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1. Progression of conventional cardiovascular risk factors and vascular disease risk in individuals: insights from the PROG-IMT consortium.

Author

Bahls M, Lorenz MW, Dörr M, Gao L, Kitagawa K, Tuomainen TP, Agewall S, Berenson G, Catapano AL, Norata GD, Bots ML, van Gilst W, Asselbergs FW, Brouwers FP, Uthoff H, Sander D, Poppert H, Hecht Olsen M, Empana JP, Schminke U, Baldassarre D, Veglia F, Franco OH, Kavousi M, de Groot E, Mathiesen EB, Grigore L, Polak JF, Rundek T, Stehouwer CD, Skilton MR, Hatzitolios AI, Savopoulos C, Ntaios G, Plichart M, McLachlan S, Lind L, Willeit P, Steinmetz H, Desvarieux M, Ikram MA, Johnsen SH, Schmidt C, Willeit J, Ducimetiere P, Price JF, Bergström G, Kauhanen J, Kiechl S, Sitzer M, Bickel H, Sacco RL, Hofman A, Völzke H, and Thompson SG

Subjects
Adult, Aged, Aged, 80 and over, Female, Humans, Male, Middle Aged, Young Adult, Biomarkers blood, Carotid Intima-Media Thickness, Cholesterol, HDL blood, Cholesterol, LDL blood, Disease Progression, Heart Disease Risk Factors, Myocardial Infarction epidemiology, Predictive Value of Tests, Prognosis, Risk Assessment, Stroke epidemiology, Time Factors, Blood Pressure, Cardiovascular Diseases diagnosis, Cardiovascular Diseases epidemiology, Cardiovascular Diseases mortality, Carotid Artery Diseases diagnostic imaging, Carotid Artery Diseases epidemiology, Carotid Artery Diseases mortality, Cholesterol blood, Dyslipidemias blood, Dyslipidemias diagnosis, Dyslipidemias epidemiology, Dyslipidemias mortality, Hypertension diagnosis, Hypertension epidemiology, Hypertension mortality, Hypertension physiopathology
Abstract

Aims: Averaged measurements, but not the progression based on multiple assessments of carotid intima-media thickness, (cIMT) are predictive of cardiovascular disease (CVD) events in individuals. Whether this is true for conventional risk factors is unclear., Methods and Results: An individual participant meta-analysis was used to associate the annualised progression of systolic blood pressure, total cholesterol, low-density lipoprotein cholesterol and high-density lipoprotein cholesterol with future cardiovascular disease risk in 13 prospective cohort studies of the PROG-IMT collaboration ( n  = 34,072). Follow-up data included information on a combined cardiovascular disease endpoint of myocardial infarction, stroke, or vascular death. In secondary analyses, annualised progression was replaced with average. Log hazard ratios per standard deviation difference were pooled across studies by a random effects meta-analysis. In primary analysis, the annualised progression of total cholesterol was marginally related to a higher cardiovascular disease risk (hazard ratio (HR) 1.04, 95% confidence interval (CI) 1.00 to 1.07). The annualised progression of systolic blood pressure, low-density lipoprotein cholesterol and high-density lipoprotein cholesterol was not associated with future cardiovascular disease risk. In secondary analysis, average systolic blood pressure (HR 1.20 95% CI 1.11 to 1.29) and low-density lipoprotein cholesterol (HR 1.09, 95% CI 1.02 to 1.16) were related to a greater, while high-density lipoprotein cholesterol (HR 0.92, 95% CI 0.88 to 0.97) was related to a lower risk of future cardiovascular disease events., Conclusion: Averaged measurements of systolic blood pressure, low-density lipoprotein cholesterol and high-density lipoprotein cholesterol displayed significant linear relationships with the risk of future cardiovascular disease events. However, there was no clear association between the annualised progression of these conventional risk factors in individuals with the risk of future clinical endpoints.

Published
2020
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2. Equalization of four cardiovascular risk algorithms after systematic recalibration: individual-participant meta-analysis of 86 prospective studies.

Author

Pennells L, Kaptoge S, Wood A, Sweeting M, Zhao X, White I, Burgess S, Willeit P, Bolton T, Moons KGM, van der Schouw YT, Selmer R, Khaw KT, Gudnason V, Assmann G, Amouyel P, Salomaa V, Kivimaki M, Nordestgaard BG, Blaha MJ, Kuller LH, Brenner H, Gillum RF, Meisinger C, Ford I, Knuiman MW, Rosengren A, Lawlor DA, Völzke H, Cooper C, Marín Ibañez A, Casiglia E, Kauhanen J, Cooper JA, Rodriguez B, Sundström J, Barrett-Connor E, Dankner R, Nietert PJ, Davidson KW, Wallace RB, Blazer DG, Björkelund C, Donfrancesco C, Krumholz HM, Nissinen A, Davis BR, Coady S, Whincup PH, Jørgensen T, Ducimetiere P, Trevisan M, Engström G, Crespo CJ, Meade TW, Visser M, Kromhout D, Kiechl S, Daimon M, Price JF, Gómez de la Cámara A, Wouter Jukema J, Lamarche B, Onat A, Simons LA, Kavousi M, Ben-Shlomo Y, Gallacher J, Dekker JM, Arima H, Shara N, Tipping RW, Roussel R, Brunner EJ, Koenig W, Sakurai M, Pavlovic J, Gansevoort RT, Nagel D, Goldbourt U, Barr ELM, Palmieri L, Njølstad I, Sato S, Monique Verschuren WM, Varghese CV, Graham I, Onuma O, Greenland P, Woodward M, Ezzati M, Psaty BM, Sattar N, Jackson R, Ridker PM, Cook NR, D'Agostino RB, Thompson SG, Danesh J, and Di Angelantonio E

Subjects
Aged, Calibration, Female, Humans, Male, Middle Aged, Prospective Studies, Risk Assessment, Algorithms, Cardiovascular Diseases etiology
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., (© The Author(s) 2018. Published by Oxford University Press on behalf of the European Society of Cardiology.)

Published
2019
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3. Cardiovascular Risk Factors Associated With Venous Thromboembolism.

Author

Gregson J, Kaptoge S, Bolton T, Pennells L, Willeit P, Burgess S, Bell S, Sweeting M, Rimm EB, Kabrhel C, Zöller B, Assmann G, Gudnason V, Folsom AR, Arndt V, Fletcher A, Norman PE, Nordestgaard BG, Kitamura A, Mahmoodi BK, Whincup PH, Knuiman M, Salomaa V, Meisinger C, Koenig W, Kavousi M, Völzke H, Cooper JA, Ninomiya T, Casiglia E, Rodriguez B, Ben-Shlomo Y, Després JP, Simons L, Barrett-Connor E, Björkelund C, Notdurfter M, Kromhout D, Price J, Sutherland SE, Sundström J, Kauhanen J, Gallacher J, Beulens JWJ, Dankner R, Cooper C, Giampaoli S, Deen JF, Gómez de la Cámara A, Kuller LH, Rosengren A, Svensson PJ, Nagel D, Crespo CJ, Brenner H, Albertorio-Diaz JR, Atkins R, Brunner EJ, Shipley M, Njølstad I, Lawlor DA, van der Schouw YT, Selmer RM, Trevisan M, Verschuren WMM, Greenland P, Wassertheil-Smoller S, Lowe GDO, Wood AM, Butterworth AS, Thompson SG, Danesh J, Di Angelantonio E, and Meade T

Subjects
Adult, Body Mass Index, Cardiovascular Diseases mortality, Coronary Disease complications, Coronary Disease mortality, Diabetes Mellitus epidemiology, Female, Humans, Male, Middle Aged, Obesity complications, Obesity epidemiology, Outcome Assessment, Health Care, Prospective Studies, Pulmonary Embolism epidemiology, Pulmonary Embolism mortality, Risk Factors, Smoking adverse effects, Smoking epidemiology, United Kingdom epidemiology, Venous Thromboembolism epidemiology, Venous Thrombosis complications, Venous Thrombosis epidemiology, Cardiovascular Diseases epidemiology, Coronary Disease epidemiology, Pulmonary Embolism complications, Venous Thromboembolism complications
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.

Published
2019
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4. Predictive value for cardiovascular events of common carotid intima media thickness and its rate of change in individuals at high cardiovascular risk - Results from the PROG-IMT collaboration.

Author

Lorenz MW, Gao L, Ziegelbauer K, Norata GD, Empana JP, Schmidtmann I, Lin HJ, McLachlan S, Bokemark L, Ronkainen K, Amato M, Schminke U, Srinivasan SR, Lind L, Okazaki S, Stehouwer CDA, Willeit P, Polak JF, Steinmetz H, Sander D, Poppert H, Desvarieux M, Ikram MA, Johnsen SH, Staub D, Sirtori CR, Iglseder B, Beloqui O, Engström G, Friera A, Rozza F, Xie W, Parraga G, Grigore L, Plichart M, Blankenberg S, Su TC, Schmidt C, Tuomainen TP, Veglia F, Völzke H, Nijpels G, Willeit J, Sacco RL, Franco OH, Uthoff H, Hedblad B, Suarez C, Izzo R, Zhao D, Wannarong T, Catapano A, Ducimetiere P, Espinola-Klein C, Chien KL, Price JF, Bergström G, Kauhanen J, Tremoli E, Dörr M, Berenson G, Kitagawa K, Dekker JM, Kiechl S, Sitzer M, Bickel H, Rundek T, Hofman A, Mathiesen EB, Castelnuovo S, Landecho MF, Rosvall M, Gabriel R, de Luca N, Liu J, Baldassarre D, Kavousi M, de Groot E, Bots ML, Yanez DN, and Thompson SG

Subjects
Aged, Cardiovascular Diseases diagnostic imaging, Cardiovascular Diseases epidemiology, Female, Humans, Male, Middle Aged, Prognosis, Risk Factors, Cardiovascular Diseases diagnosis, Carotid Intima-Media Thickness, Intersectoral Collaboration
Abstract

Aims: Carotid intima media thickness (CIMT) predicts cardiovascular (CVD) events, but the predictive value of CIMT change is debated. We assessed the relation between CIMT change and events in individuals at high cardiovascular risk., Methods and Results: From 31 cohorts with two CIMT scans (total n = 89070) on average 3.6 years apart and clinical follow-up, subcohorts were drawn: (A) individuals with at least 3 cardiovascular risk factors without previous CVD events, (B) individuals with carotid plaques without previous CVD events, and (C) individuals with previous CVD events. Cox regression models were fit to estimate the hazard ratio (HR) of the combined endpoint (myocardial infarction, stroke or vascular death) per standard deviation (SD) of CIMT change, adjusted for CVD risk factors. These HRs were pooled across studies. In groups A, B and C we observed 3483, 2845 and 1165 endpoint events, respectively. Average common CIMT was 0.79mm (SD 0.16mm), and annual common CIMT change was 0.01mm (SD 0.07mm), both in group A. The pooled HR per SD of annual common CIMT change (0.02 to 0.43mm) was 0.99 (95% confidence interval: 0.95-1.02) in group A, 0.98 (0.93-1.04) in group B, and 0.95 (0.89-1.04) in group C. The HR per SD of common CIMT (average of the first and the second CIMT scan, 0.09 to 0.75mm) was 1.15 (1.07-1.23) in group A, 1.13 (1.05-1.22) in group B, and 1.12 (1.05-1.20) in group C., Conclusions: We confirm that common CIMT is associated with future CVD events in individuals at high risk. CIMT change does not relate to future event risk in high-risk individuals.

Published
2018
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5. The use of repeated blood pressure measures for cardiovascular risk prediction: a comparison of statistical models in the ARIC study.

Author

Sweeting MJ, Barrett JK, Thompson SG, and Wood AM

Subjects
Bias, Biomarkers, Blood Pressure, Computer Simulation, Female, Humans, Longitudinal Studies, Male, Middle Aged, Models, Statistical, Risk Factors, Survival Analysis, Blood Pressure Determination statistics & numerical data, Cardiovascular Diseases epidemiology, Regression Analysis, Risk Assessment methods
Abstract

Many prediction models have been developed for the risk assessment and the prevention of cardiovascular disease in primary care. Recent efforts have focused on improving the accuracy of these prediction models by adding novel biomarkers to a common set of baseline risk predictors. Few have considered incorporating repeated measures of the common risk predictors. Through application to the Atherosclerosis Risk in Communities study and simulations, we compare models that use simple summary measures of the repeat information on systolic blood pressure, such as (i) baseline only; (ii) last observation carried forward; and (iii) cumulative mean, against more complex methods that model the repeat information using (iv) ordinary regression calibration; (v) risk-set regression calibration; and (vi) joint longitudinal and survival models. In comparison with the baseline-only model, we observed modest improvements in discrimination and calibration using the cumulative mean of systolic blood pressure, but little further improvement from any of the complex methods. © 2016 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd., (© 2016 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.)

Published
2017
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6. Use of Repeated Blood Pressure and Cholesterol Measurements to Improve Cardiovascular Disease Risk Prediction: An Individual-Participant-Data Meta-Analysis.

Author

Paige E, Barrett J, Pennells L, Sweeting M, Willeit P, Di Angelantonio E, Gudnason V, Nordestgaard BG, Psaty BM, Goldbourt U, Best LG, Assmann G, Salonen JT, Nietert PJ, Verschuren WMM, Brunner EJ, Kronmal RA, Salomaa V, Bakker SJL, Dagenais GR, Sato S, Jansson JH, Willeit J, Onat A, de la Cámara AG, Roussel R, Völzke H, Dankner R, Tipping RW, Meade TW, Donfrancesco C, Kuller LH, Peters A, Gallacher J, Kromhout D, Iso H, Knuiman M, Casiglia E, Kavousi M, Palmieri L, Sundström J, Davis BR, Njølstad I, Couper D, Danesh J, Thompson SG, and Wood A

Subjects
Adult, Aged, Blood Pressure, Female, Humans, Middle Aged, Risk Factors, Blood Pressure Determination, Cardiovascular Diseases epidemiology, Cholesterol blood, Risk Assessment methods
Abstract

The added value of incorporating information from repeated blood pressure and cholesterol measurements to predict cardiovascular disease (CVD) risk has not been rigorously assessed. We used data on 191,445 adults from the Emerging Risk Factors Collaboration (38 cohorts from 17 countries with data encompassing 1962-2014) with more than 1 million measurements of systolic blood pressure, total cholesterol, and high-density lipoprotein cholesterol. Over a median 12 years of follow-up, 21,170 CVD events occurred. Risk prediction models using cumulative mean values of repeated measurements and summary measures from longitudinal modeling of the repeated measurements were compared with models using measurements from a single time point. Risk discrimination (C-index) and net reclassification were calculated, and changes in C-indices were meta-analyzed across studies. Compared with the single-time-point model, the cumulative means and longitudinal models increased the C-index by 0.0040 (95% confidence interval (CI): 0.0023, 0.0057) and 0.0023 (95% CI: 0.0005, 0.0042), respectively. Reclassification was also improved in both models; compared with the single-time-point model, overall net reclassification improvements were 0.0369 (95% CI: 0.0303, 0.0436) for the cumulative-means model and 0.0177 (95% CI: 0.0110, 0.0243) for the longitudinal model. In conclusion, incorporating repeated measurements of blood pressure and cholesterol into CVD risk prediction models slightly improves risk prediction., (© The Author(s) 2017. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health.)

Published
2017
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7. Modeling the costs and long-term health benefits of screening the general population for risks of cardiovascular disease: a review of methods used in the literature.

Author

Epstein D, García-Mochón L, Kaptoge S, and Thompson SG

Subjects
Adult, Aged, Aged, 80 and over, Algorithms, Cardiovascular Diseases diagnosis, Cardiovascular Diseases therapy, Cost-Benefit Analysis, Female, Health Promotion, Humans, Hydroxymethylglutaryl-CoA Reductase Inhibitors economics, Hydroxymethylglutaryl-CoA Reductase Inhibitors therapeutic use, Male, Middle Aged, Primary Prevention economics, Quality-Adjusted Life Years, Reproducibility of Results, Risk Factors, Cardiovascular Diseases economics, Cardiovascular Diseases prevention & control, Models, Economic, Risk Assessment methods
Abstract

Background: Strategies for screening and intervening to reduce the risk of cardiovascular disease (CVD) in primary care settings need to be assessed in terms of both their costs and long-term health effects. We undertook a literature review to investigate the methodologies used., Methods: In a framework of developing a new health-economic model for evaluating different screening strategies for primary prevention of CVD in Europe (EPIC-CVD project), we identified seven key modeling issues and reviewed papers published between 2000 and 2013 to assess how they were addressed., Results: We found 13 relevant health-economic modeling studies of screening to prevent CVD in primary care. The models varied in their degree of complexity, with between two and 33 health states. Programmes that screen the whole population by a fixed cut-off (e.g., predicted 10-year CVD risk >20 %) identify predominantly elderly people, who may not be those most likely to benefit from long-term treatment. Uncertainty and model validation were generally poorly addressed. Few studies considered the disutility of taking drugs in otherwise healthy individuals or the budget impact of the programme., Conclusions: Model validation, incorporation of parameter uncertainty, and sensitivity analyses for assumptions made are all important components of model building and reporting, and deserve more attention. Complex models may not necessarily give more accurate predictions. Availability of a large enough source dataset to reliably estimate all relevant input parameters is crucial for achieving credible results. Decision criteria should consider budget impact and the medicalization of the population as well as cost-effectiveness thresholds.

Published
2016
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8. Carotid Intima-Media Thickness Progression as Surrogate Marker for Cardiovascular Risk

Author

Willeit, Peter, Tschiderer, Lena, Allara, Elias, Reuber, Kathrin, Seekircher, Lisa, Gao, Lu, Liao, Ximing, Lonn, Eva, Gerstein, Hertzel C, Yusuf, Salim, Brouwers, Frank P, Asselbergs, Folkert W, Van Gilst, Wiek, Anderssen, Sigmund A, Grobbee, Diederick E, Kastelein, John JP, Visseren, Frank LJ, Ntaios, George, Hatzitolios, Apostolos I, Savopoulos, Christos, Nieuwkerk, Pythia T, Stroes, Erik, Walters, Matthew, Higgins, Peter, Dawson, Jesse, Gresele, Paolo, Guglielmini, Giuseppe, Migliacci, Rino, Ezhov, Marat, Safarova, Maya, Balakhonova, Tatyana, Sato, Eiichi, Amaha, Mayuko, Nakamura, Tsukasa, Kapellas, Kostas, Jamieson, Lisa M, Skilton, Michael, Blumenthal, James A, Hinderliter, Alan, Sherwood, Andrew, Smith, Patrick J, Van Agtmael, Michiel A, Reiss, Peter, Van Vonderen, Marit GA, Kiechl, Stefan, Klingenschmid, Gerhard, Sitzer, Matthias, Stehouwer, Coen DA, Uthoff, Heiko, Zou, Zhi-Yong, Cunha, Ana R, Neves, Mario F, Witham, Miles D, Park, Hyun-Woong, Lee, Moo-Sik, Bae, Jang-Ho, Bernal, Enrique, Wachtell, Kristian, Kjeldsen, Sverre E, Olsen, Michael H, Preiss, David, Sattar, Naveed, Beishuizen, Edith, Huisman, Menno V, Espeland, Mark A, Schmidt, Caroline, Agewall, Stefan, Ok, Ercan, Aşçi, Gülay, De Groot, Eric, Grooteman, Muriel PC, Blankestijn, Peter J, Bots, Michiel L, Sweeting, Michael J, Thompson, Simon G, Lorenz, Matthias W, PROG-IMT And The Proof-ATHERO Study Groups, Group, PROG-IMT Study, Group, Proof-ATHERO Study, Vascular Medicine, Medical Psychology, APH - Mental Health, APH - Personalized Medicine, Experimental Vascular Medicine, ACS - Atherosclerosis & ischemic syndromes, Global Health, APH - Aging & Later Life, AGEM - Amsterdam Gastroenterology Endocrinology Metabolism, ACS - Pulmonary hypertension & thrombosis, Ege Üniversitesi, MUMC+: Centrum voor Chronische Zieken (3), MUMC+: MA Med Staf Artsass Interne Geneeskunde (9), RS: Carim - V01 Vascular complications of diabetes and metabolic syndrome, MUMC+: HVC Pieken Maastricht Studie (9), Interne Geneeskunde, MUMC+: MA Interne Geneeskunde (3), Allara, Elias [0000-0002-1634-8330], Apollo - University of Cambridge Repository, Cardiovascular Centre (CVC), Internal medicine, Nephrology, and ACS - Diabetes & metabolism

Subjects
Male, medicine.medical_specialty, Carotid Artery, Common, carotid intima-media thickness, LDL TREATMENT STRATEGIES, Myocardial Infarction, surrogate marker, TYPE-2 DIABETES-MELLITUS, HIV-INFECTED PATIENTS, 030204 cardiovascular system & hematology, Impaired glucose tolerance, EXTENDED-RELEASE NIACIN, 03 medical and health sciences, 0302 clinical medicine, IMPAIRED GLUCOSE-TOLERANCE, ESTROGEN PLUS PROGESTIN, cardiovascular disease, Physiology (medical), Internal medicine, BASE-LINE CHARACTERISTICS, medicine, Humans, CORONARY-HEART-DISEASE, 030212 general & internal medicine, Myocardial infarction, cardiovascular diseases, Stroke, Randomized Controlled Trials as Topic, clinical trials as topic, business.industry, Surrogate endpoint, Middle Aged, medicine.disease, Common, Clinical trial, meta-analysis, ARTERIAL-WALL THICKNESS, Intima-media thickness, JAPAN STATIN TREATMENT, Heart Disease Risk Factors, Relative risk, Meta-analysis, Cardiology, cardiovascular system, Female, Carotid Artery, Cardiology and Cardiovascular Medicine, business
Abstract

Background: To quantify the association between effects of interventions on carotid intima-media thickness (cIMT) progression and their effects on cardiovascular disease (CVD) risk. Methods: We systematically collated data from randomized, controlled trials. cIMT was assessed as the mean value at the common-carotid-artery; if unavailable, the maximum value at the common-carotid-artery or other cIMT measures were used. the primary outcome was a combined CVD end point defined as myocardial infarction, stroke, revascularization procedures, or fatal CVD. We estimated intervention effects on cIMT progression and incident CVD for each trial, before relating the 2 using a Bayesian meta-regression approach. Results: We analyzed data of 119 randomized, controlled trials involving 100 667 patients (mean age 62 years, 42% female). Over an average follow-up of 3.7 years, 12 038 patients developed the combined CVD end point. Across all interventions, each 10 mu m/y reduction of cIMT progression resulted in a relative risk for CVD of 0.91 (95% Credible Interval, 0.87-0.94), with an additional relative risk for CVD of 0.92 (0.87-0.97) being achieved independent of cIMT progression. Taken together, we estimated that interventions reducing cIMT progression by 10, 20, 30, or 40 mu m/y would yield relative risks of 0.84 (0.75-0.93), 0.76 (0.67-0.85), 0.69 (0.59-0.79), or 0.63 (0.52-0.74), respectively. Results were similar when grouping trials by type of intervention, time of conduct, time to ultrasound follow-up, availability of individual-participant data, primary versus secondary prevention trials, type of cIMT measurement, and proportion of female patients. Conclusions: the extent of intervention effects on cIMT progression predicted the degree of CVD risk reduction. This provides a missing link supporting the usefulness of cIMT progression as a surrogate marker for CVD risk in clinical trials., Austrian Science Fund (FWF)Austrian Science Fund (FWF) [P 32488]; Dr-Johannes-and-Hertha-Tuba Foundation; German Research FoundationGerman Research Foundation (DFG) [DFG Lo 1569/2-1, DFG Lo 1569/2-3]; excellence initiative "Competence Centers for Excellent Technologies" (COMET) of the Austrian Research Promotion Agency (FFG) "Research Center of Excellence in Vascular Ageing: Tyrol, VAS-Cage" - Bundesministerium fur Verkehr, Innovation und Technologie (B [843536]; Bundesministerium fur Bildung, Wissenschaft und Forschung (BMWFW); Wirtschaftsagentur Wien; Standortagentur Tirol, This work was supported by the Austrian Science Fund (FWF; P 32488); the Dr-Johannes-and-Hertha-Tuba Foundation; the German Research Foundation (DFG Lo 1569/2-1 and DFG Lo 1569/2-3); and the excellence initiative "Competence Centers for Excellent Technologies" (COMET) of the Austrian Research Promotion Agency (FFG) "Research Center of Excellence in Vascular Ageing: Tyrol, VAS-Cage" (K-Project No. 843536), funded by Bundesministerium fur Verkehr, Innovation und Technologie (BMVIT), Bundesministerium fur Bildung, Wissenschaft und Forschung (BMWFW), Wirtschaftsagentur Wien, and Standortagentur Tirol.

Published
2020

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

Author

Pennells, Lisa, Kaptoge, Stephen, Wood, Angela, Sweeting, Mike, Zhao, Xiaohui, White, Ian, Burgess, Stephen, Willeit, Peter, Bolton, Thomas, Moons, Karel G M, van der Schouw, Yvonne T, Selmer, Randi, Khaw, Kay-Tee, Gudnason, Vilmundur, Assmann, Gerd, Amouyel, Philippe, Salomaa, Veikko, Kivimaki, Mika, Nordestgaard, Børge G, Blaha, Michael J, Kuller, Lewis H, Brenner, Hermann, Gillum, Richard F, Meisinger, Christa, Ford, Ian, Knuiman, Matthew W, Rosengren, Annika, Lawlor, Debbie A, Völzke, Henry, Cooper, Cyrus, Marín Ibañez, Alejandro, Casiglia, Edoardo, Kauhanen, Jussi, Cooper, Jackie A, Rodriguez, Beatriz, Sundström, Johan, Barrett-Connor, Elizabeth, Dankner, Rachel, Nietert, Paul J, Davidson, Karina W, Wallace, Robert B, Blazer, Dan G, Björkelund, Cecilia, Donfrancesco, Chiara, Krumholz, Harlan M, Nissinen, Aulikki, Davis, Barry R, Coady, Sean, Whincup, Peter H, Jørgensen, Torben, Ducimetiere, Pierre, Trevisan, Maurizio, Engström, Gunnar, Crespo, Carlos J, Meade, Tom W, Visser, Marjolein, Kromhout, Daan, Kiechl, Stefan, Daimon, Makoto, Price, Jackie F, Gómez de la Cámara, Agustin, Wouter Jukema, J, Lamarche, Benoît, Onat, Altan, Simons, Leon A, Kavousi, Maryam, Ben-Shlomo, Yoav, Gallacher, John, Dekker, Jacqueline M, Arima, Hisatomi, Shara, Nawar, Tipping, Robert W, Roussel, Ronan, Brunner, Eric J, Koenig, Wolfgang, Sakurai, Masaru, Pavlovic, Jelena, Gansevoort, Ron T, Nagel, Dorothea, Goldbourt, Uri, Barr, Elizabeth L M, Palmieri, Luigi, Njølstad, Inger, Sato, Shinichi, Monique Verschuren, W M, Varghese, Cherian V, Graham, Ian, Onuma, Oyere, Greenland, Philip, Woodward, Mark, Ezzati, Majid, Psaty, Bruce M, Sattar, Naveed, Jackson, Rod, Ridker, Paul M, Cook, Nancy R, D'Agostino, Ralph B, Thompson, Simon G, Danesh, John, Di Angelantonio, Emanuele, Simpson, Lara M, Pressel, Sara L, Couper, David J, Nambi, Vijay, Matsushita, Kunihiro, Folsom, Aaron R, Shaw, Jonathan E, Magliano, Dianna J, Zimmet, Paul Z, Wannamethee, S Goya, Willeit, Johann, Santer, Peter, Egger, Georg, Casas, Juan Pablo, Amuzu, Antointtte, Tikhonoff, Valérie, Sutherland, Susan E, Cushman, Mary, Søgaard, Anne Johanne, Håheim, Lise Lund, Ariansen, Inger, Tybjærg-Hansen, Anne, Jensen, Gorm B, Schnohr, Peter, Giampaoli, Simona, Vanuzzo, Diego, Panico, Salvatore, Balkau, Beverley, Bonnet, Fabrice, Marre, Michel, de la Cámara, Agustin Gómez, Rubio Herrera, Miguel Angel, Friedlander, Yechiel, McCallum, John, McLachlan, Stela, Guralnik, Jack, Phillips, Caroline L, Wareham, Nick, Schöttker, Ben, Saum, Kai-Uwe, Holleczek, Bernd, Tolonen, Hanna, Vartiainen, Erkki, Jousilahti, Pekka, Harald, Kennet, D’Agostino, Ralph B, Massaro, Joseph M, Pencina, Michael, Vasan, Ramachandran, Kayama, Takamasa, Kato, Takeo, Oizumi, Toshihide, Jespersen, Jørgen, Møller, Lars, Bladbjerg, Else Marie, Chetrit, A, Wilhelmsen, Lars, Lissner, Lauren, Dennison, Elaine, Kiyohara, Yutaka, Ninomiya, Toshiharu, Doi, Yasufumi, Nijpels, Giel, Stehouwer, Coen D A, Kazumasa, Yamagishi, Iso, Hiroyasu, Kurl, Sudhir, Tuomainen, Tomi-Pekka, Salonen, Jukka T, Deeg, Dorly J H, Nilsson, Peter M, Hedblad, Bo, Melander, Olle, De Boer, Ian H, DeFilippis, Andrew Paul, Verschuren, W M Monique, Watt, Graham, Tverdal, Aage, Kirkland, Susan, Shimbo, Daichi, Shaffer, Jonathan, Bakker, Stephan J L, van der Harst, Pim, Hillege, Hans L, Dallongeville, Jean, Schulte, Helmut, Trompet, Stella, Smit, Roelof A J, Stott, David J, Després, Jean-Pierre, Cantin, Bernard, Dagenais, Gilles R, Laughlin, Gail, Wingard, Deborah, Aspelund, Thor, Eiriksdottir, Gudny, Gudmundsson, Elias Freyr, Ikram, Arfan, van Rooij, Frank J A, Franco, Oscar H, Rueda-Ochoa, Oscar L, Muka, Taulant, Glisic, Marija, Tunstall-Pedoe, Hugh, Howard, Barbara V, Zhang, Ying, Jolly, Stacey, Davey-Smith, George, Can, Günay, Yüksel, Hüsniye, Nakagawa, Hideaki, Morikawa, Yuko, Miura, Katsuyuki, Ingelsson, Martin, Giedraitis, Vilmantas, Gaziano, J Michael, Shipley, Martin, Arndt, Volker, Cook, Nancy, Ibañez, Alejandro Marín, Geleijnse, Johanna M, Epidemiology, Læknadeild (HÍ), Faculty of Medicine (UI), Heilbrigðisvísindasvið (HÍ), School of Health Sciences (UI), Háskóli Íslands, University of Iceland, Pennells, Lisa [0000-0002-8594-3061], Kaptoge, Stephen [0000-0002-1155-4872], Wood, Angela [0000-0002-7937-304X], Sweeting, Michael [0000-0003-0980-8965], Zhao, Xiaohui [0000-0001-9922-2815], Burgess, Stephen [0000-0001-5365-8760], Danesh, John [0000-0003-1158-6791], Di Angelantonio, Emanuele [0000-0001-8776-6719], Apollo - University of Cambridge Repository, Nutrition and Health, APH - Aging & Later Life, APH - Societal Participation & Health, APH - Health Behaviors & Chronic Diseases, Cardiovascular Centre (CVC), Groningen Kidney Center (GKC), Groningen Institute for Organ Transplantation (GIOT), Lifestyle Medicine (LM), Life Course Epidemiology (LCE), AGEM - Endocrinology, metabolism and nutrition, Internal medicine, Epidemiology and Data Science, İÜC, Lisa, Pennell, Stephen, Kaptoge, Angela, Wood, Mike, Sweeting, Xiaohui, Zhao, Ian, White, Stephen, Burge, Peter, Willeit, Thomas, Bolton, Karel G M, Moon, Yvonne T, van der Schouw, Randi, Selmer, Kay-Tee, Khaw, Vilmundur, Gudnason, Gerd, Assmann, Philippe, Amouyel, Veikko, Salomaa, Mika, Kivimaki, Børge G, Nordestgaard, Michael J, Blaha, Lewis H, Kuller, Hermann, Brenner, Richard F, Gillum, Christa, Meisinger, Ian, Ford, Matthew W, Knuiman, Annika, Rosengren, Debbie A, Lawlor, Henry, Völzke, Cyrus, Cooper, Alejandro, Marín Ibañez, Edoardo, Casiglia, Jussi, Kauhanen, Jackie A, Cooper, Beatriz, Rodriguez, Johan, Sundström, Elizabeth, Barrett-Connor, Rachel, Dankner, Paul J, Nietert, Karina W, Davidson, Robert B, Wallace, Dan G, Blazer, Cecilia, Björkelund, Chiara, Donfrancesco, Harlan M, Krumholz, Aulikki, Nissinen, Barry R, Davi, Sean, Coady, Peter H, Whincup, Torben, Jørgensen, Pierre, Ducimetiere, Maurizio, Trevisan, Gunnar, Engström, Carlos J, Crespo, Tom W, Meade, Marjolein, Visser, Daan, Kromhout, Stefan, Kiechl, Makoto, Daimon, Jackie F, Price, Agustin, Gómez de la Cámara, J, Wouter Jukema, Benoît, Lamarche, Altan, Onat, Leon A, Simon, Maryam, Kavousi, Yoav, Ben-Shlomo, John, Gallacher, Jacqueline M, Dekker, Hisatomi, Arima, Nawar, Shara, Robert W, Tipping, Ronan, Roussel, Eric J, Brunner, Wolfgang, Koenig, Masaru, Sakurai, Jelena, Pavlovic, Ron T, Gansevoort, Dorothea, Nagel, Uri, Goldbourt, Elizabeth L M, Barr, Luigi, Palmieri, Inger, Njølstad, Shinichi, Sato, W M, Monique Verschuren, Cherian V, Varghese, Ian, Graham, Oyere, Onuma, Philip, Greenland, Mark, Woodward, Majid, Ezzati, Bruce M, Psaty, Sattar, W Tipping, Naveerobert, M Simpson, Lara, L Pressel, Sara, J Couper, David, Nambi, Vijay, Matsushita, Kunihiro, R Folsom, Aaron, E Shaw, Jonathan, J Magliano, Dianna, Z Zimmet, Paul, W Knuiman, Matthew, H Whincup, Peter, Goya Wannamethee, S, Willeit, Johann, Santer, Peter, Egger, Georg, Pablo Casas, Juan, Amuzu, Antoinette, Ben-Shlomo, Yoav, Gallacher, John, Tikhonoff, Valérie, Casiglia, Edoardo, E Sutherland, Susan, J Nietert, Paul, Cushman, Mary, M Psaty, Bruce, Johanne Søgaard, Anne, Lund Håheim, Lise, Ariansen, Inger, Tybjærg-Hansen, Anne, B Jensen, Gorm, Schnohr, Peter, Giampaoli, Simona, Vanuzzo, Diego, Panico, Salvatore, Palmieri, Luigi, Balkau, Beverley, Bonnet, Fabrice, Marre, Michel, Gómez de la Cámara, Agustin, Angel Rubio Herrera, Miguel, Friedlander, Yechiel, Mccallum, John, Mclachlan, Stela, Guralnik, Jack, L Phillips, Caroline, Khaw, Kay-Tee, Wareham, Nick, Schöttker, Ben, Saum, Kai-Uwe, Holleczek, Bernd, Nissinen, Aulikki, Tolonen, Hanna, Donfrancesco, Chiara, Vartiainen, Erkki, Jousilahti, Pekka, Harald, Kennet, B D’Agostino, Ralph, M Massaro, Joseph, Pencina, Michael, Vasan, Ramachandran, Kayama, Takamasa, Kato, Takeo, Oizumi, Toshihide, Jespersen, Jørgen, Møller, Lar, Marie Bladbjerg, Else, Chetrit, A, Rosengren, Annika, Wilhelmsen, Lar, Björkelund, Cecilia, Lissner, Lauren, Nagel, Dorothea, Dennison, Elaine, Kiyohara, Yutaka, Ninomiya, Toshiharu, Doi, Yasufumi, Rodriguez, Beatriz, Nijpels, Giel, A Stehouwer, Coen D, Sato, Shinichi, Kazumasa, Yamagishi, Iso, Hiroyasu, Goldbourt, Uri, Salomaa, Veikko, Kurl, Sudhir, Tuomainen, Tomi-Pekka, T Salonen, Jukka, Visser, Marjolein, H Deeg, Dorly J, W Meade, Tom, M Nilsson, Peter, Hedblad, Bo, Melander, Olle, H De Boer, Ian, Paul DeFilippis, Andrew, M Monique Verschuren, W, Sattar, Naveed, Watt, Graham, Meisinger, Christa, Koenig, Wolfgang, H Kuller, Lewi, Tverdal, Aage, F Gillum, Richard, A Cooper, Jackie, Kirkland, Susan, Shimbo, Daichi, Shaffer, Jonathan, Ducimetiere, Pierre, L Bakker, Stephan J, van der Harst, Pim, L Hillege, Han, J Crespo, Carlo, Amouyel, Philippe, Dallongeville, Jean, Assmann, Gerd, Schulte, Helmut, Trompet, Stella, J Smit, Roelof A, J Stott, David, T van der Schouw, Yvonne, Després, Jean-Pierre, Cantin, Bernard, R Dagenais, Gille, Laughlin, Gail, Wingard, Deborah, Trevisan, Maurizio, Aspelund, Thor, Eiriksdottir, Gudny, Freyr Gudmundsson, Elia, Ikram, Arfan, A van Rooij, Frank J, H Franco, Oscar, L Rueda-Ochoa, Oscar, Muka, Taulant, Glisic, Marija, Tunstall-Pedoe, Hugh, Völzke, Henry, V Howard, Barbara, Zhang, Ying, Jolly, Stacey, Davey-Smith, George, Can, Günay, Yüksel, Hüsniye, Nakagawa, Hideaki, Morikawa, Yuko, Miura, Katsuyuki, Njølstad, Inger, Ingelsson, Martin, Giedraitis, Vilmanta, M Ridker, Paul, Michael Gaziano, J, Kivimaki, Mika, Shipley, Martin, J Brunner, Eric, Arndt, Volker, Brenner, Hermann, Cook, Nancy, Ford, Ian, Marín Ibañez, Alejandro, M Geleijnsed, Johanna, Rod, Jackson, Paul M, Ridker, Nancy R, Cook, Ralph B, D'Agostino, Simon G, Thompson, John, Danesh, and Emanuele, Di Angelantonio

Subjects
Male, Cardiac & Cardiovascular Systems, Nutrition and Disease, Prevention and Epidemiology, PREDICTION, Áhættuþættir, 030204 cardiovascular system & hematology, GUIDELINES, 0302 clinical medicine, Risk Factors, Voeding en Ziekte, FRAMINGHAM, Discrimination, Medicine, Cardiac and Cardiovascular Systems, Blóðrásarsjúkdómar, Prospective Studies, Prospective cohort study, Non-U.S. Gov't, 1102 Cardiorespiratory Medicine and Haematology, CALIBRATION, Kardiologi, Framingham Risk Score, Emerging Risk Factors Collaboration, SCORES, Research Support, Non-U.S. Gov't, Incidence (epidemiology), Middle Aged, Cardiovascular disease, Justice and Strong Institutions, Risk prediction, ddc, 3. Good health, Cardiovascular Diseases, Meta-analysis, Cohort, Calibration, Female, Risk assessment, Cardiology and Cardiovascular Medicine, Algorithm, Life Sciences & Biomedicine, Algorithms, SDG 16 - Peace, Risk algorithms, DISEASE PREVENTION, Research Support, Risk Assessment, VALIDATION, 03 medical and health sciences, Clinical Research, Journal Article, Humans, ddc:610, Risk factor, VLAG, Aged, Science & Technology, business.industry, SDG 16 - Peace, Justice and Strong Institutions, 030229 sport sciences, R1, STATIN USE, Cardiovascular System & Hematology, Cardiovascular System & Cardiology, business, PRIMARY PREVENTION, TASK-FORCE
Abstract

Publisher's version (útgefin grein), 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., The work of the co-ordinating centre was funded by the UK Medical Research Council (G0800270), British Heart Foundation (SP/09/ 002), British Heart Foundation Cambridge Cardiovascular Centre of Excellence, UK National Institute for Health Research Cambridge Biomedical Research Centre, European Research Council (268834), and European Commission Framework Programme 7 (HEALTH-F2-2012-279233). The Emerging Risk Factor Collaboration’s website https://www.phpc.cam.ac.uk/ceu/erfc/list-of-studies/ has compiled a list provided by investigators of some of the funders of the component studies in this analysis. I.W. was supported by the Medical Research Council Unit Programme MC_UU_12023/21. M.K. is supported by the Netherlands Organization for Scientific Research (NWO) Veni grant (Veni, 91616079). J.P. is supported by Erasmus Mundus Western Balkans (ERAWEB), a project funded by the European Commission.

Published
2019

10. Cardiovascular risk factors are 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, Zoller, Bengt, Assmann, Gerd, Gudnason, Vilmundur, Folsom, Aaron R., Arndt, Volker, Fletcher, Astrid, Norman, Paul E., Nordestgaard, Borge G., Kitamura, Akihiko, Mahmoodi, Bakhtawar K., Whincup, Peter H., Knuiman, Matthew, Salomaa, Veikko, Meisinger, Christa, Koenig, Wolfgang, Kavousi, Maryam, Voelzke, Henry, Cooper, Jackie A., Ninomiya, Toshiharu, Casiglia, Edoardo, Rodriguez, Beatriz, Ben-Shlomo, Yoav, Despres, Jean-Pierre, Simons, Leon, Barrett-Connor, Elizabeth, Bjorkelund, Cecilia, Notdurfter, Marlene, Kromhout, Daan, Price, Jackie, Sutherland, Susan E., Sundstroem, Johan, Kauhanen, Jussi, Gallacher, John, Beulens, Joline W. J., Dankner, Rachel, Cooper, Cyrus, Giampaoli, Simona, Deen, Jason F., Gomez de la Camara, Agustin, 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, Njolstad, Inger, Lawlor, Deborah A., van der Schouw, Yvonne T., Selmer, Randi Marie, Trevisan, Maurizio, Verschuren, W. M. Monique, Greenland, Philip, Wassertheil-Smoller, Sylvia, Lowe, Gordon D. O., Wood, Angela M., Butterworth, Adam S., Thompson, Simon G., Danesh, John, Di Angelantonio, Emanuele, Meade, Tom, Rosamond, Wayne, Whitsel, Eric, Cushman, Mary, Barr, Elizabeth L. M., Shaw, Jonathan E., Zimmet, Paul Z., Kiechl, Stefan, Weger, Siegfried, Willeit, Johann, Amuzu, Antoinette, Dale, Caroline, Casas, Juan P., Tikhonoff, Valerie, Nietert, Paul, Tybjaerg-Hansen, Anne, Frikke-Schmidt, Ruth, Jensen, Gorm B., Lora Pablos, David, Cancelas Navia, Pilar, McLachlan, Stela, Schoettker, Ben, Saum, Kai-Uwe, Holleczek, Bernd, Ariansen, Inger, Meyer, Haakon E., Haheim, Lise Lund, Vartiainen, Erkki, Jousilahti, Pekka, Harald, Kennet, Wilhelmsen, Lars, Dennison, Elaine, Syddall, Holly, Westbury, Leo, Flicker, Leon, Hankey, Graeme J., Golledge, Jonathan, Doi, Yasufumi, Kiyohara, Yutaka, Elders, Petra, Stehouwer, Coen, Jensen, Majken, Iso, Hiroyasu, Yamagishi, Kazumasa, Sudhir, Kurl, Tuomainen, Tomi-Pekka, Salonen, Jukka T., Boer, Jolanda M. A., Blokstra, Anneke, Melander, Olle, Nilsson, Peter M., Engstrom, Gunnar, Palmieri, Luigi, Vanuzzo, Diego, Peters, Annette, Thorand, Barbara, Heier, Margit, Hu, Frank B., Manson, JoAnn E., Meijer, Karina, Gansevoort, Ron T., Schulte, Helmut, Sluijs, Ivonne, Cantin, Bernard, Lamarche, Benoit, Dagenais, Gilles R., McEvoy, Linda, Laughlin, Gail, Daniels, Lori B., Aspelund, Thor, Gudmundsson, Elias Freyr, Thorsson, Bolli, Leening, Maarten J. G., Ikram, M. Arfan, Franco, Oscar H., Tunstall-Pedoe, Hugh, Werner, Andre, Devereux, Richard, Jolly, Stacey, Smith, George Davey, Can, Gunay, Yuksel, Husniye, Altay, Servet, Ingelsson, Martin, Giedraitis, Vilmantas, Claessen, Heiner, Rothenbacher, Dietrich, Parikh, Nisha, I, Eaton, Charles, Kivimaki, Mika, Feskens, Edith, Geleijnse, Johanna M., Spackman, Sarah, Walker, Matthew, Cardiovascular Centre (CVC), Groningen Kidney Center (GKC), Real World Studies in PharmacoEpidemiology, -Genetics, -Economics and -Therapy (PEGET), Kaptoge, Stephen [0000-0002-1155-4872], Pennells, Lisa [0000-0002-8594-3061], Burgess, Stephen [0000-0001-5365-8760], Bell, Steven [0000-0001-6774-3149], Sweeting, Michael [0000-0003-0980-8965], Wood, Angela [0000-0002-7937-304X], Butterworth, Adam [0000-0002-6915-9015], Danesh, John [0000-0003-1158-6791], Di Angelantonio, Emanuele [0000-0001-8776-6719], Apollo - University of Cambridge Repository, Epidemiology, ACS - Diabetes & metabolism, APH - Health Behaviors & Chronic Diseases, Epidemiology and Data Science, and ACS - Heart failure & arrhythmias

Subjects
Male, Outcome Assessment, ALCOHOL-CONSUMPTION, Coronary Disease, 030204 cardiovascular system & hematology, Body Mass Index, 0302 clinical medicine, Risk Factors, Outcome Assessment, Health Care, Cardiac and Cardiovascular Systems, 030212 general & internal medicine, Prospective Studies, DEEP-VEIN THROMBOSIS, Prospective cohort study, Original Investigation, Venous Thrombosis, education.field_of_study, Kardiologi, Hazard ratio, INDIVIDUAL-PARTICIPANT DATA, Venous Thromboembolism, Middle Aged, Biobank, 3. Good health, Pulmonary embolism, ddc, Cardiovascular Diseases, OBESITY, Female, SMOKING, Cardiology and Cardiovascular Medicine, Cohort study, Adult, medicine.medical_specialty, PULMONARY-EMBOLISM, Population, 03 medical and health sciences, Internal medicine, medicine, Journal Article, Diabetes Mellitus, Online First, Life Science, Humans, CORONARY-HEART-DISEASE, ddc:610, Obesity, Pulmonary Embolism, Smoking, United Kingdom, Risk factor, education, METAANALYSIS, VLAG, Global Nutrition, Wereldvoeding, business.industry, Research, DIABETES-MELLITUS, medicine.disease, Health Care, LIPOPROTEIN, business, Body mass index
Abstract

Key Points Question To what extent are established cardiovascular risk factors associated with risk of venous thromboembolism (VTE)? Findings In this analysis of individual participant data from the Emerging Risk Factors Collaboration and the UK Biobank including 1.1 million participants, among a panel of several established cardiovascular risk factors, older age, smoking, and greater adiposity were consistently associated with higher VTE risk. Meaning There is overlap in at least some of the major population determinants of important venous and arterial thrombotic diseases., This analysis of data from the Emerging Risk Factors Collaboration and the UK Biobank estimates the associations of major cardiovascular risk factors with venous thromboembolism., 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.

Published
2019

11. The use of repeated blood pressure measures for cardiovascular risk prediction: a comparison of statistical models in the ARIC study

Author

Sweeting, Michael J., Barrett, Jessica K., Thompson, Simon G., Wood, Angela M., Sweeting, Michael J [0000-0003-0980-8965], Thompson, Simon G [0000-0002-5274-7814], and Apollo - University of Cambridge Repository

Subjects
Male, repeat measures, Models, Statistical, joint models, C-index, Blood Pressure, Blood Pressure Determination, regression calibration, Middle Aged, C‐index, Risk Assessment, Survival Analysis, Bias, Cardiovascular Diseases, Risk Factors, cardiovascular risk prediction, Humans, Regression Analysis, Computer Simulation, Female, Longitudinal Studies, Research Articles, Biomarkers, Research Article
Abstract

Many prediction models have been developed for the risk assessment and the prevention of cardiovascular disease in primary care. Recent efforts have focused on improving the accuracy of these prediction models by adding novel biomarkers to a common set of baseline risk predictors. Few have considered incorporating repeated measures of the common risk predictors. Through application to the Atherosclerosis Risk in Communities study and simulations, we compare models that use simple summary measures of the repeat information on systolic blood pressure, such as (i) baseline only; (ii) last observation carried forward; and (iii) cumulative mean, against more complex methods that model the repeat information using (iv) ordinary regression calibration; (v) risk-set regression calibration; and (vi) joint longitudinal and survival models. In comparison with the baseline-only model, we observed modest improvements in discrimination and calibration using the cumulative mean of systolic blood pressure, but little further improvement from any of the complex methods. © 2016 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.

Published
2016
Full Text
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12. Use of Repeated Blood Pressure and Cholesterol Measurements to Improve Cardiovascular Disease Risk Prediction : An Individual-Participant-Data Meta-Analysis

Author

Paige, Ellie, Barrett, Jessica, Pennells, Lisa, Sweeting, Michael, Willeit, Peter, Di Angelantonio, Emanuele, Gudnason, Vilmundur, Nordestgaard, Børge G, Psaty, Bruce M, Goldbourt, Uri, Best, Lyle G, Assmann, Gerd, Salonen, Jukka T, Nietert, Paul J, Verschuren, W M Monique, Brunner, Eric J, Kronmal, Richard A, Salomaa, Veikko, Bakker, Stephan J L, Dagenais, Gilles R, Sato, Shinichi, Jansson, Jan-Håkan, Willeit, Johann, Onat, Altan, de la Cámara, Agustin Gómez, Roussel, Ronan, Völzke, Henry, Dankner, Rachel, Tipping, Robert W, Meade, Tom W, Donfrancesco, Chiara, Kuller, Lewis H, Peters, Annette, Gallacher, John, Kromhout, Daan, Iso, Hiroyasu, Knuiman, Matthew, Casiglia, Edoardo, Kavousi, Maryam, Palmieri, Luigi, Sundström, Johan, Davis, Barry R, Njølstad, Inger, Couper, David, Danesh, John, Thompson, Simon G, Wood, Angela, Clinicum, Department of Public Health, University of Helsinki, Barrett, Jessica [0000-0003-1889-9803], Pennells, Lisa [0000-0002-8594-3061], Sweeting, Michael [0000-0003-0980-8965], Di Angelantonio, Emanuele [0000-0001-8776-6719], Danesh, John [0000-0003-1158-6791], Thompson, Simon [0000-0002-5274-7814], Wood, Angela [0000-0002-7937-304X], Apollo - University of Cambridge Repository, and Epidemiology

Subjects
Adult, MODELS, Blood Pressure, Cholesterol/blood, Risk Assessment, risk prediction, Systematic Reviews, Meta- and Pooled Analyses, SDG 3 - Good Health and Well-being, cardiovascular disease, MULTIPLE, Humans, risk factors, CORONARY-HEART-DISEASE, longitudinal measurements, Cardiac and Cardiovascular Systems, Risk Assessment/methods, repeated measurements, Human Nutrition & Health, Aged, Cardiovascular Disease, Longitudinal Measurements, Repeated Measurements, Risk Factors, Risk Prediction, repeated risk factors, Kardiologi, Cardiovascular Diseases/epidemiology, Humane Voeding & Gezondheid, Blood Pressure Determination, Public Health, Global Health, Social Medicine and Epidemiology, Middle Aged, 3142 Public health care science, environmental and occupational health, VARIABILITY, Folkhälsovetenskap, global hälsa, socialmedicin och epidemiologi, Cholesterol, Cardiovascular Diseases, Female
Abstract

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

Published
2017

13. Modeling the costs and long-term health benefits of screening the general population for risks of cardiovascular disease: a review of methods used in the literature

Author

Epstein, David, García-Mochón, Leticia, Kaptoge, Stephen, Thompson, Simon G, Kaptoge, Stephen [0000-0002-1155-4872], Thompson, Simon [0000-0002-5274-7814], and Apollo - University of Cambridge Repository

Subjects
Adult, Male, Cost-Benefit Analysis, Economics, Econometrics and Finance (miscellaneous), Health Promotion, Risk Assessment, Risk Factors, Humans, Aged, Literature review, Aged, 80 and over, Original Paper, Primary prevention, Health Policy, Cost-effectiveness analysis, H510, Statins, Reproducibility of Results, Middle Aged, Cardiovascular disease, Models, Economic, Cardiovascular Diseases, Screening, Female, I180, Quality-Adjusted Life Years, Hydroxymethylglutaryl-CoA Reductase Inhibitors, Algorithms
Abstract

Background Strategies for screening and intervening to reduce the risk of cardiovascular disease (CVD) in primary care settings need to be assessed in terms of both their costs and long-term health effects. We undertook a literature review to investigate the methodologies used. Methods In a framework of developing a new health-economic model for evaluating different screening strategies for primary prevention of CVD in Europe (EPIC-CVD project), we identified seven key modeling issues and reviewed papers published between 2000 and 2013 to assess how they were addressed. Results We found 13 relevant health-economic modeling studies of screening to prevent CVD in primary care. The models varied in their degree of complexity, with between two and 33 health states. Programmes that screen the whole population by a fixed cut-off (e.g., predicted 10-year CVD risk >20 %) identify predominantly elderly people, who may not be those most likely to benefit from long-term treatment. Uncertainty and model validation were generally poorly addressed. Few studies considered the disutility of taking drugs in otherwise healthy individuals or the budget impact of the programme. Conclusions Model validation, incorporation of parameter uncertainty, and sensitivity analyses for assumptions made are all important components of model building and reporting, and deserve more attention. Complex models may not necessarily give more accurate predictions. Availability of a large enough source dataset to reliably estimate all relevant input parameters is crucial for achieving credible results. Decision criteria should consider budget impact and the medicalization of the population as well as cost-effectiveness thresholds. Electronic supplementary material The online version of this article (doi:10.1007/s10198-015-0753-2) contains supplementary material, which is available to authorized users.

Published
2016

14. Instrumental variable analysis with a nonlinear exposure-outcome relationship

Author

Burgess, Stephen, Davies, Neil M, Thompson, Simon G, EPIC-InterAct Consortium, Burgess, Stephen [0000-0001-5365-8760], Thompson, Simon [0000-0002-5274-7814], and Apollo - University of Cambridge Repository

Subjects
Causality, Europe, Cardiovascular Diseases, Risk Factors, Linear Models, Humans, Multicenter Studies as Topic, Prospective Studies, Mendelian Randomization Analysis, Epidemiologic Methods, Body Mass Index
Abstract

BACKGROUND: Instrumental variable methods can estimate the causal effect of an exposure on an outcome using observational data. Many instrumental variable methods assume that the exposure-outcome relation is linear, but in practice this assumption is often in doubt, or perhaps the shape of the relation is a target for investigation. We investigate this issue in the context of Mendelian randomization, the use of genetic variants as instrumental variables. METHODS: Using simulations, we demonstrate the performance of a simple linear instrumental variable method when the true shape of the exposure-outcome relation is not linear. We also present a novel method for estimating the effect of the exposure on the outcome within strata of the exposure distribution. This enables the estimation of localized average causal effects within quantile groups of the exposure or as a continuous function of the exposure using a sliding window approach. RESULTS: Our simulations suggest that linear instrumental variable estimates approximate a population-averaged causal effect. This is the average difference in the outcome if the exposure for every individual in the population is increased by a fixed amount. Estimates of localized average causal effects reveal the shape of the exposure-outcome relation for a variety of models. These methods are used to investigate the relations between body mass index and a range of cardiovascular risk factors. CONCLUSIONS: Nonlinear exposure-outcome relations should not be a barrier to instrumental variable analyses. When the exposure-outcome relation is not linear, either a population-averaged causal effect or the shape of the exposure-outcome relation can be estimated.

Published
2014

15. Risk thresholds for alcohol consumption: combined analysis of individual-participant data for 599 912 current drinkers in 83 prospective studies

Author

Angela M Wood, Stephen Kaptoge, Adam S Butterworth, Peter Willeit, Samantha Warnakula, Thomas Bolton, Ellie Paige, Dirk S Paul, Michael Sweeting, Stephen Burgess, Steven Bell, William Astle, David Stevens, Albert Koulman, Randi M Selmer, W M Monique Verschuren, Shinichi Sato, Inger Njølstad, Mark Woodward, Veikko Salomaa, Børge G Nordestgaard, Bu B Yeap, Astrid Fletcher, Olle Melander, Lewis H Kuller, Beverley Balkau, Michael Marmot, Wolfgang Koenig, Edoardo Casiglia, Cyrus Cooper, Volker Arndt, Oscar H Franco, Patrik Wennberg, John Gallacher, Agustín Gómez de la Cámara, Henry Völzke, Christina C Dahm, Caroline E Dale, Manuela M Bergmann, Carlos J Crespo, Yvonne T van der Schouw, Rudolf Kaaks, Leon A Simons, Pagona Lagiou, Josje D Schoufour, Jolanda M A Boer, Timothy J Key, Beatriz Rodriguez, Conchi Moreno-Iribas, Karina W Davidson, James O Taylor, Carlotta Sacerdote, Robert B Wallace, J Ramon Quiros, Rosario Tumino, Dan G Blazer, Allan Linneberg, Makoto Daimon, Salvatore Panico, Barbara Howard, Guri Skeie, Timo Strandberg, Elisabete Weiderpass, Paul J Nietert, Bruce M Psaty, Daan Kromhout, Elena Salamanca-Fernandez, Stefan Kiechl, Harlan M Krumholz, Sara Grioni, Domenico Palli, José M Huerta, Jackie Price, Johan Sundström, Larraitz Arriola, Hisatomi Arima, Ruth C Travis, Demosthenes B Panagiotakos, Anna Karakatsani, Antonia Trichopoulou, Tilman Kühn, Diederick E Grobbee, Elizabeth Barrett-Connor, Natasja van Schoor, Heiner Boeing, Kim Overvad, Jussi Kauhanen, Nick Wareham, Claudia Langenberg, Nita Forouhi, Maria Wennberg, Jean-Pierre Després, Mary Cushman, Jackie A Cooper, Carlos J Rodriguez, Masaru Sakurai, Jonathan E Shaw, Matthew Knuiman, Trudy Voortman, Christa Meisinger, Anne Tjønneland, Hermann Brenner, Luigi Palmieri, Jean Dallongeville, Eric J Brunner, Gerd Assmann, Maurizio Trevisan, Richard F Gillum, Ian Ford, Naveed Sattar, Mariana Lazo, Simon G Thompson, Pietro Ferrari, David A Leon, George Davey Smith, Richard Peto, Rod Jackson, Emily Banks, Emanuele Di Angelantonio, John Danesh, Adam Butterworth, Monique Verschuren, Salomaa Veikko, Astrid Flecther, Manuela Bergmann, Carlos Crespo, Jolanda M.A Boer, J. Ramon Quiros, Eric B Rimm, Dan G Blazer III, Jean-Pierre Dallongeville, Richard F Gillumn, Ian Ford Ford, Simon Thompson, Epidemiology, Wood, Angela M, Kaptoge, Stephen, Butterworth, Adam S, Willeit, Peter, Warnakula, Samantha, Bolton, Thoma, Paige, Ellie, Paul, Dirk S, Sweeting, Michael, Burgess, Stephen, Bell, Steven, Astle, William, Stevens, David, Koulman, Albert, Selmer, Randi M, Verschuren, W M Monique, Sato, Shinichi, Njølstad, Inger, Woodward, Mark, Salomaa, Veikko, Nordestgaard, Børge G, Yeap, Bu B, Fletcher, Astrid, Melander, Olle, Kuller, Lewis H, Balkau, Beverley, Marmot, Michael, Koenig, Wolfgang, Casiglia, Edoardo, Cooper, Cyru, Arndt, Volker, Franco, Oscar H, Wennberg, Patrik, Gallacher, John, de la Cámara, Agustín Gómez, Völzke, Henry, Dahm, Christina C, Dale, Caroline E, Bergmann, Manuela M, Crespo, Carlos J, van der Schouw, Yvonne T, Kaaks, Rudolf, Simons, Leon A, Lagiou, Pagona, Schoufour, Josje D, Boer, Jolanda M A, Key, Timothy J, Rodriguez, Beatriz, Moreno-Iribas, Conchi, Davidson, Karina W, Taylor, James O, Sacerdote, Carlotta, Wallace, Robert B, Quiros, J Ramon, Tumino, Rosario, Blazer, Dan G, Linneberg, Allan, Daimon, Makoto, Panico, Salvatore, Howard, Barbara, Skeie, Guri, Strandberg, Timo, Weiderpass, Elisabete, Nietert, Paul J, Psaty, Bruce M, Kromhout, Daan, Salamanca-Fernandez, Elena, Kiechl, Stefan, Krumholz, Harlan M, Grioni, Sara, Palli, Domenico, Huerta, José M, Price, Jackie, Sundström, Johan, Arriola, Larraitz, Arima, Hisatomi, Travis, Ruth C, Panagiotakos, Demosthenes B, Karakatsani, Anna, Trichopoulou, Antonia, Kühn, Tilman, Grobbee, Diederick E, Barrett-Connor, Elizabeth, van Schoor, Natasja, Boeing, Heiner, Overvad, Kim, Kauhanen, Jussi, Wareham, Nick, Langenberg, Claudia, Forouhi, Nita, Wennberg, Maria, Després, Jean-Pierre, Cushman, Mary, Cooper, Jackie A, Rodriguez, Carlos J, Sakurai, Masaru, Shaw, Jonathan E, Knuiman, Matthew, Voortman, Trudy, Meisinger, Christa, Tjønneland, Anne, Brenner, Hermann, Palmieri, Luigi, Dallongeville, Jean, Brunner, Eric J, Assmann, Gerd, Trevisan, Maurizio, Gillum, Richard F, Ford, Ian, Sattar, Naveed, Lazo, Mariana, Thompson, Simon G, Ferrari, Pietro, Leon, David A, Smith, George Davey, Peto, Richard, Jackson, Rod, Banks, Emily, Di Angelantonio, Emanuele, Danesh, John, Timo Strandberg / Principal Investigator, Department of Medicine, Clinicum, HUS Internal Medicine and Rehabilitation, APH - Personalized Medicine, APH - Aging & Later Life, and Epidemiology and Data Science

Subjects
Male, FLOATING ABSOLUTE RISK, Alcohol abuse, BLOOD-PRESSURE, 030204 cardiovascular system & hematology, Alcohol Drinking/adverse effects, 0302 clinical medicine, Cardiovascular Disease, Prospective Studies, 030212 general & internal medicine, Myocardial infarction, Prospective cohort study, Human Nutrition & Health, media_common, Medicine(all), VDP::Medisinske Fag: 700::Helsefag: 800::Samfunnsmedisin, sosialmedisin: 801, Research Support, Non-U.S. Gov't, Humane Voeding & Gezondheid, Hazard ratio, Substance Abuse, Public Health, Global Health, Social Medicine and Epidemiology, ASSOCIATION, General Medicine, Middle Aged, ddc, 3. Good health, Substance abuse, Cardiovascular Diseases, CARDIOVASCULAR-DISEASE, MENDELIAN RANDOMIZATION, Female, Risk assessment, STROKE, Human, Alcohol Drinking, Lower risk, Article, 03 medical and health sciences, SDG 3 - Good Health and Well-being, Research Support, N.I.H., Extramural, US ADULTS, medicine, Journal Article, Life Science, Humans, media_common.cataloged_instance, CORONARY-HEART-DISEASE, ddc:610, Cardiovascular Diseases/etiology, European union, Beroendelära, METAANALYSIS, business.industry, MORTALITY, medicine.disease, Folkhälsovetenskap, global hälsa, socialmedicin och epidemiologi, 3121 General medicine, internal medicine and other clinical medicine, VDP::Medical disciplines: 700::Health sciences: 800::Community medicine, Social medicine: 801, business, Demography
Abstract

BACKGROUND: Low-risk limits recommended for alcohol consumption vary substantially across different national guidelines. To define thresholds associated with lowest risk for all-cause mortality and cardiovascular disease, we studied individual-participant data from 599 912 current drinkers without previous cardiovascular disease.METHODS: We did a combined analysis of individual-participant data from three large-scale data sources in 19 high-income countries (the Emerging Risk Factors Collaboration, EPIC-CVD, and the UK Biobank). We characterised dose–response associations and calculated hazard ratios (HRs) per 100 g per week of alcohol (12·5 units per week) across 83 prospective studies, adjusting at least for study or centre, age, sex, smoking, and diabetes. To be eligible for the analysis, participants had to have information recorded about their alcohol consumption amount and status (ie, non-drinker vs current drinker), plus age, sex, history of diabetes and smoking status, at least 1 year of follow-up after baseline, and no baseline history of cardiovascular disease. The main analyses focused on current drinkers, whose baseline alcohol consumption was categorised into eight predefined groups according to the amount in grams consumed per week. We assessed alcohol consumption in relation to all-cause mortality, total cardiovascular disease, and several cardiovascular disease subtypes. We corrected HRs for estimated long-term variability in alcohol consumption using 152 640 serial alcohol assessments obtained some years apart (median interval 5·6 years [5th–95th percentile 1·04–13·5]) from 71 011 participants from 37 studies.FINDINGS: In the 599 912 current drinkers included in the analysis, we recorded 40 310 deaths and 39 018 incident cardiovascular disease events during 5·4 million person-years of follow-up. For all-cause mortality, we recorded a positive and curvilinear association with the level of alcohol consumption, with the minimum mortality risk around or below 100 g per week. Alcohol consumption was roughly linearly associated with a higher risk of stroke (HR per 100 g per week higher consumption 1·14, 95% CI, 1·10–1·17), coronary disease excluding myocardial infarction (1·06, 1·00–1·11), heart failure (1·09, 1·03–1·15), fatal hypertensive disease (1·24, 1·15–1·33); and fatal aortic aneurysm (1·15, 1·03–1·28). By contrast, increased alcohol consumption was log-linearly associated with a lower risk of myocardial infarction (HR 0·94, 0·91–0·97). In comparison to those who reported drinking >0–≤100 g per week, those who reported drinking >100–≤200 g per week, >200–≤350 g per week, or >350 g per week had lower life expectancy at age 40 years of approximately 6 months, 1–2 years, or 4–5 years, respectively.INTERPRETATION: In current drinkers of alcohol in high-income countries, the threshold for lowest risk of all-cause mortality was about 100 g/week. For cardiovascular disease subtypes other than myocardial infarction, there were no clear risk thresholds below which lower alcohol consumption stopped being associated with lower disease risk. These data support limits for alcohol consumption that are lower than those recommended in most current guidelines.FUNDING: UK Medical Research Council, British Heart Foundation, National Institute for Health Research, European Union Framework 7, and European Research Council.

Published
2018

16. Predictive value for cardiovascular events of common carotid intima media thickness and its rate of change in individuals at high cardiovascular risk – Results from the PROG-IMT collaboration

Author

Sathanur R. Srinivasan, Christine Espinola-Klein, Albert Hofman, Jing Liu, Eric de Groot, Tomi-Pekka Tuomainen, Helmuth Steinmetz, Ta-Chen Su, Carmen Suárez, Maria Rosvall, Stela McLachlan, Raffaele Izzo, Marcus Dörr, Liliana Grigore, Gunnar Engström, Stein Harald Johnsen, David Yanez, Giel Nijpels, Giuseppe Danilo Norata, Fabrizio Veglia, Matthias W. Lorenz, Dong Zhao, Lu Gao, Wuxiang Xie, Damiano Baldassarre, Mauro Amato, Ellisiv B. Mathiesen, Michiel L. Bots, Dirk Sander, Joseph F. Polak, Matthieu Plichart, Holger Poppert, Matthias Sitzer, Ralph L. Sacco, Irene Schmidtmann, Heiko Uthoff, Manuel F. Landecho, Horst Bickel, Gerald S. Berenson, Oscar H. Franco, Bo Hedblad, Kazuo Kitagawa, Daniel Staub, Jackie F. Price, Caroline Schmidt, Alberico L. Catapano, Tatjana Rundek, Thapat Wannarong, M. Arfan Ikram, Kathrin Ziegelbauer, Alfonso Friera, Francesco Rozza, Kimmo Ronkainen, Jacqueline M. Dekker, Peter Willeit, Samuela Castelnuovo, Coen D.A. Stehouwer, Shuhei Okazaki, Pierre Ducimetière, Stefan Blankenberg, Johann Willeit, Oscar Beloqui, Maryam Kavousi, Henry Völzke, Kuo-Liong Chien, Grace Parraga, Bernhard Iglseder, Rafael Gabriel, Lena Bokemark, Stefan Kiechl, Cesare R. Sirtori, Göran Bergström, Jussi Kauhanen, Simon G. Thompson, Nicola De Luca, Lars Lind, Jean Philippe Empana, Moïse Desvarieux, Ulf Schminke, Hung-Ju Lin, Elena Tremoli, Lorenz, Matthias W, Gao, Lu, Ziegelbauer, Kathrin, Norata, Giuseppe Danilo, Empana, Jean Philippe, Schmidtmann, Irene, Lin, Hung-Ju, Mclachlan, Stela, Bokemark, Lena, Ronkainen, Kimmo, Amato, Mauro, Schminke, Ulf, Srinivasan, Sathanur R, Lind, Lar, Okazaki, Shuhei, Stehouwer, Coen D A, Willeit, Peter, Polak, Joseph F, Steinmetz, Helmuth, Sander, Dirk, Poppert, Holger, Desvarieux, Moise, Ikram, M Arfan, Johnsen, Stein Harald, Staub, Daniel, Sirtori, Cesare R, Iglseder, Bernhard, Beloqui, Oscar, Engström, Gunnar, Friera, Alfonso, Rozza, Francesco, Xie, Wuxiang, Parraga, Grace, Grigore, Liliana, Plichart, Matthieu, Blankenberg, Stefan, Su, Ta-Chen, Schmidt, Caroline, Tuomainen, Tomi-Pekka, Veglia, Fabrizio, Völzke, Henry, Nijpels, Giel, Willeit, Johann, Sacco, Ralph L, Franco, Oscar H, Uthoff, Heiko, Hedblad, Bo, Suarez, Carmen, Izzo, Raffaele, Zhao, Dong, Wannarong, Thapat, Catapano, Alberico, Ducimetiere, Pierre, Espinola-Klein, Christine, Chien, Kuo-Liong, Price, Jackie F, Bergström, Göran, Kauhanen, Jussi, Tremoli, Elena, Dörr, Marcu, Berenson, Gerald, Kitagawa, Kazuo, Dekker, Jacqueline M, Kiechl, Stefan, Sitzer, Matthia, Bickel, Horst, Rundek, Tatjana, Hofman, Albert, Mathiesen, Ellisiv B, Castelnuovo, Samuela, Landecho, Manuel F, Rosvall, Maria, Gabriel, Rafael, de Luca, Nicola, Liu, Jing, Baldassarre, Damiano, Kavousi, Maryam, de Groot, Eric, Bots, Michiel L, Yanez, David N, Thompson, Simon G, Lorenz, Matthias W [0000-0002-7565-1751], Apollo - University of Cambridge Repository, General practice, APH - Health Behaviors & Chronic Diseases, Epidemiology and Data Science, Interne Geneeskunde, RS: CARIM - R3.01 - Vascular complications of diabetes and the metabolic syndrome, MUMC+: HVC Pieken Maastricht Studie (9), MUMC+: MA Interne Geneeskunde (3), Epidemiology, Neurology, Radiology & Nuclear Medicine, Pirro, Matteo, and PROG-IMT study group

Subjects
Male, Myocardial Infarction, lcsh:Medicine, PROGRESSION, Cardiovascular Medicine, 030204 cardiovascular system & hematology, Vascular Medicine, Biochemistry, Carotid Intima-Media Thickness, Geographical locations, DISEASE, 0302 clinical medicine, Risk Factors, Germany, Medicine and Health Sciences, Medicine, Cardiac and Cardiovascular Systems, Myocardial infarction, skin and connective tissue diseases, lcsh:Science, ARTERY INTIMA, Stroke, Intersectoral Collaboration, POPULATION, Cardiovascular Diseases/diagnosis, METABOLIC SYNDROME, education.field_of_study, Kardiologi, Multidisciplinary, Agricultural and Biological Sciences(all), VDP::Medical disciplines: 700::Clinical medical disciplines: 750, Hazard ratio, VDP::Medisinske Fag: 700::Klinisk medisinske fag: 750, Middle Aged, Prognosis, Predictive value, 3. Good health, Europe, Neurology, Italy, Cardiovascular Diseases, HYPERTENSIVE MEN, Cardiology, cardiovascular system, Female, Research Article, medicine.medical_specialty, High cardiovascular risk, Cerebrovascular Diseases, Science, Population, 030209 endocrinology & metabolism, ATHEROSCLEROSIS RISK, Arbetsmedicin och miljömedicin, 03 medical and health sciences, Carotid intima media thickness (CIMT), Internal medicine, Humans, European Union, ddc:610, cardiovascular diseases, education, Aged, Sweden, Biochemistry, Genetics and Molecular Biology(all), Proportional hazards model, business.industry, lcsh:R, Health Risk Analysis, Correction, Occupational Health and Environmental Health, Atherosclerosis, medicine.disease, Confidence interval, Health Care, Intima-media thickness, MYOCARDIAL-INFARCTION, Medical Biophysics, lcsh:Q, VASCULAR RISK, sense organs, Carotid intima media thickness , Cardiovascular risk, People and places, Metabolic syndrome, business, FOLLOW-UP, 030217 neurology & neurosurgery, Genetics and Molecular Biology(all)
Abstract

AIMS: Carotid intima media thickness (CIMT) predicts cardiovascular (CVD) events, but the predictive value of CIMT change is debated. We assessed the relation between CIMT change and events in individuals at high cardiovascular risk. METHODS AND RESULTS: From 31 cohorts with two CIMT scans (total n = 89070) on average 3.6 years apart and clinical follow-up, subcohorts were drawn: (A) individuals with at least 3 cardiovascular risk factors without previous CVD events, (B) individuals with carotid plaques without previous CVD events, and (C) individuals with previous CVD events. Cox regression models were fit to estimate the hazard ratio (HR) of the combined endpoint (myocardial infarction, stroke or vascular death) per standard deviation (SD) of CIMT change, adjusted for CVD risk factors. These HRs were pooled across studies. In groups A, B and C we observed 3483, 2845 and 1165 endpoint events, respectively. Average common CIMT was 0.79mm (SD 0.16mm), and annual common CIMT change was 0.01mm (SD 0.07mm), both in group A. The pooled HR per SD of annual common CIMT change (0.02 to 0.43mm) was 0.99 (95% confidence interval: 0.95-1.02) in group A, 0.98 (0.93-1.04) in group B, and 0.95 (0.89-1.04) in group C. The HR per SD of common CIMT (average of the first and the second CIMT scan, 0.09 to 0.75mm) was 1.15 (1.07-1.23) in group A, 1.13 (1.05-1.22) in group B, and 1.12 (1.05-1.20) in group C. CONCLUSIONS: We confirm that common CIMT is associated with future CVD events in individuals at high risk. CIMT change does not relate to future event risk in high-risk individuals. The PROG-IMT project, which includes this publication, has been funded by the German Research Foundation (Deutsche Forschungsgemeinschaft, www.dfg.de) under the grants DFG Lo 1569/2-1 and DFG Lo 1569/2-3, received by MWL. The DFG had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. Simon Thompson is supported by the British heart Foundation (CH/12/2/29428). Some of the contributing studies were funded by different parties, as listed in the acknowledgement section. Here, too, the funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. Sí

Published
2018

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