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Genome-wide significant loci for metformin response in type 2 diabetes reported elsewhere have not replicated in the Diabetes Prevention Program (DPP). To assess pharmacogenetic interactions in pre-diabetes, we conducted a genome-wide association study (GWAS) in the DPP. Cox proportional hazards models tested associations with diabetes incidence in metformin (MET, n=876) and placebo (PBO, n=887) arms. Multiple linear regression assessed association with one-year change in metformin-related quantitative traits, adjusted for baseline trait, age, sex, and 10 ancestry principal components. We tested for gene-by-treatment interaction. No significant associations emerged for diabetes incidence. We identified four genome-wide significant variants after correcting for correlated traits (pENOSF1 (minor allele frequency [MAF]AFR=0.07, MAFEUR=0.002) was associated with an increase in % glycated hemoglobin (per minor allele β=0.39 [95% CI 0.28, 0.50], p=2.8×10-12). Rs145591055 near OMSR (MAF=0.10 in American Indians), was associated with weight loss (kg) (per G allele β=-7.55 [95% CI -9.88, -5.22], p=3.2×10-10) in MET. Neither variant was significant in PBO; gene-by-treatment interaction was significant for both variants (p(G×T)

Genome-wide significant loci for metformin response in type 2 diabetes reported elsewhere have not replicated in the Diabetes Prevention Program (DPP). To assess pharmacogenetic interactions in pre-diabetes, we conducted a genome-wide association study (GWAS) in the DPP. Cox proportional hazards models tested associations with diabetes incidence in metformin (MET, n=876) and placebo (PBO, n=887) arms. Multiple linear regression assessed association with one-year change in metformin-related quantitative traits, adjusted for baseline trait, age, sex, and 10 ancestry principal components. We tested for gene-by-treatment interaction. No significant associations emerged for diabetes incidence. We identified four genome-wide significant variants after correcting for correlated traits (p

OBJECTIVE Maturity-onset diabetes of the young (MODY) is frequently misdiagnosed as type 1 or type 2 diabetes. Correct diagnosis may result in a change in clinical treatment and impacts prediction of complications and familial risk. In this study, we aimed to assess the prevalence of MODY in multiethnic youth under age 20 years with a clinical diagnosis of type 2 diabetes. RESEARCH DESIGN AND METHODS We evaluated whole-exome sequence data of youth with a clinical diagnosis of type 2 diabetes. We considered participants to have MODY if they carried a MODY gene variant classified as likely pathogenic (LP) or pathogenic (P) according to current guidelines. RESULTS Of 3,333 participants, 93 (2.8%) carried an LP/P variant in HNF4A (16 participants), GCK (23), HNF1A (44), PDX1 (5), INS (4), and CEL (1). Compared with those with no LP/P variants, youth with MODY had a younger age at diagnosis (12.9 ± 2.5 vs. 13.6 ± 2.3 years, P = 0.002) and lower fasting C-peptide levels (3.0 ± 1.7 vs. 4.7 ± 3.5 ng/mL, P < 0.0001). Youth with MODY were less likely to have hypertension (6.9% vs. 19.5%, P = 0.007) and had higher HDL cholesterol (43.8 vs. 39.7 mg/dL, P = 0.006). CONCLUSIONS By comprehensively sequencing the coding regions of all MODY genes, we identified MODY in 2.8% of youth with clinically diagnosed type 2 diabetes; importantly, in 89% (n = 83) the specific diagnosis would have changed clinical management. No clinical criterion reliably separated the two groups. New tools are needed to find ideal criteria for selection of individuals for genetic testing.

There is a limited understanding of how genetic loci associated with glycemic traits and type 2 diabetes (T2D) influence the response to antidiabetic medications. Polygenic scores provide increasing power to detect patterns of disease predisposition that might benefit from a targeted pharmacologic intervention. In the Study to Understand the Genetics of the Acute Response to Metformin and Glipizide in Humans (SUGAR-MGH), we constructed weighted polygenic scores using known genome-wide significant associations for T2D, fasting glucose, and fasting insulin, comprising 65, 43, and 13 single nucleotide polymorphisms, respectively. Multiple linear regression tested for associations between scores and glycemic traits as well as pharmacodynamic end points, adjusting for age, sex, race, and BMI. A higher T2D score was nominally associated with a shorter time to insulin peak, greater glucose area over the curve, shorter time to glucose trough, and steeper slope to glucose trough after glipizide. In replication, a higher T2D score was associated with a greater 1-year hemoglobin A1c reduction to sulfonylureas in the Genetics of Diabetes Audit and Research in Tayside Scotland (GoDARTS) study (P = 0.02). Our findings suggest that individuals with a higher genetic burden for T2D experience a greater acute and sustained response to sulfonylureas.

Objective: Maturity-onset diabetes of the young (MODY) is frequently misdiagnosed as type 1 or type 2 diabetes. Correct diagnosis may result in a change in clinical treatment and impacts prediction of complications and familial risk. In this study, we aimed to assess the prevalence of MODY in multi-ethnic youth under age 20 years with a clinical diagnosis of type 2 diabetes. Research design and methods: We evaluated whole-exome sequence data of youth with a clinical diagnosis of type 2 diabetes. We considered participants to have MODY if they carried a MODY gene variant classified as likely pathogenic (LP) or pathogenic (P) according to current guidelines. Results: 93 of 3,333 participants (2.8%) carried an LP/P variant in HNF4A (16 participants), GCK (23), HNF1A (44), PDX1 (5), INS (4), and CEL (1). Compared with those with no LP/P variants, youth with MODY had a younger age at diagnosis (12.9 ± 2.5 vs 13.6 ± 2.3 years, P=0.002) and lower fasting C-peptide levels (3.0 ± 1.7 vs 4.7 ± 3.5 ng/mL, Pvs 19.5%, P=0.007) and had higher HDL cholesterol (43.8 vs 39.7 mg/dL, P=0.006). Conclusions: By comprehensively sequencing the coding regions of all MODY genes, we identified MODY in 2.8% of youth with clinically diagnosed type 2 diabetes; importantly, in 89% (n=83) the specific diagnosis would have changed clinical management. No clinical criterion reliably separated the two groups. New tools are needed to find ideal criteria to select individuals for genetic testing.

Youth-onset type 2 diabetes (T2D) has a strong genetic predisposition and is regarded as a phenotypic extreme of adult-onset T2D. In this study, we aimed to investigate the contribution of rare coding variants to the genetic basis of youth-onset T2D by analyzing whole-exome sequences of 3,005 persons with youth-onset T2D (diagnosed before 20 years of age) from the Progress in Diabetes Genetics in Youth (ProDiGY) consortium and 9,777 ancestry matched adult controls. We identified three genes with aggregate rare variant associations reaching exome-wide significance (P Disclosure S. Kwak: None. L. M. Laffel: Consultant; Self; AstraZeneca, Boehringer Ingelheim International GmbH, Dexcom, Inc., Dompe, Insulogic LLC, Janssen Pharmaceuticals, Inc., Laxmi Therapeutic Devices, LifeScan, Lilly Diabetes, Medtronic, Provention Bio, Inc. E. M. Isganaitis: None. M. W. Haymond: Advisory Panel; Self; Daiichi Sankyo, Zealand Pharma A/S, Other Relationship; Self; AstraZeneca, Stock/Shareholder; Self; Xeris Pharmaceuticals, Inc. L. L. Levitsky: Consultant; Self; Eli Lilly and Company. T. I. Pollin: None. J. C. Florez: Consultant; Self; Goldfinch Bio, Inc., Other Relationship; Self; Novo Nordisk. J. Flannick: None. On behalf of the prodigy consortium: n/a. S. Srinivasan: None. L. Chen: None. J. Todd: None. E. T. Jensen: None. J. Divers: None. A. K. Mottl: Advisory Panel; Self; Bayer U. S. C. Pihoker: None. R. Gandica: None. Funding National Institutes of Health (1R01DK125490-01)

Genome-wide significant (GWS) loci for glycemic response to MET in type 2 diabetes (T2D) reported elsewhere have not replicated in the DPP. Thus, to assess whether pharmacogenetic interactions may differ in impaired glucose tolerance (IGT), we conducted a GWAS in the DPP for MET response, defined by diabetes incidence and change in quantitative traits (fasting glucose, 2-hour glucose on oral glucose tolerance test, hemoglobin A1c [HbA1c], fasting insulin, insulin sensitivity index, and weight). Samples were genotyped on the Illumina HumanCore Exome array and imputed on the 1000 Genomes Phase3 panel. Cox proportional hazards models tested associations with diabetes incidence in MET (n=876) and placebo (PBO, n=887) arms. Multiple linear regression using an additive model evaluated for association with 1-year change in quantitative traits, adjusted for baseline trait, age, sex, and 10 ancestry principal components. We tested for gene-by-environment (G×E) interaction between MET and PBO. Results were filtered to minor allele frequency (MAF) >1% and imputation score >0.7. We identified 3 GWS variants after correcting for correlated traits (P In summary, a GWAS for MET response in participants with IGT revealed novel associations that require validation in ancestry-specific populations and may have implications for tailored therapy. Disclosure J. H. Li: None. R. L. Hanson: None. S. E. Kahn: Advisory Panel; Self; Bayer AG, Boehringer Ingelheim International GmbH, Eli Lilly and Company, Merck & Co., Inc., Novo Nordisk, Pfizer Inc. W. C. Knowler: None. T. I. Pollin: None. J. C. Florez: Consultant; Self; Goldfinch Bio, Inc., Other Relationship; Self; Novo Nordisk. D. Research group: None. J. A. Perry: None. K. A. Jablonski: None. L. Chen: None. S. Srinivasan: None. J. N. Todd: None. M. Harden: None. J. M. Mercader: None. P. W. Franks: Advisory Panel; Self; Zoe Global Limited, Consultant; Self; Eli Lilly and Company, Novo Nordisk. Funding National Institute of Diabetes and Digestive and Kidney Diseases (U01DK048489)

There is a limited understanding of how genetic loci associated with glycemic traits and type 2 diabetes (T2D) influence the response to anti-diabetes medications. Polygenic scores provide increasing power to detect patterns of disease predisposition that might benefit from a targeted pharmacologic intervention. In the Study to Understand the Genetics of the Acute Response to Metformin and Glipizide in Humans (SUGAR-MGH), we constructed weighted polygenic scores using known genome-wide significant associations for T2D, fasting glucose (FG), and fasting insulin (FI), comprised of 65, 43, and 13 single nucleotide polymorphisms, respectively. Multiple linear regression tested for associations between scores and glycemic traits as well as pharmacodynamic endpoints, adjusting for age, sex, race, and body mass index (BMI). A higher T2D score was nominally associated with a shorter time to insulin peak, greater glucose area over the curve, shorter time to glucose trough, and steeper slope to glucose trough after glipizide. In replication, a higher T2D score was associated with a greater 1-year HbA1c reduction to sulfonylureas in the Genetics of Diabetes Audit and Research, Tayside and Scotland (GoDARTS) study (p=0.02). Our findings suggest that individuals with a higher genetic burden for T2D experience a greater acute and sustained response to sulfonylureas.

Type 2 diabetes (T2D) in youth is associated with a pro-atherogenic lipid profile characterized by high triglycerides (TG), high low-density lipoprotein cholesterol (LDL-c), and low high-density lipoprotein cholesterol (HDL-c). Genome wide association studies (GWAS) in adults have discovered more than 100 variants associated with lipid traits, but the association of genetic variants to dyslipidemia in youth with T2D remains unknown. Therefore, we genotyped 206,928 variants and imputed 17,642,824 variants in 1,076 youth with T2D from the Treatment Options for type 2 diabetes in Adolescents and Youth (TODAY) and SEARCH for Diabetes in Youth (SEARCH) as part of ProDiGY, a collaboration with type 2 diabetes Genetic Exploration by Next-generation sequencing in multi-Ethnic Samples (T2D-GENES). Using a linear mixed model, we performed association testing for TG, LDL-c and HDL-c adjusted for age, sex, body mass index (BMI) Z-score, HbA1c, and genetic relationship matrix within each sub-study followed by meta-analyses for each lipid trait. We identified a novel variant between a deletion on chromosome 3 (3:67817380_AT/A_Deletion:RP11-81N13.1) that was associated with TG levels at genome wide level of significance (P-value=2.26×10-8, β=0.21±0.09). We also identified a genome-wide significant variant rs247617 (P-value=5.12×10-9, β=2.52±0.43) between HERFUD1 and CETP for association with HDL-c. This variant has been previously reported to be associated with HDL-c in adults in the Global Lipids Genetics Consortium (GLGC). We did not find any genome-wide significant variants in the LDL-c analysis. In summary, our genetic analyses of lipid traits in youth with T2D identified one novel and one previously known locus. Further multi-phenotype analysis may uncover additional loci associated with lipid traits in youth with T2D. Disclosure L. Chen: None. S. Srinivasan: None. N. Santoro: None. J. Todd: None. J. Divers: None. A.S. Shah: None. S. Gidding: Research Support; Self; Color Genomics. B.K. Burke: None. M.W. Haymond: Advisory Panel; Self; Daiichi Sankyo Company, Limited, Zealand Pharma A/S. Stock/Shareholder; Self; Xeris Pharmaceuticals, Inc. Other Relationship; Self; AstraZeneca. L. Lange: None. S.M. Marcovina: None. A. Chait: Consultant; Self; MedImmune. J. Flannick: Consultant; Self; Decibel Therapeutics. S. Caprio: None. J.C. Florez: None. Funding National Institutes of Health

Whether genetics can help predict response to type 2 diabetes (T2D) drugs is unknown. We set out to examine whether genetic risk scores (GRSs) composed of single nucleotide polymorphisms (SNPs) known to be associated with glycemic traits and T2D are associated with glycemic traits and the acute response to metformin and glipizide in SUGAR-MGH. We measured plasma glucose, insulin, glucagon, proinsulin, c-peptide, GLP-1, and GIP levels in 1,000 participants naïve to antidiabetes medications at 0, 30, 60, 90, 120, 180, and 240 minutes following administration of 5 mg glipizide (Visit 1, day 1), and during a 75-g oral glucose tolerance test following a short metformin course (Visit 2, day 8). We constructed a T2D, fasting glucose (FG), and fasting insulin (FI) GRS (with 65, 43 and 13 SNPs respectively) by summing the number of risk alleles carried by each person based on known genome-wide significant associations. Linear regression tested for associations between GRSs and glycemic traits as well as pharmacodynamic endpoints, adjusting for age, sex, race, and BMI. The FG GRS predicted FG in our cohort (β=0.46 increase in FG in mg/dL*allele, P Disclosure J.H. Li: None. L. Szczerbinski: None. V. Kaur: None. J. Todd: None. J.C. Florez: None. Funding National Institute of Diabetes and Digestive and Kidney Diseases (R01DK-088214, R03DK-077675, P30-DK-036836); Joslin Clinical Research Center; Harvard Catalyst; National Center for Research Resources/National Center for Advancing Translational Sciences (M01-RR-01066, 1UL1-RR-025758-04, 88UL1-TR-000170-05; Harvard University

Obesity has been posited as an independent risk factor for diabetic kidney disease (DKD), but establishing causality from observational data is problematic. We aimed to test whether obesity is causally related to DKD using Mendelian randomization, which exploits the random assortment of genes during meiosis. In 6,049 subjects with type 1 diabetes, we used a weighted genetic risk score (GRS) comprised of 32 validated BMI loci as an instrument to test the relationship of BMI with macroalbuminuria, end-stage renal disease (ESRD), or DKD defined as presence of macroalbuminuria or ESRD. We compared these results with cross-sectional and longitudinal observational associations. Longitudinal analysis demonstrated a U-shaped relationship of BMI with development of macroalbuminuria, ESRD, or DKD over time. Cross-sectional observational analysis showed no association with overall DKD, higher odds of macroalbuminuria (for every 1 kg/m2 higher BMI, odds ratio [OR] 1.05, 95% CI 1.03–1.07, P < 0.001), and lower odds of ESRD (OR 0.95, 95% CI 0.93–0.97, P < 0.001). Mendelian randomization analysis showed a 1 kg/m2 higher BMI conferring an increased risk in macroalbuminuria (OR 1.28, 95% CI 1.11–1.45, P = 0.001), ESRD (OR 1.43, 95% CI 1.20–1.72, P < 0.001), and DKD (OR 1.33, 95% CI 1.17–1.51, P < 0.001). Our results provide genetic evidence for a causal link between obesity and DKD in type 1 diabetes. As obesity prevalence rises, this finding predicts an increase in DKD prevalence unless intervention should occur.