0.25 mmol/L (0.20-0.29), higher (p=4.4x10(-24)). The odds ratio for coronary heart disease was 1.18 (95% CI 1.11-1.26; p=2.6x10(-7)) per C allele, which was concordant with the hazard ratio of 1.10 (95% CI 1.08-1.12) per 16% higher triglyceride concentration recorded in prospective studies. -1131T>C was significantly associated with higher VLDL particle concentration (mean difference per C allele 12.2 nmol/L [95% CI 7.7-16.7]; p=9.3x10(-8)) and smaller HDL particle size (0.14 nm [0.08-0.20]; p=7.0x10(-5)), factors that could mediate the effects of triglyceride. INTERPRETATION: These data are consistent with a causal association between triglyceride-mediated pathways and coronary heart disease. FUNDING: British Heart Foundation, UK Medical Research Council, Novartis.
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26. Glycated hemoglobin measurement and prediction of cardiovascular disease

Author: Emerging Risk Factors Collaboration, Di Angelantonio, Emanuele, Gao, Pei, Khan, Hassan, Butterworth, Adam, Wormser, David, Kaptoge, Stephen, Kondapally Seshasai, Sreenivasa Rao, Thompson, Alex, Sarwar, Nadeem, Willeit, Peter, Ridker, Paul M, Barr, Elizabeth LM, Khaw, Kay-Tee, Psaty, Bruce M, Brenner, Hermann, Balkau, Beverley, Dekker, Jacqueline M, Lawlor, Debbie A, Daimon, Makoto, Willeit, Johann, Njølstad, Inger, Nissinen, Aulikki, Brunner, Eric J, Kuller, Lewis H, Price, Jackie F, Sundström, Johan, Knuiman, Matthew W, Feskens, Edith JM, Verschuren, WMM, Wald, Nicholas, Bakker, Stephan JL, Whincup, Peter H, Ford, Ian, Goldbourt, Uri, Gómez-De-La-Cámara, Agustín, Gallacher, John, Simons, Leon A, Rosengren, Annika, Sutherland, Susan E, Björkelund, Cecilia, Blazer, Dan G, Wassertheil-Smoller, Sylvia, Onat, Altan, Marín Ibañez, Alejandro, Casiglia, Edoardo, Jukema, J Wouter, Simpson, Lara M, Giampaoli, Simona, Nordestgaard, Børge G, Selmer, Randi, Wennberg, Patrik, Kauhanen, Jussi, Salonen, Jukka T, Dankner, Rachel, Barrett-Connor, Elizabeth, Kavousi, Maryam, Gudnason, Vilmundur, Evans, Denis, Wallace, Robert B, Cushman, Mary, D'Agostino, Ralph B, Umans, Jason G, Kiyohara, Yutaka, Nakagawa, Hidaeki, Sato, Shinichi, Gillum, Richard F, Folsom, Aaron R, Van Der Schouw, Yvonne T, Moons, Karel G, Griffin, Simon, Sattar, Naveed, Wareham, Nicholas, Selvin, Elizabeth, Thompson, Simon, and Danesh, John
Subjects: Male, Emerging Risk Factors Collaboration, Glycated Hemoglobin A, Cholesterol, HDL, Coronary Disease, Middle Aged, Risk Assessment, 3. Good health, Stroke, C-Reactive Protein, Predictive Value of Tests, Diabetes Mellitus, Humans, Female, Prospective Studies, Aged
Abstract: IMPORTANCE: The value of measuring levels of glycated hemoglobin (HbA1c) for the prediction of first cardiovascular events is uncertain. OBJECTIVE: To determine whether adding information on HbA1c values to conventional cardiovascular risk factors is associated with improvement in prediction of cardiovascular disease (CVD) risk. DESIGN, SETTING, AND PARTICIPANTS: Analysis of individual-participant data available from 73 prospective studies involving 294,998 participants without a known history of diabetes mellitus or CVD at the baseline assessment. MAIN OUTCOMES AND MEASURES: Measures of risk discrimination for CVD outcomes (eg, C-index) and reclassification (eg, net reclassification improvement) of participants across predicted 10-year risk categories of low (

27. Lipoprotein(a) Concentration and the Risk of Coronary Heart Disease, Stroke, and Nonvascular Mortality

Author: Emerging Risk Factors Collaboration, Erqou S, Kaptoge S, Pl, Perry, Di Angelantonio E, Thompson A, Ir, White, Sm, Marcovina, Collins R, Sg, Thompson, and Danesh J

28. Cardiovascular Risk Factors Associated With Venous Thromboembolism

Author: Gregson, John, Kaptoge, Stephen, Bolton, Thomas, Pennells, Lisa, Willeit, Peter, Burgess, Stephen, Bell, Steven, Sweeting, Michael, Rimm, Eric B, Kabrhel, Christopher, Zöller, Bengt, Assmann, Gerd, Gudnason, Vilmundur, Folsom, Aaron R, Arndt, Volker, Fletcher, Astrid, Norman, Paul E, Nordestgaard, Børge G, Kitamura, Akihiko, Mahmoodi, Bakhtawar K, Whincup, Peter H, Knuiman, Matthew, Salomaa, Veikko, Meisinger, Christa, Koenig, Wolfgang, Kavousi, Maryam, Völzke, Henry, Cooper, Jackie A, Ninomiya, Toshiharu, Casiglia, Edoardo, Rodriguez, Beatriz, Ben-Shlomo, Yoav, Després, Jean-Pierre, Simons, Leon, Barrett-Connor, Elizabeth, Björkelund, Cecilia, Notdurfter, Marlene, Kromhout, Daan, Price, Jackie, Sutherland, Susan E, Sundström, Johan, Kauhanen, Jussi, Gallacher, John, Beulens, Joline WJ, Dankner, Rachel, Cooper, Cyrus, Giampaoli, Simona, Deen, Jason F, Gómez De La Cámara, Agustín, Kuller, Lewis H, Rosengren, Annika, Svensson, Peter J, Nagel, Dorothea, Crespo, Carlos J, Brenner, Hermann, Albertorio-Diaz, Juan R, Atkins, Robert, Brunner, Eric J, Shipley, Martin, Njølstad, Inger, Lawlor, Deborah A, Van Der Schouw, Yvonne T, Selmer, Randi Marie, Trevisan, Maurizio, Verschuren, WM Monique, Greenland, Philip, Wassertheil-Smoller, Sylvia, Lowe, Gordon DO, Wood, Angela M, Butterworth, Adam S, Thompson, Simon G, Danesh, John, Di Angelantonio, Emanuele, Meade, Tom, and Emerging Risk Factors Collaboration
Subjects: 2. Zero hunger, Adult, Male, Venous Thrombosis, Smoking, Coronary Disease, Venous Thromboembolism, Middle Aged, 16. Peace & justice, United Kingdom, 3. Good health, Body Mass Index, Cardiovascular Diseases, Risk Factors, Outcome Assessment, Health Care, Diabetes Mellitus, Humans, Female, Obesity, Prospective Studies, Pulmonary Embolism
Abstract: IMPORTANCE: It is uncertain to what extent established cardiovascular risk factors are associated with venous thromboembolism (VTE). OBJECTIVE: To estimate the associations of major cardiovascular risk factors with VTE, ie, deep vein thrombosis and pulmonary embolism. DESIGN, SETTING, AND PARTICIPANTS: This study included individual participant data mostly from essentially population-based cohort studies from the Emerging Risk Factors Collaboration (ERFC; 731 728 participants; 75 cohorts; years of baseline surveys, February 1960 to June 2008; latest date of follow-up, December 2015) and the UK Biobank (421 537 participants; years of baseline surveys, March 2006 to September 2010; latest date of follow-up, February 2016). Participants without cardiovascular disease at baseline were included. Data were analyzed from June 2017 to September 2018. EXPOSURES: A panel of several established cardiovascular risk factors. MAIN OUTCOMES AND MEASURES: Hazard ratios (HRs) per 1-SD higher usual risk factor levels (or presence/absence). Incident fatal outcomes in ERFC (VTE, 1041; coronary heart disease [CHD], 25 131) and incident fatal/nonfatal outcomes in UK Biobank (VTE, 2321; CHD, 3385). Hazard ratios were adjusted for age, sex, smoking status, diabetes, and body mass index (BMI). RESULTS: Of the 731 728 participants from the ERFC, 403 396 (55.1%) were female, and the mean (SD) age at the time of the survey was 51.9 (9.0) years; of the 421 537 participants from the UK Biobank, 233 699 (55.4%) were female, and the mean (SD) age at the time of the survey was 56.4 (8.1) years. Risk factors for VTE included older age (ERFC: HR per decade, 2.67; 95% CI, 2.45-2.91; UK Biobank: HR, 1.81; 95% CI, 1.71-1.92), current smoking (ERFC: HR, 1.38; 95% CI, 1.20-1.58; UK Biobank: HR, 1.23; 95% CI, 1.08-1.40), and BMI (ERFC: HR per 1-SD higher BMI, 1.43; 95% CI, 1.35-1.50; UK Biobank: HR, 1.37; 95% CI, 1.32-1.41). For these factors, there were similar HRs for pulmonary embolism and deep vein thrombosis in UK Biobank (except adiposity was more strongly associated with pulmonary embolism) and similar HRs for unprovoked vs provoked VTE. Apart from adiposity, these risk factors were less strongly associated with VTE than CHD. There were inconsistent associations of VTEs with diabetes and blood pressure across ERFC and UK Biobank, and there was limited ability to study lipid and inflammation markers. CONCLUSIONS AND RELEVANCE: Older age, smoking, and adiposity were consistently associated with higher VTE risk.

29. Association of Cardiometabolic Multimorbidity With Mortality

Author: Di Angelantonio, Emanuele, Kaptoge, Stephen, Wormser, David, Willeit, Peter, Butterworth, Adam S., Bansal, Narinder, O’Keeffe, Linda M., Gao, Pei, Wood, Angela M., Burgess, Stephen, Freitag, Daniel F., Pennells, Lisa, Peters, Sanne A., Hart, Carole L., Haheim, Lise Lund, Gillum, Richard F., Nordestgaard, Børge G., Psaty, Bruce M., Yeap, Bu B., Knuiman, Matthew W., Nietert, Paul J., Kauhanen, Jussi, Salonen, Jukka T., Kuller, Lewis H., Simons, Leon A., Van Der Schouw, Yvonne T., Barrett-Connor, Elizabeth, Selmer, Randi, Crespo, Carlos J., Rodriguez, Beatriz, Verschuren, W. M. Monique, Salomaa, Veikko, Svärdsudd, Kurt, Van Der Harst, Pim, Björkelund, Cecilia, Wilhelmsen, Lars, Wallace, Robert B., Brenner, Hermann, Amouyel, Philippe, Barr, Elizabeth L. M., Iso, Hiroyasu, Onat, Altan, Trevisan, Maurizio, D'Agostino Sr., Ralph B., Cooper, Cyrus, Kavousi, Maryam, Welin, Lennart, Roussel, Ronan, Hu, Frank B., Sato, Shinichi, Davidson, Karina W., Howard, Barbara V., Leening, Maarten J. G., Rosengren, Annika, Dörr, Marcus, Deeg, Dorly J. H., Kiechl, Stefan, Stehouwer, Coen D. A., Nissinen, Aulikki, Giampaoli, Simona, Donfrancesco, Chiara, Kromhout, Daan, Price, Jackie F., Peters, Annette, Meade, Tom W., Casiglia, Edoardo, Lawlor, Debbie A., Gallacher, John, Nagel, Dorothea, Franco, Oscar H., Assmann, Gerd, Dagenais, Gilles R., Jukema, J. Wouter, Sundström, Johan, Woodward, Mark, Brunner, Eric J., Khaw, Kay-Tee, Wareham, Nicholas J., Whitsel, Eric A., Njølstad, Inger, Hedblad, Bo, Wassertheil-Smoller, Sylvia, Engström, Gunnar, Rosamond, Wayne D., Selvin, Elizabeth, Sattar, Naveed, Thompson, Simon G., Danesh, John, and Emerging Risk Factors Collaboration
Subjects: Cardiovascular system--Diseases, Epidemiology, Diabetes, Mortality, Medical sciences, 3. Good health
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.

30. Assessing risk prediction models using individual participant data from multiple studies

Author: Pennells, Lisa, Kaptoge, Stephen, White, Ian R, Thompson, Simon G, Wood, Angela M, and Emerging Risk Factors Collaboration
Subjects: Male, Models, Statistical, C index, inverse variance, Coronary Disease, individual participant data, Middle Aged, D measure, Risk Assessment, meta-analysis, risk prediction, C-Reactive Protein, Meta-Analysis as Topic, Risk Factors, Data Interpretation, Statistical, Humans, Female, weighting, Prospective Studies, coronary heart disease, 10. No inequality, Proportional Hazards Models
Abstract: Individual participant time-to-event data from multiple prospective epidemiologic studies enable detailed investigation into the predictive ability of risk models. Here we address the challenges in appropriately combining such information across studies. Methods are exemplified by analyses of log C-reactive protein and conventional risk factors for coronary heart disease in the Emerging Risk Factors Collaboration, a collation of individual data from multiple prospective studies with an average follow-up duration of 9.8 years (dates varied). We derive risk prediction models using Cox proportional hazards regression analysis stratified by study and obtain estimates of risk discrimination, Harrell's concordance index, and Royston's discrimination measure within each study; we then combine the estimates across studies using a weighted meta-analysis. Various weighting approaches are compared and lead us to recommend using the number of events in each study. We also discuss the calculation of measures of reclassification for multiple studies. We further show that comparison of differences in predictive ability across subgroups should be based only on within-study information and that combining measures of risk discrimination from case-control studies and prospective studies is problematic. The concordance index and discrimination measure gave qualitatively similar results throughout. While the concordance index was very heterogeneous between studies, principally because of differing age ranges, the increments in the concordance index from adding log C-reactive protein to conventional risk factors were more homogeneous.

31. C-reactive protein, fibrinogen, and cardiovascular risk.

Author: Kaptoge S, Thompson SG, Danesh J, Emerging Risk Factors Collaboration, Kaptoge, Stephen, Thompson, Simon G, and Danesh, John
Published: 2013
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31 results on '"Emerging Risk Factors Collaboration"'

1. Separate and combined associations of body-mass index and abdominal adiposity with cardiovascular disease: collaborative analysis of 58 prospective studies

2. The Emerging Risk Factors Collaboration: analysis of individual data on lipid, inflammatory and other markers in over 1.1 million participants in 104 prospective studies of cardiovascular diseases

3. Diabetes mellitus, fasting blood glucose concentration, and risk of vascular disease: a collaborative meta-analysis of 102 prospective studies

4. Equalization of four cardiovascular risk algorithms after systematic recalibration : individual-participant meta-analysis of 86 prospective studies

5. Meta-analysis of non-linear exposure-outcome relationships using individual participant data: A comparison of two methods

6. Cardiovascular Risk Factors Associated with Venous Thromboembolism

7. Equalization of four cardiovascular risk algorithms after systematic recalibration: individual-participant meta-analysis of 86 prospective studies

8. Major lipids, apolipoproteins, and risk of vascular disease

9. Glycated hemoglobin measurement and prediction of cardiovascular disease

10. Association of Cardiometabolic Multimorbidity With Mortality

11. Separate and combined associations of body-mass index andabdominal adiposity with cardiovascular disease: collaborative analysis of 58prospective studies

12. C-reactive protein concentration and risk of coronary heart disease, stroke, and mortality: an individual participant meta-analysis

13. Lipoprotein(a) concentration and the risk of coronary heart disease, stroke, and nonvascular mortality

14. Glycated hemoglobin measurement and prediction of cardiovascular disease

15. Assessing risk prediction models using individual participant data from multiple studies

16. Meta-analysis of non-linear exposure-outcome relationships using individual participant data: A comparison of two methods.

17. C-Reactive Protein, Fibrinogen, and Cardiovascular Disease Prediction

18. Separate and combined associations of body-mass index and abdominal adiposity with cardiovascular disease: collaborative analysis of 58 prospective studies

19. Association of Cardiometabolic Multimorbidity With Mortality

20. Statistical methods for the time-to-event analysis of individual participant data from multiple epidemiological studies

21. Statistical methods for the time-to-event analysis of individual participant data from multiple epidemiological studies

22. Cardiovascular Disease Risk Prediction Factors—Reply

23. Cardiovascular Disease Risk Prediction Factors—Reply

24. Statistical methods for the time-to-event analysis of individual participant data from multiple epidemiological studies.

25. Triglyceride-mediated pathways and coronary disease: collaborative analysis of 101 studies

26. Glycated hemoglobin measurement and prediction of cardiovascular disease

27. Lipoprotein(a) Concentration and the Risk of Coronary Heart Disease, Stroke, and Nonvascular Mortality

28. Cardiovascular Risk Factors Associated With Venous Thromboembolism

29. Association of Cardiometabolic Multimorbidity With Mortality

30. Assessing risk prediction models using individual participant data from multiple studies

31. C-reactive protein, fibrinogen, and cardiovascular risk.

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31 results on '"Emerging Risk Factors Collaboration"'

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