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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

Diabetic kidney disease (DKD) is a heritable but poorly understood complication of diabetes. To identify genetic variants predisposing to DKD, we performed genome-wide association analyses in 19,406 individuals with type 1 diabetes (T1D) using a spectrum of DKD definitions basedon albuminuria and renal function. We identified 16 genome-wide significant loci. The variant with the strongest association (rs55703767) is a common missense mutation in the collagen type IV alpha 3 chain(COL4A3)gene, which encodes a major structural component of the glomerular basement membrane (GBM) implicated in heritable nephropathies. The rs55703767 minor allele (Asp326Tyr) is protective against several definitions of DKD, including albuminuria and end-stage renal disease. Three other loci are in or near genes with known or suggestive involvement in DKD(BMP7)or renal biology (COLEC11andDDR1). The 16 DKD-associated loci provide novel insights into the pathogenesis of DKD, identifying potential biological targets for prevention and treatment.

PURPOSE OF REVIEW: To provide an update on knowledge the role of genetics in youth-onset type 2 diabetes (T2D). RECENT FINDINGS: The prevalence of T2D, once thought to be exclusively a disease of adults, has increased by over 35% since 2001. Youth with T2D tend to have higher rates of complications, more aggressive disease, with more rapid loss of beta-cell function and a less favorable response to treatment than adults. Obesity is the most important risk factor for T2D, and the rise in childhood overweight and obesity appears responsible for the dramatic increase in T2D in youth. However, some obese children do not develop T2D, consistent with genetic differences in susceptibility to the disease in the setting of obesity/insulin resistance, currently far less well characterized in youth than in adults. Recent studies have begun to show associations of several established adult T2D genetic risk variants with youth T2D and related glycemic quantitative traits, including the strongest known cross-population T2D genetic contributor TCF7L2. Maturity onset diabetes of the young (MODY), a diabetes subtype distinct from type 1 diabetes (T1D) and T2D, is now known to result from a highly penetrant gene mutation in one of several genes. MODY has been shown to account for or contribute to at least 4.5% of clinically diagnosed T2D, even among those who are overweight or obese, impacting treatment decisions. The recently formed ProDiGY (Progress in Diabetes Genetics in Youth) Consortium is using genome-wide association studies and whole exome sequencing to understand the genetic architecture of T2D in youth, including how it differs from that of adults. SUMMARY: The limited amount of research conducted to date on the genetics of youth-onset T2D, which tends to be a more aggressive disease than adult T2D, suggests some overlap with genes involved in adult T2D and a sizeable influence of highly penetrant monogenic diabetes variants. The ProDiGY Consortium is expected to provide a more comprehensive understanding of youth T2D genetics.

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.

BACKGROUND A fundamental precept of the carbohydrate–insulin model of obesity is that insulin secretion drives weight gain. However, fasting hyperinsulinemia can also be driven by obesity-induced insulin resistance. We used genetic variation to isolate and estimate the potentially causal effect of insulin secretion on body weight. METHODS Genetic instruments of variation of insulin secretion [assessed as insulin concentration 30 min after oral glucose (insulin-30)] were used to estimate the causal relationship between increased insulin secretion and body mass index (BMI), using bidirectional Mendelian randomization analysis of genome-wide association studies. Data sources included summary results from the largest published metaanalyses of predominantly European ancestry for insulin secretion (n = 26037) and BMI (n = 322154), as well as individual-level data from the UK Biobank (n = 138541). Data from the Cardiology and Metabolic Patient Cohort study at Massachusetts General Hospital (n = 1675) were used to validate genetic associations with insulin secretion and to test the observational association of insulin secretion and BMI. RESULTS Higher genetically determined insulin-30 was strongly associated with higher BMI (β = 0.098, P = 2.2 × 10−21), consistent with a causal role in obesity. Similar positive associations were noted in sensitivity analyses using other genetic variants as instrumental variables. By contrast, higher genetically determined BMI was not associated with insulin-30. CONCLUSIONS Mendelian randomization analyses provide evidence for a causal relationship of glucose-stimulated insulin secretion on body weight, consistent with the carbohydrate–insulin model of obesity.

The increasing prevalence of Type 2 diabetes has emphasized the need to optimize treatment regimens. Metformin, the most widely used oral agent, is recommended as first-line drug therapy by multiple professional organizations. Response to metformin varies significantly at the individual level; this heterogeneity may be explained in part by genetic factors. Understanding these underlying factors may aid with tailoring treatment for individual patients as well as with designing improved Type 2 diabetes therapies. The past 10 years have seen substantial progress in the understanding of the pharmacogenetics of metformin response. The majority of this work has focused on genes involved in the pharmacokinetics of metformin. Owing to the uncertainty surrounding its mechanism of action, studies of pharmacodynamic genetics have been relatively few; genome-wide approaches have the potential to illuminate the molecular details of metformin response. In this review we summarize current knowledge about metformin pharmacogenetics and suggest directions for future investigation.

Context: Extracellular nucleotide receptors are expressed in pancreatic B-cells. Purinergic signaling via these receptors may regulate pancreatic B-cell function. Objective: We hypothesized that purinergic signaling might influence glucose regulation and sought evidence in human studies of glycemic variation and a mouse model of purinergic signaling dysfunction. Design: In humans, we mined genome-wide meta-analysis data sets to examine purinergic signaling genes for association with glycemic traits and type 2 diabetes. We performed additional testing in two genomic regions (P2RX4/P2RX7 and P2RY1) in a cohort from the Prevalence, Prediction, and Prevention of Diabetes in Botnia (n = 3504), which includes more refined measures of glucose homeostasis. In mice, we generated a congenic model of purinergic signaling dysfunction by crossing the naturally hypomorphic C57BL6 P2rx7 allele onto the 129SvJ background. Results: Variants in five genes were associated with glycemic traits and in three genes with diabetes risk. In the Prevalence, Prediction, and Prevention of Diabetes in Botnia study, the minor allele in the missense functional variant rs1718119 (A348T) in P2RX7 was associated with increased insulin sensitivity and secretion, consistent with its known effect on increased pore function. Both male and female P2x7-C57 mice demonstrated impaired glucose tolerance compared with matched P2x7-129 mice. Insulin tolerance testing showed that P2x7-C57 mice were also less responsive to insulin than P2x7-129 mice. Conclusions: We show association of the purinergic signaling pathway in general and hypofunctioning P2X7 variants in particular with impaired glucose homeostasis in both mice and humans.

ClinicalTrials.gov NCT01762046; Journal Article; Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov't; OBJECTIVE Genome-wide association studies have uncovered a large number of genetic variants associated with type 2 diabetes or related phenotypes. In many cases the causal gene or polymorphism has not been identified, and its impact on response to anti-hyperglycemic medications is unknown. The Study to Understand the Genetics of the Acute Response to Metformin and Glipizide in Humans (SUGAR-MGH, NCT01762046) is a novel resource of genetic and biochemical data following glipizide and metformin administration. We describe recruitment, enrollment, and phenotyping procedures and preliminary results for the first 668 of our planned 1,000 participants enriched for individuals at risk of requiring anti-diabetic therapy in the future. METHODS All individuals are challenged with 5 mg glipizide × 1; twice daily 500 mg metformin × 2 days; and 75-g oral glucose tolerance test following metformin. Genetic variants associated with glycemic traits and blood glucose, insulin, and other hormones at baseline and following each intervention are measured. RESULTS Approximately 50% of the cohort is female and 30% belong to an ethnic minority group. Following glipizide administration, peak insulin occurred at 60 minutes and trough glucose at 120 minutes. Thirty percent of participants experienced non-severe symptomatic hypoglycemia and required rescue with oral glucose. Following metformin administration, fasting glucose and insulin were reduced. Common genetic variants were associated with fasting glucose levels. CONCLUSIONS SUGAR-MGH represents a viable pharmacogenetic resource which, when completed, will serve to characterize genetic influences on pharmacological perturbations, and help establish the functional relevance of newly discovered genetic loci to therapy of type 2 diabetes. TRIAL REGISTRATION ClinicalTrials.gov NCT01762046. Yes