Using genetics to inform new therapeutics for diabetes

References

• Guariguata L, Dr W, Hambleton I, et al. Global estimates of diabetes prevalence for 2013 and projections for 2035. Diabetes Res Clin Pract. 2014;103:137–149.
   PubMed Web of Science ®Google Scholar
• Hu FB. Globalization of diabetes: the role of diet, lifestyle, and genes. Diabetes Care. 2011;34:1249–1257.
   PubMed Web of Science ®Google Scholar
• Majeed A, El-Sayed AA, Khoja T, et al. Diabetes in the middle-east and north Africa: an update. Diabetes Res Clin Pract. 2014;103:218–222.
   PubMed Web of Science ®Google Scholar
• Ramachandran A, Snehalatha C, Ma RCW. Diabetes in SOUTH-East Asia: an update. Diabetes Res Clin Pract. 2014;103:231–237.
   PubMed Web of Science ®Google Scholar
• Aschner P, Aguilar-Salinas C, Aguirre L, et al. Diabetes in South and Central America: an update. Diabetes Res Clin Pract. 2014;103:238–243.
   PubMed Web of Science ®Google Scholar
• 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. In: Minelli C, editor. PLOS Med. Vol. 13. 2016. pp. e1002179.
   PubMed Web of Science ®Google Scholar
• Type TR. 2 diabetes: etiology and reversibility. Diabetes Care. 2013;36:1047–1055.
   PubMed Web of Science ®Google Scholar
• Tuomi T, Santoro N, Caprio S, et al. The many faces of diabetes: a disease with increasing heterogeneity. Lancet (London, England). 2014;383:1084–1094.
   PubMed Web of Science ®Google Scholar
• 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. 2016;49:17–26.
   PubMed Web of Science ®Google Scholar
• American Diabetes Association. Standards of medical care in diabetes. Diabetes Care. 2017;40:S1–S131.
   PubMed Web of Science ®Google Scholar
• Atkinson MA, Eisenbarth GS, Michels AW. Type 1 diabetes. Lancet (London, England). 2014;383:69–82.
   PubMed Web of Science ®Google Scholar
• Farmer A, Diagnosis FR. classification, and treatment of diabetes. Bmj. 2011;342:d3319.
   PubMedGoogle Scholar
• Shields BM, Peters JL, Cooper C, et al. Can clinical features be used to differentiate type 1 from type 2 diabetes? A systematic review of the literature. BMJ Open. 2015;5:e009088.
   PubMed Web of Science ®Google Scholar
• Collins FS, Varmus H. A new initiative on precision medicine. N Engl J Med. 2015;372:793–795.
   PubMed Web of Science ®Google Scholar
• 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.
   PubMed Web of Science ®Google Scholar
• Oram RA, Patel K, Hill A, et al. A type 1 diabetes genetic risk score can aid discrimination between type 1 and type 2 diabetes in young adults. Diabetes Care. 2016;39(3):337–344.
   PubMed Web of Science ®Google Scholar
• Läll K, Mägi R, Morris A, et al. Personalized risk prediction for type 2 diabetes: the potential of genetic risk scores. Genet Med. 2016;19(3):322–329.
   PubMed Web of Science ®Google Scholar
• Walford GA, Porneala BC, Dauriz M, et al. Metabolite traits and genetic risk provide complementary information for the prediction of future type 2 diabetes. Diabetes Care. 2014;37:2508–2514.
   PubMed Web of Science ®Google Scholar
• Wilson PWF, Meigs JB, Sullivan L, et al. Prediction of incident diabetes mellitus in middle-aged adults. Arch Intern Med. 2007;167:1068.
   PubMed Web of Science ®Google Scholar
• Keating BJ. Advances in risk prediction of type 2 diabetes: integrating genetic scores with framingham risk models. Diabetes. 2015;64:1495–1497.
   PubMed Web of Science ®Google Scholar
• Rossetti L, Smith D, Shulman GI, et al. Correction of hyperglycemia with phlorizin normalizes tissue sensitivity to insulin in diabetic rats. J Clin Invest. 1987;79:1510–1515.
   PubMed Web of Science ®Google Scholar
• Marsenic O. Glucose control by the kidney: an emerging target in diabetes. Am J Kidney Dis. 2009;53:875–883.
   PubMed Web of Science ®Google Scholar
• Bailey C. The current drug treatment landscape for diabetes and perspectives for the future. Clin Pharmacol Ther. 2015;98:170–184.
   PubMed Web of Science ®Google Scholar
• GoDARTS and UKPDS Diabetes Pharmacogenetics Study Group, Wellcome Trust Case Control Consortium 2, Zhou K, et al. Common variants near ATM are associated with glycemic response to metformin in type 2 diabetes. Nat Genet. 2011;43:117–120.
   PubMed Web of Science ®Google Scholar
• Zhou K, Yee SW, Seiser EL, et al. Variation in the glucose transporter gene SLC2A2 is associated with glycemic response to metformin. Nat Genet. 2016;48:1055–1059.
   PubMed Web of Science ®Google Scholar
• Kim J, Lee H-Y, Ahn J, et al. NHX-5, an endosomal Na+/H+ exchanger, is associated with metformin action. J Biol Chem. 2016;291:18591–18599.
   PubMed Web of Science ®Google Scholar
• Stocker SL, Morrissey KM, Yee SW, et al. The effect of novel promoter variants in MATE1 and MATE2 on the pharmacokinetics and pharmacodynamics of metformin. Clin Pharmacol Ther. 2013;93:186–194.
   PubMed Web of Science ®Google Scholar
• Choi JH, Yee SW, Ramirez AH, et al. A common 5ʹ-UTR variant in MATE2-K is associated with poor response to metformin. Clin Pharmacol Ther. 2011;90:674–684.
   PubMed Web of Science ®Google Scholar
• Daniels MA, Kan C, Willmes DM, et al. Pharmacogenomics in type 2 diabetes: oral antidiabetic drugs. Pharmacogenomics J. 2016;16:399–410.
   PubMed Web of Science ®Google Scholar
• Maruthur NM, Gribble MO, Bennett WL, et al. The pharmacogenetics of type 2 diabetes: A systematic review. Diabetes Care. 2014;37:876–886.
   PubMed Web of Science ®Google Scholar
• Tkáč I. Genetics of drug response in type 2 diabetes. Curr Diab Rep. 2015;15:43.
   PubMed Web of Science ®Google Scholar
• Chen X, Liu L, He W, et al. Association of the ADRA2A polymorphisms with the risk of type 2 diabetes: A meta-analysis. Clin Biochem. 2013;46:722–726.
   PubMed Web of Science ®Google Scholar
• Rosengren AH, Braun M, Mahdi T, et al. Reduced insulin exocytosis in human pancreatic beta-cells with gene variants linked to type 2 diabetes. Diabetes. 2012;61:1726–1733.
   PubMed Web of Science ®Google Scholar
• Talmud PJ, Cooper JA, Gaunt T, et al. Variants of ADRA2A are associated with fasting glucose, blood pressure, body mass index and type 2 diabetes risk: meta-analysis of four prospective studies. Diabetologia. 2011;54:1710–1719.
   PubMed Web of Science ®Google Scholar
• Tang Y, Axelsson AS, Spégel P, et al. Genotype-based treatment of type 2 diabetes with an α2A-adrenergic receptor antagonist. Sci Transl Med. 2014;6(257):257ra139. doi: 10.1126/scitranslmed.3009934.
   PubMed Web of Science ®Google Scholar
• Zhang M, Luo H, Xi Z. Drug repositioning for diabetes based on “omics” data mining, In: Paulmurugan R, editor. PLoS One. 2015;10(5):e0126082.
   PubMed Web of Science ®Google Scholar
• Pfeffer MA, Claggett B, Diaz R, et al. Lixisenatide in patients with type 2 diabetes and acute coronary syndrome. N Engl J Med. 2015;373:2247–2257.
   PubMed Web of Science ®Google Scholar
• Marso SP, Daniels GH, Brown-Frandsen K, et al. Liraglutide and cardiovascular outcomes in type 2 diabetes. N Engl J Med. 2016;375:311–322.
   PubMed Web of Science ®Google Scholar
• Marso SP, Bain SC, Consoli A, et al. Semaglutide and cardiovascular outcomes in Patients with Type 2 Diabetes. N Engl J Med. 2016;376:NEJMoa1607141.
   Google Scholar
• Scott RA, Freitag DF, Li L, et al. A genomic approach to therapeutic target validation identifies a glucose-lowering GLP1R variant protective for coronary heart disease. Sci Transl Med. 2016;8:341ra76.
   PubMed Web of Science ®Google Scholar
• Merino J, Leong A, Posner D et al. Genetically Driven Hyperglycemia Increases Risk of Coronary Artery Disease Separately From Type 2 Diabetes. Diabetes Care. 2017;40(5):687–693.
   Google Scholar
• Turner N, Zeng X-Y, Osborne B, et al. Repurposing drugs to target the diabetes epidemic. Trends Pharmacol Sci. 2016;37:379–389.
   PubMed Web of Science ®Google Scholar
• Zeevi D, Korem T, Zmora N, et al. Personalized nutrition by prediction of glycemic responses. Cell. 2015;163:1079–1095.
   PubMed Web of Science ®Google Scholar
• Forslund K, Hildebrand F, Nielsen T, et al. Disentangling type 2 diabetes and metformin treatment signatures in the human gut microbiota. Nature. 2015;528:262–266.
   PubMed Web of Science ®Google Scholar
• Lukovac S, Belzer C, Pellis L, et al. Differential modulation by Akkermansia muciniphila and Faecalibacterium prausnitzii of host peripheral lipid metabolism and histone acetylation in mouse gut organoids. MBio. 2014;5:e01438–14.
   PubMed Web of Science ®Google Scholar
• Plovier H, Everard A, Druart C, et al. A purified membrane protein from Akkermansia muciniphila or the pasteurized bacterium improves metabolism in obese and diabetic mice. Nat Med. 2016;23:107–113.
   PubMed Web of Science ®Google Scholar
• Hatoum IJ, Greenawalt DM, Cotsapas C, et al. Weight loss after gastric bypass is associated with a variant at 15q26.1. Am J Hum Genet. 2013;92:827–834.
   PubMed Web of Science ®Google Scholar
• Pedersen HK, Gudmundsdottir V, Pedersen MK, et al. Ranking factors involved in diabetes remission after bariatric surgery using machine-learning integrating clinical and genomic biomarkers. Npj Genomic Med. 2016;1:16035.
   Google Scholar
• Fløyel T, Kaur S, Pociot F. Genes affecting β-cell function in type 1 diabetes. Curr Diab Rep. 2015;15:97.
   PubMed Web of Science ®Google Scholar
• Noble JA, Erlich HA. Genetics of type 1 diabetes. Cold Spring Harb Perspect Med. 2012;2:a007732.
   PubMed Web of Science ®Google Scholar
• Pociot F, Akolkar B, Concannon P, et al. Genetics of type 1 diabetes: what’s next? Diabetes. 2010;59:1561–1571.
   PubMed Web of Science ®Google Scholar
• Erlich H, Valdes AM, Noble J, et al. HLA DR-DQ haplotypes and genotypes and type 1 diabetes risk. Diabetes. 2008;57:1084–1092.
   PubMed Web of Science ®Google Scholar
• Grant SFA, Thorleifsson G, Reynisdottir I, et al. Variant of transcription factor 7-like 2 (TCF7L2) gene confers risk of type 2 diabetes. Nat Genet. 2006;38:320–323.
   PubMed Web of Science ®Google Scholar
• Mayans S, Lackovic K, Lindgren P, et al. TCF7L2 polymorphisms are associated with type 2 diabetes in northern Sweden. Eur J Hum Genet. 2007;15:342–346.
   PubMed Web of Science ®Google Scholar
• Hayashi T, Iwamoto Y, Kaku K, et al. Replication study for the association of TCF7L2 with susceptibility to type 2 diabetes in a Japanese population. Diabetologia. 2007;50:980–984.
   PubMed Web of Science ®Google Scholar
• Groves CJ, Zeggini E, Minton J, et al. Association analysis of 6,736 U.K. subjects provides replication and confirms TCF7L2 as a type 2 diabetes susceptibility gene with a substantial effect on individual risk. Diabetes. 2006;55:2640–2644.
   PubMed Web of Science ®Google Scholar
• Ng MCY, Tam CHT, Lam VKL, et al. Replication and identification of novel variants at TCF7L2 associated with type 2 diabetes in Hong Kong Chinese. J Clin Endocrinol Metab. 2007;92:3733–3737.
   PubMed Web of Science ®Google Scholar
• Morris AP, Voight BF, Teslovich TM, et al. Large-scale association analysis provides insights into the genetic architecture and pathophysiology of type 2 diabetes. Nat Genet. 2012;44:981–990.
   PubMed Web of Science ®Google Scholar
• Kooner JS, Saleheen D, Sim X, et al. Genome-wide association study in individuals of South Asian ancestry identifies six new type 2 diabetes susceptibility loci. Nat Genet. 2011;43:984–989.
   PubMed Web of Science ®Google Scholar
• Zeggini E, Weedon MN, Lindgren CM, et al. Replication of genome-wide association signals in UK samples reveals risk loci for type 2 diabetes. Science. 2007;316(80):1336–1341.
   PubMedGoogle Scholar
• Cho YS, Chen C-H, Hu C, et al. Meta-analysis of genome-wide association studies identifies eight new loci for type 2 diabetes in east Asians. Nat Genet. 2012;44:67–72.
   Web of Science ®Google Scholar
• Voight BF, Scott LJ, Steinthorsdottir V, et al. Twelve type 2 diabetes susceptibility loci identified through large-scale association analysis. Nat Genet. 2010;42:579–589.
   PubMed Web of Science ®Google Scholar
• Zeggini E, Scott LJ, Saxena R, et al. Meta-analysis of genome-wide association data and large-scale replication identifies additional susceptibility loci for type 2 diabetes. Nat Genet. 2008;40:638–645.
   PubMed Web of Science ®Google Scholar
• Steinthorsdottir V, Thorleifsson G, Reynisdottir I, et al. A variant in CDKAL1 influences insulin response and risk of type 2 diabetes. Nat Genet. 2007;39:770–775.
   PubMed Web of Science ®Google Scholar
• Sladek R, Rocheleau G, Rung J, et al. A genome-wide association study identifies novel risk loci for type 2 diabetes. Nature. 2007;445:881–885.
   PubMed Web of Science ®Google Scholar
• Scott LJ, Mohlke KL, Bonnycastle LL, et al. A genome-wide association study of type 2 diabetes in Finns detects multiple susceptibility variants. Science. 2007;316(80):1341–1345.
   PubMedGoogle Scholar
• Diabetes Genetics Initiative of Broad Institute of Harvard and MIT, Lund University, and Novartis Institutes for BioMedical Research, et al. Genome-wide association analysis identifies loci for type 2 diabetes and triglyceride levels. Science. 2007;316(80):1331–1336.
   PubMedGoogle Scholar
• Mahajan A, Go MJ, Zhang W, et al. Genome-wide trans-ancestry meta-analysis provides insight into the genetic architecture of type 2 diabetes susceptibility. Nat Genet. 2014;46:234–244.
   PubMed Web of Science ®Google Scholar
• Watkins RA, Evans-Molina C, Blum JS, et al. Established and emerging biomarkers for the prediction of type 1 diabetes: a systematic review. Transl Res. 2014;164:110–121.
   PubMed Web of Science ®Google Scholar
• Flannick J, Florez JC. Type 2 diabetes: genetic data sharing to advance complex disease research. Nat Rev Genet. 2016;17:535–549.
   PubMed Web of Science ®Google Scholar
• Fuchsberger C, Flannick J, Teslovich TM, et al. The genetic architecture of type 2 diabetes. Nature. 2016;536:41–47.
   PubMed Web of Science ®Google Scholar
• McCarthy MI. Painting a new picture of personalised medicine for diabetes. Diabetologia. 2017;60:793–799.
   PubMed Web of Science ®Google Scholar
• Bhatt HB. Thoughts on the progression of type 2 diabetes drug discovery. Expert Opin Drug Discov. 2015;10:107–110.
   PubMed Web of Science ®Google Scholar
• Zhang Z-J, Zheng Z-J, Kan H, et al. Reduced risk of colorectal cancer with metformin therapy in patients with type 2 diabetes: A meta-analysis. Diabetes Care. 2011;34:2323–2328.
   PubMed Web of Science ®Google Scholar
• Woods A, Leiper JM, Carling D. To the Editor. Nat Genet. 2012;44:360–361.
   PubMedGoogle Scholar
• Yee SW, Chen L, Giacomini KM. The role of ATM in response to metformin treatment and activation of AMPK. Nat Genet. 2012;44:359–360.
   PubMed Web of Science ®Google Scholar
• Florez JC, Barrett-Connor E, Jablonski KA, et al. The C allele of ATM rs11212617 does not associate with metformin response in the diabetes prevention program. Diabetes Care. 2012;35:1864–1867.
   PubMed Web of Science ®Google Scholar
• Zhou K, Donnelly L, Yang J, et al. Heritability of variation in glycaemic response to metformin: a genome-wide complex trait analysis. Lancet Diabetes Endocrinol. 2014;2:481–487.
   PubMed Web of Science ®Google Scholar
• Segrè AV, Wei N, Altshuler D, et al. Pathways targeted by antidiabetes drugs are enriched for multiple genes associated with type 2 diabetes risk. Diabetes. 2015;64:1470–1483.
   PubMed Web of Science ®Google Scholar
• Yki-Järvinen H. Thiazolidinediones. N Engl J Med. 2004;351:1106–1118.
   PubMed Web of Science ®Google Scholar
• Ashburn TT, Thor KB. Drug repositioning: identifying and developing new uses for existing drugs. Nat Rev Drug Discov. 2004;3:673–683.
   PubMed Web of Science ®Google Scholar
• Park J, Lee D-S, Christakis NA, et al. The impact of cellular networks on disease comorbidity. Mol Syst Biol. 2009;5:262.
   PubMed Web of Science ®Google Scholar
• Zhou X, Menche J, A-L B, et al. Human symptoms–disease network. Nat Commun. 2014;5:4212.
   PubMed Web of Science ®Google Scholar
• Hu JX, Thomas CE, Brunak S. Network biology concepts in complex disease comorbidities. Nat Rev Genet. 2016;17:615–629.
   PubMed Web of Science ®Google Scholar
• Hidalgo CA, Blumm N, Barabási A-L, et al. A dynamic network approach for the study of human phenotypes. Meyers LA, Editor. PLoS Comput. Biol. 2009;5:e1000353.
   PubMed Web of Science ®Google Scholar
• Li J-W, Lee H-M, Wang Y, et al. Interactome-transcriptome analysis discovers signatures complementary to GWAS Loci of Type 2 Diabetes. Sci Rep. 2016;6:35228.
   PubMed Web of Science ®Google Scholar
• Bagley SC, Sirota M, Chen R, et al. Constraints on biological mechanism from disease comorbidity using electronic medical records and database of genetic variants. In: Kann M, editor. PLoS Comput. Biol. Vol. 12. 2016. pp. e1004885.
   PubMed Web of Science ®Google Scholar
• Li L, Ruau DJ, Patel CJ, et al. Disease risk factors identified through shared genetic architecture and electronic medical records. Sci Transl Med. 2014;6:234ra57.
   PubMed Web of Science ®Google Scholar
• Bonora E. Protection of pancreatic beta-cells: is it feasible? Nutr Metab Cardiovasc Dis. 2008;18:74–83.
   PubMed Web of Science ®Google Scholar
• Dooley J, Tian L, Schonefeldt S, et al. Genetic predisposition for beta cell fragility underlies type 1 and type 2 diabetes. Nat Genet. 2016;48:519–527.
   PubMed Web of Science ®Google Scholar
• Mukherjee A, Morales-Scheihing D, Butler PC, et al. Type 2 diabetes as a protein misfolding disease. Trends Mol Med. 2015;21:439–449.
   PubMed Web of Science ®Google Scholar
• Nissen SE, Wolski K. Effect of rosiglitazone on the risk of myocardial infarction and death from cardiovascular causes. N Engl J Med. 2007;356:2457–2471.
   PubMed Web of Science ®Google Scholar
• Center for Drug Evaluation and Research at the U.S. Food and Drug Administration (FDA). Guidance for industry diabetes mellitus — evaluating cardiovascular risk in new antidiabetic therapies to treat type 2 diabetes [internet]. 2008.
   Google Scholar
• Powell N, Walker MM, Talley NJ. The mucosal immune system: master regulator of bidirectional gut–brain communications. Nat Rev Gastroenterol Hepatol. 2017;14:143–159.
   PubMed Web of Science ®Google Scholar
• Brock C, Søfteland E, Gunterberg V, et al. Diabetic autonomic neuropathy affects symptom generation and brain-gut axis. Diabetes Care. 2013;36:3698–3705.
   PubMed Web of Science ®Google Scholar
• Perry RJ, Peng L, Barry NA, et al. Acetate mediates a microbiome–brain–β-cell axis to promote metabolic syndrome. Nature. 2016;534:213–217.
   PubMed Web of Science ®Google Scholar
• Qin J, Li Y, Cai Z, et al. A metagenome-wide association study of gut microbiota in type 2 diabetes. Nature. 2012;490:55–60.
   PubMed Web of Science ®Google Scholar
• Rubino F, Nathan DM, Eckel RH, et al. Metabolic surgery in the treatment algorithm for type 2 diabetes: A joint statement by international diabetes organizations. Diabetes Care. 2016;39:861–877.
   PubMed Web of Science ®Google Scholar
• Sjöström L, Peltonen M, Jacobson P, et al. Association of bariatric surgery with long-term remission of type 2 diabetes and with microvascular and macrovascular complications. Jama. 2014;311:2297–2304.
   PubMed Web of Science ®Google Scholar
• Yu J, Zhou X, Li L, et al. The long-term effects of bariatric surgery for type 2 diabetes: systematic review and meta-analysis of randomized and non-randomized evidence. Obes Surg. 2015;25:143–158.
   PubMed Web of Science ®Google Scholar
• American Diabetes Association. 7. Obesity management for the treatment of type 2 diabetes. Diabetes Care. 2017;40:S57–S63.
   PubMedGoogle Scholar
• Mingrone G, Panunzi S, De Gaetano A, et al. Bariatric surgery versus conventional medical therapy for type 2 diabetes. N Engl J Med. 2012;366:1577–1585.
   PubMed Web of Science ®Google Scholar
• Mingrone G, Panunzi S, De Gaetano A, et al. Bariatric-metabolic surgery versus conventional medical treatment in obese patients with type 2 diabetes: 5 year follow-up of an open-label, single-centre, randomised controlled trial. Lancet (London, England). 2015;386:964–973.
   PubMed Web of Science ®Google Scholar
• Purnell JQ, Selzer F, Wahed AS, et al. Type 2 diabetes remission rates after laparoscopic gastric bypass and gastric banding: results of the longitudinal assessment of bariatric surgery study. Diabetes Care. 2016;39:1101–1107.
   PubMed Web of Science ®Google Scholar
• Still CD, Wood GC, Chu X, et al. Clinical factors associated with weight loss outcomes after Roux-en-Y gastric bypass surgery. Obes. 2014;22:888–894.
   PubMed Web of Science ®Google Scholar
• Rubino F, Schauer PR, Kaplan LM, et al. Metabolic surgery to treat type 2 diabetes: clinical outcomes and mechanisms of action. Annu Rev Med. 2010;61:393–411.
   PubMed Web of Science ®Google Scholar
• Allen RE, Hughes TD, Ng JL, et al. Mechanisms behind the immediate effects of Roux-en-Y gastric bypass surgery on type 2 diabetes. Theor Biol Med Model. 2013;10:45.
   PubMed Web of Science ®Google Scholar
• Batterham RL, Cummings DE. Mechanisms of diabetes improvement following bariatric/metabolic surgery. Diabetes Care. 2016;39:893–901.
   PubMed Web of Science ®Google Scholar
• Mosinski JD, Kirwan JP. Longer-term physiological and metabolic effects of gastric bypass surgery. Curr Diab Rep. 2016;16:50.
   PubMed Web of Science ®Google Scholar
• Tremaroli V, Karlsson F, Werling M, et al. Roux-en-Y gastric bypass and vertical banded gastroplasty induce long-term changes on the human gut microbiome contributing to fat mass regulation. Cell Metab. 2015;22:228–238.
   PubMed Web of Science ®Google Scholar
• Berisha SZ, Serre D, Schauer P, et al. Changes in whole blood gene expression in obese subjects with type 2 diabetes following bariatric surgery: A pilot study. In: Ludgate M, editor. PLoS One. Vol. 6. 2011. pp. e16729.
   PubMed Web of Science ®Google Scholar
• Nilsson EK, Ernst B, Voisin S, et al. Roux-en Y gastric bypass surgery induces genome-wide promoter-specific changes in DNA methylation in whole blood of obese patients. In: Manco M, editor. PLoS One. Vol. 10. 2015. pp. e0115186.
   Google Scholar
• Benton MC, Johnstone A, Eccles D, et al. An analysis of DNA methylation in human adipose tissue reveals differential modification of obesity genes before and after gastric bypass and weight loss. Genome Biol. 2015;16:8.
   PubMed Web of Science ®Google Scholar
• Flum DR, Belle SH, King WC, et al. Perioperative safety in the longitudinal assessment of bariatric surgery. N Engl J Med. 2009;361:445–454.
   PubMed Web of Science ®Google Scholar
• Meeuwsen S, Horgan GW, Elia M. The relationship between BMI and percent body fat, measured by bioelectrical impedance, in a large adult sample is curvilinear and influenced by age and sex. Clin Nutr. 2010;29:560–566.
   PubMed Web of Science ®Google Scholar
• Panunzi S, De Gaetano A, Carnicelli A, et al. Predictors of remission of diabetes mellitus in severely obese individuals undergoing bariatric surgery: do BMI or procedure choice matter? A meta-analysis. Ann Surg. 2015;261:459–467.
   PubMed Web of Science ®Google Scholar
• Sjöholm K, Pajunen P, Jacobson P, et al. Incidence and remission of type 2 diabetes in relation to degree of obesity at baseline and 2 year weight change: the Swedish Obese Subjects (SOS) study. Diabetologia. 2015;58:1448–1453.
   PubMed Web of Science ®Google Scholar
• Cummings DE, Cohen RV. Beyond BMI: the need for new guidelines governing the use of bariatric and metabolic surgery. Lancet Diabetes Endocrinol. 2014;2:175–181.
   PubMed Web of Science ®Google Scholar
• Still CD, Wood GC, Benotti P, et al. Preoperative prediction of type 2 diabetes remission after Roux-en-Y gastric bypass surgery: A retrospective cohort study. Lancet Diabetes Endocrinol. 2014;2:38–45.
   PubMed Web of Science ®Google Scholar
• U.S. Food and Drug Administration. Table of pharmacogenomic biomarkers in drug labeling [Internet]. Center for Drug Evaluation and Research; [ cited 2016 Jul 11]. Available from: https://www.fda.gov/drugs/scienceresearch/researchareas/pharmacogenetics/ucm083378.htm.
   Google Scholar
• Omori S, Tanaka Y, Takahashi A, et al. Association of CDKAL1, IGF2BP2, CDKN2A/B, HHEX, SLC30A8, and KCNJ11 with susceptibility to type 2 diabetes in a Japanese Population. Diabetes. 2008;57:791–795.
   PubMed Web of Science ®Google Scholar
• Boesgaard TW, Žilinskaitė J, Vänttinen M, et al. The common SLC30A8 Arg325Trp variant is associated with reduced first-phase insulin release in 846 non-diabetic offspring of type 2 diabetes patients—the EUGENE2 study. Diabetologia. 2008;51:816–820.
   PubMed Web of Science ®Google Scholar
• Jing YL, Sun QM, Bi Y, et al. SLC30A8 polymorphism and type 2 diabetes risk: evidence from 27 study groups. Nutr Metab Cardiovasc Dis. 2011;21:398–405.
   PubMed Web of Science ®Google Scholar
• Tabara Y, Osawa H, Kawamoto R, et al. Replication study of candidate genes associated with type 2 diabetes based on genome-wide screening. Diabetes. 2009;58:493–498.
   PubMed Web of Science ®Google Scholar
• Merriman C, Huang Q, Rutter GA, et al. Lipid-tuned zinc transport activity of human ZnT8 protein correlates with risk for type-2 diabetes. J Biol Chem. 2016;291:26950–26957.
   PubMed Web of Science ®Google Scholar
• Nicolson TJ, Bellomo EA, Wijesekara N, et al. Insulin storage and glucose homeostasis in mice null for the granule zinc transporter znt8 and studies of the type 2 diabetes-associated variants. Diabetes. 2009;58:2070–2083.
   PubMed Web of Science ®Google Scholar
• Kim I, Kang ES, Yim YS, et al. A low-risk ZnT-8 allele (W325) for post-transplantation diabetes mellitus is protective against cyclosporin A-induced impairment of insulin secretion. Pharmacogenomics J. 2011;11:191–198.
   PubMed Web of Science ®Google Scholar
• Flannick J, Thorleifsson G, Beer NL, et al. Loss-of-function mutations in SLC30A8 protect against type 2 diabetes. Nat Genet. 2014;46:357–363.
   PubMed Web of Science ®Google Scholar
• Fellmann C, Gowen BG, Lin P-C, et al. Cornerstones of CRISPR-Cas in drug discovery and therapy. Nat Rev Drug Discov. 2017;16:89–100.
   PubMed Web of Science ®Google Scholar
• Plenge RM. Disciplined approach to drug discovery and early development. Sci Transl Med. 2016;8:349ps15.
   PubMed Web of Science ®Google Scholar
• Barretina J, Caponigro G, Stransky N, et al. The cancer cell line encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature. 2012;483:603–607.
   PubMed Web of Science ®Google Scholar
• Garnett MJ, Edelman EJ, Heidorn SJ, et al. Systematic identification of genomic markers of drug sensitivity in cancer cells. Nature. 2012;483:570–575.
   PubMed Web of Science ®Google Scholar
• Lyssenko V, Laakso M. Genetic screening for the risk of type 2 diabetes: worthless or valuable? Diabetes Care. 2013;36:S120–S126.
   PubMed Web of Science ®Google Scholar