Diabetes

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.

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.

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.

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.

AimsThe distribution of abdominal adipose depots and their mechanistic links to type 2 diabetes remain incompletely understood. This study elucidated the relationship between type 2 diabetes presence and quantitative abdominal imaging traits, including hepatic steatosis, liver and spleen size, and adipose distribution, using unenhanced computed tomography (CT) scans from a large-scale, racially diverse, disease-focused medical biobank. Materials and MethodsDeep learning algorithms were applied to abdominal CT scans to automatically quantify image-derived phenotypes, including spleen-hepatic attenuation difference (SHAD) for hepatic steatosis, liver and spleen volumes (LV and SV, respectively), visceral and subcutaneous adipose tissue (VAT and SAT, respectively), and visceral-to-subcutaneous fat ratio (VSR). ResultsDiabetic individuals demonstrated a greater degree of hepatic steatosis and central adiposity than those without diabetes. Liver attenuation was lower (47.6 vs. 52.4 Hounsfield units (HU); lower values indicate greater steatosis), SHAD was higher (-5.41 vs. -8.41 HU; more positive values indicate greater steatosis), and steatosis prevalence increased (38.4% vs. 21.4%) (all p<2.2x10-{superscript 1}). VSR was also elevated (0.64 vs. 0.54, p=5.86x10-{superscript 1}3). These trends remained significant after stratification by sex. Multivariate analyses revealed independent associations of diabetes with SHAD (OR 1.04), LV (OR 1.59), SV (OR 3.95), VAT (OR 1.23), SAT (OR 1.05), and VSR (OR 2.27), after adjusting for age, sex, race, and BMI. ConclusionsHepatic steatosis, hepatomegaly, and visceral adiposity on CT imaging are predictive of type 2 diabetes presence. Notably, VSR showed a stronger association with diabetes than BMI, underscoring how body-fat distribution, rather than overall adiposity, more accurately reflects metabolic disease risk.

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.

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.

Aims/hypothesisImmunotherapies such as Teplizumab can preserve residual beta cell function in individuals with newly diagnosed type 1 diabetes (T1D), but treatment response is variable. Currently, no biomarker exists to identify individuals most likely to benefit from immunotherapy. We believe that baseline serum metabolomic profiles can distinguish individuals who respond to treatment from nonresponders and predict therapeutic response. MethodsBaseline serum samples from 41 individuals newly diagnosed with T1D enrolled in the AbATE trial (NCT00129259) were analyzed to identify metabolic predictors of response to Teplizumab therapy in the AbATE trial. Responders to Teplizumab, as per study protocol, were defined as individuals who exhibited less than a 40% decline in baseline C-peptide levels at 2 years after start of treatment. We analyzed baseline serum samples using a semi-targeted metabolomics approach via liquid chromatography-high-resolution tandem mass spectrometry. Metabolites that were significantly different between responders and nonresponders were identified (P < 0.05), and the significant metabolites were used to train a supervised Random Forest model to predict treatment response. Model performance was evaluated using a 70/30 training/testing split, 5-fold cross-validation, bootstrap resampling (1,000 iterations), and permutation testing (1,000 permutations). ResultsWe identified 15 significantly different metabolites at baseline between responders and nonresponders (P < 0.05). These metabolites included amino acids and their derivatives, tricarboxylic acid (TCA) cycle intermediates, and microbially derived metabolites. At baseline, responders exhibited higher levels of TCA cycle metabolites, amino acid derivatives, and microbial metabolites, whereas nonresponders showed elevated levels of glutamate and acylcarnitines. The Random Forest classifier achieved an accuracy of 0.769 and an area under the receiver operating characteristic curve (AUC) of 0.881 in the test dataset. Cross-validation yielded a mean AUC of 0.856 (SD 0.156; 95% CI 0.719-0.992). Bootstrap analysis produced a test AUC 95% CI of 0.619-1.000, and permutation testing confirmed significance (p = 0.012). Conclusions/interpretationBaseline serum metabolomic signatures can predict responders to Teplizumab with high accuracy. This could potentially be applicable when considering other immunotherapies in preventative efforts in T1D. Trial registrationClinicalTrials.gov NCT00129259. Research in ContextO_ST_ABSWhat is already known about this subject?C_ST_ABSO_LITeplizumab can delay beta cell decline in individuals with newly diagnosed T1D, but treatment response varies. C_LIO_LINo validated biomarkers currently exist to predict which individuals will respond to immunotherapy. C_LIO_LIMetabolomic profiling has shown potential for identifying metabolic signatures associated with disease progression and immune activity in T1D. C_LI What is the key question?O_LICan baseline serum metabolomic profiles predict which individuals with newly diagnosed T1D will respond to Teplizumab therapy? C_LI What are the new findings?O_LIFifteen baseline metabolites differed significantly between responders and nonresponders, including amino acid derivatives, tricarboxylic acid cycle intermediates, and microbially derived metabolites. C_LIO_LIResponders exhibited metabolic signatures consistent with preserved beta cell function and enhanced mitochondrial and immune-regulatory activity. C_LIO_LIA Random Forest model developed using these metabolites accurately predicted treatment response (AUC 0.881), demonstrating strong predictive potential. C_LI How might this impact on clinical practice in the foreseeable future?O_LIBaseline metabolomic profiling could support personalized treatment strategies by identifying individuals most likely to benefit from treatment with Teplizumab or other immunotherapies. C_LI

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.

Prediabetes (PD), which includes impaired glucose tolerance (IGT) and impaired fasting glucose (IFG), is a dysfunctional metabolic state that often progresses to type 2 diabetes (T2D). Standard screening tools such as random glucose and hemoglobin A1c frequently miss early or intermittent dysglycemia and cannot distinguish underlying physiological differences relevant for targeted intervention. Although the oral glucose tolerance test (OGTT) detects more PD and T2D and identifies high-risk individuals earlier, its clinical use is limited by the need for repeated venous sampling and in-clinic administration. We introduce the mobile OGTT (mOGTT), which leverages continuous glucose monitoring (CGM) to capture high-resolution glycemic responses to a standardized glucose challenge outside clinical settings. In a population spanning a broad range of glycemic health, we establish preliminary normative mOGTT values, characterize their relationship to clinical OGTT thresholds, and assess concordance with A1c. We show that CGM-derived analogs of OGTT metrics improve detection and phenotyping of dysglycemia and propose the Glucose Challenge Response Index (GCRI), a composite measure of glycemic health. Finally, we demonstrate the generalizability of GCRI-based subphenotypes in an out-of-sample cohort. These results help facilitate an efficient and scalable approach for conveniently detecting and quantifying early-stage glucose dysregulation.

BackgroundCurrent glycemic monitoring exhibits a critical temporal gap between fructosamine (14-21 days) and HbA1c (90-120 days), limiting timely therapeutic adjustments. The Glycated Protein Precipitation Index (GPPI) represents a novel biomarker potentially bridging this interval. ObjectiveTo validate GPPIs correlation with established glycemic markers and determine its clinical utility for intermediate-term glycemic assessment. Methods: This prospective validation study will enroll 200 diabetic patients and 100 normoglycemic controls. GPPI leverages differential precipitation of glycated versus native serum proteins using trichloroacetic acid at optimized concentrations, followed by colorimetric quantification with bromophenol blue at 595nm. Primary endpoint: correlation coefficient between GPPI and HbA1c (target r>0.85). Secondary endpoints include analytical precision (CV<5%), interference assessment, reference interval establishment, and cost-effectiveness analysis. Simultaneous HbA1c, fasting glucose, and continuous glucose monitoring (subset n=50) will provide comparative data. Deming regression, Bland-Altman analysis, and ROC curves will assess method agreement and diagnostic performance. Expected OutcomesGPPI should reflect 6-8 week glycemic control with strong HbA1c correlation, offering cost reduction from $15-30 to $0.24 per test. Simplified visual interpretation enables field deployment in resource-limited settings, potentially democratizing diabetes monitoring for underserved populations globally.

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.

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.

ObjectiveTo introduce and evaluate the clinical utility of the "adipo-B index" as a novel metric of the adipose tissue-pancreatic beta cell axis. To our knowledge, no prior clinical metric has integrated adipose tissue insulin resistance and pancreatic beta-cell function into a single index applicable across therapeutic classes. MethodsTreatment-naive subjects with T2DM received monotherapy with modified traditional diet for diabetes (MJDD, n=61), canagliflozin (n=67), pioglitazone (n=54), or sitagliptin (n=63). Correlations between the baseline and changes in adipo-IR or adipo-B and clinical parameters were analyzed. This is a prospective, non-randomized observational study. ResultsAt baseline, among all the subjects, adipo-B significantly correlated with FBG, HbA1c, non-HDL-C and BMI, while adipo-IR did not. At 3 months, across all therapeutic strategies, significant negative correlations were observed between the changes in ({Delta})adipo-B and baseline adipo-B. By contrast, in MJDD, canagliflozin and pioglitazone, significant negative correlations were seen between {Delta}adipo-IR and baseline adipo-IR, while with sitagliptin, no correlations were noted. {Delta}adipo-B, but not {Delta}adipo-IR, correlated with the improvements of glycemic (FBG, HbA1c) and lipid (non-HDL-C) parameters across all these therapies. While significant correlations were seen between {Delta}adipo-B and {Delta}adipo-IR with MJDD, pioglitazone and sitagliptin, canagliflozin uniquely "decoupled" this axis. With sitagliptin and pioglitazone, adipo-B improved despite weight gain. ConclusionThe adipo-B index is a superior indicator of systemic metabolic status and therapeutic response and could serve as a useful tool for precision therapy for diabetes.

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.

Obesity-driven type 2 diabetes (T2D) is characterized by pathological alterations in visceral white adipose tissue (vWAT). While microRNAs (miRNAs) are key post-transcriptional regulators, comprehensive human vWAT profiling across metabolic states remains limited. This study characterized vWAT miRNA expression in lean, obese, and obese+T2D individuals to identify regulatory networks associated with metabolic failure. Deep miRNA sequencing was performed on vWAT samples from a discovery cohort, followed by validation via qPCR in an independent replication cohort. Differentially expressed miRNAs across the three groups were bioinformatically integrated with matched mRNA transcriptomic data to construct functional regulatory modules and identify enriched pathways underlying metabolic impairment. Several miRNAs exhibited robust and reproducible differential expression between obesity and obesity with T2D. Integrated miRNA-mRNA analyses revealed coherent regulatory modules involving inflammation, lipid metabolism, insulin signaling, and iron homeostasis. Specifically, miR-141-3p, miR-200b-3p, miR-15b-3p, miR-12136, and miR-585-3p showed consistent differential expression. Notably, miR-141-3p and miR-200b-3p were markedly upregulated and inversely associated with metabolic stress-related genes, including TF and FBXO32. Several miRNAs correlated with clinical markers of metabolic dysfunction, supporting their biomarker potential. By comparing lean, obese, and diabetic populations, this study provides a comprehensive characterization of the vWAT miRNA landscape and identifies specific miRNA-mRNA regulatory circuits that orchestrate the transition from healthy adiposity to pathological adipose tissue dysfunction. These findings pinpoint novel molecular drivers of type 2 diabetes progression and offer potential targets for therapeutic intervention in metabolic endocrine disorders.

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).

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.