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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.

Objective: To examine the effect of different patterns of durable glycemic control on the development of comorbidities among youth with type 2 diabetes and to assess the impact of fasting glucose variability on the clinical course of type 2 diabetes. Research Design and Methods: 457 TODAY participants, mean age 14 years and diabetes duration Results: Higher baseline HbA1c concentration, lower beta-cell function, and maternal history of diabetes were strongly associated with loss of glycemic control in youth with type 2 diabetes. Higher cumulative HbA1c concentration over 4 years and greater fasting glucose variability over a year’s time within 3 years of diagnosis were related to higher prevalence of dyslipidemia, nephropathy, and retinopathy progression over the subsequent 10 years. A coefficient of variability in fasting glucose at or above 8.3% predicted future loss of glycemic control and development of comorbidities. Conclusions: Higher baseline HbA1c concentration and fasting glucose variability during year 1 accurately predicted youth with type 2 diabetes who will experience metabolic decompensation and comorbidities and may be useful tools for clinicians when considering early intensification of therapy.

The prevalence of type 2 diabetes in youth has increased substantially, yet the genetic underpinnings remain largely unexplored. To identify genetic variants predisposing to youth-onset type 2 diabetes, we formed ProDiGY, a multiethnic collaboration of three studies (TODAY, SEARCH, and T2D-GENES) with 3,006 youth case subjects with type 2 diabetes (mean age 15.1 ± 2.9 years) and 6,061 diabetes-free adult control subjects (mean age 54.2 ± 12.4 years). After stratifying by principal component–clustered ethnicity, we performed association analyses on ∼10 million imputed variants using a generalized linear mixed model incorporating a genetic relationship matrix to account for population structure and adjusting for sex. We identified seven genome-wide significant loci, including the novel locus rs10992863 in PHF2 (P = 3.2 × 10−8; odds ratio [OR] = 1.23). Known loci identified in our analysis include rs7903146 in TCF7L2 (P = 8.0 × 10−20; OR 1.58), rs72982988 near MC4R (P = 4.4 × 10−14; OR 1.53), rs200893788 in CDC123 (P = 1.1 × 10−12; OR 1.32), rs2237892 in KCNQ1 (P = 4.8 × 10−11; OR 1.59), rs937589119 in IGF2BP2 (P = 3.1 × 10−9; OR 1.34), and rs113748381 in SLC16A11 (P = 4.1 × 10−8; OR 1.04). Secondary analysis with 856 diabetes-free youth control subjects uncovered an additional locus in CPEB2 (P = 3.2 × 10−8; OR 2.1) and consistent direction of effect for diabetes risk. In conclusion, we identified both known and novel loci in the first genome-wide association study of youth-onset type 2 diabetes.

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.

Objective: Real-time continuous glucose monitoring (CGM) is effective for diabetes management in T1D and adults with T2D, but has not been assessed in youth with T2D. The objective of this study was to assess the feasibility and acceptability of CGM use in youth with T2D. Methods: Youth (13- 21 years old) with T2D ≥ 6 months and A1c > 7%, on any FDA approved treatment regimen were included. After a blinded run-in period, participants were given a Dexcom G6 CGM system for 12 weeks. The use and acceptability of the real-time CGM were evaluated by sensor usage, surveys, and focus group qualitative data. Results: Youth (n=8) had median age 19.4 (IQR 15.5-20.6) years, were 75% female, 50% hispanic/latino ethnicity, 50% non-English speaking at home, and 91% had public insurance. Baseline A1c was 11.7 (±2.8)% with mean diabetes duration of 2.5 (IQR 1.4 - 5.1) years and 75% were on basal insulin. Of the 7 participants who have completed the CGM intervention, all reported CGM was a positive experience (50% very positive, 50% slightly positive), easy to use (50% very easy, 50% easy), and useful (83% useful, 17% useful). All participants desired to continue to use real-time CGM in the future. 85% were “extremely” likely to recommend real-time CGM to friends. 67% of participants self-reported eating fewer meals while using CGM. All participants used sensors for the duration of the study. Conclusion: Real-time CGM is feasible and acceptable for youth with type 2 diabetes. Larger randomized controlled trials are needed to assess effects on glycemic control, healthy lifestyle changes, and diabetes distress. Disclosure H. Chesser: None. S. Srinivasan: None. J. C. Wong: Advisory Panel; Self; Provention Bio, Inc., Research Support; Self; Dexcom, Inc., Tandem Diabetes Care.

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)

Polygenic scores (PS) of T2D genetic variants based on pathophysiologic mechanisms underlying diabetes have been developed. We investigated whether T2D cluster PS influences response to interventions for diabetes prevention. We examined 2,647 DPP participants with impaired glucose tolerance and genetic data randomized to intensive lifestyle, metformin or placebo arms for diabetes prevention. We generated five PS clusters including β-cell related clusters (β-cell, proinsulin) and insulin resistance clusters (liver/lipid, obesity, lipodystrophy). We examined associations of PS with baseline and 1-year glycemic traits after intervention derived from oral glucose tolerance tests using general linear models, and with diabetes incidence over an average period of 3 years using Cox Regression Models. Models were adjusted for sex, age, and 10 principal components, respective baseline trait, and treatment group. At one-year follow-up, β-cell PS was associated with reduction in the insulinogenic index and corrected insulin response, in each treatment group and, statistically significant, in all combined (P We conclude that high genetic burden in β-cell cluster PS is associated with greater risk of diabetes and a decrease in insulin secretion, seen across all treatment groups. Despite interventions with intensive lifestyle and metformin, participants with high β-cell cluster genetic burden had an increased risk of diabetes and worsening in insulin secretion similar to placebo group. These data suggest that alternate therapies may be needed to prevent β-cell decline in this subset of prediabetes patients. Disclosure L. K. Billings: Advisory Panel; Self; Bayer Inc., Lilly Diabetes, Novo Nordisk, Sanofi. S. Raghavan: None. S. Srinivasan: None. J. Xu: None. J. C. Florez: Consultant; Self; Goldfinch Bio, Inc., Other Relationship; Self; Novo Nordisk. M. Udler: None. D. Research group: None. K. A. Jablonski: None. P. W. Franks: Advisory Panel; Self; Zoe Global Limited, Consultant; Self; Eli Lilly and Company, Novo Nordisk. R. B. Goldberg: None. M. Hivert: 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. C. Lee: None. J. Merino: None. Funding National Institute of Diabetes and Digestive and Kidney Diseases (U01DK048489)

OBJECTIVE The rs7903146 T allele in transcription factor 7 like 2 (TCF7L2) is strongly associated with type 2 diabetes (T2D), but the mechanisms for increased risk remain unclear. We evaluated the physiologic and hormonal effects of TCF7L2 genotype before and after interventions that influence glucose physiology. RESEARCH DESIGN AND METHODS We genotyped rs7903146 in 608 individuals without diabetes and recorded biochemical data before and after 1) one dose of glipizide (5 mg) on visit 1 and 2) a 75-g oral glucose tolerance test (OGTT) performed after administration of metformin 500 mg twice daily over 2 days. Incretin levels were measured in 150 of the 608 participants. RESULTS TT risk-allele homozygotes had 1.6 mg/dL higher baseline fasting glucose levels and 2.5 pg/mL lower glucagon levels per T allele than carriers of other genotypes at baseline. In a subset of participants, the T allele was associated with higher basal glucagon-like peptide 1 (GLP-1) levels at visit 1 (β = 1.52, P = 0.02 and β = 0.96, P = 0.002 for total and active GLP-1, respectively), and across all points of the OGTT after metformin administration. Regarding drug response, the T allele was associated with a shorter time (β = −7.00, P = 0.03) and a steeper slope (β = 0.23, P = 0.04) to trough glucose levels after glipizide administration, and lower visit 2 fasting glucose level adjusted for visit 1 fasting glucose level (β = −1.02, P = 0.04) and a greater decline in glucose level between visits (β = −1.61, P = 0.047) after metformin administration. CONCLUSIONS Our findings demonstrate that common variation at TCF7L2 influences acute responses to both glipizide and metformin in people without diabetes and highlight altered incretin signaling as a potential mechanism by which TCF7L2 variation increases T2D risk.

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

It is often difficult to distinguish between type 1 (T1D) and type 2 diabetes (T2D), particularly in young adulthood. Both T1D and T2D have significant genetic contributions to risk and understanding their genetic overlap may offer clinical insight. We examined whether a T1D genetic risk score (GRS) is associated with clinical features and diagnosis of T2D in the Framingham Heart Study (FHS). We constructed a T1D GRS based on 79 SNPs from fine mapping results, with SNPs weighted by published effect sizes. The GRS included both HLA (27 SNPs) and non-HLA (52 SNPs) regions, and was applied to 1503 participants with T2D. Multivariable regression models assessed the association of the GRS with age of T2D onset, BMI, HbA1C, HOMA-β and HOMA-IR at the exam of diagnosis. Case-control analysis of 1503 cases and 5649 controls was done using multivariate generalized estimating equations (GEE) logistic models to determine the association between the T1D GRS and T2D risk with age and sex as covariates. In FHS participants, the mean age was 57.2 y, BMI was 30 kg/m2, and HbA1c 6.05%. The T1D GRS was not associated with T2D age of onset, or BMI, HbA1c, HOMA-β and HOMA-IR after excluding participants on medications for T2D. There was a trend towards lower HOMA-β and HOMA-IR values in the top quintile of the GRS (n=244, mean=2.47, SD= 0.7 for ln(HOMA-β) and mean=2.88, SD=0.8 for ln(HOMA-IR)) when compared to the lowest quintile of the GRS (n=260, mean= 2.53, SD=0.8 for ln(HOMA-β) and mean=2.92, SD=0.8 for ln(HOMA-IR)). The T1D GRS was associated with T2D (β = 0.per SD increase in GRS, SE= 0.03, OR =1.1, P = 0.04). When the non-HLA and HLA components of the GRS were tested separately, the results remained significant only for the non-HLA T1D GRS (β = 0.per SD increase in GRS, SE= 0.03, OR=1.1, P= 0.02). A T1D GRS of non-HLA SNPs is associated with T2D in the FHS participants. Our further analysis with increased sample size in multi-ethnic populations may refine the impact of the T1D GRS to characterize individuals with T2D. Disclosure S. Srinivasan: None. A. Leong: None. M. Udler: None. B.C. Porneala: None. J.B. Meigs: None. S.S. Rich: None. J. Dupuis: None.

The prevalence of type 2 diabetes is increasing in youth, particularly in certain ethnic groups, and studies have highlighted differences between youth- and adult-onset disease. However, its genetic determinants remain largely unexplored. To identify genetic variants predisposing to type 2 diabetes in youth, we formed ProDiGY, a multi-ethnic collaboration of the TODAY, SEARCH for Diabetes in Youth, and T2D-GENES studies, with 30young cases (mean age= 15.1 y ±2.8, 63% female, 22% white, 36% African American, 42% Hispanic) and 6061 diabetes-free adult controls (mean age = 54.2 y ±12.4; 57% female, 24% white, 18% African American, 58% Hispanic). After stratifying by self-reported and principal component-clustered ethnicity, we performed association analyses on 206,928 directly genotyped variants using a generalized linear mixed model using a genetic relationship matrix to account for population structure. We identified 5 genome-wide significant loci, including the novel locus rs13130484 in GNPDA2 (P= 1.4 ×10-8, odds ratio [OR]= 1.24), which has nominal association with type 2 diabetes in adults. Known loci identified in our analysis include rs4132670 in the well-known type 2 diabetes locus TCF7L2 (P= 4.2×10-13, OR 1.35) in high linkage disequilibrium with causal SNP rs7903146 (R2=0.83), rs11662368 near the obesity locus MC4R (P= 3.9×10-9, OR =1.31), rs11257655 in RN7SL232P (P= 5.7×10-12, OR= 1.33), and rs7075724 in CDC123 (P= 8.3×10-11, OR= 1.29); effect sizes are greater for these latter three loci than those found in adults (Pdiff= 8.2×10-5, 2.4×10-5, and 1.7×10-6, respectively). Secondary analysis with 856 diabetes-free youth controls demonstrated consistent direction of effect but no additional loci. In conclusion, we identified known and novel genetic loci predisposing to type 2 diabetes in youth. Our future analysis of ∼10 million variants holds the potential to uncover additional new type 2 diabetes susceptibility loci. Disclosure S. Srinivasan: None. J. Todd: None. L. Chen: None. J. Divers: None. S. Gidding: None. S. Chernausek: Advisory Panel; Self; Novo Nordisk Inc.. R. Gubitosi-Klug: None. M.M. Kelsey: Other Relationship; Self; Daiichi Sankyo Company, Limited, Merck Sharp & Dohme Corp.. R. Shah: None. M. Black: None. L.E. Wagenknecht: None. J.M. Mercader: None. A. Manning: None. J. Flannick: None. D. Dabelea: None. J.C. Florez: Consultant; Self; Intarcia Therapeutics, Inc.. Consultant; Spouse/Partner; Santen.