Diabetologia

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

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

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.

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.

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.

Background and AimsAdult pancreas-derived islet progenitor cells (IPCs) have recently been shown to expand in culture and differentiate into endocrine-like organoids. However, translation of this approach to a clinically compatible workflow requires cell enrichment strategies and validation using tissue obtained during real-world clinical procedures. Here, we adapted our previously described IPC platform to non-endocrine pancreatic tissue fractions generated during clinical islet isolation procedures and evaluated their capacity to generate functional islet organoids. MethodsNon-endocrine pancreatic tissue fractions obtained during clinical islet isolation were expanded ex vivo and enriched using fluorescence-activated cell sorting (FACS) for CD81 and CD9, surface markers previously identified in IPC populations. Sorted cells were expanded, induced to form IPC clusters, and differentiated with ISX9 to generate islet organoids. Differentiation was assessed by gene expression analysis, flow cytometry, immunofluorescence, calcium flux assays, glucose-stimulated insulin and glucagon secretion, and single-cell RNA sequencing. ResultsClinically derived non-endocrine cell fractions yielded expandable IPC populations expressing progenitor-associated markers. FACS-purified and expanded CD81+/CD9+ IPCs were enriched with BMPR1A and P2RY1. Sorted cells generated three-dimensional BMPR1A+ and RGS16+ IPC clusters. IPC clusters differentiated into islet organoids with upregulated expression of canonical beta-and alpha-cell transcription factors. Single-cell transcriptomic profiling revealed activation of coordinated endocrine gene programs and alignment with reference human islet endocrine signatures, while the undifferentiated IPC compartment was marked by enrichment of PTX3, FST, CEMIP, and GREM1. Terminally differentiated cells exhibited depolarization-induced calcium influx and glucose-regulated insulin and glucagon secretion. ConclusionsThese findings establish an adaptable workflow for expansion and production of functional islet organoids recovered from clinically derived pancreatic tissue. This strategy may provide an unlimited autologous source of adult progenitor-derived islets for future islet cell replacement therapies in diabetes.

(1) Aims and hypothesisLoss-of-function mutations in SLC30A8, encoding the zinc ion (Zn2+) transporter ZnT8 in pancreatic beta cells, lower type 2 diabetes risk dose-dependently, but the underlying mechanisms remain unclear. Here, we combine proteomic, transcriptomic and functional approaches in human stem cell-derived islet-like clusters bearing common alleles or the inactivating variant R138X. We hypothesized that this variant protects against the deleterious effect of Zn2+ depletion on cell survival and function. (2) MethodsHuman embryonic stem cells INS(GFP/w) (MEL1), and CRISPR/Cas9-derived heterozygous or homozygous R138X lines were differentiated into stem cell-derived islet-like clusters. Intracellular Zn2+ levels were reduced using the chelator N,N,N',N'-tetrakis(2-pyridylmethyl)-1,2-ethanediamine (TPEN). Apoptosis was assessed by TUNEL staining and protein expression by immunofluorescence. Glucose-stimulated calcium (Ca2+) dynamics were measured using the intracellular probe (Cal590) and insulin secretion by homogenous time-resolved fluorescence. Transcriptomic profiling was performed by bulk mRNA sequencing and proteomics by liquid chromatography-tandem mass spectrometry. (3) ResultsIntracellular Zn2+ depletion increased apoptosis in wild-type islet-like clusters, whereas R138X clusters were protected. R138X heterozygous clusters showed a mild increase in GCG+ cells and R138X homozygous clusters exhibited increased NKX6.1+ cells, without affecting polyhormonal populations. These changes were reversed under Zn2+ depletion. Transcriptomic and proteomic analyses, assessing genotype effects while accounting for Zn2+ depletion, showed that R138X clusters (versus wild-type) exhibited upregulation of genes and proteins involved in vesicle trafficking, secretion, Ca{superscript 2} signaling and mitochondrial metabolism, consistent with enhanced glucose-stimulated insulin secretion in homozygous clusters. Conversely, genes and proteins associated with extracellular matrix remodeling, metal-ion handling, apoptosis and cellular stress were downregulated. R138X clusters displayed altered Ca2+ signaling, with decreased area under the curve and oscillation amplitude, but increased frequency. These differences were reversed by TPEN, while Zn2+ depletion impaired Ca2+ response in wild-type clusters. Despite lowered overall activity, R138X homozygous clusters showed enhanced overall cell-cell connectivity, reversed by TPEN treatment. The opposite effects were observed in R138X heterozygous clusters, showing improved connectivity and activity under Zn2+ depletion. (4) Conclusion and interpretationIntracellular Zn2+ depletion compromises islet-like cluster identity and function, while the R138X variant confers protection against these effects. Under Zn2+-depleted conditions, ZnT8 deficiency promotes a more mature and metabolically active state of the R138X clusters, with enhanced Ca2+ signaling and insulin secretion, supported by a structural remodeling and the downregulation of apoptosis and cellular stress. These findings highlight the therapeutic potential of targeting ZnT8 in type 2 diabetes and support its relevance for further improving cell-based therapies. Research in ContextO_ST_ABSWhat is already know about this subject?C_ST_ABSO_LIRare inactivating mutations in the insulin granule-associated zinc transporter gene, SLC30A8/ZnT8, drive lowered type 2 diabetes risk. C_LIO_LIPrevious studies have indicated that apoptosis is lowered, and glucose-stimulated insulin secretion enhanced, after ZnT8 inactivation. C_LIO_LIThe molecular mechanisms underlying these changes are unclear. C_LI What is the key question?O_LIHow do inactivating mutations in SL30A8/ZnT8 lead to lowered apoptosis and enhanced insulin secretion from stem cell-derived islet-like clusters, and is altered susceptibility to intracellular zinc depletion involved? C_LI What are the new findings?O_LIThe rare inactivating R138X mutation in SLC30A8 leads to gene dose-dependent changes in the transcriptome and proteome of islet-like clusters. C_LIO_LIChanges include upregulation of maturity and downregulation of immaturity genes. C_LIO_LIDepletion of intracellular Zn2+ exaggerates the protective effects of the inactivating mutation on apoptosis and insulin secretion C_LI How might this impact on clinical practice in the foreseeable future?O_LIOur findings suggest that careful monitoring of both dietary zinc intake and of circulating levels of zinc ions, whose effects are mitigated in SLC30A8 mutation carriers, may be helpful in some populations to lower diabetes risk. C_LI

Background: Diet is a modifiable risk factor for cardiometabolic disease, yet establishing causality remains challenging. Mendelian randomisation (MR) leverages genetic variants as instrumental variables (IVs) to enable causal inference. Method: Using two-sample MR, we assessed the causal effects of four principal component-derived dietary patterns (DPs) - Unhealthy, Healthy, Meat-based, Pescatarian - on twelve cardiometabolic outcomes: body mass index, coronary artery disease, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, total cholesterol, triglycerides, systolic blood pressure, diastolic blood pressure, type 2 diabetes, fasting glucose and insulin, and glycated haemoglobin. Two sets of IVs were employed: conventional genome-wide significant variants associated with each DP, rigorously filtered for pleiotropy and directionality; and biologically informed variants in chemosensory receptor genes, given the role of taste and smell perception in shaping food choices. Results: Using conventional IVs, the Pescatarian DP reduced fasting insulin ({beta}IVW = -0.10 pmolL-1 per SD increase in Pescatarian DP score, 95% Confidence interval [CI] [-0.15, -0.04]; P = 1.19x10-3), which survived multiple sensitivity analyses. Associations between the Unhealthy DP and elevated blood pressure and glycated haemoglobin were likely undermined by heterogeneity and pleiotropy, with insufficient IVs for robust sensitivity testing. Chemosensory receptors yielded null findings, reflecting insufficient power. Conclusion: Rigorously filtered conventional IVs supported the causal nature of well-established diet-disease relationships, demonstrating MR's utility in strengthening causal inference in nutritional epidemiology. Chemosensory IVs demonstrated limited utility for DPs, likely reflecting the heterogeneous and complex sensory profiles of overall diets. Future efforts should consider using guideline-based dietary scores to facilitate translation of findings.

Background: Parental diabetes and obesity influence offspring phenotype, but their relative contributions remain unclear. Aim: To examine the relative contributions of parental diabetes and obesity to offspring overweight-obesity and glucose intolerance. Methods: We studied 200 offspring of mothers with diabetes in pregnancy (ODM; 176 indexes, 24 siblings), 176 mothers (133 gestational diabetes (GDM), 22 type 1, 21 type 2 diabetes), and 150 fathers. Controls included 177 offspring of non-diabetic mothers (ONDM), 177 mothers without diabetes in pregnancy, and 163 fathers. Overweight-obesity was defined by WHO criteria, central obesity as waist-to-height ratio >0.5, and glucose intolerance by ADA criteria (fasting glucose for <10 years; oral glucose tolerance test (OGTT) for >=10 years). Generalized linear mixed-effects models assessed parental determinants of offspring outcomes. Results: ODM were more overweight-obese, centrally obese, and glucose intolerant than ONDM. Younger ODM had higher capillary glucose (5.6 vs 5.1 mmol/L, p<0.001). Among ODM >=10 years, 37% had prediabetes and 5% diabetes versus 20% and 0% in ONDM. Overweight-obesity was associated with maternal (OR 7.81; 95% CI 2.19-27.85), paternal (OR 6.21; 95% CI 1.57-24.53), and biparental obesity (OR 9.59; 95% CI 2.73-33.69), but not parental diabetes. Glucose intolerance was associated only with maternal diabetes in pregnancy (OR 3.90; 95% CI 2.05-7.41). Conclusions: Preventing offspring obesity will require addressing parental obesity, whereas preventing glucose intolerance will require optimal glycemic control in the mothers before and during pregnancy.

Importance Women have been under-represented in clinical trials of type 2 diabetes mellitus (T2D), and evidence on sex differences in effectiveness of T2D treatments remains limited. Objective To assess sex differences in comparative effectiveness and safety of four second-line antidiabetic agents: glucagon-like peptide-1 receptor agonists (GLP-1RA), sodium-glucose cotransporter-2 inhibitors (SGLT2i), dipeptidyl peptidase-4 inhibitors (DPP4i), and sulfonylureas (SU). Design Retrospective cohort study using an active-comparator new-user design, following each participant till treatment discontinuation or end of data. Setting Multinational study across ten real-world databases from the Observational Health Data Sciences and Informatics (OHDSI) network in the United States, United Kingdom, Germany, and Spain. Participants 5.15 million adults with T2D who initiated one of the four second-line therapies following metformin during 1992-2021. Exposures GLP-1RA, SGLT2i, DPP4i, or SU. Main Outcomes and Measures Cardiovascular effectiveness as measured through 7 outcomes (major adverse cardiovascular events and glycemic control) and safety through 18 outcomes as highlighted by ADA guideline. Hazard ratios (HRs) are estimated separately for women and men using propensity score-stratified Cox models with empirical calibration. Sex differences were tested using Z-tests on log-HR differences. Results Drug initiation rates differed by sex with 9.28% of women initiating on GLP-1RA, 11.91% SGLT2i, 27.81% DPP4i, and 50.99% SU; the rates among the men were 5.41%, 12.84%, 24.64%, and 57.10%. No significant sex differences were observed for cardiovascular effectiveness outcomes. Several safety outcomes showed significant sex differences that are consistent across drug comparisons. Focusing on GLP-1RA compared to SGLT2i for brevity, GLP-1RA users experienced the following comparative benefits and risks: higher risk of acute pancreatitis among women (HR 1.39 [1.13, 1.70]) while non-differential risk among men (HR 0.91 [0.74, 1.12]) with p = 0.005 for the test of difference; non-differential risk of hypotension among women (HR 1.08 [0.98, 1.19]) while lower risk among men (HR 0.87 [0.78, 0.96]) with p = 0.003. Where no sex differences were found, our findings were consistent with existing evidence. Conclusions and Relevance This large-scale multinational study on antidiabetic agents identified clinically relevant sex differences, which are biologically plausible but previously lacked clinical evidence. Our findings reinforce the importance of tailoring T2D management according to sex.

Background and aims: Direct evidence to connect early life metabolism with cardiometabolic diseases in old age is limited due to the rarity of multi-decadal biochemical follow-up studies. To gain deeper insight into metabolic ageing, we conducted a longitudinal study that integrates serial data on clinical biomarkers, metabolomics and clinical events across the human life course. Methods: Children born in 1962-1992 were included from four European cohorts. Time-series of clinical biomarkers and metabolomics data were available for 8,653 participants (ages 0-49 years, 142 molecular and four physiological variables). Comparable data for 13,795 UK Biobank participants at two visits (ages 40-79 years) were linked with retrospective and prospective records of diabetes and cardiovascular disease. Lifetime metabolic trajectories were reconstructed by unsupervised machine learning and local polynomial regression. Results: A stable stratification in metabolic health emerged in children between ages 3 and 12 years and persisted to old age. We summarized this population pattern by assigning each participant into one of seven metabolic subgroups with characteristic biomarker trajectories. Two subgroups (MetDys TG+ and MetDys TG-) featured increased waist-height ratio from childhood, persistently higher C-reactive protein throughout life and rapidly increasing fasting insulin between 30 and 49 years of age. Both subgroups exhibited high risk for diabetes (HR > 13) and ischemic heart disease (HR > 2.5) when compared against the lowest risk subgroup (High HDL ApoB-). Conclusions: This life-course analysis shows that metabolic dysfunction associated with excess weight gain begins in early childhood and is associated with cardiometabolic morbidity in later life.

Objective: To investigate the association of the modified cardiometabolic index (MCMI) with cardiovascular-kidney-metabolic (CKM) syndrome staging, all-cause and cardiovascular mortality, and compare its predictive performance with traditional indices. Methods: This prospective cohort study included 5,189 adults with CKM syndrome (stages 0-4) from NHANES 1999-2018 (median follow-up 10.4 years). Associations were assessed using polynomial/ordinal logistic regression, Cox models, and restricted cubic splines. Mediation analysis explored diabetes' role. Competing risks (Fine-Gray), E-values, and sensitivity analyses ensured robustness. Predictive performance was compared using C-index and AUC. Results: MCMI showed a "decelerating increase" nonlinear association with CKM staging (adjusted OR=3.90, 95%CI: 3.38-4.50). For all-cause mortality, MCMI>3.5 exhibited a threshold effect (Q4 vs Q1: HR=1.412, 1.046-1.907); RCS curves identified MCMI<3.5 as a safety interval. For cardiovascular mortality, MCMI showed a fluctuating nonlinear pattern with low-risk (3.0-3.5) and high-risk (<2.5 or >4.0) intervals. Diabetes mediated 45.5% of MCMI-cardiovascular mortality risk (total HR=1.374, indirect HR=1.141). Competing risks revealed substantial underestimation of true effects (Q4 vs Q1 sHR=3.25, trend P<0.001). MCMI remained independently associated with all-cause mortality after extensive adjustments (HR=1.22, 1.05-1.40); E-values (1.73/1.29) indicated robustness. MCMI demonstrated superior predictive performance over CMI and TyG (mean AUC difference 0.0243). Conclusions: MCMI is an independent predictor of CKM progression and mortality. Its cardiovascular mortality risk is predominantly mediated by diabetes. MCMI>3.5 may serve as a clinical cut-off, outperforming traditional metabolic indices for CKM risk stratification. Keywords: modified cardiometabolic index, cardiovascular-kidney-metabolic syndrome, all-cause mortality, cardiovascular mortality, diabetes mellitus, competing risks model, cohort study, risk prediction

Aims: Although metabolic dysregulation is implicated in DCM, the involvement of metabolic syndrome (MetS) remains unclear. This study aims to systematically examine MetS in DCM pathogenesis. Materials and methods: By leveraging 378,837 UK Biobank participants, instead of the conventional binary MetS, we calculated a continuous metabolic risk score (MRS) and evaluated its influence on DCM risk within a multi-model evidence framework. Bidirectional weighted quantile sum regression identified key MRS components, a nested case-control study assessed 14-year pre-diagnosis MRS trajectories, mediation analyses evaluated MRS mediating lifestyle-DCM links and inflammation mediating MRS-DCM relationships, and Mendelian randomization (MR) evaluated causality for genetically predicted MetS and components on DCM. Results: During a median follow-up period of 13.4 years (interquartile range 12.7~14.1 years), 820 (0.2%) participants developed DCM. Higher MRS (HR=1.26 [1.18~1.34]) was associated with increased DCM risk, and such an association persisted across all robustness assessments even among non-MetS individuals. Waist circumference (WC, HR=1.36 [1.28~1.45], weight=0.58) and glycated hemoglobin (HR=1.23 [1.16~1.30], weight=0.22) dominated MRS' risk contribution. The trajectories of MRS diverged in cases approximately 10 years pre-diagnosis. MRS mediated 5.1~26.2% of lifestyle-related DCM risk, while inflammation mediated 16.4% of the MRS-DCM association. MR analysis further confirmed causal effects of MetS (OR=1.65 [1.45~1.88]), WC (OR=1.79 [1.58~2.03]) on DCM risk. Conclusions: Metabolic dysfunction, which was dominated by central adiposity and hyperglycemia, plays a key role in the occurrence of DCM. Early intervention targeting metabolic factors may prevent DCM onset.

Background & AimsDiabetes mellitus has been associated with both intestinal barrier dysfunction and peripheral neuropathy leading to increased risk of infection. The mucus layer forms a physical barrier against pathogens and is a critical component of the intestinal barrier that may be impaired in diabetes. This study aimed to assess how diabetes impacts goblet cells (GCs), mucus layer integrity, and innervation in the colon. MethodsFluorescence microscopy was used to investigate GCs, the mucus layer, and innervation in the colon of db/db mice. Custom open-access image analysis pipelines were developed to quantify GC numbers, location and content, mucus thickness, bacterial colonization, and innervation density in intestinal tissue sections. We also treated mice with the clinically used glucagon-like peptide 1 receptor (GLP-1R) agonist liraglutide to assess its capacity to reverse pathological changes to GCs and the mucus layer in a model of established type 2 diabetes (T2DM). ResultsThe mucus layer was significantly thinner in the colon of db/db mice with established diabetes and bacteria more readily colonized the epithelium and crypts. Intercrypt GC numbers were significantly reduced in db/db mice. However, there were significantly more GCs per crypt, and crypts were elongated in the db/db colon. Innervation was reduced in the mucosa and external muscle of the colon, consistent with diabetic neuropathic changes. Liraglutide treatment increased the size of GCs but had no effect on GC numbers, mucus thickness, or innervation in this model of established T2DM. ConclusionsMucus barrier dysfunction and GC hyperplasia is evident in the db/db colon. Increased microbial penetrability through the mucus layer suggests potential implications for the increased risk of gastrointestinal infection in diabetes.

Genome-guided dietary advice is a goal of precision nutrition. However, the contribution of gene-diet interactions (GxDs) to disease risk remains unclear, hindering the identification of diet-outcome pairs more likely amenable to genetic-based recommendations. We thus implemented a two-step approach: first, we comprehensively assessed the contributions of genome-wide GxDs to cardiometabolic outcomes across a broad array of dietary exposures in UK Biobank participants (N = 141,144 to 325,989). Second, we selected the 20 significant diet-outcome pairs from the 713 pairs tested (p < 7.0 x 10-5) and derived GxD polygenic scores. In an independent sample, all scores were nominally associated with their corresponding outcomes, with 12 of 20 polygenic scores Bonferroni significant (p < 0.0025). Further analyses revealed GxD polygenic scores were associated with clinical outcomes such as incident gout, suggesting translational potential. Altogether, these results showcase the promise of GxD scores to inform precision nutrition.

Background: Single-nephron glomerular filtration rate (GFR) represents a nephron-level functional index that may reveal key pathophysiological mechanisms driving progression in patients with diabetic nephropathy. However, its clinical relevance remains incompletely understood. This cross-sectional study assessed single-nephron estimated GFR (eGFR) across different chronic kidney disease (CKD) stages in patients with advanced diabetic nephropathy. Methods: Nephron number was estimated as the number of nonglobally sclerotic glomeruli per kidney using computed tomography-derived cortical volume combined with biopsy stereology. Single-nephron eGFR was calculated by dividing eGFR by the nephron number of both kidneys. Patients were stratified according to CKD stage at kidney biopsy. Associations between CKD stages and single-nephron eGFR were evaluated using multivariable linear regression models adjusted for age, sex, urinary protein excretion, and eGFR. Results: The study included 105 patients with biopsy-proven diabetic nephropathy and overt proteinuria (median age 59 years, 83% male, HbA1c 6.6%, 57% had nephrotic range proteinuria). The percentage of globally sclerotic glomeruli, mesangial expansion score, and prevalence of nodular lesions increased significantly with advancing CKD stage. Median nephron number declined from 529,178 to 224,458 per kidney, whereas glomerular volume remained constant. Single-nephron eGFR decreased markedly with CKD stage and remained significantly inversely associated with CKD stage after adjustment for clinicopathologic covariates (P for trend <0.001). Conclusion: In overt diabetic nephropathy, single-nephron eGFR decreased with advancing CKD stage, despite relatively preserved glomerular volume. At this stage of disease, structural alterations specific to diabetic nephropathy may impair effective single-nephron filtration capacity.

BackgroundMetabolically healthy obesity (MHO) is unstable, with up to 80% of individuals progressing to metabolically abnormal obesity (MAO), yet mechanisms underlying this transition remain unclear. African Americans bear a disproportionate burden of obesity-related cardiovascular disease. Circulating extracellular vesicles (EVs) mediate inter-organ communication and may drive MAO-related vascular dysfunction. MethodsAdults of African ancestry were classified as metabolically healthy lean (MHL, n=14), MHO (n=9), or MAO (n=16). Plasma-derived EVs were characterized and their microRNA cargo profiled. Human coronary artery endothelial cells were treated with EVs from each group to assess nitric oxide signaling, oxidative stress, inflammatory activation, and mitochondrial dynamics. ResultsMHO participants exhibited preserved insulin sensitivity and lower inflammation compared with MAO despite comparable adiposity. EVs from MHO carried a distinct microRNA signature enriched in miR-148a-5p, miR-181c-5p, and miR-1255a, linked to antioxidant and matrix regulatory pathways. MAO EVs were enriched in miR-3613-3p, miR-6842-3p, and miR-326, targeting inflammation and insulin resistance pathways. Compared with both MHL and MHO EVs, MAO EVs suppressed endothelial nitric oxide synthase phosphorylation and reduced nitric oxide bioavailability, with increased reactive oxygen species and ICAM-1 expression. MHO EVs induced an intermediate phenotype with disrupted mitochondrial morphology, supporting a graded continuum of endothelial stress. ConclusionsMHO represents a biologically active intermediate state. Circulating EVs from MHO individuals convey molecular signals that impair endothelial and mitochondrial function, predisposing to vascular injury and progression toward MAO. EV-associated microRNAs are mechanistic mediators and candidate biomarkers of metabolic and vascular deterioration in obesity. CLINICAL PERSPECTIVEO_ST_ABSWhat Is New?C_ST_ABSO_LIThis study systematically investigated extracellular vesicles derived from metabolically healthy obese individuals to define direct vesicle effects on endothelial function using integrated omics coupled to functional outputs. C_LIO_LIExtracellular vesicles from metabolically healthy obesity convey a distinct molecular and biological signature that distinguishes lean and metabolically abnormal obesity. C_LIO_LIMetabolic health status, rather than obesity alone, drives extracellular vesicle-mediated endothelial nitric oxide signaling, oxidative stress, inflammation, and mitochondrial dynamics. C_LI What Are the Clinical Implications?O_LIThese findings explain why some individuals with obesity exhibit preserved vascular function while others develop early endothelial dysfunction. C_LIO_LIStratifying obesity by metabolic health status improves cardiovascular risk assessment beyond body mass index alone. C_LIO_LITargeting extracellular vesicle signaling pathways represents a novel strategy to prevent metabolically healthy individuals from progressing to metabolically abnormal obesity. C_LI

The ability to understand and affect the course of complex, multi-system diseases like diabetes has been limited by a lack of well-designed, high-quality and large multimodal datasets. The NIH Bridge2AI AI-READI project (aireadi.org) aims to address this shortfall by generating an AI-ready dataset to support AI discoveries in type 2 diabetes mellitus (T2DM). This manual of procedures provides a detailed description of the AI-READI protocol.

Variant databases ClinVar and gnomAD are the backbone of clinical variant interpretation, but their population composition is skewed toward European ancestry. Whether this skew creates systematic classification disadvantages for non-European patients with monogenic diabetes has not been examined at the database level. ClinVar variant_summary (GRCh38, April 2026; 4,421,188 variants) was cross-referenced with gnomAD v4.0 genome data for 17 monogenic diabetes genes. Annotation coverage and variant classification rates were computed stratified by genetic ancestry group (AFR, AMR, EAS, SAS, MID, NFE, FIN, ASJ). Of 14,691 gnomAD variants across the 17 genes, only 29.7% had any ClinVar classification (range: 12.7%-61.3% by gene). Among classified variants, non-Finnish European (NFE) variants had the highest variant of uncertain significance (VUS) rate (32.1%) and the lowest benign/likely benign fraction (41.6%), consistent with a large submission volume without functional follow-up. African-ancestry (AFR) variants showed the second-highest VUS rate (29.2%), not statistically distinguishable from NFE after Bonferroni correction, while all other non-European groups had significantly lower rates (all p < 0.001). GCK showed a pattern inversion - non-European VUS rate (18.5%) exceeding European (15.0%) - consistent with progressive reclassification in European populations absent in non-European cohorts. Annotation coverage and VUS divergence were uncorrelated (r = -0.15, p = 0.57). The primary equity problem is a 70% annotation gap combined with a non-European curation deficit, not a simple VUS excess. Ancestry-stratified evaluation of ClinGen Variant Curation Expert Panel (VCEP) criteria performance is warranted across disease domains.