Lipoprotein signatures of cholesteryl ester transfer protein and HMG-CoA reductase inhibition
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RESEARCH ARTICLE Lipoprotein signatures of cholesteryl ester transfer protein and HMG-CoA reductase inhibition Johannes Kettunen 1,2‡ , Michael V. HolmesID 3,4,5,6‡ , Elias AllaraID 7,8‡ , Olga Anufrieva 1‡ , Pauli OhukainenID 1 , Clare Oliver-WilliamsID 7,9 , Qin Wang 1 , Therese Tillin 10 , Alun D. HughesID 10 , Mika Ka ¨ho ¨nen 11 , Terho Lehtima ¨ki 12 , Jorma Viikari 13,14 , Olli T. Raitakari 15,16 , Veikko SalomaaID 2 , Marjo-Riitta Ja ¨rvelinID 17,18,19,20,21 , Markus Perola 2,22,23 , George Davey SmithID 6,24 , Nish Chaturvedi 10 , John Danesh 7,8,25,26 , Emanuele Di Angelantonio 7,8‡ , Adam S. ButterworthID 7,8‡ , Mika Ala-KorpelaID 1,6,24,27,28,29‡ * 1Computational Medicine, Faculty of Medicine, University of Oulu and Biocenter Oulu, Oulu, Finland, 2National Institute for Health and Welfare, Helsinki, Finland, 3Medical Research Council Population Health Research Unit, University of Oxford, Oxford, United Kingdom, 4Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom, 5National Institute for Health Research, Oxford Biomedical Research Centre, Oxford University Hospital, Oxford, United Kingdom, 6Medical Research Council Integrative Epidemiology Unit at the University of Bristol, Bristol, United Kingdom, 7British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom, 8National Institute for Health Research Blood and Transplant Research Unit in Donor Health and Genomics, University of Cambridge, Cambridge, United Kingdom, 9Homerton College, University of Cambridge, Cambridge, United Kingdom, 10 Institute of Cardiovascular Science, University College London, London, United Kingdom, 11 Department of Clinical Physiology, University of Tampere and Tampere University Hospital, Tampere, Finland, 12 Department of Clinical Chemistry, Fimlab Laboratories, Finnish Cardiovascular Research Center Tampere, Faculty of Medicine and Health Technologies, University of Tampere, Tampere, Finland, 13 Department of Medicine, University of Turku, Turku, Finland, 14 Division of Medicine, Turku University Hospital, Turku, Finland, 15 Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, Finland, 16 Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital, Turku, Finland, 17 Center for Life Course Health Research, Faculty of Medicine, University of Oulu, Oulu, Finland, 18 Biocenter Oulu, University of Oulu, Oulu, Finland, 19 Unit of Primary Health Care, Oulu University Hospital, OYS, Oulu, Finland, 20 Department of Epidemiology and Biostatistics, MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London, London, United Kingdom, 21 Department of Life Sciences, College of Health and Life Sciences, Brunel University London, United Kingdom, 22 Diabetes and Obesity Research Program, University of Helsinki, Helsinki, Finland, 23 Estonian Genome Center, University of Tartu, Tartu, Estonia, 24 Population Health Science, Bristol Medical School, University of Bristol, Bristol, United Kingdom, 25 Wellcome Trust Sanger Institute, Hinxton, United Kingdom, 26 British Heart Foundation Cambridge Centre of Excellence, Department of Medicine, University of Cambridge, Cambridge, United Kingdom, 27 NMR Metabolomics Laboratory, School of Pharmacy, University of Eastern Finland, Kuopio, Finland, 28 Systems Epidemiology, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia, 29 Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Faculty of Medicine, Nursing and Health Sciences, The Alfred Hospital, Monash University, Melbourne, Victoria, Australia ‡ JK, MVH, EA, and OA are joint first authors on this work. ED, ASB, and MAK are joint senior authors on this work. *[email protected] Abstract Cholesteryl ester transfer protein (CETP) inhibition reduces vascular event risk, but confusion surrounds its effects on low-density lipoprotein (LDL) cholesterol. Here, we clarify associations of genetic inhibition of CETP on detailed lipoprotein measures and compare those PLOS Biology | https://doi.org/10.1371/journal.pbio.3000572 December 20, 2019 1 / 19 a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 OPEN ACCESS Citation: Kettunen J, Holmes MV, Allara E, Anufrieva O, Ohukainen P, Oliver-Williams C, et al. (2019) Lipoprotein signatures of cholesteryl ester transfer protein and HMG-CoA reductase inhibition. PLoS Biol 17(12): e3000572. https://doi.org/ 10.1371/journal.pbio.3000572 Academic Editor: Heather J. Cordell, Newcastle University, UNITED KINGDOM Received: March 28, 2019 Accepted: November 29, 2019 Published: December 20, 2019 Copyright: ©2019 Kettunen et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: The datasets used in the current study are available from the cohorts through application process for researchers who meet the criteria for access to confidential data. For the NFBCs please contact the project center ([email protected]) and visit the website (www.oulu.fi/nfbc) for more information. At the time of publication the NFBC66 genome-wide data is unable to be transferred outside of the University of Oulu. Please contact [email protected] for more information on availability. Aggregated statistical YFS data may be accessed through the
to genetic inhibition of 3-hydroxy-3-methylglutaryl-coenzyme A reductase (HMGCR). We used an allele associated with lower CETP expression (rs247617) to mimic CETP inhibition and an allele associated with lower HMGCR expression (rs12916) to mimic the well-known effects of statins for comparison. The study consists of 65,427 participants of European ancestries with detailed lipoprotein subclass profiling from nuclear magnetic resonance spectroscopy. Genetic associations were scaled to 10% reduction in relative risk of coronary heart disease (CHD). We also examined observational associations of the lipoprotein subclass measures with risk of incident CHD in 3 population-based cohorts totalling 616 incident cases and 13,564 controls during 8-year follow-up. Genetic inhibition of CETP and HMGCR resulted in near-identical associations with LDL cholesterol concentration estimated by the Friedewald equation. Inhibition of HMGCR had relatively consistent associations on lower cholesterol concentrations across all apolipoprotein B-containing lipoproteins. In contrast, the associations of the inhibition of CETP were stronger on lower remnant and very-lowdensity lipoprotein (VLDL) cholesterol, but there were no associations on cholesterol concentrations in LDL defined by particle size (diameter 18–26 nm) (−0.02 SD LDL defined by particle size; 95% CI: −0.10 to 0.05 for CETP versus −0.24 SD, 95% CI −0.30 to −0.18 for HMGCR). Inhibition of CETP was strongly associated with lower proportion of triglycerides in all high-density lipoprotein (HDL) particles. In observational analyses, a higher triglyceride composition within HDL subclasses was associated with higher risk of CHD, independently of total cholesterol and triglycerides (strongest hazard ratio per 1 SD higher triglyceride composition in very large HDL 1.35; 95% CI: 1.18–1.54). In conclusion, CETP inhibition does not appear to affect size-specific LDL cholesterol but is likely to lower CHD risk by lowering concentrations of other atherogenic, apolipoprotein B-containing lipoproteins (such as remnant and VLDLs). Inhibition of CETP also lowers triglyceride composition in HDL particles, a phenomenon reflecting combined effects of circulating HDL, triglycerides, and apolipoprotein Bcontaining particles and is associated with a lower CHD risk in observational analyses. Our results reveal that conventional composite lipid assays may mask heterogeneous effects of emerging lipid-altering therapies. Introduction Definitive evidence on the causal role of low-density lipoproteins (LDLs) in cardiovascular disease comes from trials of LDL cholesterol lowering compounds [1], which have shown beneficial effects on risk of coronary heart disease (CHD) and stroke. Consistent effects have been seen for drugs acting on related pathways, such as 3-hydroxy-3-methylglutaryl-coenzyme A reductase (HMGCR) inhibitors, i.e., statins, and proprotein convertase subtilisin-kexin type 9 (PCSK9) inhibitors [2], both of which up-regulate hepatic LDL receptor expression, and for drugs acting on other pathways, such as ezetimibe [3], which inhibits intestinal absorption of cholesterol [4]. However, trials of drugs primarily designed to alter concentrations of lipids other than LDL cholesterol have had mixed results [5,6]. One such example is the class of drugs designed to inhibit cholesteryl ester transfer protein (CETP), a lipid transport protein responsible for the exchange of triglycerides and cholesteryl esters between apolipoprotein B-containing particles and high-density lipoprotein (HDL) particles. CETP inhibitors were developed initially on the Lipoprotein Signatures of CETP and HMGCR PLOS Biology | https://doi.org/10.1371/journal.pbio.3000572 December 20, 2019 2 / 19 data controller on case by case basis for scientific research. Please contact the project center ([email protected]) and visit the website (http:// youngfinnsstudy.utu.fi) for more information. Regarding the FINRISK 1997 cohort and the DILGOM study, requests for data availability should be addressed to the THL Biobank as instructed in https://thl.fi/en/web/thl-biobank/for-researchers. More information on the SABRE study can be found at https://mrc.ukri.org/research/facilitiesand-resources-for-researchers/cohort-directory/ southall-and-brent-revisited-sabre/ and data access can be requested via email ([email protected]). For the INTERVAL study please contact the project center ([email protected]) and visit the website (http://www.intervalstudy.org.uk) for more information. Funding: JK was funded through Academy of Finland (grant numbers 297338 and 307247) and Novo Nordisk Foundation (NNF17OC0026062). MVH works in a Unit that receives funding from the UK Medical Research Council and is supported by a British Heart Foundation Intermediate Clinical Research Fellowship (FS/18/23/33512). PO was supported by the Emil Aaltonen Foundation. COW has been awarded prize money by Novartis UK. MAK was supported by a Senior Research Fellowship from the National Health and Medical Research Council (NHMRC) of Australia (APP1158958) and by Sigrid Juselius Foundation. GDS and MAK work in a Unit that receives funds from the University of Bristol and UK Medical Research Council (MC_UU_12013/1). The Northern Finland Birth Cohorts (MRJ) were supported by the Academy of Finland (EGEAproject, 285547), University Hospital Oulu, Biocenter, University of Oulu, Finland (75617), NHLBI grant 5R01HL087679-02 through the STAMPEED program (1RL1MH083268-01), ERDF European Regional Development Fund Grant no. 539/2010 A31592, EU H2020–PHC-2014 DynaHEALTH action (No. 633595), EU H2020HCO-2004 iHEALTH Action (643774), EU H2020SC1-2016-2017 LifeCycle Action (grant agreement No 733206), and MRC Grant nro MR/M013138/1. The Young Finns Study (OTR) has been financially supported by the Academy of Finland: grants 286284, 134309 (Eye), 126925, 121584, 124282, 129378 (Salve), 117787 (Gendi), and 41071 (Skidi); the Social Insurance Institution of Finland; Competitive State Research Financing of the Expert Responsibility area of Kuopio, Tampere and Turku University Hospitals (grant X51001); Juho Vainio Foundation; Paavo Nurmi Foundation; Finnish Foundation for Cardiovascular Research; Finnish Cultural Foundation; Tampere Tuberculosis Foundation; Emil Aaltonen Foundation; Yrjo¨
basis of their HDL cholesterol raising effects. Although accumulating genetic evidence suggests that HDL cholesterol concentration is unlikely to be causally related to CHD [7–9], there were 2 strong reasons to believe that CETP inhibition may still reduce vascular risk: (i) genetic studies of CETP variants have shown associations with CHD [10,11] and (ii) some CETP inhibitors not only increase HDL cholesterol but also appear to lower LDL cholesterol as measured by conventional assays [12,13]. The findings from the phase III REVEAL study, the largest CETP trial to date, showed that treatment with the CETP inhibitor anacetrapib led to a reduction in risk of coronary events that was proportional to the reduction in non-HDL cholesterol [14]. Interestingly, anacetrapib appeared to have discrepant effects based on the assay used to quantify LDL cholesterol (using beta-quant, direct or Friedewald estimation) [13]. This discrepant effect was also identified in a genetic study that approximated a factorial clinical trial of CETP inhibition and statin therapy [15]. Thus, although both CETP inhibitors and statins lower Friedewald-estimated LDL cholesterol, which also includes cholesterol carried by other lipoprotein particles, it is possible that the drugs have differential effects on the concentration and composition of lipids in different apolipoprotein B–containing lipoproteins. In this study, we used the established approach of exploiting genetic variants near the protein-coding genes of drug targets to investigate detailed lipid and lipoprotein subclass signatures of CETP inhibition in large population-based studies. We compared the association of an allele associated with lower CETP expression (to mimic CETP inhibition) with HMGCR expression associated variant (to proxy statin treatment) to gauge into how these 2 therapies alter the lipoprotein milieu. The genetic effects of HMGCR inhibition were analysed to provide a valuable control because these effects are already well understood [16]. We also examined the role of lipoprotein composition in CHD for the first time. We present findings that the triglyceride composition, in contrast to circulating concentrations, of HDL particles is associated with CHD. Results Data from 62,400 individuals with extensive lipoprotein subclass profiling and genotypes were available. We combined data from 5 adult cohorts (mean age range from 31–52 years) and one cohort of adolescents (mean age 16 years) for the genetic analyses in which 51% of participants of all 6 studies were female. Study-specific and pooled estimates from meta-analyses of genetic and observational analyses for all 191 traits are presented in Supporting S1–S15 Figs. Scaled to 10% reduction in relative risk of CHD, CETP rs247617 and HMGCR rs12916 had near-identical associations with Friedewald-estimated LDL cholesterol (Fig 1) and similar associations for apolipoprotein B. In contrast, when LDL cholesterol was defined on the basis of cholesterol transported in LDL based on particle size (diameter 18–26 nm) and measured via nuclear magnetic resonance (NMR) spectroscopy, CETP expression lowering allele had no association with this size-specific LDL cholesterol (0.02 SDs; 95% CI: −0.10 to 0.05). Although HMGCR expression lowering allele had a relatively consistent association with individual apolipoprotein B–containing lipoproteins (effect estimates ranging from −0.25 for intermediate-density lipoprotein [IDL] cholesterol to −0.18 for very-low-density lipoprotein [VLDL] cholesterol), CETP expression lowering allele had the most pronounced associations with VLDL cholesterol, a weaker association with IDL cholesterol, but no association with LDL cholesterol defined by particle size or cholesterol transported by any of the large, medium, or small LDL subclasses (Fig 1). When examining triglycerides in apolipoprotein B–containing particles, CETP expression lowering allele associated with lower circulating triglyceride concentrations in VLDL and IDL subclasses, whereas HMGCR expression lowering allele had weaker effects on these measures, Lipoprotein Signatures of CETP and HMGCR PLOS Biology | https://doi.org/10.1371/journal.pbio.3000572 December 20, 2019 3 / 19 Jahnsson Foundation; Signe and Ane Gyllenberg Foundation; and Diabetes Research Foundation of Finnish Diabetes Association. The INTERVAL trial (JD) was funded by NHSBT and the NIHR Blood and Transplant Research Unit in Donor Health and Genomics (NIHR BTRU-2014-10024). The trial’s coordinating centre at the Department of Public Health and Primary Care at the University of Cambridge, Cambridge, UK, has received core support from the UK Medical Research Council (G0800270), British Heart Foundation (SP/09/002), and the NIHR Cambridge Biomedical Research Centre. The NIHR Blood and Transplant Research Unit (BTRU) in Donor Health and Genomics is supported by grant NIHR BTRU-2014-10024. Dr Allara was supported by a NIHR BTRU PhD Studentship while this study was performed. This work has received support from the EU/EFPIA Innovative Medicines Initiative Joint Undertaking BigData@Heart grant n˚ 116074. Investigators at the University of Oxford, Oxford, UK, have been supported by the Research and Development Programme of NHSBT, the NHSBT Howard Ostin Trust Fund, and the NIHR Oxford Biomedical Research Centre through the programme grant NIHR-RP-PG-0310-1004. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: The authors have declared that no competing interests exist. Abbreviations: Apo A-I, apolipoprotein A-I; ApoB, apolipoprotein B; C, cholesterol; CETP, cholesteryl ester transfer protein; CHD, coronary heart disease; CoA, coenzyme A; HDL, high-density lipoprotein; HMGCR, 3-hydroxy-3-methyl-glutaryl-coenzyme A reductase; IDL, intermediate-density lipoprotein; LDL, low-density lipoprotein; NMR, nuclear magnetic resonance; OR, odds ratio; PCSK9, proprotein convertase subtilisin-kexin type 9; SNP, single-nucleotide polymorphism; TG, triglycerides; VLDL, very-low-density lipoprotein.
except in LDL subclasses (Fig 2). CETP expression lowering allele had a very strong association with higher HDL cholesterol (0.84; 95% CI: 0.76–0.92) but HMGCR did not (0.04; 95% CI: −0.02 to 0.10; Fig 3). Similarly, CETP expression lowering allele was associated with lower total quantity of triglycerides in HDL particles (−0.23; 95% CI: −0.31 to −0.15) but HMGCR expression lowering allele was not (−0.03; 95% CI: −0.09 to 0.02). The lipoprotein particle structure is biophysically constrained, generating strong correlations between lipid measures within individual lipoprotein subclasses [19–22]. Notable differences in lipid concentrations in subclass particles would therefore suggest changes in the compositional proportions of these lipids. For genetic inhibition of CETP, the effects on circulating triglyceride concentrations in all HDL subclasses were weaker (XL-HDL and L-HDL) or even in the opposite direction (M-HDL and S-HDL) than the effects on cholesterol concentration in these subclasses (Fig 3). Examining the genetic associations with the particle lipid compositions, the relative amount of triglycerides (in relation to all lipid molecules in the particles) was remarkably diminished in all HDL subclass particles by genetic inhibition of CETP (Fig 4). Genetic inhibition of HMGCR did not associate with triglyceride concentration or composition of any HDL subclass. These associations are in line with the known physiological roles of CETP and HMGCR and their inhibition [23,24]. In addition, as expected, CETP expression lowering allele associated with higher compositions of triglycerides in most VLDL subclass Fig 1. Associations of genetic variants in CETP rs247617 (red) and HMGCR rs12916 (blue) with circulating apolipoprotein B and cholesterol concentrations in size-specific apolipoprotein B particles. Estimates represent the standardized difference in lipoprotein trait, with per allele associations scaled to a 10% lower relative risk of CHD. Analyses were adjusted for age, sex, genotyping batch, and 10 genetic principal components. The circles refer to the effect estimates and the horizontal bars to the 95% CIs. Closed circles represent statistical significance of associations at P<0.002 and open circles associations that are nonsignificant at this threshold. The lipoprotein subclasses are defined by particle size [17–19]: potential chylomicrons and the largest VLDL particles (XXL-VLDL; average particle diameter �75 nm); 5 different VLDL subclasses, i.e., very large (average particle diameter 64.0 nm), large (53.6 nm), medium (44.5 nm), small (36.8 nm), and very small VLDL (31.3 nm); IDL (28.6 nm); and 3 LDL subclasses, i.e., large (25.5 nm), medium (23.0 nm), and small LDL (18.7 nm). Underlying data can be found in S1 Data. ApoB, apolipoprotein B; C, cholesterol; CHD, coronary heart disease; IDL, intermediate-density lipoprotein; LDL, low-density lipoprotein; VLDL, very-low-density lipoprotein. https://doi.org/10.1371/journal.pbio.3000572.g001 Lipoprotein Signatures of CETP and HMGCR PLOS Biology | https://doi.org/10.1371/journal.pbio.3000572 December 20, 2019 4 / 19
particles and HMGCR expression lowering allele showed directionally similar albeit weaker associations. To understand the clinical relevance of these HDL-related compositional changes arising from CETP inhibition, beyond lowering the cholesterol concentrations of apolipoprotein B– containing lipoprotein particles, we studied the observational associations of lipoprotein subclass lipid concentrations and compositions with CHD in 3 prospective population cohorts totalling 616 incident cases and 13,564 controls during an 8-year follow-up. The triglyceride concentration of HDL was associated with incident CHD when adjusted for nonlipid cardiovascular risk factors (Fig 5). However, when serum cholesterol and serum triglycerides were added to the model, as expected, the associations attenuated. In contrast, the triglyceride compositions of all the HDL subclass particles were positively associated with CHD, independent of circulating concentrations of cholesterol and triglycerides, with hazard ratios around 1.3 for all HDL subclasses (Fig 5). Adjusting for LDL-C had only very minor effects on the associations of both circulating HDL-related triglyceride concentrations and the triglyceride compositions of HDL particles. In addition to the compositional enrichment of triglycerides in HDL particles, the compositional enrichment of cholesteryl esters in the largest VLDL particles (XXL-VLDL and XL-VLDL) was also observationally associated with greater risk of CHD (S11 Fig). The genetic inhibition of CETP lowered the cholesteryl ester composition of these VLDL particles, i.e., was acting toward decreased risk of CHD (S2 Fig). Fig 2. Associations of genetic variants in CETP rs247617 (red) and HMGCR rs12916 (blue) with circulating triglyceride concentrations in size-specific apolipoprotein B particles. Estimates represent the standardized difference in lipoprotein trait, with per allele associations scaled to a 10% lower relative risk of CHD. Analyses were adjusted for age, sex, genotyping batch, and 10 genetic principal components. The circles refer to the effect estimates and the horizontal bars to the 95% CIs. Closed circles represent statistical significance of associations at P<0.002 and open circles associations that are nonsignificant at this threshold. The lipoprotein subclasses are defined by particle size [17–19]: potential chylomicrons and the largest VLDL particles (XXL-VLDL; average particle diameter �75 nm); 5 different VLDL subclasses, i.e., very large (average particle diameter 64.0 nm), large (53.6 nm), medium (44.5 nm), small (36.8 nm), and very small VLDL (31.3 nm); IDL (28.6 nm); and 3 LDL subclasses, i.e., large (25.5 nm), medium (23.0 nm), and small LDL (18.7 nm). Underlying data can be found in S1 Data. CHD, coronary heart disease; IDL, intermediate-density lipoprotein; LDL, low-density lipoprotein; TG, triglyceride; VLDL, very-low-density lipoprotein. https://doi.org/10.1371/journal.pbio.3000572.g002 Lipoprotein Signatures of CETP and HMGCR PLOS Biology | https://doi.org/10.1371/journal.pbio.3000572 December 20, 2019 5 / 19
To further investigate the novel relation of the triglyceride composition of HDL particles (and thereby potentially the inhibition of CETP) with incident CHD, we performed systematic analyses focusing on 3 fundamental measures that characterize the overall lipoprotein profile fairly well, namely, total serum triglyceride, HDL-C, and apolipoprotein B concentration, and gradually adjusted the association between HDL particle triglyceride composition and incident CHD. The results are presented in Fig 6. Adjusting the associations between HDL particle triglyceride compositions and incident CHD with total triglycerides, HDL-C and apolipoprotein B had all very similar minor effects. However, a combined adjustment for apolipoprotein B and HDL-C almost abolished the associations similarly to apolipoprotein B and triglycerides. Discussion We used genetic variants in CETP and HMGCR to gain insight into the expected effects of therapeutic inhibition of CETP and HMG-CoA reductase on circulating lipoproteins and lipids. Our data show that although CETP and HMGCR have near-identical effects on Friedewald-estimated LDL cholesterol, this result masks a very different association of CETP and HMGCR with size-specific LDL cholesterol. Genetic inhibition of HMGCR showed similar effects with cholesterol across the apolipoprotein B–containing lipoproteins but genetic inhibition of CETP showed stronger associations with larger apolipoprotein B particles, namely, VLDL and remnant cholesterol [25], but no association with cholesterol carried specifically in LDL particles defined by size. Fig 3. Associations of genetic variants in CETP rs247617 (red) and HMGCR rs12916 (blue) with circulating apolipoprotein A-I as well as cholesterol and triglyceride concentrations in size-specific HDL particles. Estimates represent the standardized difference in lipoprotein trait, with per allele associations scaled to a 10% lower relative risk of CHD. Analyses were adjusted for age, sex, genotyping batch, and 10 genetic principal components. The circles refer to the effect estimates and the horizontal bars to the 95% CIs. Closed circles represent statistical significance of associations at P<0.002 and open circles associations that are nonsignificant at this threshold. The lipoprotein subclasses are defined by particle size [17–19]: the 4 size-specific HDL subclasses are very large (average particle diameter 14.3 nm), large (12.1 nm), medium (10.9 nm), and small HDL (8.7 nm). Underlying data can be found in S1 Data. Apo A-I, apolipoprotein A-I; C, cholesterol; CHD, coronary heart disease; HDL, high-density lipoprotein; TG, triglycerides. https://doi.org/10.1371/journal.pbio.3000572.g003 Lipoprotein Signatures of CETP and HMGCR PLOS Biology | https://doi.org/10.1371/journal.pbio.3000572 December 20, 2019 6 / 19
Friedewald-estimated LDL cholesterol (as well as ‘direct’ assays) are nonspecific measures of cholesterol [26–28]. For example, in addition to the cholesterol in size-specific LDL particles, Friedewald LDL cholesterol also includes, to varying degrees, cholesterol in IDL, VLDL, and lipoprotein(a) [29]. This nonspecificity of commonly used “LDL” cholesterol assays is under-recognized and underlies the prevailing opinion that inhibitors of HMGCR and CETP both alter LDL cholesterol. However, our data show this not to be the case: using NMR spectroscopy-based lipoprotein particle quantification, which defines individual lipoprotein subclasses based on particle size [18,19,21], our findings demonstrate that CETP has negligible effect on cholesterol in size-specific LDL particles. As the inhibition of CETP affects the IDL subclass similarly to all the LDL subclasses, the “LDL cholesterol” via beta-quantification would also be only minimally affected. In this way, the use of a composite lipid measure can obscure differential associations of a therapy or gene [20] with individual constituents of the composite and can have clinical ramifications. For example, if a trial is powered to a given reduction in Friedewald LDL cholesterol, under the naïve assumption that the drug uniformly alters all the subcomponents, then the trial may not have the expected result if the drug has differential effects on these subcomponents. This is exemplified in the recent phase III ACCELERATE trial of evacetrapib, which was terminated for futility, and was powered to a difference Fig 4. Associations of genetic variants in CETP rs247617 (red) and HMGCR rs12916 (blue) with the triglyceride composition of size-specific lipoprotein particles. Estimates represent the standardized difference in lipoprotein trait, with per allele associations scaled to a 10% lower relative risk of CHD. Analyses were adjusted for age, sex, genotyping batch, and 10 genetic principal components. The circles refer to the effect estimates and the horizontal bars to the 95% CIs. Closed circles represent statistical significance of associations at P<0.002 and open circles associations that are nonsignificant at this threshold. The lipoprotein subclasses are defined by particle size [17–19]: potential chylomicrons and the largest VLDL particles (XXL-VLDL; average particle diameter �75 nm); 5 different VLDL subclasses, i.e., very large (average particle diameter 64.0 nm), large (53.6 nm), medium (44.5 nm), small (36.8 nm), and very small (31.3 nm); IDL (28.6 nm); and 3 LDL subclasses, i.e., large (25.5 nm), medium (23.0 nm), and small (18.7 nm). The 4 size-specific HDL subclasses are very large (average particle diameter 14.3 nm), large (12.1 nm), medium (10.9 nm), and small (8.7 nm). Underlying data can be found in S1 Data. Apo B, apolipoprotein B; CHD, coronary heart disease; HDL, high-density lipoprotein; IDL, intermediate-density lipoprotein; LDL, low-density lipoprotein; TG, triglycerides; VLDL, very-low-density lipoprotein. https://doi.org/10.1371/journal.pbio.3000572.g004 Lipoprotein Signatures of CETP and HMGCR PLOS Biology | https://doi.org/10.1371/journal.pbio.3000572 December 20, 2019 7 / 19
Fig 5. Observational associations of circulating triglyceride concentrations and triglyceride composition in lipoprotein subclass particles and risk of incident CHD. (Left panel) Black: Hazard ratios for incident CHD per SD higher triglyceride concentration within each size-specific lipoprotein subclass adjusted for traditional risk factors. Pink: adjusted for traditional risk factors, serum cholesterol, and serum triglycerides. (Right panel) Black: Hazard ratios for incident CHD per SD higher percentage of triglycerides (of all lipid molecules) within each size-specific lipoprotein subclass adjusted for traditional risk factors. Pink: adjusted for traditional risk factors, serum cholesterol, and serum triglycerides. Basic risk factors include age, sex, mean arterial pressure, smoking, diabetes mellitus, lipid medication, geographical region in FINRISK, and ethnicity in SABRE. The horizontal bars to the 95% CIs. Closed circles represent statistical significance of associations at P<0.002 and open circles associations that are nonsignificant at this threshold. The horizontal bars refer to the 95% CIs. Underlying data can be found in S1 Data. Apo B, apolipoprotein B; CHD, coronary heart disease; HDL, highdensity lipoprotein; IDL, intermediate-density lipoprotein; LDL, low-density lipoprotein; VLDL, very-low-density lipoprotein; TG, triglycerides. https://doi.org/10.1371/journal.pbio.3000572.g005 Fig 6. Observational associations of circulating triglyceride concentrations and triglyceride composition in lipoprotein subclass particles and risk of incident CHD with multiple adjustments. Hazard ratios for incident CHD per SD higher circulating triglyceride concentrations (upper part) and triglyceride composition (lower part) in lipoprotein subclass particles within each size-specific lipoprotein subclass adjusted for traditional risk factors and gradually for 3 fundamental measures that characterize the overall lipoprotein profile pretty well, namely, total serum triglyceride (total TG), HDL cholesterol (HDL-C), and apolipoprotein B (apoB) concentration. Closed circles represent statistical significance of associations at P<0.002 and open circles associations that are nonsignificant at this threshold. The horizontal bars refer to the 95% CIs. Underlying data can be found in S1 Data. Apo B, apolipoprotein B; C, cholesterol; CHD, coronary heart disease; HDL, high-density lipoprotein; IDL, intermediate-density lipoprotein; LDL, low-density lipoprotein; TG, triglycerides; VLDL, very-low-density lipoprotein. https://doi.org/10.1371/journal.pbio.3000572.g006 Lipoprotein Signatures of CETP and HMGCR PLOS Biology | https://doi.org/10.1371/journal.pbio.3000572 December 20, 2019 8 / 19
in LDL cholesterol based on a composite assay [12]. The differential effects of CETP inhibition on composite markers such as Friedewald and directly-quantified LDL cholesterol compared to apolipoprotein B concentrations identified in the subsequent phase III REVEAL trial of anacetrapib [13] suggest that had ACCELERATE used an alternative measure of proatherogenic lipoproteins (e.g., apolipoprotein B or non-HDL-C [14]) to gauge the expected vascular effect, the trial may have been more appropriately powered. This highlights the need to understand, in detail, the consequences of lipid-modifying therapies on lipoproteins and lipids in order to be able to gauge whether a composite measure (such as Friedewald LDL cholesterol) can be reliably used as an indicator of the likely beneficial effect of a therapy. This is unlikely to be limited to assays for LDL cholesterol. For example, assays that quantify triglycerides measure the summation of triglycerides across multiple lipoprotein particle categories. Drugs currently under development that target triglycerides (such as apolipoprotein C-III inhibitors [30]) have differential effects on triglycerides in lipoprotein subclass particles as demonstrated in a recent genetic study [31]. If triglycerides within different lipoprotein subclasses have heterogeneous effects on vascular disease, a clinical trial powered to the overall concentration of circulating triglycerides may give an inaccurate portrayal of the cardiovascular consequences arising from apolipoprotein C-III inhibition. Another key finding is that the lipid compositions of lipoprotein particles can associate with disease risk independently of total lipid concentrations. Although genetic inhibition of CETP increased circulating concentrations of cholesterol in all HDL subclasses, the triglyceride composition, i.e., the percentage of triglyceride molecules of all the lipid molecules in the particle, was markedly lower in all HDL particles. Intriguingly, our observational analyses, the first to explore lipoprotein particle lipid composition with CHD outcomes, revealed that triglyceride enrichment of HDL particles associates with higher risk for future CHD, independently of total circulating cholesterol and triglycerides. The largest hazard ratio for the triglyceride enrichment in medium HDL subclass particles was of a similar magnitude (approximately 1.3) as that for LDL cholesterol and apolipoprotein B [32]. However, this phenomenon appears to be due to combined effects of circulating HDL and apolipoprotein B-containing particles, maybe in connection to CETP function and the circulating amount of total triglycerides, not an intrinsic indication of the role of HDL particle lipid composition in CHD. Key strengths of our analyses include the availability of detailed measurements of blood lipoprotein subclass concentrations and compositions from general population studies with incident CHD events, together with the availability of genome-wide genotyping. We used single CETP and HMGCR variants as genetic proxies for therapeutic inhibition (i.e., instruments in the Mendelian randomization analyses), assuming that they are not pleiotropic. This assumption is justifiable on the basis that the SNPs were selected in cis-regions and alter gene expression and together with the fact that (1) the CETP genetic variant recapitulated the effects of CETP enzyme activity in relation to the role the enzyme has in shuttling esterified cholesterol from HDL to apolipoprotein B-containing particles in exchange for triglycerides [23] and that (2) prospective population-based data of patients taking statins with blood sampling before and after the commencement of therapy showed that genetic variants in HMGCR robustly recapitulated the effects of statin therapy on lipoprotein subclasses and lipids [16]. In conclusion, we have shown that, in contrast to genetic inhibition of HMG-CoA (proxying statin therapy), genetic inhibition of CETP does not alter circulating size-specific LDL cholesterol concentrations. This is masked by using conventional, nonspecific assays for LDL cholesterol and may be problematic for ongoing and future clinical trials of lipid lowering therapies, especially when a nonspecific marker of lipids is used to derive an expected effect of a drug with risk of disease. The basis for the reduction in CHD risk seen with CETP inhibition appears to be due to the lowering of atherogenic non-LDL lipoprotein particles. Our findings Lipoprotein Signatures of CETP and HMGCR PLOS Biology | https://doi.org/10.1371/journal.pbio.3000572 December 20, 2019 9 / 19
S1 Data. Data underlying Figs 1–6. (XLSX) S2 Data. Data underlying S1–S15 Figs. (XLSX) Author Contributions Conceptualization: Johannes Kettunen, Michael V. Holmes, Pauli Ohukainen, Emanuele Di Angelantonio, Adam S. Butterworth, Mika Ala-Korpela. Formal analysis: Johannes Kettunen, Elias Allara, Olga Anufrieva, Clare Oliver-Williams. Funding acquisition: Johannes Kettunen, Mika Ala-Korpela. Investigation: Johannes Kettunen, Michael V. Holmes, Elias Allara, Emanuele Di Angelantonio, Adam S. Butterworth, Mika Ala-Korpela. Methodology: Johannes Kettunen, Michael V. Holmes, Elias Allara, Olga Anufrieva, Qin Wang, George Davey Smith, Emanuele Di Angelantonio, Adam S. Butterworth, Mika AlaKorpela. Project administration: Johannes Kettunen, Michael V. Holmes, Adam S. Butterworth, Mika Ala-Korpela. Resources: Johannes Kettunen, Mika Ala-Korpela. Supervision: Johannes Kettunen, Michael V. Holmes, Adam S. Butterworth, Mika AlaKorpela. Validation: Johannes Kettunen, Michael V. Holmes, Adam S. Butterworth, Mika Ala-Korpela. Visualization: Olga Anufrieva. Writing – original draft: Johannes Kettunen, Michael V. Holmes, Mika Ala-Korpela. Writing – review & editing: Johannes Kettunen, Michael V. Holmes, Elias Allara, Olga Anufrieva, Pauli Ohukainen, Clare Oliver-Williams, Qin Wang, Therese Tillin, Alun D. Hughes, Mika Ka¨ho¨nen, Terho Lehtima¨ki, Jorma Viikari, Olli T. Raitakari, Veikko Salomaa, Marjo-Riitta Ja¨rvelin, Markus Perola, George Davey Smith, Nish Chaturvedi, John Danesh, Emanuele Di Angelantonio, Adam S. Butterworth, Mika Ala-Korpela. References 1. Collins R, Reith C, Emberson J, Armitage J, Baigent C, Blackwell L, et al. Interpretation of the evidence for the efficacy and safety of statin therapy. Lancet. 2016; 388:2532–61. https://doi.org/10.1016/S01406736(16)31357-5 PMID: 27616593 2. Sabatine MS, Giugliano RP, Keech AC, Honarpour N, Wiviott SD, Murphy SA, et al. Evolocumab and Clinical Outcomes in Patients with Cardiovascular Disease. N Engl J Med. 2017; 376:1713–22. https:// doi.org/10.1056/NEJMoa1615664 PMID: 28304224 3. Cannon CP, Blazing MA, Giugliano RP, McCagg A, White JA, Theroux P, et al. Ezetimibe Added to Statin Therapy after Acute Coronary Syndromes. N Engl J Med. 2015; 372:2387–97. https://doi.org/10. 1056/NEJMoa1410489 PMID: 26039521 4. Silverman MG, Ference BA, Im K, Wiviott SD, Giugliano RP, Grundy SM, et al. Association Between Lowering LDL-C and Cardiovascular Risk Reduction Among Different Therapeutic Interventions: A Systematic Review and Meta-analysis. JAMA. 2016; 316:1289–97. https://doi.org/10.1001/jama.2016. 13985 PMID: 27673306 Lipoprotein Signatures of CETP and HMGCR PLOS Biology | https://doi.org/10.1371/journal.pbio.3000572 December 20, 2019 16 / 19
5. Group HTC, Landray MJ, Haynes R et al. Effects of extended-release niacin with laropiprant in high-risk patients. N Engl J Med. 2014; 371:203–12. https://doi.org/10.1056/NEJMoa1300955 PMID: 25014686 6. Schwartz GG, Olsson AG, Abt M, Ballantyne CM, Barter PJ, Brumm J, et al. Effects of dalcetrapib in patients with a recent acute coronary syndrome. N Engl J Med. 2012; 367:2089–99. https://doi.org/10. 1056/NEJMoa1206797 PMID: 23126252 7. Holmes MV, Asselbergs FW, Palmer TM, Drenos F, Lanktree MB, Nelson CP, et al. Mendelian randomization of blood lipids for coronary heart disease. Eur Heart J. 2015; 36:539–50. https://doi.org/10.1093/ eurheartj/eht571 PMID: 24474739 8. Voight BF1, Peloso GM, Orho-Melander M, Frikke-Schmidt R, Barbalic M, Jensen MK, et al. Plasma HDL cholesterol and risk of myocardial infarction: a mendelian randomisation study. Lancet. 2012; 380:572–80. https://doi.org/10.1016/S0140-6736(12)60312-2 PMID: 22607825 9. White J, Swerdlow DI, Preiss D, Fairhurst-Hunter Z, Keating BJ, Asselbergs FW, et al. Association of Lipid Fractions With Risks for Coronary Artery Disease and Diabetes. JAMA Cardiol. 2016; 1:692–9. https://doi.org/10.1001/jamacardio.2016.1884 PMID: 27487401 10. Thompson A, Di Angelantonio E, Sarwar N, Erqou S, Saleheen D, Dullaart RP, et al. Association of cholesteryl ester transfer protein genotypes with CETP mass and activity, lipid levels, and coronary risk. JAMA. 2008; 299:2777–88. https://doi.org/10.1001/jama.299.23.2777 PMID: 18560005 11. Webb TR, Erdmann J, Stirrups KE, Stitziel NO, Masca NG, Jansen H, et al. Systematic Evaluation of Pleiotropy Identifies 6 Further Loci Associated With Coronary Artery Disease. J Am Coll Cardiol. 2017; 69:823–36. https://doi.org/10.1016/j.jacc.2016.11.056 PMID: 28209224 12. Lincoff AM, Nicholls SJ, Riesmeyer JS, Barter PJ, Brewer HB, Fox KAA, et al. Evacetrapib and Cardiovascular Outcomes in High-Risk Vascular Disease. N Engl J Med 2017; 376:1933–42. https://doi.org/ 10.1056/NEJMoa1609581 PMID: 28514624 13. HPS3/TIMI55–REVEAL Collaborative Group, Bowman L, Hopewell JC, Chen F, Wallendszus K, Stevens W, et al. Effects of Anacetrapib in Patients with Atherosclerotic Vascular Disease. N Engl J Med. 2017; 377:1217–27. https://doi.org/10.1056/NEJMoa1706444 PMID: 28847206 14. Holmes MV, Davey Smith G. Dyslipidaemia: REVEALing the effect of CETP inhibition in cardiovascular disease. Nat Rev Cardiol. 2017; 14:635–6. https://doi.org/10.1038/nrcardio.2017.156 PMID: 28980665 15. Ference BA, Kastelein JJP, Ginsberg HN, Chapman MJ, Nicholls SJ, Ray KK, et al. Association of Genetic Variants Related to CETP Inhibitors and Statins With Lipoprotein Levels and Cardiovascular Risk. JAMA. 2017; 318:947–56. https://doi.org/10.1001/jama.2017.11467 PMID: 28846118 16. Wu¨rtz P, Wang Q, Soininen P, Kangas AJ, Fatemifar G, Tynkkynen T, et al. Metabolomic Profiling of Statin Use and Genetic Inhibition of HMG-CoA Reductase. J Am Coll Cardiol. 2016; 67:1200–10. https://doi.org/10.1016/j.jacc.2015.12.060 PMID: 26965542 17. Soininen P, Kangas AJ, Wu¨rtz P, Tukiainen T, Tynkkynen T, Laatikainen R, et al. High-throughput serum NMR metabonomics for cost-effective holistic studies on systemic metabolism. Analyst. 2009; 134:1781–5. https://doi.org/10.1039/b910205a PMID: 19684899 18. Soininen P, Kangas AJ, Wurtz P, Suna T, Ala-Korpela M. Quantitative serum nuclear magnetic resonance metabolomics in cardiovascular epidemiology and genetics. Circ Cardiovasc Genet. 2015; 8:192–206. https://doi.org/10.1161/CIRCGENETICS.114.000216 PMID: 25691689 19. Wang J, Stanča ´kova ´A, Soininen P, Kangas AJ, Paananen J, Kuusisto J, et al. Lipoprotein subclass profiles in individuals with varying degrees of glucose tolerance: a population-based study of 9399 Finnish men. J Intern Med. 2012; 272:562–72. https://doi.org/10.1111/j.1365-2796.2012.02562.x PMID: 22650159 20. Tukiainen T, Kettunen J, Kangas AJ, Lyytika ¨inen LP, Soininen P, Sarin AP, et al. Detailed metabolic and genetic characterization reveals new associations for 30 known lipid loci. Hum Mol Genet. 2012; 21:1444–55. https://doi.org/10.1093/hmg/ddr581 PMID: 22156771 21. Lounila J, Ala-Korpela M, Jokisaari J, Savolainen MJ, Kesaniemi YA. Effects of orientational order and particle size on the NMR line positions of lipoproteins. Phys Rev Lett. 1994; 72:4049–52. https://doi.org/ 10.1103/PhysRevLett.72.4049 PMID: 10056366 22. Kumpula LS, Kumpula JM, Taskinen MR, Jauhiainen M, Kaski K, Ala-Korpela M. Reconsideration of hydrophobic lipid distributions in lipoprotein particles. Chem Phys Lipids. 2008; 155:57–62. https://doi. org/10.1016/j.chemphyslip.2008.06.003 PMID: 18611396 23. Barter PJ, Kastelein JJ. Targeting cholesteryl ester transfer protein for the prevention and management of cardiovascular disease. J Am Coll Cardiol. 2006; 47:492–9. https://doi.org/10.1016/j.jacc.2005.09. 042 PMID: 16458126 24. Istvan ES, Deisenhofer J. Structural mechanism for statin inhibition of HMG-CoA reductase. Science. 2001; 292:1160–4. https://doi.org/10.1126/science.1059344 PMID: 11349148 Lipoprotein Signatures of CETP and HMGCR PLOS Biology | https://doi.org/10.1371/journal.pbio.3000572 December 20, 2019 17 / 19
25. Varbo A, Nordestgaard BG. Remnant lipoproteins. Curr Opin Lipidol. 2017; 28:300–7. https://doi.org/ 10.1097/MOL.0000000000000429 PMID: 28548974 26. Friedewald WT, Levy RI, Fredrickson DS. Estimation of the concentration of low-density lipoprotein cholesterol in plasma, without use of the preparative ultracentrifuge. Clin Chem. 1972; 18:499–502. PMID: 4337382 27. Martin SS, Blaha MJ, Elshazly MB, Toth PP, Kwiterovich PO, Blumenthal RS, Jones SR. Comparison of a novel method vs the Friedewald equation for estimating low-density lipoprotein cholesterol levels from the standard lipid profile. JAMA 2013; 310:2061–8. https://doi.org/10.1001/jama.2013.280532 PMID: 24240933 28. Niemi J, Ma ¨kinen VP, Heikkonen J, Tenkanen L, Hiltunen Y, Hannuksela ML, et al. Estimation of VLDL, IDL, LDL, HDL2, apoA-I, and apoB from the Friedewald inputs—apoB and IDL, but not LDL, are associated with mortality in type 1 diabetes. Ann Med. 2009; 41:451–61. https://doi.org/10.1080/ 07853890902893392 PMID: 19412820 29. Nauck M, Warnick GR, Rifai N. Methods for measurement of LDL-cholesterol: a critical assessment of direct measurement by homogeneous assays versus calculation. Clin Chem. 2002; 48:236–54. PMID: 11805004 30. Gaudet D, Alexander VJ, Baker BF, Brisson D, Tremblay K, Singleton W, et al. Antisense Inhibition of Apolipoprotein C-III in Patients with Hypertriglyceridemia. N Engl J Med. 2015; 373:438–47. https://doi. org/10.1056/NEJMoa1400283 PMID: 26222559 31. Drenos F, Davey Smith G, Ala-Korpela M, Kettunen J, Wu¨rtz P, Soininen P, et al. Metabolic Characterization of a Rare Genetic Variation Within APOC3 and Its Lipoprotein Lipase-Independent Effects. Circ Cardiovasc Genet. 2016; 9:231–9. https://doi.org/10.1161/CIRCGENETICS.115.001302 PMID: 27114411 32. Wu¨rtz P, Havulinna AS, Soininen P, Tynkkynen T, Prieto-Merino D, Tillin T, et al. Metabolite profiling and cardiovascular event risk: a prospective study of 3 population-based cohorts. Circulation. 2015; 131:774–85. https://doi.org/10.1161/CIRCULATIONAHA.114.013116 PMID: 25573147 33. Sabatti C, Service SK, Hartikainen AL, Pouta A, Ripatti S, Brodsky J, et al. Genome-wide association analysis of metabolic traits in a birth cohort from a founder population. Nat Genet. 2009; 41:35–46. https://doi.org/10.1038/ng.271 PMID: 19060910 34. Jarvelin MR, Hartikainen-Sorri AL, Rantakallio P. Labour induction policy in hospitals of different levels of specialisation. Br J Obstet Gynaecol. 1993; 100:310–5. https://doi.org/10.1111/j.1471-0528.1993. tb12971.x PMID: 8494831 35. Raitakari OT, Juonala M, Ro ¨nnemaa T, Keltikangas-Ja ¨rvinen L, Ra ¨sa ¨nen L, Pietika ¨inen M, et al. Cohort profile: the cardiovascular risk in Young Finns Study. Int J Epidemiol. 2008; 37:1220–6. https://doi.org/ 10.1093/ije/dym225 PMID: 18263651 36. Konttinen H, Silventoinen K, Sarlio-Lahteenkorva S, Mannisto S, Haukkala A. Emotional eating and physical activity self-efficacy as pathways in the association between depressive symptoms and adiposity indicators. Am J Clin Nutr. 2010; 92:1031–9. https://doi.org/10.3945/ajcn.2010.29732 PMID: 20861176 37. Borodulin K, Vartiainen E, Peltonen M, Jousilahti P, Juolevi A, Laatikainen T, et al. Forty-year trends in cardiovascular risk factors in Finland. Eur J Public Health. 2015; 25:539–46. https://doi.org/10.1093/ eurpub/cku174 PMID: 25422363 38. Moore C, Sambrook J, Walker M, Tolkien Z, Kaptoge S, Allen D, et al. The INTERVAL trial to determine whether intervals between blood donations can be safely and acceptably decreased to optimise blood supply: study protocol for a randomised controlled trial. Trials. 2014; 15:363. https://doi.org/10.1186/ 1745-6215-15-363 PMID: 25230735 39. Tillin T, Hughes AD, Mayet J, Whincup P, Sattar N, Forouhi NG, et al. The relationship between metabolic risk factors and incident cardiovascular disease in Europeans, South Asians, and African Caribbeans: SABRE (Southall and Brent Revisited)—a prospective population-based study. J Am Coll Cardiol. 2013; 61:1777–86. https://doi.org/10.1016/j.jacc.2012.12.046 PMID: 23500273 40. Tillin T, Forouhi NG, McKeigue PM, Chaturvedi N, Group SS. Southall And Brent REvisited: Cohort profile of SABRE, a UK population-based comparison of cardiovascular disease and diabetes in people of European, Indian Asian and African Caribbean origins. Int J Epidemiol. 2012; 41:33–42. https://doi.org/ 10.1093/ije/dyq175 PMID: 21044979 41. Inouye M1, Kettunen J, Soininen P, Silander K, Ripatti S, Kumpula LS, et al. Metabonomic, transcriptomic, and genomic variation of a population cohort. Mol Syst Biol. 2010; 6:441. https://doi.org/10.1038/ msb.2010.93 PMID: 21179014 42. Kettunen J, Demirkan A, Wu¨rtz P, Draisma HH, Haller T, Rawal R, et al. Genome-wide study for circulating metabolites identifies 62 loci and reveals novel systemic effects of LPA. Nat Commun. 2016; 7:11122. https://doi.org/10.1038/ncomms11122 PMID: 27005778 Lipoprotein Signatures of CETP and HMGCR PLOS Biology | https://doi.org/10.1371/journal.pbio.3000572 December 20, 2019 18 / 19
43. Kettunen J, Tukiainen T, Sarin AP, Ortega-Alonso A, Tikkanen E, Lyytika ¨inen LP, et al. Genome-wide association study identifies multiple loci influencing human serum metabolite levels. Nat Genet. 2012; 44:269–76. https://doi.org/10.1038/ng.1073 PMID: 22286219 44. Wurtz P, Kangas AJ, Soininen P, Lawlor DA, Davey Smith G, Ala-Korpela M. Quantitative Serum NMR Metabolomics in Large-Scale Epidemiology: A Primer on -Omic Technology. Am J Epidemiol. 2017; 186:1084–96. https://doi.org/10.1093/aje/kwx016 PMID: 29106475 45. Willer CJ, Schmidt EM, Sengupta S, Peloso GM5, Gustafsson S, Kanoni Set al. Discovery and refinement of loci associated with lipid levels. Nat Genet. 2013; 45:1274–83. https://doi.org/10.1038/ng.2797 PMID: 24097068 46. Swerdlow DI, Preiss D, Kuchenbaecker KB, Holmes MV, Engmann JE, Shah T, et al. HMG-coenzyme A reductase inhibition, type 2 diabetes, and bodyweight: evidence from genetic analysis and randomised trials. Lancet. 2015; 385:351–61. https://doi.org/10.1016/S0140-6736(14)61183-1 PMID: 25262344 47. Do R, Willer CJ, Schmidt EM, Sengupta S, Gao C, Peloso GM, et al. Common variants associated with plasma triglycerides and risk for coronary artery disease. Nat Genet. 2013; 45:1345–52. https://doi.org/ 10.1038/ng.2795 PMID: 24097064 48. GTEx Consortium. The Genotype-Tissue Expression (GTEx) project. Nat Genet. 2013; 45:580–5. https://doi.org/10.1038/ng.2653 PMID: 23715323 49. Nikpay M, Goel A, Won HH, Hall LM, Willenborg C, Kanoni S, et al. A comprehensive 1,000 Genomesbased genome-wide association meta-analysis of coronary artery disease. Nat Genet. 2015; 47:1121– 30. https://doi.org/10.1038/ng.3396 PMID: 26343387 Lipoprotein Signatures of CETP and HMGCR PLOS Biology | https://doi.org/10.1371/journal.pbio.3000572 December 20, 2019 19 / 19