Diabetes Care

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

ObjectivesIt is not known which clinical features optimally differentiate type 1 and 2 diabetes at diagnosis. We aimed to determine which clinical features differentiate adult-onset type 1 and 2 diabetes at diagnosis and develop classification models combining these features with and without islet-autoantibodies. DesignA prospective cohort study with prediction model development and validation. SettingUK primary and secondary care. Participants1800 adults ([≥]18 years) diagnosed with diabetes in the previous 12 months, excluding known secondary or monogenic diabetes. Main outcome measuresType 1 and 2 diabetes defined by a combination of insulin treatment and endogenous insulin production (measured using C-peptide) assessed [≥]three years after diabetes diagnosis. ResultsEleven clinical features and routinely measured biomarkers discriminated type 1 from type 2 diabetes independently of diagnosis age and BMI. Lower age-at-diagnosis, BMI and waist-hip ratio, unintentional weight-loss, and higher presentation HbA1c or glucose were the most discriminative features, with other features only weakly discriminative. Models integrating clinical features with and without islet-autoantibodies, developed in those age 18-50 years at diabetes diagnosis, had high performance in internal validation (clinical features only: AUCROC (95% CI) 0.94 (0.93, 0.96), clinical features and islet-autoantibodies: AUCROC 0.97 (0.96, 0.98)), and maintained high discrimination in older adults (age >50 at diagnosis; clinical features only: AUCROC 0.93 (0.90, 0.96), clinical features and islet-autoantibodies: AUCROC 0.97 (0.94, 0.99)). Simplifying the models to a point-based score (the StartRight Score) resulted in similar performance. These models had higher performance than current clinical guidance. In UK primary care data models were strongly predictive of outcomes associated with type 1 diabetes, including in those initially treated as type 2 diabetes. ConclusionsLower age-at-diagnosis, BMI, and wait-hip ratio, unintentional weight-loss and high presentation glycaemia are the most discriminative features for diagnosis of type 1 diabetes in adults. Models combining routine clinical features, with or without islet-autoantibodies, have high accuracy and could assist clinical classification and prioritisation of classification biomarker testing. Study registrationhttps://clinicaltrials.gov/study/NCT03737799 Summary boxesO_ST_ABSSection 1: What is already known on this topicC_ST_ABSO_LIMost type 1 diabetes occurs in adults, but differentiating it from type 2 diabetes, which is much more common, is challenging, and misclassification is common. C_LIO_LIAge-at-diagnosis and BMI are currently the only clinical features robustly shown to distinguish between type 1 and type 2 diabetes at diagnosis; many other features included in textbooks and guidelines have little supporting evidence. C_LIO_LIGuideline bodies, including the UK National Institute for Health and Care Excellence (NICE), have identified a need for evidence on what features discriminate type 1 and 2 diabetes in adults, and how these features can be combined to improve diagnosis. C_LI Section 2: What this study addsO_LIThis is the first study to prospectively assess the utility of clinical features for diabetes subtype at diagnosis. C_LIO_LIThe five most discriminative routine clinical features for distinguishing type 1 from type 2 diabetes at diagnosis are age-at-diagnosis, BMI, waist-hip ratio, pre-diagnosis unintentional weight-loss, and presentation glycaemia (HbA1c or glucose). C_LIO_LIMany features included in current guidelines were only very weakly discriminative of subtype, and no single clinical feature was able to adequately differentiate between type 1 and type 2 diabetes alone. C_LIO_LIA clinical prediction model combining ten routinely available clinical features, with or without islet-autoantibodies, as both a prototype calculator and a points-based score (the StartRight Score), had high accuracy in differentiating type 1 from type 2 diabetes and outperforms current clinical guidance and islet-autoantibody assessment alone. C_LI

BackgroundType 2 diabetes (T2D) represents a major global health burden, with over 700 GWAS loci identified. Translation to biological mechanisms remains challenging. This study employs systematic post-GWAS functional annotation to characterize the RPTOR locus, encoding Raptor, a scaffold protein critical for mTORC1 signaling and beta-cell function. MethodsWe analyzed 31 GWAS credible sets containing rs12950541 (chr17:80760693 G>A) using Open Targets Platform v24.12, encompassing 20 metabolic traits. L2G scoring, colocalization analysis, and QTL mapping in GTEx v8 were performed. Independent Variant Effect Predictor (VEP) analysis of the linkage disequilibrium (LD) block was conducted to characterize all variants in LD (D [≥] 0.7) with rs12950541. RNA-protein interaction networks were predicted using RNAct/catRAPID for key RPTOR transcripts and functionally enriched using ToppGene. Drug target and novelty analyses were performed using ChEMBL, PubMed, and ClinicalTrials.gov databases. Phenome-wide associations and regulatory annotations were obtained from the T2D Knowledge Portal. ResultsRPTOR was consistently ranked #1 L2G gene across all 31 credible sets (mean score 0.428, range 0.383-0.503). T2D showed strong GWAS-GWAS colocalizations (H4>0.8) with adiposity traits. Skeletal muscle demonstrated strongest QTL evidence with sQTL at P=1.21x10-16 and multiple eQTLs/tuQTLs. Critically, zero GWAS-QTL colocalizations and zero QTL in pancreatic islets, adipose, or liver highlight an "eQTL gap." VEP analysis of 140 LD partners revealed exclusively non-coding variants (100% MODIFIER impact), including 24 regulatory region variants and 2 transcription factor binding site variants. RNAct analysis revealed that the NMD transcript RPTOR-208 shows stronger RNA-protein interactions than the canonical transcript, with predicted binding partners including sulfonylurea receptors (ABCC8/ABCC9), IGF1R, and chromatin remodelers, enriched for glucose-mediated signaling and SWI/SNF complex pathways. ABCC8 is confirmed as the molecular target of sulfonylurea drugs (ChEMBL: CHEMBL2071), and literature analysis confirms that the RPTOR-ABCC8 RNA-protein interaction is completely novel, with no prior publications linking RPTOR transcript biology to sulfonylurea receptor function. T2DKP PheWAS confirmed 78 significant associations across 18 phenotype groups, revealing effects on acute insulin response, insulin sensitivity, HDL cholesterol, hepatic enzymes, and sleep traits, with transcription factor binding analysis showing that rs12950541 directly enhances p300 enhancer marking while reducing CTCF insulator binding. ConclusionsSeven convergent lines of evidence support rs12950541 as a strong candidate regulatory variant at RPTOR. Integration of post-GWAS annotation, VEP characterization, RNA-protein interaction networks, and translational drug target analysis converges on a regulatory mechanism involving splicing, chromatin remodeling, and metabolic signaling pathways. The novel predicted interaction between RPTOR-208 and ABCC8/ABCC9 suggests a previously unrecognized molecular bridge between mTORC1 signaling and KATP channel-mediated insulin secretion, with potential implications for understanding sulfonylurea-mTOR pathway crosstalk in T2D.

Objective: Despite the complex and non-linear progression of diabetes, its shared pathways with atherosclerotic cardiovascular disease (ASCVD) are conventionally described using models based on single time points. We identified longitudinal diabetes clusters before diagnosis using deep learning and studied their association with ASCVD events and mortality. Methods: We analyzed 157,670 visits from 15,871 adults (25-65 years) without diabetes from four pooled U.S. cohorts (median follow-up: 22 years [IQR: 9-30]). A gated recurrent unit model with decay (GRU-D) was used to predict 1-year risk of diabetes or censoring within 10 years, by learning longitudinal embeddings across 25 clinical characteristics and biomarkers. Parallel Factor Analysis-2 (PARAFAC-2) and Gaussian mixture models (GMM) were used to group longitudinal participant representations as clusters. Landmark time Cox proportional hazards regressions, relative to last observation in the training window, were used to study covariate-adjusted associations of clusters with ASCVD and mortality. Prognostic utility of clusters beyond the PREVENT risk score was assessed using Harrell's C-index. Findings were replicated in a fifth cohort. Results: The analytic sample was aged 49 years [SD: 11], 58% female, and 68% white; 1,202 (8%) developed diabetes within the first 10 years. We identified five clusters (Cluster A to E) that differed in their clinical characteristics over time. Cluster E (46%) had the highest cumulative incidence of diabetes in the study period, followed by Cluster C (40%) and Cluster A (38%). Cluster C, which was defined by older age, high blood pressure, and suboptimal renal function at the first visit, had higher rates of ASCVD (HR: 1.09, 95%CI: 0.98-1.21) and mortality (HR: 1.08, 95%CI: 1.00-1.16), relative to Cluster A despite being similar in age and BMI at the first visit. Relative to Cluster A, all other clusters had similar or lower rates of ASCVD and mortality. We observed substantial cluster effects for three clusters (Clusters C to E), which were based on only two cohorts. The two clusters (Clusters A and B) that included participants from all four cohorts were reproduced in the fifth cohort and showed similar rates of outcomes. Clusters did not improve ASCVD prognosis, relative to a model that included only the PREVENT risk score. Conclusions: Longitudinal clusters reveal substantial heterogeneity in the period before diabetes diagnosis, and their risk for ASCVD and mortality. However, clusters discovered may, in part, be explained by cohort effects from variations in recruitment and visit patterns after recruitment.

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: Acute pancreatitis (AP) is an established risk factor for diabetes, with approximately 20% of children developing either prediabetes or diabetes within one year of their first episode. Little is known about the diabetes pathophysiology or which individuals are at highest risk. We aimed to evaluate whether genetic risk scores (GRS) for type 1 (T1D) and polygenic risk scores (PRS) type 2 diabetes (T2D) are associated with progression to dysglycemia following AP. Methods: Clinical data were available for 123 children (mean age (IQR), 12 (8-15) years; mean body mass index (BMI), 21.8) with AP who were followed for >1 year. Array genotyping coupled with imputation using the TOPMed reference panel was performed. Genetic ancestry was predicted using a random forest classifier. GRS for T1D and T2D were calculated using either an ancestry-appropriate (T1D-GRS) or a multi-ancestry (T2D-PRS) weighted framework. To evaluate risk compared to the population we used predefined GRS thresholds from UK Biobank. Results: Among the 123 subjects, 24 developed dysglycemia (5 with diabetes and 19 with prediabetes). The majority (75.6%, n=93) of children were of European ancestry. Comparison of the T1D-GRS burden with the UK BioBank showed numerically higher proportions for any given threshold. At the top 5% threshold, 9.7% of our cohort were classified as high-risk compared to 5% in UK Biobank (p<0.05). The elevated T1D-GRS could be primarily attributed to non-HLA variants and was more enriched in those testing positive for [&ge;]1 islet-autoantibody. The T2D-PRS was also elevated in the dysglycemic group but only reached statistical significance in those who were obese. Conclusion: These findings highlight the potential role of both T1D-GRS and T2D-PRS in investigating diabetes susceptibility following AP.

Metformin has been linked to mortality benefits in type 2 diabetes that may extend beyond glycemic control, but population-level evidence connecting these benefits to inflammation-related pathways remains limited. Using NHANES 2013-2018 data with mortality follow-up through 2019, we examined associations between metformin use and four inflammatory markers, including neutrophil-to-lymphocyte ratio (NLR), monocyte-to-lymphocyte ratio (MLR), serum albumin, and high-sensitivity C-reactive protein (hs-CRP), and evaluated their relevance to all-cause and cardiovascular mortality. Among 2,122 adults with self-reported diabetes (60% metformin users; 2,116 with valid mortality follow-up), survey-weighted linear regression adjusted for demographic, socioeconomic, and metabolic covariates showed metformin use was associated with lower NLR ({beta} = - 0.35; 95% CI -0.57, -0.14), lower MLR ({beta} = -0.04; 95% CI -0.06, -0.02), and higher serum albumin ({beta} = +0.11 g/dL; 95% CI 0.06, 0.16); the hs-CRP association was directionally consistent but not significant. Associations for NLR and MLR were essentially unchanged after BMI and HbA1c adjustment, remained robust in an active comparator analysis against sulfonylurea monotherapy, and were consistent across propensity score and overlap weighting sensitivity analyses. Survey-weighted Cox regression linked metformin to lower all-cause (HR 0.64; 95% CI 0.48, 0.86) and cardiovascular mortality (HR 0.49; 95% CI 0.26, 0.94). NLR was independently associated with all-cause mortality, with the highest tertile carrying nearly twice the hazard of the lowest, and inclusion of NLR or MLR modestly attenuated the metformin-mortality association. Metformin use is associated with a distinct cellular immune-inflammation profile in adults with type 2 diabetes, supporting further investigation of non-glycemic pathways relevant to its long-recognized clinical benefits.

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.

Aims CGM devices report glucose only within fixed limits (typically 40-400 mg/dL; 2.2-22.2 mmol/L), truncating extreme values to a boundary ("capping"). We characterised prevalence, duration, and consequences of capping in type 1 diabetes trial data. Materials and Methods We analysed 46,990,617 CGM readings from 948 participants across four publicly available clinical trial datasets (Dexcom G4 Platinum or G6 sensors). Capping prevalence, run duration, and associations with age, HbA1c and sex were characterised across all datasets. In the 77 participants of the Replace-BG trial CGM-plus-blood glucose monitor (BGM) arm, CGM-derived metrics were compared with contemporaneous BGM measurements across 1,162 non-overlapping 14-day windows. Results Between 93.5% and 100% of participants had at least one capped reading, and capped values comprised 0.47-0.98% of all readings. In the three datasets for which duration could be calculated, over 70% of upper-cap runs exceeded 15 minutes and over one third exceeded 60 minutes. Upper-limit capping was inversely associated with age (Spearman {rho} -0.20 to -0.47, p[&le;]0.002) in three of the datasets, and positively associated with baseline HbA1c ({rho} 0.39-0.62, p<0.001) in all four datasets. A within-participant analysis showed that capping burden did not predict CGM-BGM divergence in any summary metric (all p>0.2), and a systematic CGM-BGM offset in mean glucose and time in range (TIR) reflected the physiological lag between blood and interstitial fluid rather than capping artefact. Conclusions Sensor limit capping is near-universal in type 1 diabetes, produces sustained periods of right-censored glucose data disproportionately affecting younger patients, and does not substantially distort standard summary metrics at the population level. Clinicians and trialists should be aware that CGM data can confirm extreme glucose events but cannot quantify their severity.

Background Chronic gastritis and duodenitis (CGD) are highly prevalent among patients with type 2 diabetes (T2D). However, the prognostic impact of their comorbidity and the potential role of MRI-derived phenotype-tailored dietary strategies remain unclear. Methods This prospective cohort study included 453,768 UK Biobank participants. Primary endpoints were myocardial infarction, stroke, end-stage renal disease (ESRD), dementia, Parkinson's disease, and all-cause mortality. Time-dependent multivariable Cox regression assessed outcome associations, while additive interaction analyses evaluated synergistic effects between T2D and CGD. Eight healthy dietary pattern scores were analyzed. Latent profile analysis classified MRI-derived body composition phenotypes based on fat distribution and organ volume. Results T2D and CGD were positively associated, and their comorbidity increased risks of cardiovascular events, ESRD, dementia, and all-cause mortality. Additive interaction analyses demonstrated synergistic effects on myocardial infarction and all-cause mortality. The comorbidity was further associated with aggravated lipid metabolic abnormalities and multiorgan atrophy. Higher adherence to the Healthful Plant-Based Diet Index (HPDI) and Dietary Approaches to Stop Hypertension (DASH) diets attenuated the excess mortality risk related to this synergy. Dietary associations varied across T2D, CGD, and comorbid populations, while MRI-based latent profiles modified diet-outcome relationships. A nomogram integrating demographic, dietary, and body composition data demonstrated reliable long-term predictive performance for myocardial infarction, stroke, and all-cause mortality. Conclusions Comorbid T2D and CGD substantially increase adverse clinical risks and exhibit synergistic effects on myocardial infarction and all-cause mortality. These findings support routine CGD screening in T2D care and provide population-based evidence for MRI-derived phenotype-tailored dietary strategies.

Objectives: Diabetes affects over 500 million people globally and glycemia is inadequately managed. Metformin is the most frequently prescribed initial treatment for type 2 diabetes globally, yet glycemic response trajectories to metformin in routine real-world care and predictors of treatment response have not been well described. We aimed to identify glycemic response trajectories in adults prescribed metformin monotherapy as initial type 2 diabetes treatment and predictors of poor glycemic response to metformin. Design: Observational cohort study using latent class mixed models to identify hemoglobin A1c (HbA1c) trajectory classes, followed by random forests machine learning to predict trajectory class membership. Setting: US Veterans Affairs Healthcare System Participants: Adults treated with metformin alone for >30 days after diabetes diagnosis with a minimum of two HbA1c measurements from 90 days prior to two years after the first metformin prescription (N=140,413). Exposures: Demographic, laboratory, vital sign, and comorbidity data were included as predictors of metformin response trajectory Main Outcomes and Measures: We included all HbA1c measurements (487,604 total) for two years after metformin initiation to define metformin glycemic response trajectories. Results: We identified three HbA1c trajectories: stably low (89.7% of sample, mean HbA1c decrease from 7.2% to 6.6%), brisk response (7.1% of sample, mean HbA1c decrease from 11.4% to 7.0%), and non-response (3.1% of sample, mean HbA1c increase from 8.9% to 10.8%). Of those in the stably low and brisk response classes at 2 years, 91% maintained HbA1c at approximately 7% on metformin alone for 5 years after drug initiation. Prediction models could accurately predict brisk response (91% accuracy) but not metformin non-response (59% accuracy). Conclusions: Most individuals treated initially with metformin monotherapy have a beneficial and durable glycemic response. Predicting individuals who will not respond to metformin may be challenging but is evident within six months with recommended glycemic surveillance. The findings support current guidelines for HbA1c surveillance when initiating diabetes treatment.

BackgroundThe 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. MethodsWe 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. ResultsConditioning on the primary sentinel variant rs12213929 (upstream of GLP1R, {beta} = 0.11; 95% CI 0.09-0.14; p = 1.94x10-17), we identified a secondary variant (rs13216992, intron of GLP1R) independently associated with BMI ({beta} = 0.10; 95% CI 0.07-0.13; p = 7.88x10-14). 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 < 2x10-16). 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. ConclusionsThe 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.

Diabetic peripheral neuropathy (DPN) is a common and disabling condition for which no disease-modifying therapies are available. Glycemic and metabolic drivers do not fully explain why only a subset of individuals with diabetes develop DPN, and genetic contributors remain poorly defined. We aimed to perform a multi-population genome-wide association study (GWAS) of DPN to highlight potential new etiological pathways and therapeutic targets. Methods We performed a multi-population GWAS of neuropathy in people with and without diabetes using the VA Million Veteran Program and UK Biobank, followed by replication in the All of Us Research Program (AoU), and gene-based and gene-set analyses to identify implicated pathways. Causal relationships between circulating serine levels and DPN were further tested using two sample Mendelian randomization. To further evaluate pathogenic potential, we analyzed rare, high impact variants in GWAS implicated genes among individuals with unresolved inherited neuropathies using the GENESIS platform. Findings Among individuals with type 2 diabetes, we identified seven genome wide significant loci (p<5x10-): PHGDH and PSPH (key serine synthesis genes), TEAD1, CYP4F11, LARGE1, FTO, and COBLL1. No loci were significant in individuals without diabetes or with type 1 diabetes. Four loci (PHGDH, TEAD1, FTO and CYP4F11) replicated in AoU (p <0.05). Mendelian randomization demonstrated that higher genetically predicted serine levels were associated with lower DPN risk, consistent with a causal role of serine metabolism in disease pathogenesis. Rare-variant burden analyses revealed associations of predicted deleterious variants with inherited neuropathy case status in PHGDH (odds ratio [OR] 12.7 [95% CI 7.9, 20.4]), PSPH (OR 8.5 [7.2, 10.2]), PHKG1 (OR 4.8 [3.7, 6.3]), and LARGE1 (OR 0.007 [0.0004, 0.1]). Interpretation Convergent genetic evidence across common and rare variation implicates serine synthesis as a key pathway in DPN. These findings link diabetic and inherited neuropathies through a shared metabolic mechanism, identifying serine metabolism as a potential therapeutic target.

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

Aims/Hypothesis: Behavioral and phenotypic characteristics do not fully explain variability in African Americans with youth-onset type 2 diabetes (Y-T2D) treated with metformin with or without liraglutide. We hypothesized that biological heterogeneity, including genetic variation in the metformin transporter OCT1, influences metformin pharmacokinetics and hepatic glucose flux. Therefore, we sought to characterize metformin pharmacokinetics in Y-T2D and evaluate genetic variants known to modulate metformin efficacy in adults to determine the mechanisms underlying variation in treatment response. Methods: We evaluated genetic variants related to metformin transport and mechanisms of action in 30 Y-T2D using a candidate-gene approach to evaluate the association of pharmacogenetic variants with fasting glucose and gluconeogenesis. In a subset of Y-T2D randomized to 3 months of metformin (n=11) or metformin and liraglutide (n=8), we constructed a metformin population pharmacokinetic model and evaluated gene variant associations. Results: A one-compartment first-order absorption and elimination pharmacokinetic model provided the optimal fit. Metformin pharmacokinetic parameters were similar by group and not related to glycemia. The rs628031_OCT1 A allele was associated with greater metformin clearance. The rs622342_OCT1 C allele was associated with lower post-treatment fractional gluconeogenesis ({beta} [95% CI] = -8.8 [-14.13, -3.47] %, Adjusted R2 = 0.56, P = 0.003). The rs7903146_TCF7L2 T allele was associated with greater reductions in fasting glucose among those treated with metformin + liraglutide ({beta} = -1.32 [-2.42, -0.22] mmol/L, Adjusted R2 = 0.8, P<0.002), but baseline glucose and gluconeogenesis (P<0.0001) were the strongest predictors of post-treatment glycemia. Conclusion/interpretation: In Y-T2D, OCT1 gene variants rs628031 and rs622342 were associated with metformin clearance and gluconeogenesis, respectively. TCF7L2 variant rs7903146 may contribute to differences in glycemic response in youth treated with metformin and liraglutide. These findings suggest genetic variants may be important for understanding variable metformin response in Y-T2D.

Importance: Despite the benefits of statin therapy in individuals with diabetes, fewer than 70% of adults with diabetes meet contemporary guidelines for statin therapy and reducing low-density lipoprotein cholesterol (LDL) to <100 mg/dL. Evidence describing delays in statin initiation after diabetes diagnosis and associated clinical outcomes may motivate process of care interventions to improve guideline recommended care in individuals newly diagnosed with type 2 diabetes mellitus (T2D). Objective: To examine the timing of statin initiation and achievement of LDL <100 mg/dL after diabetes diagnosis, and to determine the association of early LDL reduction among statin initiators with incident atherosclerotic cardiovascular disease (ASCVD). Design: Retrospective observational cohort study using data from 2005-2021 Setting: Veterans Affairs Health Care System (VA) Participants: Individuals with newly diagnosed T2D Exposure: Primary exposure was ASCVD risk based on ACC/AHA Pooled Cohort Equations; secondary exposure was LDL <100 mg/dL in the first year after T2D diagnosis among statin initiators Main Outcomes and Measures: Co-primary outcomes were initiation of statin therapy and achievement of LDL <100 mg/dL within 5 years of diabetes diagnosis; incident 5-year ASCVD was a secondary outcome. Results: Among 100,406 individuals with newly diagnosed T2D, 59,615 were prescribed statin therapy within five years (59.4%), and 44,783 (57.5%) of those with LDL above goal achieved LDL <100 mg/dL within 5 years. Relative to those at low (<7.5%) 10-year ASCVD risk, individuals at intermediate (7.5-20%) and high (>20%) risk were more likely to be initiated on a statin (intermediate: Hazard Ratio [HR] 1.14 [95% CI 1.11, 1.17]; high: HR 1.16 [95% CI 1.13, 1.19]) and to achieve LDL <100 mg/dL (intermediate: HR 1.23 [95% CI 1.19, 1.26]; high: HR 1.34 [95% CI 1.30, 1.38]). Among those prescribed a statin within one year of diabetes diagnosis, achieving LDL <100 mg/dL in the first year after diabetes diagnosis was associated with lower risk of 5-year incident ASCVD (HR 0.84 [95% CI 0.77, 0.92]). Conclusions and Relevance: Gaps in guideline-directed primary prevention of ASCVD arise early following initial diabetes diagnosis. Guideline recommended early LDL lowering among statin initiators was associated with improved clinical outcomes.

ImportanceWomen 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. ObjectiveTo 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). DesignRetrospective cohort study using an active-comparator new-user design, following each participant till treatment discontinuation or end of data. SettingMultinational 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. Participants5.15 million adults with T2D who initiated one of the four second-line therapies following metformin during 1992-2021. ExposuresGLP-1RA, SGLT2i, DPP4i, or SU. Main Outcomes and MeasuresCardiovascular 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. ResultsDrug 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 RelevanceThis 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.

Wolfram syndrome is a rare autosomal recessive disorder caused by pathogenic variants in the WFS1 gene, characterized by early-onset diabetes mellitus, optic atrophy, sensorineural hearing loss, arginine vasopressin deficiency, and progressive neurodegeneration. The condition selectively affects pancreatic {beta} cells and neurons via chronic endoplasmic reticulum (ER) stress, and no proven disease-modifying therapy currently exists. Diabetes mellitus is typically the first manifestation, presenting at a mean age of 6 years as an insulin-dependent phenotype with preserved C-peptide and negative diabetes-related autoantibodies. Glucagon-like peptide-1 receptor agonists (GLP-1 RAs) are well-established agents in the management of type 2 diabetes, augmenting glucose-dependent insulin secretion, suppressing glucagon, slowing gastric emptying, and promoting satiety. Preclinical evidence further suggests that GLP-1 RAs preserve {beta}-cell mass, attenuate ER stress, and confer neuroprotective effects, properties of particular therapeutic relevance to Wolfram syndrome. We conducted a retrospective cohort study of 84 participants with genetically confirmed Wolfram syndrome and insulin-dependent diabetes mellitus enrolled in the Washington University Wolfram Syndrome International Registry and Clinical Study. Clinical data were extracted from medical records; for participants concurrently enrolled in the Tracking Neurodegeneration in Early Wolfram Syndrome study, longitudinal data were obtained from that source as well. Thirty-five percent of eligible participants had received a GLP-1 RA at some point during follow-up. We characterize the prevalence of GLP-1 RA use, documented rationale for initiation, observed effects on glycemic control and visual outcomes, adverse effects, and reasons for discontinuation. No statistically significant changes in hemoglobin A1c (HbA1c) or body mass index (BMI) were observed. Visual acuity declined significantly at two years, consistent with expected disease progression. Gastrointestinal adverse effects were common and contributed to frequent discontinuation. These observational data provide important clinical context and a foundation for future prospective trials evaluating GLP-1 RAs as a potential disease-modifying strategy in Wolfram syndrome.

Aims/hypothesisType 2 diabetes (T2D) frequently co-occurs with other chronic conditions and has been suggested as a key driver of multimorbidity development. However, the temporal dynamics of how T2D influences multimorbidity progression are not well understood. We aimed to investigate how T2D influences the rate of multimorbidity progression. MethodsWe analyzed data from the UK Biobank, a prospective population-based cohort study (n=502,368, median age 58 years [range 37-73 years], 46% male at baseline) with a median follow-up of 15.3 year. 8.7% of participants were diagnosed with T2D over the follow-up period. We counted the current number of morbidities (among 80 long-term chronic conditions) identified through hospital admission records using ICD-10 diagnosis codes. We modeled the age-specific transition rates between multimorbidity states using multistate models tailored for time-split data (i.e., 2 to 3 morbidities, T2D (with 1 morbidity) toT2D (with 2 morbidities), etc.). Age was modeled using natural splines with an interaction term with T2D, adjusting for sex, education, ethnicity, smoking, and body mass index. ResultsThe total follow-up time was 7.5 million person-years (PY), of which 0.33 million PY was in T2D. Individuals with T2D consistently experienced higher transition rates between morbidity transition states. For example, the transition rate from 2 to 3 morbidities was 7.94 per 100 PY without the presence of T2D, compared to 18.35 per 100 PY with T2D (rate ratio=2.31, 95% CI: 2.28-2.34). The excess in transition rates was most pronounced from states with few comorbidities. Further, the transition rates were consistently influenced by T2D status and age, with younger individuals with T2D showing the most accelerated progression. Conclusion/interpretationOur study suggests that T2D is associated with accelerated development of subsequent multimorbidity, highlighting T2D as a critical contributor of chronic disease accumulation. The progression of disease accumulation is more pronounced in younger age groups, which suggests different underlying mechanisms of multimorbidity progression across age, warranting further investigation. The findings indicate the need for early intervention among younger people with T2D to slow multimorbidity progression.

Type 2 Diabetes (T2D) is a growing public health burden in West Africa, yet the effectiveness of lifestyle interventions for glycemic control in this region remains unclear. This systematic review and meta-analysis evaluated the impact of lifestyle interventions on Fasting Blood Glucose (FBG) and Glycated Hemoglobin (HbA1c) levels among adults with T2D in West Africa. A systematic search of PubMed, Scopus, Africa Journals Online, and Cairn.info was conducted following PRISMA guidelines. Randomized controlled trials (RCTs) and quasi-experimental studies evaluating lifestyle interventions (physical activity, dietary modification, and combined/educational interventions) for glycemic control in adults with T2D in West Africa were included. Meta-analysis was performed via a random-effects model with restricted maximum likelihood (REML) estimation, using mean differences (MD) as the effect measure for both FBG and HbA1c outcomes. Heterogeneity was assessed via I2 statistics, and sensitivity, subgroup, and meta-regression analyses were conducted to examine potential moderators of the observed heterogeneity. Ten studies comprising 645 participants were included. Six studies reported FBG outcomes; however, two were excluded from the FBG meta-analysis due to missing control group post-test values and absence of a control group respectively, leaving four studies for pooling. A separate set of four studies contributed to the HbA1c meta-analysis. For FBG, lifestyle interventions were associated with reduction in FBG levels (pooled MD = -1.81 mmol/L, 95% CI: -2.33 to -1.30, p < 0.001), with moderate heterogeneity (I2 = 50.76%). The certainty of evidence assessed using the GRADE approach was rated as low for FBG outcomes and very low for HbA1c outcomes, reflecting concerns about imprecision and inconsistency across studies. Leave-one-out sensitivity analysis confirmed robustness of this finding, with estimates ranging from -1.707 to -2.087 mmol/L. Neither intervention duration nor sample size significantly moderated FBG effect sizes, with the model explaining approximately 15.7% of observed heterogeneity. For HbA1c, lifestyle interventions were also associated with reduction in HbA1c levels (pooled MD = -1.044%, 95% CI: -1.594 to -0.495, p = 0.0002), though heterogeneity was exceptionally high (I2 = 98.08%), limiting interpretability of the pooled estimate. Exploratory meta-regression identified intervention duration and sample size as statistically associated with HbA1c effect size, though the model was saturated given the small number of studies and findings should not be interpreted as confirmatory evidence of moderation. Conclusion: Lifestyle interventions, including supervised physical activity, dietary modification, and community-based diabetes education, were generally associated with improvements in glycemic control among adults with type 2 diabetes in West Africa. Evidence was more consistent for fasting blood glucose, while findings for HbA1c were highly heterogeneous and should be interpreted with caution. These results suggest potential benefit, but variability across studies highlights the need for more standardized and rigorously designed trials in the region.