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05-19-2018 | Risk factors | Review | Article

Prioritising Risk Factors for Type 2 Diabetes: Causal Inference through Genetic Approaches

Journal: Current Diabetes Reports

Authors: Laura B. L. Wittemans, Luca A. Lotta, Claudia Langenberg

Publisher: Springer US

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Abstract

Purpose of the Review

Causality has been demonstrated for few of the many putative risk factors for type 2 diabetes (T2D) emerging from observational epidemiology. Genetic approaches are increasingly being used to infer causality, and in this review, we discuss how genetic discoveries have shaped our understanding of the causal role of factors associated with T2D.

Recent Findings

Genetic discoveries have led to the identification of novel potential aetiological factors of T2D, including the protective role of peripheral fat storage capacity and specific metabolic pathways, such as the branched-chain amino acid breakdown. Consideration of specific genetic mechanisms contributing to overall lipid levels has suggested that distinct physiological processes influencing lipid levels may influence diabetes risk differentially. Genetic approaches have also been used to investigate the role of T2D and related metabolic traits as causal risk factors for other disease outcomes, such as cancer, but comprehensive studies are lacking.

Summary

Genome-wide association studies of T2D and metabolic traits coupled with high-throughput molecular phenotyping and in-depth characterisation and follow-up of individual loci have provided better understanding of aetiological factors contributing to T2D.
Literature
1.
Willi C, Bodenmann P, Ghali WA, Faris PD, Cornuz J. Active smoking and the risk of type 2 diabetes—a systematic review and meta-analysis. JAMA. 2007;298:2654–64. CrossRefPubMed
2.
Hu FB, Manson JE, Stampfer MJ, Graham C, Liu S, Solomon CG, et al. Diet, lifestyle, and the risk of type 2 diabetes mellitus in women. N Engl J Med. 2001;345:790–7. CrossRefPubMed
3.
Hu FB, Li TY, Colditz GA, Willett WC, Manson JE. Television watching and other sedentary behaviors in relation to risk of obesity and type 2 diabetes mellitus in women. J Am Med Assoc. 2003;289:1785–91. CrossRef
4.
The InterAct Consortium. Dietary fibre and incidence of type 2 diabetes in eight European countries: the EPIC-InterAct Study and a meta-analysis of prospective studies. Diabetologia. 2015;58:1394–408. CrossRefPubMedCentral
5.
O’Connor L, Imamura F, Lentjes MAH, Khaw K-T, Wareham NJ, Forouhi NG. Prospective associations and population impact of sweet beverage intake and type 2 diabetes, and effects of substitutions with alternative beverages. Diabetologia. 2015;58:1474–83. CrossRefPubMedPubMedCentral
6.
Emdin CA, Anderson SG, Woodward M, Rahimi K. Usual blood pressure and risk of new-onset diabetes evidence from 4.1 million adults and a meta-analysis of prospective studies. J Am Coll Cardiol. 2015;66:1552–62. CrossRefPubMedPubMedCentral
7.
• Abbasi A, Sahlqvist A-S, Lotta L, et al. Aetiological and predictive biomarkers of the risk of developing type 2 diabetes: a systematic review. PLoS One. 2016;11:e0163721. A systematic literature review of blood-based and urinary biomarkers reported for type 2 diabetes, and an evaluation of the evidence for predictive value and causality for each of the reported biomarkers. CrossRefPubMedPubMedCentral
8.
Lindström J, Louheranta A, Mannelin M, Rastas M, Salminen V, Eriksson J, et al. The Finnish Diabetes Prevention Study (DPS). Diabetes Care. 2003;26:3230–6. CrossRefPubMed
9.
Diabetes Prevention Program Research Group. Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin. N Engl J Med. 2002;346:393–403. CrossRefPubMedCentral
10.
Ramachandran A, Snehalatha C, Mary S, Mukesh B, Bhaskar AD, Vijay V, et al. The Indian Diabetes Prevention Programme shows that lifestyle modification and metformin prevent type 2 diabetes in Asian Indian subjects with impaired glucose tolerance (IDPP-1). Diabetologia. 2006;49:289–97. CrossRefPubMed
11.
Dunbar JA, Hernan AL, Janus ED, et al. Challenges of diabetes prevention in the real world: results and lessons from the Melbourne Diabetes Prevention Study. BMJ Open Diabetes Res Care. 2015;3:e000131. CrossRefPubMedPubMedCentral
12.
Li G, Zhang P, Wang J, et al. The long-term effect of lifestyle interventions to prevent diabetes in the China Da Qing Diabetes Prevention Study: a 20-year follow-up study. Lancet. 2008;371:1783–9. CrossRefPubMed
13.
Hulley S, Grady D, Bush T, Furberg C, Herrington D, Riggs B, et al. Randomized trial of estrogen plus progestin for secondary prevention of coronary heart disease in postmenopausal women. JAMA. 1998;280:605–13. CrossRefPubMed
14.
Stampfer MJ, Colditz GA. Estrogen replacement therapy and coronary heart disease: a quantitative assessment of the epidemiologic evidence. Prev Med. 1991;20:47–63. CrossRefPubMed
15.
Nelson MR, Tipney H, Painter JL, et al. The support of human genetic evidence for approved drug indications. Nat Genet. 2015;47:856–60. CrossRefPubMed
16.
Hurle MR, Nelson MR, Agarwal P, Cardon LR. Trial watch: impact of genetically supported target selection on R&D productivity. Nat Rev Drug Discov. 2016;15:596–7. CrossRefPubMed
17.
Altshuler D. Developing medicines to prevent the development and alter the course severe genetic diseases. In: Chief Medical Officer Annual Report 2016 Generation Genome. (Chapter 4); 2017.
18.
Keavney B. Genetic epidemiological studies of coronary heart disease. Int J Epidemiol. 2002;31:730–6. CrossRefPubMed
19.
Ebrahim S, Davey SG. Mendelian randomization: can genetic epidemiology help redress the failures of observational epidemiology? Hum Genet. 2008;123:15–33. CrossRefPubMed
20.
Burgess S, Bowden J, Fall T, Ingelsson E, Thompson. Sensitivity analyses for robust causal inference from Mendelian randomization analyses with multiple genetic variants. Epidemiology 2017:28:30–42.
21.
Bowden J, Davey Smith G, Burgess S. Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. Int J Epidemiol. 2015;44:512–25. CrossRefPubMedPubMedCentral
22.
Bowden J, Davey Smith G, Haycock PC, Burgess S. Consistent estimation in Mendelian randomization with some invalid instruments using a weighted median estimator. Genet Epidemiol. 2016;40:304–14. CrossRefPubMedPubMedCentral
23.
Flannick J, Florez JC. Type 2 diabetes: genetic data sharing to advance complex disease research. Nat Rev Genet. 2016;17:535–49. CrossRefPubMed
24.
•• Mahajan A, Taliun D, Thurner M, et al. Fine-mapping of an expanded set of type 2 diabetes loci to single-variant resolution using high-density imputation and islet-specific epigenome maps. bioRxiv 2018:245506. doi: https://​doi.​org/​10.​1101/​245506. This paper describes the most recent and largest ever genome-wide association study on type 2 diabetes, including more than 74,000 cases and 824,000 controls of European descent. Based on dense reference panels for imputation and integration of regulatory annotations, significant progress at fine-mapping of associated genetic loci was made.
25.
Scott RA, Scott LJ, Magi R, Marullo L, Gaulton KJ, Kaakinen M, et al. An expanded genome-wide association study of type 2 diabetes in Europeans. Diabetes. 2017;66:2888–902. CrossRefPubMedPubMedCentral
26.
• Mahajan A, Wessel J, Willems S, et al. Refining the accuracy of validated target identification through coding variant fine-mapping in type 2 diabetes. Nat. Genet. 2018;50:559–71. In this trans-ethnic exome-wide association analysis for type 2 diabetes on 81,400 cases and 371,000 controls, 40 coding variants were identified and for 16 of these evidence based on fine-mapping was found that they are the causal variant at that locus.
27.
International Diabetes Federation. IDF Diabetes Atlas Eighth Edition 2017.
28.
Lean MEJ, Leslie WS, Barnes AC, et al. Primary care-led weight management for remission of type 2 diabetes (DiRECT): an open-label, cluster-randomised trial. Lancet. 2017;391:541–51. CrossRefPubMed
29.
• Dale CE, Fatemifar G, Palmer TM, et al. Causal associations of adiposity and body fat distribution with coronary heart disease, stroke subtypes, and type 2 diabetes mellitus: a Mendelian randomization analysis. Circulation. 2017;135:2373–88. Mendelian randomization study corroborating the role of both overall and central adiposity as risk factors for coronary heart disease and type 2 diabetes. CrossRefPubMedPubMedCentral
30.
Corbin LJ, Richmond RC, Wade KH, Burgess S, Bowden J, Smith GD, et al. Body mass index as a modifiable risk factor for type 2 diabetes: refining and understanding causal estimates using Mendelian randomisation. Diabetes. 2016;65:3002–7. CrossRefPubMedPubMedCentral
31.
• Emdin CA, Khera AV, Natarajan P, Klarin D, Zekavat SM, Hsiao AJ, et al. Genetic association of waist-to-hip ratio with cardiometabolic traits, type 2 diabetes, and coronary heart disease. JAMA. 2017;317:626–34. This Mendelian randomization study found supportive evidence that waist-to-hip ratio is an independent risk factor for type 2 diabetes and coronary heart disease. CrossRefPubMedPubMedCentral
32.
• Lu Y, Day FR, Gustafsson S, et al. New loci for body fat percentage reveal link between adiposity and cardiometabolic disease risk. Nat Commun. 2016;7:10495. This study conducted the largest GWAS for total body fat percentage and reported for two loci opposite directions of effect on body fat percentage and diabetes risk. CrossRefPubMedPubMedCentral
33.
Kilpeläinen TO, Zillikens MC, Stančákova A, et al. Genetic variation near IRS1 associates with reduced adiposity and an impaired metabolic profile. Nat Genet. 2011;43:753–60. CrossRefPubMedPubMedCentral
34.
• Minster RL, Hawley NL, Su CT, et al. A thrifty variant in CREBRF strongly influences body mass index in Samoans. Nat Genet. 2016;48:1049–54. In this study on a Samoan population, a coding variant in CREBRF with a very strong increasing effect on body mass index but a risk-reducing effect on type 2 diabetes was identified through GWAS and targeted sequencing. CrossRefPubMedPubMedCentral
35.
•• Lotta LA, Gulati P, Day FR, et al. Integrative genomic analysis implicates limited peripheral adipose storage capacity in the pathogenesis of human insulin resistance. Nat Genet. 2017;49:17–26. Based on genetic variants identified for three hallmarks of insulin resistance, the authors find evidence that shared mechanisms may exist between common insulin resistance not primarily driven by overweight and insulin-resistant lipodystrophy syndromes. CrossRefPubMed
36.
Barroso I, Gurnell M, Crowley VEF, et al. Dominant negative mutations in human PPARγ associated with severe insulin resistance, diabetes mellitus and hypertension. Nature. 1999;402:880–3. CrossRefPubMed
37.
Manning A, Hivert M-F, Scott RA, et al. A genome-wide approach accounting for body mass index identifies genetic variants influencing fasting glycemic traits and insulin resistance. Nat Genet. 2012;44:659–69. CrossRefPubMedPubMedCentral
38.
Scott RA, Lagou V, Welch RP, et al. Large-scale association analyses identify new loci influencing glycemic traits and provide insight into the underlying biological pathways. Nat Genet. 2012;44:991–1005. CrossRefPubMedPubMedCentral
39.
Scott RA, Fall T, Pasko D, et al. Common genetic variants highlight the role of insulin resistance and body fat distribution in type 2 diabetes, independently of obesity. Diabetes. 2014;63:4378–87. CrossRefPubMedPubMedCentral
40.
Yaghootkar H, Scott RA, White CC, et al. Genetic evidence for a normal-weight “metabolically obese” phenotype linking insulin resistance, hypertension, coronary artery disease, and type 2 diabetes. Diabetes 2014:63:4369–4377.
41.
Semple RK, Savage DB, Cochran EK, Gorden P, O’Rahilly S. Genetic syndromes of severe insulin resistance. Endocr Rev. 2011;32:498–514. CrossRefPubMed
42.
Ramachandran A, Wan Ma RC, Snehalatha C. Diabetes in Asia. Lancet. 2010;375:408–18. CrossRefPubMed
43.
Babai MA, Arasteh P, Hadibarhaghtalab M, Naghizadeh MM, Salehi A, Askari A, et al. Defining a BMI cut-off point for the Iranian population: the shiraz heart study. PLoS One. 2016;11:e0160639. CrossRefPubMedPubMedCentral
44.
Frank LK, Heraclides A, Danquah I, Bedu-Addo G, Mockenhaupt FP, Schulze MB. Measures of general and central obesity and risk of type 2 diabetes in a Ghanaian population. Trop Med Int Heal. 2013;18:141–51. CrossRef
45.
Virtue S, Vidal-Puig A. Adipose tissue expandability, lipotoxicity and the metabolic syndrome—an allostatic perspective. Biochim Biophys Acta - Mol Cell Biol Lipids. 1801;2010:338–49.
46.
Danforth E. Failure of adipocyte differentiation causes type II diabetes mellitus? Nat Genet. 2000;26:13. CrossRefPubMed
47.
Sattar N, Preiss D, Murray HM, et al. Statins and risk of incident diabetes: a collaborative meta-analysis of randomised statin trials. Lancet. 2010;375:735–42. CrossRefPubMed
48.
Haase CL, Tybjærg-Hansen A, Nordestgaard BG, Frikke-Schmidt R. HDL cholesterol and risk of type 2 diabetes: a Mendelian Randomization Study. Diabetes. 2015;64:3328–33. CrossRefPubMed
49.
Qi Q, Liang L, Doria A, Hu FB, Qi L. Genetic predisposition to dyslipidemia and type 2 diabetes risk in two prospective cohorts. Diabetes. 2012;61:745–52. CrossRefPubMedPubMedCentral
50.
Maneka N, De Silva G, Freathy RM, et al. Mendelian randomization studies do not support a role for raised circulating triglyceride levels influencing type 2 diabetes, glucose levels, or insulin resistance. Diabetes. 2011;60:1008–18. CrossRef
51.
• Burgess S, Thompson SG. Multivariable Mendelian randomization: the use of pleiotropic genetic variants to estimate causal effects. Am J Epidemiol. 2015;181:251–60. This paper presents a multivariable Mendelian randomization method that allows to assess the independent causal role of genetically correlated exposures. CrossRefPubMedPubMedCentral
52.
Mora S, Otvos JD, Rifai N, Rosenson RS, Buring JE, Ridker PM. Lipoprotein particle profiles by nuclear magnetic resonance compared with standard lipids and apolipoproteins in predicting incident cardiovascular disease in women. Circulation. 2009;119:931–9. CrossRefPubMedPubMedCentral
53.
Wilson PWF, Meigs JB, Sullivan L, Fox CS, Nathan DM, D’Agostino RAB. Prediction of incident diabetes mellitus in middle-aged adults: the Framingham Offspring Study. Arch Intern Med. 2007;167:1068–74. CrossRefPubMed
54.
Gupta AK, Dahlof B, Dobson J, Sever PS, Wedel H, Poulter NR. Determinants of new-onset diabetes randomized in the Anglo-Scandinavian cardiac outcomes trial—blood pressure lowering arm and the relative influence of antihypertensive medication. Diabetes Care. 2008;31:982–8. CrossRefPubMed
55.
•• 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. Using multivariable Mendelian randomization, the authors provide evidence that higher LDL and HDL cholesterol, and possibly also higher triglycerides, protect against type 2 diabetes. CrossRefPubMedPubMedCentral
56.
•• Fall T, Xie W, Poon W, et al. Using genetic variants to assess the relationship between circulating lipids and type 2 diabetes. Diabetes. 2015;64:2676–84. This earlier study also uses multivariable Mendelian randomization to assess the independent causal effect sizes of the three lipid fractions, and reported a protective effect of LDL and HDL cholesterol, but not of triglycerides, against type 2 diabetes. CrossRefPubMed
57.
Besseling J, Kastelein JJP, Defesche JC, Hutten BA, Hovingh GK. Association between familial hypercholesterolemia and prevalence of type 2 diabetes mellitus. JAMA. 2015;313:1029–36. CrossRefPubMed
58.
Xu H, Ryan KA, Jaworek TJ, et al. Familial hypercholesterolemia and type 2 diabetes in the old order Amish. Diabetes. 2017;66:2054–8. CrossRefPubMedPubMedCentral
59.
•• Lotta LA, Sharp SJ, Burgess S, et al. Association between low-density lipoprotein cholesterol—lowering genetic variants and risk of type 2 diabetes. JAMA. 2016;316:1383–91. This study shows that LDL-lowering variants located in/near genes that represent established or emerging targets for LDL-lowering drugs increase risk of diabetes to a different extent, whereas all variants are associated with a similar risk reduction in coronary heart disease. CrossRefPubMedPubMedCentral
60.
•• Liu DJ, Peloso GM, Yu H, et al. Exome-wide association study of plasma lipids in >300,000 individuals. Nat Genet. 2017;49:1758–66. In this exome-wide association analyses for lipid fractions, LDL-lowering variants were found to have a heterogeneous effect on type 2 diabetes. Associations of triglyceride-lowering variants with diabetes risk was found to be mechanisms-dependent. CrossRefPubMedPubMedCentral
61.
Burgess S, Freitag DF, Khan H, Gorman DN, Thompson SG. Using multivariable Mendelian randomization to disentangle the causal effects of lipid fractions. PLoS One. 2014;9:e108891. CrossRefPubMedPubMedCentral
62.
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. JAMA. 2016;316:1289–97. CrossRefPubMed
63.
Do R, Willer CJ, Schmidt EM, et al. Common variants associated with plasma triglycerides and risk for coronary artery disease. Nat Genet. 2013;45:1345–52. CrossRefPubMedPubMedCentral
64.
Helgadottir A, Gretarsdottir S, Thorleifsson G, et al. Variants with large effects on blood lipids and the role of cholesterol and triglycerides in coronary disease. Nat Genet. 2016;48:634–9. CrossRefPubMed
65.
Ye Z, Sharp SJ, Burgess S, Scott RA, Imamura F, InterAct Consortium, et al. Association between circulating 25-hydroxyvitamin D and incident type 2 diabetes: a Mendelian randomisation study. Lancet Diabetes Endocrinol. 2015;3:35–42. CrossRefPubMedPubMedCentral
66.
Mente A, Meyre D, Lanktree MB, et al. Causal relationship between adiponectin and metabolic traits: a Mendelian Randomization Study in a multiethnic population. PLoS One. 2013;8:e66808. CrossRefPubMedPubMedCentral
67.
Yaghootkar H, Lamina C, Scott RA, et al. Mendelian randomization studies do not support a causal role for reduced circulating adiponectin levels in insulin resistance and type 2 diabetes. Diabetes. 2013;62:3589–98. CrossRefPubMedPubMedCentral
68.
Sluijs I, Holmes M V, van der Schouw YT, et al. A Mendelian Randomization Study of circulating uric acid and type 2 diabetes. Diabetes 2015:64:3028–3036.
69.
Prins BP, Abbasi A, Wong A, et al. Investigating the causal relationship of C-reactive protein with 32 complex somatic and psychiatric outcomes: a large-scale cross-consortium Mendelian Randomization Study. PLoS Med. 2016;13:e1001976. CrossRefPubMedPubMedCentral
70.
Noordam R, Smit RAJ, Postmus I, Trompet S, van Heemst D. Assessment of causality between serum gamma-glutamyltransferase and type 2 diabetes mellitus using publicly available data: a Mendelian randomization study. Int J Epidemiol. 2016;45:1953–60. PubMed
71.
Lee YS, Cho Y, Burgess S, Davey Smith G, Relton CL, Shin S-Y, et al. Serum gamma-glutamyl transferase and risk of type 2 diabetes in the general Korean population: a Mendelian Randomization Study. Hum Mol Genet. 2016;25:3877–86. CrossRefPubMed
72.
Liu J, Au Yeung SL, Lin SL, Leung GM, Schooling CM. Liver enzymes and risk of ischemic heart disease and type 2 diabetes mellitus: a Mendelian Randomization Study. Sci Rep. 2016;6:38813. CrossRefPubMedPubMedCentral
73.
Wang Q, Kangas AJ, Soininen P, et al. Sex hormone-binding globulin associations with circulating lipids and metabolites and the risk for type 2 diabetes: observational and causal effect estimates. Int J Epidemiol. 2015;44:623–37. CrossRefPubMed
74.
Ding EL, Song Y, Manson JE, Hunter DJ, Lee CC, Rifai N, et al. Sex hormone–binding globulin and risk of type 2 diabetes in women and men. N Engl J Med. 2009;361:1152–63. CrossRefPubMedPubMedCentral
75.
Jujić A, Nilsson PM, Engström G, Hedblad B, Melander O, Magnusson M. Atrial natriuretic peptide and type 2 diabetes development—biomarker and genotype association study. PLoS One. 2014;9:e89201. CrossRefPubMedPubMedCentral
76.
Pfister R, Sharp S, Luben R, et al. Mendelian Randomization Study of B-type natriuretic peptide and type 2 diabetes: evidence of causal association from population studies. PLoS Med. 2011;8:e1001112. CrossRefPubMedPubMedCentral
77.
Abbasi A, Deetman PE, Corpeleijn E, et al. Bilirubin as a potential causal factor in type 2 diabetes risk: a Mendelian randomization study. Diabetes. 2015;64:1459–69. CrossRefPubMed
78.
Floegel A, Stefan N, Yu Z, et al. Identification of serum metabolites associated with risk of type 2 diabetes using a targeted metabolomic approach. Diabetes. 2013;62:639–48. CrossRefPubMedPubMedCentral
79.
Walford GA, Ma Y, Clish C, Florez JC, Wang TJ, Gerszten RE. Metabolite profiles of diabetes incidence and intervention response in the diabetes prevention program. Diabetes. 2016;65:1424–33. CrossRefPubMedPubMedCentral
80.
Shi L, Brunius C, Lehtonen M, Auriola S, Bergdahl IA, Rolandsson O, Hanhineva K, Landberg R. Plasma metabolites associated with type 2 diabetes in a Swedish population—a case-control study nested in a prospective cohort. Diabetologia 2018 (online publication ahead of press). doi: doi: https://​doi.​org/​10.​1007/​s00125-017-4521-y.
81.
Peddinti G, Cobb J, Yengo L, Froguel P, Kravić J, Balkau B, et al. Early metabolic markers identify potential targets for the prevention of type 2 diabetes. Diabetologia. 2017;60:1740–50. CrossRefPubMedPubMedCentral
82.
Lu Y, Wang Y, Ong CN, Subramaniam T, Choi HW, Yuan JM, et al. Metabolic signatures and risk of type 2 diabetes in a Chinese population: an untargeted metabolomics study using both LC-MS and GC-MS. Diabetologia. 2016;59:2349–59. CrossRefPubMed
83.
Forouhi NG, Koulman A, Sharp SJ, et al. Differences in the prospective association between individual plasma phospholipid saturated fatty acids and incident type 2 diabetes: the EPIC-InterAct Case-Cohort Study. Lancet Diabetes Endocrinol. 2014;2:810–8. CrossRefPubMedPubMedCentral
84.
Suvitaival T, Bondia-Pons I, Yetukuri L, Pöhö P, Nolan JJ, Hyötyläinen T, et al. Lipidome as a predictive tool in progression to type 2 diabetes in Finnish men. Metabolism. 2018;78:1–12. CrossRefPubMed
85.
Rhee EP, Cheng S, Larson MG, et al. Lipid profiling identifies a triacylglycerol signature of insulin resistance and improves diabetes prediction in humans. J Clin Invest. 2011;121:1402–11. CrossRefPubMedPubMedCentral
86.
Lu Y, Wang Y, Zou L, Liang X, Ong CN, Tavintharan S, et al. Serum lipids in association with type 2 diabetes risk and prevalence in a Chinese population. J Clin Endocrinol Metab. 2018;103:671–80. CrossRefPubMed
87.
Belongie KJ, Ferrannini E, Johnson K, Andrade-Gordon P, Hansen MK, Petrie JR. Identification of novel biomarkers to monitor β-cell function and enable early detection of type 2 diabetes risk. PLoS One. 2017;12:e0182932. CrossRefPubMedPubMedCentral
88.
• Chambers JC, Loh M, Lehne B, Drong A, Kriebel J, Motta V, et al. Epigenome-wide association of DNA methylation markers in peripheral blood from Indian Asians and Europeans with incident type 2 diabetes: a nested case-control study. Lancet Diabetes Endocrinol. 2015;3:526–34. This is the largest methylation-wide association thus far conducted for type 2 diabetes. CrossRefPubMedPubMedCentral
89.
Wang TJ, Larson MG, Vasan RS, et al. Metabolite profiles and the risk of developing diabetes. Nat Med. 2011;17:448–53. CrossRefPubMedPubMedCentral
90.
Ruiz-Canela M, Toledo E, Clish CB, et al. Plasma branched-chain amino acids and incident cardiovascular disease in the PREDIMED trial. Clin Chem. 2016;62:582–92. CrossRefPubMedPubMedCentral
91.
Mayers JR, Wu C, Clish CB, et al. Elevation of circulating branched-chain amino acids is an early event in human pancreatic adenocarcinoma development. Nat Med. 2014;20:1193–8. CrossRefPubMedPubMedCentral
92.
Shin S-Y, Fauman EB, Petersen A-K, et al. An atlas of genetic influences on human blood metabolites. Nat Genet. 2014;46:543–50. CrossRefPubMedPubMedCentral
93.
Kettunen J, Demirkan A, Würtz P, et al. Genome-wide study for circulating metabolites identifies 62 loci and reveals novel systemic effects of LPA. Nat Commun. 2016;7:11122. CrossRefPubMedPubMedCentral
94.
Draisma HHM, Pool R, Kobl M, et al. Genome-wide association study identifies novel genetic variants contributing to variation in blood metabolite levels. Nat Commun. 2015;6:7208. CrossRefPubMedPubMedCentral
95.
•• Sun BB, Maranville JC, Peters JE, et al. Consequences of natural perturbations in the human plasma proteome. 2017 bioRxiv 134551. doi: https://​doi.​org/​10.​1101/​134551. The largest GWAS thus far conducted on 3,000 plasma proteins, highlighting several examples of how integration of genetics and proteomics can increase understanding of disease mechanisms and prioritise proteins as drug targets.
96.
Bonder MJ, Luijk R, Zhernakova DV, et al. Disease variants alter transcription factor levels and methylation of their binding sites. Nat Genet. 2017;49:131–8. CrossRefPubMed
97.
Yengo L, Sidorenko J, Kemper KE, et al. Meta-analysis of genome-wide association studies for height and body mass index in ~700,000 individuals of European ancestry. bioRxiv 2018:274654. doi: https://​doi.​org/​10.​1101/​274654.
98.
• Lotta LA, Scott RA, Sharp SJ, et al. Genetic predisposition to an impaired metabolism of the branched-chain amino acids and risk of type 2 diabetes: a Mendelian randomisation analysis. PLOS Med. 2016;13:e1002179. This study demonstrates how integration of metabolomics and large-scale genetic discoveries for specific metabolites can propose metabolic pathways as novel disease pathways. CrossRefPubMedPubMedCentral
99.
•• Elliott HR, Shihab HA, Lockett GA, Holloway JW, McRae AF, Davey Smith G, et al. Role of DNA methylation in type 2 diabetes etiology: using genotype as a causal anchor. Diabetes. 2017;66:1713–22. In this study, genetic loci associated with methylation markers previously associated with type 2 diabetes were identified and used to distinguish methylation markers that may be on the causal path to type 2 diabetes from those that are non-causal. CrossRefPubMedPubMedCentral
100.
Bhaskaran K, Douglas I, Forbes H, dos-Santos-Silva I, Leon DA, Smeeth L. Body-mass index and risk of 22 specific cancers: a population-based cohort study of 5.24 million UK adults. Lancet 2014:384:755–765.
101.
Pearson-Stuttard J, Zhou B, Kontis V, Bentham J, Gunter MJ, Ezzati M. Worldwide burden of cancer attributable to diabetes and high body-mass index: a comparative risk assessment. Lancet Diabetes Endocrinol. 2017;6:95–104. CrossRefPubMed
102.
Staley JR, Blackshaw J, Kamat MA, et al. PhenoScanner: a database of human genotype-phenotype associations. Bioinformatics. 2016;32:3207–9. CrossRefPubMedPubMedCentral
103.
Hemani G, Jie Zheng J, Kaitlin H, et al. MR-Base: a platform for systematic causal inference across the phenome using billions of genetic associations. bioRxiv 2016:078972. doi: https://​doi.​org/​10.​1101/​078972.
104.
Nead KT, Sharp SJ, Thompson DJ, et al. Evidence of a causal association between insulinemia and endometrial cancer: a Mendelian randomization analysis. J Natl Cancer Inst. 2015;107:djv178. CrossRefPubMedPubMedCentral
105.
Painter JN, O’Mara TA, Marquart L, et al. Genetic risk score Mendelian randomization shows that obesity measured as body mass index, but not waist:hip ratio, is causal for endometrial cancer. Cancer Epidemiol Biomark Prev. 2016;25:1503–10. CrossRef
106.
Guo Y, Warren Andersen S, Shu X-O, et al. Genetically predicted body mass index and breast cancer risk: Mendelian randomization analyses of data from 145,000 women of European descent. PLoS Med. 2016;13:e1002105. CrossRefPubMedPubMedCentral
107.
Zhao Z, Wen W, Michailidou K, Bolla MK, Wang Q, Zhang B, et al. Association of genetic susceptibility variants for type 2 diabetes with breast cancer risk in women of European Ancestry. Cancer Causes Control. 2016;27:679–93. CrossRefPubMedPubMedCentral
108.
Hou N, Zheng Y, Gamazon ER, et al. Genetic susceptibility to type 2 diabetes and breast cancer risk in women of European and African Ancestry. Cancer Epidemiol Biomark Prev. 2012;21:552–6. CrossRef
109.
• Carreras-Torres R, Johansson MBM, Haycock PC, et al. Obesity, metabolic factors and risk of different histological types of lung cancer: a Mendelian randomization study. PLoS One. 2017;12:e0177875. Mendelian randomization study on the role of metabolic risk factors in risk of specific types of lung cancer suggesting causal roles for body mass index in lung cancer sub-types, and for fasting insulin in overall lung cancer. CrossRefPubMedPubMedCentral
110.
• Carreras-Torres R, Johansson M, Gaborieau V, Haycock PC, Wade KH, Relton CL, et al. The role of obesity, type 2 diabetes, and metabolic factors in pancreatic cancer: a Mendelian Randomization Study. J Natl Cancer Inst. 2017;109:djx012. Genetic study on the causal role of metabolic risk factors in pancreatic cancer risk. Findings suggest strong effects of body mass index and fasting insulin levels. CrossRefPubMedCentral
111.
• Jarvis D, Mitchell JS, Law PJ, et al. Mendelian randomisation analysis strongly implicates adiposity with risk of developing colorectal cancer. Br J Cancer. 2016;115:266–72. Mendelian randomization study reporting a strong causal effect size of body mass index and waist-to-hip ratio on risk of colorectal cancer. CrossRefPubMedPubMedCentral

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Access the latest news and expert insight from the ADA 82nd Scientific Sessions