Cholesteryl Ester Transfer Protein as a Drug Target for Cardiovascular Disease

Cholesteryl Ester Transfer Protein as a Drug Target for Cardiovascular Disease | 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 Article Cholesteryl Ester Transfer Protein as a Drug Target for Cardiovascular Disease Amand Schmidt, Nicholas Hunt, Maria Gordillo-Maranon, Pimphen Charoen, and 20 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-78818/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 24 Sep, 2021 Read the published version in Nature Communications → Version 1 posted You are reading this latest preprint version Abstract Drug development of cholesteryl ester transfer protein (CETP) inhibition to prevent coronary heart disease (CHD) has yet to deliver licensed medicines. To distinguish compound from drug target failure, we compared evidence from clinical trials and Mendelian randomization (MR) results. Findings from meta-analyses of CETP inhibitor trials (≥ 24 weeks follow-up) were used to judge between-compound heterogeneity in treatment effects. Genetic data were extracted on 190 + pharmacologically relevant outcomes; spanning 480,698 − 21,770 samples and 74,124-4,373 events. Drug target MR of protein concentration was used to determine the on-target effects of CETP inhibition and compared to that of PCSK9 modulation. Fifteen eligible CETP inhibitor trials of four compounds were identified, enrolling 79,961 participants. There was a high degree of heterogeneity in effects on lipids, lipoproteins, blood pressure, and clinical events. For example, dalcetrapib and evacetrapib showed a neutral effect, torcetrapib increased, and anacetrapib decreased cardiovascular disease (CVD); heterogeneity p-value < 0.001. In drug target MR analysis, lower CETP concentration (per \(\mu\) g/ml) was associated with CHD (odds ratio 0.95; 95%CI 0.91; 0.99), heart failure (0.95; 95%CI 0.92; 0.99), chronic kidney disease (0.94 95%CI 0.91; 0.98), and age-related macular degeneration (1.69; 95%CI 1.44; 1.99). Lower PCSK9 concentration was associated with a lower risk of CHD, heart failure, atrial fibrillation and stroke, and increased risk of Alzheimer’s disease and asthma. In conclusion, previous failures of CETP inhibitors are likely compound related. CETP inhibition is expected to reduce risk of CHD, heart failure, and kidney disease, but potentially increase risk of age-related macular disease. Drug Discovery, Design, & Development Cardiac & Cardiovascular Systems Drug target validation Mendelian randomization CETP PCSK9 drug therapy cardiovascular disease Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction The causal role of low-density lipoprotein cholesterol (LDL-C) in coronary heart disease (CHD) has been established through randomized controlled trials (RCTs) of different LDL-C lowering drug classes 1 2 3,4 and by Mendelian randomization (MR) studies 5 . Circulating high-density lipoprotein cholesterol (HDL-C) shows an inverse association with CHD in nonrandomized studies 6 . MR studies utilizing genetic variants associated with HDL-C selected throughout the genome have provided inconclusive evidence on the causal role of HDL-C as a biomarker 5 , 7 . Findings from RCTs of niacin 8 and cholesteryl ester transfer protein (CETP) inhibitors 9 , developed to prevent CHD by raising HDL-C have also been disappointing. For example, of the four CETP inhibitors that have progressed to phase 3 clinical trials, none have received market authorization (Supplementary Table 1). Six other CETP inhibitors (e.g. obicetrapib) are still in active development, raising important questions about the validity of CETP as a therapeutic target 10 . One interpretation is that HDL-C is not causally related to CHD, and that raising HDL-C as a therapeutic strategy will be an ineffective approach for CHD prevention. As a result, the reduction in CHD events observed in a large RCT of anacetrapib (odds ratio [OR] 0.91 95%CI 0.85; 0.97) 11 , was attributed to its effect on LDL-C rather than to its HDL-C raising action 10 . However, analysis of lipoprotein sub-classes measured using nuclear magnetic resonance (NMR) spectroscopy suggests that, unlike LDL-C, HDL-C particles encompasses several lipoprotein sub-fractions that have differential associations with CHD: some fractions being associated with higher and others with lower CHD risk 12 , 13 . Additionally, failures of CETP inhibitors might be related to the developed compounds rather than the drug target itself, either because of inadequate target engagement or a competing off-target action. Compound related failures can be addressed by developing an improved CETP inhibitor, whereas target failure affects all CETP inhibitors. To address these uncertainties, we performed a drug target MR study 14 of CETP, focusing on variants within the encoding gene (acting in cis ) that are associated with circulating CETP concentration, to directly model the effects of pharmacological action on this target by a clean drug with no off-target actions. To evaluate potentially diverse effects of drug target perturbation, we combined drug target MR with a phenome-wide scan of over 190 disease biomarkers or clinical end-points relevant to cardiovascular as well as non-cardiovascular outcomes 15 . We compared drug target MR effect estimates to compound-specific effect estimates derived from a systematic review and meta-analysis of CETP inhibitor RCTs. Assuming the developed CETP inhibitors sufficiently engaged the drug target, on-target failures would result in consistent treatment effects across all compounds, which should be similar to the on-target effect modelled through MR. Finally, drug target MR analyses of CETP and PCSK9, an archetypal LDL-C lowering drug target, were compared on their effects profile. Methods Systematic review and meta-analyses of CETP inhibitor effects CETP inhibitor trials with at least 24 weeks of follow-up (irrespective of phase) were identified through a systematic review using a pre-specified search strategy (See Appendix) of MEDLINE and OVID, supplemented by clinicaltrials.gov. Parallel-group RCTs were included regardless of comparator (placebo or active therapy) with no additional exclusion criteria. Treatment effects were extracted (by NH and AFS) on lipids, lipoproteins, blood pressure, the incidence of all-cause mortality (ACM) and cardiovascular endpoints: any cardiovascular disease (CVD, defined as CV death, myocardial infarction (MI), any stroke, and angina hospitalization), fatal CVD (FCVD), any MI (including CHD), fatal MI (FMI), any stroke (ST; including ischemic, hemorrhagic and other strokes), ischemic stroke (IST), hemorrhagic stroke (HST), and heart failure (HF). Treatment effects on continuous traits (mean differences) were extracted as the between group difference in change from baseline 16 . Additional data were extracted on compound dose and potency, trial participants, and setting. Compound-specific clinical trial data were meta-analysed using the inverse-variance weighted method using both fixed and random effects. We used the Q-statistic 17 , 18 to test for the presence of between compound heterogeneity. Mendelian randomisation analysis Drug target MR analysis 14 utilises ( cis )-variants in, or near, a drug target encoding gene to obtain a causal estimate of the protein effect on multiple outcomes. Specifically, genetic associations with an outcome (e.g. CHD) are regressed on genetic associations with the drug target protein concentration or, alternatively, with biomarkers distal to the protein. Under the assumption that all the effects of the genetic variants on an outcome are mediated by the drug target protein (no-horizontal pleiotropy), the slope represents an estimate of the drug target effect. Here we used genetic effect estimates on the concentration of the encoded protein (CETP or PCSK9) as the primary exposure of interest, repeating the analyses using genetic effect association with LDL-C (for CETP and PCSK9), HDL-C (for CETP), and triglycerides (TG; for CETP), representing biomarkers known to be affected by the corresponding protein (available from the GLGC 19 consortium). To reduce the risk of “weak-instrument bias” 20 , we selected genetic variants with an F-statistic of 15 or higher (Supplemental Tables 2–7). We used a two-staged MR-paradigm, where genetic associations with the exposure and outcome were derived in independent samples, ensuring that any remaining weak-instrument bias attenuates towards the null (conservative estimates) 20 . Given the differences in coverage between the various outcome GWAS, variants were clumped to an R-squared of 0.40 after linking the exposure variants to a specific outcome GWAS (maximizing precision). Residual linkage disequilibrium (LD) was modelled using a generalised least squares (GLS) 21 , 22 IVW-estimator, and an external correlation structure (random 5,000 UK biobank, (UKB) sample). The possibility of bias inducing horizontal pleiotropy was minimized by focussing on a cis genetic region, excluding variants with large leverage or outlier statistics 14 , 23 and using the Q-statistic to identify remaining violations 23 , 24 . Findings from the cis -MR analysis of CETP were compared to effects observed in trials (for outcomes shared by the trial and MR analyses) using hierarchical clustering. Selection of genetic instruments Genetic associations with CETP concentration (protein quantitative trait loci; pQTLs) were extracted from a GWAS on circulating CETP concentration 25 . Genetic variants were selected based on residency within a narrow window around CETP (Chr 16: bp: 56,961,923 to 56,985,845; GRCh38) 25 . For the PCSK9 drug target MR, we selected variants associated with PCSK9 concentration 26 using the following window: 55,037,447 to 55,066,852 bp (Chr 1; GRCh38). CETP variants with a minor allele frequency (MAF) below 0.05 were removed. For PCSK9 , this threshold was reduced to 0.01, ensuring rs11591147 was included (the top hit with PCSK9 concentration 26 ). Genetic associations with outcomes of interest GWAS data were available for over 190 outcome traits (see Supplementary Methods and Table 8) including 60,801 CHD cases from CardiogramplusC4D 27 ; 40,585 stroke cases (subtypes) from MEGASTROKE 28 ; 47,309 HF cases from HERMES 29 , 60,620 atrial fibrillation (AF) cases from AFgen 30 , 71,880 Alzheimer’s disease (AD) 31 cases from a meta-analysis of the PGC-ALZ, IGAP, 16,144 age-related macular degeneration (AMD) events from IAMDGC 32 , 33 , and genetic associations with NMR measured circulating lipoprotein subfractions and other metabolites were available from a meta-analysis of Kettunen et al. 34 , and UCLEB 35 (n: 33,029). Results are presented as mean difference (MD) or odds ratio (OR) with 95% confidence interval (95%CI) coded towards the drug target effect direction; i.e., towards lower circulating protein, LDL-C, and TG concentration, and a higher HDL-C concentration. CETP concentration was reported as \(\mu\) g/ml while PCSK9 concentration was reported as log-transformed ng/ml. Results Effects of different CETP inhibitors in trials We identified 15 RCTs of CETP inhibitors with at least 24 weeks of follow-up, including four different compounds (six anacetrapib, four dalcetrapib, four torcetrapib and one evacetrapib study), all evaluated against placebo (Supplementary Table 9) and involving 79,961 participants. Participants received either torcetrapib 60–120 mg, evacetrapib 130 mg, anacetrapib 100 mg, or dalcetrapib 600–900 mg per day, reflecting differences in compound potency (Supplementary Table 9, and Supplementary results). The longest follow-up times was a median of 49 months for anacetrapib in the REVEAL trial, 31 months for dalcetrapib in the DAL-OUTCOMES trial, 24 months for torcetrapib in the RADIANCE 1 and ILLUSTRATE trials and 26 months for evacetrapib ACCELERATE trial. All four compounds increased HDL-C and reduced LDL-C, but the magnitude of effect differed between compounds (Fig. 1 , Supplement Table 10). Anacetrapib and evacetrapib had the largest HDL-C increasing effect, 130% (95%CI 127; 133) and 132% (95%CI 130; 133) respectively, followed by torcetrapib 52% (95%CI 49; 55) and dalcetrapib 29% (95%CI 23; 43); heterogeneity p-value < 0.001. The reduction in LDL-C was − 38% (95%CI -40; -36) for anacetrapib, -37% (95%CI -38; -36) for evacetrapib, -20% (95%CI -24; -17) for torcetrapib, and − 1% (95%CI -1.1; -0.9) for dalcetrapib. The CETP inhibitor effects were similarly heterogenous (interaction p-value < 0.001) for TG, apolipoprotein A1, B, lp(a), and systolic/diastolic blood pressure (SBP/DBP); Fig. 1 and Supplemental Fig. 1 and Table 10. CETP inhibitors also differed in their effect on clinical outcomes (Fig. 1 ). Torcetrapib increased risk of all-cause mortality (OR 1.56 95%CI 1.14; 2.12), while evacetrapib decreased all-cause mortality (OR 0.84 95%CI 0.71; 1.00); heterogeneity p-value = 0.009. Similarly, torcetrapib increased any CVD (OR 1.22 95% 1.08; 1.38), while anacetrapib decreased CVD (OR 0.93 95%CI 0.87; 1.00); heterogeneity p-value 0.002. Anacetrapib reduced any MI risk (OR 0.89 95%CI 0.80; 0.99), with the remaining compounds showing a neutral MI effect; heterogeneity p-value 0.046. On-target effects of CETP inhibition using drug target MR Lower genetically instrumented CETP concentration was associated with lower LDL-C -0.08 (mmol/L, 95%CI -0.08; -0.08), TG -0.09 (mmol/L, 95%CI -0.09; -0.09), Lp[a] -2.13 (nmol/L, 95%CI -1.52; -2.74), apolipoprotein B -0.03 (g/L, 95%CI -0.03; -0.03), and higher HDL-C 0.24 (mmol/L, 95%CI 0.24; 0.24), and apolipoprotein A1 0.14 (g/L, 95%CI 0.14; 0.14); Fig. 2 (full details in Supplementary Table 11). Lower CETP concentrations were significantly associated with a lower blood pressure (-0.2 mmHg for SBP and − 0.12 for DBP), lower concentration of blood glucose (-0.02 mmol/L), HbA1c (-0.10 mmol/mol), lower cell counts for leukocytes (-0.03 × 10 9 cells/L), lymphocytes (-0.02 × 10 9 cells/L), and monocytes (-0.01 × 10 9 cells/L). These findings were consistent in cis -MR analysis weighted by LDL-C, HDL-C and TG (Supplemental Fig. 2). Lower genetically instrumented CETP concentration was associated with CHD (OR 0.95; 95%CI 0.91; 0.99), HF (OR 0.95; 95%CI 0.92; 0.99), CKD (OR 0.94 95%CI 0.91; 0.98), and AMD (OR 1.31 95%CI 1.22; 1.40); Fig. 3 and Supplemental Table 12. The magnitude and direction of effects were consistent in LDL-C, HDL-C and TG weighted analyses (Supplemental Fig. 2, Table 12). On-target effects of PCSK9 inhibition using drug target MR We compared the drug target MR results of CETP lowering to those for PCSK9, using genetic instruments on PCSK9 concentration. Lower PCSK9 concentration (Fig. 2 , Supplemental Table 11) was associated with lower LDL-C (-0.57 mmol/L), apolipoprotein B (-0.15 mmol/L), lp[a] (-3.54 nmol/L) and HDL-C (-0.03 mmol/L). We additionally observed an association with carotid intima-media thickness (-0.02 mm), SBP (-1.20 mmHg), blood urea nitrogen (BUN: -0.04 mg/dl), HbA1c (-0.25 mmol/mol), and higher estimated-GFR (eGFR: 0.01 per SD), C-reactive protein (CRP: 0.41 mg/L), pulse rate (1.22 bpm), and blood cell counts (Fig. 2 , and Supplemental Table 11). Lower PCSK9 concentration was associated with the following clinical endpoints (Fig. 3 , Supplementary table 12): CHD (OR 0.69 95%CI 0.59; 0.81), any stroke (OR 0.79 95%CI 0.69; 0.91), any ischemic stroke (OR 0.86 95%CI 0.76; 0.97), large artery stroke (OR 0.64 95%CI 0.47; 0.87), HF (OR 0.79 95%CI 0.71; 0.87), AF (OR 0.90 95%CI 0.83; 0.97), CKD (OR 0.83 95%CI 0.72; 0.94), multiple sclerosis (MS; OR 0.69 95%CI 0.50; 0.96), and increased risk of asthma (OR 1.97 95%CI 1.56; 2.48) and AD (OR 2.43 95%CI 1.93; 3.06). The LDL-C weighted analysis was consistent with these findings (Fig. 2 , Supplemental Fig. 2 and Table 12). Lipoprotein sub fraction profiles based on NMR spectroscopy Drug target MR showed that lower CETP concentration was associated with wide ranging effects on lipoprotein sub-fraction size and content including medium, large and extra-large HDL-C subfractions, and lower extra-small, small, and medium VLDL sub-fractions (Fig. 4 ). Lower CETP concentration had a minimal effect on total LDL-C measured through NMR spectroscopy: -0.01 SD (95%CI -0.05; 0.02) for LDL-C compared to a HDL-C effect of 0.51 SD (95%CI 0.46; 0.56). Lower CETP was however strongly associated with decreased mean LDL-C diameter − 0.24 SD (95%CI -0.29; -0.20). The PCSK9 NMR profile was narrower than that for CETP, with lower PCSK9 associated only with lower medium and large LDL-C subfractions, IDL, and extra small VLDL. Comparing effects of CETP inhibitors to drug target MR effects of CETP modulation CETP inhibitors could be directly compared to the on-target MR effects of lower CETP concentration for their effect on lipids, lipoprotein, blood pressure and any MI (Fig. 5 ). Both torcetrapib and dalcetrapib showed biomarker profiles distinct from that of genetically instrumented lower CETP concentration. For torcetrapib this difference was driven by an increasing effect on SBP and DBP. For dalcetrapib this difference was due to attenuated lipid associations. Anacetrapib and evacetrapib displayed a similar risk factor profile that most closely reflected the on-target association of lower CETP concentration modelled genetically, and hence clustered most closely to on-target CETP modulation. Discussion We found substantial heterogeneity in the effects of four CETP-inhibitors (anacetrapib, evacetrapib, dalcetrapib and torcetrapib) on major lipid fractions, blood pressure, all-cause mortality and cardiovascular outcomes, suggesting between-compound differences in the efficacy of CETP inhibition, off-target actions or both. The effects profile of anacetrapib and evacetrapib on blood lipids and cardiovascular end-points most closely matched the effects of genetically-instrumented reductions in CETP concentration suggesting that anacetrapib and evacetrapib are effective CETP inhibitors. The reduction in cardiovascular events seen in the REVEAL trial of anacetrapib (median follow-up 1,497 days; Supplementary Table 3) is consistent with the drug target MR results presented here. The manufacturer, Merck, did not seek marketing authorization for this drug citing an anticipated lack of regulatory support 36 . The evacetrapib ACCELERATE trial was terminated for futility after a median follow-up of 791 days, a time point before the benefits of anacetrapib emerged in the REVEAL trial (see Fig. 1 of ref 11 ). Taken together, the presented RCT and drug target MR findings, suggest that CETP is a viable target to manage CVD risk. The heterogeneous clinical effects of evaluated CETP inhibitors, e.g. the increased risk of mortality and CVD by torcetrapib or the modest LDL-C effect of dalcetrapib, are likely to be compound - rather than target-related 37 . As well as enabling a separation of on- vs off-target effects of CETP inhibition, drug target MR analysis facilitate an investigation of CETP effect beyond those investigated in clinical trials. The drug target MR analyses showed that lower CETP concentration was additionally associated with not only with CHD (OR 0.95 per µg/ml CETP concentration; 95%CI 0.91; 0.99), but also HF (OR 0.95; 95%CI 0.92; 0.99) and CKD (OR 0.94; 95%CI 0.91; 0.98), but with a higher risk of AMD (OR 1.31; 95%CI 1.22; 1.40). Similar to the on-target effects of CETP, genetically-instrumented PCSK9 concentration was associated with a lower risk of CHD, HF and CKD, and additionally with any stroke, ischemic stroke, AF, MS, as well as an increased risk of asthma and AD 38 . We showed that CETP and PCSK9 had distinct effect patterns on different lipoprotein sub-fractions, with lower CETP being associated with higher HDL-C and lower VLDL-C sub-fractions, and PCSK9 with lower LDL-C sub-fractions alone. These findings suggest that, although sharing salutary effects on clinical endpoints, the mechanisms through which the effects of CETP and PCSK9 inhibition are mediated are likely to be target-specific and cannot, on present evidence, be attributed to selected shared actions or a single pathway e.g. on LDL-C or apolipoprotein B 39 . Some prior drug target MR studies have attempted to quantify the anticipated effect of a drug targeting the same protein. For example, the anticipated effect of CETP inhibition on CHD risk is a reduction of 40% when weighted by one mmol/L lower LDL-C concentration (Supplemental figure S2). While of potential interest, there are some caveats that suggest that drug target MR analysis may be more useful as a reliable test of effect direction, and when multiple outcomes are considered, the rank order of effects. This is because drugs that inhibit a target do so usually by modifying its function not its concentration, whereas genetic variants used in MR analysis usually affect protein expression and therefore concentration. However, for enzymes like CETP, activity reflects both the amount of available protein as well as activity per unit concentration. Thus, on both theoretical grounds and through numerous empirical examples 39 – 41 , MR analyses using variants in a gene encoding a drug target that affect its expression (or activity) have reproduced the effect direction of compounds with pharmacological action on the same protein 39 – 41 . Given the typically non-linear drug dose-response, the small downstream effects of genetic variants on the level or function of a protein may underestimate the potential treatment effect of a drug. MR analyses assess the effect of target modulation in any tissue, whereas, certain tissues may be in accessible to a drug either because of its chemistry or the anatomical or physiological barriers. Furthermore, RCTs are closely monitored, and followed for a fixed period, allowing for exploration of induction-times 11 . MR estimates are considered to reflect a life-long exposure, but in the absence of serial assessment, possible changes across age are difficult to explore, as are disease induction-times. For these reasons we suggest that drug target MR offers a robust indication of effect direction but may not directly anticipate the effect magnitude of pharmacologically interfering with a protein. Findings such as the observed increased risk of AMD (CETP), asthma (PCSK9), Alzheimer’s in (PCSK9), therefore need to be considered in the context of both the duration of drug exposure and the potential for a drug to access the relevant tissues. Our findings add to prior drug target MR analyses of CETP and PCSK9 which did not have access to genetic associations with protein concentration and weighted by downstream effects of the drug target on HDL-C or LDL-C, respectively. Here we showed consistency in the findings of MR analyses of CETP weighted through the concentration of the encoded protein (a more direct proxy of target modulation) and through HDL-C, TG, and LDL-C. Such “biomarker weighted” drug target MR should not be confused with MR analyses designed to evaluate the causal relevance of major lipid fractions; utilising genetic variants from throughout the genome 14 . In the presence of post-translation pleiotropy 14 , where perturbation of a protein affects multiple downstream biomarkers, some of which may lie on the causal pathway to disease and others not, biomarker weighted drug target MRs do not provide evidence on the possible mediating pathway of the drug target on disease 14 and instead reflect drug target effects. In conclusion, previous failures of CETP inhibitors are likely related to suboptimal target inhibition (dalcetrapib), off-target effects (torcetrapib) or insufficiently long follow-up (evacetrapib). The present drug target MR analysis, consistent with findings from the anacetrapib trials, anticipates that on-target CETP inhibition decreases CVD risk. MR analyses additionally suggests a reduction in kidney disease risk, but an increased risk of age-related macular degeneration. Declarations Author’s contributions AFS, ADH, CF, contributed to the idea and design of the study. AFS and NH performed the systematic-review and meta-analysis. AFS, NH, and CF performed the analyses. AFS drafted the manuscript. All authors provided critical input on the analyses and the drafted manuscript. Conflict of interest statements AFS has received Servier funding for unrelated work. MZ conducted this research as an employee of BenevolentAI. Since completing the work MZ is now a full-time employee of GlaxoSmithKline. None of the remaining authors have a competing interest to declare. DAL has received support from Roche Diagnostics and Medtronic Ltd for research unrelated to this paper. TRG receives funding from GlaxoSmithKline and Biogen Funding and role of funding sources AFS is supported by BHF grant PG/18/5033837 and the UCL BHF Research Accelerator AA/18/6/34223. CF and AFS received additional support from the National Institute for Health Research University College London Hospitals Biomedical Research Centre. MGM is supported by a BHF Fellowship FS/17/70/33482. ADH is an NIHR Senior Investigator. We further acknowledge support from the Rosetrees and Stoneygate Trust. The UCLEB Consortium is supported by a British Heart Foundation Programme Grant (RG/10/12/28456). TRG receives support from the UK Medical Research Council (MC_UU_00011/4). DOMK is supported by the Dutch Science Organization (ZonMW-VENI Grant 916.14.023). Alun DH receives support from the UK Medical Research (MC_UU_12019/1). MK is supported by the UK Medical Research Council (MR/S011676/1, MR/R024227/1), National Institute on Aging (NIH), US (R01AG062553) and the Academy of Finland (311492). DAL is supported by a Bristol BHF Accelerator Award (AA/18/7/34219), and works in a unit that recieves support from the University of Bristol and the UK Medical Research Council (MC_UU_00011/6). DAL is a National Institute of Health Research Senior Investigator (NF-0616-10102). Guarantor Amand F Schmidt performed the here presented analyses, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Acknowledgement : This research has been conducted using the UK Biobank Resource under Application Number 12113. The authors are grateful to UK Biobank participants. UK Biobank was established by the Wellcome Trust medical charity, Medical Research Council, Department of Health, Scottish Government, and the Northwest Regional Development Agency. It has also had funding from the Welsh Assembly Government and the British Heart Foundation. Prior postings and presentations The preliminary meta-analysis of RCT data were presented at BPS 2018 by NH. The preprint version of this manuscript has been deposited on medrxiv. Data availability All data are publicly available, as described in the methods section. Please contact AFS for access to specific files, data, or analysis scripts. 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Genome-wide association and Mendelian randomisation analysis provide insights into the pathogenesis of heart failure. Nat. Commun. 11 , 1–12 (2020). Nielsen, J. B. et al. Biobank-driven genomic discovery yields new insight into atrial fibrillation biology. Nat. Genet. 50 , 1234–1239 (2018). Jansen, I. E. et al. Genome-wide meta-analysis identifies new loci and functional pathways influencing Alzheimer’s disease risk. Nat. Genet. 51 , 404–413 (2019). Fritsche, L. G. et al. A large genome-wide association study of age-related macular degeneration highlights contributions of rare and common variants. Nat. Genet. 48 , 134–143 (2016). Burgess, S. & Davey Smith, G. Mendelian Randomization Implicates High-Density Lipoprotein Cholesterol–Associated Mechanisms in Etiology of Age-Related Macular Degeneration. Ophthalmology 124 , 1165–1174 (2017). Kettunen, J. et al. Genome-wide study for circulating metabolites identifies 62 loci and reveals novel systemic effects of LPA. Nat. Commun. 7 , 1–9 (2016). Shah, T. et al. Population genomics of cardiometabolic traits: design of the University College London-London School of Hygiene and Tropical Medicine-Edinburgh-Bristol (UCLEB) Consortium. PloS One 8 , e71345 (2013). Merck halts development of anacetrapib. https://www.healio.com/news/cardiology/20171012/merck-halts-development-of-anacetrapib. Mohammadpour, A. H. & Akhlaghi, F. Future of Cholesteryl Ester Transfer Protein (CETP) Inhibitors: A Pharmacological Perspective. Clin. Pharmacokinet. 52 , 615–626 (2013). Williams, D. M., Finan, C., Schmidt, A. F., Burgess, S. & Hingorani, A. D. Lipid lowering and Alzheimer disease risk: A mendelian randomization study. Ann. Neurol. 87 , 30–39 (2020). Ference, B. A. et al. Association of Genetic Variants Related to CETP Inhibitors and Statins With Lipoprotein Levels and Cardiovascular Risk. JAMA 318 , 947–956 (2017). Ference, B. A. et al. Variation in PCSK9 and HMGCR and Risk of Cardiovascular Disease and Diabetes. N. Engl. J. Med. 375 , 2144–2153 (2016). Ference, B. A. How to use Mendelian randomization to anticipate the results of randomized trials. Eur. Heart J. 39 , 360–362 (2018). Additional Declarations Yes there is potential Competing Interest. AFS has received Servier funding for unrelated work. MZ conducted this research as an employee of BenevolentAI. Since completing the work MZ is now a full-time employee of GlaxoSmithKline. None of the remaining authors have a competing interest to declare. DAL has received support from Roche Diagnostics and Medtronic Ltd for research unrelated to this paper. TRG receives funding from GlaxoSmithKline and Biogen Supplementary Files AFSchmidtAppendix.pdf AFSchmidtAppendix.pdf Cite Share Download PDF Status: Published Journal Publication published 24 Sep, 2021 Read the published version in Nature Communications → 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-78818","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":2887729,"identity":"49806a57-6d97-4012-86c2-714b8234d8e7","order_by":0,"name":"Amand 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London","correspondingAuthor":false,"prefix":"","firstName":"Aroon","middleName":"","lastName":"Hingorani","suffix":""},{"id":2887752,"identity":"775ab142-f585-4c43-8436-66e1d4075ff5","order_by":23,"name":"Chris Finan","email":"","orcid":"https://orcid.org/0000-0002-3319-1937","institution":"Institute of Cardiovascular Science, Faculty of Population Health, University College London","correspondingAuthor":false,"prefix":"","firstName":"Chris","middleName":"","lastName":"Finan","suffix":""}],"badges":[],"createdAt":"2020-09-16 11:01:50","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-78818/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-78818/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41467-021-25703-3","type":"published","date":"2021-09-24T04:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":2727443,"identity":"4953c1e7-8e9c-433e-bd81-de5ae00a7bb0","added_by":"auto","created_at":"2020-10-01 17:20:00","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":116669,"visible":true,"origin":"","legend":"Differences in CETP-inhibitor effects on lipids, blood pressure and clinical endpoints. \n\nN.B Results are based on a fixed effect compound specific meta-analyses with differences between compounds tested using a Q-test (Heterogeneity). *** indicates a p-value \u003c 0.001 for the Q-test. LDL: LDL-C, HDL: HDL-C, TG: triglycerides, ApoA1: apolipoprotein A1, ApoB: apolipoprotein B, S/DBP: systolic/diastolic blood pressure ACM: All-cause mortality, CVD: cardiovascular disease, FCVD: fatal-CVD, MI: myocardial infarction, FMI: fatal-MI, ST: any stroke, IST: Ischemic stroke, HST: haemorrhagic stroke, HF: heart failure. \n","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-78818/v1/1.png"},{"id":2727446,"identity":"9fa4f201-3fd4-46a3-85d9-00a8f5074360","added_by":"auto","created_at":"2020-10-01 17:20:00","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":85578,"visible":true,"origin":"","legend":"Drug target Mendelian randomization estimates of lower CETP and PCSK9 weighted by genetic associations with protein concentration or downstream lipid. \n\nN.B The rows represent the quantitative outcomes and the columns represent the intermediate variables (approximating) drug target concentration. Cells are coloured by effect direction times -log10(p-value), with the mean difference (the slope coefficient) provide for MR results with a p-value smaller than 0.05. The p-values was truncated at 10-16 ensuring sufficient variation in the colour code.\n","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-78818/v1/2.png"},{"id":2727447,"identity":"afba198a-a0b3-40e1-8cb1-25fc2f875893","added_by":"auto","created_at":"2020-10-01 17:20:00","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":99344,"visible":true,"origin":"","legend":"The drug target Mendelian randomization effects of lower CETP and PCSK9 concentration on clinical end-points. \n\nN.B CHD: coronary heart disease, HF: heart failure, AF: atrial fibrillation, T2DM: type 2 diabetes mellitus, CKD: chronic kidney disease, IBD: inflammatory bowel disease, CD: Crohn’s disease, UC: ulcerative colitis, MS: multiple sclerosis, AMD: age-related macular degeneration.\n","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-78818/v1/3.png"},{"id":2727448,"identity":"7b7578af-7c60-4112-9e17-d366619cef9e","added_by":"auto","created_at":"2020-10-01 17:20:01","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":89351,"visible":true,"origin":"","legend":"The drug target Mendelian randomization effects of lower CETP and PCSK9 concentration on NMR-measured metabolites. \n\nN.B Results are provided as -log10(p-values) times effect direction, with the x-axis limits set to ±16. Bars semi-transparent and plotted on-top of each other to directly compare the two drug targets in their NMR measured lipids effect estimates. The vertical lines at ±1.3 represent the traditional p-value threshold of 0.05.\n","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-78818/v1/4.png"},{"id":2727449,"identity":"0641212a-7227-4434-87eb-d88f32de6418","added_by":"auto","created_at":"2020-10-01 17:20:01","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":42871,"visible":true,"origin":"","legend":"A cluster analysis comparing the on-target Mendelian randomization effect of lower CETP concentration to effects from CETP inhibiting compounds. \n\nN.B Clustering was performed on the square root of the -log10(p-values) × effect direction, with the p-value truncated to 10-60 to ensure enough difference between the CETP compound effect on changes in lipids. Associations with a p-value below 0.05 are indicated with a star. The dendrograms represent clustering by outcome (rows) and compound/drug target (columns). \n","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-78818/v1/5.png"},{"id":15779153,"identity":"f627d859-38b7-40ef-9933-53efc89a5a7c","added_by":"auto","created_at":"2021-11-22 15:34:17","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1027123,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-78818/v1/bc2b2e9e-59e8-4096-a7a5-77d48bc61ed5.pdf"},{"id":2727444,"identity":"489d8215-675d-4df7-adec-95cb08a9f43a","added_by":"auto","created_at":"2020-10-01 17:20:00","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":224163,"visible":true,"origin":"","legend":"AFSchmidtAppendix.pdf","description":"","filename":"AFSchmidtAppendix.pdf","url":"https://assets-eu.researchsquare.com/files/rs-78818/v1/AFSchmidtAppendix.pdf"}],"financialInterests":"\u003cb\u003eYes\u003c/b\u003e there is potential Competing Interest.\nAFS has received Servier funding for unrelated work. MZ conducted this research as an employee of BenevolentAI. Since completing the work MZ is now a full-time employee of GlaxoSmithKline. None of the remaining authors have a competing interest to declare. DAL has received support from Roche Diagnostics and Medtronic Ltd for research unrelated to this paper. TRG receives funding from GlaxoSmithKline and Biogen","formattedTitle":"Cholesteryl Ester Transfer Protein as a Drug Target for Cardiovascular Disease","fulltext":[{"header":"Introduction","content":" \u003cp\u003eThe causal role of low-density lipoprotein cholesterol (LDL-C) in coronary heart disease (CHD) has been established through randomized controlled trials (RCTs) of different LDL-C lowering drug classes\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e 2 3,4\u003c/sup\u003e and by Mendelian randomization (MR) studies\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eCirculating high-density lipoprotein cholesterol (HDL-C) shows an inverse association with CHD in nonrandomized studies\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. MR studies utilizing genetic variants associated with HDL-C selected throughout the genome have provided inconclusive evidence on the causal role of HDL-C as a biomarker\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Findings from RCTs of niacin\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e and cholesteryl ester transfer protein (CETP) inhibitors\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e, developed to prevent CHD by raising HDL-C have also been disappointing. For example, of the four CETP inhibitors that have progressed to phase 3 clinical trials, none have received market authorization (Supplementary Table\u0026nbsp;1). Six other CETP inhibitors (e.g. obicetrapib) are still in active development, raising important questions about the validity of CETP as a therapeutic target\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. One interpretation is that HDL-C is not causally related to CHD, and that raising HDL-C as a therapeutic strategy will be an ineffective approach for CHD prevention. As a result, the reduction in CHD events observed in a large RCT of anacetrapib (odds ratio [OR] 0.91 95%CI 0.85; 0.97)\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e, was attributed to its effect on LDL-C rather than to its HDL-C raising action\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eHowever, analysis of lipoprotein sub-classes measured using nuclear magnetic resonance (NMR) spectroscopy suggests that, unlike LDL-C, HDL-C particles encompasses several lipoprotein sub-fractions that have differential associations with CHD: some fractions being associated with higher and others with lower CHD risk \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Additionally, failures of CETP inhibitors might be related to the developed compounds rather than the drug target itself, either because of inadequate target engagement or a competing off-target action. Compound related failures can be addressed by developing an improved CETP inhibitor, whereas target failure affects \u003cem\u003eall\u003c/em\u003e CETP inhibitors.\u003c/p\u003e \u003cp\u003eTo address these uncertainties, we performed a drug target MR study\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e of CETP, focusing on variants within the encoding gene (acting in \u003cem\u003ecis\u003c/em\u003e) that are associated with circulating CETP concentration, to directly model the effects of pharmacological action on this target by a clean drug with no off-target actions. To evaluate potentially diverse effects of drug target perturbation, we combined drug target MR with a phenome-wide scan of over 190 disease biomarkers or clinical end-points relevant to cardiovascular as well as non-cardiovascular outcomes\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. We compared drug target MR effect estimates to compound-specific effect estimates derived from a systematic review and meta-analysis of CETP inhibitor RCTs. Assuming the developed CETP inhibitors sufficiently engaged the drug target, on-target failures would result in consistent treatment effects across all compounds, which should be similar to the on-target effect modelled through MR. Finally, drug target MR analyses of CETP and PCSK9, an archetypal LDL-C lowering drug target, were compared on their effects profile.\u003c/p\u003e "},{"header":"Methods","content":" \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSystematic review and meta-analyses of CETP inhibitor effects\u003c/h2\u003e \u003cp\u003eCETP inhibitor trials with at least 24 weeks of follow-up (irrespective of phase) were identified through a systematic review using a pre-specified search strategy (See Appendix) of MEDLINE and OVID, supplemented by clinicaltrials.gov. Parallel-group RCTs were included regardless of comparator (placebo or active therapy) with no additional exclusion criteria. Treatment effects were extracted (by NH and AFS) on lipids, lipoproteins, blood pressure, the incidence of all-cause mortality (ACM) and cardiovascular endpoints: any cardiovascular disease (CVD, defined as CV death, myocardial infarction (MI), any stroke, and angina hospitalization), fatal CVD (FCVD), any MI (including CHD), fatal MI (FMI), any stroke (ST; including ischemic, hemorrhagic and other strokes), ischemic stroke (IST), hemorrhagic stroke (HST), and heart failure (HF). Treatment effects on continuous traits (mean differences) were extracted as the between group difference in change from baseline\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Additional data were extracted on compound dose and potency, trial participants, and setting. Compound-specific clinical trial data were meta-analysed using the inverse-variance weighted method using both fixed and random effects. We used the Q-statistic\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e to test for the presence of between compound heterogeneity.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eMendelian randomisation analysis\u003c/h2\u003e \u003cp\u003eDrug target MR analysis\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e utilises (\u003cem\u003ecis\u003c/em\u003e)-variants in, or near, a drug target encoding gene to obtain a causal estimate of the protein effect on multiple outcomes. Specifically, genetic associations with an outcome (e.g. CHD) are regressed on genetic associations with the drug target protein concentration or, alternatively, with biomarkers distal to the protein. Under the assumption that all the effects of the genetic variants on an outcome are mediated by the drug target protein (no-horizontal pleiotropy), the slope represents an estimate of the drug target effect. Here we used genetic effect estimates on the concentration of the encoded protein (CETP or PCSK9) as the primary exposure of interest, repeating the analyses using genetic effect association with LDL-C (for CETP and PCSK9), HDL-C (for CETP), and triglycerides (TG; for CETP), representing biomarkers known to be affected by the corresponding protein (available from the GLGC\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e consortium).\u003c/p\u003e \u003cp\u003eTo reduce the risk of \u0026ldquo;weak-instrument bias\u0026rdquo;\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e, we selected genetic variants with an F-statistic of 15 or higher (Supplemental Tables\u0026nbsp;2\u0026ndash;7). We used a two-staged MR-paradigm, where genetic associations with the exposure and outcome were derived in independent samples, ensuring that any remaining weak-instrument bias attenuates towards the null (conservative estimates)\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Given the differences in coverage between the various outcome GWAS, variants were clumped to an R-squared of 0.40 \u003cem\u003eafter\u003c/em\u003e linking the exposure variants to a specific outcome GWAS (maximizing precision). Residual linkage disequilibrium (LD) was modelled using a generalised least squares (GLS)\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e IVW-estimator, and an external correlation structure (random 5,000 UK biobank, (UKB) sample). The possibility of bias inducing horizontal pleiotropy was minimized by focussing on a \u003cem\u003ecis\u003c/em\u003e genetic region, excluding variants with large leverage or outlier statistics\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e and using the Q-statistic to identify remaining violations\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eFindings from the \u003cem\u003ecis\u003c/em\u003e-MR analysis of CETP were compared to effects observed in trials (for outcomes shared by the trial and MR analyses) using hierarchical clustering.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eSelection of genetic instruments\u003c/h2\u003e \u003cp\u003eGenetic associations with CETP concentration (protein quantitative trait loci; pQTLs) were extracted from a GWAS on circulating CETP concentration\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Genetic variants were selected based on residency within a narrow window around \u003cem\u003eCETP\u003c/em\u003e (Chr 16: bp: 56,961,923 to 56,985,845; GRCh38)\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. For the PCSK9 drug target MR, we selected variants associated with PCSK9 concentration\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e using the following window: 55,037,447 to 55,066,852\u0026nbsp;bp (Chr 1; GRCh38). \u003cem\u003eCETP\u003c/em\u003e variants with a minor allele frequency (MAF) below 0.05 were removed. For \u003cem\u003ePCSK9\u003c/em\u003e, this threshold was reduced to 0.01, ensuring rs11591147 was included (the top hit with PCSK9 concentration\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eGenetic associations with outcomes of interest\u003c/h2\u003e \u003cp\u003eGWAS data were available for over 190 outcome traits (see Supplementary Methods and Table\u0026nbsp;8) including 60,801 CHD cases from CardiogramplusC4D\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e; 40,585 stroke cases (subtypes) from MEGASTROKE\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e; 47,309 HF cases from HERMES\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e, 60,620 atrial fibrillation (AF) cases from AFgen \u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e, 71,880 Alzheimer\u0026rsquo;s disease (AD)\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e cases from a meta-analysis of the PGC-ALZ, IGAP, 16,144 age-related macular degeneration (AMD) events from IAMDGC\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e,\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e, and genetic associations with NMR measured circulating lipoprotein subfractions and other metabolites were available from a meta-analysis of Kettunen \u003cem\u003eet al.\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e, and UCLEB\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e (n: 33,029).\u003c/p\u003e \u003cp\u003eResults are presented as mean difference (MD) or odds ratio (OR) with 95% confidence interval (95%CI) coded towards the drug target effect direction; i.e., towards lower circulating protein, LDL-C, and TG concentration, and a higher HDL-C concentration. CETP concentration was reported as \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\mu\\)\u003c/span\u003e\u003c/span\u003eg/ml while PCSK9 concentration was reported as log-transformed ng/ml.\u003c/p\u003e \u003c/div\u003e "},{"header":"Results","content":" \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eEffects of different CETP inhibitors in trials\u003c/h2\u003e \u003cp\u003eWe identified 15 RCTs of CETP inhibitors with at least 24 weeks of follow-up, including four different compounds (six anacetrapib, four dalcetrapib, four torcetrapib and one evacetrapib study), all evaluated against placebo (Supplementary Table\u0026nbsp;9) and involving 79,961 participants. Participants received either torcetrapib 60\u0026ndash;120\u0026nbsp;mg, evacetrapib 130\u0026nbsp;mg, anacetrapib 100\u0026nbsp;mg, or dalcetrapib 600\u0026ndash;900\u0026nbsp;mg per day, reflecting differences in compound potency (Supplementary Table\u0026nbsp;9, and Supplementary results). The longest follow-up times was a median of 49 months for anacetrapib in the REVEAL trial, 31 months for dalcetrapib in the DAL-OUTCOMES trial, 24 months for torcetrapib in the RADIANCE 1 and ILLUSTRATE trials and 26 months for evacetrapib ACCELERATE trial.\u003c/p\u003e \u003cp\u003eAll four compounds increased HDL-C and reduced LDL-C, but the magnitude of effect differed between compounds (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Supplement Table\u0026nbsp;10). Anacetrapib and evacetrapib had the largest HDL-C increasing effect, 130% (95%CI 127; 133) and 132% (95%CI 130; 133) respectively, followed by torcetrapib 52% (95%CI 49; 55) and dalcetrapib 29% (95%CI 23; 43); heterogeneity p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001. The reduction in LDL-C was \u0026minus;\u0026thinsp;38% (95%CI -40; -36) for anacetrapib, -37% (95%CI -38; -36) for evacetrapib, -20% (95%CI -24; -17) for torcetrapib, and \u0026minus;\u0026thinsp;1% (95%CI -1.1; -0.9) for dalcetrapib. The CETP inhibitor effects were similarly heterogenous (interaction p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001) for TG, apolipoprotein A1, B, lp(a), and systolic/diastolic blood pressure (SBP/DBP); Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Supplemental Fig.\u0026nbsp;1 and Table\u0026nbsp;10.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eCETP inhibitors also differed in their effect on clinical outcomes (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Torcetrapib increased risk of all-cause mortality (OR 1.56 95%CI 1.14; 2.12), while evacetrapib decreased all-cause mortality (OR 0.84 95%CI 0.71; 1.00); heterogeneity p-value\u0026thinsp;=\u0026thinsp;0.009. Similarly, torcetrapib increased any CVD (OR 1.22 95% 1.08; 1.38), while anacetrapib decreased CVD (OR 0.93 95%CI 0.87; 1.00); heterogeneity p-value 0.002. Anacetrapib reduced any MI risk (OR 0.89 95%CI 0.80; 0.99), with the remaining compounds showing a neutral MI effect; heterogeneity p-value 0.046.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eOn-target effects of CETP inhibition using drug target MR\u003c/h2\u003e \u003cp\u003eLower genetically instrumented CETP concentration was associated with lower LDL-C -0.08 (mmol/L, 95%CI -0.08; -0.08), TG -0.09 (mmol/L, 95%CI -0.09; -0.09), Lp[a] -2.13 (nmol/L, 95%CI -1.52; -2.74), apolipoprotein B -0.03 (g/L, 95%CI -0.03; -0.03), and higher HDL-C 0.24 (mmol/L, 95%CI 0.24; 0.24), and apolipoprotein A1 0.14 (g/L, 95%CI 0.14; 0.14); Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e (full details in Supplementary Table\u0026nbsp;11).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eLower CETP concentrations were significantly associated with a lower blood pressure (-0.2\u0026nbsp;mmHg for SBP and \u0026minus;\u0026thinsp;0.12 for DBP), lower concentration of blood glucose (-0.02\u0026nbsp;mmol/L), HbA1c (-0.10\u0026nbsp;mmol/mol), lower cell counts for leukocytes (-0.03\u0026thinsp;\u0026times;\u0026thinsp;10\u003csup\u003e9\u003c/sup\u003e cells/L), lymphocytes (-0.02\u0026thinsp;\u0026times;\u0026thinsp;10\u003csup\u003e9\u003c/sup\u003e cells/L), and monocytes (-0.01\u0026thinsp;\u0026times;\u0026thinsp;10\u003csup\u003e9\u003c/sup\u003e cells/L). These findings were consistent in \u003cem\u003ecis\u003c/em\u003e-MR analysis weighted by LDL-C, HDL-C and TG (Supplemental Fig.\u0026nbsp;2).\u003c/p\u003e \u003cp\u003eLower genetically instrumented CETP concentration was associated with CHD (OR 0.95; 95%CI 0.91; 0.99), HF (OR 0.95; 95%CI 0.92; 0.99), CKD (OR 0.94 95%CI 0.91; 0.98), and AMD (OR 1.31 95%CI 1.22; 1.40); Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Supplemental Table\u0026nbsp;12. The magnitude and direction of effects were consistent in LDL-C, HDL-C and TG weighted analyses (Supplemental Fig.\u0026nbsp;2, Table\u0026nbsp;12).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eOn-target effects of PCSK9 inhibition using drug target MR\u003c/h2\u003e \u003cp\u003eWe compared the drug target MR results of CETP lowering to those for PCSK9, using genetic instruments on PCSK9 concentration. Lower PCSK9 concentration (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Supplemental Table\u0026nbsp;11) was associated with lower LDL-C (-0.57\u0026nbsp;mmol/L), apolipoprotein B (-0.15\u0026nbsp;mmol/L), lp[a] (-3.54\u0026nbsp;nmol/L) and HDL-C (-0.03\u0026nbsp;mmol/L). We additionally observed an association with carotid intima-media thickness (-0.02\u0026nbsp;mm), SBP (-1.20\u0026nbsp;mmHg), blood urea nitrogen (BUN: -0.04\u0026nbsp;mg/dl), HbA1c (-0.25\u0026nbsp;mmol/mol), and higher estimated-GFR (eGFR: 0.01 per SD), C-reactive protein (CRP: 0.41\u0026nbsp;mg/L), pulse rate (1.22\u0026nbsp;bpm), and blood cell counts (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, and Supplemental Table\u0026nbsp;11).\u003c/p\u003e \u003cp\u003eLower PCSK9 concentration was associated with the following clinical endpoints (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Supplementary table 12): CHD (OR 0.69 95%CI 0.59; 0.81), any stroke (OR 0.79 95%CI 0.69; 0.91), any ischemic stroke (OR 0.86 95%CI 0.76; 0.97), large artery stroke (OR 0.64 95%CI 0.47; 0.87), HF (OR 0.79 95%CI 0.71; 0.87), AF (OR 0.90 95%CI 0.83; 0.97), CKD (OR 0.83 95%CI 0.72; 0.94), multiple sclerosis (MS; OR 0.69 95%CI 0.50; 0.96), and increased risk of asthma (OR 1.97 95%CI 1.56; 2.48) and AD (OR 2.43 95%CI 1.93; 3.06). The LDL-C weighted analysis was consistent with these findings (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Supplemental Fig.\u0026nbsp;2 and Table\u0026nbsp;12).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eLipoprotein sub fraction profiles based on NMR spectroscopy\u003c/h2\u003e \u003cp\u003eDrug target MR showed that lower CETP concentration was associated with wide ranging effects on lipoprotein sub-fraction size and content including medium, large and extra-large HDL-C subfractions, and lower extra-small, small, and medium VLDL sub-fractions (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Lower CETP concentration had a minimal effect on total LDL-C measured through NMR spectroscopy: -0.01 SD (95%CI -0.05; 0.02) for LDL-C compared to a HDL-C effect of 0.51 SD (95%CI 0.46; 0.56). Lower CETP was however strongly associated with decreased mean LDL-C diameter \u0026minus;\u0026thinsp;0.24 SD (95%CI -0.29; -0.20). The PCSK9 NMR profile was narrower than that for CETP, with lower PCSK9 associated only with lower medium and large LDL-C subfractions, IDL, and extra small VLDL.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eComparing effects of CETP inhibitors to drug target MR effects of CETP modulation\u003c/h2\u003e \u003cp\u003eCETP inhibitors could be directly compared to the on-target MR effects of lower CETP concentration for their effect on lipids, lipoprotein, blood pressure and any MI (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Both torcetrapib and dalcetrapib showed biomarker profiles distinct from that of genetically instrumented lower CETP concentration. For torcetrapib this difference was driven by an increasing effect on SBP and DBP. For dalcetrapib this difference was due to attenuated lipid associations. Anacetrapib and evacetrapib displayed a similar risk factor profile that most closely reflected the on-target association of lower CETP concentration modelled genetically, and hence clustered most closely to on-target CETP modulation.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e "},{"header":"Discussion","content":" \u003cp\u003eWe found substantial heterogeneity in the effects of four CETP-inhibitors (anacetrapib, evacetrapib, dalcetrapib and torcetrapib) on major lipid fractions, blood pressure, all-cause mortality and cardiovascular outcomes, suggesting between-compound differences in the efficacy of CETP inhibition, off-target actions or both. The effects profile of anacetrapib and evacetrapib on blood lipids and cardiovascular end-points most closely matched the effects of genetically-instrumented reductions in CETP concentration suggesting that anacetrapib and evacetrapib are effective CETP inhibitors.\u003c/p\u003e \u003cp\u003eThe reduction in cardiovascular events seen in the REVEAL trial of anacetrapib (median follow-up 1,497 days; Supplementary Table\u0026nbsp;3) is consistent with the drug target MR results presented here. The manufacturer, Merck, did not seek marketing authorization for this drug citing an anticipated lack of regulatory support\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. The evacetrapib ACCELERATE trial was terminated for futility after a median follow-up of 791 days, a time point before the benefits of anacetrapib emerged in the REVEAL trial (see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e of ref\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e). Taken together, the presented RCT and drug target MR findings, suggest that CETP is a viable target to manage CVD risk. The heterogeneous clinical effects of evaluated CETP inhibitors, e.g. the increased risk of mortality and CVD by torcetrapib or the modest LDL-C effect of dalcetrapib, are likely to be compound - rather than target-related \u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAs well as enabling a separation of on- vs off-target effects of CETP inhibition, drug target MR analysis facilitate an investigation of CETP effect beyond those investigated in clinical trials. The drug target MR analyses showed that lower CETP concentration was additionally associated with not only with CHD (OR 0.95 per \u0026micro;g/ml CETP concentration; 95%CI 0.91; 0.99), but also HF (OR 0.95; 95%CI 0.92; 0.99) and CKD (OR 0.94; 95%CI 0.91; 0.98), but with a higher risk of AMD (OR 1.31; 95%CI 1.22; 1.40). Similar to the on-target effects of CETP, genetically-instrumented PCSK9 concentration was associated with a lower risk of CHD, HF and CKD, and additionally with any stroke, ischemic stroke, AF, MS, as well as an increased risk of asthma and AD\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. We showed that CETP and PCSK9 had distinct effect patterns on different lipoprotein sub-fractions, with lower CETP being associated with higher HDL-C and lower VLDL-C sub-fractions, and PCSK9 with lower LDL-C sub-fractions alone. These findings suggest that, although sharing salutary effects on clinical endpoints, the mechanisms through which the effects of CETP and PCSK9 inhibition are mediated are likely to be target-specific and cannot, on present evidence, be attributed to selected shared actions or a single pathway e.g. on LDL-C or apolipoprotein B\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eSome prior drug target MR studies have attempted to quantify the anticipated effect of a drug targeting the same protein. For example, the anticipated effect of CETP inhibition on CHD risk is a reduction of 40% when weighted by one mmol/L lower LDL-C concentration (Supplemental figure S2). While of potential interest, there are some caveats that suggest that drug target MR analysis may be more useful as a reliable test of effect direction, and when multiple outcomes are considered, the rank order of effects. This is because drugs that inhibit a target do so usually by modifying its function not its concentration, whereas genetic variants used in MR analysis usually affect protein expression and therefore concentration. However, for enzymes like CETP, activity reflects both the amount of available protein as well as activity per unit concentration. Thus, on both theoretical grounds and through numerous empirical examples\u003csup\u003e\u003cspan additionalcitationids=\"CR40\" citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e, MR analyses using variants in a gene encoding a drug target that affect its expression (or activity) have reproduced the effect direction of compounds with pharmacological action on the same protein\u003csup\u003e\u003cspan additionalcitationids=\"CR40\" citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. Given the typically non-linear drug dose-response, the small downstream effects of genetic variants on the level or function of a protein may underestimate the potential treatment effect of a drug. MR analyses assess the effect of target modulation in any tissue, whereas, certain tissues may be in accessible to a drug either because of its chemistry or the anatomical or physiological barriers. Furthermore, RCTs are closely monitored, and followed for a fixed period, allowing for exploration of induction-times\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. MR estimates are considered to reflect a life-long exposure, but in the absence of serial assessment, possible changes across age are difficult to explore, as are disease induction-times. For these reasons we suggest that drug target MR offers a robust indication of effect direction but may not directly anticipate the effect magnitude of pharmacologically interfering with a protein. Findings such as the observed increased risk of AMD (CETP), asthma (PCSK9), Alzheimer\u0026rsquo;s in (PCSK9), therefore need to be considered in the context of both the duration of drug exposure and the potential for a drug to access the relevant tissues.\u003c/p\u003e \u003cp\u003eOur findings add to prior drug target MR analyses of CETP and PCSK9 which did not have access to genetic associations with protein concentration and weighted by downstream effects of the drug target on HDL-C or LDL-C, respectively. Here we showed consistency in the findings of MR analyses of CETP weighted through the concentration of the encoded protein (a more direct proxy of target modulation) and through HDL-C, TG, and LDL-C. Such \u0026ldquo;biomarker weighted\u0026rdquo; drug target MR should not be confused with MR analyses designed to evaluate the causal relevance of major lipid fractions; utilising genetic variants from throughout the genome\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. In the presence of post-translation pleiotropy\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e, where perturbation of a protein affects multiple downstream biomarkers, some of which may lie on the causal pathway to disease and others not, biomarker weighted drug target MRs do not provide evidence on the possible mediating pathway of the drug target on disease\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e and instead reflect drug target effects.\u003c/p\u003e \u003cp\u003eIn conclusion, previous failures of CETP inhibitors are likely related to suboptimal target inhibition (dalcetrapib), off-target effects (torcetrapib) or insufficiently long follow-up (evacetrapib). The present drug target MR analysis, consistent with findings from the anacetrapib trials, anticipates that on-target CETP inhibition decreases CVD risk. MR analyses additionally suggests a reduction in kidney disease risk, but an increased risk of age-related macular degeneration.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor\u0026rsquo;s contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAFS, ADH, CF, contributed to the idea and design of the study. AFS and NH performed the systematic-review and meta-analysis. AFS, NH, and CF performed the analyses. AFS drafted the manuscript. All authors provided critical input on the analyses and the drafted manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest statements \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAFS has received Servier funding for unrelated work. MZ conducted this research as an employee of BenevolentAI. Since completing the work MZ is now a full-time employee of GlaxoSmithKline. None of the remaining authors have a competing interest to declare. DAL has received support from Roche Diagnostics and Medtronic Ltd for research unrelated to this paper. TRG receives funding from GlaxoSmithKline and Biogen\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding and role of funding sources\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAFS is supported by BHF grant PG/18/5033837 and the UCL BHF Research Accelerator AA/18/6/34223. CF and AFS received additional support from the National Institute for Health Research University College London Hospitals Biomedical Research Centre. MGM is supported by a BHF Fellowship FS/17/70/33482. ADH is an NIHR Senior Investigator. We further acknowledge support from the Rosetrees and Stoneygate Trust. The UCLEB Consortium is supported by a British Heart Foundation Programme Grant (RG/10/12/28456). TRG receives support from the UK Medical Research Council (MC_UU_00011/4). DOMK is supported by the Dutch Science Organization (ZonMW-VENI Grant 916.14.023). Alun DH receives support from\u0026nbsp; the UK Medical Research\u0026nbsp; (MC_UU_12019/1). MK is supported by the UK Medical Research Council (MR/S011676/1, MR/R024227/1), National Institute on Aging (NIH), US (R01AG062553) and the Academy of Finland (311492). DAL is supported by a Bristol BHF Accelerator Award (AA/18/7/34219), and works in a unit that recieves support from the University of Bristol and the UK Medical Research Council (MC_UU_00011/6). DAL is a National Institute of Health Research Senior Investigator (NF-0616-10102).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGuarantor \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAmand F Schmidt performed the here presented analyses, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e:\u003c/p\u003e\n\u003cp\u003eThis research has been conducted using the UK Biobank Resource under Application Number 12113. The authors are grateful to UK Biobank participants. UK Biobank was established by the Wellcome Trust medical charity, Medical Research Council, Department of Health, Scottish Government, and the Northwest Regional Development Agency. It has also had funding from the Welsh Assembly Government and the British Heart Foundation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePrior postings and presentations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe preliminary meta-analysis of RCT data were presented at BPS 2018 by NH. The preprint version of this manuscript has been deposited on medrxiv.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data are publicly available, as described in the methods section. Please contact AFS for access to specific files, data, or analysis scripts.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eCollins, R. \u003cem\u003eet al.\u003c/em\u003e Interpretation of the evidence for the effi cacy and safety of statin therapy. \u003cem\u003eThe Lancet\u003c/em\u003e \u003cstrong\u003e388\u003c/strong\u003e, 2532\u0026ndash;2561 (2016).\u003c/li\u003e\n\u003cli\u003eCannon, C. P. \u003cem\u003eet al.\u003c/em\u003e Ezetimibe Added to Statin Therapy after Acute Coronary Syndromes. \u003cem\u003eN Engl J Med\u003c/em\u003e \u003cstrong\u003e372\u003c/strong\u003e, 2387\u0026ndash;2397 (2015).\u003c/li\u003e\n\u003cli\u003eSchmidt, A. F., Pearce, L. S., Wilkins, J. T., Casas, J. P. \u0026amp; Hingorani, A. D. 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Heart J.\u003c/em\u003e \u003cstrong\u003e39\u003c/strong\u003e, 360\u0026ndash;362 (2018).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Drug target validation, Mendelian randomization, CETP, PCSK9, drug therapy, cardiovascular disease ","lastPublishedDoi":"10.21203/rs.3.rs-78818/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-78818/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDrug development of cholesteryl ester transfer protein (CETP) inhibition to prevent coronary heart disease (CHD) has yet to deliver licensed medicines. To distinguish compound from drug target failure, we compared evidence from clinical trials and Mendelian randomization (MR) results. Findings from meta-analyses of CETP inhibitor trials (\u0026ge;\u0026thinsp;24 weeks follow-up) were used to judge between-compound heterogeneity in treatment effects. Genetic data were extracted on 190\u0026thinsp;+\u0026thinsp;pharmacologically relevant outcomes; spanning 480,698\u0026thinsp;\u0026minus;\u0026thinsp;21,770 samples and 74,124-4,373 events. Drug target MR of protein concentration was used to determine the on-target effects of CETP inhibition and compared to that of PCSK9 modulation. Fifteen eligible CETP inhibitor trials of four compounds were identified, enrolling 79,961 participants. There was a high degree of heterogeneity in effects on lipids, lipoproteins, blood pressure, and clinical events. For example, dalcetrapib and evacetrapib showed a neutral effect, torcetrapib increased, and anacetrapib decreased cardiovascular disease (CVD); heterogeneity p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001. In drug target MR analysis, lower CETP concentration (per \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\mu\\)\u003c/span\u003e\u003c/span\u003eg/ml) was associated with CHD (odds ratio 0.95; 95%CI 0.91; 0.99), heart failure (0.95; 95%CI 0.92; 0.99), chronic kidney disease (0.94 95%CI 0.91; 0.98), and age-related macular degeneration (1.69; 95%CI 1.44; 1.99). Lower PCSK9 concentration was associated with a lower risk of CHD, heart failure, atrial fibrillation and stroke, and increased risk of Alzheimer\u0026rsquo;s disease and asthma. In conclusion, previous failures of CETP inhibitors are likely compound related. CETP inhibition is expected to reduce risk of CHD, heart failure, and kidney disease, but potentially increase risk of age-related macular disease.\u003c/p\u003e","manuscriptTitle":"Cholesteryl Ester Transfer Protein as a Drug Target for Cardiovascular Disease","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2020-10-01 17:18:31","doi":"10.21203/rs.3.rs-78818/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nature-communications","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"NCOMMS","sideBox":"Learn more about [Nature Communications](http://www.nature.com/ncomms/)","snPcode":"","submissionUrl":"https://mts-ncomms.nature.com/","title":"Nature Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature Communications","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"75bd2bc1-ef1a-4181-b265-08566dbf1cd0","owner":[],"postedDate":"October 1st, 2020","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":667169,"name":"Drug Discovery, Design, \u0026 Development"},{"id":667170,"name":"Cardiac \u0026 Cardiovascular Systems"}],"tags":[{"value":"feature-on-social-media","date":"2020-09-29 21:50:08"}],"updatedAt":"2021-11-22T15:33:20+00:00","versionOfRecord":{"articleIdentity":"rs-78818","link":"https://doi.org/10.1038/s41467-021-25703-3","journal":{"identity":"nature-communications","isVorOnly":false,"title":"Nature Communications"},"publishedOn":"2021-09-24 04:00:00","publishedOnDateReadable":"September 24th, 2021"},"versionCreatedAt":"2020-10-01 17:18:31","video":"","vorDoi":"10.1038/s41467-021-25703-3","vorDoiUrl":"https://doi.org/10.1038/s41467-021-25703-3","workflowStages":[]},"version":"v1","identity":"rs-78818","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-78818","identity":"rs-78818","version":["v1"]},"buildId":"_2-kVJe1T_tPrBINL-cwx","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}