Diabetes Care

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.

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.

Identifying individuals at risk of early onset type 1 diabetes (diagnosed <2 years) would be highly beneficial in reducing risk of severe diabetic ketoacidosis (DKA) for those with extreme autoimmunity. We aimed to investigate whether genetic variation contributes to heterogeneity in age of type 1 diabetes onset, focusing on those diagnosed <2 years and ages previously defined by histological differences. We carried out association testing on 6773 individuals with type 1 diabetes and tested for heterogeneity in Human Leukocyte Antigen (HLA) variants across stratified age groups (594 diagnosed <2 years, 2241 diagnosed 2-7 years, 3094 diagnosed 7-13 years, 844 diagnosed 13+ years). We used a 67 SNP type 1 diabetes genetic risk score (T1D-GRS) to quantify aggregated genetic risk and assessed its utility in screening for type 1 diabetes <2 years. We observed higher T1D-GRSs as age of onset decreased in type 1 diabetes and found that DR3-DQ2 homozygosity was most strongly associated with <2 years onset (log-OR=4.27). The T1D-GRS showed high discriminative ability for <2 years onset type 1 diabetes onset (AUC=0.94) and correctly identified 88% of type 1 diabetes cases at the 85th population centile. We have shown higher genetic risk for very early onset T1D and suggest T1D-GRSs in newborn screening is likely to be particularly sensitive to those with younger type 1 diabetes onset.

IntroductionPolygenic risk scores (PRS), metabolomics, and proteomics have each shown promise in improving type 2 diabetes risk prediction, but their combined utility beyond established clinical models remains unclear. We aimed to evaluate whether integrating multi-omics biomarkers enhances 10-year type 2 diabetes risk prediction beyond single-omics extensions and the clinical Cambridge Diabetes Risk Score (CDRS), which includes HbA1c measurements. MethodsWe analysed data from 23,325 UK Biobank participants without diagnosed diabetes at baseline. Data for a PRS for type 2 diabetes, 11 metabolites, and 15 proteins were added to the CDRS to develop multi-omics prediction models. Model performance was evaluated using Harrells C-index and the net reclassification index (NRI). ResultsDuring 10 years of follow-up, 719 participants developed incident type 2 diabetes. Among individual omics layers, proteomics contributed the greatest improvement in predictive performance, increasing the C-index from 0.857 (clinical CDRS) to 0.880 ({Delta}C-index; +0.023; P < 0.001), with an NRI of 30.0%. The full multi-omics model, further significantly increased the C- index compared to a model combining the clinical CDRS with proteomics data (C-index, 0.886; {Delta}C-index; +0.006; P < 0.033). ConclusionIntegrating proteomics, metabolomics, and a diabetes-PRS into a clinical model substantially improves type 2 diabetes risk prediction beyond single-omics extensions. However, the C-index difference between the proteomics extended and full multi-omics extended models is small, and the clinical models extended with proteomics data would be easier to translate into routine care because it needs only the measurement of 15 proteins.

BackgroundProteogenomic analyses in biobanks provide opportunities to improve understanding of aetiology and drug discovery for type 2 diabetes (T2D). MethodsWe identified proteins (Olink Explore) associated with glycaemic traits and/or T2D with observational designs in UK Biobank (UKB-EUR, n =33,301). The Bayesian non-negative matrix factorisation (bNMF) was applied to cluster T2D-associated proteins incorporating their phenotypic associations with 43 metabolic/anthropometric traits. For clusters leading proteins (top 10% by ranking), two-steps colocalization and bidirectional Mendelian randomization were used to investigate three-way (i.e., protein-metabolic/anthropometric traits-T2D) relationships. We performed equivalent genetic analyses in China Kadoorie Biobank (CKB-EAS, n=2,029) to investigate shared/distinct findings. Results1,793 proteins were observationally associated with glycaemic traits and/or T2D in UKB-EUR, which were classified by bNMF into five clusters (Adiposity, Reduced-adiposity, Lipids, Liver, Kidney) where 906 proteins were cluster-leading. We triangulated observational and genetic evidence identifying five (B4GAT1, DNER, ENO3, HOMX2, OMG), one (ENTR1) and three (RTBDN, TSPAN8, NCR3LG1) proteins potentially affecting T2D in UKB-EUR, CKB-EAS, and both, respectively. In UKB-EUR, six (CD34, FGFBP3, GALNT10, KHK, MENT, MXRA8) were affected by T2D and five (GSTA1, GSTA3, MEGF9, NCAN, SHBG) were bidirectionally associated with T2D. The genetic analyses also revealed potential pathways in T2D aetiology (e.g., effects of RTBDN and TSPAN8 on T2D via BMI and SHBG respectively). ConclusionThis study identified multiple candidate proteins involved in the development of T2D that may make useful biomarkers for monitoring disease onset and progression in the future. These findings may inform molecular sub-phenotyping of T2D and more personalised T2D management.

BACKGROUNDCardiovascular disease (CVD) is a leading cause of diabetes-related mortality in Mexico. Although diabetes subgroups capture underlying disease heterogeneity, their association and utility for risk prediction for fatal CVD in Mexican adults remain unclear. METHODSWe analyzed 24,943 adults with diabetes from the Mexico City Prospective Study. Participants were classified into mild obesity-related (MOD), severe insulin-deficient (SIDD), severe insulin-resistant (SIRD), and mild age-related (MARD) diabetes using a self-normalizing neural network algorithm. Fatal CVD was defined as death from ischemic heart disease or stroke (ICD-10 I20-I25, I60-I69). SCORE2-Diabetes was recalibrated and validated overall and by diabetes subgroup. Cox proportional hazards models were used to estimate subgroup-specific risk, and sequential models evaluated the incremental predictive value of diabetes subgroups combined with SCORE2-Diabetes and traditional risk factors. RESULTSOver a median follow-up of 19.3 years (IQR 12.7-20.6), 2,223 fatal CVD events (8.9%) were recorded. SIDD was the most prevalent subgroup (50.6%), followed by SIRD (17.3%), MARD (16.8%), and MOD (15.4%). SIDD and MARD showed the highest adjusted risk of fatal CVD (RR 1.58 [95%CI 1.38-1.81] and 1.35 [1.13-1.60]), whereas MOD and SIRD had lower risk. Recalibrated SCORE2-Diabetes demonstrated adequate discrimination overall (c-statistic 0.759, 95%CI 0.745-0.773) and for most subgroups but underperformed in MARD, with recalibration improving risk assessment. The combination of diabetes subgroups and SCORE2-Diabetes improved prediction for fatal CVD outcomes. CONCLUSIONSDiabetes subgroups show heterogeneity in fatal CVD risk in Mexican adults. SIDD and MARD identify high-risk individuals and integration subgroup classification with SCORE2-Diabetes enhances risk prediction.

The detection of monogenic diabetes illustrates the potential of precision medicine, with treatments tailored to specific genes and diagnosis involving targeted genetic testing. Current detection criteria are derived from White populations. We investigated detection of monogenic diabetes in an unselected multiethnic cohort comprising 1,706 participants diagnosed with diabetes before the age of 30-years. Using broad biomarker criteria (triple pancreatic antibody negative and detectable C-peptide) to select for next generation sequencing of monogenic diabetes genes, we found a non-significantly different minimum cohort prevalence of monogenic diabetes of 2.1% in White, 2.0% in South Asian, 2.5% in African-Caribbean, and 3.6% in Mixed participants. The detection rate, however, varied significantly (17.7% in White, 5.3%in South Asian, 8.0% in African-Caribbean, and 15.2% in Mixed participants, p<0.001). Those without monogenic diabetes showed significant variations in BMI. No difference in phenotype of monogenic diabetes across ancestry groups was observed. Non-white ethnicity participants were significantly more likely to have undiagnosed monogenic diabetes than White with on average a 10-year duration before receiving a correct diagnosis. By applying ancestry-specific BMI cut-offs (White <30, South Asian <27, African-Caribbean and Mixed <35 kg/m{superscript 2}), the overall detection rate increased from 8.8 to 16%, reducing the number needed to test to identify one case from 11 to 6 and boosting detection rates to 39, 11, 9 and 26% in White, South Asian, African-Caribbean and Mixed-ethnicity participants, respectively. These findings were validated in an external real-world dataset. Applying broad biomarker criteria for initial selection, mitigates clinical biases leading to misclassification of monogenic diabetes in non-White ethnicities. However, further tailoring criteria with ethnic-specific BMI cut-offs doubled detection rates, improving cost-effectiveness by minimising unnecessary testing. Our study highlights the need to develop precision medicine approaches accounting for phenotypic variation across diverse populations, to ensure accurate diagnoses and cost-efficient healthcare provision.

BackgroundGenetic risk scores (GRS) for type 1 diabetes (T1D) have been developed primarily in European populations, limiting their generalisability across ancestries. Indians differ from Europeans in clinical characteristics of T1D and overall genetic architecture, yet systematic evaluation of T1D GRS performance in multi-regional Indian cohorts is lacking. MethodsThe study included 597 T1D patients and 3347 non-diabetic controls from different regions in India. Genotyping, imputation, quality control analysis, and construction of the 67-SNPs T1D GRS were performed using standardised pipelines. Discriminative performance was assessed using Receiver Operative Curve-Area under Curve (ROC-AUC) analysis, and optimal thresholds were derived using Youdens index. HLA-DQ diplotype frequencies were compared, and association analysis was conducted using multivariable logistic regression. FindingsT1D GRS showed consistent discriminative performance across Indian cohorts [ROC-AUC=0.84 (range=0{middle dot}78-0{middle dot}87)], supporting its comprehensive use for T1D classification in India. Notably, its performance was lower in islet cell autoantibody (IA) negative compared with IA positive T1D patients (ROC-AUC, 0{middle dot}75 vs 0{middle dot}85) and in adult-onset than in childhood-onset patients (0{middle dot}74 vs 0{middle dot}84). We observed a lower frequency of protective HLA-DQ diplotypes and a strong association of HLA-DQ81 containing diplotypes in childhood-onset T1D. Application of an India-specific T1D GRS score improved the sensitivity than the European cut-off. InterpretationT1D GRS is a valuable unified diagnostic tool in Indians, but its performance varies by islet cell autoantibody status and age at onset, likely reflecting population-specific HLA architecture. European-derived T1D GRS thresholds under-classify the genetic risk, highlighting the importance of ancestry-aware optimisation in Indians. FundingCDRC grant CDRC202111026 and CSIR Intramural Grant P50. Research in contextO_ST_ABSEvidence before this studyC_ST_ABSPrevious studies have shown that a 67-SNPs T1D genetic risk score (GRS) can distinguish T1D patients from non-diabetic controls and other forms of diabetes, but its performance varies across ancestries. Islet cell autoantibodies (IA) have important diagnostic value for classifying type 1 diabetes (T1D). However, their prevalence in India varies widely, with up to one-quarter of patients testing negative, limiting their clinical utility. Evidence supporting the use of the T1D GRS in India, combined with IA antibodies status is limited to a single cohort representing one linguistic group. The applicability of T1D GRS across multi-centric clinical settings has not been systematically evaluated. Added value of this studyThis study validates the 67-SNPs T1D GRS across multiple Indian cohorts representing major linguistic groups, supporting its use as a unified diagnostic tool. Differences in T1DGRS performance between childhood-and adult-onset T1D are linked to enrichment of protective HLA-DQ diplotypes in adult-onset disease, providing genetic insight into disease heterogeneity. The study also demonstrates that European-derived GRS thresholds systematically under-classify genetic risk in Indians and the population-specific threshold is essential. Implications of all the available evidenceThe European-derived T1D GRS can be applied across Indian clinical settings with consistent discriminative performance. However, its utility is influenced by islet cell autoantibody status and the age at onset of disease. Ancestry-aware threshold optimisation substantially improves diagnostic accuracy and is essential for equitable implementation of T1D GRS in Indians. Larger studies are needed to identify population-specific risk variants and further refine genetic tools for clinical diagnosis.

BackgroundThe pancreas is essential for metabolic homeostasis. Alterations in morphology and parenchymal integrity may impact proper function but are not routinely used for risk stratification. Here, we propose an AI-pipeline to quantify pancreas volume and fat content from MRI to identify individuals at high-risk for cardiometabolic disease in the general population. MethodsWe quantified pancreas volume (milliliters, mL) and intrapancreatic fat content (defined as fat fraction; FF, %) from MRI of UK Biobank (UKB) and German National Cohort (NAKO) participants using deep learning. We 1) analyzed differences in volume and FF across age and sex, 2) computed percentile-curves and z-scores adjusted for age and sex to identify high-risk volumes/FF, and 3) conducted Cox regression to assess associations between z-score categories (volume: reference, z=-1 to 1; low, z=<-1; high, z>1; FF: low, z<1; moderate, z=0-1; high, z>1) and incident outcomes (diabetes, major adverse cardiovascular events (MACE), all-cause mortality) after adjustment for risk factors. ResultsAmong 63,548 UKB and NAKO-participants (57.7{+/-}12.8 years; BMI: 26.3{+/-}4.4 kg/m2, 46.9% female), automated pancreas analysis revealed a positive association between both volume and FF and age. In 33,099 UKB-participants (median 4.8 years follow-up), z-score categories were associated with incident diabetes (low volume, aHR:1.59, 95%CI[1.20-2.11]; high FF, aHR:1.70, 95%CI[1.31-2.19]), MACE (high volume, aHR: 0.79, 95%CI[0.61-1.01]; high FF, aHR: 1.32, 95%CI[1.01-1.73]), and all-cause mortality (low volume, aHR: 1.48, 95%CI[1.16-1.90]) beyond risk factors. Adding z-score categories to a baseline model including risk factors improved discrimination of future diabetes (volume:0.781 to 0.784, p=0.004; FF:0.781 to 0.787, p<0.001) and mortality (volume:0.781 to 0.787, p<0.001) ConclusionsDeviations from normalized pancreas volume and FF predicted cardiometabolic outcomes beyond known risk factors and alcohol intake. This automated approach identifies high-risk individuals who may benefit from cardiometabolic/endocrinology referral.

BackgroundThe diagnostic accuracy of HbA1C for prediabetes has been questioned due to its discordance with fasting plasma glucose (FPG) and 2 h oral glucose tolerance test (OGTT) glucose in non-white populations. This study aims to estimate concordance in the diagnosis of prediabetes using HbA1C FPG, and OGTT in a Filipino-American cohort. MethodsCross-sectional data from 149 Filipino-Americans without known diabetes living in the San Francisco Bay Area were used to compare prevalence of prediabetes as diagnosed by HbA1C, versus diagnosis by FPG and OGTT. ResultsThirty nine percent of subjects met the diagnosis of prediabetes using any one of the measures. Overall agreement between HbA1C, FPG and OGTT was low. Prevalence was 8.1% by FPG, 8.7% by OGTT and 35% by HbA1C. BMI, waist-hip ratio, insulin, HOMA-IR, blood pressure, and triglycerides were significantly higher in those with prediabetes by HbA1C versus normal HbA1C. ConclusionsThere is significant discordance between HbA1C, FPG, and OGTT in diagnosing prediabetes in a Filipino-American cohort. HbA1C detected four times as many individuals with prediabetes than FPG or OGTT. Individuals classified with prediabetes by HbA1C had indicators of more insulin resistance compared to individuals with normal HbA1C suggesting that HbA1C appears to detect true metabolic abnormalities on the path to diabetes as opposed to detecting false positives. These results have important implications for diabetes and prediabetes screening in Filipinos.

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.

Stratifying progression from early-stage type 1 diabetes to clinical disease is essential for optimally timing disease-modifying therapies. We previously developed a progression likelihood score (PLS) that includes quantitative IA-2 autoantibody (IA-2A) measurements. This study aligned IA-2A thresholds used for PLS calculation between the radiobinding assay (RBA) and a commercially available RSR IA-2A ELISA to support broader clinical application. Serum samples from 349 children with stage 1 type 1 diabetes were analyzed using both assays. IA-2A positivity was similar by RBA (61.6%) and ELISA (59.0%). Centile-based alignment of ELISA-positive samples defined thresholds corresponding to RBA IA-2A categories. ELISA-derived PLS low (PLS < 0.5), moderate (PLS 0.5-4.0) and high (PLS > 4.0) risk groups stratified progression to stage 3 disease comparably to RBA-derived groups. The 3-year progression rate for children with an ELISA IA-2A PLS >4.0 was 52.4% (95% CI, 30.5- 66.1), similar to the RBA-derived PLS >4.0 group (58.7%; 95% CI, 37.1-72.8). These results demonstrate that the commercial ELISA can be used for PLS-based risk stratification.

ObjectiveTo examine dose-response associations of vigorous intermittent lifestyle physical activity micropatterns (VILPA; bouts [&le;]1 minute) and its moderate-to-vigorous equivalent (MV-ILPA; bouts [&le;]3 minutes) with incident type 2 diabetes. Research Design and MethodsProspective data from UK Biobank accelerometry sub-study participants who reported no leisure-time exercise and had no type 2 diabetes at baseline were analysed. Daily duration and frequency of VILPA and MV-ILPA were derived from wrist-worn accelerometry. Incident type 2 diabetes was ascertained through linked primary care, hospital and death records. Dose-response associations were examined using multivariable-adjusted Fine-Gray subdistribution hazard models accounting for competing risks. ResultsAmong 22,706 participants (mean age 62.3 years; 56.4% female), 665 developed type 2 diabetes over an average follow-up of 7.9 years. Daily durations of VILPA and MV-ILPA showed inverse, non-linear L-shaped associations with incident type 2 diabetes. Median durations of 3.9 minutes/day of VILPA and 25.3 minutes/day of MV-ILPA were associated with 36% and 46% lower risk of type 2 diabetes, respectively, compared with those completing no VILPA or 3.9 minutes/day of MV-ILPA. Daily VILPA frequency showed a near-linear inverse association, with 10.4 bouts/day (median) corresponding to a hazard ratio (HR) of 0.64 (95% CI 0.51-0.81) compared with 0 bouts/day. Daily MV-ILPA frequency showed a U-shaped pattern, with risk reductions plateauing at approximately 56 bouts/day (HR 0.54, 95% CI 0.39-0.76). ConclusionsAmong adults who do not do leisure-time exercise, accruing brief, intermittent bouts of moderate-to-vigorous intensity physical activity during day-to-day routines may be a promising strategy for prevention of type 2 diabetes.

Aims/HypothesisUnderstanding heterogeneous patient responses to various glucose-lowering therapies is crucial for advancing personalized treatment approaches and optimizing outcomes for type 2 diabetes mellitus. While average treatment effects are known for many drug classes, patient responses may differ by underlying clinical and demographic factors. We hypothesize that major glucose-lowering drug classes exhibit heterogeneous treatment effects (HTE) across patient subgroups defined by key clinical and demographic characteristics. MethodsThis is a large-scale observational cohort study replicated in six data-sources across the Observational Health Data Sciences and Informatics network. New-user, active-comparator cohorts were constructed for patients with type 2 diabetes mellitus initiating one of the nine antihyperglycemic drug classes. Large-scale propensity score adjustment for measured confounding, empirical calibration using negative control outcomes, and random-effects meta-analysis were employed to estimate calibrated hazard ratios (HRs). HTE was assessed by comparing differences in log HRs across 10 demographic and clinical subgroups. ResultsEvidence of HTE was observed across hyperlipidemia, hypertension, obesity, and sex subgroups. Biguanides (vs. DPP-4i) were protective against acute myocardial infarction in patients with hyperlipidemia, and against heart failure hospitalization in patients with obesity. SGLT-2 inhibitors (vs. GLP-1 receptor agonists) reduced stroke risk only in non-obese patients. Sex-specific patterns also emerged: women taking GLP-1 receptor agonists had a higher risk of diarrhea, and women taking SGLT-2 inhibitors had a lower risk of stroke compared with DPP-4 inhibitors; these associations were not seen for male patients. ConclusionsThis hypothesis-generating study identified several potential signals (blood pressure status, lipid status, obesity status, and sex) where there exists treatment effect heterogeneity for several classes of type 2 diabetes mellitus drugs. These preliminary findings highlight the potential for personalized type 2 diabetes mellitus treatment recommendations based on patient characteristics.

Type 2 diabetes (T2D) affects 11.1% of the global population, underscoring the need for biomarkers that inform treatment response and glycemic outcomes. We evaluated the association between the FTO variant rs9939609-A and glycemic control in a Mexican population. A total of 174 individuals living with T2D from Merida and Sisal, Yucatan, were included, of whom 85% were receiving oral hypoglycemic agents as main treatment. Glycemic control was defined cross-sectionally as good ([&le;]130 mg/dL, n=63) or poor (>130 mg/dL, n= 111) with fasting glucose. Linear mixed models incorporating relevant covariates and a family random intercept were used. Effect size estimates were transformed to logit odds ratios. After adjustment for age, sex, BMI, years with T2D, and treatment, we observed a significant association in the additive (OR = 1.15 [1.003-1.31]) and recessive (OR = 1.51 [1.03-2.23]) models. To conclude, rs9939609-A may be associated with poorer glycemic control despite pharmacologic therapy.

1PurposeThe KIND (KINder mit Diabetes) cohort investigates diabetic peripheral neuropathy (DPN) in paediatric type 1 diabetes (T1D). Current guidelines recommend DPN screening at puberty or from 11 years and 2-5 years after T1D diagnosis, yet subclinical neurophysiological changes occur within the first 2 years. The cohort examines: (1) longitudinal associations between glycaemic metrics (HbA1c and continuous glucose monitoring-derived variability metrics) and peripheral nerve function and structure; (2) comparative predictive value of different variability metrics; (3) developmental trajectories of nerve maturation in T1D versus controls; (4) effects of residual beta-cell function on neuropathy progression; and (5) how early signs of DPN differ between patients with multiple daily injections (MDI) and continuous subcutaneous insulin infusion (CSII) therapy. ParticipantsThis prospective cohort, initiated in June 2019, continuously enrols children and young adults with T1D ([&le;]21 years) at two Swiss centres. The care-embedded design integrates research into quarterly diabetes care and annual comprehensive assessments. So far, 141 T1D cohort participants (median age: 12.2; IQR: [8.4; 14.3] years; 47.5 % female) and 103 healthy controls (10.9 [7.5; 14.2] years; 53.4 % female) were recruited. Controls for neurophysiological examinations comprise measurements from the healthy, contralateral side of children with limb injuries in the surgical outpatient clinic of the Childrens hospital of Eastern Switzerland (OKS). Multimodal assessments comprise nerve conduction studies (peroneal, tibial, median motor and sensory) and high-resolution ultrasound, with development-adjusted analyses distinguishing diabetes effects from normal growth. Findings to dateCross-sectional analysis showed reduced nerve conduction velocities across all nerves, particularly peroneal in T1D patients (n=53) compared to healthy controls (n=50). Height-adjusted peroneal velocity (dNCV) correlated negatively with glucose variability (SD: r=-0.45, p=0.009), HbA1c (r=-0.27, p=0.049). During the first five years, dNCV correlated negatively with diabetes duration (r=-0.41, p=0.004), independent of glycaemic control. The cross-sectional area of the median nerve increased on average by 0.217 mm{superscript 2} per 1% HbA1c (p=0.004) and was already detectable with diabetes duration <2 years. Longitudinal analysis (n=45, 21.4{+/-}8.6 months) demonstrated that HbA1c changes predicted dNCV changes ({beta}=-0.59, p=0.014), and in some patients, early impairment was reversible with improved glycaemic control. Future plansA planned 2026 extension of this continuously recruited and prospectively followed cohort will integrate physical activity measures (Swiss National Science Foundation Grant 10006264). Future analyses will compare glycaemic variability metrics as predictors of functional and structural nerve changes, investigate their temporal relationships as well as influencing factors, and examine residual beta-cell function effects across developmental stages. All data produced in the present study may be made available upon reasonable request and in accordance with legal and ethical requirements.

Age-defined type 1 diabetes (T1D) endotypes, T1DE1 and T1DE2, are characterized by reproducible differences in pancreatic immunopathology and clinical course. In particular, these endotypes differ in the extent and composition of lymphocytic insulitis and in the extent of loss of insulin-producing {beta} cell mass, at diagnosis. However, blood-based biomarkers that may distinguish these endotypes and inform the underlying immune-islet biology axis at diagnosis remain limited. Here, we characterized the clinical features and profiled circulating microRNAs (miRNAs) in plasma from two independent INNODIA cohorts of individuals with newly diagnosed stage 3 T1D (discovery, n=115; replication, n=147), stratified into age-defined endotypes (T1DE1, <7 years; T1DE2, [&ge;]13 years; and intermediate T1DInt, 7-12 years). Differential-expression and age-adjusted models were coupled to orthogonal ddPCR validation. Putative miRNAs cellular sources were inferred using reference miRNA expression atlases. Biological context was explored via correlations of miRNAs with whole-blood transcriptomics. Clinically, T1DE1 was associated with lower {beta}-cell function and higher first-year C-peptide decline, alongside distinct islet autoantibody patterns, consistent with an immunologically aggressive endotype. Small RNA-seq analysis and ddPCR validation identified a reproducible signature in which miR-150-5p, a B-and T-lymphocyte related miRNA, and miR-375-3p, a {beta} cell enriched molecule, were consistently increased in T1DE1 compared with T1DE2 across both cohorts. MiR-150-5p retained robust association with T1DE1 even after age adjustment, and neither miRNA was associated with age in non-T1D pediatric datasets, supporting T1D endotype specificity. The increased circulating miR-150-5p signal was not explained by differences in peripheral blood B-or T-cell frequencies in high-parameter flow-cytometry subsets, and its levels correlated inversely with whole-blood expression of the immune-associated miR-150-5p target genes MPPE1 and RABGAP1L. Finally, applying a rule-based combined classifier (miR-150-5p and miR-375-3p "high") achieved re-stratification of T1D individuals, including those in the intermediate age group, into two miRNA-defined groups with distinct {beta} cell functional trajectories. Collectively, these data suggest circulating miR-150-5p and miR-375-3p as non-invasive biomarkers linked to endotype-associated biology at T1D diagnosis, with potential utility for endotype-centered stratification and trial enrichment.

Prediabetes and type 2 diabetes (T2D) are metabolic disorders characterized by insulin resistance and {beta}-cell dysfunction. To understand the molecular mechanisms driving the transition from prediabetes to T2D, we performed a longitudinal proteogenomic analysis on 458 participants from the Prediabetes Lifestyle Intervention Study (PLIS). We identified 185 plasma proteins to be differentially expressed between conditions, 36 of which predict future T2D-onset. Integrating genetic data from 321 individuals, we generated a genome-wide protein quantitative trait loci (pQTL) map, identifying 86 differential and 700 shared cis-pQTLs between prediabetes and T2D. Mediation analysis revealed 60 putative causal links connecting allele-driven plasma protein expression to clinical traits, identifying body fat distribution, insulin resistance, and {beta}-cell function as central drivers of pathogenesis. Collectively, these findings highlight specific proteins underlying disease progression and substantiate the view that prediabetes and T2D are not distinct conditions, but rather stages on a unified metabolic spectrum.

ObjectivesAn update to the NICE Type 2 diabetes (T2DM) guideline in February 2022 recommended an SGLT2 inhibitor be offered to people with heart failure (HF) or cardiovascular disease (CVD) as comorbidities and considered for people at high CVD risk. We report uptake of this guideline in England by September 2023. DesignObservational cohort study. SettingGeneral practices contributing to the Clinical Practice Research Data Link, linked to hospital admission records. Participants587,826 people aged over 18 with T2DM on 1st September 2023, stratified according to their CVD category (low CVD risk score; high CVD risk score; CVD only; HF only; CVD and HF) and CKD status, and further by age, gender, ethnicity, deprivation, and T2DM diagnosis duration. Main outcome measuresPercentage of patients with a current SGLT2 inhibitor prescription; odds ratios for association between patient characteristics and a current prescription. ResultsIn people with T2DM, the percentage with a current SGLT2 inhibitor prescription was 20.2% and 19.9% respectively for people at low and high CVD risk, 19.5% for people with CVD, and 30.5% for people with CVD and HF. In age-stratified analyses, uptake ordered from lowest to highest was as follows: low CVD risk score, high CVD risk score, CVD only, HF only, CVD and HF. In models adjusted for clinical and patient characteristics uptake was lower in people aged >70, women, Black people, and people living in areas of higher deprivation. ConclusionsWhilst prescribing of SGLT2 inhibitors continues to rise in England, an opportunity remains to increase uptake in people with T2DM and to reduce inequalities. We report inequalities by ethnicity and deprivation, and lower uptake for people with CVD without HF than people with HF, despite an equal guideline recommendation for these two groups. Additional evidence is needed to interpret the age and gender inequalities we report. What is already known on this topicO_LIIn 2020 approximately 10% of people with type 2 diabetes (T2DM) and cardiovascular disease (CVD) and 14% of people with T2DM but without CVD in England had a current SGLT2 inhibitor prescription. C_LIO_LIIn 2022 NICE recommended that an SGLT2 inhibitor should be offered to people with T2DM with heart failure or CVD, and considered for people with T2DM at high risk of CVD; network meta-analyses have found 10% to 40% lower odds of cardiovascular mortality with treatment in these groups. C_LIO_LIUptake of NICE guidelines in general practice has historically been variable, although higher when accompanied by pay-for-performance schemes such as the Quality and Outcomes Framework. C_LI What this study addsO_LIBy September 2023 the percentage of people with T2DM with a current SGLT2 inhibitor prescription had reached 19.5% in those with CVD as a comorbidity, compared to 19.9% in those at high risk of CVD, and 30.5% in those with heart failure. C_LIO_LIWomen, people of Black ethnicity, and people living in areas of high deprivation had lower odds of a current prescription in analyses adjusted for age, gender, cardiovascular comorbidity, and renal function. C_LIO_LIThere remains an opportunity to increase uptake of SGLT2 inhibitors for people with T2DM in line with NICE guidelines in general practice in England, and to reduce inequalities in uptake. C_LI

BackgroundDysbiosis of gut microbiota plays a key role in type 1 diabetes mellitus (T1DM). Fecal microbiota transplantation represents a novel therapeutic avenue. We hypothesize that youth-derived fecal microbiota transplantation (yFMT) can remodel the gut microecosystem and improve clinical outcomes. This study aims to investigate the efficacy and safety of orally administered yFMT capsules in adults with T1DM. Methods and analysisThis single-center, randomized, double-blind, placebo-controlled pilot study will enroll adults with T1DM who have suboptimal glycemic outcomes (glycated hemoglobin[HbA1c] of 7-14% and time in range [TIR] <70%). Following a 17-day run-in period for insulin optimization, continuous glucose monitoring(CGM) wearing, baseline assessments and bowel preparation, participants will be randomly allocated (1:1) to take yFMT or placebo capsules for consecutive 6 days, alongside their standard insulin therapy, and then complete a 12-week follow-up. The primary efficacy endpoint is the change from baseline in the rate of achieving the composite target of TIR>70% and time below range<4% at 4 and 12 weeks post-randomization. Secondary efficacy endpoints comprise changes from baseline at weeks 4 and 12 in other glycemic metrics (including HbA1c, fasting glucose, 2-hour postprandial glucose, and additional CGM metrics), C-peptide, immune responses, infection markers, and gut microbiota composition. Changes from baseline at week 12 in serum metabolomic profiles will also be assessed, encompassing bile acids, short-chain fatty acids, and other related metabolites. Safety endpoints include the incidence of adverse events and serious adverse events. DiscussionOur findings will offer new insight into the feasibility and effects of oral yFMT in adult with T1DM and provide the necessary evidence to power a subsequent multicenter large-scale study. Exploratory biomarker analyses conducted within this study may further pave the way for future individualized microbiome-based therapeutics. Trial registrationChinese Clinical Trial Registry identifier: ChiCTR2500111955 (November 7, 2025).