Author + information
- Received July 20, 2016
- Revision received September 21, 2016
- Accepted October 4, 2016
- Published online December 19, 2016.
- Connor A. Emdin, DPhila,b,
- Amit V. Khera, MDa,b,
- Pradeep Natarajan, MDa,b,
- Derek Klarin, MDa,b,
- Hong-Hee Won, PhDc,
- Gina M. Peloso, PhDb,d,
- Nathan O. Stitziel, MD, PhDe,
- Akihiro Nomura, MDa,b,
- Seyedeh M. Zekavat, BSca,b,
- Alexander G. Bick, MDb,
- Namrata Gupta, PhDb,
- Rosanna Asselta, PhDf,
- Stefano Duga, PhDf,
- Piera Angelica Merlini, MDg,
- Adolfo Correa, MDh,
- Thorsten Kessler, MDi,j,
- James G. Wilson, MDk,
- Matthew J. Bown, MDl,
- Alistair S. Hall, MDm,
- Peter S. Braund, PhDl,
- Nilesh J. Samani, MDl,
- Heribert Schunkert, MDi,
- Jaume Marrugat, MD, PhDn,
- Roberto Elosua, MD, PhDn,
- Ruth McPherson, MDo,
- Martin Farrall, PhDp,
- Hugh Watkins, MD, PhDp,
- Cristen Willer, PhDq,
- Gonçalo R. Abecasis, PhDr,
- Janine F. Felix, MD, PhDs,
- Ramachandran S. Vasan, MDt,u,
- Eric Lander, PhDb,
- Daniel J. Rader, MDv,
- John Danesh, MSc, DPhilw,x,y,
- Diego Ardissino, MDz,aa,
- Stacey Gabriel, PhDb,
- Danish Saleheen, MDbb,
- Sekar Kathiresan, MDa,b,∗ (, )
- CHARGE–Heart Failure Consortium,
- CARDIoGRAM Exome Consortium
- aCenter for Human Genetic Research, Cardiovascular Research Center and Cardiology Division, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
- bProgram in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts
- cSamsung Advanced Institute for Health Sciences and Technology, Sungkyunkwan University, Samsung Medical Center, Seoul, Republic of Korea
- dDepartment of Biostatistics, Boston University School of Public Health, Boston, Massachusetts
- eDepartments of Medicine, Genetics, and the McDonnell Genome Institute, Washington University School of Medicine, St. Louis, Missouri
- fDepartment of Biomedical Sciences, Humanitas University and Humanitas Clinical and Research Center, Milan, Italy
- gOspedale Niguarda, Milan, Italy
- hJackson Heart Study, Department of Medicine, University of Mississippi Medical Center, Jackson, Mississippi
- iDeutsches Herzzentrum München, Technische Universität München, Deutsches Zentrum für Herz-Kreislauf-Forschung, München, Germany
- jMunich Heart Alliance, München, Germany
- kDepartment of Physiology and Biophysics, University of Mississippi Medical Center, Jackson, Mississippi
- lDepartments of Cardiovascular Sciences and NIHR Leicester Cardiovascular Biomedical Research Unit, University of Leicester, Leicester, United Kingdom
- mLeeds Institute of Cardiovascular and Metabolic Medicine, Leeds University, Leeds, United Kingdom
- nCardiovascular Epidemiology & Genetics, IMIM (Hospital del Mar Research Institute), Barcelona, Spain
- oUniversity of Ottawa Heart Institute, Ottawa, Ontario, Canada
- pDivision of Cardiovascular Medicine, Radcliffe Department of Medicine and the Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, United Kingdom
- qDepartment of Computational Medicine and Bioinformatics, Department of Human Genetics, and Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan
- rCenter for Statistical Genetics, Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, Michigan
- sDepartment of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
- tNational Heart, Lung, and Blood Institute's and Boston University's Framingham Heart Study, Framingham, Massachusetts
- uSections of Cardiology, Preventive Medicine and Epidemiology, Department of Medicine, Boston University Schools of Medicine and Public Health, Boston, Massachusetts
- vDepartment of Genetics, University of Pennsylvania, Philadelphia, Pennsylvania
- wPublic Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
- xWellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, United Kingdom
- yNational Institute for Health Research Blood and Transplant Research Unit in Donor Health and Genomics, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
- zDivision of Cardiology, Azienda Ospedaliero-Universitaria di Parma, University of Parma, Parma, Italy
- aaASTC: Associazione per lo Studio Della Trombosi in Cardiologia, Pavia, Italy
- bbBiostatistics and Epidemiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
- ↵∗Reprint requests and correspondence:
Dr. Sekar Kathiresan, Cardiovascular Research Center & Center for Human Genetics, Massachusetts General Hospital, 185 Cambridge Street, CPZN 5.252, Boston, Massachusetts 02114.
Background Genomic analyses have suggested that the LPA gene and its associated plasma biomarker, lipoprotein(a) (Lp[a]), represent a causal risk factor for coronary heart disease (CHD). As such, lowering Lp(a) levels has emerged as a therapeutic strategy. Beyond target identification, human genetics may contribute to the development of new therapies by defining the full spectrum of beneficial and adverse consequences and by developing a dose–response curve of target perturbation.
Objectives The goal of this study was to establish the full phenotypic impact of LPA gene variation and to estimate a dose–response curve between genetically altered plasma Lp(a) and risk for CHD.
Methods We leveraged genetic variants at the LPA gene from 3 data sources: individual-level data from 112,338 participants in the U.K. Biobank; summary association results from large-scale genome-wide association studies; and LPA gene sequencing results from case subjects with CHD and control subjects free of CHD.
Results One SD genetically lowered Lp(a) level was associated with a 29% lower risk of CHD (odds ratio [OR]: 0.71; 95% confidence interval [CI]: 0.69 to 0.73), a 31% lower risk of peripheral vascular disease (OR: 0.69; 95% CI: 0.59 to 0.80), a 13% lower risk of stroke (OR: 0.87; 95% CI: 0.79 to 0.96), a 17% lower risk of heart failure (OR: 0.83; 95% CI: 0.73 to 0.94), and a 37% lower risk of aortic stenosis (OR: 0.63; 95% CI: 0.47 to 0.83). We observed no association with 31 other disorders, including type 2 diabetes and cancer. Variants that led to gain of LPA gene function increased the risk for CHD, whereas those that led to loss of gene function reduced the CHD risk.
Conclusions Beyond CHD, genetically lowered Lp(a) levels are associated with a lower risk of peripheral vascular disease, stroke, heart failure, and aortic stenosis. As such, pharmacological lowering of plasma Lp(a) may influence a range of atherosclerosis-related diseases.
Lipoprotein(a) (Lp[a]) is a circulating lipoprotein in which the constituent apolipoprotein B on a low-density lipoprotein (LDL) particle is modified by the covalent addition of another protein, namely apolipoprotein(a) (1,2). Higher plasma Lp(a) levels are associated with an increased risk for incident coronary heart disease (CHD) (3), heritable, and largely determined by variation in the LPA gene, which encodes apolipoprotein(a) (2). Genetic variants in LPA that increase Lp(a) levels also increase CHD risk, suggesting that Lp(a) is a causal risk factor for development of CHD (4–6). Consequently, lowering Lp(a) levels has emerged as a therapeutic strategy to reduce the risk of CHD (2,7).
Beyond identifying a therapeutic target gene, human genetics may help estimate the probable efficacy and safety of pharmacological modulation (8). Although LPA variants have been consistently reported to be associated with CHD (5,6) and aortic valve stenosis (9,10), there is uncertainty around the full spectrum of phenotypic consequences. Previous studies have reported conflicting evidence on whether LPA variants are associated with other cardiovascular diseases, such as stroke (11,12). In addition, observational epidemiology has associated lower plasma levels of Lp(a) with increased risks of cancer (13) and diabetes (14).
Deoxyribonucleic acid (DNA) sequence variants might also provide a mechanism to estimate a dose–response curve. In particular, the simultaneous identification of gain-of-function variants as well as loss-of-function variants and an analysis of phenotypic effects can reveal a dose–response curve even before a clinical trial is initiated.
In the present study, we leveraged genetic variants across the allele frequency spectrum and 3 large data sources to evaluate the phenotypic consequences of genetically lowered Lp(a) levels. The effect of a genetically mediated 1 SD decrease in Lp(a) levels on cardiometabolic disease and range of other disorders was estimated.
The overall study design is shown in Figure 1. Several data sources were leveraged to provide greater power for estimating the effect of genetically lowered Lp(a) level on cardiometabolic traits and outcomes, to conduct a phenome-wide association study, and to examine the effect of rare loss-of-function variants in the LPA gene on risk of CHD.
Individual-level data from 112,338 individuals of European ancestry from the U.K. Biobank, a large population-based cohort (Online Appendix), were used (15). Characteristics of individuals are provided in Online Table 1. These individual-level data were supplemented with summary results from 7 genome-wide association study (GWAS) consortia examining blood lipid levels, anthropometric traits, glycemic traits, diabetes, CHD, heart failure (HF), and renal dysfunction, all predominantly containing individuals of European descent (Online Appendix, Table 1) (16–23). Our estimates for CHD were derived from the CARDIoGRAM (Coronary Artery Disease Genome wide Replication and Meta-analysis) Exome Consortium analysis of up to 42,335 CHD case subjects and 78,240 control subjects. Finally, LPA gene sequences from 15,251 participants of European ancestry from the Myocardial Infarction Genetics (MIGen) Consortium were used.
In the primary analysis, we examined the effect of genetically lowered Lp(a) level on 9 different cardiometabolic diseases: CHD; stroke; HF; atrial fibrillation; aortic stenosis; peripheral vascular disease (PVD); venous thromboembolism; diabetes; and chronic kidney disease (CKD) (Online Table 2). We also examined the effect of genetically lowered Lp(a) level on 15 cardiometabolic quantitative traits (Online Appendix): waist-to-hip ratio; waist circumference; hip circumference; body mass index; systolic blood pressure; diastolic blood pressure; total cholesterol; LDL cholesterol; high-density lipoprotein (HDL) cholesterol; triglyceride levels; fasting glucose; fasting insulin; 2-h glucose; glycosylated hemoglobin; and serum creatinine–derived estimated glomerular filtration rate (eGFR). All traits were standardized (i.e., reported in units of SDs) to allow for comparisons among traits. Using the U.K. Biobank cohort, a phenome-wide association study was also conducted for 28 additional diseases, including endocrine, renal, urological, gastrointestinal, neurological, musculoskeletal, respiratory, and neoplastic disorders.
DNA sequence variants
To estimate the effect of genetically lowered Lp(a) level on a wide range of phenotypes, individual-level data from U.K. Biobank were combined with summary-level data from large-scale GWAS. Four single nucleotide polymorphisms (SNPs) in the LPA gene were used that have been previously associated with plasma Lp(a) levels: rs10455872, rs3798220, rs41272114, and rs143431368 (Online Table 3). Together, rs10455872 and rs3798220 explain approximately 36% of variation in plasma Lp(a) levels (5); the other 2 (rs41272114 and rs143431368) are loss-of-function variants associated with lower Lp(a) levels.
To standardize the estimates to a 1 SD decrease in Lp(a) levels, estimates of the effect of each variant on Lp(a) levels from the ARIC (Atherosclerosis Risk In Communities) study were used (Online Table 3, Online Appendix). ARIC is a community-based study of 15,792 white and black participants, ages 45 to 64 years, who were first enrolled in 1987 (24). The analysis was restricted to 2,758 individuals of European ancestry in the ARIC cohort who had Lp(a) levels measured at the baseline visit by using a double-antibody enzyme-linked immunosorbent assay (25). Participants fasted for 12 to 24 h before blood collection. Plasma was separated from cells with centrifugation within 1 h of collection and stored at –70°C. Analyses were performed within 2 weeks. The assay was shown to have high internal reliability in a validation study in ARIC (r = 0.90) and in a separate comparison versus a newer assay calibrated by using International Federation of Clinical Chemistry reference material (r = 0.88) (26). Linear regression was used, adjusting for age, sex, and 5 principal components of ancestry, to estimate the association between each variant and Lp(a) level in an additive model. Because Lp(a) levels were non-normally distributed, log-transformed Lp(a) levels were used, as previously described (5).
For analyses of both U.K. Biobank and summary-level data, a gene variant score was created out of the 4 SNPs. For each variant, we modeled the Lp(a)-lowering allele and weighted by the effect of each SNP on log-transformed Lp(a) levels in SD units (Online Table 3). The effect of this gene variant score on each trait and outcome was then examined, standardized per SD decrease in log-transformed Lp(a) levels.
For U.K. Biobank, an LPA gene variant score was generated in units of SD Lp(a) by multiplying each variant by its effect on Lp(a) levels. This gene variant score was then included in a logistic regression analysis adjusting for age, sex, 10 principal components of ancestry, and a dummy variable for array type. For the summary-level data, this approach is equivalent to an inverse variance–weighted, fixed effects meta-analysis of the effect of each variant on a trait or outcome of interest, divided by the effect of each variant on Lp(a) levels (27).
For the primary outcomes (the 9 cardiometabolic diseases), a Bonferroni-adjusted level of significance of p = 0.05/9 = 0.0056 was set. For the secondary analysis of cardiometabolic traits, which included 15 traits, a level of significance of p = 0.05/15 = 0.003 was set. For the phenome-wide association study of 28 phenotypes, a level of significance of p = 0.05/28 = 0.0018 was set.
Loss-of-function variant analysis
To examine whether loss-of-function variants in the LPA gene influence CHD risk, whole exome sequencing data from the MIGen Consortium were used (Online Appendix). This consortium is composed of 10 coronary artery disease case-control studies (28,29). Loss-of-function variants were defined as follows: 1) nonsense mutations that resulted in early termination of the apolipoprotein(a) protein; 2) frameshift mutations due to insertions or deletions of DNA; or 3) splice-site mutations that resulted in an incorrectly spliced protein. These loss-of-function variants in the MIGen Consortium were combined with loss-of-function variants that were genotyped (either directly or imputed) in the U.K. Biobank. Variants are provided in Online Tables 4 and 5. We analyzed rare variants (<1%) separately to a common loss-of-function variant in the LPA gene (rs41272114, allele frequency of 3.8% in U.K. Biobank) (30,31).
We tested for the association of CHD with the presence of a loss-of-function variant using logistic regression. In the MIGen Consortium, the analysis was adjusted for sex, 5 principal components of ancestry, and a dummy variable for each cohort. We did not adjust for age in the MIGen Consortium because cases in some cohorts were selected for early-onset myocardial infarction, resulting in age being significantly and inversely associated with the presence of CHD. In the U.K. Biobank, the analysis was adjusted for age, sex, 10 principal components of ancestry, and array type.
All analyses were performed by using R version 3.2.3 software (The R Project for Statistical Computing, Vienna, Austria).
We first estimated the effect of LPA gene variant score on plasma Lp(a) levels in ARIC participants. Variants rs3798220 and rs10455872 altered Lp(a) levels by 0.98 and 0.91 SD, respectively, whereas rs41272114 and rs143431368 altered Lp(a) levels by 0.62 SD and 0.92 SD (Online Table 3). In this study, 1 SD in Lp(a) levels equaled 28 mg/dl. The distribution of LPA gene variant score in the U.K. Biobank is provided (Online Table 6).
We examined the effect of genetically lowered Lp(a) level on 9 different cardiometabolic diseases (Central Illustration). Genetically lowered Lp(a), a 1 SD genetic decrease, was associated with a 29% lower risk of CHD (odds ratio [OR]: 0.71; 95% confidence interval [CI]: 0.69 to 0.73; p = 3.2 × 10–90). Genetically lowered Lp(a) had similar strengths of association with CHD across subpopulations (Online Figure 1). Beyond CHD, genetically lowered Lp(a) level was associated with a 31% lower risk of PVD (OR: 0.69; 95% CI: 0.59 to 0.80; p = 1.9 × 10–6), a 13% lower risk of stroke (OR: 0.87; 95% CI: 0.79 to 0.96; p = 0.004), a 37% lower risk of aortic stenosis (OR: 0.63; 95% CI: 0.47 to 0.83; p = 0.0011), and a 17% lower risk of HF (OR: 0.83; 95% CI: 0.73 to 0.94; p = 0.0045).
Although genetically lowered Lp(a) levels were only nominally associated with a 9% lower risk of CKD (OR: 0.91; 95% CI: 0.81 to 1.00; p = 0.043), it was highly significantly associated with the underlying quantitative trait (eGFR), as described later. Genetically lowered Lp(a) level was not associated with diabetes, venous thromboembolism, or atrial fibrillation. To examine if the association of genetically lowered Lp(a) with HF and aortic stenosis was mediated by CHD, we excluded participants with CHD in the U.K. Biobank (n = 4,461). After exclusion, a 1 SD genetic decrease in Lp(a) levels had similar strengths of association with HF (OR: 0.84; 95% CI: 0.66 to 1.07; n = 107,877) and aortic stenosis (OR: 0.70; 95% CI: 0.49 to 0.99; n = 107,877). A sensitivity analysis excluding those with prevalent aortic stenosis (n = 193) yielded a similar strength for the association between a 1 SD decrease in Lp(a) levels and HF (OR: 0.85; 95% CI: 0.72 to 1.02; n = 112,145).
In contrast to the effects of Lp(a) on cardiometabolic disorders, we found no association of genetically lowered Lp(a) with any of 28 different disorders, including 4 gastrointestinal disorders, 3 endocrine disorders, 2 renal/urological disorders, 3 psychiatric disorders, 4 musculoskeletal disorders, 4 respiratory disorders, and 8 different cancers (all p > 0.01) (Figure 2).
We next estimated the effect of LPA gene variant score on 15 quantitative traits (Figure 3). A significant association of genetically lowered Lp(a) with improved kidney function was observed: a 0.04 SD (95% CI: 0.02 to 0.05) increase in eGFR per SD genetically lowered Lp(a) (p = 1.4 × 10–5). This scenario corresponds to an approximate 2.0 ml/min increase in eGFR per SD lower Lp(a). As expected, a 1 SD genetically lowered Lp(a) was associated with total cholesterol and LDL cholesterol (0.14 SD decrease in total cholesterol [95% CI: 0.11 to 0.16; p = 3.5 × 10–27) and a 0.14 SD decrease in LDL cholesterol (95% CI: 0.11 to 0.16; p = 4.7 × 10–27). These estimates correspond, approximately, to a 5.6 mg/dl decrease in total cholesterol and a 4.9 mg/dl decrease in LDL cholesterol. We found no significant association of LPA genetic risk score with waist-to-hip ratio, waist circumference, hip circumference, body mass index, systolic blood pressure, diastolic blood pressure, HDL cholesterol, triglycerides, fasting glucose, fasting insulin, 2-h glucose, or glycosylated hemoglobin (p > 0.05 for each). LPA gene variant risk score remained unassociated with systolic and diastolic blood pressures when use of antihypertensive therapy was not accounted for (0 SD [95% CI: –0.02 to 0.01] and 0 SD [95% CI: –0.01 to 0.02] per SD lower Lp[a], respectively).
Figure 4 provides a dose–response curve for CHD derived from gain and loss-of-function variants at the LPA gene locus. The impact of LPA variation on CHD risk is directly proportional to its effect on circulating Lp(a) levels. The Lp(a)-increasing alleles of common variants rs3798220 and rs10455872, which increased Lp(a) levels by 0.98 and 0.91 SD, respectively, increased risk of CHD by 57% (OR: 1.57; 95% CI: 1.46 to 1.69) and 38% (OR: 1.38; 95% CI: 1.33 to 1.43). Rare synonymous variants, which had no significant effect on Lp(a) levels, also had no significant effect on CHD (OR: 0.98; 95% CI: 0.86 to 1.12). A common loss-of-function variant rs41272114, which decreased Lp(a) levels by 0.62 SD, was associated with a 12% lower risk of CHD (OR: 0.88; 95% CI: 0.84 to 0.93; p = 3.4 × 10–7). Presence of a rare (allele frequency <1%) loss-of-function variant in the LPA gene was associated with a 24% lower risk of CHD (OR: 0.76; 95% CI: 0.59 to 0.98; p = 0.033) (Online Figure 2).
To evaluate the phenotypic consequences of genetically lowered Lp(a) levels, we leveraged the following: 1) 4 DNA sequence variants that alter plasma Lp(a) level; 2) individual-level genotype and phenotype data from >100,000 participants in the U.K. Biobank; 3) summary genetic association results from 7 large-scale GWAS; and 4) LPA gene sequences in >15,000 participants. We found that 1 SD genetically lowered Lp(a) was associated with a range of atherosclerosis-related diseases, including CHD, PVD, stroke, HF, and aortic stenosis, but was not associated with 31 other different diseases in a phenome-wide association study.
These data allow for several conclusions. First, using naturally occurring DNA sequence variation, a dose–response relationship between perturbation of Lp(a) and risk for CHD was provided. We examined the effects of both common and rare variants, as well as gain-of-function variants that increase Lp(a) levels and loss-of-function variants that decrease Lp(a) levels. The effects of these different variants on CHD were consistently proportional to their effect on Lp(a). Consistent with 2 recent reports (30,32), a low-frequency loss-of-function variant (rs41272114) and a burden of rare loss-of-function variants in LPA protected against CHD. In combination, these results suggest that greater pharmacological reductions in Lp(a) levels should produce proportionally greater reductions in CHD risk, thus supporting intensive Lp(a) lowering.
Second, these results suggest that Lp(a) inhibition may be a viable therapeutic strategy to prevent a range of diseases beyond CHD. This study extends previous research demonstrating that LPA variants are associated with cardiovascular disease (5,6,11,12,33,34). In a report of up to 12,716 individuals from 35 case-control studies, LPA variants were associated with peripheral arterial disease, ischemic stroke, and coronary artery disease (11). In contrast, in an analysis of 14,465 individuals in the Heart Protection Study, LPA variants were associated with PVD but not with stroke (12). Our results suggest that LPA variants are associated with PVD, stroke, and HF. Furthermore, our report of a significant association with aortic stenosis is consistent with recent analyses demonstrating a significant effect of LPA variants on aortic valve calcification and stenosis (9,10). Inclusion of these diseases in composite endpoints of trials of Lp(a)-reducing therapies (in addition to CHD) may increase the likelihood of a positive trial outcome, highlighting the potential benefits of genetic analyses for trial design and clinical drug development.
Third, a surprising finding of this study was that genetically lowered Lp(a) was associated with a modest but significant improvement in kidney function as assessed by 2 phenotypes—eGFR and prevalence of CKD. This lower risk of CKD may be mediated through a reduction in renal atherosclerotic burden. These findings are consistent with a recent GWAS of metabolites that revealed a strong association between LPA rs10455872 and creatinine levels (35). These results implicate Lp(a) metabolism in the development of CKD.
This study’s major strength was the scale and variety of data sources, which improved our power to detect an effect of genetically lowered Lp(a) on a wide range of diseases and cardiometabolic traits. Our use of the largest available cohorts provided requisite power to demonstrate that genetic Lp(a) lowering was associated with a lower risk of PVD, stroke, HF, and CKD. Our use of the U.K. Biobank allowed us to examine the association of genetic LPA variants across a wide range of noncardiovascular diseases, for which we failed to find an association.
Several study limitations deserve mention. First, our use of a 2-sample design, with exposure estimates from ARIC and outcome estimates from the U.K. Biobank and various GWAS, prevented us from examining whether the effect of LPA variants differed according to baseline levels of Lp(a). Second, our phenome-wide association study might have been underpowered to detect a significant effect of Lp(a) on many of the outcomes. Because the U.K. Biobank develops validated phenotypes and accumulates a greater number of events, a phenome-wide association study may be better-powered to detect an effect on different disorders. Third, we used prevalent events based on a verbal interview with a nurse for our phenome-wide association study of 28 different disorders. Although these events are likely to be of greater specificity than coded hospitalization data, they have not been independently validated. Finally, our population was limited to individuals of European ancestry, and our results may not be generalizable to individuals of different ancestry. Indeed, both Lp(a) levels and the number of Kringle IV domains in Lp(a) have been shown to vary substantially with ancestry, suggesting that the impact of Lp(a) on cardiovascular disease may also differ by ancestry (36).
Genetically decreased Lp(a) was associated with a range of cardiometabolic disorders, including CHD, stroke, PVD, aortic stenosis, HF, and renal dysfunction. Pharmacological lowering of Lp(a) levels may reduce the risk of these disorders.
COMPETENCY IN MEDICAL KNOWLEDGE: A genetic predisposition to lower blood levels of Lp(a) was associated with protection from coronary artery disease, stroke, PVD, aortic stenosis, HF, and CKD but was not associated with type 2 diabetes, gastrointestinal disorders, or specific cancers.
TRANSLATIONAL OUTLOOK: Further research should be conducted to determine whether more intensive lowering of Lp(a) levels results in proportionally greater reductions in cardiovascular risk.
For an expanded Methods section, as well as supplemental tables and figures, please see the online version of this article.
The views expressed in this manuscript are those of the authors and do not necessarily represent the views of the National Heart, Lung, and Blood Institute, the National Institutes of Health, or the U.S. Department of Health and Human Services. The REGICOR study was supported by the Spanish Ministry of Economy and Innovation through the Carlos III Health Institute (Red Investigación Cardiovascular RD12/0042, PI09/90506), European Funds for Development (ERDF-FEDER), and by the Catalan Research and Technology Innovation Interdepartmental Commission (2014SGR240). Samples for the Leicester cohort were collected as part of projects funded by the British Heart Foundation (British Heart Foundation Family Heart Study, RG2000010; U.K. Aneurysm Growth Study, CS/14/2/30841) and the National Institute for Health Research (NIHR Leicester Cardiovascular Biomedical Research Unit Biomedical Research Informatics Centre for Cardiovascular Science, IS_BRU_0211_20033). The Munich MI Study is supported by the German Federal Ministry of Education and Research (BMBF) in the context of the e:Med program (e:AtheroSysMed) and the FP7 European Union project CVgenes@target (261123). Additional grants were received from the Fondation Leducq (CADgenomics: Understanding Coronary Artery Disease Genes, 12CVD02). This study was also supported through the Deutsche Forschungsgemeinschaft cluster of excellence “Inflammation at Interfaces” and SFB 1123. The Italian Atherosclerosis, Thrombosis, and Vascular Biology (ATVB) Study was supported by a grant from RFPS-2007-3-644382 and Programma di ricerca Regione-Università 2010-2012 Area 1–Strategic Programmes–Regione Emilia-Romagna. Funding for the exome sequencing project was provided by RC2 HL103010 (HeartGO), RC2 HL102923 (LungGO), and RC2 HL102924 (WHISP). Exome sequencing was performed through RC2 HL102925 (BroadGO) and RC2 HL102926 (SeattleGO). Exome sequencing in ATVB, PROCARDIS, Ottawa, and the Southern German Myocardial Infarction Study was supported by 5U54HG003067 (to Drs. Lander and Gabriel). For a full list of CHARGE-HF (Cohorts for Heart and Aging Research in Genomic Epidemiology–Heart Failure) working group members contributing to this work and for CHARGE-HF acknowledgements, please see PMID 20445134. Mr. Emdin is supported by the Rhodes Trust. Dr. Khera is supported by a John S. Ladue Memorial Fellowship in Cardiology and a KL2/Catalyst Medical Research Investigator Training award (an appointed KL2 award: TR001100) from Harvard Catalyst and has received consulting fees from Merck and Amarin. Dr. Won was supported by Basic Science Research Program through the National Research Foundation of Korea funded by the Ministry of Science, ICT & Future Planning (2016R1C1B2007920). Dr. Peloso is supported by the National Heart, Lung, and Blood Institute of the National Institutes of Health under award number K01HL125751. Dr. Stitziel has received funding from K08HL114642 and R01HL131961; has received a research grant from AstraZeneca; and has received consulting fees from Regeneron. Dr. Farrall has received the British Heart Foundation Centre of Research Excellence, Oxford (RE/13/1/30181), award; the Wellcome Trust core award (090532/Z/09/Z); and funding from the Wellcome Trust Institutional Support Scheme. Dr. Abecasis has served as a consultant for Regeneron. Dr. Rader has received consulting fees from Aegerion Pharmaceuticals, Alnylam Pharmaceuticals, Eli Lilly and Company, Pfizer, Sanofi, and Novartis; is an inventor on a patent related to lomitapide that is owned by the University of Pennsylvania and licensed to Aegerion Pharmaceuticals; and is a cofounder of Vascular Strategies and Staten Biotechnology. Dr. Danesh has served as a consultant for Takeda; has been a member of the Novartis Cardiovascular & Metabolic Advisory Board, the International Cardiovascular and Metabolism Research and Development portfolio committee for Novartis, the Merck Sharp & Dohme UK Atherosclerosis Advisory Board, the Pfizer Population Research Advisory Panel, and the Sanofi Advisory Board; and has received funding from the British Heart Foundation, the BUPA Foundation, diaDexus, European Research Council, the European Union, Evelyn Trust, Fogarty International Centre, GlaxoSmithKline, Merck, the National Heart, Lung, and Blood Institute, the National Health Service Blood and Transplant, the National Institute for Health Research, the National Institute of Neurological Disorders and Stroke, Novartis, Pfizer, Roche, Sanofi, Takeda, The Wellcome Trust, U.K. Biobank, the University of British Columbia, and the U.K. Medical Research Council. Dr. Ardissino has received speaker fees from AstraZeneca, Boehringer Ingelheim, Johnson & Johnson, Bayer, Daiichi-Sankyo, GlaxoSmithKline, Eli Lilly and Company, Boston Scientific, Bristol-Myers Squibb, Menarini Group, Novartis, and Sanofi; and research grants from GlaxoSmithKline, Eli Lilly and Company, Pfizer, and Novartis. Dr. Saleheen has received grants from Pfizer, Regeneron, and the National Institutes of Health. Dr. Kathiresan is supported by a research scholar award from the Massachusetts General Hospital, the Donovan Family Foundation, and R01 HL127564; has received grants from Bayer Healthcare, Aegerion Pharmaceuticals, and Regeneron Pharmaceuticals; has received consulting fees from Merck, Novartis, Sanofi, AstraZeneca, Alnylam Pharmaceuticals, Leerink Partners, Noble Insights, Quest Diagnostics, Genomics PLC, and Eli Lilly and Company; and holds equity in San Therapeutics and Catabasis Pharmaceuticals. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose. Drs. Emdin and Khera contributed equally to this work.
- Abbreviations and Acronyms
- coronary heart disease
- confidence interval
- chronic kidney disease
- deoxyribonucleic acid
- estimated glomerular filtration rate
- genome-wide association study
- high-density lipoprotein
- heart failure
- low-density lipoprotein
- odds ratio
- peripheral vascular disease
- single nucleotide polymorphism
- Received July 20, 2016.
- Revision received September 21, 2016.
- Accepted October 4, 2016.
- American College of Cardiology Foundation
- Utermann G.
- Tsimikas S.,
- Hall J.L.
- Sandholzer C.,
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