Use of bempedoic acid in an out-patient setting: an assessment of efficacy heterogeneity

Use of bempedoic acid in an out-patient setting: an assessment of efficacy heterogeneity | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Use of bempedoic acid in an out-patient setting: an assessment of efficacy heterogeneity Amro Maarouf, Manjit K Shergill, Alan F Jones, John Marriott, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6727985/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objectives We describe decreases in total cholesterol (TC), non-high density lipoprotein-cholesterol (non-HDL-C) and low density lipoprotein-cholesterol (LDL-C) following bempedoic acid treatment in an out-patient setting and compare the results to those observed in phase 3 efficacy trials. Methods We analysed a cohort of 113 patients not achieving LDL-C targets commenced on bempedoic acid after previous treatment with statins and ezetimibe using an intention-to-treat approach. We compared pre and post bempedoic acid treatment lipids (3-months) in the total cohort using paired t-tests. Baseline patient characteristics associated with LDL-C decrease was established via linear/multiple regression analyses. Results Following bempedoic acid treatment absolute reduction (mean ± SD) in TC, non-HDL-C, and LDL-C values were 1.1 ± 1.0mmol/L, 1.0 ± 1.0mmol/L, and 1.0 ± 0.9mmol/L, respectively, whilst percentage reductions (mean ± SD) were 16.4 ± 14.1%, 18.8 ± 17.2%, and 23.2 ± 20.5%, respectively. Significant decreases in the lipids were observed in every subgroup studied. The LDL-C decrease following bempedoic acid was independently greater in ex-smokers, individuals on ezetimibe at baseline, and those with higher baseline LDL-C values. Conclusions Our results show that the LDL-C decrease seen with bempedoic acid was comparable to that observed in the phase 3 efficacy trials. However, efficacy heterogeneity was observed in some of the subgroups studied such as patients on ezetimibe monotherapy or with higher LDL-C at baseline, the latter in accordance with the Wilder principle. The use of effect scores for identified patient subgroups might predict treatment response, enabling optimisation of lipid lowering efficacy. Bempedoic acid cardiovascular disease hypercholesterolaemia low density lipoprotein cholesterol Wilder principle Figures Figure 1 Figure 2 Introduction The World Health Organisation in 2021 estimated that atherogenic cardiovascular disease (CVD) annually accounted for around 17.9 million deaths [ 1 ]. Many prospective studies have shown dyslipidaemia to be a predictive factor of CVD [ 2 ]. Low density lipoprotein-cholesterol (LDL-C) lowering has been an important aspect of CVD risk reduction since the pivotal Scandinavian Simvastatin Survival Study in 1994 [ 3 ], this based on the lipid hypothesis [ 2 , 4 ]. The Cholesterol Treatment Trialist’ (CTT) collaboration analysed 5 trials including 39,612 patients which compared greater vs lesser efficacious statins (dose of type) and, 21 trials including 129,526 patients which compared statins vs placebo [ 5 ]. When the results of all 26 randomised controlled trials (RCT) were pooled, a 1.0 mmol/L LDL-C reduction was associated with a 22% relative risk reduction (RRR) of CVD (Rate Ratio (RR) = 0·78, 95% Confidence Interval (CI) = (0.76, 0.80); p < 0·0001) [ 5 ]. Additionally non-statin lipid lowering agents such as ezetimibe and Proprotein Convertase Subtilisin/Kexin Type-9 (PCSK9) inhibitors have been shown to lower CVD in accordance with the findings of the CTT collaboration [ 2 , 5 ]. The Dyslipidaemia clinics run by the University Hospitals Birmingham NHS Foundation Trust have used the LDL-C targets set by the European Society of Cardiology and European Atherosclerosis Society since 2019 [ 6 ]. The current treatment pathway used in our clinics to reduce LDL-C include statins, ezetimibe, bempedoic acid (prescribed since October 2020), PSCK9 inhibitors (prescribed since 2016) and inclisiran (prescribed since 2022). Bempedoic acid is a prodrug, that following activation in the liver, decreases hepatic cholesterol synthesis by inhibiting adenosine triphosphate citrate lyase [ 7 ]. Thus, bempedoic acid, which is not activated in skeletal muscle, is potentially useful in patients not reaching the assigned LDL-cholesterol targets because of statin discontinuation or dose reduction [ 8 – 10 ].The Cholesterol Lowering via Bempedoic acid, an ACL-Inhibiting Regimen (CLEAR) study programme comprising 4 RCTs (CLEAR Serenity, CLEAR Tranquillity, CLEAR Wisdom and CLEAR Harmony) evaluated lipid lowering efficacy and safety of bempedoic acid [ 9 , 11 – 13 ]. The mean LDL-C decreases in the treatment (bempedoic acid) arms after 12-weeks ranged between 15% and 24% (CLEAR Serenity − 24%, baseline LDL-C = 4.1mmol/L, CLEAR Tranquillity − 24%, baseline LDL-C = 3.3mmol/L, 15%, CLEAR Wisdom - baseline LDL-C = 3.1mmol/L, and CLEAR Harmony − 17%, baseline LDL-C = 2.7mmol/L) [ 10 ]. The CLEAR Outcomes RCT including 13,970 (6992 - bempedoic acid, 6978 – placebo) patients at high CVD risk with a median follow-up of 40.6 months showed bempedoic acid being associated with significantly (hazard ratio = 0.87, 95% CI = (0.79, 0.96), p = 0.004) lower major adverse cardiovascular events [ 14 ]. Bempedoic acid was made available to the Dyslipidemia clinics at Good Hope and Birmingham Heartlands Hospitals (part of University Hospitals Birmingham NHS Foundation Trust) between 01.10.2020 and 28.04.2021 prior to the National Institute for Health and Care Excellence (NICE) technology appraisal guidance (TAG) being published via a pre-reimbursement access scheme [ 15 ]. The aim of this audit was initially to evaluate the efficacy of bempedoic acid in the total cohort and selected subgroups, then compare it against the efficacy observed in the CLEAR RCTs [ 9 , 11 – 14 ] and finally establish predictors of LDL-C change that may account for efficacy heterogeneity. Materials and Methods This study is part of an ongoing lipid clinic audit programme carried out by the Department of Clinical Biochemistry at Good Hope and Birmingham Heartlands Hospitals (University of Birmingham NHS Foundation Trust) to evaluate the efficacy of lipid lowering agents. Previously, audits on fibrates, statins, ezetimibe and PCSK9 inhibitor therapy had been completed and published [ 16 – 21 ]. More recently, our group were part of a UK wide multicenter audit that assessed treatment efficacy of Bempedoic acid [ 22 ]. Approval for the current audit was received from University Hospitals Birmingham NHS Foundation Trust (Ref: CARMS-17932). A list of all 113 patients started on bempedoic acid between 01.10.2020 and 28.04.2021 had been maintained and clinical and lipid profile data were obtained from the hospital electronic patient records. The mean age ± standard deviation (SD) of the 113 patients started on bempedoic acid was 62.5 ± 10.6 years. All patients commenced on bempedoic acid had not achieved their LDL-C targets based on the guidelines adopted by the clinics [ 6 ]. Statin intolerance was evident in 100 (88.5%) of the patients. The cohort characteristics regarding sex, ethnicity, diabetes/smoking/CVD status, details of ongoing lipid lowering therapy and baseline LDL-C are shown in Table 1 . All patients attended the consultant led specialist Dyslipidemia clinics carried out at Good Hope Hospital (consultant: SR) and Birmingham Heartlands Hospital (consultant: AFJ) taking referrals from primary care across the West Midlands county, UK as well as from secondary care (Cardiology, Stroke and Vascular Medicine). It was usual practice to initially establish LDL-C targets based on evidence and guidelines and then decide on the treatment pathway [ 6 ]. The clinics would usually consider statins initially, taking into account pharmacokinetic characteristics of the drug. Atorvastatin would usually be first line with rosuvastatin substituted in intolerant patients. If both statins led to clinical adverse effects, simvastatin, pravastatin and fluvastatin were considered. Ezetimibe was initiated as second line in patients not achieving LDL-C targets. Following statins and ezetimibe, other lipid lowering agents such as PCSK9 inhibitors (depending upon funding thresholds being met) or bempedoic acid would be commenced. Statin and ezetimibe (both agents would usually have been tried prior bempedoic acid) intolerance was high as shown in Table 1 ; only 25 (22.1%) patients were on a statin/ezetimibe combination when bempedoic acid was initiated. This could have been due to bempedoic acid being commenced only in patients not achieving LDL-C targets, thus our cohort would arguably demonstrate high statin and ezetimibe intolerance. Table 1 Changes in TC, non-HDL-C and LDL-C following bempedoic acid in the total cohort and patients stratified by baseline characteristics. Patient groups Pre-bempedoic acid (mean ± SD) Post-bempedoic acid (mean ± SD) Decrease in lipids (mean ± SD), (n), p (paired t-test) TC (mmol/L) Non-HDL-C (mmol/L) LDL-C (mmol/L) TC (mmol/L) Non-HDL-C (mmol/L) LDL-C (mmol/L) TC (mmol/L) Non-HDL-C (mmol/L) LDL-C (mmol/L) Total cohort 6.2 ± 1.5 (n = 113) 4.8 ± 1.5 (n = 111) 3.9 ± 1.3 (n = 111) 5.1 ± 1.1 (n = 110) 3.8 ± 1.1 (n = 110) 2.9 ± 1.6 (n = 106) 1.1 ± 1.0 (n = 110), p < 0.0001 1.0 ± 1.0 (n = 108), p < 0.0001 1.0 ± 0.9 (n = 105), p < 0.0001 Stratification by baseline discrete variables Sex Males 5.9 ± 1.6 (n = 35) 4.8 ± 1.6 (n = 33) 3.7 ± 1.3 (n = 33) 4.8 ± 1.1 (n = 33) 3.8 ± 1.1 (n = 33) 2.7 ± 0.9 (n = 31) 1.2 ± 0.9 (n = 33), p < 0.0001 1.1 ± 0.8 (n = 31), p < 0.0001 1.0 ± 0.8 (n = 30), p < 0.0001 Females 6.3 ± 1.5 (n = 78) 4.9 ± 1,5 (n = 78) 3.9 ± 1.3 (n = 78) 5.2 ± 1.1 (n = 77) 3.9 ± 1.2 (n = 77) 3.0 ± 1.1 (n = 75) 1.1 ± 1.0 (n = 77), p < 0.0001 1.0 ± 1.0 (n = 77), p < 0.0001 1.0 ± 0.9 (n = 75), p < 0.0001 Ethnicity White caucasian 6.2 ± 1.5 (n = 86) 4.9 ± 1.4 (n = 85) 3.9 ± 1.3 (n = 85) 5.1 ± 1.1 (n = 83) 3.7 ± 1.1 (n = 83) 2.8 ± 1.0 (n = 80) 1.2 ± 1.0 (n = 83), p < 0.0001 1.1 ± 1.0 (n = 82), p < 0.0001 1.1 ± 0.9 (n = 80), p < 0.0001 Non-white caucasian 6.2 ± 1.7 (n = 27) 4.8 ± 1.8 (n = 26) 3.9 ± 1.5 (n = 26) 5.2 ± 1.2 (n = 27) 4.1 ± 1.4 (n = 27) 3.2 ± 1.2 (n = 26) 1.0 ± 0.9 (n = 27), p < 0.0001 0.8 ± 0.8 (n = 26), p < 0.0001 0.7 ± 0.7 (n = 25), p = 0.0001 Diabetes status T2DM 6.0 ± 1.9 (n = 19) 4.6 ± 1.8 (n = 19) 3.7 ± 1.6 (n = 19) 4.8 ± 1.5 (n = 18) 3.6 ± 1.4 (n = 18) 2.7 ± 1.3 (n = 18) 1.2 ± 1.1 (n = 18), p = 0.0004 1.0 ± 1.0 (n = 18), p = 0.0005 1.0 ± 0.9 (n = 18), p = 0.0002 No T2DM 6.3 ± 1.4 (n = 94) 4.9 ± 1.4 (n = 92) 3.9 ± 1.3 (n = 92) 5.2 ± 1.0 (n = 92) 3.9 ± 1.1 (n = 92) 2.9 ± 1.0 (n = 88) 1.1 ± 1.0 (n = 92), p < 0.0001 1.0 ± 0.9 (n = 90), p < 0.0001 1.0 ± 0.9 (n = 87), p < 0.0001 Smoking status Non-smokers 6.2 ± 1.5 (n = 80) 4.8 ± 1.5 (n = 79) 3.9 ± 1.3 (n = 79) 5.2 ± 1.0 (n = 77) 3.9 ± 1.2 (n = 77) 3.0 ± 1.1 (n = 73) 1.0 ± 1.0 (n = 77), p < 0.0001 0.9 ± 1.0 (n = 76), p < 0.0001 0.9 ± 0.9 (n = 73), p < 0.0001 Ex-smokers 5.8 ± 1.7 (n = 16) 4.5 ± 1.6 (n = 16) 3.5 ± 1.3 (n = 16) 4.6 ± 1.1 (n = 16) 3.7 ± 1.0 (n = 16) 2.3 ± 0.8 (n = 16) 1.2 ± 1.0 (n = 16), p = 0.0003 1.1 ± 0.9 (n = 16), p = 0.0002 1.2 ± 1.0 (n = 16), p = 0.0003 Current-smokers 6.8 ± 1.3 (n = 17) 5.4 ± 1.3 (n = 16) 4.3 ± 1.1 (n = 16) 5.2 ± 1.2 (n = 17) 4.0 ± 1.2 (n = 17) 3.0 ± 1.0 (n = 17) 1.6 ± 1.0 (n = 17), p < 0.0001 1.5 ± 0.9 (n = 16), p < 0.0001 1.4 ± 0.7 (n = 16), p < 0.0001 CVD status Primary prevention 6.5 ± 1.4 (n = 93) 5.1 ± 1.5 (n = 91) 4.1 ± 1.3 (n = 91) 5.3 ± 1.0 (n = 90) 4.0 ± 1.1 (n = 90) 3.0 ± 1.0 (n = 86) 1.2 ± 1.0 (n = 90), p < 0.0001 1.1 ± 1.0 (n = 88), p < 0.0001 1.1 ± 0.9 (n = 85), p < 0.0001 Secondary prevention 4.8 ± 1.0 (n = 20) 3.7 ± 1.0 (n = 20) 2.8 ± 0.9 (n = 20) 4.1 ± 0.9 (n = 20) 3.0 ± 0.9 (n = 20) 2.1 ± 0.9 (n = 20) 0.7 ± 0.9 (n = 20), p = 0.0013 0.7 ± 0.8 (n = 20), p = 0.0030 0.7 ± 0.8 (n = 20), p = 0.0010 Ongoing lipid lowering agents None 7.0 ± 1.4 (n = 37) 5.6 ± 1.4 (n = 36) 4.5 ± 1.3 (n = 36) 5.7 ± 1.1 (n = 37) 4.5 ± 1.1 (n = 37) 3.4 ± 1.1 (n = 37) 1.2 ± 1.0 (n = 37), p < 0.0001 1.1 ± 1.0 (n = 36), p < 0.0001 1.1 ± 0.9 (n = 36), p < 0.0001 Ezetimibe 6.3 ± 1.1 (n = 24) 4.8 ± 1.1 (n = 24) 3.8 ± 1.1 (n = 23) 4.8 ± 0.9 (n = 22) 3.5 ± 0.9 (n = 22) 2.4 ± 9.8 (n = 19) 1.5 ± 1.0 (n = 22), p < 0.0001 1.3 ± 0.9 (n = 22), p < 0.0001 1.4 ± 0.9 (n = 19), p < 0.0001 Statins and Ezetimibe 5.3 ± 1.4 (n = 25) 4.0 ± 1.4 (n = 24) 3.2 ± 1.1 (n = 25) 4.6 ± 0.8 (n = 25) 3.3 ± 0.8 (n = 25) 2.6 ± 0.8 (n = 25) 0.7 ± 0.9 (n = 25), p = 0.0014 0.6 ± 1.0 (n = 24), p = 0.0036 0.6 ± 0.8 (n = 25), p = 0.0017 Statins 6.0 ± 1.7 (n = 27) 4.6 ± 1.6 (n = 27) 3.7 ± 1.4 (n = 27) 4.9 ± 1.2 (n = 26) 3.7 ± 1.2 (n = 26) 2.8 ± 1.1 (n = 25) 1.1 ± 1.1 (n = 26), p < 0.0001 1.0 ± 0.9 (n = 26), p < 0.0001 0.9 ± 0.7 (n = 25), p < 0.0001 Stratification by median LDL-C (3.65mmol/L) LDL-C < 3.65mmol/L 5.0 ± 0.9 (n = 56) 3.7 ± 0.8 (n = 55) 2.8 ± 0.4 (n = 56) 4.4 ± 0.8 (n = 54) 3.2 ± 0.8 (n = 54) 2.3 ± 0.8 (n = 53) 0.6 ± 0.7 (n = 54), p < 0.0001 0.5 ± 0.7 (n = 53), p < 0.0001 0.5 ± 0.6 (n = 52), p < 0.0001 LDL-C ≥ 3.65mmol/L 7.4 ± 1.1 (n = 55) 6.0 ± 1.1 (n = 55) 4.9 ± 0.9 (n = 55) 5.7 ± 1.0 (n = 54) 4.5 ± 1.0 (n = 54) 3.5 ± 0.9 (n = 52) 1.7 ± 1.0 (n = 54), p < 0.0001 1.5 ± 0.9 (n = 54), p < 0.0001 1.5 ± 0.8 (n = 53), p < 0.0001 Within the audit period, 41 (36.3%) of the patients discontinued bempedoic acid and a lipid profile check/clinic appointment was arranged as soon as possible. In the 72 (63.7%) individuals continuing bempedoic acid, lipid profiles at or close to 12-weeks post-bempedoic acid initiation were conducted just prior to the follow-up appointment. The median (inter quartile range) follow-up was 3.3 (2.3–5.1) months. No other changes to lipid lowering therapy took place during the audit period. Total cholesterol (TC), HDL-C and triglycerides (TG) measurements were carried out on the Abbott Alinity c system using the supplied kit reagents. LDL-C was calculated via the Friedwald algorithm [ 23 ], as was the case in the CLEAR RCTs [ 9 , 11 – 13 ]. Analytical performance of the Abbott Alinity c system has previously been extensively evaluated and reported [24.25]. Statistical analysis Baseline TC, non-HDL-C and LDL-C were normally distributed (skewness kurtosis test, p > 0.05), hence paired and unpaired t-tests were used to compare intra- and intergroup changes in TC, non-HDL-C and LDL-C following bempedoic acid in the total cohort and subgroups stratified by baseline characteristics. Statistical analyses were performed on the entire cohort and not just on the individuals who continued with the treatment. No differences (p > 0.05, using logistic regression) in baseline characteristics were observed between individuals continuing or stopping bempedoic acid regarding sex, ethnicity, diabetes/smoking/CVD status, ongoing lipid lowering therapy and LDL-C. Comparison of pre and post bempedoic acid lipid values : Paired t-tests were used to determine if changes in TC, non-HDL-C and LDL-C were significant in the total cohort and subgroups stratified by sex, ethnicity, diabetes/smoking/CVD status, ongoing lipid lowering therapy, and baseline LDL-C levels stratified by the median value of 3.65mmol/L. Factors associated with LDL-C changes following bempedoic acid treatment : Linear regression analysis was performed to establish factors that predicted LDL-C decrease, with all significant independent variables included in a single multivariate regression model. For non-continuous independent variables, a single characteristic of the variable was chosen as the reference category and the other characteristics of that variable were compared to the reference category (factorisation) regarding associations with the selected dependent variable. Stata version 14 (College Station, TX) was used for all the statistical analysis. Results Bempedoic acid treatment led to a significant decrease (paired t-test) in TC, non-HDL-C and LDL-C in the total cohort and the various subgroups as shown in Table 1 . The principal analyses were conducted on an intention-to-treat basis, hence the efficacy values seen in Table 1 could be considered conservative as indicated by the decreases in TC, non-HDL-C and LDL-C (mean ± SD) being significantly lower (TC = 0.8 ± 0.8, n = 39, non-HDL-C = 0.7 ± 0.8, n = 38, LDL-C = 0.7 ± 0.9, n = 37) in the individuals discontinuing bempedoic acid compared to their counterparts (TC = 1.3 ± 1.0, n = 71, non-HDL-C = 1.2 ± 1.0, n = 70, LDL-C = 1.2 ± 0.8, n = 68) who continued the medication; unpaired t-test: p < 0.0034, 0.0099, and 0.0034 for changes in TC, non-HDL-C, and LDL-C, respectively. Table 2 shows subgroups based on baseline characteristics that predicted bempedoic acid efficacy (change in LDL-C) using separate linear regression models; ex-smokers (compared to non-smokers) – Model 5, statin monotherapy (compared to those on ezetimibe monotherapy) Model 7, and lower levels of LDL-C that were significantly associated with decreased LDL-C lowering – Models 8 & 9. The LDL-C decrease was greater (coefficient (c) = 0.46, 95% CI = (0.0222, 0.89), p = 0.040) in individuals on ezetimibe monotherapy compared to the remaining participants (no lipid lowering therapy, statin monotherapy and statin and ezetimibe treated) combined together as the reference group. In view of the above findings, we did not study efficacy in the patient cohort stratified by single, dual, or triple therapies. When the patients were stratified by the median baseline LDL-C value of 3.65mmol/L, patients with LDL-C ≥ 3.65mmol/L were associated with a significantly greater LDL-C decrease than individuals with LDL-C < 3.65mmol/L (reference) – Model 9. Table 2 Baseline factors associated with LDL-C decrease in patients treated with bempedoic acid; individual factors analysed via linear regression models (1–9) followed by a multiple regression model (10) including all significant factors from the earlier models. Independent Variable Dependent variable (outcome): decrease in LDL-C (mmol/L) (at baseline) Model c (95% CI), p R-squared Age (years) Model 1 -0.0042 (-0.021, 0.012), p = 0.61 0.00 Males Model 2 0.073 (-0.31, 0.45), p = 0.70 0.00 Females reference Non-white caucasians Model 3 -0.25 (-0.77, 0.27), p = 0.34 0.01 White caucasians reference T2DM Model 4 -0.0096 (-0.46, 0.44), p = 0.97 0.00 No T2DM reference Current smokers Model 5 0.32 (-0.16, 0.79), p = 0.19 0.05 Ex-smokers 0.52 (0.048, 1.00), p = 0.031 Non-smokers reference Secondary prevention Model 6 -0.39 (-0.82, 0.042), p = 0.079 0.30 Primary prevention reference No lipid lowering agents Model 7 -0.23 (-0.71, 0.25(, p = 0.34 0.10 Statin monotherapy -0.79 (-1.30, -0.27), p = 0.003 Statins and ezetimibe -0.46 (-0.97, 0.054), p = 0.079 Ezetimibe monotherapy reference LDL-C (mmol/L) Model 8 0.42 (0.32, 0.52), p < 0.001 0.41 LDL-C ≥ 3.65mmol/L Model 9 0.99 (0.71, 1.27), p < 0.001 0.32 LDL-C < 3.65mmol/L reference Multiple regression model including significant independent variable from Models 1–9. LDL-C (mmol/L) Model 10 0.43 (0.33, 0.54), p < 0.001 0.50 Current smokers 0.27 (-0.083, 0.63), p = 0.13 Ex-smokers 0.48 (0.12, 0.83), p = 0.009 Non-smokers reference No lipid lowering agents -0.51 (-0.88, -0.14), p = 0.007 Statin monotherapy -0.46 (-0.86, -0.068), p = 0.022 Statins and ezetimibe -0.43 (-0.82, -0.042), p = 0.030 Ezetimibe monotherapy reference Multiple regression analysis (Model 10) with LDL-C decrease as the dependent variable was then performed with baseline LDL-C, smoking status and lipid lowering therapy at baseline included as independent variables, i,e. factors that reached statistical significance in Models 1–8. LDL-C stratified by the median were omitted and LDL-C was used as a continuous variable. Higher baseline LDL-C, ex-smokers and ezetimibe monotherapy independently showed significantly greater LDL-C reduction following bempedoic acid treatment – Model 10. Months of follow-up was not associated with LDL-C change (c = 0.037, 95% CI = (-0.051, 0.13), p = 0.40). We could not carry out meaningful statistics in patients with higher than median LDL-C and ezetimibe monotherapy at baseline in view of modest patient numbers (9 patients); LDL-C decrease (mean ± SD): 1.9 ± 0.8mmol/L. Models 8 and 9 (Table 2 ) indicated that pre-treatment LDL-C values were associated with LDL-C decrease following bempedoic acid initiation; higher baseline LDL-C concentrations were related to greater LDL-C decreases. This is evident in Fig. 1 where LDL-C (x-axis) was plotted against the decrease in LDL-C (y-axis) following bempedoic acid treatment. We then determined whether these effects were due to a fixed percentage decrease in LDL-C following bempedoic acid therapy (this could also show the above pattern). Importantly, baseline LDL-C was also significantly associated with percentage changes in LDL-C (c = 4.92, 95% CI = (2.11, 7.74), p = 0.001). Baseline LDL-C was also associated with a change in LDL-C when the patients were stratified by the median LDL-C of 3.65mmol/L (Fig. 2 and footnote). Table 3 shows that this phenomenon was also evident for all of the subgroups stratified by the other baseline characteristics, apart from current smokers (p = 0.057, n = 16). Interestingly, baseline LDL-C was significantly associated with bempedoic acid related LDL-C decrease in both, patients who tolerated the drug (c = 0.47, 95% CI = (0.36, 0.58), p < 0.001, n = 68) and those who discontinued it (c = 0.35, 95% CI = 0.19, 0.52), p < 0.001, n = 37). Table 3 Associations between baseline LDL-C and decrease in LDL-C carried out via linear regression in the various patient subgroups stratified by baseline characteristics. Linear regression models (LDL-C vs decrease in LDL-C) in subgroups (at baseline) c (95% CI), p n Males 0.46 (0.32, 0.60), p < 0.001 30 Females 0.40 (0.28, 0.53), p < 0.001 75 Non-white caucasians 0.30 (0.13, 0.46), p = 0.001 25 White caucasians 0.47 (0.35, 0.58), p < 0.001 80 T2DM 0.31 (0.068, 0.56), p = 0.016 18 No T2DM 0.45 (0.34, 0.56), p < 0.001 87 Current smokers 0.32 (-0.011, 0.65), p = 0.057 16 Ex-smokers 0.61 (0.36, 0.85), p < 0.001 16 Non-smokers 0.40 (0.29, 0.51), p < 0.001 73 Secondary prevention 0.39 (0.020, 0.76), p = 0.040 20 Primary prevention 0.45 (0.34, 0.56), p < 0.001 85 No lipid lowering agents 0.40 (0.19, 0.61), p < 0.001 36 Statin monotherapy 0.32 (0.17, 0.48), p < 0.001 25 Statins and ezetimibe 0.52 (0.30, 0.74), p < 0.001 25 Ezetimibe monotherapy 0.57 (0.28, 0.86), p = 0.001 19 Discussion This was a real-world study of 113 out-patients treated with bempedoic acid prior to the NICE TAG in April 2021. We adopted an intention-to-treat approach in this audit which appeared to result in conservative estimates of bempedoic acid efficacy. Paired t-tests (Table 1 ) were used to compare pre and post bempedoic acid TC, Non-HDL-C and LDL-C in the total cohort and patient subgroups whilst linear and multiple regression analyses (Table 2 ) were performed to establish baseline predictors (independent variables) of LDL-C change (dependent variable). This was followed by further study of the association (Table 3 ) between baseline LDL-C (independent variable) and change in LDL-C (dependent variable) using linear regression models. Comparison of outcomes with previous RCTs Following bempedoic acid treatment, absolute reduction (mean ± SD) in TC, non-HDL-C, and LDL-C values were 1.1 ± 1.0mmol/L, 1.0 ± 1.0mmol/L, and 1.0 ± 0.9mmol/L, respectively, whilst percentage reductions (mean ± SD) were 16.4 ± 14.1%, 18.8 ± 17.2%, and 23.2 ± 20.5%, respectively. The mean LDL-C reduction in the present study was similar to the mean decreases observed in the CLEAR Tranquility (24%) and CLEAR Serenity (24%) treatment arms and higher than in the CLEAR Wisdom (15%) and CLEAR Harmony (17%) treatment arms [ 10 ]. This could be due to baseline characteristics (including baseline LDL-C values and co-therapies) as seen in Table 2 leading to efficacy heterogeneity. Our findings, if validated, suggest that an understanding of cohort characteristics is essential to interpret differences in study outcomes. Significant decreases in the lipid values were observed in every subgroup that we studied (Table 1 ). LDL-C reduction is currently the cornerstone of lipid lowering regarding CVD prevention [ 2 , 4 , 6 ]. The results from the CTT collaboration suggests a RRR in CVD of 22% per 1mmol/L decrease in LDL-C [ 4 , 5 ]. The lipid hypothesis is based on agents such as resins, statins, ezetimibe, PCSK9 inhibitors and bempedoic acid use being associated with lowering of CVD [ 2 , 14 ]. Absolute risk reduction (ARR) is a function of absolute risk (AR) and RRR, hence we can hypothesize that bempedoic acid treatment in our patients tolerating the drug (mean ± SD = 1.2 ± 0.8mmol/L) would have yielded a significant ARR in CVD, especially in patients with high AR [ 4 ]. The LDL-C decrease following bempedoic acid was independently greater in certain subgroups; ex-smokers, individuals on ezetimibe at baseline, and those with higher baseline LDL-C values. We can offer no explanation for the increased efficacy in ex-smokers. Smoking cessation, whilst increasing HDL-C levels does not appear to change LDL-C values [ 26 , 27 ]. The LDL-C lowering efficacy of bempedoic acid appeared greater in patients on ezetimibe monotherapy compared to individuals not on lipid lowering agents, statin monotherapy and statin/ezetimibe combination (Table 2 ). We speculate that this could be due to ezetimibe up-regulating cholesterol synthesis [ 28 ]. Sudhop et al. demonstrated that cholesterol synthesis significantly increased by 89% in patients on ezetimibe [ 28 ]. Since bempedoic acid acts to decrease cholesterol synthesis by inhibiting adenosine triphosphate citrate lyase [ 7 ], it is feasible that the LDL-C lowering efficacy of the drug could perhaps be greater in individuals with an increased rate of cholesterol synthesis. The rate of synthesis would be reduced when statins are added to ezetimibe, hence the LDL-C lowering efficacy of bempedoic acid would perhaps be attenuated in patients also on statin and ezetimibe [ 29 ]. Impact of the Wilder Effect (principle of initial value) Baseline LDL-C values were associated with both an absolute and percentage change in LDL-C following bempedoic acid treatment (higher baseline LDL-C led to greater decrease in LDL-C). The association between baseline LDL-C and change in LDL-C is in accordance with the Wilder principle which suggests that the response to an intervention is dependent on the baseline values [ 30 ]. This was also observed in most subgroups (Table 3 ) and previous audits from our centre have also demonstrated this phenomenon [ 16 , 18 , 19 ]. This observation has clinical significance as high LDL-C levels are associated with greater AR of CVD and a more pronounced LDL-C reduction would lead to increased RRR [ 4 ]. Thus, ARR is perhaps even greater in patients with high baseline LDL-C values. As percentage decrease in LDL-C was also associated with baseline LDL-C, it is likely that it is a real effect and not a statistical aberration, although potential mechanisms have not been elucidated. Strengths and weaknesses There are a range of strengths and weaknesses in the present study. It was a real-world single arm longitudinal study with a small sample size with relatively short follow-up. This study contributed 113 patients to a national audit that included 221 patients commenced on bempedoic acid assessing the attainment of lipid targets [ 22 ]. The mean efficacy results from the multi-center audit was identical to that observed in our audit; TC: -1.1mmol/L, Non-HDL-C: -1.0mmol/L and LDL-C: -1.0mmol/L. However, these results would have been influenced by the efficacy of our study group (113 patients) which comprised nearly half (51.1%) of the multi-center audit cohort. In this analysis, we studied efficacy patterns prior to the NICE TAG being issued (which introduced extra selection criteria including statin intolerance as a pre-requisite) as this would have introduced selection bias [ 15 ]. Thus, all patients started on bempedoic acid were included with no sample size calculation based on previously reported efficacy. The take up rate over a near 7-month period may be considered slow for a new pharmaceutical agent; the coronavirus lockdowns in the UK could have had an impact ( https://www.instituteforgovernment.org.uk/sites/default/files/timeline-lockdown-web.pdf ). The intention though was to compare the lipid lowering results with published efficacy outcomes at 3 months. Regression towards the mean is possible, although the observed Wilder phenomenon may counter this [ 30 ]. The patient numbers were modest, as we wished to evaluate efficacy in patients not achieving LDL-C targets prior to the NICE technology assessment being published, as that could have reshaped the cohort characteristics. The small cohort affected the depth of subgroup analyses and study of interactions. We would have liked to study efficacy in patients with higher baseline LDL-C and ezetimibe monotherapy, but patient numbers were modest. Thus, it is essential to conduct a further study with a larger cohort and longer follow-up to verify the efficacy heterogeneity observed. Conclusions Our results show that bempedoic acid led to a reduction in LDL-C in the total cohort with efficacy comparable to that reported in phase 3 trials [ 10 ]. However, efficacy heterogeneity was observed in the subgroups studied. The response was more pronounced in patients taking existing ezetimibe monotherapy and those with higher baseline LDL-C. Wang et al emphasize the importance of heterogeneity of treatment effects and propose the use of effect scores relating to various patient subgroups to predict treatment response [ 31 ]. It would be interesting if this approach was adopted to optimize lipid lowering efficacy of all therapeutic agents prescribed, thus resulting in maximal CVD prevention. Declarations Funding statement The authors received no financial support for the research, authorship, and/or publication of this article. Declaration of competing interests SR has received research grants, travel grants and speakers’ honoraria from Daiichi Sankyo UK Ltd AM, MKS, AFJ and JM declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article Author contributions: Conception – SR, AM Data collection – SR, AM, MKS Analysis – SR, AM, JM Manuscript preparation – SR, AM, MKS, JM, AFJ Data availability The datasets generated and analyzed during the current study are available from the corresponding author on reasonable request. Ethical considerations Approval for the collection, analysis and publication of this retrospectively obtained and anonymised audit data was received from University Hospitals Birmingham NHS Foundation Trust (Ref: CARMS-17932). Ethics approval was therefore not required. Consent to participate Not applicable Consent for publication Not applicable References Cardiovascular diseases (CVDs). – World Health Organisation [online] 2021 https://www.who.int/health-topics/cardiovascular-diseases#tab=tab_1 (accessed 28.02.2024). Ramachandran S, Bhartia M, König CS. The Lipid Hypothesis: From Resins to Proprotein Convertase Subtilisin/Kexin Type 9 Inhibitors. Front Cardiovasc Med. 2020;5:5:1–35. Scandinavian Simvastatin Survival Study Group. Randomised trial of cholesterol lowering in 4444 patients with coronary heart disease: the Scandinavian Simvastatin Survival Study (4S). Lancet. 1994;344(8934):1383–9. https://doi.org/10.1016/S0140-6736(94)90566-5 . König CS, Mann A, McFarlane R, Marriott J, Price M, Ramachandran S. Age and the Residual Risk of Cardiovascular Disease following Low Density Lipoprotein-Cholesterol Exposure. Biomedicines. 2023;11(12):3208. https://doi.org/10.3390/biomedicines11123208 . Baigent C, Blackwell L, Emberson J, et al. Efficacy and safety of more intensive lowering of LDL cholesterol: a meta-analysis of data from 170,000 participants in 26 randomized trials. Lancet. 2010;376(9753):1670–81. https://doi.org/10.1016/S0140-6736(10)61350-5 . Mach F, Baigent C, Catapano AL, ESC Committee for Practice Guidelines (CPG); ESC National Cardiac Societies. 2019 ESC/EAS guidelines for the management of dyslipidaemias: Lipid modification to reduce cardiovascular risk. Atherosclerosis. 2019;290:140–205. https://doi.org/10.1016/j.atherosclerosis.2019.08.014 . Authors/Task Force Members. Pinkosky SL, Newton RS, Day EA, et al. Liver-specific ATP-citrate lyase inhibition by bempedoic acid decreases LDL-C and attenuates atherosclerosis. Nat Commun. 2016;7(1):13457. https://doi.org/10.1038/ncomms13457 . Selva-O’Callaghan A, Alvarado-Cardenas M, Pinal-Fernández I, et al. Statin-induced myalgia and myositis: an update on pathogenesis and clinical recommendations. Expert Rev Clin Immunol. 2018;14(3):215–24. https://doi.org/10.1080/1744666X.2018.1440206 . Ballantyne CM, Banach M, Mancini GJ, et al. Efficacy and safety of bempedoic acid added to ezetimibe in statin-intolerant patients with hypercholesterolemia: a randomized, placebo-controlled study. Atherosclerosis. 2018;277:195–203. https://doi.org/10.1016/j.atherosclerosis.2018.06.002 . Chaplin S. Bempedoic acid for primary hypercholesterolaemia. Prescriber. 2021;32(1):29–31. Laufs U, Banach M, Mancini GJ, et al. Efficacy and safety of bempedoic acid in patients with hypercholesterolemia and statin intolerance. J Am Heart Assoc. 2019;8(7):e011662. https://doi.org/10.1161/JAHA.118.011662 . Goldberg AC, Leiter LA, Stroes ES, et al. Effect of bempedoic acid vs placebo added to maximally tolerated statins on low-density lipoprotein cholesterol in patients at high risk for cardiovascular disease: the CLEAR wisdom randomized clinical trial. JAMA. 2019;322(18):1780–8. https://doi.org/10.1001/jama.2019.16585 . Ray KK, Bays HE, Catapano AL, et al. Safety and efficacy of bempedoic acid to reduce LDL cholesterol. N Engl J Med. 2019;380(11):1022–32. https://doi.org/10.1056/NEJMoa1803917 . Nissen SE, Lincoff AM, Brennan D, et al. Bempedoic acid and cardiovascular outcomes in statin-intolerant patients. N Engl J Med. 2023;388(15):1353–64. https://doi.org/10.1056/NEJMoa2215024 . National Institute for Health and Care Excellence (NICE). Bempedoic Acid with Ezetimibe for Treating Primary Hypercholesterolaemia or Mixed Dyslipidaemia. Technology Appraisal Guidance [TA694].; 2021. Ramachandran S, Abbas A, Saraf S, et al. Significant increase in high-density lipoprotein cholesterol with fibrates is associated with low pretreatment high-density lipoprotein cholesterol: findings from an outpatient clinic setting. Metab Syndr Relat Disord. 2012;10(3):189–94. https://doi.org/10.1089/met.2011.0112 . Abbas A, Saraf S, Ramachandran S, et al. Fibrates and estimated glomerular filtration rate: observations from an outpatient clinic setting and clinical implications. Postgrad Med J. 2012;88(1043):503–6. https://doi.org/10.1136/postgradmedj-2011-130594 . Gandhi N, Lenton R, Bhartia M, et al. Effect of fibrate treatment on liver function tests in patients with the metabolic syndrome. Springerplus. 2014;3:1–7. https://doi.org/10.1186/2193-1801-3-14 . Collins MW, König CS, Abbas A, et al. Association between triglyceride and high-density lipoprotein cholesterol change following fibrate therapy. Diabetes Metab Syndr. 2014;8(4):212–5. https://doi.org/10.1016/j.dsx.2014.09.004 . Groves C, Shetty C, Strange RC, et al. A study in high-risk, maximally pretreated patients to determine the potential use of PCSK9 inhibitors at various thresholds of total and LDL cholesterol levels. Postgrad Med J. 2017;93(1098):205–8. https://doi.org/10.1136/postgradmedj-2016-134062 . Ramachandran S, Bhartia M, Jones A et al. PCSK9 Inhibitors in diabetic dyslipidaemia. RSSDI Diabetes Update 2018:374–378. Jaypee Brothers Medical Publishers Ltd (ISBN: 978-93-5270-618-1). Ramachandran S, Maarouf A, Mitchell K, et al. A UK multicentre audit of the management of patients with primary hypercholesterolaemia or mixed dyslipidaemia with bempedoic acid against published lipid-lowering treatment targets. Drugs Context. 2024;13. https://doi.org/10.7573/dic.2024-2-4 . :2024-2-4. Friedewald WT, Levy RI, Fredrickson DS. Estimation of the concentration of low-density lipoprotein cholesterol in plasma, without use of the preparative ultracentrifuge. Clin Chem. 1972;18(6):499–502. PMID: 4337382. Westgard S, Petrides V, Schneider S, et al. Assessing precision, bias and sigma-metrics of 53 measurands of the Alinity ci system. Clin Biochem. 2017;50(18):1216–21. https://doi.org/10.1016/j.clinbiochem.2017.09.005 . Kanani FZ, Haider Kazmi A, Kaleem B. Sigma metrics of Alinity ci system–a study on thirty-nine clinical chemistry and immunoassay parameters. Adv Lab Med. 2021;2(2):267–75. https://doi.org/10.1515/almed-2021-0001 . van der Plas A, Antunes M, Pouly S, et al. Meta-analysis of the effects of smoking and smoking cessation on triglyceride levels. Toxicol Rep. 2023;10:367–75. https://doi.org/10.1016/j.toxrep.2023.03.001 . Maeda K, Noguchi Y, Fukui T. The effects of cessation from cigarette smoking on the lipid and lipoprotein profiles: a meta-analysis. Prev Med. 2003;37(4):283–90. https://doi.org/10.1016/s0091-7435(03)00110-5 . Sudhop T, Lütjohann D, Kodal A, et al. Inhibition of intestinal cholesterol absorption by ezetimibe in humans. Circulation. 2002;106(15):1943–8. https://doi.org/10.1161/01.cir.0000034044.95911.dc . Duan Y, Gong K, Xu S, et al. Regulation of cholesterol homeostasis in health and diseases: from mechanisms to targeted therapeutics. Signal Transduct Target Ther. 2022;7(1):265. https://doi.org/10.1038/s41392-022-01125-5 . Messerli FH, Bangalore S, Schmieder RE. Wilder's principle: pre-treatment value determines post-treatment response. Eur Heart J. 2015;36(9):576–9. https://doi.org/10.1093/eurheartj/ehu467 . Wang G, Heagerty PJ, Dahabreh IJ. Using effect scores to characterize heterogeneity of treatment effects. JAMA. 2024;331(14):1225–6. https://doi.org/10.1001/jama.2024.3376 . Additional Declarations Competing interest reported. SR has received research grants, travel grants and speakers’ honoraria from Daiichi Sankyo UK Ltd AM, MKS, AFJ and JM declared no potential competing interest with respect to the research, authorship, and/or publication of this article Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6727985","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":463089822,"identity":"8f9f8339-367a-4477-bbcc-7c659847a044","order_by":0,"name":"Amro Maarouf","email":"","orcid":"","institution":"Department of Clinical Biochemistry, University Hospitals Birmingham NHS Foundation Trust","correspondingAuthor":false,"prefix":"","firstName":"Amro","middleName":"","lastName":"Maarouf","suffix":""},{"id":463089826,"identity":"36ddeee8-7a71-4bbf-aeeb-640f24e1f6b0","order_by":1,"name":"Manjit K Shergill","email":"","orcid":"","institution":"Department of Clinical Biochemistry, University Hospitals Birmingham NHS Foundation Trust","correspondingAuthor":false,"prefix":"","firstName":"Manjit","middleName":"K","lastName":"Shergill","suffix":""},{"id":463089827,"identity":"e35faf01-962b-482d-b2f7-3bd0c31c834b","order_by":2,"name":"Alan F Jones","email":"","orcid":"","institution":"Department of Clinical Biochemistry, University Hospitals Birmingham NHS Foundation Trust","correspondingAuthor":false,"prefix":"","firstName":"Alan","middleName":"F","lastName":"Jones","suffix":""},{"id":463089828,"identity":"da556a54-a9ab-4ff4-a240-9eb31bbf42e2","order_by":3,"name":"John Marriott","email":"","orcid":"","institution":"Institute of Clinical Sciences, University of Birmingham","correspondingAuthor":false,"prefix":"","firstName":"John","middleName":"","lastName":"Marriott","suffix":""},{"id":463089829,"identity":"48fe03cc-c287-46f0-a918-85e538a04920","order_by":4,"name":"Sudarshan Ramachandran","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABE0lEQVRIiWNgGAWjYHACAwglAcQ8QMzPwMMGpA4AMRuRWiQbSNZicICAFvkZyRuYK2q2yfPPbn724O0euzzjG7nHHjDuuGPPIJGWgNWKG2kFjGeO3TacceeYueGcZ8nFZjfy0g0YzzxLbJBIO4BVi0SOAWMD223GDRIJZtI8B5gTt93IMZNgbDucwCCR3oDdYSAt/27bb5BI/wbUUp+4eQZEiz0uLQw3gFoa224nbpDIAdlyGMwAaWHE6bAzzwoONvbdTp5xI6dMcs6B44kzzrwxN0hsO5zYxvMMq/fl25M3Pmz4dtu2f0b6Nok3B6oT+9tzzB58BDqMnz3NAKvDGCBRgAZAxuOMyFEwCkbBKBgFBAEA0URm/rQ610wAAAAASUVORK5CYII=","orcid":"","institution":"Department of Clinical Biochemistry, University Hospitals Birmingham NHS Foundation Trust","correspondingAuthor":true,"prefix":"","firstName":"Sudarshan","middleName":"","lastName":"Ramachandran","suffix":""}],"badges":[],"createdAt":"2025-05-22 22:08:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6727985/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6727985/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":83784844,"identity":"9bcfb971-fc04-4127-b9cd-47ca1e596763","added_by":"auto","created_at":"2025-06-02 16:32:50","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":236312,"visible":true,"origin":"","legend":"\u003cp\u003eA plot of the association between baseline LDL-C and the decrease in LDL-C following bempedoic acid.\u003c/p\u003e\n\u003cp\u003eFootnote: The trend-line is based on the regression analysis seen in Model 8 (Table 2)\u003c/p\u003e","description":"","filename":"BempedoicacidWilder20042024Fig1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6727985/v1/cfc328cfb19cdb403d457c12.jpg"},{"id":83784841,"identity":"a405573e-7be4-43a1-aa5c-1c17de88ddf2","added_by":"auto","created_at":"2025-06-02 16:32:50","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":271558,"visible":true,"origin":"","legend":"\u003cp\u003eA plot of the association between baseline LDL-C and the decrease in LDL-C following bempedoic acid in patients stratified by the median baseline LDL-C (3.65mmol/L).\u003c/p\u003e\n\u003cp\u003eFootnote:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRegression analyses \u003c/strong\u003e(dependent variable: decrease in LDL-C (mmol/L), independent\u003c/p\u003e\n\u003cp\u003evariable: baseline LDL-C (mmol/L)\u003c/p\u003e\n\u003cp\u003eLDL-C \u0026lt; 3.65mmol/L: c=0.29, 95% CI = (0.0045, 0.57), p=0.047, n=53\u003c/p\u003e\n\u003cp\u003eLDL-C ≥ 3.65mmol/L: c=0.37, 95% CI = (0.14, 0.59), p=0.002, n=52\u003c/p\u003e","description":"","filename":"BempedoicacidWilderbymedianbaselineLDLC20042024Fig2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6727985/v1/5324df2417b82adb4a4f2649.jpg"},{"id":85886673,"identity":"cd024b9a-a697-4fef-a070-e5d47079d7fd","added_by":"auto","created_at":"2025-07-02 17:53:24","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2153521,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6727985/v1/9a455185-9edf-49e1-8b23-a29d7a3c8e05.pdf"}],"financialInterests":"Competing interest reported. SR has received research grants, travel grants and speakers’ honoraria from Daiichi Sankyo UK Ltd\nAM, MKS, AFJ and JM declared no potential competing interest with respect to the research, authorship, and/or publication of this article","formattedTitle":"Use of bempedoic acid in an out-patient setting: an assessment of efficacy heterogeneity","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe World Health Organisation in 2021 estimated that atherogenic cardiovascular disease (CVD) annually accounted for around 17.9\u0026nbsp;million deaths [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Many prospective studies have shown dyslipidaemia to be a predictive factor of CVD [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Low density lipoprotein-cholesterol (LDL-C) lowering has been an important aspect of CVD risk reduction since the pivotal Scandinavian Simvastatin Survival Study in 1994 [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], this based on the lipid hypothesis [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The Cholesterol Treatment Trialist\u0026rsquo; (CTT) collaboration analysed 5 trials including 39,612 patients which compared greater vs lesser efficacious statins (dose of type) and, 21 trials including 129,526 patients which compared statins vs placebo [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. When the results of all 26 randomised controlled trials (RCT) were pooled, a 1.0 mmol/L LDL-C reduction was associated with a 22% relative risk reduction (RRR) of CVD (Rate Ratio (RR)\u0026thinsp;=\u0026thinsp;0\u0026middot;78, 95% Confidence Interval (CI) = (0.76, 0.80); p\u0026thinsp;\u0026lt;\u0026thinsp;0\u0026middot;0001) [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Additionally non-statin lipid lowering agents such as ezetimibe and Proprotein Convertase Subtilisin/Kexin Type-9 (PCSK9) inhibitors have been shown to lower CVD in accordance with the findings of the CTT collaboration [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe Dyslipidaemia clinics run by the University Hospitals Birmingham NHS Foundation Trust have used the LDL-C targets set by the European Society of Cardiology and European Atherosclerosis Society since 2019 [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. The current treatment pathway used in our clinics to reduce LDL-C include statins, ezetimibe, bempedoic acid (prescribed since October 2020), PSCK9 inhibitors (prescribed since 2016) and inclisiran (prescribed since 2022). Bempedoic acid is a prodrug, that following activation in the liver, decreases hepatic cholesterol synthesis by inhibiting adenosine triphosphate citrate lyase [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Thus, bempedoic acid, which is not activated in skeletal muscle, is potentially useful in patients not reaching the assigned LDL-cholesterol targets because of statin discontinuation or dose reduction [\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].The Cholesterol Lowering via Bempedoic acid, an ACL-Inhibiting Regimen (CLEAR) study programme comprising 4 RCTs (CLEAR Serenity, CLEAR Tranquillity, CLEAR Wisdom and CLEAR Harmony) evaluated lipid lowering efficacy and safety of bempedoic acid [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The mean LDL-C decreases in the treatment (bempedoic acid) arms after 12-weeks ranged between 15% and 24% (CLEAR Serenity \u0026minus;\u0026thinsp;24%, baseline LDL-C\u0026thinsp;=\u0026thinsp;4.1mmol/L, CLEAR Tranquillity \u0026minus;\u0026thinsp;24%, baseline LDL-C\u0026thinsp;=\u0026thinsp;3.3mmol/L, 15%, CLEAR Wisdom - baseline LDL-C\u0026thinsp;=\u0026thinsp;3.1mmol/L, and CLEAR Harmony \u0026minus;\u0026thinsp;17%, baseline LDL-C\u0026thinsp;=\u0026thinsp;2.7mmol/L) [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. The CLEAR Outcomes RCT including 13,970 (6992 - bempedoic acid, 6978 \u0026ndash; placebo) patients at high CVD risk with a median follow-up of 40.6 months showed bempedoic acid being associated with significantly (hazard ratio\u0026thinsp;=\u0026thinsp;0.87, 95% CI = (0.79, 0.96), p\u0026thinsp;=\u0026thinsp;0.004) lower major adverse cardiovascular events [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBempedoic acid was made available to the Dyslipidemia clinics at Good Hope and Birmingham Heartlands Hospitals (part of University Hospitals Birmingham NHS Foundation Trust) between 01.10.2020 and 28.04.2021 prior to the National Institute for Health and Care Excellence (NICE) technology appraisal guidance (TAG) being published via a pre-reimbursement access scheme [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. The aim of this audit was initially to evaluate the efficacy of bempedoic acid in the total cohort and selected subgroups, then compare it against the efficacy observed in the CLEAR RCTs [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan additionalcitationids=\"CR12 CR13\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] and finally establish predictors of LDL-C change that may account for efficacy heterogeneity.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003eThis study is part of an ongoing lipid clinic audit programme carried out by the Department of Clinical Biochemistry at Good Hope and Birmingham Heartlands Hospitals (University of Birmingham NHS Foundation Trust) to evaluate the efficacy of lipid lowering agents. Previously, audits on fibrates, statins, ezetimibe and PCSK9 inhibitor therapy had been completed and published [\u003cspan additionalcitationids=\"CR17 CR18 CR19 CR20\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. More recently, our group were part of a UK wide multicenter audit that assessed treatment efficacy of Bempedoic acid [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Approval for the current audit was received from University Hospitals Birmingham NHS Foundation Trust (Ref: CARMS-17932). A list of all 113 patients started on bempedoic acid between 01.10.2020 and 28.04.2021 had been maintained and clinical and lipid profile data were obtained from the hospital electronic patient records. The mean age \u0026plusmn; standard deviation (SD) of the 113 patients started on bempedoic acid was 62.5 \u0026plusmn; 10.6 years. All patients commenced on bempedoic acid had not achieved their LDL-C targets based on the guidelines adopted by the clinics [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Statin intolerance was evident in 100 (88.5%) of the patients. The cohort characteristics regarding sex, ethnicity, diabetes/smoking/CVD status, details of ongoing lipid lowering therapy and baseline LDL-C are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. All patients attended the consultant led specialist Dyslipidemia clinics carried out at Good Hope Hospital (consultant: SR) and Birmingham Heartlands Hospital (consultant: AFJ) taking referrals from primary care across the West Midlands county, UK as well as from secondary care (Cardiology, Stroke and Vascular Medicine). It was usual practice to initially establish LDL-C targets based on evidence and guidelines and then decide on the treatment pathway [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. The clinics would usually consider statins initially, taking into account pharmacokinetic characteristics of the drug. Atorvastatin would usually be first line with rosuvastatin substituted in intolerant patients. If both statins led to clinical adverse effects, simvastatin, pravastatin and fluvastatin were considered. Ezetimibe was initiated as second line in patients not achieving LDL-C targets. Following statins and ezetimibe, other lipid lowering agents such as PCSK9 inhibitors (depending upon funding thresholds being met) or bempedoic acid would be commenced. Statin and ezetimibe (both agents would usually have been tried prior bempedoic acid) intolerance was high as shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e; only 25 (22.1%) patients were on a statin/ezetimibe combination when bempedoic acid was initiated. This could have been due to bempedoic acid being commenced only in patients not achieving LDL-C targets, thus our cohort would arguably demonstrate high statin and ezetimibe intolerance.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eChanges in TC, non-HDL-C and LDL-C following bempedoic acid in the total cohort and patients stratified by baseline characteristics.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePatient groups\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003ePre-bempedoic acid (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003ePost-bempedoic acid (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003eDecrease in lipids (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD), (n), p (paired t-test)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTC (mmol/L)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-HDL-C (mmol/L)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLDL-C (mmol/L)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTC (mmol/L)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNon-HDL-C (mmol/L)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLDL-C (mmol/L)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eTC (mmol/L)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNon-HDL-C (mmol/L)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eLDL-C (mmol/L)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal cohort\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5 (n\u0026thinsp;=\u0026thinsp;113)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5 (n\u0026thinsp;=\u0026thinsp;111)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.3 (n\u0026thinsp;=\u0026thinsp;111)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1 (n\u0026thinsp;=\u0026thinsp;110)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1 (n\u0026thinsp;=\u0026thinsp;110)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.6 (n\u0026thinsp;=\u0026thinsp;106)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0 (n\u0026thinsp;=\u0026thinsp;110), p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0 (n\u0026thinsp;=\u0026thinsp;108), p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9 (n\u0026thinsp;=\u0026thinsp;105), p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStratification by baseline discrete variables\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMales\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.6 (n\u0026thinsp;=\u0026thinsp;35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.6 (n\u0026thinsp;=\u0026thinsp;33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1.3 (n\u0026thinsp;=\u0026thinsp;33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1 (n\u0026thinsp;=\u0026thinsp;33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1 (n\u0026thinsp;=\u0026thinsp;33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9 (n\u0026thinsp;=\u0026thinsp;31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9 (n\u0026thinsp;=\u0026thinsp;33), p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8 (n\u0026thinsp;=\u0026thinsp;31), p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8 (n\u0026thinsp;=\u0026thinsp;30), p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFemales\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5 (n\u0026thinsp;=\u0026thinsp;78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1,5 (n\u0026thinsp;=\u0026thinsp;78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.3 (n\u0026thinsp;=\u0026thinsp;78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1 (n\u0026thinsp;=\u0026thinsp;77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2 (n\u0026thinsp;=\u0026thinsp;77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1 (n\u0026thinsp;=\u0026thinsp;75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0 (n\u0026thinsp;=\u0026thinsp;77), p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0 (n\u0026thinsp;=\u0026thinsp;77), p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9 (n\u0026thinsp;=\u0026thinsp;75), p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEthnicity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWhite caucasian\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5 (n\u0026thinsp;=\u0026thinsp;86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4 (n\u0026thinsp;=\u0026thinsp;85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.3 (n\u0026thinsp;=\u0026thinsp;85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1 (n\u0026thinsp;=\u0026thinsp;83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1 (n\u0026thinsp;=\u0026thinsp;83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0 (n\u0026thinsp;=\u0026thinsp;80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0 (n\u0026thinsp;=\u0026thinsp;83), p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0 (n\u0026thinsp;=\u0026thinsp;82), p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9 (n\u0026thinsp;=\u0026thinsp;80), p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNon-white caucasian\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.7 (n\u0026thinsp;=\u0026thinsp;27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.8 (n\u0026thinsp;=\u0026thinsp;26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5 (n\u0026thinsp;=\u0026thinsp;26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2 (n\u0026thinsp;=\u0026thinsp;27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4 (n\u0026thinsp;=\u0026thinsp;27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2 (n\u0026thinsp;=\u0026thinsp;26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9 (n\u0026thinsp;=\u0026thinsp;27), p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8 (n\u0026thinsp;=\u0026thinsp;26), p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7 (n\u0026thinsp;=\u0026thinsp;25), p\u0026thinsp;=\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDiabetes status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eT2DM\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.9 (n\u0026thinsp;=\u0026thinsp;19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.6\u0026thinsp;\u0026plusmn;\u0026thinsp;1.8 (n\u0026thinsp;=\u0026thinsp;19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1.6 (n\u0026thinsp;=\u0026thinsp;19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5 (n\u0026thinsp;=\u0026thinsp;18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.6\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4 (n\u0026thinsp;=\u0026thinsp;18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1.3 (n\u0026thinsp;=\u0026thinsp;18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1 (n\u0026thinsp;=\u0026thinsp;18), p\u0026thinsp;=\u0026thinsp;0.0004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0 (n\u0026thinsp;=\u0026thinsp;18), p\u0026thinsp;=\u0026thinsp;0.0005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9 (n\u0026thinsp;=\u0026thinsp;18), p\u0026thinsp;=\u0026thinsp;0.0002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNo T2DM\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4 (n\u0026thinsp;=\u0026thinsp;94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4 (n\u0026thinsp;=\u0026thinsp;92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.3 (n\u0026thinsp;=\u0026thinsp;92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0 (n\u0026thinsp;=\u0026thinsp;92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1 (n\u0026thinsp;=\u0026thinsp;92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0 (n\u0026thinsp;=\u0026thinsp;88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0 (n\u0026thinsp;=\u0026thinsp;92), p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9 (n\u0026thinsp;=\u0026thinsp;90), p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9 (n\u0026thinsp;=\u0026thinsp;87), p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSmoking status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNon-smokers\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5 (n\u0026thinsp;=\u0026thinsp;80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5 (n\u0026thinsp;=\u0026thinsp;79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.3 (n\u0026thinsp;=\u0026thinsp;79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0 (n\u0026thinsp;=\u0026thinsp;77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2 (n\u0026thinsp;=\u0026thinsp;77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1 (n\u0026thinsp;=\u0026thinsp;73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0 (n\u0026thinsp;=\u0026thinsp;77), p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0 (n\u0026thinsp;=\u0026thinsp;76), p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9 (n\u0026thinsp;=\u0026thinsp;73), p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEx-smokers\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.7 (n\u0026thinsp;=\u0026thinsp;16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.6 (n\u0026thinsp;=\u0026thinsp;16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.3 (n\u0026thinsp;=\u0026thinsp;16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.6\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1 (n\u0026thinsp;=\u0026thinsp;16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0 (n\u0026thinsp;=\u0026thinsp;16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8 (n\u0026thinsp;=\u0026thinsp;16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0 (n\u0026thinsp;=\u0026thinsp;16), p\u0026thinsp;=\u0026thinsp;0.0003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9 (n\u0026thinsp;=\u0026thinsp;16), p\u0026thinsp;=\u0026thinsp;0.0002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0 (n\u0026thinsp;=\u0026thinsp;16), p\u0026thinsp;=\u0026thinsp;0.0003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCurrent-smokers\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.3 (n\u0026thinsp;=\u0026thinsp;17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.3 (n\u0026thinsp;=\u0026thinsp;16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1 (n\u0026thinsp;=\u0026thinsp;16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2 (n\u0026thinsp;=\u0026thinsp;17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2 (n\u0026thinsp;=\u0026thinsp;17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0 (n\u0026thinsp;=\u0026thinsp;17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.6\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0 (n\u0026thinsp;=\u0026thinsp;17), p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9 (n\u0026thinsp;=\u0026thinsp;16), p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7 (n\u0026thinsp;=\u0026thinsp;16), p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCVD status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePrimary prevention\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4 (n\u0026thinsp;=\u0026thinsp;93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5 (n\u0026thinsp;=\u0026thinsp;91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.3 (n\u0026thinsp;=\u0026thinsp;91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0 (n\u0026thinsp;=\u0026thinsp;90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1 (n\u0026thinsp;=\u0026thinsp;90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0 (n\u0026thinsp;=\u0026thinsp;86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0 (n\u0026thinsp;=\u0026thinsp;90), p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0 (n\u0026thinsp;=\u0026thinsp;88), p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9 (n\u0026thinsp;=\u0026thinsp;85), p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSecondary prevention\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0 (n\u0026thinsp;=\u0026thinsp;20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0 (n\u0026thinsp;=\u0026thinsp;20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9 (n\u0026thinsp;=\u0026thinsp;20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9 (n\u0026thinsp;=\u0026thinsp;20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9 (n\u0026thinsp;=\u0026thinsp;20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9 (n\u0026thinsp;=\u0026thinsp;20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9 (n\u0026thinsp;=\u0026thinsp;20), p\u0026thinsp;=\u0026thinsp;0.0013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8 (n\u0026thinsp;=\u0026thinsp;20), p\u0026thinsp;=\u0026thinsp;0.0030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8 (n\u0026thinsp;=\u0026thinsp;20), p\u0026thinsp;=\u0026thinsp;0.0010\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOngoing lipid lowering agents\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNone\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4 (n\u0026thinsp;=\u0026thinsp;37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.6\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4 (n\u0026thinsp;=\u0026thinsp;36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.3 (n\u0026thinsp;=\u0026thinsp;36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1 (n\u0026thinsp;=\u0026thinsp;37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1 (n\u0026thinsp;=\u0026thinsp;37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1 (n\u0026thinsp;=\u0026thinsp;37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0 (n\u0026thinsp;=\u0026thinsp;37), p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0 (n\u0026thinsp;=\u0026thinsp;36), p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9 (n\u0026thinsp;=\u0026thinsp;36), p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEzetimibe\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1 (n\u0026thinsp;=\u0026thinsp;24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1 (n\u0026thinsp;=\u0026thinsp;24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1 (n\u0026thinsp;=\u0026thinsp;23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9 (n\u0026thinsp;=\u0026thinsp;22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9 (n\u0026thinsp;=\u0026thinsp;22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.4\u0026thinsp;\u0026plusmn;\u0026thinsp;9.8 (n\u0026thinsp;=\u0026thinsp;19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0 (n\u0026thinsp;=\u0026thinsp;22), p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9 (n\u0026thinsp;=\u0026thinsp;22), p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9 (n\u0026thinsp;=\u0026thinsp;19), p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStatins and Ezetimibe\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4 (n\u0026thinsp;=\u0026thinsp;25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4 (n\u0026thinsp;=\u0026thinsp;24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1 (n\u0026thinsp;=\u0026thinsp;25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8 (n\u0026thinsp;=\u0026thinsp;25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8 (n\u0026thinsp;=\u0026thinsp;25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8 (n\u0026thinsp;=\u0026thinsp;25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9 (n\u0026thinsp;=\u0026thinsp;25), p\u0026thinsp;=\u0026thinsp;0.0014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.6\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0 (n\u0026thinsp;=\u0026thinsp;24), p\u0026thinsp;=\u0026thinsp;0.0036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8 (n\u0026thinsp;=\u0026thinsp;25), p\u0026thinsp;=\u0026thinsp;0.0017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStatins\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.7 (n\u0026thinsp;=\u0026thinsp;27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.6\u0026thinsp;\u0026plusmn;\u0026thinsp;1.6 (n\u0026thinsp;=\u0026thinsp;27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4 (n\u0026thinsp;=\u0026thinsp;27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2 (n\u0026thinsp;=\u0026thinsp;26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2 (n\u0026thinsp;=\u0026thinsp;26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1 (n\u0026thinsp;=\u0026thinsp;25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1 (n\u0026thinsp;=\u0026thinsp;26), p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9 (n\u0026thinsp;=\u0026thinsp;26), p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7 (n\u0026thinsp;=\u0026thinsp;25), p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStratification by median LDL-C (3.65mmol/L)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLDL-C\u0026thinsp;\u0026lt;\u0026thinsp;3.65mmol/L\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9 (n\u0026thinsp;=\u0026thinsp;56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8 (n\u0026thinsp;=\u0026thinsp;55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4 (n\u0026thinsp;=\u0026thinsp;56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8 (n\u0026thinsp;=\u0026thinsp;54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8 (n\u0026thinsp;=\u0026thinsp;54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8 (n\u0026thinsp;=\u0026thinsp;53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7 (n\u0026thinsp;=\u0026thinsp;54), p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7 (n\u0026thinsp;=\u0026thinsp;53), p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6 (n\u0026thinsp;=\u0026thinsp;52), p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLDL-C\u0026thinsp;\u0026ge;\u0026thinsp;3.65mmol/L\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1 (n\u0026thinsp;=\u0026thinsp;55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1 (n\u0026thinsp;=\u0026thinsp;55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9 (n\u0026thinsp;=\u0026thinsp;55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0 (n\u0026thinsp;=\u0026thinsp;54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0 (n\u0026thinsp;=\u0026thinsp;54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9 (n\u0026thinsp;=\u0026thinsp;52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0 (n\u0026thinsp;=\u0026thinsp;54), p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9 (n\u0026thinsp;=\u0026thinsp;54), p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8 (n\u0026thinsp;=\u0026thinsp;53), p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eWithin the audit period, 41 (36.3%) of the patients discontinued bempedoic acid and a lipid profile check/clinic appointment was arranged as soon as possible. In the 72 (63.7%) individuals continuing bempedoic acid, lipid profiles at or close to 12-weeks post-bempedoic acid initiation were conducted just prior to the follow-up appointment. The median (inter quartile range) follow-up was 3.3 (2.3\u0026ndash;5.1) months. No other changes to lipid lowering therapy took place during the audit period. Total cholesterol (TC), HDL-C and triglycerides (TG) measurements were carried out on the Abbott Alinity c system using the supplied kit reagents. LDL-C was calculated via the Friedwald algorithm [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], as was the case in the CLEAR RCTs [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Analytical performance of the Abbott Alinity c system has previously been extensively evaluated and reported [24.25].\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eBaseline TC, non-HDL-C and LDL-C were normally distributed (skewness kurtosis test, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05), hence paired and unpaired t-tests were used to compare intra- and intergroup changes in TC, non-HDL-C and LDL-C following bempedoic acid in the total cohort and subgroups stratified by baseline characteristics. Statistical analyses were performed on the entire cohort and not just on the individuals who continued with the treatment. No differences (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05, using logistic regression) in baseline characteristics were observed between individuals continuing or stopping bempedoic acid regarding sex, ethnicity, diabetes/smoking/CVD status, ongoing lipid lowering therapy and LDL-C.\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eComparison of pre and post bempedoic acid lipid values\u003c/b\u003e: Paired t-tests were used to determine if changes in TC, non-HDL-C and LDL-C were significant in the total cohort and subgroups stratified by sex, ethnicity, diabetes/smoking/CVD status, ongoing lipid lowering therapy, and baseline LDL-C levels stratified by the median value of 3.65mmol/L.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eFactors associated with LDL-C changes following bempedoic acid treatment\u003c/b\u003e: Linear regression analysis was performed to establish factors that predicted LDL-C decrease, with all significant independent variables included in a single multivariate regression model. For non-continuous independent variables, a single characteristic of the variable was chosen as the reference category and the other characteristics of that variable were compared to the reference category (factorisation) regarding associations with the selected dependent variable. Stata version 14 (College Station, TX) was used for all the statistical analysis.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eBempedoic acid treatment led to a significant decrease (paired t-test) in TC, non-HDL-C and LDL-C in the total cohort and the various subgroups as shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The principal analyses were conducted on an intention-to-treat basis, hence the efficacy values seen in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e could be considered conservative as indicated by the decreases in TC, non-HDL-C and LDL-C (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD) being significantly lower (TC\u0026thinsp;=\u0026thinsp;0.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8, n\u0026thinsp;=\u0026thinsp;39, non-HDL-C\u0026thinsp;=\u0026thinsp;0.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8, n\u0026thinsp;=\u0026thinsp;38, LDL-C\u0026thinsp;=\u0026thinsp;0.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9, n\u0026thinsp;=\u0026thinsp;37) in the individuals discontinuing bempedoic acid compared to their counterparts (TC\u0026thinsp;=\u0026thinsp;1.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0, n\u0026thinsp;=\u0026thinsp;71, non-HDL-C\u0026thinsp;=\u0026thinsp;1.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0, n\u0026thinsp;=\u0026thinsp;70, LDL-C\u0026thinsp;=\u0026thinsp;1.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8, n\u0026thinsp;=\u0026thinsp;68) who continued the medication; unpaired t-test: p\u0026thinsp;\u0026lt;\u0026thinsp;0.0034, 0.0099, and 0.0034 for changes in TC, non-HDL-C, and LDL-C, respectively.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows subgroups based on baseline characteristics that predicted bempedoic acid efficacy (change in LDL-C) using separate linear regression models; ex-smokers (compared to non-smokers) \u0026ndash; Model 5, statin monotherapy (compared to those on ezetimibe monotherapy) Model 7, and lower levels of LDL-C that were significantly associated with decreased LDL-C lowering \u0026ndash; Models 8 \u0026amp; 9. The LDL-C decrease was greater (coefficient (c)\u0026thinsp;=\u0026thinsp;0.46, 95% CI = (0.0222, 0.89), p\u0026thinsp;=\u0026thinsp;0.040) in individuals on ezetimibe monotherapy compared to the remaining participants (no lipid lowering therapy, statin monotherapy and statin and ezetimibe treated) combined together as the reference group. In view of the above findings, we did not study efficacy in the patient cohort stratified by single, dual, or triple therapies. When the patients were stratified by the median baseline LDL-C value of 3.65mmol/L, patients with LDL-C\u0026thinsp;\u0026ge;\u0026thinsp;3.65mmol/L were associated with a significantly greater LDL-C decrease than individuals with LDL-C\u0026thinsp;\u0026lt;\u0026thinsp;3.65mmol/L (reference) \u0026ndash; Model 9.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline factors associated with LDL-C decrease in patients treated with bempedoic acid; individual factors analysed via linear regression models (1\u0026ndash;9) followed by a multiple regression model (10) including all significant factors from the earlier models.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndependent Variable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eDependent variable (outcome): decrease in LDL-C (mmol/L)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(at baseline)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ec (95% CI), p\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eR-squared\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge (years)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eModel 1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0042 (-0.021, 0.012), p\u0026thinsp;=\u0026thinsp;0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMales\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eModel 2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.073 (-0.31, 0.45), p\u0026thinsp;=\u0026thinsp;0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFemales\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ereference\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNon-white caucasians\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eModel 3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.25 (-0.77, 0.27), p\u0026thinsp;=\u0026thinsp;0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWhite caucasians\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ereference\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eT2DM\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eModel 4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0096 (-0.46, 0.44), p\u0026thinsp;=\u0026thinsp;0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNo T2DM\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ereference\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCurrent smokers\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eModel 5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.32 (-0.16, 0.79), p\u0026thinsp;=\u0026thinsp;0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEx-smokers\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.52 (0.048, 1.00), p\u0026thinsp;=\u0026thinsp;0.031\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNon-smokers\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ereference\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSecondary prevention\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eModel 6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.39 (-0.82, 0.042), p\u0026thinsp;=\u0026thinsp;0.079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePrimary prevention\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ereference\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNo lipid lowering agents\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003eModel 7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.23 (-0.71, 0.25(, p\u0026thinsp;=\u0026thinsp;0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStatin monotherapy\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e-0.79 (-1.30, -0.27), p\u0026thinsp;=\u0026thinsp;0.003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStatins and ezetimibe\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.46 (-0.97, 0.054), p\u0026thinsp;=\u0026thinsp;0.079\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEzetimibe monotherapy\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ereference\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLDL-C (mmol/L)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eModel 8\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.42 (0.32, 0.52), p\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLDL-C\u0026thinsp;\u0026ge;\u0026thinsp;3.65mmol/L\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eModel 9\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.99 (0.71, 1.27), p\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLDL-C\u0026thinsp;\u0026lt;\u0026thinsp;3.65mmol/L\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ereference\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMultiple regression model including significant independent variable from Models 1\u0026ndash;9.\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLDL-C (mmol/L)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"7\" rowspan=\"8\"\u003e \u003cp\u003e\u003cb\u003eModel 10\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.43 (0.33, 0.54), p\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"7\" rowspan=\"8\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCurrent smokers\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.27 (-0.083, 0.63), p\u0026thinsp;=\u0026thinsp;0.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEx-smokers\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.48 (0.12, 0.83), p\u0026thinsp;=\u0026thinsp;0.009\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNon-smokers\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ereference\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNo lipid lowering agents\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e-0.51 (-0.88, -0.14), p\u0026thinsp;=\u0026thinsp;0.007\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStatin monotherapy\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e-0.46 (-0.86, -0.068), p\u0026thinsp;=\u0026thinsp;0.022\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStatins and ezetimibe\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e-0.43 (-0.82, -0.042), p\u0026thinsp;=\u0026thinsp;0.030\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEzetimibe monotherapy\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ereference\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eMultiple regression analysis (Model 10) with LDL-C decrease as the dependent variable was then performed with baseline LDL-C, smoking status and lipid lowering therapy at baseline included as independent variables, i,e. factors that reached statistical significance in Models 1\u0026ndash;8. LDL-C stratified by the median were omitted and LDL-C was used as a continuous variable. Higher baseline LDL-C, ex-smokers and ezetimibe monotherapy independently showed significantly greater LDL-C reduction following bempedoic acid treatment \u0026ndash; Model 10. Months of follow-up was not associated with LDL-C change (c\u0026thinsp;=\u0026thinsp;0.037, 95% CI = (-0.051, 0.13), p\u0026thinsp;=\u0026thinsp;0.40). We could not carry out meaningful statistics in patients with higher than median LDL-C and ezetimibe monotherapy at baseline in view of modest patient numbers (9 patients); LDL-C decrease (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD): 1.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8mmol/L.\u003c/p\u003e \u003cp\u003eModels 8 and 9 (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) indicated that pre-treatment LDL-C values were associated with LDL-C decrease following bempedoic acid initiation; higher baseline LDL-C concentrations were related to greater LDL-C decreases. This is evident in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e where LDL-C (x-axis) was plotted against the decrease in LDL-C (y-axis) following bempedoic acid treatment. We then determined whether these effects were due to a fixed percentage decrease in LDL-C following bempedoic acid therapy (this could also show the above pattern). Importantly, baseline LDL-C was also significantly associated with percentage changes in LDL-C (c\u0026thinsp;=\u0026thinsp;4.92, 95% CI = (2.11, 7.74), p\u0026thinsp;=\u0026thinsp;0.001). Baseline LDL-C was also associated with a change in LDL-C when the patients were stratified by the median LDL-C of 3.65mmol/L (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and footnote). Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows that this phenomenon was also evident for all of the subgroups stratified by the other baseline characteristics, apart from current smokers (p\u0026thinsp;=\u0026thinsp;0.057, n\u0026thinsp;=\u0026thinsp;16). Interestingly, baseline LDL-C was significantly associated with bempedoic acid related LDL-C decrease in both, patients who tolerated the drug (c\u0026thinsp;=\u0026thinsp;0.47, 95% CI = (0.36, 0.58), p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, n\u0026thinsp;=\u0026thinsp;68) and those who discontinued it (c\u0026thinsp;=\u0026thinsp;0.35, 95% CI\u0026thinsp;=\u0026thinsp;0.19, 0.52), p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, n\u0026thinsp;=\u0026thinsp;37).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociations between baseline LDL-C and decrease in LDL-C carried out via linear regression in the various patient subgroups stratified by baseline characteristics.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eLinear regression models (LDL-C vs decrease in LDL-C) in subgroups\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(at baseline)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ec (95% CI), p\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMales\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.46 (0.32, 0.60), p\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFemales\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.40 (0.28, 0.53), p\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNon-white caucasians\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.30 (0.13, 0.46), p\u0026thinsp;=\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWhite caucasians\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.47 (0.35, 0.58), p\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eT2DM\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.31 (0.068, 0.56), p\u0026thinsp;=\u0026thinsp;0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNo T2DM\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.45 (0.34, 0.56), p\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e87\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCurrent smokers\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.32 (-0.011, 0.65), p\u0026thinsp;=\u0026thinsp;0.057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEx-smokers\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.61 (0.36, 0.85), p\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNon-smokers\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.40 (0.29, 0.51), p\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e73\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSecondary prevention\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.39 (0.020, 0.76), p\u0026thinsp;=\u0026thinsp;0.040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePrimary prevention\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.45 (0.34, 0.56), p\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e85\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNo lipid lowering agents\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.40 (0.19, 0.61), p\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStatin monotherapy\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.32 (0.17, 0.48), p\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStatins and ezetimibe\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.52 (0.30, 0.74), p\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEzetimibe monotherapy\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.57 (0.28, 0.86), p\u0026thinsp;=\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis was a real-world study of 113 out-patients treated with bempedoic acid prior to the NICE TAG in April 2021. We adopted an intention-to-treat approach in this audit which appeared to result in conservative estimates of bempedoic acid efficacy. Paired t-tests (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) were used to compare pre and post bempedoic acid TC, Non-HDL-C and LDL-C in the total cohort and patient subgroups whilst linear and multiple regression analyses (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) were performed to establish baseline predictors (independent variables) of LDL-C change (dependent variable). This was followed by further study of the association (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) between baseline LDL-C (independent variable) and change in LDL-C (dependent variable) using linear regression models.\u003c/p\u003e\n\u003ch3\u003eComparison of outcomes with previous RCTs\u003c/h3\u003e\n\u003cp\u003eFollowing bempedoic acid treatment, absolute reduction (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD) in TC, non-HDL-C, and LDL-C values were 1.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0mmol/L, 1.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0mmol/L, and 1.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9mmol/L, respectively, whilst percentage reductions (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD) were 16.4\u0026thinsp;\u0026plusmn;\u0026thinsp;14.1%, 18.8\u0026thinsp;\u0026plusmn;\u0026thinsp;17.2%, and 23.2\u0026thinsp;\u0026plusmn;\u0026thinsp;20.5%, respectively. The mean LDL-C reduction in the present study was similar to the mean decreases observed in the CLEAR Tranquility (24%) and CLEAR Serenity (24%) treatment arms and higher than in the CLEAR Wisdom (15%) and CLEAR Harmony (17%) treatment arms [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. This could be due to baseline characteristics (including baseline LDL-C values and co-therapies) as seen in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e leading to efficacy heterogeneity. Our findings, if validated, suggest that an understanding of cohort characteristics is essential to interpret differences in study outcomes.\u003c/p\u003e \u003cp\u003eSignificant decreases in the lipid values were observed in every subgroup that we studied (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). LDL-C reduction is currently the cornerstone of lipid lowering regarding CVD prevention [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. The results from the CTT collaboration suggests a RRR in CVD of 22% per 1mmol/L decrease in LDL-C [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. The lipid hypothesis is based on agents such as resins, statins, ezetimibe, PCSK9 inhibitors and bempedoic acid use being associated with lowering of CVD [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Absolute risk reduction (ARR) is a function of absolute risk (AR) and RRR, hence we can hypothesize that bempedoic acid treatment in our patients tolerating the drug (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u0026thinsp;=\u0026thinsp;1.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8mmol/L) would have yielded a significant ARR in CVD, especially in patients with high AR [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe LDL-C decrease following bempedoic acid was independently greater in certain subgroups; ex-smokers, individuals on ezetimibe at baseline, and those with higher baseline LDL-C values. We can offer no explanation for the increased efficacy in ex-smokers. Smoking cessation, whilst increasing HDL-C levels does not appear to change LDL-C values [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. The LDL-C lowering efficacy of bempedoic acid appeared greater in patients on ezetimibe monotherapy compared to individuals not on lipid lowering agents, statin monotherapy and statin/ezetimibe combination (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). We speculate that this could be due to ezetimibe up-regulating cholesterol synthesis [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Sudhop \u003cem\u003eet al.\u003c/em\u003e demonstrated that cholesterol synthesis significantly increased by 89% in patients on ezetimibe [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Since bempedoic acid acts to decrease cholesterol synthesis by inhibiting adenosine triphosphate citrate lyase [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], it is feasible that the LDL-C lowering efficacy of the drug could perhaps be greater in individuals with an increased rate of cholesterol synthesis. The rate of synthesis would be reduced when statins are added to ezetimibe, hence the LDL-C lowering efficacy of bempedoic acid would perhaps be attenuated in patients also on statin and ezetimibe [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eImpact of the Wilder Effect (principle of initial value)\u003c/h3\u003e\n\u003cp\u003eBaseline LDL-C values were associated with both an absolute and percentage change in LDL-C following bempedoic acid treatment (higher baseline LDL-C led to greater decrease in LDL-C). The association between baseline LDL-C and change in LDL-C is in accordance with the Wilder principle which suggests that the response to an intervention is dependent on the baseline values [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. This was also observed in most subgroups (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) and previous audits from our centre have also demonstrated this phenomenon [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. This observation has clinical significance as high LDL-C levels are associated with greater AR of CVD and a more pronounced LDL-C reduction would lead to increased RRR [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Thus, ARR is perhaps even greater in patients with high baseline LDL-C values. As percentage decrease in LDL-C was also associated with baseline LDL-C, it is likely that it is a real effect and not a statistical aberration, although potential mechanisms have not been elucidated.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStrengths and weaknesses\u003c/h2\u003e \u003cp\u003eThere are a range of strengths and weaknesses in the present study. It was a real-world single arm longitudinal study with a small sample size with relatively short follow-up. This study contributed 113 patients to a national audit that included 221 patients commenced on bempedoic acid assessing the attainment of lipid targets [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. The mean efficacy results from the multi-center audit was identical to that observed in our audit; TC: -1.1mmol/L, Non-HDL-C: -1.0mmol/L and LDL-C: -1.0mmol/L. However, these results would have been influenced by the efficacy of our study group (113 patients) which comprised nearly half (51.1%) of the multi-center audit cohort.\u003c/p\u003e \u003cp\u003eIn this analysis, we studied efficacy patterns prior to the NICE TAG being issued (which introduced extra selection criteria including statin intolerance as a pre-requisite) as this would have introduced selection bias [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Thus, all patients started on bempedoic acid were included with no sample size calculation based on previously reported efficacy. The take up rate over a near 7-month period may be considered slow for a new pharmaceutical agent; the coronavirus lockdowns in the UK could have had an impact (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.instituteforgovernment.org.uk/sites/default/files/timeline-lockdown-web.pdf\u003c/span\u003e\u003cspan address=\"https://www.instituteforgovernment.org.uk/sites/default/files/timeline-lockdown-web.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The intention though was to compare the lipid lowering results with published efficacy outcomes at 3 months. Regression towards the mean is possible, although the observed Wilder phenomenon may counter this [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. The patient numbers were modest, as we wished to evaluate efficacy in patients not achieving LDL-C targets prior to the NICE technology assessment being published, as that could have reshaped the cohort characteristics. The small cohort affected the depth of subgroup analyses and study of interactions. We would have liked to study efficacy in patients with higher baseline LDL-C and ezetimibe monotherapy, but patient numbers were modest. Thus, it is essential to conduct a further study with a larger cohort and longer follow-up to verify the efficacy heterogeneity observed.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eOur results show that bempedoic acid led to a reduction in LDL-C in the total cohort with efficacy comparable to that reported in phase 3 trials [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. However, efficacy heterogeneity was observed in the subgroups studied. The response was more pronounced in patients taking existing ezetimibe monotherapy and those with higher baseline LDL-C. Wang \u003cem\u003eet al\u003c/em\u003e emphasize the importance of heterogeneity of treatment effects and propose the use of effect scores relating to various patient subgroups to predict treatment response [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. It would be interesting if this approach was adopted to optimize lipid lowering efficacy of all therapeutic agents prescribed, thus resulting in maximal CVD prevention.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eThe authors received no financial support for the research, authorship, and/or publication of this article.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of competing interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSR has received research grants, travel grants and speakers\u0026rsquo; honoraria from Daiichi Sankyo UK Ltd\u003c/p\u003e\n\u003cp\u003eAM, MKS, AFJ and JM\u0026nbsp;declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConception \u0026ndash; SR, AM\u003c/p\u003e\n\u003cp\u003eData collection \u0026ndash; SR, AM, MKS\u003c/p\u003e\n\u003cp\u003eAnalysis \u0026ndash; SR, AM, JM\u003c/p\u003e\n\u003cp\u003eManuscript preparation \u0026ndash; SR, AM, MKS, JM, AFJ\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical considerations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eApproval for the collection, analysis and publication of this retrospectively obtained and anonymised audit data was received from University Hospitals Birmingham NHS Foundation Trust (Ref: CARMS-17932). Ethics approval was therefore not required.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNot applicable\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eCardiovascular diseases (CVDs). \u0026ndash; World Health Organisation [online] 2021 \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.who.int/health-topics/cardiovascular-diseases#tab=tab_1\u003c/span\u003e\u003cspan address=\"https://www.who.int/health-topics/cardiovascular-diseases#tab=tab_1\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (accessed 28.02.2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRamachandran S, Bhartia M, K\u0026ouml;nig CS. 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Using effect scores to characterize heterogeneity of treatment effects. JAMA. 2024;331(14):1225\u0026ndash;6. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1001/jama.2024.3376\u003c/span\u003e\u003cspan address=\"10.1001/jama.2024.3376\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Bempedoic acid, cardiovascular disease, hypercholesterolaemia, low density lipoprotein cholesterol, Wilder principle","lastPublishedDoi":"10.21203/rs.3.rs-6727985/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6727985/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjectives\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe describe decreases in total cholesterol (TC), non-high density lipoprotein-cholesterol (non-HDL-C) and low density lipoprotein-cholesterol (LDL-C) following bempedoic acid treatment in an out-patient setting and compare the results to those observed in phase 3 efficacy trials.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe analysed a cohort of 113 patients not achieving LDL-C targets commenced on bempedoic acid after previous treatment with statins and ezetimibe using an intention-to-treat approach. We compared pre and post bempedoic acid treatment lipids (3-months) in the total cohort using paired t-tests. Baseline patient characteristics associated with LDL-C decrease was established via linear/multiple regression analyses.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFollowing bempedoic acid treatment absolute reduction (mean ± SD) in TC, non-HDL-C, and LDL-C values were 1.1 ± 1.0mmol/L, 1.0 ± 1.0mmol/L, and 1.0 ± 0.9mmol/L, respectively, whilst percentage reductions (mean ± SD) were 16.4 ± 14.1%, 18.8 ± 17.2%, and 23.2 ± 20.5%, respectively. Significant decreases in the lipids were observed in every subgroup studied. The LDL-C decrease following bempedoic acid was independently greater in ex-smokers, individuals on ezetimibe at baseline, and those with higher baseline LDL-C values.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur results show that the LDL-C decrease seen with bempedoic acid was comparable to that observed in the phase 3 efficacy trials. However, efficacy heterogeneity was observed in some of the subgroups studied such as patients on ezetimibe monotherapy or with higher LDL-C at baseline, the latter in accordance with the Wilder principle. The use of effect scores for identified patient subgroups might predict treatment response, enabling optimisation of lipid lowering efficacy.\u003c/p\u003e","manuscriptTitle":"Use of bempedoic acid in an out-patient setting: an assessment of efficacy heterogeneity","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-02 16:32:45","doi":"10.21203/rs.3.rs-6727985/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f2744a8c-acee-4266-a77f-544384e24ed6","owner":[],"postedDate":"June 2nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-07-02T17:53:10+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-02 16:32:45","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6727985","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6727985","identity":"rs-6727985","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}