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Association of Genetically Enhanced Lipoprotein Lipase–Mediated Lipolysis and Low-Density Lipoprotein Cholesterol–Lowering Alleles With Risk of Coronary Disease and Type 2 Diabetes

Lotta, Luca A., Stewart, Isobel D., Sharp, Stephen J., Day, Felix R., Burgess, Stephen, Luan, Jian’an, Bowker, Nicholas, Cai, Lina, Li, Chen, Wittemans, Laura B. L., Kerrison, Nicola D., Khaw, Kay-Tee, McCarthy, Mark I., O’Rahilly, Stephen, Scott, Robert A., Savage, David B., Perry, John R. B., Langenberg, Claudia, Wareham, Nicholas J.·2018·JAMA Cardiology
DOI 10.1001/jamacardio.2018.2866PMC PMC6217943
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Figure 1. Associations of Genotype Category With Cardiometabolic Disease Outcomes in 2 × 2 Factorial Genetic Analyses Associations of each genetic score group with risk of coronary artery disease and type 2 diabetes compared with the reference group. The reference group includes those with a low-density lipoprotein cholesterol (LDL-C)–lowering score and a triglyceride-lowering LPL score less than or equal to the median score; the genetically lower triglyceride levels only group, those with a triglyceride-lowering LPL score greater than the median but an LDL-C–lowering score less than or equal to the median; the genetically lower LDL-C levels only group, those with an LDL-C–lowering score greater than the median but a triglyceride-lowering LPL score less than or equal to the median; and the group with both exposures, those with both scores greater than the median. Analyses include individual-level genetic data from 390 470 participants of the UK Biobank,32 EPIC-Norfolk,31 and EPIC-InterAct30 studies. Median values and interquartile ranges for lipid levels in a given genotype category are from the EPIC-Norfolk study. To convert LDL-C level to micromoles per liter, multiply by 0.0259. To convert triglyceride level to micromoles per liter, multiply by 0.0113. IQR indicates interquartile range; LPL, lipoprotein lipase; NA, not applicable; OR, odds ratio.
Figure 1. Associations of Genotype Category With Cardiometabolic Disease Outcomes in 2 × 2 Factorial Genetic Analyses Associations of each genetic score group with risk of coronary artery disease and type 2 diabetes compared with the reference group. The reference group includes those with a low-density lipoprotein cholesterol (LDL-C)–lowering score and a triglyceride-lowering LPL score less than or equal to the median score; the genetically lower triglyceride levels only group, those with a triglyceride-lowering LPL score greater than the median but an LDL-C–lowering score less than or equal to the median; the genetically lower LDL-C levels only group, those with an LDL-C–lowering score greater than the median but a triglyceride-lowering LPL score less than or equal to the median; and the group with both exposures, those with both scores greater than the median. Analyses include individual-level genetic data from 390 470 participants of the UK Biobank,32 EPIC-Norfolk,31 and EPIC-InterAct30 studies. Median values and interquartile ranges for lipid levels in a given genotype category are from the EPIC-Norfolk study. To convert LDL-C level to micromoles per liter, multiply by 0.0259. To convert triglyceride level to micromoles per liter, multiply by 0.0113. IQR indicates interquartile range; LPL, lipoprotein lipase; NA, not applicable; OR, odds ratio.CC BY · PubMed Central
Figure 2. Associations of Triglyceride-Lowering LPL Alleles With Cardiometabolic Disease Outcomes in Individuals Above or Below the Median of the Population Distribution of Low-Density Lipoprotein Cholesterol (LDL-C)–Lowering Genetic Variants Analyses include individual-level genetic data from 390 470 participants of the UK Biobank,32 EPIC-Norfolk,31 and EPIC-InterAct30 studies.
Figure 2. Associations of Triglyceride-Lowering LPL Alleles With Cardiometabolic Disease Outcomes in Individuals Above or Below the Median of the Population Distribution of Low-Density Lipoprotein Cholesterol (LDL-C)–Lowering Genetic Variants Analyses include individual-level genetic data from 390 470 participants of the UK Biobank,32 EPIC-Norfolk,31 and EPIC-InterAct30 studies.CC BY · PubMed Central
Figure 3. Associations of Triglyceride-Lowering LPL Alleles With Cardiometabolic Disease Outcomes Within Quintiles of the Population Distribution of Genetic Variants at 58 Low-Density Lipoprotein Cholesterol (LDL-C)–Associated Genetic Loci Data are from the UK Biobank,32 EPIC-Norfolk,31 and EPIC-InterAct30 studies. Median values and interquartile ranges for lipid levels within each stratum are from the EPIC-Norfolk study. To convert LDL-C level to micromoles per liter, multiply by 0.0259. To convert triglyceride level to micromoles per liter, multiply by 0.0113. IQR, interquartile range; OR, odds ratio.
Figure 3. Associations of Triglyceride-Lowering LPL Alleles With Cardiometabolic Disease Outcomes Within Quintiles of the Population Distribution of Genetic Variants at 58 Low-Density Lipoprotein Cholesterol (LDL-C)–Associated Genetic Loci Data are from the UK Biobank,32 EPIC-Norfolk,31 and EPIC-InterAct30 studies. Median values and interquartile ranges for lipid levels within each stratum are from the EPIC-Norfolk study. To convert LDL-C level to micromoles per liter, multiply by 0.0259. To convert triglyceride level to micromoles per liter, multiply by 0.0113. IQR, interquartile range; OR, odds ratio.CC BY · PubMed Central
Figure 4. Associations of Loss-of-Function Alleles With Cardiometabolic Disease Outcomes in ANGPTL4 and ANGPTL3 A, Associations of the ANGPTL4 p.Glu40Lys loss-of-function allele with cardiometabolic disease outcomes. Groups with genetically higher or lower low-density lipoprotein cholesterol (LDL-C) levels were defined on the basis of the median value of the 58-variant LDL-C–lowering genetic score. Associations are scaled to represent the odds ratio (OR) per SD of genetically lower triglyceride levels. Data are from the UK Biobank,32 EPIC-Norfolk,31 and EPIC-InterAct30 studies. B, Associations of different genetic exposures associated with lower LDL-C levels with protection against coronary disease. A clear log-linear relationship between genetic difference in LDL-C level and lower risk is observed for several mechanisms, while ANGPTL3 loss-of-function variants are outliers in this relationship. For individual variants, the estimates represent per-allele differences; for quintiles of the LDL-C score, the difference is compared with the bottom quintile; for the overall genetic score, the difference is per SD of genetically lower LDL-C level; and for ANGPTL3 variants, the difference is in carriers compared with noncarriers.
Figure 4. Associations of Loss-of-Function Alleles With Cardiometabolic Disease Outcomes in ANGPTL4 and ANGPTL3 A, Associations of the ANGPTL4 p.Glu40Lys loss-of-function allele with cardiometabolic disease outcomes. Groups with genetically higher or lower low-density lipoprotein cholesterol (LDL-C) levels were defined on the basis of the median value of the 58-variant LDL-C–lowering genetic score. Associations are scaled to represent the odds ratio (OR) per SD of genetically lower triglyceride levels. Data are from the UK Biobank,32 EPIC-Norfolk,31 and EPIC-InterAct30 studies. B, Associations of different genetic exposures associated with lower LDL-C levels with protection against coronary disease. A clear log-linear relationship between genetic difference in LDL-C level and lower risk is observed for several mechanisms, while ANGPTL3 loss-of-function variants are outliers in this relationship. For individual variants, the estimates represent per-allele differences; for quintiles of the LDL-C score, the difference is compared with the bottom quintile; for the overall genetic score, the difference is per SD of genetically lower LDL-C level; and for ANGPTL3 variants, the difference is in carriers compared with noncarriers.CC BY · PubMed Central

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Research works printed but not linked: 34

  1. Eckel, 1989, Lipoprotein lipase: a multifunctional enzyme relevant to common metabolic diseases., N Engl J Med, 320, 1060, 10.1056/NEJM198904203201607 · DOI
  2. Sagoo, 2008, Seven lipoprotein lipase gene polymorphisms, lipid fractions, and coronary disease: a HuGE association review and meta-analysis., Am J Epidemiol, 168, 1233, 10.1093/aje/kwn235 · DOI · Paper page
  3. Musunuru, 2010, Exome sequencing, ANGPTL3 mutations, and familial combined hypolipidemia., N Engl J Med, 363, 2220, 10.1056/NEJMoa1002926 · DOI · Paper page
  4. Lotta, 2017, Integrative genomic analysis implicates limited peripheral adipose storage capacity in the pathogenesis of human insulin resistance., Nat Genet, 49, 17, 10.1038/ng.3714 · DOI
  5. Taskinen, 1987, Lipoprotein lipase in diabetes., Diabetes Metab Rev, 3, 551, 10.1002/(ISSN)1099-0895 · DOI
  6. Liu, 2017, Exome-wide association study of plasma lipids in >300,000 individuals., Nat Genet, 49, 1758, 10.1038/ng.3977 · DOI · Paper page
  7. Mahajan, 2018, Refining the accuracy of validated target identification through coding variant fine-mapping in type 2 diabetes., Nat Genet, 50, 559, 10.1038/s41588-018-0084-1 · DOI · Paper page
  8. Larsson, 2014, Identification of a small molecule that stabilizes lipoprotein lipase in vitro and lowers triglycerides in vivo., Biochem Biophys Res Commun, 450, 1063, 10.1016/j.bbrc.2014.06.114 · DOI
  9. Geldenhuys, 2014, A novel lipoprotein lipase (LPL) agonist rescues the enzyme from inhibition by angiopoietin-like 4 (ANGPTL4)., Bioorg Med Chem Lett, 24, 2163, 10.1016/j.bmcl.2014.03.021 · DOI
  10. Gaudet, 2014, Targeting APOC3 in the familial chylomicronemia syndrome., N Engl J Med, 371, 2200, 10.1056/NEJMoa1400284 · DOI · Paper page
  11. Würtz, 2016, Metabolomic profiling of statin use and genetic inhibition of HMG-CoA reductase., J Am Coll Cardiol, 67, 1200, 10.1016/j.jacc.2015.12.060 · DOI · Paper page
  12. Sattar, 2016, Lipid-lowering efficacy of the PCSK9 inhibitor evolocumab (AMG 145) in patients with type 2 diabetes: a meta-analysis of individual patient data., Lancet Diabetes Endocrinol, 4, 403, 10.1016/S2213-8587(16)00003-6 · DOI
  13. Leiter, 2017, Lipid-lowering efficacy and safety of alirocumab in patients with or without diabetes: a sub-analysis of ODYSSEY COMBO II., Diabetes Obes Metab, 19, 989, 10.1111/dom.12909 · DOI
  14. Plenge, 2013, Validating therapeutic targets through human genetics., Nat Rev Drug Discov, 12, 581, 10.1038/nrd4051 · DOI · Paper page
  15. Day, 1999, EPIC-Norfolk: study design and characteristics of the cohort: European Prospective Investigation of Cancer., Br J Cancer, 80, 95
  16. Sudlow, 2015, UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age., PLoS Med, 12, e1001779, 10.1371/journal.pmed.1001779 · DOI · Paper page
  17. Morris, 2012, Large-scale association analysis provides insights into the genetic architecture and pathophysiology of type 2 diabetes., Nat Genet, 44, 981, 10.1038/ng.2383 · DOI · Paper page
  18. Scott, 2012, Large-scale association analyses identify new loci influencing glycemic traits and provide insight into the underlying biological pathways., Nat Genet, 44, 991, 10.1038/ng.2385 · DOI
  19. Manning, 2012, A genome-wide approach accounting for body mass index identifies genetic variants influencing fasting glycemic traits and insulin resistance., Nat Genet, 44, 659, 10.1038/ng.2274 · DOI
  20. Willer, 2013, Discovery and refinement of loci associated with lipid levels., Nat Genet, 45, 1274, 10.1038/ng.2797 · DOI
  21. Riboli, 1997, The EPIC Project: rationale and study design: European Prospective Investigation Into Cancer and Nutrition., Int J Epidemiol, 26, S6, 10.1093/ije/26.suppl_1.S6 · DOI
  22. Ference, 2015, Effect of naturally random allocation to lower low-density lipoprotein cholesterol on the risk of coronary heart disease mediated by polymorphisms in NPC1L1, HMGCR, or both: a 2 × 2 factorial mendelian randomization study., J Am Coll Cardiol, 65, 1552, 10.1016/j.jacc.2015.02.020 · DOI · Paper page
  23. Yin, 2009, Genetic variation in ANGPTL4 provides insights into protein processing and function., J Biol Chem, 284, 13213, 10.1074/jbc.M900553200 · DOI · Paper page
  24. Gusarova, 2018, Genetic inactivation of ANGPTL4 improves glucose homeostasis and is associated with reduced risk of diabetes., Nat Commun, 9, 2252, 10.1038/s41467-018-04611-z · DOI · Paper page
  25. Jarcho, 2015, Proof that lower is better—LDL cholesterol and IMPROVE-IT., N Engl J Med, 372, 2448, 10.1056/NEJMe1507041 · DOI
  26. Burgess, 2016, Combining information on multiple instrumental variables in mendelian randomization: comparison of allele score and summarized data methods., Stat Med, 35, 1880, 10.1002/sim.v35.11 · DOI
  27. Machiela, 2015, LDlink: a web-based application for exploring population-specific haplotype structure and linking correlated alleles of possible functional variants., Bioinformatics, 31, 3555, 10.1093/bioinformatics/btv402 · DOI · Paper page
  28. McQuaid, 2010, Femoral adipose tissue may accumulate the fat that has been recycled as VLDL and nonesterified fatty acids., Diabetes, 59, 2465, 10.2337/db10-0678 · DOI
  29. Fall, 2015, Using genetic variants to assess the relationship between circulating lipids and type 2 diabetes., Diabetes, 64, 2676, 10.2337/db14-1710 · DOI
  30. Lotta, 2016, Association between low-density lipoprotein cholesterol-lowering genetic variants and risk of type 2 diabetes: a meta-analysis., JAMA, 316, 1383, 10.1001/jama.2016.14568 · DOI · Paper page
  31. Besseling, 2015, Association between familial hypercholesterolemia and prevalence of type 2 diabetes mellitus., JAMA, 313, 1029, 10.1001/jama.2015.1206 · DOI
  32. Swerdlow, 2015, HMG-coenzyme A reductase inhibition, type 2 diabetes, and bodyweight: evidence from genetic analysis and randomised trials., Lancet, 385, 351, 10.1016/S0140-6736(14)61183-1 · DOI · Paper page
  33. Willer, 2008, Newly identified loci that influence lipid concentrations and risk of coronary artery disease., Nat Genet, 40, 161, 10.1038/ng.76 · DOI · Paper page
  34. Voight, 2012, Plasma HDL cholesterol and risk of myocardial infarction: a mendelian randomisation study., Lancet, 380, 572, 10.1016/S0140-6736(12)60312-2 · DOI · Paper page

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Metadata

Authors
Lotta, L. A., Stewart, I. D., Sharp, S. J., Day, F. R., Burgess, S., Luan, J., Bowker, N., Cai, L., Li, C., Wittemans, L. B. L., Kerrison, N. D., Khaw, K.-T., McCarthy, M. I., O’Rahilly, S., Scott, R. A., Savage, D. B., Perry, J. R. B., Langenberg, C., & Wareham, N. J.
Affiliations
  • MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, United Kingdom
  • MRC Biostatistics Unit, University of Cambridge, Cambridge, United Kingdom
  • Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
  • Oxford Centre for Diabetes, Endocrinology, and Metabolism, University of Oxford, Oxford, United Kingdom
  • Metabolic Research Laboratories, Institute of Metabolic Science, University of Cambridge, Cambridge, United Kingdom
Venue
JAMA Cardiology
Publisher
American Medical Association (AMA)
Published
2018-10-01
Volume
3
Issue
10
Pages
957
ISSN
2380-6583
DOI
10.1001/jamacardio.2018.2866
PMC
PMC6217943
Cited by
72
References
24
Language
en

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