Diabetes Care

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.

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.

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.

IntroductionDysglycemia is a well-established risk factor for cardiovascular disease (CVD); yet traditional glycemic traits, including fasting plasma glucose (FPG) and HbA1c, do not capture dynamic glucose fluctuations that may inform CVD risk. We cross-sectionally investigated the association of continuous glucose monitor (CGM)-derived metrics and 2-h post-prandial glucose (2-h PPG) with estimated 10-year CVD risk among individuals without diabetes. MethodsWe included 1,360 Framingham Heart Study participants (Third Generation, New Offspring Spouse, and Omni 2 cohorts at exam 4) without prevalent diabetes or CVD who had [&ge;]3 days of CGM data and completed a mixed meal tolerance test (MMTT) with corresponding 2-PPG. We included 7 CGM summary metrics and defined data-driven glucotypes according to CGM measures of glycemic burden and variability. The 10-year CVD risk was estimated using the Predicting Risk of CVD EVENTs (PREVENT) base equations. We performed linear regression on standardized glycemic traits and glucotypes with log-transformed PREVENT risk scores and multinomial regression to relate standardized CGM metrics and 2-h PPG with PREVENT categories (low <5%[reference], borderline 5-<7.5%, intermediate/high [&ge;]7.5%). All models were adjusted for FPG and body mass index (BMI). ResultsAmong participants (55.9% women, 43.4% with prediabetes), mean age was 59.3 years, and mean BMI was 27.9 kg/m2. All CGM-derived metrics and 2-h PPG were positively associated with higher overall 10-year CVD risk (per 1 SD increase of each exposure variable, {beta} range: 0.06-0.16, all p<0.001). A glucotype representing high glycemic burden and high glycemic variability was associated with higher overall 10-year CVD risk, compared with the glucotype representing low glycemic burden and low glycemic variability. Higher CGM-derived metrics and 2-h PPG were also associated with higher odds being in the intermediate/high CVD risk (OR range: 1.20-1.65, all p<0.001), adjusting for FPG and BMI. ConclusionDynamic glycemic traits, including novel glucotypes that capture glycemic burden and variability, may provide novel insights into CVD risk prevention among individuals without T2D.

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.

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

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

BackgroundGLP-1 receptor agonists (GLP-1RAs) and SGLT2 inhibitors (SGLT2Is) have established cardiovascular benefits for patients with type 2 diabetes mellitus (T2DM), with similar class-level effectiveness found in previous studies. However, real-world comparative effectiveness assessments of individual agents remain limited. ObjectivesTo compare the cardiovascular effectiveness of individual GLP-1RAs and SGLT2Is. MethodsWe conducted a multi-national, retrospective, new-user active-comparator cohort study using 10 US and non-US administrative claims and electronic health record databases. The study included 1,245,211 adults with T2DM receiving metformin who initiated second-line therapy with one of six GLP-1RAs (albiglutide, dulaglutide, exenatide, liraglutide, lixisenatide, semaglutide) or one of four SGLT2Is (canagliflozin, dapagliflozin, empagliflozin, ertugliflozin). Empagliflozin (393,499; 31.6%), semaglutide (235,585; 18.9%), dapagliflozin (208,666; 16.8%), and dulaglutide (207,348; 16.8%) were most commonly used. A secondary subgroup analysis included 316,242 patients with established cardiovascular diseases (CVD). Primary outcomes were 3-point major adverse cardiovascular events (MACE: acute myocardial infarction, stroke, sudden cardiac death) and 4-point MACE (adding hospitalization/ER visit with heart failure). Secondary outcomes included the individual components. Hazard ratios (HRs) were estimated for pairwise agent comparisons while on-treatment (per-protocol) and over total follow-up using Cox proportional hazards models, with propensity score adjustments, negative control calibration, and pre-specified study diagnostics to guard against potential confounding. Random-effects meta-analysis produced summary HR estimates across data sources that passed diagnostics. ResultsAcross the study cohort, individual GLP-1RAs and SGLT2Is demonstrated broadly similar cardiovascular effectiveness, both within and across drug classes. For example, semaglutide and empagliflozin showed comparable risks for 3-point MACE (meta-analytic HR 1.05; 95% CI 0.79-1.39) and 4-point MACE (meta-analytic HR 0.95; 95% CI 0.81-1.12), with consistent findings in the CVD subgroup. Study diagnostics confirmed adequate equipoise, covariate balance and statistical power to detect similarity in HRs between 0.8 and 1.2 for commonly used agents. ConclusionsIn this large-scale real-world study, individual GLP-1RAs and SGLT2Is exhibited largely comparable cardiovascular benefits, including in patients with established CVD. These findings align with network meta-analytic estimates from major cardiovascular outcome trials and broadly support current treatment guidelines. Clinical choices should be guided by relevant factors such as safety, adherence, tolerability, cost, and patient preference, where further work is needed.

ObjectivesTo estimate potential launch prices of generic semaglutide following patent expiry from 2026 and to quantify the global obesity and type 2 diabetes (T2DM) burden in countries where generic access may become possible. MethodsWe used World Bank population data and World Obesity and Diabetes Atlas prevalence estimates to calculate obesity and T2DM burden in countries where semaglutide patents expire in 2026 or were not filed. Patent status was identified using MedsPaL and cross-checked with regional databases. We updated established cost-plus pricing methodologies using 2024-2025 Indian API shipment data to estimate production costs for oral and injectable semaglutide, incorporating formulation, packaging, taxation, and profit assumptions. ResultsTen countries with 2026 patent expiry represent 44% of the global population and 48% of the global obesity burden. No patent filings were identified in 150 additional countries. By the end of 2026, generic injectable semaglutide could be distributed in 160 countries where 69% of global T2DM and 84% of clinical obesity occurs. Estimated generic injectable costs ranged from $28-140 per person-year, while oral formulations ranged from $186-380 per person-year. Injection devices contributed disproportionately to total cost. ConclusionPatent expiry could substantially expand access to semaglutide at dramatically lower prices, particularly in high-burden settings. However, device costs, secondary patents, and health system constraints may limit equitable uptake without coordinated policy action. Study ImportanceO_ST_ABSWhat is already known about this subject?C_ST_ABSO_LISemaglutide is highly effective for obesity and cardiometabolic disease but remains unaffordable in many low- and middle-income countries due to high branded prices and patent protections. C_LIO_LIPrevious cost-plus analyses show that generic competition can substantially reduce prices of essential medicines after patent expiry. C_LI What are the new findings in your manuscript?O_LIUsing 2024-2025 API shipment data, we estimate generic injectable semaglutide could be produced for $28-140 per person-year following 2026 patent expiry. C_LIO_LIBy 2026, generic semaglutide could be available in 160 countries comprising 69% of global T2DM and 84% of clinical obesity burden. C_LI How might your results change the direction of research or the focus of clinical practice?O_LIProvides an evidence base for procurement planning and price negotiations ahead of patent expiry. C_LIO_LIHighlights the importance of addressing device costs and secondary patents to ensure equitable global access. C_LI

Uric acid (UA) is traditionally regarded as a metabolic risk marker; however, its dynamic behavior during glucose-lowering therapy remains incompletely understood. We compared UA responses to a modified traditional Japanese diet (MJDD) and the DPP-4 inhibitor alogliptin in patients with early-stage type 2 diabetes mellitus (T2DM). In this prospective observational study, drug-naive patients received MJDD (n=58) or alogliptin (n=52) monotherapy for 3 months. Changes ({Delta}) in serum UA were analyzed in relation to glycemic control, insulin resistance, adipose tissue insulin resistance (adipo-IR), and beta-cell function. Both interventions significantly reduced fasting blood glucose and HbA1c while paradoxically increasing serum UA and HOMA-B. Baseline UA was the primary determinant of {Delta}UA in both cohorts. MJDD significantly reduced body mass index, insulin, free fatty acids, HOMA-R, and adipo-IR, with effects most pronounced in subjects with baseline BMI >25. In contrast, alogliptin selectively reduced adipo-IR in leaner subjects (BMI <25). Across both treatments, {Delta}UA correlated positively with {Delta}HOMA-B and inversely with {Delta}HbA1c. Notably, during MJDD, {Delta}UA showed a paradoxical negative correlation with {Delta}BMI and {Delta}FBG, and a positive correlation with {Delta}FFA. Patients exhibiting the greatest UA increases demonstrated the most marked improvements in beta-cell function and, with MJDD, the greatest weight loss. These findings indicate that MJDD and alogliptin exert distinct metabolic effects in early T2DM, yet both link rising UA to enhanced beta-cell function, suggesting that UA may serve as a dynamic pharmacometabolic biomarker reflecting therapy-specific metabolic adaptation rather than metabolic deterioration.

ObjectivesTo characterize ultra-processed food (UPF) circulating metabolic signatures associated with Crohns disease (CD) and to localize key metabolic mediators linking UPF intake to CD risk. DesignProspective cohort study. SettingTwo large multi-center cohorts (UK Biobank [UKB] and Whitehall II [WHII] study) across the UK and an Eastern multi-center cohort ONE-IBD Study from China. ParticipantsUK Biobank discovery cohort (n=10,229) for signature derivation, internal validation cohort (n=91,306), external validation cohort Whitehall-II (n=7,893), and three additional cohorts (two Western and ONE-IBD) for validation of key metabolic drivers. Main outcome measuresPrimary outcomes were UPF-related circulating metabolic signatures and their associations with CD risk; secondary outcomes included evidence supporting causal roles of candidate metabolites and genetic pathways assessed by Mendelian randomization, colocalization, and gene-environment analysis. ResultsA UPF metabolic signature of 73 metabolites was constructed and validated across cohorts (Spearman {rho}: 0.20-0.25). More pronounced UPF metabolic signature was associated with increased CD risk (HRper SD=2.65, 95% CI 1.57-4.48). WGCNA revealed a cluster enriched in fatty acids. Within this cluster, docosahexaenoic acid (DHA) emerged as the strongest, which mediated 17.1% of the UPF-CD association. External validation in ONE-IBD supported DHA as the strongest associated metabolite with UPF and CD. Mendelian randomization supported a causal protective effect of DHA on CD (OR=0.72, 95% CI 0.61- 0.83; P<0.001), with colocalization implicating rs174546 in the FADS1 gene. ConclusionThe adverse effects of UPF on CD risk may be driven by a relative deficiency of protective metabolites such as DHA, apart from additive harm to metabolic depletion. This reframes UPF-related risk and highlighting potential targets for precision nutrition in CD prevention.

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.

Background and AimsThe glucagon-like peptide-1 receptor agonist (GLP-1 RA) semaglutide has demonstrated efficacy for the secondary prevention of cardiovascular disease among patients with overweight/obesity without diabetes mellitus. However, the comparative effectiveness of GLP-1 RA versus other antiobesity medications (e.g. phentermine-topiramate) not been evaluated. MethodsThis was a retrospective, observational, cohort study using target trial emulation methodology using the Truveta electronic health record database of more than 120 million patients. Adult patients with a body mass index (BMI) >=27 kg/m2, a history of cardiovascular disease (prior ischemic stroke, transient ischemic attack, or myocardial infarction, or known coronary artery disease, heart failure, or peripheral artery disease) without diabetes mellitus were included in the study. The primary endpoint was time to first major adverse cardiovascular or cerebrovascular event (MACCE, defined as stroke or myocardial infarction). ResultsIn total, 35,240 were included in the bupropion-naltrexone versus GLP-1 RA comparison, and 27,051 were included in the phentermine-topiramate versus GLP-1 RA comparison. In the pre-weighting cohort, GLP-1 RA use was associated with decreased hazard of MACCE compared to bupropion-naltrexone (HR 0.50 [95% confidence interval (CI) 0.36-0.69]) and phentermine-topiramate (HR 0.43 [95% CI 0.30-0.60]). In the propensity score-overlap weighted cohort, GLP-1 RA prescription was not associated with a lower hazard of MACCE than bupropion-naltrexone (aHR 0.69 [95% CI 0.47-1.00]) but was associated with a lower hazard compared to phentermine-topiramate (aHR 0.61 [95% CI 0.41-0.91]; adjusted absolute rate difference 0.98 per 1000 person-years). ConclusionsPrescription of a GLP-1 RA was associated with a lower risk of subsequent MACCE than phentermine-topiramate.

BackgroundWhile multi-omics approaches, incorporating polygenic risk scores (PRS), metabolomics, and proteomics have shown promise in predicting major adverse cardiovascular events (MACE), their added value beyond cardiovascular disease (CVD) risk factors remains underexplored. We aimed to assess whether integrating multi-omics biomarkers into the SCORE2 model improves the prediction of MACE in apparently healthy individuals. MethodsThis study included 24,042 UK Biobank participants without CVD or diabetes mellitus, aged 40-69 years. Multi-omics biomarkers were fitted in sex-specific models including the variables of SCORE2 and 9 metabolites, 12 proteins, and a PRS for CVD in males, as well as 7 metabolites, 11 proteins, and a PRS for CVD in females. The performance of the SCORE2 model and its multi-omics extensions was compared using Harrells C-index and the net reclassification index (NRI) in a training and test set (70% and 30% of study population). ResultsIn 10-year follow-up, 1,204 MACE events occurred. Integrating multi-omics biomarkers into SCORE2 significantly improved the predictive performance (C-index: 0.708 to 0.769, P<0.001; NRI=26.2%). In males, the C-index improved from 0.682 to 0.752 ({Delta}C-index=+0.070, P<0.001; NRI=12.4%), while in females, it increased from 0.724 to 0.782 ({Delta}C-index=+0.058, P<0.001; NRI=30.4%). However, full multi-omics measurements may not be needed because the combination of proteomics and PRS yielded comparable performance in males (C-index=0.749) and females (C-index=0.782). ConclusionsIntegrating a protein panel and a PRS significantly improves MACE risk prediction by the SCORE2 model, which includes HDL and total cholesterol. Adding further metabolites has limited additional predictive value.

Background and AimMASLD affects 30-38% of Indian adults, yet the contribution of genetic risk variants to disease susceptibility and fibrosis progression remains poorly characterised. We investigated the association of 12 candidate SNPs with MASLD susceptibility and fibrosis severity in North Indian patients, benchmarking allele frequencies against IndiGenomes and global populations. MethodsSixty-nine MASLD patients (75.4% male; median BMI 29.8 kg/m{superscript 2}) from a tertiary care liver clinic in New Delhi were genotyped for 12 SNPs using Illumina custom BeadChip array and Sanger sequencing. Patients were stratified by liver stiffness measurement (LSM): significant fibrosis ([&ge;]8 kPa, n=38) versus no significant fibrosis (<8 kPa, n=31). Allele frequencies were compared with IndiGenomes ([~]1,020 Indian individuals) and 1000 Genomes populations. ResultsPNPLA3 rs738409 G allele was the strongest within-cohort predictor of significant fibrosis (allelic OR 2.89, 95% CI 1.35-6.19, P=0.006; dominant model OR 3.94, P=0.008), with carriers demonstrating higher LSM (median 15.6 vs. 7.5 kPa, P=0.005). SAMM50 rs3761472 (OR 2.12, P=0.065) and FTO rs9939609 (OR 2.08, P=0.089) showed non-significant trends. In the population-level comparison, APOC3 rs2854116 T allele was the only variant significantly enriched after Bonferroni correction (64.0% vs. 47.9%; OR 1.93, 95% CI 1.35-2.77, P<0.001), followed by PNPLA3 (33.3% vs. 24.1%, OR 1.57, P=0.019) and SAMM50 (31.2% vs. 22.6%, OR 1.55, P=0.028). Notably, APOC3 showed no association with fibrosis (OR 0.96, P=1.000), suggesting a role in susceptibility rather than progression. All SNPs were in Hardy-Weinberg equilibrium. ConclusionsThis study reveals a dissociation between genetic determinants of MASLD susceptibility and fibrosis progression in North Indian patients. APOC3 rs2854116 predisposes to MASLD at the population level, while PNPLA3 rs738409 drives fibrosis severity within established disease, underscoring the need for ancestry-specific genetic risk stratification. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=112 SRC="FIGDIR/small/26347059v1_ufig1.gif" ALT="Figure 1"> View larger version (69K): org.highwire.dtl.DTLVardef@187f189org.highwire.dtl.DTLVardef@25d3borg.highwire.dtl.DTLVardef@13704e9org.highwire.dtl.DTLVardef@1238cce_HPS_FORMAT_FIGEXP M_FIG C_FIG

Both short and long sleep duration have been associated with poor glycemic control and an increased risk of developing type 2 diabetes mellitus. Although sleep duration may differentially modify the effects of genetic risk factors for type 2 diabetes, this has not been systematically investigated. In the present study, we conducted genome-wide gene by sleep duration meta-analyses, separately assessing interactions of short and long sleep, for fasting glucose, fasting insulin, and hemoglobin A1c in up to 489,309 individuals without diabetes from seven different population groups. In total, 16 loci were identified to interact with sleep duration -- six with short sleep and ten with long sleep. Of these, four loci were identified through cross-population meta-analysis. Mapped genes exhibit pathway connections to pericyte apoptosis, NMDA receptor activity, the GLUT1 receptor, neurological health, and sleep architecture. Eleven loci (VRK2, PCDH7, TFAP2A, CAP2, PAPPA, ZCCHC2, MYH9, SGIP1, JAKMIP3, RRAS2, MAPT) have not been reported in previous glycemic trait genome-wide association studies. Interaction loci identify divergent biological mechanisms for short and long sleep duration influencing glycemic control, suggesting specific pathways of intervention for precision medicine approaches to diabetes prevention and management. Article HighlightsO_LIThe biological mechanisms of how sleep duration impacts type 2 diabetes pathogenesis and glycemic control are unclear. C_LIO_LIThis study reveals 16 loci (11 novel) that interact with either short or long sleep duration to influence hemoglobin A1c, fasting glucose, or fasting insulin. Short and long sleep duration loci were non-overlapping. C_LIO_LIRegulation of copper and diacylglycerol levels appear as distinct cellular mechanisms implicated by long and short sleep duration loci respectively. C_LIO_LIIdentified gene targets present insight for potential type 2 diabetes therapeutic design approaches related to JIP1-JNK interaction disruption, pericyte health, NMDA receptor activity, anti-inflammatory and leptin-enhancing dietary supplements, and serpins. C_LI

BackgroundGestational exposures may contribute to the newborns lifetime risk of inflammatory bowel disease (IBD). While gestational influences are associated with IBD onset, the causality and confounding of such exposures are difficult to ascertain. The neonatal metabolome provides a metabolic snapshot of gestational influences. ObjectiveWe tested the neonatal metabolomes ability to predict future IBD, to assess whether gestational exposures are reflected in early molecular precursors of the disease. MethodsWe profiled dried blood spots from 520 newborns who later developed IBD and matched controls using high-resolution untargeted mass spectrometry metabolomics (1,350 QC-passing metabolites). Genotyping was available for 1,009 of these individuals. PERMANOVA confirmed assay sensitivity to gestational exposures, gradient boosting was used for prediction. ResultsThe neonatal metabolome significantly captured maternal smoking, birth weight, and gestational age (p < 0.001), but explained minimal variance in IBD status (R2 = 0.09%, p = 0.390) and showed no predictive power for IBD (AUC = 0.51, 95% CI 0.50-0.52, p = 0.585). Stratifying by disease subtype and age of onset did not improve performance. In contrast, genetic risk scores were modestly predictive (CD: AUC = 0.64, p < 5.11x10-14; UC: AUC = 0.63, p < 7.65x10-{superscript 1}{superscript 2}), but uncorrelated with neonatal metabolomic profiles (CD: p = 0.650; UC: p = 0.970), suggesting a later-age effect. ConclusionsUsing a large, comprehensively profiled cohort, we demonstrate that neonatal metabolomic profiles sensitively capture gestational signatures, but not the overall future IBD risk. Our findings suggest that most IBD risk accumulates later in life, beyond gestational molecular imprints.

BackgroundFatal insulin intoxication remains difficult to diagnose because insulin undergoes rapid degradation after death, limiting the reliability of direct biochemical measurements. This creates diagnostic uncertainty when objective molecular confirmation of insulin excess are required. We hypothesised that insulin excess induces systemic metabolic alterations that persist beyond insulin degradation and can be captured using postmortem metabolomics in a forensic setting. MethodsHigh-resolution mass spectrometry (HRMS)-based metabolomics was applied to a national cohort comprising 51 fatal insulin intoxications. Orthogonal partial least squares-discriminant analysis (OPLS-DA) models were trained on cases collected between 2017-2022 to identify insulin-associated metabolite features using a shared-and-unique-structures approach. Performance was evaluated using two temporally distinct test sets (2023-2024): a matched validation cohort and a heterogeneous forensic cohort reflecting biological variability. ResultsHere we show that an insulin-associated metabolomic fingerprint comprising 91 features demonstrated reproducible discrimination across independent cohorts. In the matched cohort (n=59, including 14 insulin cases), insulin intoxication classification achieved 100% sensitivity and 73% specificity within the applicability domain. In the heterogeneous cohort (n=154, including 14 insulin cases), 100% sensitivity was maintained with a 72% specificity despite increased biological variability. Univariate analyses demonstrated significant alterations across multiple metabolite classes, including acylcarnitines, fatty acids/lipids, and purine/nucleoside metabolites, with moderate effect sizes, consistent with systemic effects of insulin-induced hypoglycaemia. ConclusionsFatal insulin intoxication is associated with a reproducible metabolomic fingerprint detectable after death. These findings demonstrate that postmortem metabolomics may serve as a complementary decision-support tool when conventional biomarkers are unreliable.

Outcomes from the COSMOS trial have reinforced the notion of flavanols as important plant-derived bioactives contributing to cardiovascular health. As discussions continue on whether specific dietary reference values for flavanols are warranted, it is possible that existing dietary guidelines emphasizing fruits and vegetables already yield sufficient flavanol intake levels. If this were the case, developing flavanol specific dietary reference values might be unnecessary. This study therefore aimed at assessing whether adherence to dietary recommendations for fruit and vegetable intake and overall diet quality achieves flavanol intake levels of 500 mg/day, the amount proven to mediate cardiovascular benefits in the COSMOS trial. Flavanol intake was objectively evaluated using two validated and complementary biomarkers, 5-(3{square},4{square}-dihydroxyphenyl)-{gamma}-valerolactone metabolites (gVLMB) and structurally related (-)-epicatechin metabolites (SREMB), in two geographically distinct studies: COSMOS (US; n=6,509) and EPIC-Norfolk (UK; n=24,154). The results showed that higher fruit and vegetable intakes and diet quality (assessed via the alternative healthy eating index-aHEI) were associated with increased flavanol intake in COSMOS. Nevertheless, fewer than 25% of participants meeting dietary guidelines achieved an estimated flavanol intake of [&ge;]500 mg/day. Similar findings were observed in EPIC-Norfolk as well as through flavanol intake simulations considering fruits and vegetables commonly consumed in the US diet. In conclusion, adherence to existing dietary guidelines does not yield flavanol intake levels comparable to those shown to provide cardiovascular benefits in COSMOS. Thus, specific dietary reference values for flavanols may still be necessary if aiming to increase the intake of these dietary compounds. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=101 SRC="FIGDIR/small/26346949v1_ufig1.gif" ALT="Figure 1"> View larger version (39K): org.highwire.dtl.DTLVardef@24faeaorg.highwire.dtl.DTLVardef@1d52a29org.highwire.dtl.DTLVardef@1c2ff33org.highwire.dtl.DTLVardef@100a384_HPS_FORMAT_FIGEXP M_FIG C_FIG

Background and ObjectivesRecent large-scale studies have consistently linked healthy dietary patterns to improved cardiometabolic health; however, the underlying biological pathways remain largely unclear, especially in non-European populations. In this study, we leverage data from four population-based cohorts (UK Biobank, NEO study, GNHS, and 10K) to investigate both common and cohort-specific biological pathways linking healthy dietary patterns to cardiometabolic disease through multi-omics profiling. Material and methodsIn each cohort, we first assessed the associations between each of the five major dietary pattern scores (i.e., AMED, hPDI, DII, AHEI, and EDIH) and cardiometabolic disease risk using Cox or logistic regression models. To explore the potential mediating role, metabolomics and proteomics measurements were incorporated into the models. All models were adjusted for relevant confounders, and false discovery rate correction was applied to account for multiple testing. ResultsWith a total of 71,679 individuals without pre-existing cardiometabolic disease across four participating cohorts (UKB: 54,024, NEO: 4,838, GNHS: 3,201, and 10K: 9,616), we confirmed that adherence to healthy dietary patterns was associated with a 5-10% reduced risk of cardiometabolic disease. Three common biological pathways were identified: (1) mediation via large HDL particles and apolipoprotein F; (2) mediation via DNAJ/Hsp40 and triglyceride-rich lipoproteins; and (3) mediation via CRHBP-regulated HPA axis activity affecting triglyceride-rich lipoproteins. ConclusionsOur integrative multi-omics analysis across diverse populations identifies novel biomarkers that connect healthy dietary patterns with cardiometabolic risk. These findings deepen our understanding of the biological mechanisms underlying diet-related disease and hold promise for enhancing the development of precision nutrition interventions.