ANGPTL8 protein-truncating variant associated with lower serum triglycerides and risk of coronary disease
Autoři:
Pyry Helkkula aff001; Tuomo Kiiskinen aff001; Aki S. Havulinna aff001; Juha Karjalainen aff001; Seppo Koskinen aff002; Veikko Salomaa aff002; Mark J. Daly aff001; Aarno Palotie aff001; Ida Surakka aff001; Samuli Ripatti aff001;
Působiště autorů:
Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
aff001; Finnish Institute for Health and Welfare, Helsinki, Finland
aff002; Analytic and Translational Genetics Unit, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, United States of America
aff003; Broad Institute of the Massachusetts Institute of Technology and Harvard University, Cambridge, Massachusetts, United States of America
aff004; Psychiatric & Neurodevelopmental Genetics Unit, Department of Psychiatry, Analytic and Translational Genetics Unit, Department of Medicine, and the Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, United States of America
aff005; Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan, United States of America
aff006; Department of Public Health, University of Helsinki, Helsinki, Finland
aff007
Vyšlo v časopise:
ANGPTL8 protein-truncating variant associated with lower serum triglycerides and risk of coronary disease. PLoS Genet 17(4): e1009501. doi:10.1371/journal.pgen.1009501
Kategorie:
Research Article
doi:
https://doi.org/10.1371/journal.pgen.1009501
Souhrn
Protein-truncating variants (PTVs) affecting dyslipidemia risk may point to therapeutic targets for cardiometabolic disease. Our objective was to identify PTVs that were associated with both lipid levels and the risk of coronary artery disease (CAD) or type 2 diabetes (T2D) and assess their possible associations with risks of other diseases. To achieve this aim, we leveraged the enrichment of PTVs in the Finnish population and tested the association of low-frequency PTVs in 1,209 genes with serum lipid levels in the Finrisk Study (n = 23,435). We then tested which of the lipid-associated PTVs were also associated with the risks of T2D or CAD, as well as 2,683 disease endpoints curated in the FinnGen Study (n = 218,792). Two PTVs were associated with both lipid levels and the risk of CAD or T2D: triglyceride-lowering variants in ANGPTL8 (-24.0[-30.4 to -16.9] mg/dL per rs760351239-T allele, P = 3.4 × 10−9) and ANGPTL4 (-14.4[-18.6 to -9.8] mg/dL per rs746226153-G allele, P = 4.3 × 10−9). The risk of T2D was lower in carriers of the ANGPTL4 PTV (OR = 0.70[0.60–0.81], P = 2.2 × 10−6) than noncarriers. The odds of CAD were 47% lower in carriers of a PTV in ANGPTL8 (OR = 0.53[0.37–0.76], P = 4.5 × 10−4) than noncarriers. Finally, the phenome-wide scan of the ANGPTL8 PTV showed that the ANGPTL8 PTV carriers were less likely to use statin therapy (68,782 cases, OR = 0.52[0.40–0.68], P = 1.7 × 10−6) compared to noncarriers. Our findings provide genetic evidence of potential long-term efficacy and safety of therapeutic targeting of dyslipidemias.
Klíčová slova:
Coronary heart disease – Type 2 diabetes – Finnish people – Cholesterol – Lipid analysis – Lipids – Medical risk factors – Type 2 diabetes risk
Zdroje
1. Kannel WB, McGee DL. Diabetes and cardiovascular disease. The Framingham study. JAMA. 1979;241(19):2035–8. Epub 1979/05/11. doi: 10.1001/jama.241.19.2035 430798.
2. Fruchart JC, Sacks F, Hermans MP, Assmann G, Brown WV, Ceska R, et al. The Residual Risk Reduction Initiative: a call to action to reduce residual vascular risk in patients with dyslipidemia. Am J Cardiol. 2008;102(10 Suppl):1K–34K. Epub 2008/12/17. doi: 10.1016/S0002-9149(08)01833-X 19068318.
3. Klarin D, Damrauer SM, Cho K, Sun YV, Teslovich TM, Honerlaw J, et al. Genetics of blood lipids among ~300,000 multi-ethnic participants of the Million Veteran Program. Nat Genet. 2018;50(11):1514–23. Epub 2018/10/03. doi: 10.1038/s41588-018-0222-9 30275531; PubMed Central PMCID: PMC6521726.
4. Liu DJ, Peloso GM, Yu H, Butterworth AS, Wang X, Mahajan A, et al. Exome-wide association study of plasma lipids in >300,000 individuals. Nat Genet. 2017;49(12):1758–66. Epub 2017/10/31. doi: 10.1038/ng.3977 29083408; PubMed Central PMCID: PMC5709146.
5. Lu X, Peloso GM, Liu DJ, Wu Y, Zhang H, Zhou W, et al. Exome chip meta-analysis identifies novel loci and East Asian-specific coding variants that contribute to lipid levels and coronary artery disease. Nat Genet. 2017;49(12):1722–30. Epub 2017/10/31. doi: 10.1038/ng.3978 29083407; PubMed Central PMCID: PMC5899829.
6. Willer CJ, Schmidt EM, Sengupta S, Peloso GM, Gustafsson S, Kanoni S, et al. Discovery and refinement of loci associated with lipid levels. Nat Genet. 2013;45(11):1274–83. Epub 2013/10/08. doi: 10.1038/ng.2797 24097068; PubMed Central PMCID: PMC3838666.
7. Surakka I, Horikoshi M, Magi R, Sarin AP, Mahajan A, Lagou V, et al. The impact of low-frequency and rare variants on lipid levels. Nat Genet. 2015;47(6):589–97. Epub 2015/05/12. doi: 10.1038/ng.3300 25961943; PubMed Central PMCID: PMC4757735.
8. Dewey FE, Gusarova V, Dunbar RL, O’Dushlaine C, Schurmann C, Gottesman O, et al. Genetic and Pharmacologic Inactivation of ANGPTL3 and Cardiovascular Disease. N Engl J Med. 2017;377(3):211–21. Epub 2017/05/26. doi: 10.1056/NEJMoa1612790 28538136; PubMed Central PMCID: PMC5800308.
9. Dewey FE, Gusarova V, O’Dushlaine C, Gottesman O, Trejos J, Hunt C, et al. Inactivating Variants in ANGPTL4 and Risk of Coronary Artery Disease. N Engl J Med. 2016;374(12):1123–33. Epub 2016/03/05. doi: 10.1056/NEJMoa1510926 26933753; PubMed Central PMCID: PMC4900689.
10. 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(5):438–47. Epub 2015/07/30. doi: 10.1056/NEJMoa1400283 26222559.
11. Gaudet D, Brisson D, Tremblay K, Alexander VJ, Singleton W, Hughes SG, et al. Targeting APOC3 in the familial chylomicronemia syndrome. N Engl J Med. 2014;371(23):2200–6. Epub 2014/12/04. doi: 10.1056/NEJMoa1400284 25470695.
12. Graham MJ, Lee RG, Brandt TA, Tai LJ, Fu W, Peralta R, et al. Cardiovascular and Metabolic Effects of ANGPTL3 Antisense Oligonucleotides. N Engl J Med. 2017;377(3):222–32. Epub 2017/05/26. doi: 10.1056/NEJMoa1701329 28538111.
13. Group HTRC, 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(13):1217–27. Epub 2017/08/30. doi: 10.1056/NEJMoa1706444 28847206.
14. Tsimikas S, Karwatowska-Prokopczuk E, Gouni-Berthold I, Tardif JC, Baum SJ, Steinhagen-Thiessen E, et al. Lipoprotein(a) Reduction in Persons with Cardiovascular Disease. N Engl J Med. 2020;382(3):244–55. Epub 2020/01/02. doi: 10.1056/NEJMoa1905239 31893580.
15. 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(10059):2532–61. Epub 2016/09/13. doi: 10.1016/S0140-6736(16)31357-5 27616593.
16. Emdin CA, Khera AV, Natarajan P, Klarin D, Won HH, Peloso GM, et al. Phenotypic Characterization of Genetically Lowered Human Lipoprotein(a) Levels. J Am Coll Cardiol. 2016;68(25):2761–72. Epub 2016/12/23. doi: 10.1016/j.jacc.2016.10.033 28007139; PubMed Central PMCID: PMC5328146.
17. Chheda H, Palta P, Pirinen M, McCarthy S, Walter K, Koskinen S, et al. Whole-genome view of the consequences of a population bottleneck using 2926 genome sequences from Finland and United Kingdom. Eur J Hum Genet. 2017;25(4):477–84. Epub 2017/02/02. doi: 10.1038/ejhg.2016.205 28145424; PubMed Central PMCID: PMC5346294.
18. Lim ET, Wurtz P, Havulinna AS, Palta P, Tukiainen T, Rehnstrom K, et al. Distribution and medical impact of loss-of-function variants in the Finnish founder population. PLoS Genet. 2014;10(7):e1004494. Epub 2014/08/01. doi: 10.1371/journal.pgen.1004494 25078778; PubMed Central PMCID: PMC4117444.
19. Peloso GM, Auer PL, Bis JC, Voorman A, Morrison AC, Stitziel NO, et al. Association of low-frequency and rare coding-sequence variants with blood lipids and coronary heart disease in 56,000 whites and blacks. Am J Hum Genet. 2014;94(2):223–32. Epub 2014/02/11. doi: 10.1016/j.ajhg.2014.01.009 24507774; PubMed Central PMCID: PMC3928662.
20. Nomura A, Won HH, Khera AV, Takeuchi F, Ito K, McCarthy S, et al. Protein-Truncating Variants at the Cholesteryl Ester Transfer Protein Gene and Risk for Coronary Heart Disease. Circ Res. 2017;121(1):81–8. Epub 2017/05/17. doi: 10.1161/CIRCRESAHA.117.311145 28506971; PubMed Central PMCID: PMC5523940.
21. Lek M, Karczewski KJ, Minikel EV, Samocha KE, Banks E, Fennell T, et al. Analysis of protein-coding genetic variation in 60,706 humans. Nature. 2016;536(7616):285–91. Epub 2016/08/19. doi: 10.1038/nature19057 27535533; PubMed Central PMCID: PMC5018207.
22. Vartiainen E, Laatikainen T, Peltonen M, Juolevi A, Mannisto S, Sundvall J, et al. Thirty-five-year trends in cardiovascular risk factors in Finland. Int J Epidemiol. 2010;39(2):504–18. Epub 2009/12/05. doi: 10.1093/ije/dyp330 19959603.
23. Dang F, Wu R, Wang P, Wu Y, Azam MS, Xu Q, et al. Fasting and Feeding Signals Control the Oscillatory Expression of Angptl8 to Modulate Lipid Metabolism. Sci Rep. 2016;6:36926. Epub 2016/11/16. doi: 10.1038/srep36926 27845381; PubMed Central PMCID: PMC5109406.
24. Fu Z, Abou-Samra AB, Zhang R. A lipasin/Angptl8 monoclonal antibody lowers mouse serum triglycerides involving increased postprandial activity of the cardiac lipoprotein lipase. Sci Rep. 2015;5:18502. Epub 2015/12/22. doi: 10.1038/srep18502 26687026; PubMed Central PMCID: PMC4685196.
25. Kersten S, Mandard S, Tan NS, Escher P, Metzger D, Chambon P, et al. Characterization of the fasting-induced adipose factor FIAF, a novel peroxisome proliferator-activated receptor target gene. J Biol Chem. 2000;275(37):28488–93. Epub 2000/06/23. doi: 10.1074/jbc.M004029200 10862772.
26. Chi X, Britt EC, Shows HW, Hjelmaas AJ, Shetty SK, Cushing EM, et al. ANGPTL8 promotes the ability of ANGPTL3 to bind and inhibit lipoprotein lipase. Mol Metab. 2017;6(10):1137–49. Epub 2017/10/17. doi: 10.1016/j.molmet.2017.06.014 29031715; PubMed Central PMCID: PMC5641604.
27. Haller JF, Mintah IJ, Shihanian LM, Stevis P, Buckler D, Alexa-Braun CA, et al. ANGPTL8 requires ANGPTL3 to inhibit lipoprotein lipase and plasma triglyceride clearance. J Lipid Res. 2017;58(6):1166–73. Epub 2017/04/18. doi: 10.1194/jlr.M075689 28413163; PubMed Central PMCID: PMC5454515.
28. Wang Y, Quagliarini F, Gusarova V, Gromada J, Valenzuela DM, Cohen JC, et al. Mice lacking ANGPTL8 (Betatrophin) manifest disrupted triglyceride metabolism without impaired glucose homeostasis. Proc Natl Acad Sci U S A. 2013;110(40):16109–14. Epub 2013/09/18. doi: 10.1073/pnas.1315292110 24043787; PubMed Central PMCID: PMC3791734.
29. 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(11):1345–52. Epub 2013/10/08. doi: 10.1038/ng.2795 24097064; PubMed Central PMCID: PMC3904346.
30. Clapham KR, Chu AY, Wessel J, Natarajan P, Flannick J, Rivas MA, et al. A null mutation in ANGPTL8 does not associate with either plasma glucose or type 2 diabetes in humans. BMC Endocr Disord. 2016;16:7. Epub 2016/01/30. doi: 10.1186/s12902-016-0088-8 26822414; PubMed Central PMCID: PMC4730725.
31. Zhou W, Nielsen JB, Fritsche LG, Dey R, Gabrielsen ME, Wolford BN, et al. Efficiently controlling for case-control imbalance and sample relatedness in large-scale genetic association studies. Nat Genet. 2018;50(9):1335–41. Epub 2018/08/15. doi: 10.1038/s41588-018-0184-y 30104761; PubMed Central PMCID: PMC6119127.
32. Gusarova V, O’Dushlaine C, Teslovich TM, Benotti PN, Mirshahi T, Gottesman O, et al. Genetic inactivation of ANGPTL4 improves glucose homeostasis and is associated with reduced risk of diabetes. Nat Commun. 2018;9(1):2252. Epub 2018/06/15. doi: 10.1038/s41467-018-04611-z 29899519; PubMed Central PMCID: PMC5997992.
33. Myocardial Infarction G, Investigators CAEC, Stitziel NO, Stirrups KE, Masca NG, Erdmann J, et al. Coding Variation in ANGPTL4, LPL, and SVEP1 and the Risk of Coronary Disease. N Engl J Med. 2016;374(12):1134–44. Epub 2016/03/05. doi: 10.1056/NEJMoa1507652 26934567; PubMed Central PMCID: PMC4850838.
34. Howie BN, Donnelly P, Marchini J. A flexible and accurate genotype imputation method for the next generation of genome-wide association studies. PLoS Genet. 2009;5(6):e1000529. Epub 2009/06/23. doi: 10.1371/journal.pgen.1000529 19543373; PubMed Central PMCID: PMC2689936.
35. Manichaikul A, Mychaleckyj JC, Rich SS, Daly K, Sale M, Chen WM. Robust relationship inference in genome-wide association studies. Bioinformatics. 2010;26(22):2867–73. Epub 2010/10/12. doi: 10.1093/bioinformatics/btq559 20926424; PubMed Central PMCID: PMC3025716.
36. 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(6):499–502. Epub 1972/06/01. 4337382.
37. Cholesterol Treatment Trialists C, Baigent C, Blackwell L, Emberson J, Holland LE, Reith C, et al. Efficacy and safety of more intensive lowering of LDL cholesterol: a meta-analysis of data from 170,000 participants in 26 randomised trials. Lancet. 2010;376(9753):1670–81. Epub 2010/11/12. doi: 10.1016/S0140-6736(10)61350-5 21067804; PubMed Central PMCID: PMC2988224.
38. Denny JC, Bastarache L, Ritchie MD, Carroll RJ, Zink R, Mosley JD, et al. Systematic comparison of phenome-wide association study of electronic medical record data and genome-wide association study data. Nat Biotechnol. 2013;31(12):1102–10. Epub 2013/11/26. doi: 10.1038/nbt.2749 24270849; PubMed Central PMCID: PMC3969265.
39. Chang CC, Chow CC, Tellier LC, Vattikuti S, Purcell SM, Lee JJ. Second-generation PLINK: rising to the challenge of larger and richer datasets. Gigascience. 2015;4:7. Epub 2015/02/28. doi: 10.1186/s13742-015-0047-8 25722852; PubMed Central PMCID: PMC4342193.
40. Benner C, Spencer CC, Havulinna AS, Salomaa V, Ripatti S, Pirinen M. FINEMAP: efficient variable selection using summary data from genome-wide association studies. Bioinformatics. 2016;32(10):1493–501. Epub 2016/01/17. doi: 10.1093/bioinformatics/btw018 26773131; PubMed Central PMCID: PMC4866522.
41. Ren J, Grundy SM, Liu J, Wang W, Wang M, Sun J, et al. Long-term coronary heart disease risk associated with very-low-density lipoprotein cholesterol in Chinese: the results of a 15-Year Chinese Multi-Provincial Cohort Study (CMCS). Atherosclerosis. 2010;211(1):327–32. Epub 2010/03/13. doi: 10.1016/j.atherosclerosis.2010.02.020 20223457.
42. Nordestgaard BG. A Test in Context: Lipid Profile, Fasting Versus Nonfasting. J Am Coll Cardiol. 2017;70(13):1637–46. Epub 2017/09/25. doi: 10.1016/j.jacc.2017.08.006 28935041.
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