Background: The relevance of blood lipid concentrations to long-term incidence of cardiovascular disease and the relevance of lipid-lowering therapy for cardiovascular disease outcomes is unclear. We investigated the cardiovascular disease risk associated with the full spectrum of bloodstream non-HDL cholesterol concentrations. We also created an easy-to-use tool to estimate the long-term probabilities for a cardiovascular disease event associated with non-HDL cholesterol and modelled its risk reduction by lipid-lowering treatment. Methods: In this risk-evaluation and risk-modelling study, we used Multinational Cardiovascular Risk Consortium data from 19 countries across Europe, Australia, and North America. Individuals without prevalent cardiovascular disease at baseline and with robust available data on cardiovascular disease outcomes were included. The primary composite endpoint of atherosclerotic cardiovascular disease was defined as the occurrence of the coronary heart disease event or ischaemic stroke. Sex-specific multivariable analyses were computed using non-HDL cholesterol categories according to the European guideline thresholds, adjusted for age, sex, cohort, and classical modifiable cardiovascular risk factors. In a derivation and validation design, we created a tool to estimate the probabilities of a cardiovascular disease event by the age of 75 years, dependent on age, sex, and risk factors, and the associated modelled risk reduction, assuming a 50% reduction of non-HDL cholesterol. Findings: Of the 524 444 individuals in the 44 cohorts in the Consortium database, we identified 398 846 individuals belonging to 38 cohorts (184 055 [48·7%] women; median age 51·0 years [IQR 40·7–59·7]). 199 415 individuals were included in the derivation cohort (91 786 [48·4%] women) and 199 431 (92 269 [49·1%] women) in the validation cohort. During a maximum follow-up of 43·6 years (median 13·5 years, IQR 7·0–20·1), 54 542 cardiovascular endpoints occurred. Incidence curve analyses showed progressively higher 30-year cardiovascular disease event-rates for increasing non-HDL cholesterol categories (from 7·7% for non-HDL cholesterol <2·6 mmol/L to 33·7% for ≥5·7 mmol/L in women and from 12·8% to 43·6% in men; p<0·0001). Multivariable adjusted Cox models with non-HDL cholesterol lower than 2·6 mmol/L as reference showed an increase in the association between non-HDL cholesterol concentration and cardiovascular disease for both sexes (from hazard ratio 1·1, 95% CI 1·0–1·3 for non-HDL cholesterol 2·6 to <3·7 mmol/L to 1·9, 1·6–2·2 for ≥5·7 mmol/L in women and from 1·1, 1·0–1·3 to 2·3, 2·0–2·5 in men). The derived tool allowed the estimation of cardiovascular disease event probabilities specific for non-HDL cholesterol with high comparability between the derivation and validation cohorts as reflected by smooth calibration curves analyses and a root mean square error lower than 1% for the estimated probabilities of cardiovascular disease. A 50% reduction of non-HDL cholesterol concentrations was associated with reduced risk of a cardiovascular disease event by the age of 75 years, and this risk reduction was greater the earlier cholesterol concentrations were reduced. Interpretation: Non-HDL cholesterol concentrations in blood are strongly associated with long-term risk of atherosclerotic cardiovascular disease. We provide a simple tool for individual long-term risk assessment and the potential benefit of early lipid-lowering intervention. These data could be useful for physician–patient communication about primary prevention strategies. Funding: EU Framework Programme, UK Medical Research Council, and German Centre for Cardiovascular Research.
Bibliographical noteFunding Information:
FJB and CW report grants from the ASPIRE Cardiovascular grant award, Pfizer, during the conduct of the study. CW reports personal fees from Amgen and Sanofi, outside the submitted work. VS reports personal fees from Novo Nordisk and Sanofi and grants from Bayer, outside the submitted work. SSö reports personal fees from Actelion and Amgen, outside the submitted work. PA reports personal fees from Total, Genoscreen, and Fondation Alzheimer, outside the submitted work. JF reports personal fees from Merck, Amgen, Servier, and Sanofi, outside the submitted work. JES reports grants from the Australian Commonwealth Department of Health and Aged Care, Abbott Australasia, Alphapharm, AstraZeneca, Aventis Pharma, Bristol-Myers Squibb, Eli Lilly, GlaxoSmithKline, Janssen-Cilag, Merck Lipha, Merck, Novartis, Novo Nordisk, Pharmacia and Upjohn, Pfizer, Sanofi Synthelabo, Servier Laboratories, the Australian Kidney Foundation, and Diabetes Australia, during the conduct of the study; and personal fees from AstraZeneca, Mylan, Boehringer Ingelheim, Sanofi, Merck, Novo Nordisk, Eli Lilly, and Abbott Laboratories, outside the submitted work. RSV is supported in part by the Evans Medical Foundation and the Jay and Louis Coffman Endowment from the Department of Medicine (Boston University School of Medicine, Boston, MA, USA). SSh reports grants from the US National Institutes of Health, outside the submitted work. JAdL reports personal fees from Amgen, Regeneron, and Esperion, outside the submitted work. TO reports grants and personal fees from Roche, Abbott, and Novartis, personal fees from Siemens and Bayer, and grants from Singulex and Somalogic, outside the submitted work. KK reports grants from the EU and the UK Medical Research Council, during the conduct of the study. UL reports personal fees from The Medicines Company, Amgen, Sanofi, Berlin Chemie, Abbott Laboratories, AstraZeneca, and Novartis, and grants and personal fees from Bayer, outside the submitted work. SB reports grants and personal fees from Abbott Laboratories, Bayer, Siemens, and ThermoFisher Scientific, grants from Singulex, and personal fees from AstraZeneca, Amgen, Medtronic, Pfizer, and Roche, outside the submitted work. All other authors declare no competing interests.
This Article was prepared using data from the US National Heart, Lung, and Blood Institute Biologic Specimen and Data Repository Information Coordinating Center for the following cohorts: Cardiovascular Health Study, Atherosclerosis Risk in Communities Study, Jackson Heart Study, Multi-Ethnic Study of Atherosclerosis, and Framingham Heart Study. The BiomarCaRE Project is funded by the EU Seventh Framework Programme ( FP7/2007–2013 ) under grant agreement HEALTH-F2–2011–278913. The activities of the MORGAM data centre have been also sustained by funding by the 9th EU Framework Programme since 1998 ( EU FP7 project CHANCES; HEALTH-F3–2010–242244). A part of the biomarker determinations in the population cohorts was funded by the UK Medical Research Council ( G0601463 , 80983 : Biomarkers in the MORGAM Populations). Additional funding was provided by the German Centre for Cardiovascular Research ( 81Z1710101 ). Further detailed funding information is provided in the appendix (pp 48–50) .
© 2019 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license
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