Diabetologia

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.

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

IntroductionCardiovascular disease (CVD) is an important complication of type 2 diabetes (T2D). Current incident CVD-prediction models use single baseline measurements and achieve moderate performance in people with T2D, with C-indices around 0.7. Modern healthcare registries contain repeated measurements of HbA1c, LDL-cholesterol and eGFR, which could carry incremental predictive value. However, the added value of trajectory measures for CVD-risk prediction remains unclear. We aimed to investigate the utility of HbA1c, LDL-cholesterol and eGFR trajectory measures for incident CVD-risk prediction in people with T2D. MethodsWe studied 83,326 people with T2D from Danish nation-wide registers, who were without a CVD-history at baseline (January 1st 2015), and had [≥]2 recorded HbA1c, LDL-cholesterol and eGFR measurements between 2012-2014. Their last measurement was considered as baseline. Across 2012-2014, three types of paired trajectory measures were calculated for each participant (mean & standard deviation (SD), median & interquartile range (IQR), and intercept & slope from a fitted growth model), for HbA1c, LDL-cholesterol, and eGFR, respectively. Reference Cox-regression models for CVD-events (ICD-10 codes assessed prospectively from 2015- 2020) included only baseline measurements (age, sex , age at T2D onset, HbA1c, LDL-cholesterol, HDL-cholesterol, eGFR, and medication use). Next, the paired trajectory measures were sequentially added to the reference model, computing Hazard Ratios, C-indices and Net reclassification index (NRI) with 95% confidence intervals. Lastly, a combined model was fitted. ResultsAt baseline, mean age was 65 (SD{+/-}12), median HbA1c was 48 (mmol/mol, IQR43-56), and 48% were female. During a median 6 years of follow-up 11,280 (14%) people had a CVD-event (ischemic heart disease: 40%; stroke: 32%; heart failure: 24%; CVD-mortality: 5%). Accounting for the reference model, trajectory measures of dispersion and change were associated with CVD-events, with hazard ratios {approx} 1.1 for HbA1c and eGFR, and >1.4 for LDL-cholesterol. Measures centrality did not show an association with CVD events. Addition of trajectory measures produced minimal gains in discrimination (C-index {Delta} +0.001-+0.003) but modest improvements in net reclassification (continuous NRI {approx} +3- +9%). ConclusionsTrajectory dispersion or change measures for HbA1c, eGFR and especially LDL-cholesterol, easily obtained from routine data, might moderately enhance incident CVD-risk prediction in people with T2D.

BackgroundDiabetic kidney disease (DKD) is a leading cause of kidney failure in individuals with type 2 diabetes (T2D), yet risk identification in routine clinical practice remains incomplete. A critical and often overlooked barrier is risk observability: how much of a patients underlying risk is actually captured in their clinical record at the time of screening. Existing prediction models evaluate performance using model-specific thresholds, making it difficult to understand how additional data sources alter real-world screening behavior or which individuals benefit when models are expanded. MethodsWe developed a series of five nested machine learning models evaluated at a one-year landmark following T2D diagnosis using data from the All of Us Research Program (N = 39,431; cases = 16,193). Each successive model added a distinct information layer -- intrinsic risk, laboratory snapshots, medication exposure, longitudinal care trajectories, and social determinants of health (SDOH) -- while retaining all prior features. All models were evaluated under a fixed screening policy targeting 90% specificity, so that the false positive rate remained constant as the information available to the model grew. External validation was conducted in the BioMe Biobank (N = 9,818) without retraining. ResultsDiscrimination improved consistently across layers, from AUROC 0.673 (M1) to 0.797 (M5). Under the fixed screening policy, sensitivity nearly doubled from 0.27 to 0.49, with a cumulative recovery of 30.4% of cases missed by the base model. Gains were driven by distinct subgroups at each transition: laboratory features identified biologically high-risk individuals; medication features captured those with high treatment intensity reflecting advanced cardiometabolic burden; longitudinal care trajectory features rescued cases with biological instability observable only through repeated measurements; and SDOH features recovered individuals with limited clinical observability, with rescue probability highest among those with the fewest recorded monitoring domains. Sparse data in the clinical record indicated low observability, not low risk. Social and genetic features each contributed most when downstream physiologic signal was limited, supporting a contextual rather than universal role for each. In BioMe, discrimination was attenuated (M4 AUROC 0.659), but the relative ordering of information layers was fully preserved, and a systematic upward shift in predicted probability distributions underscored the need for recalibration before deployment in a new setting. ConclusionsDKD risk detection in T2D is substantially improved by integrating complementary information layers under a fixed clinical screening policy, with gains arising from distinct domains that identify at-risk individuals in different clinical contexts. The layered landmark framework introduced here reveals how risk observability -- shaped by monitoring intensity, healthcare engagement, and access -- determines what a screening model can detect, and provides a foundation for context-aware EHR-based screening that accounts for data availability at the time of risk assessment. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=140 SRC="FIGDIR/small/26351384v1_ufig1.gif" ALT="Figure 1"> View larger version (51K): org.highwire.dtl.DTLVardef@1cc7f4borg.highwire.dtl.DTLVardef@b92956org.highwire.dtl.DTLVardef@48ffbcorg.highwire.dtl.DTLVardef@8dc627_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOGraphical abstract.C_FLOATNO Study design and layered DKD screening framework The top row defines the cohort timeline, in which predictors are derived from clinical data collected between T2D diagnosis and the 1-year landmark, and incident DKD is ascertained after the landmark. The second row depicts the nested model architecture, in which five successive models sequentially incorporate intrinsic risk, laboratory snapshot features, medication exposure, longitudinal care trajectories, and social determinants of health, while retaining all features from prior layers. The third row summarizes model development in the All of Us Research Program (N = 39,431) and external validation in the BioMe Biobank (N = 9,818), where the same trained models and risk thresholds were applied without retraining. The bottom row highlights the three evaluation domains: predictive performance, fixed-policy screening, and missed-case recovery context. DKD, diabetic kidney disease; T2D, type 2 diabetes; PRS, polygenic risk scores; AUROC, area under the receiver operating characteristic curve; AUPRC, area under the precision-recall curve; PPV, positive predictive value; SHAP, SHapley Additive exPlanations. C_FIG

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.

Background: Genetic variants impacting red blood cell biology disrupt the relationship between glycaemia and glycated haemoglobin (HbA1c), with implications for diagnosis and management of type 2 diabetes (T2D). Thalassaemia trait is estimated to affect 350 million people globally, but its impact on T2D and related outcomes is not clear. Methods: We explored associations between thalassaemia trait, HbA1c, and T2D diagnosis and complications in 43,088 British Bangladeshi and Pakistani participants in the Genes & Health study with linked multisource England National Health Service (NHS) electronic health record data and whole exome sequencing. Findings: 2,490 participants (5.8%) were heterozygous carriers of ClinVar pathogenic / likely pathogenic thalassaemia variants, however 3 in 4 of these were not diagnosed with thalassaemia in their NHS health records. rs33950507, a common variant causal for HbE thalassaemia, was associated with increased HbA1c (beta=0.13, 95%CI:0.08-0.18, p=7.8x10-8), but not glucose levels (beta=0.01, 95%CI:-0.04-0.06, P=0.72). rs33950507 was associated with increased hazards of prediabetes (HR=1.38, 95%CI:1.26-1.52, p=2.2x10-6) and T2D (HR=1.11, 95%CI:1.01-1.22, p=0.03), and reduced hazards of diabetic eye disease (HR=0.74, 95%CI:0.56-0.96, p=0.02) and cerebrovascular disease (HR=0.44, 95%CI:0.20-0.94, p=0.03). Sensitivity analyses suggested mediation by overdiagnosis and overtreatment of T2D. Interpretation: Alternatives to HbA1c, and/or precision medicine approaches to defining and managing hyperglycaemia, are needed, particularly on a global scale. This may be particularly relevant to individuals from ancestral groups among whom erythrocytic traits are more common but often undiagnosed. Funding: Wellcome Trust, MRC, NIHR, Barts Charity, Genes & Health Industry Consortium

Background: The glucagon-like peptide-1 receptor (GLP1R) is a key regulator of glucose metabolism and appetite and a major therapeutic target for type 2 diabetes (T2D) and obesity. Genetic studies have implicated the GLP1R locus in both body mass index (BMI) and T2D, but it remains unclear whether their underlying genetic associations are the same. Methods: We analyzed 431,107 participants of genetically inferred European ancestry from the Million Veteran Program. Within 500 kb of GLP1R, we performed locus-wide linear regression models for BMI and logistic regression models for T2D, adjusted for age, sex, and 10 principal components. We identified primary and secondary BMI sentinel variants using conditional analyses and evaluated their associations with T2D. Bayesian fine-mapping was used to construct credible sets of GLP1R locus for BMI and T2D. Results: Conditioning on the primary sentinel variant rs12213929 (upstream of GLP1R, {beta} = 0.11; 95% CI 0.09-0.14; p = 1.94E-17), we identified a secondary variant (rs13216992, intron of GLP1R) independently associated with BMI ({beta} = 0.10; 95% CI 0.07-0.13; p = 7.88E-14). The two sentinel variants showed low linkage disequilibrium (r2 = 0.03). A two-variant allelic burden score (0-4; sum of the rs12213929 G-allele count and rs13216992 C-allele count) showed that participants with 4 risk alleles had 0.47 kg/m2 higher BMI than those with 0 risk alleles (95% CI 0.39-0.55; p < 2E-16). Both variants were associated with higher T2D risk, but with distinct patterns after BMI adjustment: the rs12213929-T2D association persisted after adjustment for BMI (OR = 1.02; 95% CI 1.01-1.03; p = 0.0004), whereas the rs13216992-T2D association was fully attenuated (OR = 1.00; 95% CI 0.99-1.01; p = 0.68). Fine-mapping identified a compact 95% BMI credible set of 17 variants and a broader 95% T2D credible set of 42 variants, with all BMI credible variants contained within the T2D set. Conclusions: The GLP1R locus harbors at least two independent BMI-associated variants that exhibit heterogeneous relationships with T2D: rs12213929 influences T2D risk partly through BMI-independent pathways, whereas rs13216992 appears to act predominantly via adiposity. These findings refine the genetic architecture at this key therapeutic target gene and provide a foundation for functional and pharmacogenomic studies to determine whether GLP1R variation can inform precision prevention and treatment of obesity and T2D.

The clinical characteristics of type 2 diabetes (T2D) differ between the sexes. For example, the risk of T2D is higher in males than in premenopausal females, whereas the risk of T2D-associated cardiovascular disease is higher in females. However, the sex-dependent mechanisms of T2D pathogenesis remain incompletely understood. Publicly available human islet datasets, such as HPAP and Humanislets.com, offer a valuable tool for uncovering the impact of biological sex on islet structure, gene expression, and function at a scale that was not previously possible. We performed an integrated analysis of data from publicly available sources to identify sex differences in baseline islet characteristics in donors without diabetes and subsequently examined these features in donors who lived with T2D. Among donors without diabetes, female islets had a greater proportion of alpha-cells compared with male islets and showed enriched expression of ribosomal and mitochondrial pathways in both beta- and alpha-cells. Measurements of mitochondrial function in female islets revealed lower spare respiratory capacity compared to male islets. Male and female islets had distinct changes in gene and protein expression in the context of T2D with female islets having greater preservation of insulin content and fewer defects in islet function. Together, these data show female islets have fewer islet impairments in T2D. This highlights the need for detailed mechanistic studies in both sexes to support effective and sex-informed interventions for T2D.

BackgroundVisceral adiposity is widely regarded as the pathogenic component of central obesity in cardiometabolic disease. However, emerging evidence suggests that abdominal subcutaneous adiposity may also confer metabolic risk in South Asian populations, although data in young, lean individuals are scarce. We investigated associations of MRI-measured abdominal subcutaneous adipose tissue (ASAT) and visceral adipose tissue (VAT) with cardiometabolic risk markers in young rural Indian adults. MethodsWe quantified ASAT and VAT using MRI in 590 participants (310 men) aged 18 years from the Pune Maternal Nutrition Study cohort. Sex-specific multiple regression models were used to examine associations with glucose-insulin indices, blood pressure, lipids, adipokines, and inflammatory markers. ResultsASAT showed broad and consistent associations with adverse cardiometabolic profiles, including higher 120-min glucose, dyslipidaemia, elevated blood pressure, leptin, CRP and leukocyte count, and lower insulin sensitivity and adiponectin, particularly in men; in women, ASAT was associated with most cardiometabolic risk markers except HDL-cholesterol. In contrast, VAT was associated with fewer risk markers and exhibited weaker, sex-specific patterns of association. Across outcomes, associations with ASAT were generally stronger than those observed for VAT. ConclusionsIn young, lean Indians, abdominal subcutaneous adiposity exhibits stronger associations with insulin resistance, dyslipidaemia and inflammation than visceral adiposity, challenging the prevailing VAT-centric paradigm derived largely from Western populations. These findings provide human evidence that the hierarchy of metabolic risk across abdominal fat depots is population-specific. This suggests genetic and early-life risk stratification, and supports early targeted preventive strategies. Research InsightsWhat is currently known about this topic? (max. 3 highlights, each < 100 characters) Indians have higher central obesity-adiposity than Europeans at similar BMI. Western data links VAT with cardiometabolic risk, while ASAT is protective. VAT & ASAT risk patterns vary across native and migrant South Asians. What is the key research question? (formatted as a question, < 100 characters) How do VAT and ASAT associate with cardiometabolic risk in lean rural Indian youth? What is new? (max. 3 highlights, each < 100 characters) ASAT shows stronger links with cardiometabolic risk than VAT in rural Indian youth. ASAT may contribute to high diabetes and CVD risk at low BMI in young Indians. How might this study influence clinical practice? (max. 1 highlight, < 100 characters) Early-life ASAT accumulation may raise later cardiometabolic risk, supporting early prevention strategies.

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.

Aims Type 2 diabetes mellitus (T2DM) increases risks of stroke and dementia, yet these risks vary across individuals. We hypothesized that clinically derived diabetes subtypes contribute to this heterogeneity. We aimed to identify data-driven subtypes using routine clinical features and examine their associations with dementia, stroke, mortality, and brain structure. Methods K-means clustering was applied to 14,353 UK Biobank participants with prevalent T2DM using age at diagnosis, body mass index, glycated hemoglobin, insulin resistance (triglyceride/HDL ratio), systolic blood pressure, and C-reactive protein. Cox models assessed associations with incident dementia (all-cause, Alzheimers disease [AD], vascular dementia [VaD]), stroke (all-cause, ischemic [IS], intracerebral hemorrhage [ICH]), and mortality. Brain MRI outcomes were analyzed in 779 participants using inverse probability-weighted linear regression. Results Three subtypes were identified: severe obesity-related inflammatory diabetes (SOID), mild metabolic diabetes (MMD, reference), and mild age-related hypertension-predominant diabetes (MARD-H). Compared with MMD, SOID showed higher risks of dementia (HR 1.24), VaD (HR 1.42), stroke (HR 1.38), IS (HR 1.48), all-cause mortality (HR 1.59), and cardiovascular death (HR 1.88). MRI showed lower gray matter volume and greater white matter hyperintensity burden in SOID. Conclusions Data-driven subtyping revealed heterogeneity in neurological risk in T2DM, with the obesity-inflammation subtype showing elevated vascular and neuroimaging risk.

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.

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.

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.

Diabetes mellitus (DM) and obesity frequently coexist. Both are associated with adipose dysfunction, yet the contribution of DM remains uncertain. Using bulk transcriptomics of subcutaneous and visceral adipose tissue (SAT and VAT, respectively), we show that DM is associated with shared and distinct patterns of differential gene expression in these depots. Gene ontology analysis of hits across depots highlighted extracellular matrix, inflammatory pathways, metabolism, axon guidance and endoplasmic reticulum stress. Histology revealed larger SAT adipocytes in people with DM, but only in the overweight category. Body mass index (BMI)-stratified transcriptomic analyses of SAT identified DM-associated hits present only in the overweight group. These were validated in plasma protein form using UK Biobank, informing our development of an adipose risk score that predicted incident DM in overweight people beyond a clinical risk score. Hence, molecular signatures of diabetic SAT can define high-risk adiposity, which may aid the targeting of clinical interventions.

Exercise is a cornerstone therapy for diabetes because working skeletal muscles take up glucose at dramatically greater rates than postprandial insulin-stimulated glucose uptake and, notably, do so without a requirement for insulin. This remarkable ability of working muscles is preserved in diabetes, when muscles become resistant to insulin. However, the mechanism of insulin-independent glucose uptake by working muscles is not fully understood. Here we describe a previously unrecognized glucose uptake pathway in muscle, which we refer to as "mSGLT" based on shared properties with the Sodium Glucose Linked Transporter family. In contrast to the abundant GLUT4 transporter, mSGLT is not regulated by insulin, requires Na,K-ATPase-2 activity, and transports the hexose -methyl-D-glucoside (MDG), a glucose derivative that is handled by SGLTs but not GLUT4. The mSGLT pathway and GLUT transport pathways are independent and additive. In addition to exercise, mSGLT imports glucose under other conditions of adrenergic stimulation, which inhibits pancreatic insulin release and reduces the insulin sensitivity of muscle. SGLT2-specific antibodies recognize a protein in muscle of similar size to the kidney SGLT2; this protein localizes to the muscle t-tubules, together with Na,K-ATPase-2 and MAP17, the regulatory subunit of SGLT2. However, skeletal muscles do not express a full-length transcript of Slc5a2 (SGLT2), and SGLT2-specific inhibitors do not inhibit mSGLT with high affinity. The novel transporter may be a muscle variant of Slc5a2 that results from post-transcriptional or post-translational mechanisms. mSGLT and its regulation offer potential muscle-specific therapeutic targets for treating hyperglycemia and other conditions when insulin-stimulated glucose disposal into muscle is impaired.

BackgroundThe Kidney Precision Medicine Project (KPMP) consortium aims to redefine chronic kidney disease (CKD) by integrating clinical, pathological, and molecular tissue data from kidney biopsies. Here, we demonstrate how biopsy data in CKD can clarify disease etiology and contribute to understandings of disease pathophysiology and clinical prognosis. MethodsThe KPMP is obtaining research kidney biopsies from individuals with CKD (defined as an estimated glomerular filtration rate [eGFR] < 60 mL/min/1.73m2 and/or albuminuria [&ge;]30 mg/g creatinine) and diabetes (enrolled as diabetes and CKD or DKD) or hypertension (enrolled as hypertension and CKD or HCKD). A team of kidney pathologists and nephrologists adjudicated the primary clinico-pathological diagnosis for 258 participants with CKD. We compared pathological features and kidney transcriptional signatures between participants with a primary adjudicated diagnosis of diabetic nephropathy and those with other causes of CKD. We developed a model using clinical and biomarker data that predicted the probability of diabetic nephropathy and tested associations of the signature with CKD progression among Chronic Renal Insufficiency Cohort (CRIC) participants with diabetes (n=229). ResultsAmong 183 participants enrolled as DKD, 102 (56%) had a primary adjudicated clinico-pathologic diagnosis of diabetic nephropathy. Among 75 participants enrolled as HCKD, 42 (56%) had a primary diagnosis of hypertension-associated kidney disease. Those with diabetic nephropathy, compared with other diagnoses, had more severe interstitial fibrosis, tubular atrophy, tubular injury, segmental sclerosis, and severe arteriolar hyalinosis, and single-nucleus and single-cell transcriptional analyses revealed upregulation of immune and inflammatory pathways and downregulation of oxidative phosphorylation. A combination of age, hemoglobin A1c, urine albumin-creatinine ratio, and serum KIM-1 and sTNFR1 predicted a clinico-pathologic diagnosis of diabetic nephropathy in the KPMP (AUC 0.82, 95% CI 0.75-0.89) and was associated with an increased risk of CKD progression among patients with diabetes enrolled in CRIC (HR 1.48 [95% CI 1.27-1.73] per 10% higher predicted probability of diabetic nephropathy). ConclusionIn common presentations of CKD, kidney biopsies may alter a priori impressions, reveal a diversity of diagnosis, structure, and function that is associated with clinical outcomes and can impact therapeutic decisions.

ObjectivesAn update to the NICE Type 2 diabetes (T2DM) guideline in February 2022 recommended an SGLT2 inhibitor be offered to people with cardiovascular disease (CVD) or heart failure (HF) as comorbidities and considered for people at high CVD risk. We report uptake of this guideline in England 18 months after its publication. DesignObservational cohort study. SettingGeneral practices contributing to the Clinical Practice Research Data Link, linked to hospital admission records. Participants587,826 people aged over 18 years with T2DM on 1st September 2023, stratified according to their CVD category (CVD only; HF only; CVD and HF; high CVD risk score; low CVD risk score) and chronic kidney disease (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 19.5% for people with CVD, 29.4% for people with HF, 30.5% for people with both CVD and HF, and 19.9% and 20.2% respectively for people at high and low CVD risk. 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 >60, 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 and to reduce inequalities in people with T2DM in 2026. 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 on the effectiveness of SGLT2 inhibitors in frailer populations. What is already known on this topic?O_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 February 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, 30.5% in those with heart failure, and 19.9% in those at high risk of CVD. 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_LI How might these results change the focus of research or clinical practice?O_LIThe results highlight the need for ongoing surveillance of uptake of NICE-recommended treatments for T2DM, and consideration of actions to address barriers to uptake. This is particularly important in the context of broader eligibility for SGLT2 inhibitor treatment in type 2 diabetes in England from 2026. C_LIO_LIThese results support the development of initiatives and quality improvement programmes to improve evidence-based prescribing and address inequalities between clinical and demographic subgroups. C_LI

BackgroundPET-CT scans of radioactive exendin-4, a ligand of the GLP-1 receptor, are claimed to provide a biomarker of pancreatic beta cell mass (BCM), although the GLP-1 receptor is expressed also in the exocrine pancreas (PX). Parotid glands may be a reference tissue for GLP-1 receptor expression in exocrine cells of the GI system. Our aims were 1. To assess biomarker(s) of BCM derived from 68Ga-NODAGA-exendin-4 PET-CT scans in participants with long-standing type 1 diabetes (T1DM) or in subjects with obesity (OBESE); 2. To investigate the relationship between biomarker(s) of BCM and a biomarker of beta cell functional mass (BCFxM) in OBESE. MethodsT1DM (n=8, Age: 50.4{+/-}3.8 yrs; T1DM duration: 34.2{+/-}3.0 yrs; BMI: 26.6{+/-}1.1 kg/m2; HbA1c: 7.5{+/-}0.36%) and OBESE (n=9; Age:48.2{+/-}2.2 yrs; BMI: 37.4{+/-}1.1 kg/m2; HbA1c: 5.4{+/-}0.17%) underwent two studies: 1) 68Ga-NODAGA-exendin-4 PET-CT scan of both PX and parotid glands 45-60 after i.v. injection and with CT-assessment of PX volume to compute biomarkers of BCM based on SUV (BCMSUV) or clearance (CLEAR; BCMCLEAR); 2) Mixed meal test (MMT), with measurement of plasma glucose, C-peptide, GLP-1 and GIP curves to assess BCFxM with state-of-art mathematical modeling. ResultsThe C-peptide response to the MMT in T1DM participants was absent or negligible, whereas the OBESE displayed a robust BCFxM. The PX volume was smaller in T1DM than in OBESE (51.7{+/-}6.6 vs 92.9{+/-}10.9 cc; p=0.007). The biomarkers of BCM, as assessed by 68Ga-NODAGA-exendin-4 SUV or CLEAR, were 6.6-fold (p=0.003) and 5.0-fold (p=0.002) lower, respectively, in T1DM than in OBESE. BCFxM was correlated in OBESE to both biomarkers of BCM (r=0.91 p<0.001, and r=0.82 p=0.006, respectively). Conclusion/interpretation68Ga-NODAGA-exendin-4 derived biomarkers of BCM can discriminate T1DM from OBESE. In OBESE 68Ga-NODAGA-exendin-4 derived BCM appears to be a pivotal determinant of the beta cell response to MMT and may be valuable to compare and monitor BCM both in research and in clinical settings. Research in contextO_ST_ABSWhat is already known about this subject?C_ST_ABSO_LIChanges in pancreatic beta cell functional mass are at the heart of alterations in glucose regulation, including diabetes mellitus. Beta cell functional mass can be assessed by mathematical modeling of the in vivo beta cell response to intravenous or oral challenges. C_LIO_LIBeta cell functional mass is the product of beta cell mass times beta cell function per mass unit, i.e. the result of two distinct entities, mass and function. No in vivo methods can dissect out beta cell mass and function. C_LIO_LIPancreatic 68Ga-exendin-4 uptake, as measured by PET-CT, has been proposed as a non-invasive biomarker of beta cell mass. However, the ratio of 3.6:1 between endocrine and exocrine pancreas 68Ga-exendin 4 uptake suggests that there is room for improvement. C_LI What are the key questions?O_LIDoes an improved 68Ga-exendin4 method provide a better separation between participants with type 1 diabetes and expected zero/nil beta cell mass vs people with nondiabetic obesity? C_LIO_LIWhat is the role of beta cell mass in determining beta cell functional mass in people living with obesity? C_LI What are the new findings?O_LIThe improvement in the quantitation of beta cell 68Ga-exendin-4 binding to beta cells resulted in a clearcut separation of participants with type 1 diabetes and expected zero/nil beta cell mass from people living with obesity C_LIO_LIIn people living with obesity, beta cell mass, as assessed by 68Ga-exendin-4 PET-CT scan, is a pivotal determinant of beta cell functional mass, as assessed by mathematical modeling of a frequently sampled mixed meal test C_LI How might this impact on clinical practice in the foreseeable future?O_LIThis method has the potential to track changes in beta cell mass both between-subjects and within-subjects over time C_LIO_LINatural history of glucose (in)tolerance and the impact of disease modifier candidates in diabetes mellitus can be assessed with the present method C_LI

0BackgroundA Disintegrin and Metalloprotease 17 (ADAM17) is a key sheddase regulating multiple inflammatory and growth factor-related pathways implicated in diabetic kidney disease (DKD). While cell-specific deletion of ADAM17 has shown renoprotective effects, the impact of global ADAM17 ablation in the context of diabetes remains incompletely understood. MethodsWe investigated the effects of tamoxifen-induced global Adam17 deletion in a murine model of type 1 diabetes induced by streptozotocin. Renal function, structural injury, inflammatory responses, stress-related signalling pathways, and fibrotic remodelling were comprehensively assessed and compared between diabetic Adam17 knockout and control mice. ResultsDespite persistent hyperglycaemia and albuminuria, diabetic Adam17 knockout mice exhibited preservation of glomerular filtration rate and marked attenuation of diabetes-associated renal injury. Global Adam17 deletion reduced mesangial expansion and structural damage, limited macrophage infiltration and chemokine expression, and significantly attenuated fibrotic remodelling. At the molecular level, Adam17 deficiency was associated with selective modulation of stress-related signalling pathways, including reduced activation of the PI3K/Akt axis and partial preservation of mitochondrial stress regulators, without evidence of a generalized suppression of cellular stress responses. ConclusionsOur findings demonstrate that global deletion of ADAM17 confers robust protection against diabetes-induced kidney injury through coordinated attenuation of inflammatory activation, stress-related signalling, and fibrotic progression. These results highlight the context-dependent role of ADAM17 in diabetic kidney disease and support the concept that therapeutic strategies targeting ADAM17-related pathways may require tissue- and disease-specific modulation to achieve renoprotective effects.