Diabetes Care

AimTo clarify whether SARS-CoV-2 infection or vaccination contribute to risk of type 1 diabetes or more severe diabetes onset in children and young adults. MethodsWe analysed cohorts of population-wide registries of young individuals from Norway (N=1,986,970) and Sweden (N=2,100,188). We used regression models to estimate adjusted rate ratios (aRR), treating exposures as time-varying, starting 30 days after registered SARS-CoV-2 positive test or vaccination. FindingsPooled results from Norway and Sweden and age-groups 12-17 and 18-29 years showed no significant increase in type 1 diabetes after documented infections (aRR 1.06, 95%CI:0.77-1.45). There was moderate heterogeneity, with a suggestive increased risk among children in Norway after infection. Pooled results for Norway and Sweden and age-groups 12-17 years and 18-29 years showed no significant association between SARS-CoV-2 vaccination and risk of type 1 diabetes (aRR 1.09, 95%CI: 0.81, 1.48). There was significant heterogeneity, primarily driven by a positive association among children and an inverse association in young adults in Sweden. While the type 1 diabetes incidence increased and diabetes ketoacidosis decreased over time during 2016-2023, no significant break in time-trends were seen after March 2020 for HbA1c, risk or severity of diabetic ketoacidosis, or islet autoantibodies, at diagnosis of type 1 diabetes. InterpretationTaken together, these results do not indicate any consistent, large effects of SARS-CoV-2 infection or -vaccination on risk of type 1 diabetes or severity at disease onset. Suggestive associations in sub-groups should be investigated further in other studies. FundingThe work was done as part of regular work at the institutions where the authors had their primary affiliation, and no specific funding was obtained for these studies.

Background Glycated haemoglobin (HbA1c) underpins type 2 diabetes (T2D) and prediabetes management worldwide and reflects both glycaemia and erythrocyte biology. A missense variant in PIEZO1 (rs563555492T), carried by 1 in 12 South Asians, has been associated with a nonglycaemic reduction in HbA1c. We aimed to further characterise this association and evaluate its clinical consequences. Methods We undertook genetic and linked health data analyses across two cohorts: 19,898 (37.4% female) South Indians from the Madras Diabetes Research Foundation (MDRF) and 43,011 (54.4% female) British Bangladeshis and British Pakistanis in Genes & Health. In MDRF, we tested associations with glycaemic and erythrocytic traits using additive genetic models. In Genes & Health we modelled diagnosis of prediabetes, T2D, and diabetic eye disease using flexible parametric survival models. Ten-year absolute risks were estimated for a population aged 40-50 years. Findings PIEZO1 rs563555492T was associated with erythrocytic traits and lower HbA1c, but not with fasting glucose, postprandial glucose, or C-peptide. This variant reduced risk of prediabetes (HR 0.63, 95% CI 0.58-0.69) and T2D (0.85, 0.78-0.93) diagnosis, and increased risk of diabetic eye disease among individuals with T2D (1.20, 1.01-1.43). Modelling suggested approximately 1,019 missed prediabetes and 303 missed T2D diagnoses per 100,000 adults over 10 years. Interpretation An ancestry-enriched PIEZO1 variant is associated with lower HbA1c independent of glycaemia, reduced prediabetes and T2D diagnosis suggesting delayed detection, and increased complication risk. Reliance on HbA1c may systematically underestimate glycaemic risk in a substantial minority of South Asians. Funding The Wellcome Trust; NIHR

Aims/hypothesisGiven the potential shared aetiology between type 1 and type 2 diabetes, we aimed to identify any genetic regions associated with both diseases. For associations where there is a shared signal and the allele that increases risk to one disease also increases risk to the other, inference about shared aetiology could be made, with the potential to develop therapeutic strategies to treat or prevent both diseases simultaneously. Alternatively, if a genetic signal colocalises with divergent effect directions, it could provide valuable biological insight into how the association affects the two diseases differently. MethodsUsing publicly available type 2 diabetes summary statistics from a genomewide association study (GWAS) meta-analysis of European ancestry individuals (74,124 cases and 824,006 controls) and type 1 diabetes GWAS summary statistics from a meta-analysis of studies on individuals from the UK and Sardinia (7,467 cases and 10,218 controls), we identified all regions of 0.5 Mb that contained variants associated with both diseases (false discovery rate<0.01). In each region, we performed forward stepwise logistic regression to identify independent association signals, then examined colocalisation of each type 1 diabetes signal with each type 2 diabetes signal using coloc. Any association with a colocalisation posterior probability of [&ge;]0.9 was considered a genuine shared association with both diseases. ResultsOf the 81 association signals from 42 genetic regions that showed association with both type 1 and type 2 diabetes, four association signals colocalised between both diseases (posterior probability [&ge;]0.9): (i) chromosome 16q23.1, near Chymotripsinogen B1 (CTRB1) / Breast Cancer Anti-Estrogen Resistance Protein 1 (BCAR1), which has been previously identified; (ii) chromosome 11p15.5, near the Insulin (INS) gene; (iii) chromosome 4p16.3, near Transmembrane protein 129 (TMEM129), and (iv) chromosome 1p31.3, near Phosphoglucomutase 1 (PGM1). In each of these regions, the effect of genetic variants on type 1 diabetes was in the opposite direction to the effect on type 2 diabetes. Use of additional datasets also supported the previously identified colocalisation on chromosome 9p24.2, near the GLIS Family Zinc Finger Protein 3 (GLIS3) gene, in this case with a concordant direction of effect. Conclusions/interpretationThat four of five association signals that colocalise between type 1 diabetes and type 2 diabetes are in opposite directions suggests a complex genetic relationship between the two diseases. Research in ContextWhat is already known about this subject? O_LIOther than insulin, there are currently no treatments for both type 1 and type 2 diabetes. C_LIO_LIFindings that genetic variants near the GLIS3 gene increase risk of both type 1 and type 2 diabetes have indicated shared genetic mechanisms at the level of the pancreatic {beta} cell. C_LI What is the key question? O_LIBy examining chromosome regions associated with both diseases, are there any more variants that affect risk of both diseases and could support common mechanisms and repositioning of therapeutics between the diseases? C_LI What are the new findings? O_LIAt current sample sizes, there is evidence that five genetic variants in different chromosome regions impact risk of developing both diseases. C_LIO_LIHowever, four of these variants have the opposite direction of effect in type 1 diabetes compared to type 2 diabetes, with only one, near GLIS3, having a concordant direction of effect. C_LI How might this impact on clinical practise in the foreseeable future? O_LIGenetic findings have furthered research in type 1 and type 2 diabetes independently, and suggest therapeutic strategies. However, our current investigation into their shared genetics suggests that repositioning of current type 2 diabetes treatments into type 1 diabetes may not be straightforward. C_LI

BackgroundType 2 diabetes (T2DM) incidence is increased after diagnosis of COVID-19. The impact of vaccination on this increase, for how long it persists, and the effect of COVID-19 on other types of diabetes remain unclear. MethodsWith NHS England approval, we studied diabetes incidence following COVID-19 diagnosis in pre-vaccination (N=15,211,471, January 2020-December 2021), vaccinated (N =11,822,640), and unvaccinated (N=2,851,183) cohorts (June-December 2021), using linked electronic health records. We estimated adjusted hazard ratios (aHRs) comparing diabetes incidence post-COVID-19 diagnosis with incidence before or without diagnosis up to 102 weeks post-diagnosis. Results were stratified by COVID-19 severity (hospitalised/non-hospitalised) and diabetes type. FindingsIn the pre-vaccination cohort, aHRS for T2DM incidence after COVID-19 (compared to before or without diagnosis) declined from 3.01 (95% CI: 2.76,3.28) in weeks 1-4 to 1.24 (1.12,1.38) in weeks 53-102. aHRS were higher in unvaccinated than vaccinated people (4.86 (3.69,6.41)) versus 1.42 (1.24,1.62) in weeks 1-4) and for hospitalised COVID-19 (pre-vaccination cohort 21.1 (18.8,23.7) in weeks 1-4 declining to 2.04 (1.65,2.51) in weeks 52-102), than non-hospitalised COVID-19 (1.45 (1.27,1.64) in weeks 1-4, 1.10 (0.98,1.23) in weeks 52-102). T2DM persisted for 4 months after COVID-19 for [~]73% of those diagnosed. Patterns were similar for Type 1 diabetes, though excess incidence did not persist beyond a year post-COVID-19. InterpretationElevated T2DM incidence after COVID-19 is greater, and persists longer, in hospitalised than non-hospitalised people. It is markedly less apparent post-vaccination. Testing for T2DM after severe COVID-19 and promotion of vaccination are important tools in addressing this public health problem. Research in contextO_ST_ABSEvidence before this studyC_ST_ABSWe searched PubMed for population-based observational studies published between December 1st 2019 and July 12th 2023 examining associations between SARS-CoV-2 infection or COVID-19 diagnosis (search string: SARS-CoV-2 or COVID* or coronavirus*) and subsequent incident diabetes (search term: diabetes). Of nineteen relevant studies; eight had a composite outcome of diabetes types, six stratified by diabetes type and five pertained to type-1-diabetes (T1DM) only. We did not identify any studies relating to gestational or other types of diabetes. Eleven studies were from the US, three from the UK, two from Germany, one from Canada, one from Denmark and one from South Korea. Most studies described cumulative relative risks (for infection versus no infection) one to two years post-SARS-CoV-2 infection of 1.2 to 2.6, though four studies found no associations with T1DM after the post-acute period. All studies lacked the power to compare diabetes relative risk by type, severity, and vaccination status in population subgroups. One study examined relative risks by vaccination status, but this used a composite outcome of diabetes and hyperlipidaemia and was conducted in a predominantly white male population. Two studies of T1DM found no evidence of elevated risk beyond 30 days after COVID-19 diagnosis, whilst two reported elevated risks at six months. Two studies of type 2 diabetes (T2DM) examined relative risks by time period post-infection: one study of US insurance claims reported a persistent association six months post-infection, whereas a large UK population-based study reported no associations after 12 weeks. However, the latter study used only primary care data, therefore COVID-19 cases were likely to have been under-ascertained. No large studies have investigated the persistence of diabetes diagnosed following COVID-19; key to elucidating the role of stress/steroid-induced hyperglycaemia. Added value of this studyThis study, which is the largest to address the question to date, analysed linked primary and secondary care health records with SARS-CoV-2 testing and COVID-19 vaccination data for 15 million people living in England. This enabled us to compare the elevation in diabetes incidence after COVID-19 diagnosis by diabetes type, COVID-19 severity and vaccination status, overall and in population subgroups. Importantly, excess diabetes incidence by time period since infection could also be quantified. Since healthcare in the UK is universal and free-at-the-point-of-delivery, almost the entire population is registered with primary care. Therefore the findings are likely to be generalisable. We found that, before availability of COVID-19 vaccination, a COVID-19 diagnosis (vs. no diagnosis) was associated with increased T2DM incidence which remained elevated by approximately 30% beyond one year after diagnosis. Though still present (with around 30% excess incidence at eight weeks), these associations were substantially attenuated in unvaccinated compared with vaccinated people. Excess incidence was greater in people hospitalised with COVID-19 than those who were not hospitalised after diagnosis. T1DM incidence was elevated up to, but not beyond, a year post COVID-19. Around 73% of people diagnosed with incident T2DM after COVID-19 still had evidence of diabetes four months after infection. Implications of all the available evidenceThere is a 30-50% elevated T2DM incidence post-COVID-19, but we report the novel finding that there is elevated incidence beyond one-year post-diagnosis. Elevated T1DM incidence did not appear to persist beyond a year, which may explain why previous studies disagree. For the first time in a general-population dataset, we demonstrate that COVID-19 vaccination reduces, but does not entirely ameliorate, excess diabetes incidence after COVID-19. This supports a policy of universal vaccination and suggests that other public health activities, such as enhanced diabetes screening after severe COVID-19, may be warranted, particularly in unvaccinated people.

ImportanceTwenty-three percent of 37.3M adults in the USA with diabetes are estimated to be undiagnosed, leading to potentially avoidable sequelae and morbidity. ObjectiveTo explore the utility of a polygenic risk score (PRS) at identifying individuals with undiagnosed diabetes and prediabetes. Design, Setting and ParticipantsIndividuals without doctor-diagnosed diabetes at study baseline in the UK Biobank (UKB) with HbA1c and BMI measurements. Participants were restricted to white individuals to use an ancestry-appropriate PRS. Undiagnosed diabetes and prediabetes were defined using HbA1c ([&ge;]6.5% and [&ge;]5.7 - <6.5%, respectively). ExposuresA diabetes PRS comprising 13,863 SNPs derived from the 23andMe Research Cohort, and measured BMI among UKB participants. ResultsOf 412,439 individuals self-reporting an absence of diagnosed diabetes and who had BMI and HbA1c measurements at baseline, 2,934 (0.7%) had undiagnosed diabetes, representing 11.9% of all (diagnosed and undiagnosed) diabetes. Nearly half (1,362, 46%) of undiagnosed diabetes cases were among individuals in the top 25% of the PRS distribution. Overweight individuals (BMI [&ge;]25 - <30 kg/m2) who were in the top 12.5% of the PRS distribution had a similar frequency of undiagnosed diabetes (0.8-1.6% frequency) as individuals with obesity (BMI [&ge;]30kg/m2) in the lowest 12.5% of the PRS distribution (0.7-1.7% frequency). Combining overweight and obesity with the PRS identified nearly all cases of undiagnosed diabetes: individuals with a BMI [&ge;]25 kg/m2 (66% of the study population) or those in the top 54-69% of the PRS identified 98-99% of undiagnosed cases. Of the 199 undiagnosed diabetes cases occurring among individuals with a normal BMI (<25kg/m2), two-thirds were among individuals in the top 50% of the PRS. Prediabetes was common (14%), with measured BMI and PRS providing additive risk. Among those in the top 12.5% PRS with BMI [&ge;]35kg/m2, 6.3% developed incident diabetes over 4 years follow-up, as compared to 0% among the bottom 12.5% PRS with BMI<25kg/m2. ConclusionsA diabetes PRS is informative at identifying undiagnosed cases. PRS may have broader utility in detecting individuals with asymptomatic disease. Key PointsO_ST_ABSQuestionC_ST_ABSDoes a polygenic risk score (PRS) have utility in identifying individuals with undiagnosed type 2 diabetes (T2D)? FindingsIn this analysis of 412,439 individuals without doctor-diagnosed diabetes, a T2D PRS performed additively to body mass index (BMI) at identifying individuals with undiagnosed diabetes. Selecting individuals on the basis of overweight/obesity or a T2D PRS identified almost all cases of undiagnosed diabetes. The majority of undiagnosed diabetes cases among individuals with normal weight occurred among those at elevated polygenic risk. MeaningA T2D PRS identifies cases of undiagnosed diabetes among individuals with and without overweight or obesity.

BackgroundType 1 diabetes is an autoimmune disease, which leads to insulin dependence. We investigated genetic evidence to support the repurposing of seven drugs, already licensed or in clinical phases of development, for prevention of type 1 diabetes. MethodsWe obtained genome-wide association study (GWAS) summary statistics for the risk of type 1 diabetes, whole-blood gene expression, and serum protein levels, and investigated genetic polymorphisms near seven potential drug target genes. We used colocalization to examine whether the same genetic variants that are associated with type 1 diabetes risk were also associated with the relevant drug target genetic proxies, and Mendelian randomization to evaluate the direction and magnitude of the associations. Furthermore, we performed Mendelian randomization analysis restricted to functional variants within the drug target genes. FindingsColocalization revealed that the blood interleukin (IL)-2 receptor subunit alpha (IL2RA) and IL-6 receptor (IL6R) gene expression levels within the corresponding genes shared the same causal variant with type 1 diabetes liability (posterior probabilities 100% and 96.3%, respectively). Odds ratios (OR) of type 1 diabetes per 1-SD increase in the genetically proxied gene expression of IL2RA and IL6R were 0.22 (95% confidence interval [CI] 0.17-0.27) and 1.98 (95% CI 1.48-2.65), respectively. Using missense variants, genetically proxied tyrosine kinase 2 (TYK2) expression levels were associated with type 1 diabetes risk (OR 0.61, 95% CI [0.54-0.70]). InterpretationOur findings support the targeting of IL-2, IL-6R and TYK2 signaling in prevention of type 1 diabetes. Further studies should assess the optimal window to intervene to prevent type 1 diabetes. FundingKyllikki ja Uolevi Lehikoisen saatio, Foundation for Pediatric Research, Finnish Cultural Foundation, JDRF International; The University of Oulu & The Research Council of Finland Profi 326291; European Unions Horizon 2020 research and innovation program under grant agreement no. 848158 (EarlyCause). Research in contextO_ST_ABSEvidence before this studyC_ST_ABSWe searched PubMed for GWAS, Mendelian randomization, colocalization, and other studies for genetic evidence of efficacy of targeting IL2RA, IL2RB, IL6R, IL6ST, IL23A, TYK2, JAK2, and JAK3 signaling in prevention of type 1 diabetes. We searched studies with combinations of "IL2RA", "IL2RB", "IL6R", "IL6ST", "IL23A", "JAK2", "JAK3", "TYK2", "colocalization", "mendelian randomization", or "GWAS" and "Diabetes Mellitus, Type 1"[Mesh]. A recent large GWAS by Robertson et al. observed five conditionally independent genome-wide significant single-nucleotide polymorphisms (SNPs) associated with the risk of type 1 diabetes in intergenic and intronic areas near IL2RA. Another large recent GWAS by Chiou et al. found six independent SNPs associated with the risk of type 1 diabetes near IL2RA. A few smaller candidate gene studies also reported associations between the risk of type 1 diabetes and eight other loci near IL2RA. Epigenetic studies reported that a few type 1 diabetes risk variants near IL2RA are associated with changes in methylation of DNA near promoters of IL2RA in white blood cells and B-cells. Both above mentioned GWAS found associations between two missense mutations in TYK2 gene (rs12720356(A>C), rs34536443(G>C)) and the risk of type 1 diabetes. The same variants protected against type 1 diabetes and some other autoimmune diseases in a Finnish FINNGEN biobank study. Smaller candidate gene studies found that the rs2304256 A/A genotype was protective against type 1 diabetes in a Southern Brazilian and in a European population. In a study of Japanese population, similar association was not seen, possibly due to differences in allele frequencies and incidence of type 1 diabetes between populations. In the same study, TYK2 promoter haplotype with genetic polymorphisms in promoter region and exon 1 decreased the promoter activity and increased the risk for type 1 diabetes. The minor 358Ala allele of the IL6R SNP rs2228145 (A>C) was found to be associated with a reduced risk of developing T1D. In addition to loci near or at IL2RA and TYK2 loci, the GWAS by Robertson et al. reported an association between rs2229238 near IL6R and the risk of type 1 diabetes. Furthermore, a candidate gene study found that the GG haplotypes of variants rs11171806 and rs2066808 (in strong LD) near IL23A was protective against T1D. We found no studies in which colocalization between the loci near the reviewed genes and type 1 diabetes was reported nor estimates from Mendelian randomization on the possible causal relationship. However, Robertson et al. studied the genetic evidence for therapeutic potential of their GWAS hits using a priority index algorithm, which combined information from GWAS of type 1 diabetes, expression quantitative loci in immune cells, evidence from chromatin conformation in immune cells, as well as protein-protein interactions and genomic annotations. Among the reported GWAS hits, priority index ranked IL2RA as the top target, followed by TYK2 (the 3rd target), JAK2 (11th), IL23 (25th), JAK3 (35th), and IL6R (50th). Added value of this studyWe found that the risk of type 1 diabetes and whole-blood gene expression of IL2RA and IL6R colocalized to rs61839660 near IL2RA and rs10908839 near IL6R, respectively. Using these loci as instruments of IL2RA and IL6R expression, Mendelian randomization indicated that whole blood IL2RA expression is associated with decreased risk of type 1 diabetes (OR 0.70 per 1 SD increase) whereas IL6R expression is associated with increased risk (OR 1.079 per 1 SD increase). Furthermore, when a missense variant rs2304256 in TYK2 was used as an instrument for TYK2 expression, Mendelian randomization indicated that an SD increase in TYK2 expression was predicted to decrease the risk of type 1 diabetes (OR 0.61). Implications of all the available evidenceWe found genetic evidence supporting the efficacy of targeting IL-2, IL-6 and TYK2 signalling in prevention of type 1 diabetes. Further research is needed to address when would be the most optimal time to intervene in the pathogenesis of type 1 diabetes.

ObjectivesTo cluster participants with prediabetes with five type 2 diabetes (T2D)-related partitioned polygenetic risk scores (pPRSs) and examine the risk of incident diabetes and the benefit of adherence to healthy lifestyle across clusters. DesignProspective cohort study SettingHispanic Community Health Study/Study of Latinos (HCHS/SOL), US; UK Biobank (UKBB), UK. Participants7,227 US Hispanic/Latinos without diabetes from HCHS/SOL, including 3,677 participants with prediabetes. 400,149 non-Hispanic whites without diabetes from UKBB, including 16,284 participants with prediabetes. Main outcome measuresPrediabetes was defined by fasting plasma glucose (fasting glucose) between 100-125 mg/dL, 2-hour oral glucose tolerance test (OGTT 2h glucose) between 140-199 mg/dL, or hemoglobin A1c (HbA1c) between 5.7% and 6.5%. Diabetes was defined by fasting glucose levels [&ge;]126 mg/dL, 2h glucose after OGTT [&ge;]200 mg/dL, HbA1c [&ge;]6.5%, current use of anti-diabetic medications, or medical record. Five pPRSs representing various pathways related to T2D were calculated based on 94 T2D-related genetic variants. Health lifestyle score was assessed with five modifiable risk factors, including body mass index (BMI), smoking, alcohol drinking, physical activity, and diet for T2D. ResultsUsing K-means consensus clustering on five pRPSs, six clusters of individuals with prediabetes were identified in HCHS/SOL, with each cluster presenting disparate patterns of pPRSs and different patterns of metabolic traits. Except cluster 3 which was not detected, the other five clusters were conformed in participants with prediabetes in UKBB, with each cluster showing the similar patterns of pPRSs to their corresponding cluster in HCHS/SOL. At baseline, proportion of impaired glucose tolerance (IGT)/impaired fasting glucose (IFG) and glycemic traits in HCHS/SOL (fasting glucose, OGTT 2h glucose, and HbA1c) were not significantly different across six clusters (P=0.13, P=0.62, P=0.35, P=0.96, respectively). In UKBB, random glucose and HbA1c at baseline did not show significant difference across five clusters (P=0.43, P=0.71, respectively). Although baseline glycemic traits were similar across clusters, cluster 6, which featured a very low proinsulin score, exhibited elevated risk of incident T2D in both cohorts (risk ratio [RR]=1.39, 95% confidence interval [95% CI]=[1.10, 1.76] vs. cluster 1 in HCHS/SOL; hazard ratio [HR]=1.29, 95% CI=[1.00, 1.69] vs. cluster 1 in UKBB; Combined RR/HR=1.34 [1.13, 1.60]). To explain the elevated risk of incident T2D in cluster 6, interactions between proinsulin score and other three pPRSs (Beta-cell score, Lipodystrophy-like score, Liver-lipid score) and sum score were detected (P for interaction=0.001, 0.04, 0.02 and 0.002, respectively). Cluster 5 showed an increased risk of incident T2D in UKBB (HR=1.35 [1.05, 1.75] vs. cluster 1) and in the combined analysis with HCHS/SOL (RR/HR=1.29 [1.08, 1.53]), although its risk of T2D was not significantly different from cluster 1 in HCHS/SOL (RR=1.23 [0.96, 1.57]). Inverse associations between the lifestyle score and risk of T2D were observed across different clusters, with a suggestively stronger association in Cluster 5 compared to Cluster 1, in both cohorts. Cluster 5 showed reduced risk of incident diabetes caused by healthy lifestyle score (RR=0.65 [0.47, 0.89], HR=0.71 [0.62, 0.81], respectively. Combined RR/HR=0.70 [0.62, 0.79]). Among individuals with a healthy lifestyle, those in Cluster 5 had a similar risk of T2D compared to those in Cluster 1 (combined RR/HR=1.03 [0.91-1.18], P>0.05). ConclusionsThis study identified genetic subtypes of prediabetes which differed in risk of progression to T2D, with two subtypes showing relatively high risk of T2D over time. Favorable relationship between healthy lifestyle and risk of T2D was observed, regardless of their genetic subtypes. Participants in one subtype with higher risk of T2D may realize extra benefits in terms of risk reduction from a healthy lifestyle.

IMPORTANCEClinical guidelines recommend against using glycated hemoglobin A1c (HbA1c) to assess glycemia in patients with two erythropoietic conditions: glucose-6-phosphate dehydrogenase (G6PD) deficiency or sickle cell disease. What remains elusive is quantifying the impact of genetic variants underlying these and other erythropoietic conditions on HbA1c levels as a clinical indicator of glycemic status. OBJECTIVETo evaluate the impact of five erythropoietic (non-glycemic) genetic variants on HbA1c levels as a clinical indicator of glycemic status in a population with African genetic ancestry. DESIGNRetrospective cohort study conducted January 2011 to November 2022. SETTINGVeterans Health Administrations (VHAs) Million Veteran Program (MVP), a genetic biobank representative of the U.S.s largest integrated healthcare system, including the longest-running electronic health record system. PARTICIPANTS84,987 MVP participants with African genetic ancestry, excluding those with type 1 or secondary diabetes or G6PD, sickle cell, or other erythropoietic conditions. EXPOSUREAny one of five erythropoietic genetic variants known or suspected to affect HbA1c levels in African ancestry. MAIN OUTCOMES AND MEASURESClinically measured HbA1c, random blood glucose, triglyceride to HDL-cholesterol ratio, body mass index, blood pressure, diagnosis of and medications for type 2 diabetes (T2D). RESULTSAll the variants had significant differences in HbA1c compared to non-carriers with the same sex, age, glucose and BMI. Males with X-linked G202A variant for G6PD deficiency (11% of males in cohort) had HbA1c levels reduced by 0.8 percentage points compared to non-carriers; female carriers had reductions over 0.3 percentage points. Overall, G202A carriers had worse dysglycemia but were less likely to be diagnosed with or prescribed medication for dysglycemia than non-carriers. Due to this variant, an estimated 1.5 million (6%) African American adults without T2D may have dysglycemia in the pre-diabetes or T2D range, but their HbA1c indicate normoglycemia. CONCLUSIONS AND RELEVANCEBy lowering HbA1c without lowering glucose level, G202A variant for G6PD deficiency--a condition common to African ancestry and largely asymptomatic and undiagnosed--could be misguiding clinical management for 9% of African American Veterans without T2D and 6% of African American adults without T2D in the U.S. and contributing to racial disparities in T2D management. KEY POINTSO_ST_ABSQUESTIONC_ST_ABSWhat is the impact of non-glycemic genetic variants on HbA1c levels as a clinical indicator of glycemic status? FINDINGSIn patients with African ancestry within a U.S. healthcare setting, carriers of G202A variant for glucose-6-phosphate dehydrogenase (G6PD) deficiency have significantly lower HbA1c--0.9% points in males--and lower rates of type 2 diabetes diagnosis and medication but higher glucose compared to non-carriers. Four additional erythropoietic variants also had significant effects on HbA1c but none on glucose. MEANINGHbA1c can underestimate dysglycemia in carriers of G202A variant, which is common in African, and very rare in European, ancestry.

ObjectiveHbA1c thresholds used to define dysglycemia in autoantibody-positive individuals at risk for type 1 diabetes do not account for age-related increases in HbA1c and may overestimate progression risk in adults. We evaluated whether age-adjusted HbA1c or a higher HbA1c threshold improves risk stratification across age groups. Research Design and MethodsWe analyzed 5,024 autoantibody-positive relatives (3,720 children and 1,304 adults) participating in the TrialNet Pathway to Prevention study. Age-related HbA1c effects were modelled using 6,273 adults from the population-based Exeter 10,000 cohort. Progression risk was compared using the standard dysglycemia threshold (HbA1c [&ge;] 5.7% [39 mmol/mol]), age-adjusted HbA1c, and an alternative threshold of HbA1c [&ge;]6.0% (42 mmol/mol). ResultsUsing HbA1c [&ge;] 5.7%, children had higher 1-year progression risk than adults among single autoantibody-positive participants (38% [95% CI 28, 47] vs. 13% [7.2, 19]) and multiple autoantibody-positive participants (55% [49, 60] vs. 38% [27, 47]; both p<0.001). Age adjustment reduced these differences; progression risk was similar among single autoantibody-positive participants (38% [28, 47] vs. 27% [13, 39]; p=0.32), with attenuated differences among multiple autoantibody-positive participants. An HbA1c threshold [&ge;]6.0% yielded comparable progression risk between adults and children across autoantibody subgroups. In post hoc analyses, adults aged <30 years had progression risk similar to children (p=0.1). ConclusionsAge-related variation in HbA1c influences dysglycemia classification in adults at risk for type 1 diabetes. Age-adjusted HbA1c or a higher HbA1c threshold ([&ge;]6.0% [42 mmol/mol]) in adults [&ge;]30 years identifies individuals with progression risk comparable to children and may improve age-specific risk stratification in prevention seungs.

BackgroundPrevious observational and Mendelian randomization studies suggested different amino acids associated with type 2 diabetes (T2D). However, these studies may suffer from confounding or the use of invalid instruments, respectively. MethodsWe extracted strong (p < 5x10-8), independent (r2 < 0.001) genetic variants associated with nine amino acids (alanine, glutamine, glycine, histidine, phenylalanine, tyrosine, isoleucine, leucine, and valine) from summary statistics of UK Biobank (N [&le;] 115,075), with exclusion of potentially pleiotropic variants. We then applied them to T2D summary statistics from DIAMANTE Consortium (without UK Biobank participants) (N = 455,313) and FinnGen study (N = 365,950), and glycemic traits (MAGIC consortium, N [&le;] 209,605). Inverse variance weighed (IVW) method was the main analysis, with multiple sensitivity analyses to assess robustness of findings. ResultsAlanine was associated with higher T2D risk, correcting for multiple testing (Odds Ratio (OR) 1.50 per SD; 95% CI 1.16 to 1.95). At nominal significance, isoleucine was associated with higher T2D risk (OR 1.13; 95% CI 1.00 to 1.27) and tyrosine was associated with lower T2D risk (OR 0.89; 95% CI 0.80 to 0.99). Alanine was also associated with lower insulin, higher glycated hemoglobin and glucose whereas isoleucine and leucine were associated with lower insulin. These associations were consistent in most sensitivity analyses. ConclusionAlaine likely contributed to higher T2D risk whilst the associations for isoleucine and tyrosine requires further verification. Whether these findings explain health effects of sources of amino acids, such as diet, should be further explored.

ContextIndividuals with youth-onset type 2 diabetes mellitus (T2DM) display substantial, but unexplained, heterogeneity in their clinical presentations and risk of complications such as diabetic neuropathy. Data-driven clustering may be useful in characterizing this heterogeneity. ObjectiveTo identify data-driven subphenotypes of newly diagnosed youth-onset T2DM and study their association with distal symmetric polyneuropathy (DSPN) at time of diagnosis. DesignCross-sectional SettingUSA Participants641 individuals with newly diagnosed T2DM aged 10-19 years from the SEARCH for Diabetes in Youth Study and the Treatment Options for Type 2 Diabetes in Adolescents and Youth (TODAY) study. Exposure(s)Body mass index, HbA1c, fasting C-peptide, systolic blood pressure, diastolic blood pressure, LDL cholesterol and HDL cholesterol Main Outcome MeasuresData-driven subphenotypes were identified from k-means clustering. The cross-sectional association of subphenotypes with DSPN, based on expert examination scores ([&ge;]2.5) from the Michigan Neuropathy Screening Instrument, were assessed using Poisson regressions with robust standard errors. ResultsAmong 641 youth-onset T2DM, 58.2% were female, with 38.2% of participants [&le;]13 years having average BMI of 34.5 kg/m2 (SD: 6.5 kg/m2), and average HbA1c of 6.1% (IQR: 5.6-7.0). Three youth-onset subphenotypes were identified: mild obesity related diabetes (yMOD, 48.5%), severe insulin deficient diabetes (ySIDD, 18.7%) and severe insulin resistant diabetes (ySIRD, 32.7%). After adjusting for covariates, the prevalence of abnormal DSPN were 2.58 (95%CI: 1.74, 3.81) and 2.02 (95%CI: 1.40, 2.93) times among those classified as the ySIDD and ySIRD subphenotypes, relative to the yMOD subphenotype. ConclusionsYouth-onset T2DM consisted of heterogeneous clinical subphenotypes with differences prevalence of DSPN. Management of youth-onset T2DM may need to consider strategies tailored to each subphenotype.

BackgroundType 2 diabetes (T2D) is a multi-organ disease defined by hyperglycemia resulting from different disease mechanisms. Using clinical parameters measured at diagnosis (age, BMI, HbA1c, HOMA2-B, HOMA2-IR and GAD autoantibodies) adult patients with diabetes have been reproducibly clustered into five subtypes, that differed clinically with respect to disease progression and outcomes.1 In this study we use genetic information to investigate if these subtypes have distinct underlying genetic drivers. MethodsGenome-wide association (GWAS) and genetic risk score (GRS) analysis was performed in Swedish (N=12230) and Finnish (N=4631) cohorts. Family history was recorded by questionnaires. ResultsSevere insulin-deficient diabetes (SIDD) and mild obesity-related diabetes (MOD) groups had the strongest family history of T2D. A GRS including known T2D loci was strongly associated with SIDD (OR per 1 SD increment [95% CI]=1.959 [1.814-2.118]), MOD (OR 1.726 [1.607-1.855]) and mild age-related diabetes (MARD) (OR 1.771 [1.671-1.879]), whereas it was less strongly associated with severe insulin-resistant diabetes (SIRD, OR 1.244 [1.157-1.337]), which was similar to severe autoimmune diabetes (SAID, OR 1.282 [1.160-1.418]). SAID showed strong association with the GRS for T1D, whereas the non-autoimmune subtype SIDD was most strongly associated with the GRS for insulin secretion rate (P<7.43x10-9). SIRD showed no association with variants in TCF7L2 or any GRS reflecting insulin secretion. Instead, only SIRD was associated with GRS for fasting insulin (P=3.10x10-8). Finally, a T2D locus, rs10824307 near the ZNF503 gene was uniquely associated with MOD (ORmeta=1.266 (1.170-1.369), P=4.3x10-9). ConclusionsNew diabetes subtypes have partially different genetic backgrounds and subtype-specific risk loci can be identified. Especially the SIRD subtype stands out by having lower heritability and less involvement of beta-cell related pathways in its pathogenesis. Research in contextO_ST_ABSEvidence before this studyC_ST_ABSIn March 2018 we suggested a novel subclassification of diabetes into five subtypes. This classification was based on clustering using clinical parameters commonly measured at diabetes diagnosis (age at diabetes onset, HbA1c, bodymass index, presence of GAD autoantibodies and HOMA2 indices for insulin resistance and secretion). These subtypes differed with respect to clinical characteristics, disease progression and risk of complications, but it remained unclear to what extent these subtypes have different underlying pathologies. In our original publication we analysed a small set of genetic risk variants for diabetes and found differential associations between subtypes, suggesting potential aetiological differences. Added value of this studyIn this study we have conducted a full genome analysis of the original ANDIS cohort, including genome-wide association studies and polygenic risk score analysis with replication in an independent cohort. We have also compared heritability and prevalence of having a family history of diabetes in the subtypes. Implications of all the available evidenceWe demonstrate that stratification into subtypes facilitates identification of genetic risk loci and that the aetiology of the subtypes is at least partially distinct. These results are especially important for the future study and treatment of individuals belonging to the severe insulin-resistant diabetes (SIRD) subtype, whose pathogenesis appears to differ substantially from that of traditional T2D.

BackgroundDiabetes subtypes with different clinical profiles have been found and replicated in adults. However, clinical heterogeneity of paediatric-onset diabetes remains unexplored. Better capture of different aetiologies of the disease could prove a powerful tool towards precision medicine. MethodsWe performed data-driven clustering analysis in patients with newly diagnosed diabetes (n = 3,064) from the Norwegian Childhood Diabetes Registry. Patients were stratified by autoantibody status and clustered based on five clinical variables: Age at diagnosis, fasting glucose, HbA1c, fasting C-peptide Z-score, and BMI Z-score. We assessed inter-cluster differences regarding severity of disease, early treatment needs, polygenic scores (PS), and serum biomarkers. FindingsWe identified two clusters of autoantibody-positive and three clusters of autoantibody-negative patients: 1) Childhood severe autoimmune diabetes (CSAID; n = 1482, 48{middle dot}37 %), characterized by childhood-onset diabetes, milder disease presentation, unaltered lipidic profiles, higher Type 1 diabetes PS, and autoantibody positivity; 2) Adolescence severe autoimmune diabetes (ASAID; n = 1252, 40{middle dot}86 %) with adolescent-onset of diabetes, more severe disease presentation, higher diabetic ketoacidosis, lipidic profiles signaling towards prolonged metabolic disease, and autoantibody positivity; 3) Childhood severe insulin-deficient diabetes (CSIDD; n = 106, 3{middle dot}46 %) and 4) Adolescence severe insulin-deficient diabetes (ASIDD; n = 144, 4{middle dot}70 %) with similar characteristics to CSAID and ASAID, respectively, but no autoantibody positivity; and 5) Adolescence severe insulin-resistant diabetes (ASIRD; n = 80, 2{middle dot}61 %), the oldest group, with the highest C-peptide, BMI Z-score, and Type 2 diabetes PS. CSAID and ASAID presented similarities to previously described endotypes for type 1 diabetes. InterpretationWe grouped patients with paediatric diabetes into five subgroups with varying clinical severity, genetic risk, and metabolic profiles. Similarities between autoantibody-positive and -negative clusters underscore the importance of adopting a personalised, multivariate approach to diabetes management that extends beyond autoantibody status. We hypothesise that our clusters may be connected to previously described endotypes of type 1 diabetes, facilitating patient classification without the need for pancreatic biopsies. Further understanding of this concept could help define the mechanisms involved in disease initiation, time to diagnosis, and progression.

BackgroundMaturity-onset diabetes of the young (MODY) is a heterogeneous group of monogenic disorders of impaired glucose-stimulated insulin secretion (GSIS). Mechanisms include {beta}-cell KATP channel dysfunction (e.g., KCNJ11 (MODY13) or ABCC8 (MODY12) mutations); however, no other {beta}-cell channelopathies have been identified in MODY. MethodsA four-generation family with autosomal dominant non-obese, non-ketotic antibody-negative diabetes, without mutations in known MODY genes, underwent exome sequencing. Whole-cell and single-channel K+ currents, Ca2+ handling, and GSIS were determined in cells expressing either mutated or wild-type (WT) protein. ResultsWe identified a novel non-synonymous genetic mutation in KCNK16 (NM_001135105: c.341T>C, p.Leu114Pro) segregating with MODY. KCNK16 is the most abundant and {beta}-cell-restricted K+ channel transcript and encodes the two-pore-domain K+ channel TALK-1. Whole-cell K+ currents in transfected HEK293 cells demonstrated drastic (312-fold increase) gain-of-function with TALK-1 Leu144Pro vs. WT, due to greater single channel activity. Glucose-stimulated cytosolic Ca2+ influx was inhibited in mouse islets expressing TALK-1 Leu114Pro (area under the curve [AUC] at 20mM glucose: Leu114Pro 60.1 vs. WT 89.1; P=0.030) and less endoplasmic reticulum calcium storage (cyclopiazonic acid-induced release AUC: Leu114Pro 17.5 vs. WT 46.8; P=0.008). TALK-1 Leu114Pro significantly blunted GSIS compared to TALK-1 WT in both mouse (52% decrease, P=0.039) and human (38% decrease, P=0.019) islets. ConclusionsOur data identify a novel MODY-associated gene, KCNK16; with a gain-of-function mutation limiting Ca2+ influx and GSIS. A gain-of-function common polymorphism in KCNK16 is associated with type 2 diabetes (T2DM); thus, our findings have therapeutic implications not only for KCNK16-associated MODY but also for T2DM.

Aims/HypothesisGCK-MODY (Glucokinase-Maturity Onset Diabetes of the Young) causes lifelong, mild hyperglycaemia with high penetrance. Variation in glycaemic phenotype among carriers remains unexplained. We hypothesised that polygenic background contributes to this variability. MethodsTo test whether polygenic background contributes to the GCK-MODY clinical phenotype, we analysed polygenic risk scores (PGS) for nine diabetes-related traits in 901 clinically referred individuals with GCK-MODY. We compared these to 7,645 non-diabetic controls and assessed associations between PGSs and glycaemic measures. Additionally, we evaluated 158 unselected GCK variant carriers from the UK Biobank to examine polygenic effects independent of clinical referral. ResultsWe observed independent polygenic enrichment for HbA1c (including both glycaemic and non- glycaemic components), fasting glucose, and type 2 diabetes in clinically referred GCK-MODY individuals (0.16-0.33 SD higher, all P < 0.003), but not for type 1 diabetes. In contrast, no such enrichment was seen in GCK pathogenic variant carriers from a clinically unselected population- based cohort. In both settings, HbA1c PGSs were associated with measured HbA1c levels in GCK carriers ({beta} = 0.91- 0.97, all P < 0.009), with effect sizes similar to those in non-carriers. GCK-MODY cases in the top HbA1c quintile had a 3-to-6-fold risk of exceeding the diabetes diagnostic HbA1c threshold ([&ge;] 48 mmol/mol) in clinically selected and clinically unselected cohort respectively. Conclusions/interpretationOur findings suggest that polygenic background and GCK variants interact to modify the glycaemic expression of GCK-MODY, influencing clinical diagnosis despite high penetrance. Our study highlights the importance of integrating both monogenic and polygenic factors to better understand phenotypic variability in monogenic diseases. Research in ContextO_ST_ABSWhat is already known about the subject?C_ST_ABSO_LIAlthough GCK-MODY shows high penetrance, individuals vary in their glycaemic phenotype, and the cause of this variability remains unclear. C_LIO_LIPolygenic background has previously been found to modify disease risk and phenotypic variability in other lower penetrant forms of MODY but its contribution to the clinical variability in GCK-MODY is largely unexplored C_LI What is the key question?O_LIDoes polygenic background for diabetes-related traits contribute to variation in the GCK- MODY phenotype? C_LI What are the new findings?O_LIWe identified that clinically referred GCK-MODY cases had an independent enrichment of HbA1c, fasting glucose, and type 2 diabetes polygenic background, potentially increasing the likelihood of clinical referral. C_LIO_LIHigher HbA1c polygenic risk in GCK-MODY was associated with elevated measured HbA1c levels and an increased probability of exceeding the diagnostic threshold for diabetes. C_LI How might this impact on clinical practice in the foreseeable future?O_LIPolygenic background shapes the clinical expression of GCK-MODY, supporting the integration of monogenic and polygenic information to explain variability in monogenic disease. C_LI

ObjectivesIt is not known which clinical features optimally differentiate type 1 and 2 diabetes at diagnosis. We aimed to determine which clinical features differentiate adult-onset type 1 and 2 diabetes at diagnosis and develop classification models combining these features with and without islet-autoantibodies. DesignA prospective cohort study with prediction model development and validation. SettingUK primary and secondary care. Participants1800 adults ([&ge;]18 years) diagnosed with diabetes in the previous 12 months, excluding known secondary or monogenic diabetes. Main outcome measuresType 1 and 2 diabetes defined by a combination of insulin treatment and endogenous insulin production (measured using C-peptide) assessed [&ge;]three years after diabetes diagnosis. ResultsEleven clinical features and routinely measured biomarkers discriminated type 1 from type 2 diabetes independently of diagnosis age and BMI. Lower age-at-diagnosis, BMI and waist-hip ratio, unintentional weight-loss, and higher presentation HbA1c or glucose were the most discriminative features, with other features only weakly discriminative. Models integrating clinical features with and without islet-autoantibodies, developed in those age 18-50 years at diabetes diagnosis, had high performance in internal validation (clinical features only: AUCROC (95% CI) 0.94 (0.93, 0.96), clinical features and islet-autoantibodies: AUCROC 0.97 (0.96, 0.98)), and maintained high discrimination in older adults (age >50 at diagnosis; clinical features only: AUCROC 0.93 (0.90, 0.96), clinical features and islet-autoantibodies: AUCROC 0.97 (0.94, 0.99)). Simplifying the models to a point-based score (the StartRight Score) resulted in similar performance. These models had higher performance than current clinical guidance. In UK primary care data models were strongly predictive of outcomes associated with type 1 diabetes, including in those initially treated as type 2 diabetes. ConclusionsLower age-at-diagnosis, BMI, and wait-hip ratio, unintentional weight-loss and high presentation glycaemia are the most discriminative features for diagnosis of type 1 diabetes in adults. Models combining routine clinical features, with or without islet-autoantibodies, have high accuracy and could assist clinical classification and prioritisation of classification biomarker testing. Study registrationhttps://clinicaltrials.gov/study/NCT03737799 Summary boxesO_ST_ABSSection 1: What is already known on this topicC_ST_ABSO_LIMost type 1 diabetes occurs in adults, but differentiating it from type 2 diabetes, which is much more common, is challenging, and misclassification is common. C_LIO_LIAge-at-diagnosis and BMI are currently the only clinical features robustly shown to distinguish between type 1 and type 2 diabetes at diagnosis; many other features included in textbooks and guidelines have little supporting evidence. C_LIO_LIGuideline bodies, including the UK National Institute for Health and Care Excellence (NICE), have identified a need for evidence on what features discriminate type 1 and 2 diabetes in adults, and how these features can be combined to improve diagnosis. C_LI Section 2: What this study addsO_LIThis is the first study to prospectively assess the utility of clinical features for diabetes subtype at diagnosis. C_LIO_LIThe five most discriminative routine clinical features for distinguishing type 1 from type 2 diabetes at diagnosis are age-at-diagnosis, BMI, waist-hip ratio, pre-diagnosis unintentional weight-loss, and presentation glycaemia (HbA1c or glucose). C_LIO_LIMany features included in current guidelines were only very weakly discriminative of subtype, and no single clinical feature was able to adequately differentiate between type 1 and type 2 diabetes alone. C_LIO_LIA clinical prediction model combining ten routinely available clinical features, with or without islet-autoantibodies, as both a prototype calculator and a points-based score (the StartRight Score), had high accuracy in differentiating type 1 from type 2 diabetes and outperforms current clinical guidance and islet-autoantibody assessment alone. C_LI

IntroductionDysglycemia is a well-established risk factor for cardiovascular disease (CVD); yet traditional glycemic traits, including fasting plasma glucose (FPG) and HbA1c, do not capture dynamic glucose fluctuations that may inform CVD risk. We cross-sectionally investigated the association of continuous glucose monitor (CGM)-derived metrics and 2-h post-prandial glucose (2-h PPG) with estimated 10-year CVD risk among individuals without diabetes. MethodsWe included 1,360 Framingham Heart Study participants (Third Generation, New Offspring Spouse, and Omni 2 cohorts at exam 4) without prevalent diabetes or CVD who had [&ge;]3 days of CGM data and completed a mixed meal tolerance test (MMTT) with corresponding 2-PPG. We included 7 CGM summary metrics and defined data-driven glucotypes according to CGM measures of glycemic burden and variability. The 10-year CVD risk was estimated using the Predicting Risk of CVD EVENTs (PREVENT) base equations. We performed linear regression on standardized glycemic traits and glucotypes with log-transformed PREVENT risk scores and multinomial regression to relate standardized CGM metrics and 2-h PPG with PREVENT categories (low <5%[reference], borderline 5-<7.5%, intermediate/high [&ge;]7.5%). All models were adjusted for FPG and body mass index (BMI). ResultsAmong participants (55.9% women, 43.4% with prediabetes), mean age was 59.3 years, and mean BMI was 27.9 kg/m2. All CGM-derived metrics and 2-h PPG were positively associated with higher overall 10-year CVD risk (per 1 SD increase of each exposure variable, {beta} range: 0.06-0.16, all p<0.001). A glucotype representing high glycemic burden and high glycemic variability was associated with higher overall 10-year CVD risk, compared with the glucotype representing low glycemic burden and low glycemic variability. Higher CGM-derived metrics and 2-h PPG were also associated with higher odds being in the intermediate/high CVD risk (OR range: 1.20-1.65, all p<0.001), adjusting for FPG and BMI. ConclusionDynamic glycemic traits, including novel glucotypes that capture glycemic burden and variability, may provide novel insights into CVD risk prevention among individuals without T2D.

IntroductionThere are a number of glycemic definitions for prediabetes; however, the heterogeneity in diabetes transition rates from prediabetes across different glycemic definitions in major US cohorts has been unexplored. We estimate the variability in risk and relative risk of adiposity based on diagnostic criteria like fasting glucose and hemoglobin A1C% (HA1C%). Research Design and MethodsWe estimated transition rate from prediabetes, as defined by fasting glucose between 100-125 and/or 110-125 mg/dL, and HA1C% between 5.7-6.5% in participant data from the Framingham Heart Study, Multi-Ethnic Study on Atherosclerosis, Atherosclerosis Risk in Communities, and the Jackson Heart Study. We estimated the heterogeneity and prediction interval across cohorts, stratifying by age, sex, and body mass index. For individuals who were prediabetic, we estimated the relative risk for obesity, blood pressure, education, age, and sex for diabetes. ResultsThere is substantial heterogeneity in diabetes transition rates across cohorts and prediabetes definitions with large prediction intervals. We observed the highest range of rates in individuals with fasting glucose of 110-125 mg/dL ranging from 2-18 per 100 person-years. Across different cohorts, the association obesity or hypertension in the progression to diabetes was consistent, yet it varied in magnitude. We provide a database of transition rates across subgroups and cohorts for comparison in future studies. ConclusionThe absolute transition rate from prediabetes to diabetes significantly depends on cohort and prediabetes definitions. Twitter SummaryNew study finds variable diabetes risk in prediabetes across US cohorts. Results highlight obesity, Black race, and hypertension as key factors, emphasizing the need for precision in diabetes care. #DiabetesResearch

BackgroundBeta-cell monogenic forms of diabetes are the area of diabetes care with the strongest support for precision medicine. We reviewed treatment of hyperglycemia in GCK-related hyperglycemia, HNF1A- HNF4A- and HNF1B-diabetes, Mitochondrial diabetes (MD) due to m.3243A>G variant, 6q24-transient neonatal diabetes (TND) and SLC19A2-diabetes. MethodsSystematic reviews with data from PubMed, MEDLINE and Embase were performed for the different subtypes. Individual and group level data was extracted for glycemic outcomes in individuals with genetically confirmed monogenic diabetes. Results147 studies met inclusion criteria with only six experimental studies and the rest being single case reports or cohort studies. Most studies had moderate or serious risk of bias. For GCK-related hyperglycemia, six studies (N=35) showed no deterioration in HbA1c on discontinuing glucose lowering therapy. A randomized trial (n=18 per group) showed that sulfonylureas (SU) were more effective in HNF1A-diabetes than in type 2 diabetes, and cohort and case studies supported SU effectiveness in lowering HbA1c. Two crossover trials (n=15 and n=16) suggested glinides and GLP-1 receptor agonists might be used in place of SU. Evidence for HNF4A-diabetes was limited. While some patients with HNF1B-diabetes (n=301) and MD (n=250) were treated with oral agents, most were on insulin. There was some support for the use of oral agents after relapse in 6q24-TND, and for thiamine improving glycemic control and reducing insulin requirement in SLC19A2-diabetes (less than half achieved insulin-independency). ConclusionThere is limited evidence to guide the treatment in monogenic diabetes with most studies being non-randomized and small. The data supports: no treatment in GCK-related hyperglycemia; SU for HNF1A-diabetes. Further evidence is needed to examine the optimum treatment in monogenic subtypes.

Gastroparesis is a serious medical condition characterized by delayed gastric emptying and symptoms of nausea, vomiting, bloating, fullness after meals, and abdominal pain. An innate immune dysregulation and injury to the interstitial cells of Cajal and other components of the enteric nervous system are likely central to the pathogenesis of gastroparesis. Thus far, little is known about the underlying genetic risk factors for gastroparesis. To ascertain these genetic risk factors for gastroparesis we conducted the first large whole-genome sequencing genome-wide association study (GWAS) analysis of gastroparesis patients. The GWAS focused on idiopathic and diabetic gastroparesis cases as compared to matched controls as well as compared idiopathic versus diabetic cases. Amongst the top variants, we report a novel genetic risk variant for idiopathic gastroparesis - genetic association of the Solute Carrier Family 15 (SLC15) locus. This signal is driven by multiple variants with top missense variant SLC15A4:NM145648:exon2:c.T716C:p.V239A, rs33990080. SLC15A4 was shown to mediate M1-prone metabolic shifts in macrophages and guards immune cells from metabolic stress. The risk variant carriers have a significantly higher nausea score at baseline. We also delineated a number of statistically significant loci including novel ones as well as previously known loci such as HLA-DQB1 - rs9273363, variant associated with Type 1 diabetes.. The GWAS picture that is emerging implicates the role of the machinery of M1 to M2 macrophage polarization in the etiology of idiopathic gastroparesis. We suggest that macrophage polarization fate could lead to the destruction of the interstitial cells of Cajal which effectively leads to abnormal gastric emptying.