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      Evaluating the relationship between circulating lipoprotein lipids and apolipoproteins with risk of coronary heart disease: A multivariable Mendelian randomisation analysis

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          Abstract

          Background

          Circulating lipoprotein lipids cause coronary heart disease (CHD). However, the precise way in which one or more lipoprotein lipid-related entities account for this relationship remains unclear. Using genetic instruments for lipoprotein lipid traits implemented through multivariable Mendelian randomisation (MR), we sought to compare their causal roles in the aetiology of CHD.

          Methods and findings

          We conducted a genome-wide association study (GWAS) of circulating non-fasted lipoprotein lipid traits in the UK Biobank (UKBB) for low-density lipoprotein (LDL) cholesterol, triglycerides, and apolipoprotein B to identify lipid-associated single nucleotide polymorphisms (SNPs). Using data from CARDIoGRAMplusC4D for CHD (consisting of 60,801 cases and 123,504 controls), we performed univariable and multivariable MR analyses. Similar GWAS and MR analyses were conducted for high-density lipoprotein (HDL) cholesterol and apolipoprotein A-I. The GWAS of lipids and apolipoproteins in the UKBB included between 393,193 and 441,016 individuals in whom the mean age was 56.9 y (range 39–73 y) and of whom 54.2% were women. The mean (standard deviation) lipid concentrations were LDL cholesterol 3.57 (0.87) mmol/L and HDL cholesterol 1.45 (0.38) mmol/L, and the median triglycerides was 1.50 (IQR = 1.11) mmol/L. The mean (standard deviation) values for apolipoproteins B and A-I were 1.03 (0.24) g/L and 1.54 (0.27) g/L, respectively. The GWAS identified multiple independent SNPs associated at P < 5 × 10 −8 for LDL cholesterol (220), apolipoprotein B ( n = 255), triglycerides (440), HDL cholesterol (534), and apolipoprotein A-I (440). Between 56%–93% of SNPs identified for each lipid trait had not been previously reported in large-scale GWASs. Almost half (46%) of these SNPs were associated at P < 5 × 10 −8 with more than one lipid-related trait. Assessed individually using MR, LDL cholesterol (odds ratio [OR] 1.66 per 1-standard-deviation–higher trait; 95% CI: 1.49–1.86; P < 0.001), triglycerides (OR 1.34; 95% CI: 1.25–1.44; P < 0.001) and apolipoprotein B (OR 1.73; 95% CI: 1.56–1.91; P < 0.001) had effect estimates consistent with a higher risk of CHD. In multivariable MR, only apolipoprotein B (OR 1.92; 95% CI: 1.31–2.81; P < 0.001) retained a robust effect, with the estimate for LDL cholesterol (OR 0.85; 95% CI: 0.57–1.27; P = 0.44) reversing and that of triglycerides (OR 1.12; 95% CI: 1.02–1.23; P = 0.01) becoming weaker. Individual MR analyses showed a 1-standard-deviation–higher HDL cholesterol (OR 0.80; 95% CI: 0.75–0.86; P < 0.001) and apolipoprotein A-I (OR 0.83; 95% CI: 0.77–0.89; P < 0.001) to lower the risk of CHD, but these effect estimates attenuated substantially to the null on accounting for apolipoprotein B. A limitation is that, owing to the nature of lipoprotein metabolism, measures related to the composition of lipoprotein particles are highly correlated, creating a challenge in making exclusive interpretations on causation of individual components.

          Conclusions

          These findings suggest that apolipoprotein B is the predominant trait that accounts for the aetiological relationship of lipoprotein lipids with risk of CHD.

          Author summary

          Why was this study done?
          • There is uncertainty regarding which lipid or apolipoprotein trait is the predominant atherogenic agent involved in the aetiology of lipids and coronary heart disease (CHD).

          • The elucidation of such is important because not only does it clarify the aetiological understanding of the pathogenesis of CHD, it also hones the focus on the lipid- or apolipoprotein-related trait that should be the focus when developing lipid-modifying interventions.

          What did the researchers do and find?
          • We used data on up to 441,016 participants from the UK Biobank to conduct genome-wide association analyses to find genetic variants reliably associated with lipoprotein lipid and apolipoprotein concentrations: this led to the identification of multiple independent genetic variants, each associated very robustly (at P < 5 × 10 −8) with LDL cholesterol (220 genetic variants), apolipoprotein B (255 genetic variants), triglycerides (440 genetic variants), HDL cholesterol (534 genetic variants), and apolipoprotein A–I (440 genetic variants). Hundreds of these variants identified were novel, to our knowledge.

          • When we explored the potential causal role of these lipids and apolipoproteins in isolation using Mendelian randomisation, we found evidence compatible with LDL cholesterol, triglycerides, and apolipoprotein B increasing the risk of CHD and HDL cholesterol and apolipoproteins A-I lowering CHD risk.

          • When we examined these lipids and apolipoproteins together in multivariable Mendelian randomisation, we found that only apolipoprotein B retained a robust relationship with the risk of CHD—the effect estimates for all other entities were either substantially attenuated to the null or reversed in direction.

          What do these findings mean?
          • The analytical approach in this study implemented through multivariable Mendelian randomisation simultaneously accounts for genetic associations with lipids and apolipoproteins and should therefore provide more reliable insights into what is the underlying driver of CHD.

          • These findings support that, amongst the repertoire of lipoprotein lipids and apolipoproteins that we investigated, apolipoprotein B has a fundamental role in the aetiology of CHD.

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          Most cited references24

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          'Mendelian randomization': can genetic epidemiology contribute to understanding environmental determinants of disease?

          Associations between modifiable exposures and disease seen in observational epidemiology are sometimes confounded and thus misleading, despite our best efforts to improve the design and analysis of studies. Mendelian randomization-the random assortment of genes from parents to offspring that occurs during gamete formation and conception-provides one method for assessing the causal nature of some environmental exposures. The association between a disease and a polymorphism that mimics the biological link between a proposed exposure and disease is not generally susceptible to the reverse causation or confounding that may distort interpretations of conventional observational studies. Several examples where the phenotypic effects of polymorphisms are well documented provide encouraging evidence of the explanatory power of Mendelian randomization and are described. The limitations of the approach include confounding by polymorphisms in linkage disequilibrium with the polymorphism under study, that polymorphisms may have several phenotypic effects associated with disease, the lack of suitable polymorphisms for studying modifiable exposures of interest, and canalization-the buffering of the effects of genetic variation during development. Nevertheless, Mendelian randomization provides new opportunities to test causality and demonstrates how investment in the human genome project may contribute to understanding and preventing the adverse effects on human health of modifiable exposures.
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            Assessing the suitability of summary data for two-sample Mendelian randomization analyses using MR-Egger regression: the role of the I2 statistic

            Background MR-Egger regression has recently been proposed as a method for Mendelian randomization (MR) analyses incorporating summary data estimates of causal effect from multiple individual variants, which is robust to invalid instruments. It can be used to test for directional pleiotropy and provides an estimate of the causal effect adjusted for its presence. MR-Egger regression provides a useful additional sensitivity analysis to the standard inverse variance weighted (IVW) approach that assumes all variants are valid instruments. Both methods use weights that consider the single nucleotide polymorphism (SNP)-exposure associations to be known, rather than estimated. We call this the `NO Measurement Error’ (NOME) assumption. Causal effect estimates from the IVW approach exhibit weak instrument bias whenever the genetic variants utilized violate the NOME assumption, which can be reliably measured using the F-statistic. The effect of NOME violation on MR-Egger regression has yet to be studied. Methods An adaptation of the I 2 statistic from the field of meta-analysis is proposed to quantify the strength of NOME violation for MR-Egger. It lies between 0 and 1, and indicates the expected relative bias (or dilution) of the MR-Egger causal estimate in the two-sample MR context. We call it I G X 2 . The method of simulation extrapolation is also explored to counteract the dilution. Their joint utility is evaluated using simulated data and applied to a real MR example. Results In simulated two-sample MR analyses we show that, when a causal effect exists, the MR-Egger estimate of causal effect is biased towards the null when NOME is violated, and the stronger the violation (as indicated by lower values of I G X 2 ), the stronger the dilution. When additionally all genetic variants are valid instruments, the type I error rate of the MR-Egger test for pleiotropy is inflated and the causal effect underestimated. Simulation extrapolation is shown to substantially mitigate these adverse effects. We demonstrate our proposed approach for a two-sample summary data MR analysis to estimate the causal effect of low-density lipoprotein on heart disease risk. A high value of I G X 2 close to 1 indicates that dilution does not materially affect the standard MR-Egger analyses for these data. Conclusions Care must be taken to assess the NOME assumption via the I G X 2 statistic before implementing standard MR-Egger regression in the two-sample summary data context. If I G X 2 is sufficiently low (less than 90%), inferences from the method should be interpreted with caution and adjustment methods considered.
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              Evaluating the potential role of pleiotropy in Mendelian randomization studies

              Abstract Pleiotropy, the phenomenon of a single genetic variant influencing multiple traits, is likely widespread in the human genome. If pleiotropy arises because the single nucleotide polymorphism (SNP) influences one trait, which in turn influences another (‘vertical pleiotropy’), then Mendelian randomization (MR) can be used to estimate the causal influence between the traits. Of prime focus among the many limitations to MR is the unprovable assumption that apparent pleiotropic associations are mediated by the exposure (i.e. reflect vertical pleiotropy), and do not arise due to SNPs influencing the two traits through independent pathways (‘horizontal pleiotropy’). The burgeoning treasure trove of genetic associations yielded through genome wide association studies makes for a tantalizing prospect of phenome-wide causal inference. Recent years have seen substantial attention devoted to the problem of horizontal pleiotropy, and in this review we outline how newly developed methods can be used together to improve the reliability of MR.
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                Author and article information

                Contributors
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: VisualizationRole: Writing – review & editing
                Role: ConceptualizationRole: InvestigationRole: MethodologyRole: SoftwareRole: Writing – review & editing
                Role: InvestigationRole: MethodologyRole: Writing – review & editing
                Role: InvestigationRole: Writing – review & editing
                Role: InvestigationRole: Writing – review & editing
                Role: ConceptualizationRole: Data curationRole: InvestigationRole: MethodologyRole: SupervisionRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: InvestigationRole: MethodologyRole: SupervisionRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: Academic Editor
                Journal
                PLoS Med
                PLoS Med
                plos
                plosmed
                PLoS Medicine
                Public Library of Science (San Francisco, CA USA )
                1549-1277
                1549-1676
                23 March 2020
                March 2020
                : 17
                : 3
                : e1003062
                Affiliations
                [1 ] Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom
                [2 ] Population Health Sciences, Bristol Medical School, University of Bristol, Barley House, Oakfield Grove, Bristol, United Kingdom
                [3 ] Systems Epidemiology, Baker Heart and Diabetes Institute, Melbourne, Australia
                [4 ] Computational Medicine, Faculty of Medicine, University of Oulu and Biocenter Oulu, Oulu, Finland
                [5 ] NMR Metabolomics Laboratory, School of Pharmacy, University of Eastern Finland, Kuopio, Finland
                [6 ] 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, Australia
                [7 ] Centre for Naturally Randomized Trials, University of Cambridge, Cambridge, United Kingdom
                [8 ] MRC/BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
                [9 ] Medical Research Council Population Health Research Unit, University of Oxford, Oxford, United Kingdom
                [10 ] Clinical Trial Service Unit & Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
                University of Pennsylvania, UNITED STATES
                Author notes

                I have read the journal's policy and the authors of this manuscript have the following competing interests: BAF reports receiving grants from Amgen, Merck & Co., Novartis, and Esperion Therapeutics; consulting or advisory board fees from Amgen, Regeneron, Sanofi, Merck & Co., Pfizer, CiVi BioPhama, and KrKA Pharmaceuticals; and grants from Merck & Co., Amgen, Novartis, Novo Nordisk, Regeneron, Sanofi, Pfizer, Eli Lilly, Mylan, Ionis, dalCOR, Silence Therapeutics, Integral Therapeutics, CiVi Pharma, KrKa Phamaceuticals, American College of Cardiology, European Atherosclerosis Society, and European Society of Cardiology. MVH 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. GDS is an Academic Editor on PLOS Medicine's editorial board. All other authors report no potential conflicts of interest.

                ‡ These authors are joint senior authors on this work.

                Author information
                http://orcid.org/0000-0002-7918-2040
                http://orcid.org/0000-0001-5188-5775
                http://orcid.org/0000-0003-4655-4511
                http://orcid.org/0000-0001-5905-1206
                http://orcid.org/0000-0002-1407-8314
                http://orcid.org/0000-0001-6617-0879
                Article
                PMEDICINE-D-19-02981
                10.1371/journal.pmed.1003062
                7089422
                32203549
                dd1ad9df-244b-4407-bc6c-881d36d177e4
                © 2020 Richardson 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.

                History
                : 15 August 2019
                : 21 February 2020
                Page count
                Figures: 3, Tables: 1, Pages: 22
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/501100000265, Medical Research Council;
                Award ID: MC_UU_00011/1 and MC_UU-00011/2
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/501100000265, Medical Research Council;
                Award ID: MC_UU_00011/1 and MC_UU-00011/2
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/501100000265, Medical Research Council;
                Award ID: MC_UU_00011/1 and MC_UU-00011/2
                Award Recipient :
                Funded by: UKRI
                Award ID: MR/S003886/1
                Award Recipient :
                Funded by: National Health and Medical Research Council (AU)
                Award ID: APP1158958
                Award Recipient :
                Funded by: Sigrid Juselius Foundation, Finland
                Award Recipient :
                Funded by: NIHR Cambridge Biomedical Research Centre (GB)
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/501100000265, Medical Research Council;
                Award ID: MRC PHRU Oxford
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/501100000274, British Heart Foundation;
                Award ID: FS/18/23/33512
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/501100013373, NIHR Oxford Biomedical Research Centre;
                Award ID: Obesity theme
                Award Recipient :
                TGR, ES, and GDS work in the Medical Research Council Integrative Epidemiology Unit at the University of Bristol, which is supported by the Medical Research Council (MC_UU_00011/1 and MC_UU-00011/2). TGR is a UKRI Innovation Research Fellow (MR/S003886/1). MAK is supported by a Senior Research Fellowship from the National Health and Medical Research Council (NHMRC) of Australia (APP1158958) and a research grant from the Sigrid Juselius Foundation, Finland. BAF is supported by the National Institute for Health Research (NIHR) Cambridge Biomedical Research Centre at the Cambridge University Hospitals NHS Foundation Trust. 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) and the National Institute for Health Research Oxford Biomedical Research Centre. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Biology and Life Sciences
                Biochemistry
                Proteins
                Lipoproteins
                Apolipoproteins
                Biology and Life Sciences
                Biochemistry
                Lipids
                Biology and Life Sciences
                Biochemistry
                Lipids
                Cholesterol
                Biology and Life Sciences
                Biochemistry
                Proteins
                Lipoproteins
                Biology and Life Sciences
                Computational Biology
                Genome Analysis
                Genome-Wide Association Studies
                Biology and Life Sciences
                Genetics
                Genomics
                Genome Analysis
                Genome-Wide Association Studies
                Biology and Life Sciences
                Genetics
                Human Genetics
                Genome-Wide Association Studies
                Medicine and Health Sciences
                Vascular Medicine
                Coronary Heart Disease
                Medicine and Health Sciences
                Cardiology
                Coronary Heart Disease
                Biology and Life Sciences
                Molecular Biology
                Macromolecular Structure Analysis
                Lipid Analysis
                Biology and Life Sciences
                Genetics
                Molecular Genetics
                Biology and Life Sciences
                Molecular Biology
                Molecular Genetics
                Custom metadata
                The data underlying the results presented in the study are available from the UK Biobank ( http://biobank.ndph.ox.ac.uk/showcase/). Derived data supporting the findings of this study are available as a Supporting information file.

                Medicine
                Medicine

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