Diabetes Care

Maturity Onset Diabetes of the Young (MODY) can present after the age of 40yrs, but its prevalence, clinical characteristics, and the utility of simple clinical features for selecting cases in this age group remain poorly defined. We analysed whole-exome and clinical data from 51,619 individuals with diabetes diagnosed after 40 years of age from two large cohorts: the UK Biobank (n = 25,012) and the US health system MyCode cohort (n = 26,607). The prevalence of MODY due to pathogenic variants in the ten most common genes was 1 in 191 (0.52%) and 1 in 633 (0.16%) in the UK and US cohorts. For subtypes with treatment implications (GCK, HNF1A, HNF4A, ABCC8, KCNJ11), prevalence was 1 in 234 and 1 in 935, respectively. GCK-MODY was most common, followed by HNF4A and lower-penetrance RFX6. Clinical features of MODY overlapped with both insulin-treated and non-insulin-treated non-MODY diabetes. Applying simple clinical criteria only increased the MODY diagnosis to 2.64% and 0.87% but missed over 86% of cases. MODY is more common than expected in later-onset diabetes but remains difficult to identify using clinical features alone. Further research is needed to develop more effective strategies for selecting individuals with later-onset diabetes for genetic testing. Article HighlightsO_ST_ABSWhy did we undertake this study?C_ST_ABSMODY can present later in life, and diagnosis can enable precision treatment. However, individuals with later-onset diabetes are rarely tested. What specific question did we ask?How common is MODY in people diagnosed with diabetes after 40 years, and can they be identified clinically? What did we find?MODY affects 1 in 191 to 633 individuals with diabetes onset after 40 years, but clinical features alone cannot reliably identify them. What are the implications?MODY is relatively common in later-onset diabetes but difficult to detect clinically, limiting routine genetic testing in this group.

ObjectivesThe high heritability of type 1 diabetes has enabled the development of polygenic risk scores (PRS) as disease risk screening tools. PRS can identify individuals at the highest genetic risk in a population, who can benefit from autoantibody and metabolic surveillance, to avoid ketoacidosis at diagnosis and access preventive therapies. However, PRS for type 1 diabetes developed from European data perform less well in non-European ancestries. We aimed to develop a PRS with comparable performance among different ancestries. MethodsUsing a the PRS-CSx method, and data from large European, East-Asian, African-American and Hispanic type 1 diabetes GWAS (Ntotal_cases=29,469), we developed a trans-ancestry PRS (TA-PS), combining a non-HLA component incorporating over a million variants, with the HLA component of a published European PRS (GRS2x). We tested the performance of the PRS using AUROC, sensitivity and specificity in a multi-ancestry T1D case-control cohort (Ntotal= 4,657; Nnon-European=556) from Montreal, Canada. We validated our results in two independent T1D case-control cohorts (CHOP-CAG and GRACE) and two population-based cohorts (All of Us and UK Biobank). ResultsIn our multi-ancestry Montreal-based cohort, TA-PS showed an AUROC of 0.89 which was significantly higher from the AUROC of 0.85 of GRS2x. At a 90th percentile cut-off, in African-Americans, the sensitivity of GRS2x was 0.32, compared to 0.56 in Europeans. For TA-PS, we obtained overall better sensitivities, ranging from 0.71 in Europeans to 0.77 in South Asians. TA-PS demonstrated slightly lower albeit acceptable specificity compared to that of GRS2x (> 0.83 across all ancestries). These results were validated in the four independent cohorts. ConclusionWe developed a trans-ancestry PRS that outperformed the European-based GRS2x. Importantly, TA-PS provides a comparable prediction in various ancestries, which supports its use in population-wide screening programs. Research in contextO_ST_ABSWhat is already known about this subject?C_ST_ABS- Polygenic risk scores (PRS) for type 1 diabetes are primarily developed using data from individuals of European ancestry. - The widely used, European-based GRS2 score shows reduced performance in non-European populations, particularly among individuals of African descent. - There are concerns regarding the equity of genetic risk prediction in population-based screening programs for T1D. What is the key question?- Can a trans-ancestry PRS provide accurate and equitable type 1 diabetes risk prediction across ancestries? What are the new findings?- A new trans-ancestry score, TA-PS, was developed by integrating an optimized non-HLA PRS to GRS2. - Compared to GRS2, in multi ancestry case-control and population-based cohorts, TA-PS improves sensitivity across all ancestry groups while maintaining high specificity. How might this impact on clinical practice in the foreseeable future?- TA-PS could provide equitable genetic risk stratification in population-wide screening programs for type 1 diabetes.

ContextDiagnosing Maturity-Onset Diabetes of the Young (MODY) is clinically important for treatment and prognosis. However, phenotype-based studies of MODY are prone to ascertainment bias, limiting accurate estimates of its population prevalence and phenotypic spectrum. ObjectiveTo apply a genotype-first approach to determine the population prevalence, penetrance, and all-cause mortality associated with MODY. MethodsWe analysed exome sequencing and clinical data from 454,275 UK Biobank participants to identify pathogenic variants in 10 established MODY genes. We assessed variant prevalence, age-dependent diabetes penetrance, and all-cause mortality by genetic aetiology over a mean follow-up of 13.4 years. ResultsPathogenic MODY variants were present in 1 in 1,052 individuals and accounted for 1.48% of diabetes cases diagnosed before age 40. GCK variants were the most frequent (1 in 2,787), demonstrating high penetrance (mean HbA1c 8.8 mmol/mol higher; 94.5% with prediabetes or diabetes) but no significant association with all-cause mortality (P=0.09). Variants in other MODY genes showed lower penetrance, with 12% of carriers developing diabetes by age 40 and 31.6% by age 60 and showed no increase in all-cause mortality (P=0.89). Penetrance varied by genetic aetiology, with HNF1A showing the highest penetrance and PDX1, NEUROD1, and RFX6 the lowest. Parental history of diabetes and polygenic risk for type 2 diabetes were important modifiers of penetrance (Hazard ratios 2.54 and 1.52 respectively, P<3.9x10-3). ConclusionsThis large-scale genotype-first study provides novel insights into MODY in the population. These findings have broad implications for genetic counselling, personalised treatment strategies, and healthcare resource allocation.

BackgroundThis study evaluates longitudinal associations between glycaemic control (mean and within-patient variability of glycated haemaglobin (HbA1c) levels) in individuals with type 2 diabetes (T2D) and major depressive disorder (MDD), focusing on the timings of these diagnoses. MethodsIn UK Biobank, T2D was defined using self-report and linked health outcome data, then validated using polygenic scores. Repeated HbA1c measurements (mmol/mol) over the 10 years following T2D diagnosis were outcomes in mixed effects models, with T2D disease duration included using restricted cubic splines. Four MDD exposures were considered: MDD diagnosis prior to T2D diagnosis (pre-T2D MDD), time between pre-T2D MDD diagnosis and T2D, new MDD diagnosis during follow-up (post-T2D MDD) and time since post-T2D MDD diagnosis. Models with and without covariate adjustment were considered. ResultsT2D diagnostic criteria were robustly associated with T2D polygenic scores. In 11,837 T2D cases (6.9 year median follow-up), pre-T2D MDD was associated with a 0.92 increase in HbA1c (95% CI: [0.00, 1.84]), but earlier pre-T2D MDD diagnosis correlated with lower HbA1c. These pre-T2D MDD effects became non-significant after covariate adjustment. Post-T2D MDD individuals demonstrated increasing HbA1c with years since MDD diagnosis (/3 = 0.51, 95% CI: [0.17, 0.86]). Retrospectively, looking across all follow-up, within-patient variability in HbA1c was 1.16 (95% CI: 1.13-1.19) times higher in post-T2D MDD. ConclusionsThe timing of MDD diagnosis is important for understanding glycaemic control in T2D. Poorer control was observed in MDD diagnosed post-T2D, highlighting the importance of depression screening in T2D, and closer monitoring for individuals who develop MDD after T2D.

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.

ObjectiveIslet autoantibodies at diagnosis are not well studied in older-adult onset (>30years) type 1 diabetes due to difficulties of accurate diagnosis. We used a type 1 diabetes genetic risk score (T1DGRS) to identify type 1 diabetes aiming to evaluate the prevalence and pattern of autoantibodies in older-adult onset type 1 diabetes. MethodsWe used a 30 variant T1DGRS in 1866 white-European individuals to genetically confirm a clinical diagnosis of new onset type 1 diabetes. We then assessed the prevalence and pattern of GADA, IA2A and ZnT8A within genetically consistent type 1 diabetes across three age groups (<18years (n=702), 18-30years (n=524) and >30years (n=588)). FindingsIn autoantibody positive cases T1DGRS was consistent with 100% type 1 diabetes in each age group. Conversely in autoantibody negative cases, T1DGRS was consistent with 93%(56/60) of <18years, 55%(37/67) of 18-30years and just 23%(34/151) of >30years having type 1 diabetes. Restricting analysis to genetically consistent type 1 diabetes showed similar proportions of positive autoantibodies across age groups (92% <18years, 92% 18-30years, 93% >30years)[p=0.87]. GADA was the most common autoantibody in older-adult onset type 1 diabetes, identifying 95% of autoantibody positive cases versus 72% in those <18years. InterpretationOlder adult-onset type 1 diabetes has identical rates but different patterns of positive autoantibodies to childhood onset. In clinically suspected type 1 diabetes in older-adults, absence of autoantibodies strongly suggests non-autoimmune diabetes. Our findings suggest the need to change guidelines from measuring islet autoantibodies where there is diagnostic uncertainty to measuring at least GADA in all suspected adult type 1 diabetes cases.

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.

ObjectivePolygenic scores strongly predict type 1 diabetes risk, but most scores were developed in European-ancestry populations. In this study, we developed a multi-ancestry polygenic score to accurately predict type 1 diabetes risk across diverse populations. Research Design and MethodsWe used recent multi-ancestry genome-wide association studies to create a type 1 diabetes multi-ancestry polygenic score (T1D MAPS). We trained the score in the Mass General Brigham (MGB) Biobank (372 individuals with type 1 diabetes) and tested the score in the All of Us program (86 individuals with type 1 diabetes). We evaluated the area under the receiver operating characteristic curve (AUC), and we compared the AUC to two published single-ancestry scores: T1D GRS2EUR and T1D GRSAFR. We also developed an updated score (T1D MAPS2) that combines T1D GRS2EUR and T1D MAPS. ResultsAmong individuals with non-European ancestry, the AUC of T1D MAPS was 0.90, significantly higher than T1D GRS2EUR (0.82, P = 0.04) and T1D GRSAFR (0.82, P = 0.007). Among individuals with European ancestry, the AUC of T1D MAPS was slightly lower than T1D GRS2EUR (0.89 vs. 0.91, P = 0.02). However, T1D MAPS2 performed equivalently to T1D GRS2EUR in European ancestry (0.91 vs. 0.91, P = 0.45) while still performing better in non-European ancestry (0.90 vs. 0.82, P = 0.04). ConclusionsA novel polygenic score improves type 1 diabetes risk prediction in non-European ancestry while maintaining high predictive power in European ancestry. These findings advance the accuracy of type 1 diabetes genetic risk prediction across diverse populations. Article HighlightsO_LIWhy did we undertake this study? Type 1 diabetes polygenic scores are highly predictive of disease risk, but their performance varies based on genetic ancestry. C_LIO_LIWhat is the specific question(s) we wanted to answer? Can we develop a polygenic score that accurately predicts type 1 diabetes risk across diverse populations? C_LIO_LIWhat did we find? Our novel polygenic score performs similarly to existing scores in European populations, and it demonstrates superior performance in non-European populations. C_LIO_LIWhat are the implications of our findings? This polygenic score will improve prediction of type 1 diabetes risk in genetically diverse populations. C_LI

Aims/hypothesisGenetic prediction of type 2 diabetes risk has proven difficult using current methods. Recent studies have shown that genetic variants associated with physical activity behavior are linked to type 2 diabetes incidence. This study investigated how a polygenic risk score (PRS) for type 2 diabetes relates to the incidence of type 2 diabetes and its comorbidities and whether incorporating genetic risk from physical activity-related traits and measured lifestyles improves prediction. We hypothesized that adding physical activity genotypes into prediction models would improve predictive accuracy. MethodsPRSs were calculated for 279,373 Finns in the FinnGen cohort (average age 62 years, 52% women). Cox proportional hazards models were used with follow-up from birth. In addition, we assessed whether predictive ability (concordance index) improved when PRSs for physical activity, sedentary time, cardiorespiratory fitness, muscle strength, and body mass index were included alongside the type 2 diabetes PRS. Finally, we assessed how smoking and body mass index changed the models predictive ability. ResultsEach standard deviation unit increase in the type 2 diabetes PRS was associated with an 8% higher risk of developing type 2 diabetes. Among individuals with type 2 diabetes, the PRS was linked to higher risks of comorbidities: 4% higher for nephropathy and retinopathy and 5% for severe cardiovascular disease, but not neuropathy. Physical activity-related PRSs were also independently associated with the risk of type 2 diabetes--lower risk for physical activity (7%), cardiorespiratory fitness (6%), and muscle strength (4%) and higher risk for sedentary time (14%) and body mass index (35%). However, physical activity-related PRSs did not significantly improve the models concordance index (0.644 before vs. 0.672 after adding all other PRSs). In contrast, including body mass index and smoking status increased predictive ability (c-index 0.744). Conclusions and applicabilityPRSs for type 2 diabetes and physical activity-related phenotypes independently predict the incidence of type 2 diabetes and comorbidities. However, adding physical activity-related scores to the model does not significantly improve prediction beyond the type 2 diabetes score. Notably, the PRS for body mass index was better than the PRS for type 2 diabetes in predicting type 2 diabetes incidence. These findings support the hypothesis that genetic pleiotropy may partially explain associations between type 2 diabetes and physical activity behavior. Summary boxesWhat is already known about this subject? O_LIGenetic factors contribute substantially to the risk of type 2 diabetes, but it is primarily a multifactorial condition in which modifiable lifestyle factors--including physical activity--play a critical role in onset and progression. C_LIO_LIGenetic variants related to physical activity behavior have been associated with type 2 diabetes. C_LIO_LIThe clinical utility of polygenic risk scores in predicting type 2 diabetes risk remains limited, as they explain only a small proportion of genetic variance and provide minimal improvement in risk prediction beyond established clinical risk factors. C_LI What is the key question? O_LICan the risk estimates for type 2 diabetes and its comorbidities be improved by incorporating genetic risk factors associated with physical activity and measured lifestyle behaviors? C_LI What are the new findings? O_LIPolygenic risk scores for both type 2 diabetes and physical activity-related phenotypes independently predict the incidence of type 2 diabetes and its comorbidities. C_LIO_LIIncorporating physical activity-related polygenic risk scores into the model does not significantly improve predictive accuracy beyond the type 2 diabetes risk score alone. C_LIO_LIThe findings support the hypothesis that genetic pleiotropy may partially explain associations between type 2 diabetes and physical activity behavior. C_LI How might this impact on clinical practice in the foreseeable future? O_LIAlthough polygenic risk scores for type 2 diabetes may aid in identifying high-risk individuals for targeted prevention, their integration into clinical practice requires further validation. C_LI

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.

AimsPopulation datasets are increasingly used to study type 1 or 2 diabetes, and inform clinical practice. However, correctly classifying diabetes type, when insulin treated, in population datasets is challenging. Many different approaches have been proposed, ranging from simple age or BMI cut offs, to complex algorithms, and the optimal approach is unclear. We aimed to compare the performance of approaches for classifying insulin treated diabetes for research studies, evaluated against two independent biological definitions of diabetes type. MethodWe compared accuracy of thirteen reported approaches for classifying insulin treated diabetes into type 1 and type 2 diabetes in two population cohorts with diabetes: UK Biobank (UKBB) n=26,399 and DARE n=1,296. Overall accuracy and predictive values for classifying type 1 and 2 diabetes were assessed using: 1) a type 1 diabetes genetic risk score and genetic stratification method (UKBB); 2) C-peptide measured at >3 years diabetes duration (DARE). ResultsAccuracy of approaches ranged from 71%-88% in UKBB and 68%-88% in DARE. All approaches were improved by combining with requirement for early insulin treatment (<1 year from diagnosis). When classifying all participants, combining early insulin requirement with a type 1 diabetes probability model incorporating continuous clinical features (diagnosis age and BMI only) consistently achieved high accuracy, (UKBB 87%, DARE 85%). Self-reported diabetes type alone had high accuracy (UKBB 87%, DARE 88%) but was available in just 15% of UKBB participants. For identifying type 1 diabetes with minimal misclassification, using models with high thresholds or young age at diagnosis (<20 years) had the highest performance. An online tool developed from all UKBB findings allows the optimum approach of those tested to be selected based on variable availability and the research aim. ConclusionSelf-reported diagnosis and models combining continuous features with early insulin requirement are the most accurate methods of classifying insulin treated diabetes in research datasets without measured classification biomarkers.

IntroductionMODY misdiagnosis remains widespread, existing remarkable variability within genetic variants across populations. While diagnostic tools are based on Caucasian cohorts, Whole Exome Sequencing (WES) studies are needed to identify new genes in non-Caucasians, as up to 77% of patients do not harbor variants of significance in MODY-known genes. In Latino populations, no WES studies have addressed MODY genomic landscape beyond its canonical genes. MethodsWe carried out WES in 17 patients with MODY, 17 patients with type 2 diabetes (T2DM) and 17 healthy controls (HC). MODY diagnosis was established according to Exeter criteria (score [&ge;]36%) in subjects with no or minimal insulin requirements. We compared the single nucleotide variant (SNV) landscape across groups. ResultsPatients with MODY present a polygenic landscape with allelic variants in canonical and non-canonical genes. Canonical MODY genes used for routine genetic diagnosis showed low discrimination utility, having similar frequencies between MODY, T2DM and HC in the Mexican population. We propose 14 genes with variants that distinguish MODY from T2DM and HC, as we detected variants in genes as MAP2K3, SYT15, TPTE, KCNJ12, PEX5, and OR2A1 in 75-100% of MODY cases while were absent in T2DM and HC. Enrichment analysis revealed involvement in synaptic vesicle trafficking, insulin/IGF pathway-mitogen activated protein kinase kinase/MAPK cascade, and insulin/IGF pathway-protein kinase B/AKT signaling cascade. DiscussionMODY presents a polygenic landscape. Besides improving our understanding of glycemic regulation pathways, the candidate genes could serve as MODY diagnostic biomarkers in Latino populations. FundingSupported by grant R-2019-785-052 from Instituto Mexicano del Seguro Social.

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.

Aims/HypothesisType 2 diabetes mellitus (T2D) is a heterogeneous condition influenced by lipid metabolism, inflammation, and genetic predisposition, all of which contribute to variable cardiovascular disease (CVD) risk. Identifying robust T2D sub-phenotypes and understanding their interactions with genetic predisposition is critical for personalized CVD risk assessment and care. This study aims to derive clinically relevant T2D sub-phenotypes and assess their association with CVD risk by employing robust methodology and replication across cohorts. MethodsWe analyzed data from the Rotterdam Study (n=1,250), applying Gaussian mixture clustering to derive T2D sub-phenotypes based on nine metabolic risk factors: age at diabetes diagnosis, sex, body mass index (BMI), fasting blood glucose, HOMA-IR, cholesterol levels (total, HDL, LDL), and C-reactive protein (CRP). Cox proportional hazard models adjusted for confounders were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for associations between T2D sub-phenotypes and a composite CVD outcome (coronary heart disease and stroke). Kaplan-Meier (KM) survival curves were created to study the risk of incident CVD across T2D sub-phenotypes, with the lowest-risk sub-phenotype as the reference group. Polygenic risk scores (PRS) for T2D, divided into tertiles, were included to explore the interaction of genetic predisposition with diabetes sub-phenotypes. Clustering was replicated in the KORA (n=243) and PROSPER (n=179) cohorts, with association analyses validated in the KORA cohort. We considered effect size and confidence intervals, not just p-values, for comprehensive result interpretation. ResultsThree distinct T2D sub-phenotypes emerged: (1) an "unspecified" sub-phenotype (53.4%) with lower levels of metabolic risk factors, (2) an "insulin-resistant" sub-phenotype (23.8%) characterized by higher BMI, HOMA-IR, and CRP, and (3) a "dyslipidemic" sub-phenotype (22.3%) with elevated total and LDL-cholesterol. Compared to the dyslipidemic sub-phenotype (reference group based on KM analyses), the adjusted HR for incident CVD was 1.04 (95% CI: 0.76, 1.42) for the unspecified sub-phenotype and 1.20 (95% CI: 0.84, 1.72) for the insulin-resistant sub-phenotype, indicating a slightly elevated risk of CVD for the insulin-resistant sub-phenotype. Among individuals with high T2D PRS, the insulin-resistant sub-phenotype exhibited the highest CVD risk (HR 2.28, 95% CI 1.13, 4.60) compared to low and medium PRS from T2D. The robustness of the sub-phenotypes and their associations with CVD risk was confirmed in independent KORA and PROSPER cohorts. Conclusions/InterpretationThese findings emphasize the importance of understanding metabolic and clinical diversity within T2D to better guide personalized management strategies. Further research through longitudinal studies, diverse populations, and advanced molecular profiling is essential to refine sub-phenotypic classifications and uncover underlying mechanisms to enhance patient outcomes

Novel subphenotypes of type 2 diabetes mellitus (T2DM) are associated with differences in response to treatment and risk of complications. The most widely replicated approach identified four subphenotypes (severe insulin-deficient diabetes [SIDD], severe insulin-resistant diabetes [SIRD], mild obesity-related diabetes [MOD], and mild age-related diabetes [MARD]). However, the widespread clinical application of this model is hindered by the limited availability of fasting insulin and glucose measurements in routine clinical settings. To address this, we pooled data of adults ([&ge;]18 years) with newly diagnosed T2DM from six cohort studies (n = 3,377) to perform de novo clustering and developed classification algorithms for each of the four subphenotypes using nine variables routinely collected in electronic health records (EHRs). After operationalizing the classification algorithms on the Epic Cosmos Research Platform, we identified that among the 727,076 newly diagnosed diabetes cases, 21.6% were classified as SIDD, 23.8% as MOD, and 40.9% as MARD. Individuals classified as SIDD were more likely to receive insulin and incretin mimetics treatment and had higher risks for microvascular complications (retinopathy, neuropathy, nephropathy). Our findings underscore the heterogeneity in newly diagnosed T2DM and validated T2DM subphenotypes in routine EHR systems. This offers possibilities for the subsequent development of treatment strategies tailored to subphenotypes.

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.

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.

Type 2 diabetes (T2D) is a chronic metabolic disorder with a significant genetic component. While large-scale population studies have identified hundreds of common genetic variants associated with T2D susceptibility, the role of rare (minor allele frequency < 0.1%) protein coding variation is less clear. To this end, we performed a gene burden analysis of 18,691 genes in 418,436 (n=32,374 T2D cases) individuals sequenced by the UK Biobank (UKBB) study to assess the impact of rare genetic variants on T2D risk. Our analysis identified T2D associations at exome-wide significance (P < 6.9x10-7) with rare, damaging variants within previously identified genes including GCK, GIGYF1, HNF1A, and TNRC6B. In addition, individuals with rare, damaging missense variants in the genes ZEB2 (N=31 carriers; OR=5.5 [95% CI=2.5-12.0]; p=6.4x10-7), MLXIPL (N=245; OR=2.3 [1.6-3.2]; p=3.2x10-7), and IGF1R (N=394; OR=2.4 [1.8-3.2]; p=1.3x10-10) have higher risk of T2D. Carriers of damaging missense variants within IGF1R were also shorter (-2.2cm [-1.8-2.7]; p=1.2x10-19) and had higher circulating protein levels of insulin-like growth factor-1 (IGF-1; 2.3 nmol/L [1.7-2.9] p=2.8x10-14), indicating relative IGF-1 resistance. A likely causal role of IGF-1 resistance on T2D was further supported by Mendelian randomisation analyses using common variants. Our results increase our understanding of the genetic architecture of T2D and highlight a potential therapeutic benefit of targeting the Growth Hormone/IGF-1 axis.

AimsThe gut microbiome has been implicated in type 2 diabetes progression, but reproducible biomarkers across studies remain limited due to technical and population heterogeneity. This study investigated whether specific gut microbiome shifts occur progressively across stages of type 2 diabetes. MethodsWe systematically reanalysed 16S rRNA datasets from 12 published studies (n=1,247 samples) after quality control, examining five groups (healthy controls, prediabetes (PD), new-onset type 2 diabetes, established type 2 diabetes, and type 2 diabetes with complications. Sequencing reads were quality-filtered, denoised, and resolved into amplicon sequence variants with genus-level taxonomic assignments using the SILVA database. Centered log-ratio (CLR)-transformed abundance data were analysed using PERMANOVA, meta-analysis with leave-one-study-out validation, differential abundance testing (Wilcoxon and ANCOM), and Random Forest classification. Eligible studies were identified through comprehensive searches of PubMed, Ovid Medline and Web of Science from June 2010 - June 2025 using predefined inclusion and exclusion criteria following PRISMA 2020 guidelines. Studies were investigated by two independent reviewers and included if they provided 16S rRNA data on adults across diabetes stages. Study quality was assessed based on metadata completeness and raw data availability. This systematic review and meta-analysis was registered in the Open Science Framework (OSF; registration https://osf.io/eth7a; embargoed until October 2026) and conducted according to PRISMA guidelines. ResultsEarly disease transitions showed minimal microbiome alterations, with only 4 genera, (notably enrichment of Allisonella and Escherichia-Shigella) were significantly different between healthy and PD (q < 0.05), and no significant genera between PD and new-onset type 2 diabetes. Advanced disease exhibited robust dysbiosis, with 9 genera differentially abundant in type 2 diabetes vs complicated type 2 diabetes and 5 genera in healthy vs complicated type 2 diabetes comparisons. Complicated type 2 diabetes was characterised by enrichment of Hungatella and [Clostridium] innocuum group and depletion of Faecalibacterium and compared to both uncomplicated type 2 diabetes and healthy controls. Random Forest classification achieved poor performance for early contrasts (AUC [&le;] 0.79) but strong discrimination for advanced disease (type 2 diabetes vs complicated type 2 diabetes: AUC = 0.89; Healthy vs complicated type 2 diabetes: AUC = 0.96). ConclusionGut microbiome alterations are subtle and inconsistent in early dysglycemia but become pronounced and reproducible with diabetic complications, suggesting microbiome-based biomarkers may be most clinically useful for identifying disease progression rather than early detection. Limitations include heterogeneity of sequencing methods and reliance on 16S rRNA data, which may restrict taxonomic and functional resolution. To our knowledge, this is the first meta-analysis to systematically evaluate gut microbiome alterations across multiple clinical stages of type 2 diabetes progression.

PurposeThe Biomarkers of heterogeneity in type 1 diabetes study cohort was set up to identify genetic, physiological and psychosocial factors explaining the observed heterogeneity in disease progression and the development of complications in people with long-standing type 1 diabetes (T1D). Data and samples are available for new studies and collaborations. ParticipantsData- and samples were collected in two subsets. 1) A prospective cohort of 611 participants aged [&ge;]16 years with [&ge;]5 years T1D duration was recruited from four Dutch Diabetes clinics between June 2016 and March 2021. At baseline and 1- and 2-year follow-up visits, physical assessments were performed, and blood and urine samples were collected. Participants completed questionnaires about diabetes-related problems, quality of life, neuropathy and impaired awareness of hypoglycaemia at baseline and at the last follow-up visit. A subgroup of participants underwent mixed-meal tolerance tests (MMTT) at baseline (n=169) and at 1-year follow-up (n=104). Genetic data and linkage to medical and administrative records were also available. 2) A second cross-sectional cohort, aiming to include 200 participants aged [&ge;]18 years with [&ge;]35 years T1D duration, was recruited from 7 centres, collecting measurements and samples plus 5-year retrospective data. Findings to dateFasting residual C-peptide secretion associated with decreased risk of impaired awareness of hypoglycaemia. Stimulated residual C-peptide was detectable in an additional 10% of individuals compared with fasting residual C-peptide secretion. MMTT measurements at 90 minutes and 120 minutes showed good concordance with the MMTT total area under the curve. An overall decrease of C-peptide at 1-year follow-up was observed. Future plansResearch groups are invited to consider the use of this data and sample collection. Future work will include additional hormones, beta-cell-directed autoimmunity, specific immune markers, microRNAs, metabolomics and gene expression data, combined with glucometrics, anthropometric/clinical data and additional markers of residual beta-cell function. Strengths and limitations of this studyO_ST_ABSStrengthsC_ST_ABS- The Biomarker cohort is a large longitudinal prospective cohort study with three time points, collecting biosamples and clinical data from participants with well-established and long-standing type 1 diabetes ([&ge;]5 years). - A subgroup with detailed clinical data underwent MMTT tests at two timepoints allowing further residual beta-cell marker studies. - The Biomarker and Long-Term type 1 Diabetes cohorts represent a "real-world" population, also including participants from non-academic/-specialised centres. Limitations- Despite the fact that data and biosamples were collected from more than 600 participants, this number may be too low for (sub) stratification of the data (e.g. insulin delivery modality, different treating centres and therapies etc.). - In the prospective group there was a relatively high dropout rate of 25% after 2 years, largely affected by the Covid-19 outbreak.