European Journal of Preventive Cardiology

Chan, Jeffrey Shi Kai; Satti, Danish Iltaf; Lee, Yan Hiu Athena; Hui, Jeremy Man Ho; Lee, Teddy Tai Loy; Chou, Oscar Hou In; Wai, Abraham Ka Chung; Ciobanu, Ana; Liu, Ying; Liu, Tong; Roever, Leonardo; Biondi-Zoccai, Giuseppe; Zhang, Qingpeng; Cheung, Bernard Man Yung; Zhou, Jiandong; Tse, Gary

doi: 10.1093/eurjpc/zwac097pmid: 35653641

Dyslipidaemia is associated with elevated cardiovascular risks, with the INTERHEART study observing a tripling of myocardial infarction (MI) risk in patients with dyslipidaemia.1 Most studies focused on mean levels or point estimates of low-density lipoprotein cholesterol (LDL-C) and high-density lipoprotein cholesterol (HDL-C), despite well-known visit-to-visit variability.2 Visit-to-visit cholesterol variability, reflecting fluctuations in cholesterol levels between visits, is prognostic for some adverse cardiovascular outcomes such as cardiac arrhythmias and mortality.3,4 Nonetheless, associations between cholesterol variability and heart failure (HF) remain unclear. This study therefore investigated the associations between LDL-C and HDL-C variabilities and the risk of new-onset HF and major adverse cardiovascular outcomes. This retrospective cohort study was performed according to the Declaration of Helsinki and approved by the local institutional review board. Patient consent was not required since deidentified data were used. Underlying data are available upon reasonable request to the corresponding authors. Data were retrieved from Clinical Data Analysis and Reporting System, a city-wide medical database in Hong Kong. Diagnoses are coded by International Classification of Diseases Ninth revision codes, which were used for identifying outcomes and comorbidities (see Supplementary material online, Table S1). Mortality data were obtained from the Hong Kong Death registry, a governmental registry of all Hong Kong citizens’ death records. Both have been used in prior publications.5 Adult patients who attended any family medicine clinic in Hong Kong between 1 January 2000–31 December 2003 with more than three sets of fasting lipid profiles were included. Exclusion criteria were known HF, prior MI, using HF medications, pregnancy, known human immunodeficiency virus infection, and less than three sets of lipid profiles. Patients were followed up until 31 December 2019. Primary outcome was new-onset HF. Secondary outcomes were cardiovascular mortality, and MI. Fasting LDL-C, HDL-C, and total cholesterol during index period were recorded, from which mean, standard deviation (SD), and coefficient of variation (CV) were calculated. As SD is only interpretable together with mean, CV (⁠ SDmean ⁠) was reported to facilitate interpretation. Student’s t-test and Fisher’s exact test were used to compare continuous and binary variables between subgroups, respectively. Univariable Cox regression identified covariates (P < 0.010; Supplementary material online, Tables S3–5) for adjustments in multivariable Cox regression which assessed associations between cholesterol measurements and outcomes. A prespecified subgroup analysis by the use of any lipid-lowering medication(s) at baseline was performed. Marginal effects were calculated by identifying partial derivatives of cholesterol measurements in Cox models for each data unit. All p-values were two-sided, with P < 0.05 considered significant. All analyses were performed on Statistical Package for Social Sciences (v25.0), Stata (v13.0), RStudio (v1.1.456), and/or Python (v3.6). In total, 155 065 patients were identified, of which 5662 were included [2152 (38.0%) males; mean age 63.3 ± 12.4 years; Supplementary material online, Figure S1 and Supplementary material online, Table S2]. Over a mean follow-up of 15.3 ± 4.6 years with 13 ± 9 sets of lipid profile (median of per-patient median between-test duration 0.9 years, interquartile range 0.5–1.1 years), new-onset HF, cardiovascular mortality, and MI occurred in 1196 (21.1%), 548 (9.7%), and 656 (11.6%) patients, respectively. Patients with baseline use of lipid-lowering medication(s) had higher variability of LDL-C, HDL-C, and total cholesterol. HDL-C measures, but not LDL-C measures, were strongly associated with new-onset HF risk (Table 1). Higher HDL-C variability was associated with significantly higher new-onset HF risk [adjusted hazard ratio (aHR) for CV 48.68 (21.37, 110.90), P < 0.001, Figure 1F]. No significant association was observed between LDL-C variability and new-onset HF risk. Lower mean HDL-C was associated with significantly higher new-onset HF risk [aHR 0.60 (0.50, 0.72), P < 0.001; Figure 1D], but not mean LDL-C. Meanwhile, cholesterol variability was not significantly associated with cardiovascular mortality, whilst higher mean LDL-C [aHR 1.37 (1.20, 1.55), P < 0.001; Supplementary material online, Figure S2A] and lower mean HDL-C [aHR 0.54 (0.41, 0.71), P < 0.001; Supplementary material online, Figure S2D)] were both associated with higher cardiovascular mortality risk. Furthermore, higher variability of LDL-C [aHR for CV 3.64 (1.81, 7.33), P < 0.001; Supplementary material online, Figure S3C] and HDL-C (aHR for CV 39.87 [13.77, 115.47], P < 0.001; Supplementary material online, Figure S3F) were associated with higher MI risk. Lower mean HDL-C was associated with higher MI risk [aHR 0.46 (0.36, 0.60), P < 0.001; Supplementary material online, Figure S3D], but not mean LDL-C. Total cholesterol measures showed congruent results. Figure 1 Open in new tabDownload slide Marginal effects of (A) mean low density lipoprotein cholesterol (LDL-C) level, (B) standarddeviation (SD) of LDL-C level, (C) coefficient of variation (CV) of LDL-C level, (D) mean high-density lipoprotein cholesterol (HDL-C) level, (E) SD of HDL-C level, and (F) CV of HDL-C level on the risk of heart failure. Figure 1 Open in new tabDownload slide Marginal effects of (A) mean low density lipoprotein cholesterol (LDL-C) level, (B) standarddeviation (SD) of LDL-C level, (C) coefficient of variation (CV) of LDL-C level, (D) mean high-density lipoprotein cholesterol (HDL-C) level, (E) SD of HDL-C level, and (F) CV of HDL-C level on the risk of heart failure. Table 1 Results of multivariable Cox regression analysis for the overall study cohort . Hazard ratio [95% confidence interval], P value . . New-onset heart failurea . Cardiovascular mortalityb . Myocardial infarctionc . Mean LDL-C 1.00 [0.91, 1.10], P = 0.946 1.37 [1.20, 1.55], P < 0.001 0.94 [0.83, 1.07], P = 0.368 SD of LDL-C 1.03 [0.85, 1.25], P = 0.745 0.94 [0.72, 1.24], P = 0.677 1.46 [1.15, 1.85], P = 0.002 CV of LDL-C 0.63 [0.36, 1.11], P = 0.109 0.19 [0.08, 0.42], P < 0.001 3.64 [1.81, 7.33], P < 0.001 Mean HDL-C 0.60 [0.50, 0.72], P < 0.001 0.54 [0.41, 0.71], P < 0.001 0.46 [0.36, 0.60], P < 0.001 SD of HDL-C 4.40 [2.72, 7.13], P < 0.001 1.05 [0.46, 2.39], P = 0.901 2.82 [1.42, 5.59], P = 0.003 CV of HDL-C 48.68 [21.37, 110.90], P < 0.001 1.63 [0.45, 5.97], P = 0.460 39.87 [13.77, 115.47], P < 0.001 Mean total cholesterol 0.89 [0.82, 0.96], P = 0.004 1.21 [1.08, 1.36], P = 0.001 0.87 [0.78, 0.98], P = 0.017 SD of total cholesterol 1.38 [1.18, 1.62], P < 0.001 1.18 [0.93, 1.51], P = 0.169 1.69 [1.39, 2.06], P < 0.001 CV of total cholesterol 5.49 [2.29, 13.15], P < 0.001 0.58 [0.16, 2.11], P = 0.406 29.94 [9.83, 91.19], P < 0.001 . Hazard ratio [95% confidence interval], P value . . New-onset heart failurea . Cardiovascular mortalityb . Myocardial infarctionc . Mean LDL-C 1.00 [0.91, 1.10], P = 0.946 1.37 [1.20, 1.55], P < 0.001 0.94 [0.83, 1.07], P = 0.368 SD of LDL-C 1.03 [0.85, 1.25], P = 0.745 0.94 [0.72, 1.24], P = 0.677 1.46 [1.15, 1.85], P = 0.002 CV of LDL-C 0.63 [0.36, 1.11], P = 0.109 0.19 [0.08, 0.42], P < 0.001 3.64 [1.81, 7.33], P < 0.001 Mean HDL-C 0.60 [0.50, 0.72], P < 0.001 0.54 [0.41, 0.71], P < 0.001 0.46 [0.36, 0.60], P < 0.001 SD of HDL-C 4.40 [2.72, 7.13], P < 0.001 1.05 [0.46, 2.39], P = 0.901 2.82 [1.42, 5.59], P = 0.003 CV of HDL-C 48.68 [21.37, 110.90], P < 0.001 1.63 [0.45, 5.97], P = 0.460 39.87 [13.77, 115.47], P < 0.001 Mean total cholesterol 0.89 [0.82, 0.96], P = 0.004 1.21 [1.08, 1.36], P = 0.001 0.87 [0.78, 0.98], P = 0.017 SD of total cholesterol 1.38 [1.18, 1.62], P < 0.001 1.18 [0.93, 1.51], P = 0.169 1.69 [1.39, 2.06], P < 0.001 CV of total cholesterol 5.49 [2.29, 13.15], P < 0.001 0.58 [0.16, 2.11], P = 0.406 29.94 [9.83, 91.19], P < 0.001 CV, coefficient of variation; SD, standard deviation. a Adjusted for age, hypertension, diabetes mellitus, atrial fibrillation, chronic obstructive pulmonary disease, ischaemic heart disease, peripheral vascular disease, stroke or transient ischaemic attack, dementia or Alzheimer’s disease, and use of lipid-lowering medication(s). b Adjusted for age, sex, hypertension, diabetes mellitus, atrial fibrillation, ischaemic heart disease, peripheral vascular disease, stroke or transient ischaemic attack, dementia or Alzheimer’s disease, and use of lipid-lowering medication(s). c Adjusted for age, sex, hypertension, diabetes mellitus, chronic obstructive pulmonary disease, ischaemic heart disease, peripheral vascular disease, stroke or transient ischaemic attack, and use of lipid-lowering medication(s). Open in new tab Table 1 Results of multivariable Cox regression analysis for the overall study cohort . Hazard ratio [95% confidence interval], P value . . New-onset heart failurea . Cardiovascular mortalityb . Myocardial infarctionc . Mean LDL-C 1.00 [0.91, 1.10], P = 0.946 1.37 [1.20, 1.55], P < 0.001 0.94 [0.83, 1.07], P = 0.368 SD of LDL-C 1.03 [0.85, 1.25], P = 0.745 0.94 [0.72, 1.24], P = 0.677 1.46 [1.15, 1.85], P = 0.002 CV of LDL-C 0.63 [0.36, 1.11], P = 0.109 0.19 [0.08, 0.42], P < 0.001 3.64 [1.81, 7.33], P < 0.001 Mean HDL-C 0.60 [0.50, 0.72], P < 0.001 0.54 [0.41, 0.71], P < 0.001 0.46 [0.36, 0.60], P < 0.001 SD of HDL-C 4.40 [2.72, 7.13], P < 0.001 1.05 [0.46, 2.39], P = 0.901 2.82 [1.42, 5.59], P = 0.003 CV of HDL-C 48.68 [21.37, 110.90], P < 0.001 1.63 [0.45, 5.97], P = 0.460 39.87 [13.77, 115.47], P < 0.001 Mean total cholesterol 0.89 [0.82, 0.96], P = 0.004 1.21 [1.08, 1.36], P = 0.001 0.87 [0.78, 0.98], P = 0.017 SD of total cholesterol 1.38 [1.18, 1.62], P < 0.001 1.18 [0.93, 1.51], P = 0.169 1.69 [1.39, 2.06], P < 0.001 CV of total cholesterol 5.49 [2.29, 13.15], P < 0.001 0.58 [0.16, 2.11], P = 0.406 29.94 [9.83, 91.19], P < 0.001 . Hazard ratio [95% confidence interval], P value . . New-onset heart failurea . Cardiovascular mortalityb . Myocardial infarctionc . Mean LDL-C 1.00 [0.91, 1.10], P = 0.946 1.37 [1.20, 1.55], P < 0.001 0.94 [0.83, 1.07], P = 0.368 SD of LDL-C 1.03 [0.85, 1.25], P = 0.745 0.94 [0.72, 1.24], P = 0.677 1.46 [1.15, 1.85], P = 0.002 CV of LDL-C 0.63 [0.36, 1.11], P = 0.109 0.19 [0.08, 0.42], P < 0.001 3.64 [1.81, 7.33], P < 0.001 Mean HDL-C 0.60 [0.50, 0.72], P < 0.001 0.54 [0.41, 0.71], P < 0.001 0.46 [0.36, 0.60], P < 0.001 SD of HDL-C 4.40 [2.72, 7.13], P < 0.001 1.05 [0.46, 2.39], P = 0.901 2.82 [1.42, 5.59], P = 0.003 CV of HDL-C 48.68 [21.37, 110.90], P < 0.001 1.63 [0.45, 5.97], P = 0.460 39.87 [13.77, 115.47], P < 0.001 Mean total cholesterol 0.89 [0.82, 0.96], P = 0.004 1.21 [1.08, 1.36], P = 0.001 0.87 [0.78, 0.98], P = 0.017 SD of total cholesterol 1.38 [1.18, 1.62], P < 0.001 1.18 [0.93, 1.51], P = 0.169 1.69 [1.39, 2.06], P < 0.001 CV of total cholesterol 5.49 [2.29, 13.15], P < 0.001 0.58 [0.16, 2.11], P = 0.406 29.94 [9.83, 91.19], P < 0.001 CV, coefficient of variation; SD, standard deviation. a Adjusted for age, hypertension, diabetes mellitus, atrial fibrillation, chronic obstructive pulmonary disease, ischaemic heart disease, peripheral vascular disease, stroke or transient ischaemic attack, dementia or Alzheimer’s disease, and use of lipid-lowering medication(s). b Adjusted for age, sex, hypertension, diabetes mellitus, atrial fibrillation, ischaemic heart disease, peripheral vascular disease, stroke or transient ischaemic attack, dementia or Alzheimer’s disease, and use of lipid-lowering medication(s). c Adjusted for age, sex, hypertension, diabetes mellitus, chronic obstructive pulmonary disease, ischaemic heart disease, peripheral vascular disease, stroke or transient ischaemic attack, and use of lipid-lowering medication(s). Open in new tab Subgroup analysis by use of lipid-lowering medication(s) (see Supplementary material online, Tables S6) found that in patients who did not use lipid-lowering medication(s) (N = 4068), associations between cholesterol measures and outcomes remained similar. Contrastingly, in patients who used lipid-lowering medication(s) (N = 1594), LDL-C and HDL-C variability were not prognostic. While lower mean HDL-C was associated with higher new-onset HF risk [aHR 0.43 (0.31, 0.61), P < 0.001], no association was observed for SD of HDL-C [aHR 1.829 (0.659, 5.078), P = 0.247], suggesting that the positive correlation between CV of HDL-C and new-onset HF risk was driven by inverse correlation for mean HDL-C. Meanwhile, HDL-C variability was not associated with MI risk, but higher total cholesterol variability remained associated with higher MI risk [aHR for CV 25.89 (3.74, 179.01), P = 0.001]. In this population-based study, we showed important but varying associations between visit-to-visit cholesterol variability and long-term risks of new-onset HF and major adverse cardiovascular outcomes, which may be partially negated by lipid-lowering medications. As far as we know, this is the first study demonstrating links between cholesterol variability and new-onset HF risk. Using population-based data with long follow-up, our results are widely applicable. Nonetheless, this study was limited by its retrospective nature, and the type of HF was unknown as echocardiographic data were unavailable. Additionally, many were excluded for having less than three sets of fasting lipid profiles during index period, limiting generalizability and possibly biasing for those indicated for lipid testing. Lastly, there may have been residual confounders, such as response to pharmacotherapy, smoking history, and blood pressure. Nonetheless, we adjusted for numerous cardiovascular risk factors which should account for most pertinent confounders. Although underlying mechanisms remain unclear, cholesterol variability’s links to atherosclerotic progression6,7 and inflammation8,9 may be important, as these play important roles in HF.10 Clinicians should be aware of the importance of controlling cholesterol fluctuations, and further investigations should explore measures which reduce such fluctuations. Authors’ contributions J.S.K.C., D.I.S., and G.T. contributed to the conception or design of the work. T.T.L.L., O.H.I.C., A.K.C.W., B.M.Y.C., and G.T. contributed to the acquisition of data for the work. T.T.L.L., O.H.I.C., and J.Z. curated the data for the work. J.S.K.C. and J.Z. contributed to formal analysis and visualization for the work. J.S.K.C. and G.T. contributed to project administration. J.S.K.C. and D.I.S. drafted the manuscript. All authors critically revised the manuscript. All authors gave final approval and agree to be accountable for all aspects of work ensuring integrity and accuracy. Supplementary material Supplementary material is available at European Journal of Preventive Cardiology. Funding None declared. Conflict of interest: G.B.-Z. has consulted for Cardionovum, Crannmedical, Innovheart, Meditrial, Opsens Medical, Replycare, and Terumo. Other authors have nothing to disclose. Data availability Underlying data are available upon reasonable request to the corresponding authors. References 1 Yusuf PS , Hawken S, Ôunpuu S, Dans T, Avezum A, Lanas F, McQueen M, Budaj A, Pais P, Varigos J, Lisheng L. Effect of potentially modifiable risk factors associated with myocardial infarction in 52 countries (the INTERHEART study): case-control study . Lancet 2004 ; 364 : 937 – 952 . Google Scholar Crossref Search ADS WorldCat 2 Nazir DJ , Roberts RS, Hill SA, McQueen MJ. Monthly intra-individual variation in lipids over a 1-year period in 22 normal subjects . Clin Biochem 1999 ; 32 : 381 – 389 . Google Scholar Crossref Search ADS WorldCat 3 Lee S , Jeevaratnam K, Liu T, Chang D, Chang C, Wong WT, Wong ICK, Lip GYH, Tse G. Risk stratification of cardiac arrhythmias and sudden cardiac death in type 2 diabetes mellitus patients receiving insulin therapy: a population-based cohort study . Clin Cardiol 2021 ; 44 : 1602 – 1612 . Google Scholar Crossref Search ADS WorldCat 4 Wang M-C , Li CI, Liu CS, Lin CH, Yang SY, Li TC, Lin CC. Effect of blood lipid variability on mortality in patients with type 2 diabetes: a large single-center cohort study . Cardiovasc Diabetol 2021 ; 20 : 228 . Google Scholar Crossref Search ADS WorldCat 5 Chan JSK , Zhou J, Lee S, Li A, Tan M, Leung KSK, Jeevaratnam K, Liu T, Roever L, Liu Y, Tse G, Zhang Q. Fragmented QRS is independently predictive of long-term adverse clinical outcomes in Asian patients hospitalized for heart failure: a retrospective cohort study . Front Cardiovasc Med 2021 ; 8 : 738417 . Google Scholar Crossref Search ADS WorldCat 6 Abela GS , Vedre A, Janoudi A, Huang R, Durga S, Tamhane U. Effect of statins on cholesterol crystallization and atherosclerotic plaque stabilization . Am J Cardiol 2011 ; 107 : 1710 – 1717 . Google Scholar Crossref Search ADS WorldCat 7 Navab M , Reddy ST, Van Lenten BJ, Fogelman AM. HDL and cardiovascular disease: atherogenic and atheroprotective mechanisms . Nat Rev Cardiol 2011 ; 8 : 222 – 232 . Google Scholar Crossref Search ADS WorldCat 8 Zhao L , Xu T, Li Y, Luan Y, Lv Q, Fu G, Zhang W. Variability in blood lipids affects the neutrophil to lymphocyte ratio in patients undergoing elective percutaneous coronary intervention: a retrospective study . Lipids Health Dis 2020 ; 19 : 124 . Google Scholar Crossref Search ADS WorldCat 9 Lee S , Zhou J, Wong WT, Liu T, Wu WKK, Wong ICK, Zhang Q, Tse G, et al. Glycemic and lipid variability for predicting complications and mortality in diabetes mellitus using machine learning . BMC Endocr Disord 2021 ; 21 : 94 . Google Scholar Crossref Search ADS WorldCat 10 Reina-Couto M , Pereira-Terra P, Quelhas-Santos J, Silva-Pereira C, Albino-Teixeira A, Sousa T. Inflammation in human heart failure: major mediators and therapeutic targets . Frontiers in Physiology 2021 ; 12 : 1753 . Google Scholar Crossref Search ADS WorldCat Author notes † J.S.K.C. and D.I.S. are co-first authors. Part of this study has been presented as an oral presentation at ESC Asia 2021. © The Author(s) 2022. Published by Oxford University Press on behalf of the European Society of Cardiology. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. © The Author(s) 2022. Published by Oxford University Press on behalf of the European Society of Cardiology.

Gupta, Rahul; Behnoush, Amir Hossein; Egeler, David; Aronow, Wilbert S

doi: 10.1093/eurjpc/zwac112pmid: 35655390

Acetaminophen, blood pressure, trials, meta-analysis Accepted manuscripts Accepted manuscripts are PDF versions of the author’s final manuscript, as accepted for publication by the journal but prior to copyediting or typesetting. They can be cited using the author(s), article title, journal title, year of online publication, and DOI. They will be replaced by the final typeset articles, which may therefore contain changes. The DOI will remain the same throughout. Article PDF first page preview Close This content is only available as a PDF. © The Author(s) 2022. Published by Oxford University Press on behalf of European Society of Cardiology. All rights reserved. For permissions, please e-mail: [email protected] This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model) © The Author(s) 2022. Published by Oxford University Press on behalf of European Society of Cardiology. All rights reserved. For permissions, please e-mail: [email protected]

Zafrir, Barak; Aker, Amir; Saliba, Walid

doi: 10.1093/eurjpc/zwac124pmid: 35707956

Lipoprotein(a), Hypercholesterolemia, Cardiovascular disease, PCSK9 inhibitors Accepted manuscripts Accepted manuscripts are PDF versions of the author’s final manuscript, as accepted for publication by the journal but prior to copyediting or typesetting. They can be cited using the author(s), article title, journal title, year of online publication, and DOI. They will be replaced by the final typeset articles, which may therefore contain changes. The DOI will remain the same throughout. Article PDF first page preview Close This content is only available as a PDF. © The Author(s) 2022. Published by Oxford University Press on behalf of European Society of Cardiology. All rights reserved. For permissions, please e-mail: [email protected] This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model) © The Author(s) 2022. Published by Oxford University Press on behalf of European Society of Cardiology. All rights reserved. For permissions, please e-mail: [email protected]

Arshad, Muhammad Sameer; Ahmed, Aymen; Ejaz, Arooba; Ahmed, Warda; Farooqi, Shaikh Muhammad Habibullah; Memon, Muhammad Mustafa; Shahid, Izza

doi: 10.1093/eurjpc/zwac171pmid: 35938308

Accepted manuscripts Accepted manuscripts are PDF versions of the author’s final manuscript, as accepted for publication by the journal but prior to copyediting or typesetting. They can be cited using the author(s), article title, journal title, year of online publication, and DOI. They will be replaced by the final typeset articles, which may therefore contain changes. The DOI will remain the same throughout. Article PDF first page preview Close This content is only available as a PDF. Author notes * Muhammad Sameer Arshad and Aymen Ahmed Co-first authors © The Author(s) 2022. Published by Oxford University Press on behalf of European Society of Cardiology. All rights reserved. For permissions, please e-mail: [email protected] This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model) © The Author(s) 2022. Published by Oxford University Press on behalf of European Society of Cardiology. All rights reserved. For permissions, please e-mail: [email protected]

Maqsood, Muhammad Haisum; Messerli, Franz H; Waters, David; Skolnick, Adam H; Maron, David J; Bangalore, Sripal

doi: 10.1093/eurjpc/zwac085pmid: 35512427

Statins are one of the most commonly prescribed medications, and it is surprising that the timing of statin administration is controversial—society guidelines are also silent on this issue.1–3 The trials conducted on the efficacy of statin administration timing have heterogeneous outcomes. Our objective was to compare the efficacy and safety of statin morning vs. evening dose by a meta-analysis of randomized controlled trials (RCTs). A time-limited search from inception until 30 October 2021 was conducted using PubMed and EMBASE databases. The following Medical Education Subject Headings were used for this search: ‘chronotherapy’, ‘conventional therapy’, ‘statins’, ‘hyperlipidemia’, ‘morning dose’, and ‘evening dose’. The inclusion criteria were RCTs that compared the effect of morning vs. evening dosing of statins on changes in at least one lipid profile parameters [LDL cholesterol (LDL-C), HDL cholesterol (HDL-C), total cholesterol (TC), and triglycerides] or adverse events. The two authors (M.H.M. and S.B.) independently selected studies based on titles, abstracts, and full-text studies, extracted data, and appraised the methodological quality of included trials using Cochrane Risk of Bias Tool (see Supplementary material online, Appendix Table S1 and Figure S1). Mean difference (MD) and odds ratio (OR) with 95% confidence intervals (CIs) were calculated using random effects model using DerSimonian–Laird method. Sub-group analyses were considered based on statin half-life and simvastatin use. Sensitivity analyses were conducted using leave-one-out and including trials with dyslipidemic patients alone. Study size effect was evaluated using cumulative meta-analysis. Funnel plots were used to assess publication bias. Analyses were conducted using Stata version 17.0. We did not register study protocol. The initial search yielded 671 reports, and finally 13 RCTs were included in the final meta-analysis (see Supplementary material online, Figure S2). The studies enrolled a total of 1129 patients (621 were randomized to evening dosing and 631 to morning dosing group; four cross-over trials), with similar doses of statin between morning and evening doses (Table 1). Table 1 Baseline characteristics of studies included for analysis Author (year) . Study design . Geographical area . Inclusion criteria . Intervention vs. comparison . Total participants . Age . Male, % . Follow-up (mo) . Outcomes of interest . Half-life . Kim (2013) RCT; multicentre Korea LDL-C ranging 100–220 mg/dL; triglyceride levels 400 mg/dL AM vs PM simvastatin 20 mg 122 58.6 44.7 2 TC, LDL, HDL, TG, ApoA1, ApoB, and adverse events Long half-life aSaito (1991) RCT; multicentre Japan Diagnosed hyperlidipaemia AM vs. PM simvastatin 2.5 mg 58 NR 24 3 TC, TG, HDL, adverse events Short half-life aSaito (1991) RCT; multicentre Japan Diagnosed hyperlipidaemia AM vs. PM simvastatin 5 mg 61 NR 19.7 3 TC, TG, HDL, adverse events Short half-life Scharnagl (2006) RCT; multicentre Austria and Germany Diagnosed IIa/b hypercholesterolaemia AM vs. PM fluvastatin XL 80 mg 229 60.4 38 2 TC, LDL, HDL, TG, and adverse events Long-half life Tharavanij (2010) RCT; single-centre Thailand Statin treatment as primary or secondary prevention AM vs. PM simvastatin 10 mg 52 54.6 36.5 3 LDL, HDL, TG Short half-life Ozaydin (2006) RCT, single-centre Turkey Single-vessel disease who underwent first elective percutaneous coronary intervention AM vs. PM Atorvastatin 40 mg first month then 10 mg 152 58.5 77.6 6 LDL, HLD, TC, TG. Long half-life Jin Yi (2014) RCT; multicentre Korea CKD and dyslipidaemia AM CR simvastatin 20 vs. PM IR Simvastatin 118 57 48 2 TC, TG, HDL, LDL, adverse events Long-half life Hunninghake (1990) RCT; multicentre United States Primary hypercholesterolaemia AM vs. PM pravastatin 91 53.6 72.5 2 TC, LDL, HDL, TG Short half-life Cilla (1996) RCT; cross-over United States Healthy subjects AM vs. PM atorvastatin 40 mg 16 33.3 56 2 TC, TG, HDL, LDL, adverse events Long half-life Kruse (1993) RCT Germany Familial hypercholesterolaemia AM vs. PM lovastatin 20 24 46.7 NR 1 TC, TG, HDL, LDL, adverse events Short half-life Fauler (2007) RCT: cross-over Austria Hyperlipidaemia AM vs. PM fluvastatin 80 mg 26 NR NR 2 TC, TG, HDL, LDL, adverse events Long-half life Heng (2019) RCT; multicentre Malaysia Hypercholesterolaemia AM vs. PM simvastatin 99 53.2 61.6 4 TC, TG, HDL, LDL Short half-life Wallace (2003) RCT; cross-over United Kingdom Primary or secondary prevention AM vs. PM simvastatin 60 66 55 2 TC, TG, HDL, LDL Short half-life Martin (2002) RCT; cross-over Japan Healthy subject AM vs. PM rosuvastatin 21 38.6 NR 2 TC, TG, HDL, LDL Long half-life Author (year) . Study design . Geographical area . Inclusion criteria . Intervention vs. comparison . Total participants . Age . Male, % . Follow-up (mo) . Outcomes of interest . Half-life . Kim (2013) RCT; multicentre Korea LDL-C ranging 100–220 mg/dL; triglyceride levels 400 mg/dL AM vs PM simvastatin 20 mg 122 58.6 44.7 2 TC, LDL, HDL, TG, ApoA1, ApoB, and adverse events Long half-life aSaito (1991) RCT; multicentre Japan Diagnosed hyperlidipaemia AM vs. PM simvastatin 2.5 mg 58 NR 24 3 TC, TG, HDL, adverse events Short half-life aSaito (1991) RCT; multicentre Japan Diagnosed hyperlipidaemia AM vs. PM simvastatin 5 mg 61 NR 19.7 3 TC, TG, HDL, adverse events Short half-life Scharnagl (2006) RCT; multicentre Austria and Germany Diagnosed IIa/b hypercholesterolaemia AM vs. PM fluvastatin XL 80 mg 229 60.4 38 2 TC, LDL, HDL, TG, and adverse events Long-half life Tharavanij (2010) RCT; single-centre Thailand Statin treatment as primary or secondary prevention AM vs. PM simvastatin 10 mg 52 54.6 36.5 3 LDL, HDL, TG Short half-life Ozaydin (2006) RCT, single-centre Turkey Single-vessel disease who underwent first elective percutaneous coronary intervention AM vs. PM Atorvastatin 40 mg first month then 10 mg 152 58.5 77.6 6 LDL, HLD, TC, TG. Long half-life Jin Yi (2014) RCT; multicentre Korea CKD and dyslipidaemia AM CR simvastatin 20 vs. PM IR Simvastatin 118 57 48 2 TC, TG, HDL, LDL, adverse events Long-half life Hunninghake (1990) RCT; multicentre United States Primary hypercholesterolaemia AM vs. PM pravastatin 91 53.6 72.5 2 TC, LDL, HDL, TG Short half-life Cilla (1996) RCT; cross-over United States Healthy subjects AM vs. PM atorvastatin 40 mg 16 33.3 56 2 TC, TG, HDL, LDL, adverse events Long half-life Kruse (1993) RCT Germany Familial hypercholesterolaemia AM vs. PM lovastatin 20 24 46.7 NR 1 TC, TG, HDL, LDL, adverse events Short half-life Fauler (2007) RCT: cross-over Austria Hyperlipidaemia AM vs. PM fluvastatin 80 mg 26 NR NR 2 TC, TG, HDL, LDL, adverse events Long-half life Heng (2019) RCT; multicentre Malaysia Hypercholesterolaemia AM vs. PM simvastatin 99 53.2 61.6 4 TC, TG, HDL, LDL Short half-life Wallace (2003) RCT; cross-over United Kingdom Primary or secondary prevention AM vs. PM simvastatin 60 66 55 2 TC, TG, HDL, LDL Short half-life Martin (2002) RCT; cross-over Japan Healthy subject AM vs. PM rosuvastatin 21 38.6 NR 2 TC, TG, HDL, LDL Long half-life a Same trials with two doses analyzed separately. AM, morning dose; CAD, coronary artery disease; NR, not reported; PM, evening dose; RCT, randomized controlled trials; TC, total cholesterol, and TG, triglycerides. All the references are included in supplementary material. Open in new tab Table 1 Baseline characteristics of studies included for analysis Author (year) . Study design . Geographical area . Inclusion criteria . Intervention vs. comparison . Total participants . Age . Male, % . Follow-up (mo) . Outcomes of interest . Half-life . Kim (2013) RCT; multicentre Korea LDL-C ranging 100–220 mg/dL; triglyceride levels 400 mg/dL AM vs PM simvastatin 20 mg 122 58.6 44.7 2 TC, LDL, HDL, TG, ApoA1, ApoB, and adverse events Long half-life aSaito (1991) RCT; multicentre Japan Diagnosed hyperlidipaemia AM vs. PM simvastatin 2.5 mg 58 NR 24 3 TC, TG, HDL, adverse events Short half-life aSaito (1991) RCT; multicentre Japan Diagnosed hyperlipidaemia AM vs. PM simvastatin 5 mg 61 NR 19.7 3 TC, TG, HDL, adverse events Short half-life Scharnagl (2006) RCT; multicentre Austria and Germany Diagnosed IIa/b hypercholesterolaemia AM vs. PM fluvastatin XL 80 mg 229 60.4 38 2 TC, LDL, HDL, TG, and adverse events Long-half life Tharavanij (2010) RCT; single-centre Thailand Statin treatment as primary or secondary prevention AM vs. PM simvastatin 10 mg 52 54.6 36.5 3 LDL, HDL, TG Short half-life Ozaydin (2006) RCT, single-centre Turkey Single-vessel disease who underwent first elective percutaneous coronary intervention AM vs. PM Atorvastatin 40 mg first month then 10 mg 152 58.5 77.6 6 LDL, HLD, TC, TG. Long half-life Jin Yi (2014) RCT; multicentre Korea CKD and dyslipidaemia AM CR simvastatin 20 vs. PM IR Simvastatin 118 57 48 2 TC, TG, HDL, LDL, adverse events Long-half life Hunninghake (1990) RCT; multicentre United States Primary hypercholesterolaemia AM vs. PM pravastatin 91 53.6 72.5 2 TC, LDL, HDL, TG Short half-life Cilla (1996) RCT; cross-over United States Healthy subjects AM vs. PM atorvastatin 40 mg 16 33.3 56 2 TC, TG, HDL, LDL, adverse events Long half-life Kruse (1993) RCT Germany Familial hypercholesterolaemia AM vs. PM lovastatin 20 24 46.7 NR 1 TC, TG, HDL, LDL, adverse events Short half-life Fauler (2007) RCT: cross-over Austria Hyperlipidaemia AM vs. PM fluvastatin 80 mg 26 NR NR 2 TC, TG, HDL, LDL, adverse events Long-half life Heng (2019) RCT; multicentre Malaysia Hypercholesterolaemia AM vs. PM simvastatin 99 53.2 61.6 4 TC, TG, HDL, LDL Short half-life Wallace (2003) RCT; cross-over United Kingdom Primary or secondary prevention AM vs. PM simvastatin 60 66 55 2 TC, TG, HDL, LDL Short half-life Martin (2002) RCT; cross-over Japan Healthy subject AM vs. PM rosuvastatin 21 38.6 NR 2 TC, TG, HDL, LDL Long half-life Author (year) . Study design . Geographical area . Inclusion criteria . Intervention vs. comparison . Total participants . Age . Male, % . Follow-up (mo) . Outcomes of interest . Half-life . Kim (2013) RCT; multicentre Korea LDL-C ranging 100–220 mg/dL; triglyceride levels 400 mg/dL AM vs PM simvastatin 20 mg 122 58.6 44.7 2 TC, LDL, HDL, TG, ApoA1, ApoB, and adverse events Long half-life aSaito (1991) RCT; multicentre Japan Diagnosed hyperlidipaemia AM vs. PM simvastatin 2.5 mg 58 NR 24 3 TC, TG, HDL, adverse events Short half-life aSaito (1991) RCT; multicentre Japan Diagnosed hyperlipidaemia AM vs. PM simvastatin 5 mg 61 NR 19.7 3 TC, TG, HDL, adverse events Short half-life Scharnagl (2006) RCT; multicentre Austria and Germany Diagnosed IIa/b hypercholesterolaemia AM vs. PM fluvastatin XL 80 mg 229 60.4 38 2 TC, LDL, HDL, TG, and adverse events Long-half life Tharavanij (2010) RCT; single-centre Thailand Statin treatment as primary or secondary prevention AM vs. PM simvastatin 10 mg 52 54.6 36.5 3 LDL, HDL, TG Short half-life Ozaydin (2006) RCT, single-centre Turkey Single-vessel disease who underwent first elective percutaneous coronary intervention AM vs. PM Atorvastatin 40 mg first month then 10 mg 152 58.5 77.6 6 LDL, HLD, TC, TG. Long half-life Jin Yi (2014) RCT; multicentre Korea CKD and dyslipidaemia AM CR simvastatin 20 vs. PM IR Simvastatin 118 57 48 2 TC, TG, HDL, LDL, adverse events Long-half life Hunninghake (1990) RCT; multicentre United States Primary hypercholesterolaemia AM vs. PM pravastatin 91 53.6 72.5 2 TC, LDL, HDL, TG Short half-life Cilla (1996) RCT; cross-over United States Healthy subjects AM vs. PM atorvastatin 40 mg 16 33.3 56 2 TC, TG, HDL, LDL, adverse events Long half-life Kruse (1993) RCT Germany Familial hypercholesterolaemia AM vs. PM lovastatin 20 24 46.7 NR 1 TC, TG, HDL, LDL, adverse events Short half-life Fauler (2007) RCT: cross-over Austria Hyperlipidaemia AM vs. PM fluvastatin 80 mg 26 NR NR 2 TC, TG, HDL, LDL, adverse events Long-half life Heng (2019) RCT; multicentre Malaysia Hypercholesterolaemia AM vs. PM simvastatin 99 53.2 61.6 4 TC, TG, HDL, LDL Short half-life Wallace (2003) RCT; cross-over United Kingdom Primary or secondary prevention AM vs. PM simvastatin 60 66 55 2 TC, TG, HDL, LDL Short half-life Martin (2002) RCT; cross-over Japan Healthy subject AM vs. PM rosuvastatin 21 38.6 NR 2 TC, TG, HDL, LDL Long half-life a Same trials with two doses analyzed separately. AM, morning dose; CAD, coronary artery disease; NR, not reported; PM, evening dose; RCT, randomized controlled trials; TC, total cholesterol, and TG, triglycerides. All the references are included in supplementary material. Open in new tab Evening dosing of a statin led to greater reduction in LDL-C [MD = −6.27 mg/dl (95% CI: −9.92 to −2.63), I2 = 37%; 12 trials] compared with morning dosing (Figure 1). The effect was more pronounced in the subgroup of trials testing short half-life statins (−11.6 mg/dl vs. −4.3 mg/dl), with a significant heterogeneity of treatment effect (Pheterogeneity = 0.06). Subgroup analysis stratified by trials of simvastatin vs. non-simvastatin (Pheterogeneity = 0.48) showed a similar effect, but without significant heterogeneity (Figure 1 and see Supplementary material online, Figure S3). Sensitivity analysis by including trials enrolling patients with dyslipidaemia only [MD = −7.17 mg/dl (95% CI: −11.46 to −2.88), I2 = 37%; 11 trials], and leave-one-out analysis resulted in no apparent difference (see Supplementary material online, Figure S3). Figure 1 Open in new tabDownload slide LDL-C, HDL-C, total cholesterol, and triglyceride change based on statin half-life. Figure 1 Open in new tabDownload slide LDL-C, HDL-C, total cholesterol, and triglyceride change based on statin half-life. There was no significant difference in HDL-C reduction between evening and morning dosing with main [MD = 0.97 mg/dl (95% CI: −0.54 to 2.47), I2 = 72%; 11 trials], sub-group, and sensitivity analyses (Figure 1 and see Supplementary material online, Figure S4). TC reduction was significantly greater with evening dosing [MD = −6.09 mg/dl (95% CI: −10.80 to −1.38), I2 = 54%; 11 trials] compared with morning dosing (Figure 1). The effect was more pronounced in the subgroup of trials testing short half-life statins (−13.0 mg/dl vs. −3.6 mg/dl), with a significant heterogeneity of treatment effect (Pheterogeneity = 0.05). Subgroup analysis stratified by trials of simvastatin vs. non-simvastatin (Pheterogeneity = 0.30) showed similar effect, but without significant heterogeneity (Figure 1 and see Supplementary material online, Figure S5). Sensitivity analysis including trials that enrolled patients with dyslipidaemia alone did not show any difference from the main analysis, but leave-one-out analysis had significant difference when Fauler et al. was removed (see Supplementary material online, Figure S5). There was no significant difference in triglyceride reduction between evening and morning dosing in main analysis [MD = −2.51 mg/dl (95% CI: −7.92 to 2.90), I2 = 27%; 11 trials], sub-group and sensitivity analyses, but leave-one-out analysis had significant difference when Scharnagl et al. was removed (Figure 1 and see Supplementary material online, Figure S6). Adverse events were similar between evening and morning dosing in main [OR = 1.22 (95% CI: 0.79–1.88), I2 = 0%; 7 trials], sub-group, and sensitivity analyses (see Supplementary material online, Figure S7). Overall, there was no evidence of small study effect or publication bias (see Supplementary material online, Figures S4–S7). This meta-analysis showed significant reduction of LDL-C and TC in patients randomized to evening compared with morning dosing despite identical statin dose strength. However, there was no significant difference in triglycerides and HDL-C. The results were consistent regardless of statin half-life or statin type used although there was a greater magnitude of benefit with short half-life statins. This is the largest meta-analysis to evaluate the efficacy of statin morning vs. evening dose. Prior trials and meta-analysis have shown heterogeneous outcomes, and as such, the guidelines have been silent on this subject.4 According to Cholesterol Treatment Trialists’ Collaborators, a 1 mmol/l reduction in LDL-C reduces relative risk of CV events by 22%.5 Reduction of 6.27 mg/dl is extrapolated to reduce the relative risk of CV events by 3.6%, a not so trivial reduction for a simple intervention. Real-world statin efficacy, however, does not have same LDL reduction effect as expected attributed to individual variability such as genetic polymorphism, LDL levels, and medication compliance.6 Evening dose is thought to increase compliance by increasing time to administer and, hence, could be assessed in future studies. The circadian rhythm of liver has highest activity over the night time and lower during the day. As such, 3-hydroxy-3-methylglutaryl coenzyme A reductase, the rate-limiting enzyme in cholesterol biosynthesis, has higher expression and hence biosynthesis at night.7–9 Evening dose statins reach plasma level peak simultaneously with HMG-CoA reductase peak expression, potentially explaining greater statin efficacy in LDL-C reduction compared with morning dose.9–11 This meta-analysis is limited by the following: (i) small RCTs in specific geographical areas limits generalizability; (ii) the duration of follow-up of included trials was short; (iii) statin adherence was not reported in most of these trials and it is unclear if night-time dosing was based on post-prandial status; and (iv) not all statins that are commercially available were represented in this meta-analysis. Evening dosing of statins significantly reduces LDL-C when compared with morning dosing regardless of statin half-life. This simple intervention should be considered in the management of patients with hypercholesterolaemia. Authors’ contributions M.H.M. was involved in (i) conception and design or analysis and interpretation of data, or both; (ii) drafting of the manuscript or revising it critically for important intellectual content; and (iii) final approval of the manuscript submitted. F.H.M., D.W., A.H.S., and D.J.M. were involved in (i) drafting of the manuscript or revising it critically for important intellectual content and (ii) final approval of the manuscript submitted. S.B. was involved in (i) conception and design or analysis, interpretation of data and supervision, or both; (ii) drafting of the manuscript or revising it critically for important intellectual content; and (iii) final approval of the manuscript submitted. Supplementary material Supplementary material is available at European Journal of Preventive Cardiology online. Acknowledgements There is no relevant acknowledgement to declare. Funding The authors declare that they did not receive funding for this research project and publication. Conflict of interest: The authors declare that they have no conflicts of interest relevant to the content of this manuscript. Data availability All manuscript data is included in the manuscript and supplementary material. References 1 The Use of Medicines in the United States: Review of 2011: IMS Institute for Healthcare Informatics; 2012 [cited 2021 September 9] . https://www.kff.org/wp-content/uploads/sites/2/2012/10/ihii_medicines_in_u.s_report_2011.pdf. 2 Jellinger PS , Handelsman Y, Rosenblit PD, Bloomgarden ZT, Fonseca VA, Garber AJ, Grunberger G, Guerin CK, Bell DSH, Mechanick JI, Pessah-Pollack R, Wyne K, Smith D, Brinton EA, Fazio S, Davidson M, Jellinger PS, Handelsman Y, Bell DSH, Bloomgarden ZT, Brinton EA, Davidson MH, Fazio S, Fonseca VA, Garber AJ, Grunberger G, Guerin CK, Mechanick JI, Pessah-Pollack R, Rosenblit PD, Smith DA, Wyne K, Bush M, Zangeneh F, Handelsman Y, Bell DSH, Bloomgarden ZT, Brinton EA, Fazio S, Fonseca VA, Garber AJ, Grunberger G, Guerin CK, Jellinger PS, Rosenblit PD, Smith DA, Wyne K, Davidson MH. American association of clinical endocrinologists and American College of Endocrinology guidelines for management of dyslipidemia and prevention of cardiovascular disease . Endocr Pract 2017 ; 23 : 1 – 87 . Google Scholar Crossref Search ADS PubMed WorldCat 3 Stone NJ , Robinson JG, Lichtenstein AH, Bairey Merz CN, Blum CB, Eckel RH, Goldberg AC, Gordon D, Levy D, Lloyd-Jones DM, McBride P, Schwartz JS, Shero ST, Smith SC Jr, Watson K, Wilson PWF, American College of Cardiology/American Heart Association Task Force on Practice Guidelines . ACC/AHA guideline on the treatment of blood cholesterol to reduce atherosclerotic cardiovascular risk in adults: a report of the American College of Cardiology/American Heart Association task force on practice guidelines . J Am Coll Cardiol 2014 ; 63 : 2889 – 2934 . Google Scholar Crossref Search ADS PubMed WorldCat 4 Awad K , Serban MC, Penson P, Mikhailidis DP, Toth PP, Jones SR, Rizzo M, Howard G, Lip GYH, Banach M. Effects of morning vs evening statin administration on lipid profile: a systematic review and meta-analysis . J Clin Lipidol 2017 ; 11 : 972 – 985.e9 . Google Scholar Crossref Search ADS PubMed WorldCat 5 Cholesterol Treatment Trialists (CTT) Collaborators , Mihaylova B, Emberson J, Blackwell L, Keech A, Simes J, Barnes EH, Voysey M, Gray A, Collins R, Baigent C. The effects of lowering LDL cholesterol with statin therapy in people at low risk of vascular disease: meta-analysis of individual data from 27 randomised trials . Lancet 2012 : 380 : 581 – 590 . Google Scholar Crossref Search ADS PubMed WorldCat 6 Attar A . Response to statin therapy in the real world . Eur J Prev Cardiol 2020 ; 28 ( 14 ): e25 – e26 . Google Scholar Crossref Search ADS WorldCat 7 Mortimer BC , Beveridge DJ, Phan CT, Lutton C, Redgrave TG. The diurnal rhythms of cholesterol metabolism and plasma clearance of model chylomicrons: comparison of normal and genetically hypercholesterolemic rats (RICO) . Comp Biochem Physiol A Mol Integr Physiol 1998 ; 120 : 671 – 680 . Google Scholar Crossref Search ADS PubMed WorldCat 8 Mayer D . The circadian rhythm of synthesis and catabolism of cholesterol . Arch Toxicol 1976 ; 36 : 267 – 276 . Google Scholar Crossref Search ADS PubMed WorldCat 9 Gnocchi D , Pedrelli M, Hurt-Camejo E, Parini P. Lipids around the clock: focus on circadian rhythms and lipid metabolism . Biology 2015 ; 4 : 104 – 132 . Google Scholar Crossref Search ADS PubMed WorldCat 10 Schlierf G , Dorow E. Diurnal patterns of triglycerides, free fatty acids, blood sugar, and insulin during carbohydrate-induction in man and their modification by nocturnal suppression of lipolysis . J Clin Invest 1973 ; 52 : 732 – 740 . Google Scholar Crossref Search ADS PubMed WorldCat 11 Fukagawa K , Gou HM, Wolf R, Tso P. Circadian rhythm of serum and lymph apolipoprotein AIV in ad libitum-fed and fasted rats . Am J Physiol 1994 ; 267 : R1385 – R1390 . Google Scholar PubMed OpenURL Placeholder Text WorldCat © The Author(s) 2022. Published by Oxford University Press on behalf of the European Society of Cardiology. All rights reserved. For permissions, please email: [email protected]. This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model)

Parati, Gianfranco; Halasz, Geza; Piepoli, Massimo F

doi: 10.1093/eurjpc/zwac222pmid: 36265176

Leong, Darryl P; Rangarajan, Sumathy; Rosengren, Annika; Oguz, Aytekin; Alhabib, Khalid F; Poirier, Paul; Diaz, Rafael; Dans, Antonio L; Iqbal, Romaina; Yusufali, Afzalhussein M; Yeates, Karen; Chifamba, Jephat; Seron, Pamela; Lopez-Lopez, Jose;

Casiglia, Edoardo; Tikhonoff, Valérie

doi: 10.1093/eurjpc/zwac119pmid: 35767312

Accepted manuscripts Accepted manuscripts are PDF versions of the author’s final manuscript, as accepted for publication by the journal but prior to copyediting or typesetting. They can be cited using the author(s), article title, journal title, year of online publication, and DOI. They will be replaced by the final typeset articles, which may therefore contain changes. The DOI will remain the same throughout. Article PDF first page preview Close This content is only available as a PDF. © The Author(s) 2022. Published by Oxford University Press on behalf of European Society of Cardiology. All rights reserved. For permissions, please e-mail: [email protected] This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model) © The Author(s) 2022. Published by Oxford University Press on behalf of European Society of Cardiology. All rights reserved. For permissions, please e-mail: [email protected]

Chobufo, Muchi Ditah; Regner, Sean R; Zeb, Irfan; Lacoste, Jordan L; Virani, Salim S; Balla, Sudarshan

doi: 10.1093/eurjpc/zwac103pmid: 35653373

AimsTo assess the current state of statin use, factors associated with non-use, and estimate the burden of potentially preventable atherosclerotic cardiovascular diseases (ASCVD) events.Methods and resultsUsing nationally representative data from the 2017 to 2020 National Health and Nutrition Examination Survey, statin use was assessed in primary prevention groups: high ASCVD risk ≥ 20%, LDL-cholesterol (LDL-C) ≥ 190 mg/dL, diabetes aged 40–75 years, intermediate ASCVD risk (7.5 to <20%) with ≥1 ASCVD risk enhancer and secondary prevention group: established ASCVD. Atherosclerotic cardiovascular disease risk was estimated using pooled cohort equations. We estimated 70 million eligible individuals (2.3 million with LDL-C ≥ 190 mg/dL; 9.4 million with ASCVD ≥ 20%; 15 million with diabetes and age 40–75years; 20 million with intermediate ASCVD risk and ≥1 risk enhancers; and 24.6 million with established ASCVD), about 30 million were on statin therapy. The proportion of individuals not on statin therapy was highest in the isolated LDL-C ≥ 190 mg/dL group (92.8%) and those with intermediate ASCVD risk plus enhancers (74.6%) followed by 59.4% with high ASCVD risk, 54.8% with diabetes, and 41.5% of those with established ASCVD groups. Increasing age and those with health insurance were more likely to be on statin therapy in both the primary and secondary prevention categories. Individuals without a routine place of care were less likely to be on statin therapy. A total of 385 000 (high-intensity statin) and 647 000 (moderate-intensity statin) ASCVD events could be prevented if all statin-eligible individuals were treated (and adherent) for primary prevention over a 10-year period.ConclusionStatin use for primary and secondary prevention of ASCVD remains suboptimal. Bridging the therapeutic gap can prevent ∼1 million ASCVD events over the subsequent 10 years for the primary prevention group. Social determinants of health such as access to care and healthcare coverage were associated with less statin treatment. Novel interventions to improve statin prescription and adherence are needed.

De Backer, Guy

doi: 10.1093/eurjpc/zwac111pmid: 35655392

This editorial refers to ‘Achievement of ESC/EAS LDL-C treatment goals after an acute coronary syndrome with statin and alirocumab’, by U. Landmesser et al., https://doi.org/10.1093/eurheartj/zwac107. In the 2021 ESC guidelines on cardiovascular disease prevention in clinical practice, it is recommended to aim at more stringent low-density lipoprotein cholesterol (LDL-C) goals in patients with established atherosclerotic cardiovascular disease (ASCVD).1 These goals are expressed in LDL-C levels to be attained and in percent reduction from the pre-treatment level. The treatment goal of LDL-C in patients with ASCVD [<1.4 mmol/L (<55 mg/dL)] is based on data from Mendelian randomization studies,2 from meta-analysis of LDL-C lowering randomized controlled trials (RCTs),3 and from results in RCTs such as IMPROVE-IT,4 FOURIER,5 and ODYSSEY OUTCOMES.6 For patients with ASCVD who experience a second vascular event within 2 years while taking maximum tolerated statin-based therapy, an even lower LDL-C goal of <1.0 mmol/L (<40 mg/dL) may be considered. In addition to this move towards a lower LDL-C level, the guidelines recommend to achieve a LDL-C reduction of at least 50% from the pre-treatment baseline value. This is based on the observation that percent LDL-C reduction provides incremental prognostic value over attained LDL-C levels in patients with ASCVD.7 All this fits with the paradigm ‘lower is better’ based on the evidence that until now no level of LDL-C has been defined below which benefit ceases or harm occurs. But in daily practice, clinicians may have problems with the implementation of these recommendations. Should these targets be applied universally or selectively to patients with ASCVD? Should we aim at a more case-based interpretation of these intensified goals for LDL-C lowering? And do we have the means of achieving these goals? In the 2021 ESC prevention guidelines, it is recommended that in patients with ASCVD a high-intensity statin is prescribed up to the highest tolerated dose to reach the LDL-C goals set for this specific risk group.1 However, even if this is applied, the results regarding LDL-C goal attainment are not that good as was illustrated in the EUROASPIRE V survey.8 In the IMPROVE-IT trial, the combination of simvastatin with ezetimibe resulted in a LDL-C median time-weighted LDL-C of 1.4 mmol/L (55 mg/dL) from a baseline of 2.4 mmol/L (93 mg/dL) illustrating that half of the patients had achieved the LDL-C goal of <1.4 mmol/L (<55 mg/dL).4 To achieve the actual more aggressive LDL-C goals in a majority of patients with ASCVD, it seems that the prevention strategy should move from the ‘high-intensity statin’ concept into a ‘tailored high-intensity lipid-lowering strategy’ including various kinds of combinations of lipid-lowering drug therapies. In this issue of the EJPC, results are presented from the ODYSSEY OUTCOMES trial documenting that in patients with a recent ACS who are not at the recommended LDL-C goal despite optimized statin therapy, this goal can be achieved in a large majority with the combination of statins with alirocumab.9 These results are promising but they are based on observations in a carefully selected and closely followed clinical trial cohort. Achieving such results over a longer-time period in the real world may be difficult. Introducing PCSK9 inhibitors in patients with a recent ACS is still difficult in various countries because of cost-related factors. Cost-effectiveness solutions are further needed considering differences between countries in health care systems and health insurance plans. Other combinations of lipid-lowering drugs become available with the introduction of an RNA-based agent that blocks PCSK9 synthesis (inclisiran) and of an inhibitor of ATP citrate lyase, a key enzyme in the cholesterol biosynthesis (bempedoic acid). Anyhow, longer-term safety data with all these drugs are necessary given the need for a lifelong therapy to prevent or halt the underlying process of atherosclerosis. Besides these caveats on cost-benefit and long-term safety issues of a new high-intensity lipid-lowering drug strategy there is also the issue of the inter-individual variation in the respons to such an approach. Real world data from the EUROASPIRE V survey show that the percentage LDL-C response to a given statin dose is not fixed across the pre-treatment range: the lower the pre-treatment LDL-C level, the smaller the percent LDL-C reduction for a given statin dose.10 This has also been observed in the VOYAGER study.11 This may suggest that in patients who start off with rather low LDL-C pre-treatment levels, the LDL-C goal level may well be achieved with an optimized statin therapy but with a < 50% reduction resulting in a higher residual risk than if >50% LDL-C reduction was reached. All adults should have at least one LDL-C level available in their routine medical record and in patients with ASCVD monitoring the effect of lipid-lowering drug therapies on LDL-C levels is recommended with assessment of LDL-C levels and percent reductions 4–6 weeks after any treatment strategy initiation or change. Moreover, all patients with established ASCVD do not have the same risk of recurrent events supporting an intensified prevention strategy in those at highest risk of relapse. This means a more personalized approach including the estimation of residual risk and monitoring for efficacy and safety. The absolute benefit of lowering LDL-C depends on the absolute risk of ASCVD and on the absolute reduction in LDL-C, so even a small absolute reduction in LDL-C may translate in a significant absolute risk reduction in patients with a recent ACS who are at an extreme risk of recurrent events. But the reverse is also true: in patients at a lower risk of recurrences selective reports of interventions resulting in a large relative risk reduction may well conceal a small and less relevant absolute risk reduction. All patients with established ASCVD belong to the very-high risk category according to guidelines but within that category large differences in residual risk exist. Using the EUROASPIRE risk model for patients with ASCVD (https://www.calconic.com/calculator-widgets/euroaspire-risk-factor-calculator/5f6223fab75b14001e1f3c67?layouts=true), a patient aged 60 years in Ireland with an LDL-C level of 3 mmol/L (116 mg/dL) but no other determinants of CV risk according to that model has a 6% chance of developing recurrent events over the coming 2 years.12 A patient of similar age, living in Turkey with the same LDL-C level but also uncontrolled diabetes, heart failure, and anxiety symptoms has a 38% chance of recurrences over 2 years. If the residual risk can be reduced by 50% in the Irish patient that would result in an absolute risk reduction of −3%/2 yrs but an even smaller residual risk reduction of 20% in the Turkish patient would result in an absolute risk reduction of −7.6%/2 yrs. Absolute CV risk is more useful in conveying the true impact of an intervention, yet is often under-reported. So, patients with established ASCVD are at very-high risk of recurrent events but this may vary a lot; the intensity of the secondary prevention approach should be adapted to the residual risk; all goals related to lifestyle changes and to the control of elevated LDL-C, arterial hypertension and dysglycaemia should consider this residual risk that can be estimated with existing models.12,13 Finally, one should not forget that if the LDL-C goals cannot be reached the risk of recurrent events can still be reduced by focusing on the other CV risk factors. 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Google Scholar Crossref Search ADS PubMed WorldCat 13 Hageman SHJ , McKay AJ, Ueda P, Gunn LH, Jernberg T, Hagström E, Bhatt DL, Steg PhG, Läll K, Mägi R, Gynnild MN, Ellekjær H, Saltvedt I, Tuñón J, Mahíllo I, Aceña Á, Kaminski K, Chlabicz M, Sawicka E, Tillman T, McEvoy JW, Di Angelantonio E, Graham I, De Bacquer D, Ray KK, Dorresteijn JAN, Visseren FLJ. Estimation of recurrent atherosclerotic cardiovascular event risk in patients with established cardiovascular disease: the updated SMART2 algorithm . Eur Heart J 2022 ; 43 : 1715 – 1727 . Google Scholar Crossref Search ADS PubMed WorldCat Author notes The opinions expressed in this article are not necessarily those of the Editors of the European Journal of Preventive Cardiology or of the European Society of Cardiology. © The Author(s) 2022. Published by Oxford University Press on behalf of the European Society of Cardiology. All rights reserved. For permissions, please email: [email protected]. This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model) © The Author(s) 2022. Published by Oxford University Press on behalf of the European Society of Cardiology. All rights reserved. For permissions, please email: [email protected].