Search

Your search keyword '"Ference, B.A."' showing total 15 results
Start Over Author "Ference, B.A."
15 results on '"Ference, B.A."'

4. SCORE2 risk prediction algorithms: new models to estimate 10-year risk of cardiovascular disease in Europe

Author

Hageman, S., Pennells, L., Ojeda, F., Kaptoge, S., Kuulasmaa, K., Vries, T. de, Xu, Z., Kee, F., Chung, R., Wood, A., McEvoy, J.W., Veronesi, G., Bolton, T., Dendale, P., Ference, B.A., Halle, M., Timmis, A., Vardas, P., Danesh, J., Graham, I., Salomaa, V., Visseren, F., Bacquer, D. de, Blankenberg, S., Dorresteijn, J., Angelantonio, E. di, Achenbach, S., Aleksandrova, K., Amiano, P., Amouyel, P., Andersson, J., Bakker, S.J.L., Costa, R.B.D., Beulens, J.W.J., Blaha, M., Bobak, M., Boer, J.M.A., Bonet, C., Bonnet, F., Boutron-Ruault, M.C., Braaten, T., Brenner, H., Brunner, F., Brunner, E.J., Brunstrom, M., Buring, J., Butterworth, A.S., Capkova, N., Cesana, G., Chrysohoou, C., Colorado-Yohar, S., Cook, N.R., Cooper, C., Dahm, C.C., Davidson, K., Dennison, E., Castelnuovo, A. di, Donfrancesco, C., Dorr, M., Dorynska, A., Eliasson, M., Engstrom, G., Ferrari, P., Ferrario, M., Ford, I., Fu, M., Gansevoort, R.T., Giampaoli, S., Gillum, R.F., Camara, A.G. de la, Grassi, G., Hansson, P.O., Huculeci, R., Hveem, K., Iacoviello, L., Ikram, M.K., Jorgensen, T., Joseph, B., Jousilahti, P., Jukema, J.W., Kaaks, R., Katzke, V., Kavousi, M., Kiechl, S., Klotsche, J., Konig, W., Kronmal, R.A., Kubinova, R., Kucharska-Newton, A., Lall, K., Lehmann, N., Leistner, D., Linneberg, A., Pablos, D.L., Lorenz, T., Lu, W.T., Luksiene, D., Lyngbakken, M., Magnussen, C., Malyutina, S., Ibanez, A.M., Masala, G., Mathiesen, E.B., Matsushita, K., Meade, T.W., Melander, O., Meyer, H.E., Moons, K.G.M., Moreno-Iribas, C., Muller, D., Munzel, T., Nikitin, Y., Nordestgaard, B.G., Omland, T., Onland, C., Overvad, K., Packard, C., Pajak, A., Palmieri, L., Panagiotakos, D., Panico, S., Perez-Cornago, A., Peters, A., Pietila, A., Pikhart, H., Psaty, B.M., Quarti-Trevano, F., Garcia, J.R.Q., Riboli, E., Ridker, P.M., Rodriguez, B., Rodriguez-Barranco, M., Rosengren, A., Roussel, R., Sacerdote, C., Sans, S., Sattar, N., Schiborn, C., Schmidt, B., Schottker, B., Schulze, M., Schwartz, J.E., Selmer, R.M., Shea, S., Shipley, M.J., Sieri, S., Soderberg, S., Sofat, R., Tamosiunas, A., Thorand, B., Tillmann, T., Tjonneland, A., Tong, T.Y.N., Trichopoulou, A., Tumino, R., Tunstall-Pedoe, H., Tybjaerg-Hansen, A., Tzoulaki, J., Heijden, A. van der, Schouw, Y.T. van der, Verschuren, W.M.M., Volzke, H., Waldeyer, C., Wareham, N.J., Weiderpass, E., Weidinger, F., Wild, P., Willeit, J., Willeit, P., Wilsgaard, T., Woodward, M., Zeller, T., Zhang, D.D., Zhou, B., SCORE2 Working Grp, ESC Cardiovasc Risk Collaboration, collaboration, SCORE2 working group and ESC Cardiovascular risk, Groningen Institute for Organ Transplantation (GIOT), Groningen Kidney Center (GKC), Cardiovascular Centre (CVC), Epidemiology, Neurology, Achenbach, S, Aleksandrova, K, Amiano, P, San Sebastian, D, Amouyel, P, Andersson, J, Bakker, S, Da Providencia Costa, R, Beulens, J, Blaha, M, Bobak, M, Boer, J, Bonet, C, Bonnet, F, Boutron-Ruault, M, Braaten, T, Brenner, H, Brunner, F, Brunner, E, Brunström, M, Buring, J, Butterworth, A, Capkova, N, Cesana, G, Chrysohoou, C, Colorado-Yohar, S, Cook, N, Cooper, C, Dahm, C, Davidson, K, Dennison, E, Di Castelnuovo, A, Donfrancesco, C, Dörr, M, Doryńska, A, Eliasson, M, Engström, G, Ferrari, P, Ferrario, M, Ford, I, Fu, M, Gansevoort, R, Giampaoli, S, Gillum, R, Gómez de la Cámara, A, Grassi, G, Hansson, P, Huculeci, R, Hveem, K, Iacoviello, L, Ikram, M, Jørgensen, T, Joseph, B, Jousilahti, P, Wouter Jukema, J, Kaaks, R, Katzke, V, Kavousi, M, Kiechl, S, Klotsche, J, König, W, Kronmal, R, Kubinova, R, Kucharska-Newton, A, Läll, K, Lehmann, N, Leistner, D, Linneberg, A, Pablos, D, Lorenz, T, Lu, W, Luksiene, D, Lyngbakken, M, Magnussen, C, Malyutina, S, Ibañez, A, Masala, G, Mathiesen, E, Matsushita, K, Meade, T, Melander, O, Meyer, H, Moons, K, Moreno-Iribas, C, Muller, D, Münzel, T, Nikitin, Y, Nordestgaard, B, Omland, T, Onland, C, Overvad, K, Packard, C, Pająk, A, Palmieri, L, Panagiotakos, D, Panico, S, Perez-Cornago, A, Peters, A, Pietilä, A, Pikhart, H, Psaty, B, Quarti-Trevano, F, Garcia, J, Riboli, E, Ridker, P, Rodriguez, B, Rodriguez-Barranco, M, Rosengren, A, Roussel, R, Sacerdote, C, S, S, Sattar, N, Schiborn, C, Schmidt, B, Schöttker, B, Schulze, M, Schwartz, J, Selmer, R, Shea, S, Shipley, M, Sieri, S, Söderberg, S, Sofat, R, Tamosiunas, A, Thorand, B, Tillmann, T, Tjønneland, A, Tong, T, Trichopoulou, A, Tumino, R, Tunstall-Pedoe, H, Tybjaerg-Hansen, A, Tzoulaki, J, van der Heijden, A, van der Schouw, Y, Verschuren, W, Völzke, H, Waldeyer, C, Wareham, N, Weiderpass, E, Weidinger, F, Wild, P, Willeit, J, Willeit, P, Wilsgaard, T, Woodward, M, Zeller, T, Zhang, D, Zhou, B, and Apollo - University of Cambridge Repository

Subjects
Male, Cardiology, RATIONALE, Blood Pressure, Disease, 030204 cardiovascular system & hematology, PROFILE, ACUTE CORONARY EVENTS, VALIDATION, Europe/epidemiology, 03 medical and health sciences, 0302 clinical medicine, SDG 3 - Good Health and Well-being, DESIGN, Clinical Research, Risk Factors, Diabetes mellitus, medicine, PARTICIPANTS, Humans, 030212 general & internal medicine, Risk factor, Aged, Primary prevention, business.industry, 10-year CVD risk, Incidence (epidemiology), Cardiovascular Diseases/epidemiology, Risk Prediction, Cardiovascular Disease, Primary Prevention, 10-year Cvd Risk, External validation, PRIMARY-CARE, Middle Aged, medicine.disease, Cardiovascular disease, Risk prediction, 3. Good health, Europe, Prediction algorithms, Blood pressure, Cardiovascular Diseases, Smoking status, Female, Cardiology and Cardiovascular Medicine, business, Algorithms, Demography
Abstract

Aims The aim of this study was to develop, validate, and illustrate an updated prediction model (SCORE2) to estimate 10-year fatal and non-fatal cardiovascular disease (CVD) risk in individuals without previous CVD or diabetes aged 40-69 years in Europe.Methods and results We derived risk prediction models using individual-participant data from 45 cohorts in 13 countries (677 684 individuals, 30 121 CVD events). We used sex-specific and competing risk-adjusted models, including age, smoking status, systolic blood pressure, and total- and HDL-cholesterol. We defined four risk regions in Europe according to country-specific CVD mortality, recalibrating models to each region using expected incidences and risk factor distributions. Region-specific incidence was estimated using CVD mortality and incidence data on 10 776 466 individuals. For external validation, we analysed data from 25 additional cohorts in 15 European countries (1 133 181 individuals, 43 492 CVD events). After applying the derived risk prediction models to external validation cohorts, C-indices ranged from 0.67 (0.65-0.68) to 0.81 (0.76-0.86). Predicted CVD risk varied several-fold across European regions. For example, the estimated 10-year CVD risk for a 50-year-old smoker, with a systolic blood pressure of 140 mmHg, total cholesterol of 5.5 mmol/L, and HDL-cholesterol of 1.3 mmol/L, ranged from 5.9% for men in low- risk countries to 14.0% for men in very high-risk countries, and from 4.2% for women in low-risk countries to 13.7% for women in very high-risk countries.Conclusion SCORE2-a new algorithm derived, calibrated, and validated to predict 10-year risk of first-onset CVD in European populations-enhances the identification of individuals at higher risk of developing CVD across Europe. Acknowledgements We thank investigators and participants of the several studies that contributed data to the Emerging Risk Factors Collaboration (ERFC). This research has been conducted using the UK Biobank Resource under Application Number 26865. Data from the Clinical Practice Research Datalink (CPRD) were obtained under licence from the UK Medicines and Healthcare products Regulatory Agency (protocol 162RMn2). CPRD uses data provided by patients and collected by the NHS as part of their care and support. We thank all EPIC participants and staff for their contribution to the study, the laboratory teams at the Medical Research Council Epidemiology Unit for sample management and Cambridge Genomic Services for genotyping, Sarah Spackman for data management and the team at the EPIC-CVD Coordinating Centre for study co-ordination and administration. Funding The ERFC co-ordinating centre was underpinned by programme grants from the British Heart Foundation (SP/09/002; RG/13/13/30194; RG/18/13/33946), BHF Centre of Research Excellence (RE/18/1/34212), the UK Medical Research Council (MR/L003120/1), and the National Institute for Health Research (NIHR) Cambridge Biomedical Research Centre (BRC1215-20014), with project-specific support received from the UK NIHR [*], British United Provident Association UK Foundation and an unrestricted educational grant from GlaxoSmithKline. A variety of funding sources have supported recruitment, follow-up, and laboratory measurements in the studies contributing data to the ERFC, which are listed on the ERFC website (www.phpc.cam.ac.uk/ceu/erfc/list-of-studies). *The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care. This work was supported by Health Data Research UK, which is funded by the UK Medical Research Council, Engineering and Physical Sciences Research Council, Economic and Social Research Council, Department of Health and Social Care (England), Chief Scientist Office of the Scottish Government Health and Social Care Directorates, Health and Social Care Research and Development Division (Welsh Government), Public Health Agency (Northern Ireland), British Heart Foundation, and Wellcome. The MORGAM Project has received funding from EU projects MORGAM (Biomed BMH4-CT98-3183), GenomEUtwin (FP5, QLG2-CT-2002-01254), ENGAGE (FP7, HEALTH-F4-2007-201413),CHANCES (FP7, HEALTH-F3-2010-242244), BiomarCaRE (FP7,HEALTH-F2-2011-278913), euCanSHare (Horizon 2020, No. 825903) and AFFECT-EU (Horizon 2020, No. 847770); and Medical Research Council, London (G0601463, No. 80983: Biomarkers in the MORGAM Populations). This has supported central coordination, workshops and part of the activities of the MORGAM Data Centre, the MORGAM Laboratories and the MORGAM Participating Centres EPIC-CVD was funded by the European Research Council (268834), and the European Commission Framework Programme 7 (HEALTH-F2-2012-279233). This work was supported by the Estonian Research Council grant PUTs (PRG687, PUT1660, PUT1665, PRG184), by European Union through the European Regional Development Fund project no. MOBERA5 (Norface Network project no 462.16.107), by the Green ICT programme under Norway Grants 2014 – 2021 (grant number EU53928), by the European Union through Horizon 2020 grant no. 810645 and through the European Regional Development Fund (Project No. 2014-2020.4.01.16-0125) and by the PRECISE4Q consortium. PRECISE4Q project has received funding from the European Union’s Horizon 2020 Research and Innovation Programme under Grant agreement 777107. This work was partly funded through the CoMorMent project. CoMorMent has received funding from the European Union’s Horizon 2020 Research and Innovation Programme under Grant agreement 847776. The KORA study was initiated and financed by the Helmholtz Zentrum Mu¨nchen—German Research Center for Environmental Health, which is funded by the German Federal Ministry of Education and Research (BMBF) and by the State of Bavaria. The KORA study was supported by a research grant from the Virtual Institute of Diabetes Research (Helmholtz Zentrum Mu¨nchen), the Clinical Cooperation Group Diabetes between Ludwig-Maximilians-Universita¨t Mu¨nchen and Helmholtz Zentrum Mu¨nchen, and by the German Diabetes Center (DDZ). The HAPIEE project, Institute, was supported by grants from the Wellcome Trust (064947/Z/01/Z; WT081081) and US National Institute on Aging (1R01 and AG23522). The co-ordination of EPIC is financially supported by International Agency for Research on Cancer (IARC) and also by the Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, which has additional infrastructure support provided by the NIHR Imperial Biomedical Research Centre (BRC). The national cohorts are supported by: Danish Cancer Society (Denmark); Ligue Contre le Cancer, Institut Gustave Roussy, Mutuelle Ge´ne´rale de l’Education Nationale, Institut National de la Sante´ et de la Recherche Me´dicale (INSERM) (France); German Cancer Aid, German Cancer Research Center (DKFZ), German Institute of Human Nutrition Potsdam Rehbruecke (DIfE), Federal Ministry of Education and Research (BMBF) (Germany); Associazione Italiana per la Ricerca sul Cancro-AIRC-Italy, Compagnia di SanPaolo and National Research Council (Italy); Dutch 2448 SCORE2 working group and ESC Cardiovascular Risk Collaboration Ministry of Public Health, Welfare and Sports (VWS), Netherlands Cancer Registry (NKR), LK Research Funds, Dutch Prevention Funds, Dutch ZON (Zorg Onderzoek Nederland), World Cancer Research Fund (WCRF), Statistics Netherlands (The Netherlands); Health Research Fund (FIS)—Instituto de Salud Carlos III (ISCIII), Regional Governments of Andalucı´a, Asturias, Basque Country, Murcia and Navarra, and the Catalan Institute of Oncology—ICO (Spain); Swedish Cancer Society, Swedish Research Council and County Councils of Ska˚ne and Va¨sterbotten (Sweden); Cancer Research UK (14136 to EPIC-Norfolk; C8221/A29017 to EPIC-Oxford), Medical Research Council (1000143 to EPIC-Norfolk; MR/M012190/1 to EPIC-Oxford) (United Kingdom)

Published
2021
Full Text
View/download PDF

5. SCORE2-OP risk prediction algorithms: estimating incident cardiovascular event risk in older persons in four geographical risk regions

Author

Vries, T.I. de, Cooney, M.T., Selmer, R.M., Hageman, S.H.J., Pennells, L.A., Wood, A., Kaptoge, S., Xu, Z., Westerink, J., Rabanal, K.S., Tell, G.S., Meyer, H.E., Igland, J., Ariansen, I., Matsushita, K., Blaha, M.J., Nambi, V., Peters, R., Beckett, N., Antikainen, R., Bulpitt, C.J., Muller, M., Emmelot-Vonk, M.H., Trompet, S., Jukema, W., Ference, B.A., Halle, M., Timmis, A.D., Vardas, P.E., Dorresteijn, J.A.N., Bacquer, D. de, Angelantonio, E. di, Visseren, F.L.J., Graham, I.M., SCORE2-OP Working Grp, ESC Cardiovasc Risk Collaboration, Internal medicine, ACS - Atherosclerosis & ischemic syndromes, and APH - Aging & Later Life

Subjects
Cardiovascular event, Male, Myocardial Infarction, Blood Pressure, 030204 cardiovascular system & hematology, 03 medical and health sciences, 0302 clinical medicine, Clinical Research, Risk Factors, Primary prevention, Environmental health, Medicine, Humans, 030212 general & internal medicine, Aged, Risk assessment, Aged, 80 and over, business.industry, 10-Year CVD risk, Cardiovascular disease, Risk prediction, Stroke, Prediction algorithms, Older persons, Cardiovascular Diseases, Heart Disease Risk Factors, Female, Cardiology and Cardiovascular Medicine, business, Algorithms
Abstract

Aims The aim of this study was to derive and validate the SCORE2-Older Persons (SCORE2-OP) risk model to estimate 5- and 10-year risk of cardiovascular disease (CVD) in individuals aged over 70 years in four geographical risk regions. Methods and results Sex-specific competing risk-adjusted models for estimating CVD risk (CVD mortality, myocardial infarction, or stroke) were derived in individuals aged over 65 without pre-existing atherosclerotic CVD from the Cohort of Norway (28 503 individuals, 10 089 CVD events). Models included age, smoking status, diabetes, systolic blood pressure, and total- and high-density lipoprotein cholesterol. Four geographical risk regions were defined based on country-specific CVD mortality rates. Models were recalibrated to each region using region-specific estimated CVD incidence rates and risk factor distributions. For external validation, we analysed data from 6 additional study populations {338 615 individuals, 33 219 CVD validation cohorts, C-indices ranged between 0.63 [95% confidence interval (CI) 0.61–0.65] and 0.67 (0.64–0.69)}. Regional calibration of expected-vs.-observed risks was satisfactory. For given risk factor profiles, there was substantial variation across the four risk regions in the estimated 10-year CVD event risk. Conclusions The competing risk-adjusted SCORE2-OP model was derived, recalibrated, and externally validated to estimate 5- and 10-year CVD risk in older adults (aged 70 years or older) in four geographical risk regions. These models can be used for communicating the risk of CVD and potential benefit from risk factor treatment and may facilitate shared decision-making between clinicians and patients in CVD risk management in older persons.

Published
2021
Full Text
View/download PDF

6. 2019 ESC/EAS Guidelines for the management of dyslipidaemias

Author

Mach, F., Baigent, C., Catapano, A.L., Koskinas, K.C., Casula, M., Badimon, L., Chapman, M.J., Backer, G.G. de, Delgado, V., Ference, B.A., Graham, I.M., Halliday, A., Landmesser, U., Mihaylova, B., Pedersen, T.R., Riccardi, G., Richter, D.J., Sabatine, M.S., Taskinen, M.R., Tokgozoglu, L., Wiklund, O., Nibouche, D., Zelveian, P.H., Siostrzonek, P., Najafov, R., Borne, P. van de, Pojskic, B., Postadzhiyan, A., Kypris, L., Spinar, J., Larsen, M.L., Eldin, H.S., Viigimaa, M., Strandberg, T.E., Ferrieres, J., Agladze, R., Laufs, U., Rallidis, L., Bajnok, L., Gudjonsson, T., Maher, V., Henkin, Y., Gulizia, M.M., Mussagaliyeva, A., Bajraktari, G., Kerimkulova, A., Latkovskis, G., Hamoui, O., Slapikas, R., Visser, L., Dingli, P., Ivanov, V., Boskovic, A., Nazzi, M., Visseren, F., Mitevska, I., Retterstol, K., Jankowski, P., Fontes-Carvalho, R., Gaita, D., Ezhov, M., Foscoli, M., Giga, V., Pella, D., Fras, Z., Isla, L.P. de, Hagstrom, E., Lehmann, R., Abid, L., Ozdogan, O., Mitchenko, O., Patel, R.S., Windecker, S., Aboyans, V., Collet, J.P., Dean, V., Fitzsimons, D., Gale, C.P., Grobbee, D., Halvorsen, S., Hindricks, G., Iung, B., Juni, P., Katus, H.A., Leclercq, C., Lettino, M., Lewis, B.S., Merkely, B., Mueller, C., Petersen, S., Petronio, A.S., Roffi, M., Shlyakhto, E., Simpson, I.A., Sousa-Uva, M., Touyz, R.M., Task Force Members, ESC Natl Cardiac Soc, and ESC Committee Practice Guidelines

Published
2020

9. Apolipoprotein B Particles and Cardiovascular Disease: A Narrative Review.

Author

Thanassoulis G., Ference B.A., Catapano A., Glavinovic T., Sniderman A.D., Pencina M., Navar A.M., Thanassoulis G., Ference B.A., Catapano A., Glavinovic T., Sniderman A.D., Pencina M., and Navar A.M.

Abstract

Importance: The conventional model of atherosclerosis presumes that the mass of cholesterol within very low-density lipoprotein particles, low-density lipoprotein particles, chylomicron, and lipoprotein (a) particles in plasma is the principal determinant of the mass of cholesterol that will be deposited within the arterial wall and will drive atherogenesis. However, each of these particles contains one molecule of apolipoprotein B (apoB) and there is now substantial evidence that apoB more accurately measures the atherogenic risk owing to the apoB lipoproteins than does low-density lipoprotein cholesterol or non-high-density lipoprotein cholesterol. Observations: Cholesterol can only enter the arterial wall within apoB particles. However, the mass of cholesterol per apoB particle is variable. Therefore, the mass of cholesterol that will be deposited within the arterial wall is determined by the number of apoB particles that are trapped within the arterial wall. The number of apoB particles that enter the arterial wall is determined primarily by the number of apoB particles within the arterial lumen. However, once within the arterial wall, smaller cholesterol-depleted apoB particles have a greater tendency to be trapped than larger cholesterol-enriched apoB particles because they bind more avidly to the glycosaminoglycans within the subintimal space of the arterial wall. Thus, a cholesterol-enriched particle would deposit more cholesterol than a cholesterol-depleted apoB particle whereas more, smaller apoB particles that enter the arterial wall will be trapped than larger apoB particles. The net result is, with the exceptions of the abnormal chylomicron remnants in type III hyperlipoproteinemia and lipoprotein (a), all apoB particles are equally atherogenic. Conclusions and Relevance: Apolipoprotein B unifies, amplifies, and simplifies the information from the conventional lipid markers as to the atherogenic risk attributable to the apoB lipoproteins..Copyright © 20

Published
2020

13. LPA variants, risk of coronary disease, and estimated clinical benefit of lipoprotein(a) lowering therapies: A mendelian randomization analysis

Author

Ference, B.A., primary, Burgess, S., additional, Staley, J.R., additional, Freitag, D.F., additional, Mason, A.M., additional, Nielsen, S.F., additional, Willeit, P., additional, Young, R., additional, Surendran, P., additional, Karthikeyan, S., additional, Bolton, T.R., additional, Peters, J.E., additional, Kamstrup, P.R., additional, Tybjærg-Hansen, A., additional, Benn, M., additional, Langsted, A., additional, Schnohr, P., additional, Vedel-Krogh, S., additional, Kobylecki, C.J., additional, Ford, I., additional, Packard, C., additional, Trompet, S., additional, Jukema, J.W., additional, Sattar, N., additional, Di Angelantonio, E., additional, Saleheen, D., additional, Howson for the CHD Exome+ Consor, J.M.M., additional, Nordestgaard, B.G., additional, Butterworth for the EPIC-CVD Consorti, A., additional, and Danesh, J., additional

Published
2018
Full Text
View/download PDF

14. Low-density lipoproteins cause atherosclerotic cardiovascular disease. 1. Evidence from genetic, epidemiologic, and clinical studies. A consensus statement from the European Atherosclerosis Society Consensus Panel.

Author

Universitat Rovira i Virgili, Ference B.A., Ginsberg H.N., Graham I., Ray K.K., Packard C.J., Bruckert E., Hegele R.A., Krauss R.M., Raal F.J., Schunkert H., Watt G.F., Borén J., Fazio S., Horton J.D., Masana L., Nicholls S.J., Nordestgaard B.G., Van De Sluis B., Taskinen M.R., Tokgözo?lu L., Landmesser U., Laufs U., Wiklund O., Stock J.K., Chapman M.J., Catapano A.L., Universitat Rovira i Virgili, and Ference B.A., Ginsberg H.N., Graham I., Ray K.K., Packard C.J., Bruckert E., Hegele R.A., Krauss R.M., Raal F.J., Schunkert H., Watt G.F., Borén J., Fazio S., Horton J.D., Masana L., Nicholls S.J., Nordestgaard B.G., Van De Sluis B., Taskinen M.R., Tokgözo?lu L., Landmesser U., Laufs U., Wiklund O., Stock J.K., Chapman M.J., Catapano A.L.

Abstract

To appraise the clinical and genetic evidence that low-density lipoproteins (LDLs) cause atherosclerotic cardiovascular disease (ASCVD).We assessed whether the association between LDL and ASCVD fulfils the criteria for causality by evaluating the totality of evidence from genetic studies, prospective epidemiologic cohort studies, Mendelian randomization studies, and randomized trials of LDL-lowering therapies. In clinical studies, plasma LDL burden is usually estimated by determination of plasma LDL cholesterol level (LDL-C). Rare genetic mutations that cause reduced LDL receptor function lead to markedly higher LDL-C and a dose-dependent increase in the risk of ASCVD, whereas rare variants leading to lower LDL-C are associated with a correspondingly lower risk of ASCVD. Separate meta-analyses of over 200 prospective cohort studies, Mendelian randomization studies, and randomized trials including more than 2 million participants with over 20 million person-years of follow-up and over 150?000 cardiovascular events demonstrate a remarkably consistent dose-dependent log-linear association between the absolute magnitude of exposure of the vasculature to LDL-C and the risk of ASCVD; and this effect appears to increase with increasing duration of exposure to LDL-C. Both the naturally randomized genetic studies and the randomized intervention trials consistently demonstrate that any mechanism of lowering plasma LDL particle concentration should reduce the risk of ASCVD events proportional to the absolute reduction in LDL-C and the cumulative duration of exposure to lower LDL-C, provided that the achieved reduction in LDL-C is concordant with the reduction in LDL particle number and that there are no competing deleterious off-target effects.Consistent evidence from numerous and

Published
2017

Catalog

Books, media, physical & digital resources
See catalog results