Introduction and Objective: Translating genetic and molecular biomarkers into clinically actionable T2D risk prediction models is challenged by uncertain incremental value beyond standard clinical factors in real-world populations. We assessed metabolomic risk score (MRS) and a polygenic risk score (PRS) for incident T2D prediction in a diverse healthcare system. Methods: We studied 36,352 adults free of diabetes at metabolite assessment in the Mass General Brigham Biobank and followed for care for ~6 years. We derived the MRS using elastic net regression in UK Biobank (N=233K; 10,707 incident cases; follow-up: ~13 years) profiled on the Nightingale platform, and a PRS constructed from T2D GWAS summary statistics. Cox models to predict incident T2D were sequentially adjusted from demographics to established clinical risk scores (BMI, blood pressure, family history, and lipids), random glucose and HbA1c to mimic varying levels of clinical data availability. Results: Both MRS and PRS were associated with incident T2D and improved discrimination in all models including those adjusted for HbA1c (P < 10-10, iC-index 0.01). Each standard deviation increase in MRS was associated with a threefold risk (age/sex adjusted model: HR 3.0, 95% CI 2.8-3.2, C-index 0.83; fully adjusted model: HR 1.7, 1.4-2.0, 0.84). Associations were stronger at higher BMI and among GLP-1 receptor agonist users (P interaction < 10-4) adjusting for clinical factors including BMI. Absolute risk estimation showed marked stratification: individuals with high MRS and PRS had ~30% 10-year T2D risk by age 40 and >50% by age 60 whereas lower risk profiles were <3% in all BMI categories. Higher MRS was also associated with increased risk of incident chronic kidney disease among individuals with T2D. Conclusion: MRS and PRS provide complementary, clinically meaningful prediction of T2D beyond HbA1c and BMI supporting the integration of molecular profiling with standard clinical risk factors for targeted T2D prevention and risk reduction. Disclosure M. Sevilla-Gonzalez: Research Support; Current; Novo Nordisk. A.M. Martinez-Muñoz: None. P.A. Hanson: None. A. Huerta: None. M. Vora: None. E.W. Karlson: None. J.C. Florez: Research Support; Current; Novo Nordisk. Consultant; Current; Alveus Therapeutics. C.J. Patel: None. J. Mercader: None. A. Leong: Other - A close family member was an employee until August 2024.; Ended; Merck & Co., Inc. Funding 1K99DK139461-01A and R01DK137993 from NIH

BACKGROUND:Polygenic risk scores (PRSs) improve prediction of the development of type 2 diabetes over the use of clinical risk factors alone; however, they perform poorly in populations of non-European ancestry, limiting their global clinical utility. We aimed to deliver comprehensive and rigorously tested multi-ancestry PRSs for prediction in type 2 diabetes. METHODS:We conducted meta-analyses using data from type 2 diabetes genome-wide association studies (GWAS) across cohorts from five major global ancestries: European, African or African American, Admixed American, South Asian, and East Asian. We used summary statistics from the GWAS to construct single-ancestry PRSs (using the continuous-shrinkage PRS-CS method) and multi-ancestry PRSs (using the PRS-CSx method), and constructed ancestry-specific linkage disequilibrium panels to model pairwise correlations between single-nucleotide polymorphisms in GWAS during PRS construction. Models were validated for association with type 2 diabetes in at least four independent cohorts per ancestry. The effect sizes of PRSs were estimated as the odds ratio (OR) per SD of the PRS, and ORs for individuals at the 90th, 95th, and 97·5th PRS percentiles were compared with the IQR as a reference. We also tested our PRS models for prediction of diabetes incidence with or without additional clinical factors, as well as microvascular complications and comorbidities. FINDINGS:Our analysis used data from 409 959 individuals with type 2 diabetes and 1 983 345 controls: respectively, 359 819 and 1 825 729 indivduals were included in the GWAS dataset, with 10 992 and 31 792 individuals in the training dataset and 39 148 and 125 824 individuals in the validation dataset. The best predictive performance for the single-ancestry PRSs was in European (incremental AUC 0·07-0·14) and East Asian (0·02-0·16) ancestries, whereas prediction was poorer for African or African American (0·02-0·03), Admixed American (0·02-0·04), and South Asian (0·02-0·04) ancestries, correlating with sample sizes in the GWAS. Compared with single-ancestry PRSs, our multi-ancestry PRSs showed higher effect sizes and smaller 95% CIs across all ancestries: OR per SD 1·73 (95% CI 1·67-1·80) in African or African American, 2·82 (2·67-2·97) in Admixed American, 2·45 (2·36-2·54) in East Asian, 2·36 (2·32-2·41) in European, and 2·23 (2·05-2·42) in South Asian ancestries. Individuals in the 97·5th PRS percentile had a 3-7 times increased risk of type 2 diabetes compared with those in the IQR (OR 3·43 [95% CI 2·80-4·21] in African or African American, 7·47 [5·64-9·89] in Admixed American, 6·62 [5·58-7·85] in East Asian, 6·25 [5·72-6·82] in European, and 4·50 [2·70-7·53] in South Asian ancestries). These PRSs were also associated with earlier onset of type 2 diabetes, higher risk of developing microvascular complications, and provide additional predictive value beyond clinical factors. In individuals with type 2 diabetes, the association between multi-ancestry PRSs and risk of microvascular complications and comorbidity was studied in populations of African, Admixed American, and European ancestries and was significant in all three ancestry groups for diabetic retinopathy (ORs per SD 1·28-1·57), diabetic nephropathy (1·25-1·58), proliferative diabetic retinopathy (1·39-2·08), and end-stage diabetic nephropathy (1·44-1·87); PRS was associated with coronary artery disease in the Admixed American ancestry group only (1·16 [95% CI 1·08-1·25]). INTERPRETATION:These validated, publicly available PRSs can improve risk stratification for type 2 diabetes onset and complications across diverse ancestries, supporting their further evaluation in clinical settings. FUNDING:The National Human Genome Research Institute of the US National Institutes of Health.

Abstract Purpose To evaluate whether initiation of GLP-1 receptor agonists (GLP-1RAs) is associated with anti-VEGF treatment burden in type 2 diabetes patients with diabetic macular edema (DME) in the IRIS ® Registry (Intelligent Research in Sight). Methods Incident GLP-1RA initiators were matched 1:1 with controls via Mahalanobis distance matching (9,896 pairs; N=19,792) on sociodemographics, DME risk factors, and factors influencing GLP-1RA prescription including hypertension, obesity, chronic kidney disease. A longitudinal mixed-effects event-study model evaluated monthly anti-VEGF injection frequency over a 36-month window (12 months before through 24 months after initiation), adjusting for DME duration. Visual acuity (VA) and central subfield thickness (CST) were secondary outcomes. Results Following GLP-1RA initiation, anti-VEGF injection trajectories did not significantly differ between the matched GLP-1RA and control cohorts (interaction coefficients −0.18 to 1.59, P>0.05). Likewise, no differences in VA were observed between cohorts (−0.05 to 0.04 logMAR, P>0.05) or CST (−14.12 to 33.58 µm, P>0.05). Conclusion In these matched cohorts, GLP-1RA initiation was not associated with the trajectory of anti-VEGF use or changes in VA or CST. Précis We used the American Academy of Ophthalmology IRIS ® Registry (Intelligent Research in Sight) to identify patients with DME. In 19,792 matched patients, there was no significant reduction in injection frequency post GLP1-RA initiation and no significant change in VA or CST.

Hypoglycemia is a preventable adverse treatment effect in diabetes patients, but genetic markers to identify those with increased susceptibility are lacking. We performed a case/control genome-wide association study (GWAS) of hypoglycemia in US Million Veteran Program (MVP) participants with medication-treated diabetes mellitus. Cases had an outpatient random serum/plasma glucose <70 mg/dL or an emergency department visit for hypoglycemia. GWAS was stratified by race/ethnicity, adjusted for age at MVP enrollment, sex, and top 10 population-specific principal components, followed by multi-population meta-analysis. Secondary analyses examined genetic associations with hypoglycemia stratified by diabetes medication exposure as well as replication in UK Biobank and the Action to Control Cardiovascular Risk in Diabetes clinical trial. The study included 72,244 (22,045 cases) non-Hispanic White participants, 24,162 (10,441 cases) non-Hispanic Black participants, and 9,196 (2,800 cases) Hispanic participants. Four loci had genome-wide significant associations with hypoglycemia in multi-population meta-analysis: rs12712928 (chromosome 2, SIX2/SIX3 locus), rs1064173 (chromosome 6, HLA-DQB1/DQA2 locus), rs35198068 (chromosome 10, TCF7L2 locus), and rs113748381 (chromosome 17, SCL16A11 locus). All four loci replicated in at least one independent cohort, and the magnitude of associations with hypoglycemia varied by diabetes type. Genome-wide analyses may complement candidate pharmacogenetic studies to identify risk markers of adverse drug effects.

Optimizing second-line therapy for type 2 diabetes is challenging due to interindividual variability in response. We conducted a pharmacogenomic genome-wide association study (GWAS) in the Glycemia Reduction Approaches in Type 2 Diabetes: A Comparative Effectiveness (GRADE) Study to identify genetic predictors of glycemic response to insulin glargine, glimepiride, liraglutide, and sitagliptin, when added to metformin in a diverse population. We identified 21 genome-wide significant loci associated with treatment response. rs1905505, a non-coding variant near SLC2A2, the gene encoding the glucose transporter GLUT2, was enriched in Africans/African Americans and conferred a 36% increased risk of treatment failure on glimepiride (p=4.83×10). Carriers had impaired β-cell function, evidenced by a lower C-peptide index during OGTT, and diminished glucose-lowering response to an acute sulfonylurea challenge. Genetic manipulation in zebrafish confirmed that slc2a2 disruption attenuates the glucose-lowering effect of glimepiride. In conclusion, genetic variation influences glycemic response to medications, with SLC2A2 emerging as a key determinant of sulfonylurea response. Clinical Trial registration number:NCT01794143.

Introduction and Objective: Individuals who carry genetic variants that lower A1C independently of glycemia may have undetected hyperglycemia. Other glycated proteins can be used as alternate glycemic measures but whether these A1C variants affect their levels is unclear. Methods: We recalled 202 patients (10% diabetes) from the Mass General Brigham Biobank enriching recruitment for the African Ancestry (AA) G6PD variant and tails of a European Ancestry (EA) polygenic score distribution comprising 123 variants reported in GWAS to affect A1C but not glucose. Subjects underwent 14 days of continuous glucose monitoring (CGM) and a blood draw measuring A1C, fructosamine (FRU), and glycated albumin (GA). We calculated A1C-Average Glucose (AG) discordance as measured A1C minus estimated-A1C from CGM-derived AG using the ADAG equation. Results: Despite similar or higher AG, GA, and FRU, A1C-AG discordance was lower in rs1050828 T allele carriers than AA noncarriers and lower in the bottom decile than the top decile of the EA polygenic score (all p ≤ 0.01, Table). A1C-AG discordance was higher and GA was higher in AA noncarriers than EA. Conclusion: Variants that lower A1C independently of glycemia, like the G6PD variant, do not appear to lower levels of other glycated proteins; yet both measured A1C and GA were higher in AA noncarriers than EA despite similar AG suggesting glycation propensity may differ by ancestry. N. Thangthaeng: None. R. Mandla: None. S. Kartik: None. M.N. Facibene: None. M. Sampson: None. D.B. Sacks: Other Relationship; Sebia, Trinity. J.M. Mercader: None. A. Leong: Other Relationship; Merck & Co., Inc. Doris Duke Foundation (2020096)

Introduction and Objective: Glycated hemoglobin (HbA1c) is the primary clinical measure of average glucose (AG). The X-linked G6PD variant has been associated with lower HbA1c but higher complication risks; yet the extent to which the variant impacts the HbA1c-AG relationship resulting in undetected hyperglycemia is unclear. Methods: In the GRADE Study, 1287 participants with type 2 diabetes including 303 non-Hispanic Blacks (NHB) underwent 10 days of continuous glucose monitoring (CGM) followed by HbA1c and glycated albumin (GA) measurements. By linear regression, we compared CGM-derived AG with HbA1c and GA. Results: Median HbA1c (~6.8%) was similar across G6PD genotypes but AG was higher in rs1050828 T-allele carriers (T, men: 160 mg/dL; CT, women: 126 mg/dL) than NHB noncarriers (CC, men: 123 mg/dL; women: 119 mg/dL). The regression slopes and intercepts of AG on HbA1c differed by race and genotype yet the relationship with GA was similar by genotype (Figure). For a predicted HbA1c of 7%, AG in hemizygous men was 25 mg/dL higher than heterozygous women, 44 mg/dL higher than noncarriers, and 12 mg/dL higher than AG calculated from regression equations used in clinical practice to convert HbA1c to AG. Conclusion: The relationship between AG and HbA1c differed by G6PD genotype and race. Using genotype specific regression equations, GA or CGM could reduce undetected hyperglycemia in diverse populations. A. Leong: Other Relationship; Merck & Co., Inc. M. Tripputi: None. L. Szczerbinski: None. S. Rosin: None. A.J. Kretowski: None. J.M. Mercader: None. N. Younes: None. J.H. Li: None. J.C. Florez: Research Support; Novo Nordisk. The National Institute of Diabetes and Digestive and Kidney Diseases (U01DK098246; U34-DK-088043); American Diabetes Association (7-22-ICTSPM-23); The National Heart Lung, and Blood Institute; The Centers for Disease Control and Prevention

Diabetes complications occur at higher rates in individuals of African ancestry. Glucose-6-phosphate dehydrogenase deficiency (G6PDdef), common in some African populations, confers malaria resistance, and reduces hemoglobin A1c (HbA1c) levels by shortening erythrocyte lifespan. In a combined-ancestry genome-wide association study of diabetic retinopathy, we identified nine loci including a G6PDdef causal variant, rs1050828 -T (Val98Met), which was also associated with increased risk of other diabetes complications. The effect of rs1050828 -T on retinopathy was fully mediated by glucose levels. In the years preceding diabetes diagnosis and insulin prescription, glucose levels were significantly higher and HbA1c significantly lower in those with versus without G6PDdef. In the Action to Control Cardiovascular Risk in Diabetes (ACCORD) trial, participants with G6PDdef had significantly higher hazards of incident retinopathy and neuropathy. At the same HbA1c levels, G6PDdef participants in both ACCORD and the Million Veteran Program had significantly increased risk of retinopathy. We estimate that 12% and 9% of diabetic retinopathy and neuropathy cases, respectively, in participants of African ancestry are due to this exposure. Across continentally defined ancestral populations, the differences in frequency of rs1050828 -T and other G6PDdef alleles contribute to disparities in diabetes complications. Diabetes management guided by glucose or potentially genotype-adjusted HbA1c levels could lead to more timely diagnoses and appropriate intensification of therapy, decreasing the risk of diabetes complications in patients with G6PDdef alleles.

Type 2 diabetes (T2D) is a heterogeneous disease that develops through diverse pathophysiological processes1,2 and molecular mechanisms that are often specific to cell type3,4. Here, to characterize the genetic contribution to these processes across ancestry groups, we aggregate genome-wide association study data from 2,535,601 individuals (39.7% not of European ancestry), including 428,452 cases of T2D. We identify 1,289 independent association signals at genome-wide significance (P < 5 × 10-8) that map to 611 loci, of which 145 loci are, to our knowledge, previously unreported. We define eight non-overlapping clusters of T2D signals that are characterized by distinct profiles of cardiometabolic trait associations. These clusters are differentially enriched for cell-type-specific regions of open chromatin, including pancreatic islets, adipocytes, endothelial cells and enteroendocrine cells. We build cluster-specific partitioned polygenic scores5 in a further 279,552 individuals of diverse ancestry, including 30,288 cases of T2D, and test their association with T2D-related vascular outcomes. Cluster-specific partitioned polygenic scores are associated with coronary artery disease, peripheral artery disease and end-stage diabetic nephropathy across ancestry groups, highlighting the importance of obesity-related processes in the development of vascular outcomes. Our findings show the value of integrating multi-ancestry genome-wide association study data with single-cell epigenomics to disentangle the aetiological heterogeneity that drives the development and progression of T2D. This might offer a route to optimize global access to genetically informed diabetes care.

Abstract Disclosure: J.H. Li: None. A. Barry: None. A. Huerta-Chagoya: None. L. Szczerbinski: None. J.M. Mercader: None. J.C. Florez: Consulting Fee; Self; AstraZeneca. Grant Recipient; Self; Novo Nordisk. Speaker; Self; Novo Nordisk, Merck. A. Leong: None. Background: Sodium-glucose cotransporter-2 inhibitors (SGLT2i) are increasingly prescribed for the treatment of type 2 diabetes, but there is interindividual variability in glycemic response. We investigated whether two genetic variants, rs33954001, a missense variant (p.His615Gln) in SLC5A1 encoding SGLT1, and rs8050500 near SLC5A2 encoding SGLT2, previously associated with glycemic traits in genome-wide association studies, had any influence on short-term glycated hemoglobin (HbA1c) lowering in patients on SGLT2i treatment. Methods: We identified 1,961 ancestrally diverse, genotyped individuals with documented exposure to a SGLT2i in the Mass General Brigham Biobank, a hospital-based biobank linked with patient medical records. Of these, 736 individuals had a baseline HbA1c ≥ 6% within 6 months of the index appearance of an SGLT2i and a follow-up HbA1c measured 6-12 months after. Genotyping was performed on the Illumina Multi-Ethnic Genotyping Array and the Illumina Global Screening Array. Variants were imputed with the TOPMed reference panel. Using linear regression models, we evaluated the additive effect of the genetic variants on HbA1c reduction, defined as baseline HbA1c minus follow-up HbA1c, adjusted for age, sex, 10 genetic ancestry principal components to account for population stratification, the number of concomitant glucose-lowering drug classes, and three covariates accounting for potential genotyping batch effects. To determine whether genetic effects varied by pretreatment glycemic control, we tested for a genotype × baseline HbA1c interaction and performed stratified analysis by baseline HbA1c (≥ 8% vs. < 8%). Results: The mean age of participants was 63.7 years and 41% were female. The baseline HbA1c was 8.0 ± 1.9%. The mean HbA1c reduction was 0.6% over a mean follow-up time of 8.7 ± 1.6 months. None of the variants were associated with HbA1c reduction (p > 0.05) in the whole dataset. However, in stratified analyses, among those with baseline HbA1c ≥ 8% (n = 382), we observed that carriers of the G allele at rs33954001 had a 0.5% greater HbA1c reduction (p = 0.048) compared to those with CC genotype. Further adjustment for baseline renal function did not modify results. In the Genotype-Tissue Expression (GTEx) portal, the G allele was associated with higher SLC5A1 expression in nerve (p = 1 × 10-8), esophageal mucosa (p = 4 × 10-5) and stomach (p = 9 × 10-5), but not kidney. In the Translational human pancreatic Islet Genotype tissue-Expression Resource (TIGER), the G allele was associated with SLC5A1 expression in pancreatic islets (p = 2 × 10-6). Conclusion: Genetic variation in SLC5A1 may partly explain heterogeneity in SGLT2i treatment effects among people with sub-optimally controlled diabetes, possibly due to differences in SLC5A1 tissue-specific expression. These findings require validation in large hospital-based biobanks or dedicated clinical trials. Presentation: 6/2/2024

Background: Patients with short bowel syndrome (SBS) dependent on home parenteral nutrition (HPN) commonly cycle infusions overnight, likely contributing to circadian misalignment and sleep disruption. Methods: The objective of this quasi -experimental, single -arm, controlled, pilot trial was to examine the feasibility, safety, and efficacy of daytime infusions of HPN in adults with SBS without diabetes. Enrolled patients were fitted with a continuous glucose monitor and wrist actigraph and were instructed to cycle their infusions overnight for 1 wk, followed by daytime for another week. The 24-h average blood glucose, the time spent >140 mg/dL or <70 mg/dL, and sleep fragmentation were derived for each week and compared using Wilcoxon signed -rank test. Patient -reported quality -of -life outcomes were also compared between the weeks. Results: Twenty patients (mean age, 51.7 y; 75% female; mean body mass index, 21.5 kg/m(2)) completed the trial. Overnight infusions started at 21:00 and daytime infusions at 09:00. No serious adverse events were noted. There were no differences in 24-h glycemia (daytime-median: 93.00 mg/dL; 95% CI: 87.7-99.9 mg/dL, compared with overnight-median: 91.1 mg/dL; 95% CI: 89.6-99.0 mg/dL; P = 0.922). During the day hours (09:00-21:00), the mean glucose concentrations were 13.5 (5.7-22.0) mg/dL higher, and the time spent <70 mg/dL was 15.0 (-170.0, 22.5) min lower with daytime than with overnight HPN. Conversely, during the night hours (21:00-09:00), the glucose concentrations were 16.6 (-23.1, -2.2) mg/dL lower with daytime than with overnight HPN. There were no differences in actigraphy-derived measures of sleep and activity rhythms; however, sleep timing was later, and light at night exposure was lower with daytime than with overnight HPN. Patients reported less sleep disruptions due to urination and fewer episodes of uncontrollable diarrhea or ostomy output with daytime HPN. Conclusions: Daytime HPN was feasible and safe in adults with SBS and, compared with overnight HPN, improved subjective sleep without increasing 24-h glucose concentrations. This trial was registered at clinicaltrials.gov as NCT04743960

Prolonged hyperglycemia leads to diabetes complications, especially in individuals with African ancestry (AFR) - a health disparity. Glucose-6-phosphate dehydrogenase deficiency (G6PDd) disproportionately affects men with AFR ancestry (prevalence 9.5% vs. 2.2% in the US population) and shortens red cell lifespan, reducing HbA1c with no effect on glucose levels. We investigated whether men with AFR ancestry and G6PDd were at increased risk of diabetes complications. We performed a multi-ethnic genome-wide association study meta-analysis of diabetic retinopathy (DR) using the VA Million Veteran Program (MVP) dataset (nmax = 192,406) and studied clinical impact with the MVP and ACCORD trial datasets. Nine significant loci were associated with DR, including a causal variant for G6PDd [rs1050828-T, OR 1.48 (95% CI 1.45 - 1.51), p = 1.99x10-90)]. Plasma glucose was much higher in those with vs. without G6PDd in the year preceding diabetes diagnosis (168 vs. 137 mg/dL, respectively) and insulin prescription (253 vs. 233), both p <0.001. In a Cox proportional hazards analysis, ACCORD participants with vs. without G6PDd had a higher likelihood of DR [HR 1.78 (1.55 - 2.04), p <0.001] and neuropathy (HR 1.37 (1.23 - 1.54), p <0.005]. A mediation analysis of G6PDd on DR showed that risk was fully attributable to higher glucose levels. In MVP participants, compared to those without G6PDd who were in the top two tertiles of HbA1c regressed onto plasma glucose, the risk of DR was increased with G6PDd (OR 1.40), but lower than the risk of DR in those without G6PDd who were in the lowest tertile of A1c vs. glucose (OR 1.61) - consistent with risk due to inadequate treatment rather than oxidative stress alone. Conclusions: Management based on both glucose and HbA1c, rather than HbA1c alone, might be needed to reduce diabetes complications in both individuals with African ancestry who have G6PDd, and other individuals with low HbA1c levels relative to their glucose levels. Disclosure J.H. Breeyear: None. J. Hellwege: None. J.S. House: None. S.L. Mitchell: None. B. Charest: None. T.B. Basnet: None. P. Reaven: Research Support; Dexcom, Inc. J.B. Meigs: None. M.K. Rhee: Research Support; Kowa Pharmaceuticals America, Inc. Y. Sun: None. O. Wilson: None. A.M. Hung: None. S.K. Iyengar: None. D.M. Rotroff: Consultant; Novo Nordisk. Research Support; Bayer Inc. J.B. Buse: Other Relationship; Novo Nordisk. Consultant; Corcept Therapeutics. Research Support; Corcept Therapeutics, Dexcom, Inc., Insulet Corporation. Consultant; Alkahest, Anji Pharmaceuticals, Aqua Medical, Altimmune Inc., AstraZeneca, Boehringer-Ingelheim, CeQur, Eli Lilly and Company, embecta, GentiBio, Glyscend Inc., Mellitus Health, Metsera, Pendulum Therapeutics, Praetego, LLC, Stability Health, Terns Pharmaceuticals, Insulet Corporation, Vertex Pharmaceuticals Incorporated, vTv Therapeutics. Other Relationship; Medtronic. Stock/Shareholder; Glyscend Inc., Mellitus Health, Pendulum Therapeutics, Praetego, LLC, Stability Health. A. Leong: Other Relationship; Merck & Co., Inc. J.M. Mercader: None. M. Brantley: None. N.S. Peachey: None. A. Motsinger-Reif: None. P.W. Wilson: None. Y. Sun: None. A. Giri: None. L.S. Phillips: Other Relationship; Diasyst, Inc. Research Support; Kowa Pharmaceuticals America, Inc., Janssen Pharmaceuticals, Inc., AbbVie Inc., Novo Nordisk, GlaxoSmithKline plc, Abbott, Sanofi-Aventis U.S., Pfizer Inc. T.L. Edwards: None. Funding NEI (F31EY033663, T32EY021453-10, R01EY025295, R01EY032159, P30-EY026877), NICHD (K12HD043483), NIAMS (K12AR084232-24), NIDDK (R01DK127083, K01DK120631, R21AI156161), NHGRI (U01HG011723, UL1TR002378)

Objective: To characterize high type 1 diabetes (T1D) genetic risk in a population where type 2 diabetes (T2D) predominates. Research Design and Methods: Characteristics typically associated with T1D were assessed in 109,594 Million Veteran Program (MVP) participants with adult-onset diabetes 2011–2021, who had T1D genetic risk scores (GRS) defined as low (0-<45%), medium (45-<90%), high (90-<95%), or highest (≥95%). Results: T1D characteristics increased progressively with higher genetic risk (p<0.001 for trend). A GRS ≥90% was more common with diabetes diagnoses before age 40 years, but 95% of those participants were diagnosed at age ≥40 years, and they resembled T2D in mean age (64.3 years) and BMI (32.3 kg/m2). Compared to the low risk group, the highest risk group was more likely to have diabetic ketoacidosis (DKA) (low 0.9% vs. highest GRS 3.7%), hypoglycemia prompting emergency visits (3.7% vs. 5.8%), outpatient plasma glucose <50 mg/dL (7.5% vs. 13.4%), a shorter median time to start insulin (3.5 vs. 1.4 years), use of a T1D diagnostic code (16.3% vs. 28.1%), low C peptide levels if tested (1.8% vs. 32.4%), and glutamic acid decarboxylase (GAD) antibodies (6.9% vs. 45.2%), all p<0.001. Conclusions: Characteristics associated with T1D were increased with higher genetic risk, and especially with the top 10% of risk. However, the age and BMI of those participants resemble T2D, and a substantial proportion did not have diagnostic testing or use of T1D diagnostic codes. T1D genetic screening could be used to aid identification of adult-onset T1D in settings in which T2D predominates

BACKGROUND:Islet autoantibodies form the foundation for type 1 diabetes (T1D) diagnosis and staging, but heterogeneity exists in T1D development and presentation. We hypothesized that autoantibodies can identify heterogeneity before, at, and after T1D diagnosis, and in response to disease-modifying therapies.METHODS:We systematically reviewed PubMed and EMBASE databases (6/14/2022) assessing 10 years of original research examining relationships between autoantibodies and heterogeneity before, at, after diagnosis, and in response to disease-modifying therapies in individuals at-risk or within 1 year of T1D diagnosis. A critical appraisal checklist tool for cohort studies was modified and used for risk of bias assessment.RESULTS:Here we show that 152 studies that met extraction criteria most commonly characterized heterogeneity before diagnosis (91/152). Autoantibody type/target was most frequently examined, followed by autoantibody number. Recurring themes included correlations of autoantibody number, type, and titers with progression, differing phenotypes based on order of autoantibody seroconversion, and interactions with age and genetics. Only 44% specifically described autoantibody assay standardization program participation.CONCLUSIONS:Current evidence most strongly supports the application of autoantibody features to more precisely define T1D before diagnosis. Our findings support continued use of pre-clinical staging paradigms based on autoantibody number and suggest that additional autoantibody features, particularly in relation to age and genetic risk, could offer more precise stratification. To improve reproducibility and applicability of autoantibody-based precision medicine in T1D, we propose a methods checklist for islet autoantibody-based manuscripts which includes use of precision medicine MeSH terms and participation in autoantibody standardization workshops.

Objective: Individuals with diabetes who carry genetic variants that lower hemoglobin A1c (HbA1c) independently of glycemia may have higher real, but undetected, hyperglycemia compared to those without these variants despite achieving similar HbA1c targets, potentially placing them at greater risk for diabetes-related complications. We sought to determine whether these genetic variants, aggregated in a polygenic score, and the large-effect African-ancestry specific missense variant in G6PD (rs1050828) that lower HbA1c were associated with higher retinopathy risk. Research Design and Methods: Using data from 29,828 type 2 diabetes cases of genetically inferred African-American/African-British and European ancestries, we calculated ancestry-specific nonglycemic HbA1c polygenic scores (ngA1cPS) composed of 122 variants associated with HbA1c at genome-wide significance, but not with glucose. We tested the association of the ngA1cPS and the G6PD variant with retinopathy, adjusting for measured HbA1c and retinopathy risk factors. Results: Participants in the bottom quintile of the ngA1cPS showed between 20 to 50% higher retinopathy prevalence, compared to those above this quintile, despite similar levels of measured HbA1c. The adjusted meta-analytic odds ratio for the bottom quintile was 1.31 (95% CI 1.0, 1.73; p=0.05) in African ancestry and 1.31 (95% CI 1.15, 1.50; p=6.5x10-5) in European ancestry. Among individuals of African ancestry with HbA1c below 7%-units, retinopathy prevalence was higher in individuals below, compared to above, the 50th percentile of the ngA1cPS regardless of sex or G6PD carrier status Conclusions: Genetic effects need to be considered to personalize HbA1c targets and improve outcomes of people with diabetes from diverse ancestries.

Objective: To characterize high type 1 diabetes (T1D) genetic risk in a population where type 2 diabetes (T2D) predominates. Research Design and Methods: Characteristics typically associated with T1D were assessed in 109,594 Million Veteran Program (MVP) participants with adult-onset diabetes 2011–2021, who had T1D genetic risk scores (GRS) defined as low (0-<45%), medium (45-<90%), high (90-<95%), or highest (≥95%). Results: T1D characteristics increased progressively with higher genetic risk (p<0.001 for trend). A GRS ≥90% was more common with diabetes diagnoses before age 40 years, but 95% of those participants were diagnosed at age ≥40 years, and they resembled T2D in mean age (64.3 years) and BMI (32.3 kg/m2). Compared to the low risk group, the highest risk group was more likely to have diabetic ketoacidosis (DKA) (low 0.9% vs. highest GRS 3.7%), hypoglycemia prompting emergency visits (3.7% vs. 5.8%), outpatient plasma glucose <50 mg/dL (7.5% vs. 13.4%), a shorter median time to start insulin (3.5 vs. 1.4 years), use of a T1D diagnostic code (16.3% vs. 28.1%), low C peptide levels if tested (1.8% vs. 32.4%), and glutamic acid decarboxylase (GAD) antibodies (6.9% vs. 45.2%), all p<0.001. Conclusions: Characteristics associated with T1D were increased with higher genetic risk, and especially with the top 10% of risk. However, the age and BMI of those participants resemble T2D, and a substantial proportion did not have diagnostic testing or use of T1D diagnostic codes. T1D genetic screening could be used to aid identification of adult-onset T1D in settings in which T2D predominates

OBJECTIVE:The clinical utility of genetic information for type 2 diabetes (T2D) prediction with polygenic score (PGS) in ancestrally diverse, real-world US healthcare systems is unclear, especially for those at low clinical phenotypic risk for T2D.RESEARCH DESIGN AND METHODS:We tested the association of PGS with T2D incidence in patients followed within a primary care practice network over 16 years in four hypothetical scenarios that varied by clinical data availability (N = 14,712): 1) age and sex, 2) age, sex, BMI, systolic blood pressure, and family history of diabetes; 3) all variables in (2) and random glucose; 4) all variables in (3), HDL, total cholesterol, and triglycerides, combined in a clinical risk score (CRS). To determine whether genetic effects differed by baseline clinical risk, we tested for interaction with the CRS.RESULTS:PGS was associated with incident diabetes in all models. Adjusting for age and sex only, the Hazard Ratio (HR) per PGS standard deviation (SD) was 1.76 (95% CI 1.68, 1.84) and the HR of top 5% of PGS vs interquartile range (IQR) was 2.80 (2.39, 3.28). Adjusting for the CRS, the HR per SD was 1.48 (1.40, 1.57) and HR of top 5% of PGS vs IQR was 2.09 (1.72, 2.55). Genetic effects differed by baseline clinical risk [(PGS-CRS interaction p =0.05; CRS below the median: HR 1.60 (1.43, 1.79); CRS above the median: HR 1.45 (1.35, 1.55)].CONCLUSIONS:Genetic information can help identify high-risk patients even among those perceived to be low risk in a clinical evaluation.