Abstract
Background We characterised the phenotypic consequence of genetic variation at the PCSK9 locus and compared findings with recent trials of pharmacological inhibitors of PCSK9.
Methods Published and individual participant level data (300,000+ participants) were combined to construct a weighted PCSK9 gene-centric score (GS). Fourteen randomized placebo controlled PCSK9 inhibitor trials were included, providing data on 79,578 participants. Results were scaled to a one mmol/L lower LDL-C concentration
Results The PCSK9 GS (comprising 4 SNPs) associations with plasma lipid and apolipoprotein levels were consistent in direction with treatment effects. The GS odds ratio (OR) for myocardial infarction (MI) was 0.53 (95%CI 0.42; 0.68), compared to a PCSK9 inhibitor effect of 0.90 (95%CI 0.86; 0.93). For ischemic stroke ORs were 0.84 (95%CI 0.57; 1.22) for the GS, compared to 0.85 (95%CI 0.78; 0.93) in the drug trials. ORs with type 2 diabetes mellitus (T2DM) were 1.29 (95% CI 1.11; 1.50) for the GS, as compared to 1.00 (95%CI 0.96; 1.04) for incident T2DM in PCSK9 inhibitor trials. No genetic associations were observed for cancer, heart failure, atrial fibrillation, chronic obstructive pulmonary disease, or Alzheimer’s disease – outcomes for which large-scale trial data were unavailable.
Conclusions Genetic variation at the PCSK9 locus recapitulates the effects of therapeutic inhibition of PCSK9 on major blood lipid fractions and MI. Apparent discordance between genetic associations and trial outcome for T2DM might be explained lack by a of statistical precision, or differences in the nature and duration of genetic versus pharmacological perturbation of PCSK9.
Funding This research was funded by the British Heart Foundation (SP/13/6/30554, RG/10/12/28456, FS/18/23/33512), UCL Hospitals NIHR Biomedical Research Centre, by the Rosetrees and Stoneygate Trusts.
Condensed abstract Evidence on the long-term efficacy and safety of therapeutic inhibition of PCSK9 is lacking. To explore potential long-term effects of PCSK9 inhibition, we characterised the phenotypic consequence of LDL-cholesterol lowering variants at the PCSK9 locus. A PCSK9 gene score comprising 4 SNPs recapitulated the effects of therapeutic inhibition of PCSK9 on major blood lipid fractions and risk of myocardial infarction, and was associated with an increased risk of type 2 diabetes. No associations with safety outcomes such as cancer, COPD, Alzheimer’s disease or atrial fibrillation were identified. Our findings suggest PCSK9 inhibition may be safe and effective during prolonged use.
Introduction
Statins and ezetimibe reduce the risk of major coronary events and ischemic stroke via lowering of low density lipoprotein-cholesterol (LDL-C) (1–3). Loss-of-function mutations in PCSK9 are associated with lower LDL-C and a reduced risk of coronary heart disease (CHD)(4, 5). Antibodies (mAbs) inhibiting PCSK9, reduce LDL-C in patients with hypercholesterolaemia, and received market access in 2015. The FOURIER and ODYSSEY OUTCOMES trials tested the efficacy of PCSK9-inhibition versus placebo on the background of statin treatment and both found that PCSK9 inhibition led to a 15% relative risk reduction of major vascular events in patients with established CVD and recent acute coronary syndrome over a median follow up of 2.2 or 2.8 years(6, 7).
Evidence is limited on the effect of PCSK9 inhibition on clinical outcomes, and on safety outcomes that might only become apparent with prolonged use. Nor is evidence available on the efficacy and safety of PCSK9 inhibitors in subjects other than the high-risk patients studied in trials. Mendelian randomisation for target validation uses naturally-occurring variation in a gene encoding a drug target to identify mechanism-based consequences of pharmacological modification of the same target(8). Such studies have previously proved useful in predicting success and failure in clinical trials and have assisted in delineating on-target from off-target actions of first-in-class drugs(9–13). For example, previous studies showed that variants in HMGCR, encoding the target for statins, were associated with lowering concentrations of LDL-C and lower risk of coronary heart disease(9) (CHD), while confirming the on-target nature of the effect of statins on higher body weight and higher risk of type 2 diabetes (T2DM)(9).
We characterised the phenotypic consequences of genetic variation at PCSK9 in a large, general population sample focussing on therapeutically relevant biomarkers, cardiovascular disease (CVD), individual CVD components and non-CVD outcomes such as cancer, Alzheimer’s disease, and chronic obstructive pulmonary disease (COPD). Effect estimates from the genetic analysis were compared to those from intervention trials where the outcomes under evaluation overlapped.
Methods
We summarise methods briefly here as they have been previously described in detail(14).
Genetic variant selection
SNPs rs11583680 (minor allele frequency [MAF] = 0.14), rs11591147 (MAF = 0.01), rs2479409 (MAF = 0.36) and rs11206510 (MAF = 0.17) were selected as genetic instruments at the PCSK9 locus based on the following criteria: (1) an LDL-C association as reported by the Global Lipids Genetics Consortium (GLGC)14; (2) low pairwise linkage disequilibrium (LD) (r2 ≤0.30) with other SNPs in the region (based on 1,000 Genomes CEU data); and (3) the combined annotation dependent depletion (CADD) score(15) which assesses potential functionality (see Appendix table 1).
Previously, we explored the between-SNP correlations (see Appendix figure 1 of Schmidt et al 2017(14)), revealing an r-squared of 0.26 between rs11206510 and rs11583680, confirming all other SNPs were approximately independent (r2 ≤ 0.07). Subsequent adjustment for the residual LD (correlation) structure did not impact results (see Appendix figure 90 of Schmidt et al 2017(14)).
Individual participant-level and summary-level data
Participating studies (Appendix table 2) provided analyses of individual participant-level data (IPD) based on a common analysis script (available from AFS), submitting summary estimates to the UCL analysis centre. These data were supplemented with public domain data from relevant genetic consortia (Appendix table 3). Studies contributing summary estimates to genetic consortia were excluded from the IPD component of the analysis to avoid duplication.
Biomarker data were collected on the major routinely measured blood lipids (LDL-C, HDL-C, triglycerides [TG], total cholesterol [TC]); apolipoproteins A1 [ApoA1] and B [ApoB], and nominal lipoprotein (Lp)(a); systolic (SBP) and diastolic (DBP) blood pressure; inflammation markers C-reactive protein (CRP), interleukin-6 (IL-6), and fibrinogen; haemoglobin; glycated haemoglobin (HbA1c); liver enzymes gamma-glutamyltransferase (GGT), alanine aminotransferase (ALT), aspartate transaminase (AST), and alkaline phosphatase (ALP); serum creatinine, and cognitive function (standardized to mean 0, and standard deviation 1, see Appendix table 5).
We focussed on individual clinical endpoints, rather than composites, which have been assessed in outcome trials, as well as disease end-points commonly seen in patients likely to be eligible for PCSK9 inhibitor treatment. Ischemic CVD endpoints studied were myocardial infarction (MI), ischemic stroke, revascularization, and angina. The following non-ischemic CVD events were considered: haemorrhagic stroke, heart failure, and atrial fibrillation. Non-CVD outcome data was collected on common chronic diseases: COPD, any cancer (including those of the breast, prostate, colon and lung), Alzheimer’s disease, and T2DM.
Finally, aggregated trial data on the effect of monoclonal PCSK9 inhibitors were compared to placebo for MI, revascularization, ischemic or haemorrhagic stroke, cancer, and T2DM abstracted from the Cochrane systematic review(6). We compared effects on biomarkers and clinical endpoints common to both the genetic analysis and trials.
Statistical analyses
In all analyses, we assumed an additive allele effect with genotypes coded as 0, 1 and 2, corresponding to the number of LDL-C lowering alleles. Continuous biomarkers were analysed using linear regression and binary endpoints using logistic regression. Study-specific associations were pooled for each SNP using the inverse variance weighted method for fixed effect meta-analysis. Study-specific associations were excluded if the SNP was not in Hardy-Weinberg equilibrium (see Appendix Table 4, based on a Holm-Bonferroni alpha criterion). We estimated the effect at the PCSK9 locus by combining all four SNPs in a gene centric score (GS).
Trial data were assembled as per Schmidt et al. 2017(6). Briefly, systematic searches were performed using the Cochrane Central Register of Controlled Trials (CENTRAL), MEDLINE, Embase, Web of Science registries, Clinicaltrials.gov and the International Clinical Trials Registry Platform databases. Data from placebo controlled trials were extracted and combined using the inverse variance weighted method for continuous data and a random-intercept logistic regression model for binary data(6).
Results are presented as mean differences (MD) or odds ratios (OR) with 95% confidence intervals (CI). Analyses were conducted using the statistical programme R version 3.4.1(16).
Role of the funding source
The funder(s) of the study played no role in study design, data collection, data analysis, data interpretation, or writing of the report. AFS had full access to all the data in the study and shares final responsibility for the decision to submit for publication with all authors.
Local ethics committees for studies contributing data to these analyses granted approval for the work.
Results
Participant level data were available from up to 246,355 individuals, and were supplemented by summary effect estimates from data repositories, resulting in a sample size of 320,170 individuals, including 95,865 cases of MI, 16,437 stroke, 11,920 ischemic stroke, 51,623 T2DM, 54,702 cancer, 25,630 Alzheimer’s disease and 12,412 of COPD.
Lipid and apolipoprotein associations
As reported previously(14), the four PCSK9 SNPs were associated with lower LDL-C blood concentrations ranging from −0.02 mmol/L (95%CI −0.03; −0.02) per allele for rs11583680 to −0.34 mmol/L (95%CI −0.36; −0.32) for rs11591147 (See Appendix Figure 1). PCSK9 SNPs associated with a lower LDL-C concentration were also associated with lower concentrations of apolipoprotein B proportionate to the LDL-C association.
Associations of the GS with the other lipids or apolipoproteins, scaled to a 1 mmol/L lower LDL-C were (Table 1): 0.05 mmol/L (95%CI 0.02; 0.07) for HDL-C, −0.07 mmol/L (95%CI −0.12; −0.01) for TG, −1.06 mmol/L (95%CI −1.12; −1.00) for TC, −0.20 g/L (95%CI −0.25; -0.18) for ApoB, 0.02 g/L (95%CI −0.01; 0.06) for ApoA1, and −4.12 mg/dL (95%CI −8.62; 0.38) for Lp(a).
The associations of the PCSK9 GS with blood-based lipid markers were directionally concordant with effects from treatment trials of therapeutic inhibition of PCSK9 (Figure 1).
Genetic associations with other biochemical and physiological measures
The GS estimates with SBP and DBP were 0.03 mmHg (95%CI −0.05; 0.10) and 0.08 mmHg (95%CI 0.0001; 0.15), respectively, per 1 mmol/L lower LDL-C. The PCSK9 GS was associated with nominally lower ALP (IU/L) −0.06 (95%CI −0.09; −0.02), but not with other liver enzymes (Table 1).
Genetic associations with ischemic cardiovascular events
The PCSK9 GS was associated with a lower risk of MI (OR 0.53; 95%CI 0.42; 0.68; 95,865 cases), which was directionally consistent with results from placebo-controlled PCSK9 inhibition trials: OR 0.90 (95%CI 0.86; 0.93), with both estimates scaled to a 1 mmol/L lower LDL-C (Figures 2 and 3). The genetic effect estimate for ischemic stroke was OR 0.84 (95%CI 0.57; 1.22; 11,920 cases), concordant in direction to that of the drugs trials (OR 0.85 95%CI 0.78; 0.93). Similarly, the PCSK9 GS association with coronary revascularization (OR 0.75 95%CI 0.44; 1.27) was directionally consistent with the PCSK9 inhibitor trials (OR 0.90; 95%CI 0.86, 0.93) (Figure 3).
Genetic associations with non-ischemic cardiovascular disease
The point estimate for the GS association with hemorrhagic stroke (Figure 2), OR 1.29 (95%CI 0.76; 2.19), was discordant to the estimate from PCSK9 inhibitor trials (OR 0.96 95%CI 0.75; 1.23) (Figure 3), although the confidence intervals overlapped. Comparing the association of PCSK9 GS with hemorrhagic and ischemic stroke indicated the GS had a differential effect (p-value = 0.02). No PCSK9 GS association was observed with atrial fibrillation (OR 0.92 95%CI 0.72; 1.18; 41 485 cases), or heart failure (OR 1.06 95%CI 0.88; 1.28; 23,763 cases) (Figure 2).
Associations with non-cardiovascular disease and related biomarkers
The PCSK9 GS was not associated with the risk of any cancer (OR 0.97: 95%CI 0.81; 1.17; 54,702 cases, see Figure 4), nor with any of 13 specific types of cancer (Appendix Figure 2). We did not observe an association with either Alzheimer’s disease or cognitive performance: for Alzheimer’s the OR was 0.91 (95%CI 0.55; 1.51) and for cognition (per standard deviation) −0.03 (95%CI −0.22; 0.16). As reported before(14) the GS was associated with T2DM (OR 1.00 95%CI 1.11; 1.50) (Figure 4), higher body weight (1.03 kg; 95%CI 0.24; 1.82), waist to hip ratio 0.006 (95%CI 0.003; 0.011) and fasting glucose 0.09 mmol/L (95%CI 0.02; 0.15). The OR for COPD was 0.89 (95%CI 0.67; 1.18).
Discussion
The genetic findings presented here show that variation in PCSK9 is associated with lower circulating LDL-C and apoB concentrations, lower risk of MI and, with lesser confidence, the risk of ischemic stroke and coronary revascularization. These effects are consistent in direction to effects observed in the first PCSK9 inhibitor trial’s(17).
A recent systematic review of trial data(18) indicated a potential dysglycaemic effect from PCSK9 inhibition. PCSK9 monoclonal antibodies were associated with increased fasting glucose (0.17 as standardized mean difference [SMD] 95%CI 0.14; 0.19) and glycosylated haemoglobin (0.10 SMD 95%CI 0.07; 0.12)(18). Recently we, and others, showed natural genetic variation PCSK9 was associated with elevated fasting glucose and T2DM(14, 19, 20) and that variation at other LDL-C-associated loci also influence risk of T2DM(21, 22). However, the FOURIER and ODYSSEY OUTCOMES trials, the largest treatment trials of PCSK9 inhibitors to date, did not find an association with risk of incident T2DM, at a median follow up of 2.2 and 2.8 years respectively. It is possible this reflects a genuine discordance between the findings from trials and genetic analyses. Alternatively, the exposure durations in the two largest trials may simply have been too short for subjects to develop T2DM. The risk increasing effect of statins on T2DM was only apparent after conducting a meta-analysis of 13 statin trials in which 4,278 T2DM cases were observed during an average follow-up of 4 years(23).
In general, inconsistencies between associations of variants in a gene encoding a drug target and the effects of the corresponding treatment are possible on a number of theoretical grounds. The effects of genetic variation (present from conception) may be mitigated by developmental adaptation or environmental changes. A null association of a genetic variant with an outcome therefore does not preclude an effect of a treatment administered in later life, when adaptive responses may no longer be available, or in the presence of a particular environment (24). We selected a subset of all genetic variants at PCSK9 that capture information on many others and which have some annotated function. However, other approaches to more fully capture the entire gene-centric effect are worthy of future investigation(25).
The association of PCSK9 variants with LDL-C and MI has been reported before(5), and was a motivating factor for the development of PCSK9 inhibiting drugs. Lotta and colleagues(19) reported a similar OR for MI of 0.60 (95%CI 0.48; 0.75) per 1 mmol/L decrease in LDL-C using the PCSK9 rs11591147 SNP. Using a seven SNP PCSK9 GS, Ference et al. reported a MI OR of 0.44 (95%CI 0.31; 0.64) per 1 mmol/L decrease in LDL-C(20). These scaled genetic effects are larger than the treatment effect observed in trials which others have noted previously(26), and ascribed to the lifelong effect of genetic variation versus the short-term effect of drug treatment in later life.
The available trial data showed PCSK9 inhibitors had a similar effect on MI (OR 0.90, 95%CI 0.86; 0.93) and ischemic stroke (OR 0.85 95%CI 0.78; 0.93). By contrast, the genetic analysis indicated a directionally concordant, but larger effect on MI (OR 0.53; 95%CI 0.42; 0.68) than ischemic stroke, (OR 0.84 95%CI 0.57; 1.22). The genetic analysis was, however, based on only 11,920 stroke cases, about one-fifth of the number of cases available for the genetic analysis of MI and as such confidence interval overlapped. We did observe a significantly differential association between PCKS9 SNPs and ischemic and hemorrhagic stroke (interaction p-value = 0.02). Findings from statin trials previously suggested LDL-C lowering through inhibition of HMG-coA reductase is associated with a reduced risk of ischemic but potentially increased risk of hemorrhagic stroke(27–29). Our findings suggest that a different effect on ischemic and hemorrhagic stroke subtypes may be eventually identified for PCSK9 inhibitors.
Despite previous concerns about a potential effect of this class of drugs on cognition(30), the genetic analysis did not reveal a significant association of the four PCSK9 variants with cognitive function or Alzheimer’s disease, nor with COPD or cancer, though this does not preclude an effect on such outcomes from drug treatment given in later life. While we explored the associations with any cancer (54702 events) as well as 12 individual cancer sites (Appendix Figure 2), we did not have data on some clinically relevant cancer types such as endometrial cancer.
This neutral effect on cognition has been recently reported by the EBBINGHAUS study, nested within the FOURIER trial, which reported a non-significant PCSK9 inhibitor effect on multiple measures of cognition confirming (using a non-inferiority design) an absence of effect(30); it should be noted that similar to the FOURIER and EBBINGHAUS (nested within the FOURIER) studies had similarly short follow-up. The absence of an effect on cognition during PCSK9 inhibitor treatment was also observed in the ODYSSEY OUTCOMES trial, which had a median follow-up(31) of 2.8 years.
Drugs (even apparently specific monoclonal antibodies) can exert actions on more than one protein if such targets belong to a family of structurally similar proteins. PCSK9, for example, is one of nine related proprotein convertases(32). Such ‘off-target’ actions, whether beneficial or deleterious, would not be shared by variants in the gene encoding the target of interest. In addition, monoclonal antibodies clear circulating PCSK9 from the blood and should not, in theory, influence any intracellular action of the protein(33).
Genetic association studies of the type conducted here tend to examine the risk of a first clinical event, whereas clinical trials such as ODYSSEY OUTCOMES focus on patients with established disease, where mechanisms may be modified. Proteins influencing the risk of a first event may also influence the risk of subsequent events, as observed in the case of the target of statin drugs that are effective in both primary and secondary prevention(1). For this and other reasons(34–36), examination of the effects of PCSK9 variants on the risk of subsequent CHD events in patients with established coronary atherosclerosis is the subject of a separate analysis led by the GENIUS-CHD consortium(36).
In conclusion, PCSK9 SNPs associated with lower LDL-C predict a substantial reduction in the risk of MI and concordant associations with a reduction in risk of ischemic stroke, but with a modestly increased risk of T2DM. We did not observe significant associations with other non-cardiovascular safety outcomes such as cancer, COPD, Alzheimer’s disease or atrial fibrillation. These findings suggest that it is likely that PCSK9 inhibitor therapy will not have any clinically meaningful effect on non-vascular diseases but it may increase risk of T2DM.
Authors’ contributions
Amand F Schmidt, Daniel I Swerdlow, Michael V Holmes, Riyaz S Patel, Folkert W Asselbergs, Juan-Pablo Casas, Brendan J Keating, Aroon D Hingorani, David Preiss, Naveed Sattar contributed to the idea and design of the study. Amand F Schmidt, Daniel I Swerdlow, Michael V Holmes, designed the analysis scripts shared with individual centres. Amand F Schmidt performed the meta-analysis and had access to all the data. Amand F Schmidt, Aroon D Hingorani, and Juan-Pablo Casas drafted the initial manuscript. Michael V Holmes, Riyaz S Patel, Folkert W. Asselbergs, Brendan J Keating, David Preiss, Naveed Sattar, Daniel I Swerdlow, Zammy Fairhurst-Hunter, Fernando Pires Hartwig, Bernardo Lessa Horta, Elina Hypponen, Christine Power, Max Moldovan, Erik van Iperen, Kees Hovingh, Ilja Demuth, Kristina Norman, Elisabeth Steinhagen-Thiessen, Juri Demuth, Lars Bertram, Christina M Lill, Stefan Coassin, Johann Willeit, Stefan Kiechl, Karin Willeit, Dan Mason, John Wright, Richard Morris, Goya Wanamethee, Peter Whincup, Yoav Ben-Shlomo, Stela McLachlan, Jackie F. Price, Mika Kivimaki, Catherine Welch, Aida Sanchez, Pedro Marques-Vidal, Andrew Nicolaides, Andrie G. Panayiotou, N. Charlotte Onland-Moret, Yvonne T. van der Schouw, Giuseppe Matullo, Giovanni Fiorito, Simonetta Guarrera, Carlotta Sacerdote, Nicholas J Wareham, Claudia Langenberg, Robert A Scott, Jian’an Luan, Martin Bobak, Sofia Malyutina, Andrzej Pajak, Ruzena Kubinova, Abdonas Tamosiunas, Hynek Pikhart, Niels Grarup, Oluf Pedersen, Torben Hansen, Allan Linneberg, Tine Jess, Jackie Cooper, Steve E Humphries, Murray Brilliant, Terrie Kitchner, Hakon Hakonarson, David S. Carrell, Catherine A. McCarty, Kirchner, H Lester, Eric B. Larson, David R. Crosslin, Mariza de Andrade, Dan M Roden, Joshua C Denny, Cara Carty, Stephen Hancock, John Attia, Elizabeth Holliday, Rodney Scott, Peter Schofield, Martin O’Donnell, Salim Yusuf, Michael Chong, Guillaume Pare, Pim van der Harst, M. Abdullah Said, Ruben N. Eppinga, Niek Verweij, Harold Snieder, Tim Christen, D.O. Mook-Kanamori, Stefan Gustafsson, Lars Lind, Erik Ingelsson, Raha Pazoki, Oscar Franco, Albert Hofman, Andre Uitterlinden, Abbas Dehghan, Alexander Teumer, Sebastian Baumeister, Marcus Dörr, Markus M. Lerch, Uwe Völker, Henry Völzke, Joey Ward, Jill P Pell, Tom Meade, Ingrid E. Christophersen, Anke H. Maitland-van der Zee, Ekaterina V. Baranova, Robin Young, Ian Ford, Archie Campbell, Sandosh Padmanabhan, Michiel L Bots, Diederick E. Grobbee, Philippe Froguel, Dorothée Thuillier, Ronan Roussel, Amelie Bonnefond, Bertrand Cariou, Melissa Smart, Yanchun Bao, Meena Kumari, Anubha Mahajan, Paul M Ridker, Daniel I. Chasman, Alex P. Reiner, Marylyn D Ritchie, Leslie A Lange, Chris Finan, Ghazaleh Fatemifar, Rupert Faraway, Spiros Denaxas, Harry Hemingway, Richard Houlston, Alex J. Cornish, Sara E. Dobbins, Kari Hemminki, Ben Kinnersley, Marc Sanson, Karim Labreche, Matthias Simon, Melissa Bondy, Philip Law, Helen Speedy, James Allan, Ni Li, Molly Went, Niels Weinhold, Gareth Morgan, Pieter Sonneveld, Björn Nilsson, Hartmut Goldschmidt, Kari Hemminki, Amit Sud, Andreas Engert, Markus Hansson, Dennis Valentine, Jemma C. Hopewell, Sudha Seshadri, Caroline Dale, and Rui Providencia E Costa were responsible for study specific analyses and/or critically revised the manuscript.
Conflict of interest statements
Dr Holmes has collaborated with Boehringer Ingelheim in research, and in accordance with the policy of the The Clinical Trial Service Unit and Epidemiological Studies Unit (University of Oxford), did not accept any personal payment. David Preiss consulted for Amgen on a single occasion but, in accordance with the policy of the Clinical Trial Service Unit (University of Oxford), did not accept any personal payment. He is an investigator on a clinical trial of the PCSK9 synthesis inhibitor, inclisiran, funded by a grant to the University of Oxford by the Medicines Company, but he receives no personal fees from this grant. Aroon Hingorani and Harry Hemingway are National Institute for Health Research Senior Investigators. Naveed Sattar consulted for Amgen and Sanofi related to PCSK9 inhibitors; and was an investigator on clinical trials of PCSK9 inhibition funded by Amgen. Naveed Sattar has also consulted for Boehringer Ingelheim, Janssen, Eli-Lilly and NovoNordisk. Daniel Swerdlow has consulted to Pfizer for work unrelated to this paper. Folkert W. Asselbergs is supported by a Dekker scholarship-Junior Staff Member 2014T001 – Netherlands Heart Foundation and UCL Hospitals NIHR Biomedical Research Centre. Kees Hovingh or his institution (AMC) received honoraria for consultancy, ad boards, and/or conduct of clinical trials from: AMGEN, Aegerion, Pfizer, Astra Zeneca, Sanofi, Regeneron, KOWA, Ionis pharmaceuticals and Cerenis. Bertrand Cariou has received research funding from Pfizer and Sanofi, received honoraria from AstraZeneca, Pierre Fabre, Janssen, Eli-Lilly, MSD Merck & Co., Novo-Nordisk, Sanofi, and Takeda, and has acted as a consultant/advisory panel member for Amgen, Eli Lilly, Novo-Nordisk, Sanofi, and Regeneron. Andrzej Pająk acted as a consultant/advisory pannel member for Amgen. Erik Ingelsson is a scientific advisor for Precision Wellness and Olink Proteomics for work unrelated to this paper. JCH is a scientific advisor to a clinical trial of PCSK9 inhibition. AE Honoraria: Takeda, BMS, Amgen; Consulting: Takeda, BMS, Amgen. SEH acknowledges BHF funding (PG008/08) and support from the UCL BRC. All other authors declare no competing interests.
Funding and role of funding sources
This research has been funded by the British Heart Foundation (SP/13/6/30554, RG/10/12/28456). The work was also supported by UCL Hospitals NIHR Biomedical Research Centre and by the Rosetrees and Stoneygate Trusts. 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. We thank the International Genomics of Alzheimer’s Project (IGAP) for providing summary results data for these analyses. The investigators within IGAP contributed to the design and implementation of IGAP and/or provided data but did not participate in analysis or writing of this report. IGAP was made possible by the generous participation of the control subjects, the patients, and their families. The i–Select chips was funded by the French National Foundation on Alzheimer’s disease and related disorders. EADI was supported by the LABEX (laboratory of excellence program investment for the future) DISTALZ grant, Inserm, Institut Pasteur de Lille, Université de Lille 2 and the Lille University Hospital. GERAD was supported by the Medical Research Council (Grant n° 503480), Alzheimer’s Research UK (Grant n° 503176), the Wellcome Trust (Grant n° 082604/2/07/Z) and German Federal Ministry of Education and Research (BMBF): Competence Network Dementia (CND) grant n° 01GI0102, 01GI0711, 01GI0420. CHARGE was partly supported by the NIH/NIA grant R01 AG033193 and the NIA AG081220 and AGES contract N01–AG–12100, the NHLBI grant R01 HL105756, the Icelandic Heart Association, and the Erasmus Medical Center and Erasmus University. ADGC was supported by the NIH/NIA grants: U01 AG032984, U24 AG021886, U01 AG016976, and the Alzheimer’s Association grant ADGC–10–196728. We acknowledge the International Consortium for Blood Pressure Genome-Wide Association Studies (Nature. 2011 Sep 11;478(7367):103-9, Nat Genet. 2011 Sep 11;43(10):1005-11).This work was supported in part by Deutsche Forschungsgemeinschaft (DFG Az Si 552/2), the University of Bonn (BONFOR O-126.0030), and Deutsche Krebshilfe (70/2385/WI2, 70/3163/WI3; PI Prof. J Schramm, Dept. Bloodwise provided funding for the study (LRF05001, LRF06002 and LRF13044) with additional support from Cancer Research UK (C1298/A8362 supported by the Bobby Moore Fund) and the Arbib Fund. Wellcome Trust [064947/Z/01/Z and 081081/Z/06/Z]; from the National Institute on Aging [1R01 AG23522-01]; and the MacArthur Foundation “MacArthur Initiative on Social Upheaval and Health” [71208]. The British Women’s Heart and Health Study is supported by the British Heart Foundation (PG/13/66/30442). Data on mortality and cancer events were routinely provided from NHS Digital to the BWHHS under data sharing agreement MR104a-Regional Heart Study (Female Cohort). British Women’s Heart and Health Study data are available to bona fide researchers for research purposes. Please refer to the BWHHS data sharing policy at www.ucl.ac.uk/british-womens-heart-health-study. Hartmut Goldschmidt acknowledges support from the Deutsche Krebshilfe, the Dietmar Hopp Foundation and the German Ministry of Education and Science (BMBF: CLIOMMICS (01ZX1309), Deutsche Krebshilfe, the Dietmar Hopp Foundation and the German Ministry of Education and Science (BMBF: CLIOMMICS (01ZX1309). The Novo Nordisk Foundation Center for Basic Metabolic Research is an independent Research Center at the University of Copenhagen partially funded by an unrestricted donation from the Novo Nordisk Foundation (www.metabol.ku.dk). This study was supported by the Farr Institute of Health Informatics Research, funded by The Medical Research Council (MR/K006584/1), in partnership with Arthritis Research UK, the British Heart Foundation, Cancer Research UK, the Economic and Social Research Council, the Engineering and Physical Sciences Research Council, the National Institute of Health Research, the National Institute for Social Care and Health Research (Welsh Assembly Government), the Chief Scientist Office (Scottish Government Health Directorates) and the Wellcome Trust. Christina M Lill is supported by the Possehl foundation, Renate MaaΒ Foundation. Mika Kivimaki was supported by MRC (K013351 and R024227) and a Helsinki Institute of Life Science fellowship. Michael Holmes is supported by a British Heart Foundation Intermediate Clinical Research Fellowship (FS/18/23/33512) and the National Institute for Health Research Oxford Biomedical Research Centre.
The funding sources had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.
Ethics committee approval
Local ethics committees for studies contributing data to these analyses granted approval for the work.
Guarantor
Amand F Schmidt performed the 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.