To investigate the associations between TyG-related indices and hypertension-diabetes comorbidity (HTN-DM) among middle-aged and older adults.

A total of 2854 participants (aged ≥ 45 years) living in a suburban community were included in this study. The collected information comprised three parts: baseline information, physical examination, and laboratory testing. Multivariable logistic regression was used to estimate the odds ratios (ORs) and 95% confidence intervals (CIs) for the associations between TyG-related indices (TyG, TyG-BMI, and TyG-BRI) and HTN-DM. Restricted cubic splines (RCS) were used to model the dose-response relationships. Receiver operating characteristic (ROC) curves were applied to evaluate the discriminative performance of these indices.

Among 2,854 participants, each 1-SD increment in TyG, TyG-BMI, and TyG-BRI conferred multivariable-adjusted ORs (95% CIs) of 3.16(2.75-3.64), 1.39(1.15-1.66), and 1.37(1.22-1.54), respectively (all P < 0.001). Corresponding ORs (95% CIs) comparing the highest versus lowest quartile were 30.99(16.97-62.97), 6.42(4.20-10.14), and 6.43(4.21-10.17), with significant dose-response trends (all P for trend < 0.001). RCS analysis revealed a linear relationship for TyG and nonlinear associations for TyG-BMI and TyG-BRI with HTN-DM. TyG showed the highest observed AUC (0.786) among the evaluated indices, followed by TyG-BMI (0.679) and TyG-BRI (0.633).Across all subgroup analyses, TyG remained consistently and positively associated with HTN-DM prevalence, and the interaction with sex was significant (P = 0.045).

This study revealed that TyG-related indices were associated with HTN-DM. RCS analysis indicated a linear association for TyG but nonlinear associations for TyG-BMI and TyG-BRI. Among the three indices, TyG showed the highest observed AUC. This finding suggests that composite indices incorporating BMI or BRI may not necessarily improve discriminative ability beyond TyG alone.

Type 2 diabetes mellitus (T2DM) and metabolic dysfunction-associated steatotic liver disease (MASLD) are interrelated metabolic disorders worldwide. We aimed to characterize global co-occurrence patterns of T2DM and MASLD from a spatial perspective and to identify shared risk factors.

We extracted incidence rates of T2DM and MASLD and exposure rates of 68 risk factors among individuals aged ≥ 20 years, derived from Global Burden of Disease Study 2021. Three co-occurrence areas were classified based on four quartiles of both diseases incidence. Interpretable machine learning and negative binomial linear mixed-effects model were applied to identify and quantify effects of risk factors. We calculated population-attributable fraction (PAF) to evaluate incidence burden of T2DM and MASLD attributable to identified risk factors.

Fifty-nine countries were classified as consistent, 71 as T2DM-dominant, and 74 as MASLD-dominant. Three areas overlap spatially with exposure rates to metabolic, environmental, and behavioral risk factors. After feature selection, we identified nine risk factors with substantial effects on the comorbidity. Functionally, T2DM incidence was mainly attributable to metabolic risks (PAF = 35.28%) and behavioral risks (11.78%), whereas MASLD incidence was attributable to behavioral risks (28.84%), environmental risks (21.65%), and metabolic risks (14.63%). Overall, 42.9% of T2DM incidence and 52.41% of MASLD incidence were attributed to cumulative exposure to risk factors, with specific combinations and variations of these factors significantly impacting the disparate co-occurrence patterns worldwide.

Comorbidity of T2DM and MASLD is a prevalent global health issue. Co-management of T2DM and MASLD can proceed from interventions targeting corresponding modifiable risk factors.

Background Diabetic autonomic neuropathy (DAN) is a common but frequently underdiagnosed complication of diabetes mellitus that affects multiple organ systems and contributes to increased morbidity and mortality. Early identification using simple screening tools is essential, particularly in resource-limited primary care settings. However, community-based data on DAN from rural India remain limited. Objectives The objective of this study is to estimate the proportion of screen-positive DAN and identify factors associated with its occurrence among individuals with type 2 diabetes mellitus in rural Warangal, Telangana. Methods A community-based cross-sectional study was conducted among 400 adults aged ≥30 years with previously diagnosed type 2 diabetes mellitus residing in the rural field practice area of Kakatiya Medical College, Warangal, during 2020-2021. Participants were selected through simple random sampling from the Non-Communicable Disease (NCD) register. Data were collected using a pre-tested semi-structured questionnaire. DAN was screened using the validated Composite Autonomic Symptom Score (COMPASS-31), with a score ≥17 indicating DAN. Descriptive statistics, binary logistic regression, and multivariable logistic regression analyses were performed to identify independent predictors. A p-value <0.05 was considered statistically significant. Results The prevalence of diabetic autonomic neuropathy was 21.0% (84/400). On multivariable logistic regression analysis, younger age (≤44 years) (adjusted odds ratio (AOR): 3.10), female sex (AOR: 1.72), illiteracy (AOR: 1.85), unemployment (AOR: 1.67), smoking (AOR: 7.95), alcohol consumption (AOR: 5.21), inadequate daily vegetable intake (AOR: 1.64), physical inactivity (AOR: 1.72), and poor glycaemic control (random blood glucose ≥200 mg/dL; AOR: 6.03) were identified as significant independent factors associated with DAN (p<0.05). Conclusion Approximately one in five individuals with type 2 diabetes in this rural population had DAN. Lifestyle-related factors, poor glycaemic control, and adverse socioeconomic characteristics were independently associated with DAN. Community-based screening using validated symptom-based tools such as COMPASS-31, along with interventions promoting glycaemic control and healthy lifestyle practices, may facilitate early detection and reduce the burden of diabetic complications in rural settings.

Senescence-associated secretory phenotype (SASP)-related inflammatory programs have been implicated in type 2 diabetes mellitus (T2DM), yet blood-based candidates have not been systematically screened and validated across independent cohorts. We adopted standardized senescence signature scoring and multi-cohort independent validation to screen SASP-related candidate genes through literature mining, differential expression analysis, and machine learning, followed by functional enrichment analysis, immune cell composition analysis, and immune cell proportion adjustment. We identified CCL8 and MMP9 as key SASP-related candidates; MMP9 was significantly positively correlated with senescence scores, and both genes were implicated in T2DM progression through E2F targets and interferon gamma response pathways. T2DM led to significant immune microenvironment alterations, and the expression changes of CCL8 and MMP9 were independent of peripheral blood mononuclear cell (PBMC) subset frequencies after CIBERSORT adjustment. Expression patterns were consistently validated across all cohorts including the large-scale GSE184050 dataset and single-cell RNA-seq dataset GSE280401, with RT-qPCR providing exploratory support in a small cohort. Collectively, this study provides supportive evidence that CCL8 and MMP9 may represent SASP-related biomarkers in T2DM pathogenesis. Moreover, we present an integrative workflow for systematic screening prioriti zation and validati on of SASP-related candidate genes across multiple cohorts.

Background Stroke carries substantial mortality and disability, making early prognostic risk stratification important. The Preadmission Comorbidities, Level of Consciousness, Age, and Neurological Deficit (PLAN) score provides a validated bedside approach to estimating adverse prognostic risk in acute ischemic stroke (AIS). The triglyceride-glucose (TyG) index is a simple, inexpensive marker derived from routinely available fasting triglyceride and glucose measurements and may reflect underlying metabolic dysfunction. However, its relationship with prognostic risk assessed by PLAN, and whether this association differs according to diabetes status, remains insufficiently characterized. Objectives This study aims (1) to assess the bivariate correlation between the TyG index and selected biochemical parameters in patients with AIS and (2) to examine the association between the TyG index and the PLAN prognostic score according to diabetes status. Materials and methods This cross-sectional study included 71 consecutive adults with AIS admitted to a tertiary care hospital in India between October 2024 and March 2026. Demographic, clinical, and laboratory data were collected using a structured pro forma. The TyG index was calculated as ln (fasting triglycerides (TG) (mg/dL) × fasting glucose (mg/dL)/2). The PLAN score was assessed during the initial clinical evaluation. Pearson or Spearman correlation analysis was performed based on the data distribution. Results The mean age of participants was 60.96 ± 12.20 years, with 39 (54.9%) males. Hypertension was the most frequent comorbidity (78.9%), followed by smoking (43.7%) and diabetes mellitus (35.2%). The mean TyG index and PLAN score were 9.36 ± 0.60 and 12.99 ± 4.19, respectively. The TyG index demonstrated significant positive correlations with fasting TG (ρ = 0.720), fasting glucose (ρ = 0.881), total cholesterol (ρ = 0.557), LDL-C (r = 0.504), and HbA1c (ρ = 0.348). In contrast, significant negative correlations were observed with hemoglobin (r = -0.242), HDL-C (ρ = -0.458), and serum albumin (ρ = -0.256) (all p < 0.05). A significant positive correlation was observed between the TyG index and PLAN score (r = 0.529, p < 0.0001), which remained significant after adjustment for clinical confounders (partial r = 0.540, 95% CI: 0.342-0.693; p < 0.0001). The association was also significant among diabetic (r = 0.608, p = 0.001) and non-diabetic patients (r = 0.520, p = 0.0002), with no significant TyG index × diabetes interaction (p = 0.306). Conclusions The TyG index showed significant associations with several metabolic and biochemical parameters and demonstrated a moderate positive association with PLAN prognostic risk among patients with AIS. This association persisted after adjustment for major clinical characteristics and was observed in both diabetic and non-diabetic patients, without evidence of effect modification by diabetes status. These findings suggest that the readily available TyG index may provide useful information regarding metabolic risk and its relationship with prognostic risk in AIS. However, the cross-sectional design precludes establishing causality or determining whether the TyG index independently predicts subsequent clinical outcomes. Larger prospective multicenter studies incorporating longitudinal outcomes and comprehensive adjustment for confounding are warranted.

The global prevalence of diabetes mellitus has intensified the demand for continuous, reliable, and patient-friendly glucose monitoring technologies. Conventional blood-based glucose testing remains invasive and unsuitable for continuous monitoring, motivating extensive research into non-invasive wearable sensing platforms. Sweat has emerged as an attractive biofluid for glucose monitoring due to its accessibility and compatibility with skin-interfaced devices; however, the low glucose concentration, complex matrix, and dynamic secretion rates pose significant analytical challenges. Metal-organic frameworks (MOFs), a class of crystalline porous materials constructed from metal nodes and organic linkers, offer unique opportunities to address these challenges owing to their exceptionally high surface areas, tunable pore architectures, and versatile chemical functionalities. Recently, MOF-based electrochemical glucose sensors have shown considerable promise, particularly when integrated into flexible and wearable systems for sweat analysis. This review critically examines recent advances in MOF-enabled wearable electrochemical glucose sensors, with emphasis on material design principles, enzymatic and non-enzymatic sensing mechanisms, device integration strategies, and key performance metrics. Current limitations related to conductivity, stability, biofouling, and physiological variability are discussed, along with emerging strategies to overcome these barriers. Finally, this review outlines future directions toward intelligent, clinically relevant MOF-based wearable glucose monitoring systems.

Current evidence for finerenone in T2DM with CKD is derived primarily from patients with microalbuminuria to macroalbuminuria, and data in those with moderate-to-heavy proteinuria remain limited. This study aimed to investigate the effect of finerenone on sustained graded improvement of 24h-UTP and its safety profile in T2DM patients with CKD and proteinuria >1 g/d.

This retrospective cohort study included patients with T2DM and CKD with proteinuria >1 g/d treated at hospital. Patients were divided into the finerenone group and the control group based on whether they received finerenone during the follow-up period. Proteinuria was graded as Stage 2 (1-3.5 g/d) and Stage 3 (>3.5 g/d). The primary endpoint was sustained graded improvement in 24h-UTP. The secondary endpoint was the percentage reduction in proteinuria from baseline at different timepoints. Propensity score weighting was applied to address baseline imbalances. Logistic regression and Kaplan-Meier analysis were used to assess the association between finerenone use and graded proteinuria improvement. A mixed model for repeated measures (MMRM) was employed to compare changes in 24h-UTP, eGFR, and serum potassium levels between the two groups. Safety outcomes included hyperkalemia incidence.

A total of 134 patients were included in this study, comprising 53 in the finerenone group and 81 in the control group. Over 12 months, 28 patients (52.8%) in the finerenone group and 27 patients (33.3%) in the control group achieved sustained graded improvement in 24h-UTP. After propensity score weighting, finerenone use was independently associated with sustained graded improvement (OR 1.26, 95% CI 1.07-1.47, P = 0.004). Kaplan-Meier analysis showed a significant difference in time to sustained improvement between groups (Log-rank P = 0.026-0.033). MMRM analysis revealed a greater reduction in 24h-UTP in the finerenone group, while eGFR and serum potassium changes did not differ significantly between groups.

In patients with T2DM and proteinuria >1 g/d, finerenone increased the probability of sustained graded proteinuria improvement, shortened the time to improvement, and was not associated with a significantly increased risk of hyperkalemia over 12 months, although the interpretation of safety findings is limited by baseline differences in kidney function and concomitant medication use between groups.

Gestational diabetes mellitus (GDM) is a common metabolic disorder of pregnancy associated with adverse maternal and fetal outcomes; early identification of at-risk women is essential for timely intervention. Serum uric acid (SUA) has emerged as a potential biomarker for predicting glucose intolerance during pregnancy. This study aimed to evaluate the association between first-trimester SUA levels and the subsequent development of GDM.

A prospective cohort study was conducted in the Department of Obstetrics and Gynaecology of a tertiary care teaching hospital from January 2024 to December 2025. A total of 212 non-diabetic antenatal women in their first trimester were enrolled using consecutive sampling. Baseline clinical assessment, anthropometric measurements, and SUA estimation were performed at enrolment using a fully automated biochemical analyzer (Dimension RXL Max, Siemens Healthineers, Erlangen, Germany). Participants were followed until 24-28 weeks of gestation and screened for GDM using the Diabetes in Pregnancy Study Group of India (DIPSI) criteria following a 75 g oral glucose challenge. Statistical analysis was performed using SPSS v25.0 (IBM Corp., Armonk, NY). Independent t-test, chi-square test, Pearson correlation, multivariate logistic regression, and receiver operating characteristic (ROC) curve analyses were used.

Of the 212 participants, 44 (20.8%) developed GDM. Women who developed GDM had significantly higher first-trimester SUA levels (5.4 ± 0.9 mg/dL vs. 3.8 ± 0.8 mg/dL; p < 0.001). SUA showed a positive correlation with second-trimester post-glucose blood sugar (r = 0.52, p < 0.001). The unadjusted odds ratio for GDM with SUA >4 mg/dL was 12.0 (95% CI: 4.8-30.0; p < 0.001). On multivariate logistic regression, elevated SUA (adjusted odds ratio [AOR] = 3.6, 95% CI: 1.8-7.1, p < 0.001), BMI ≥25 kg/m² (AOR = 2.9), and positive family history of diabetes (AOR = 2.4) were independent predictors. ROC analysis demonstrated an area under the curve (AUC) of 0.87 at a cut-off of 4.1 mg/dL (sensitivity 80%, specificity 70%).

Elevated first-trimester SUA levels are significantly associated with, and independently predict, GDM. SUA may serve as a simple, inexpensive, and widely available biomarker for early identification of women at increased risk of GDM. Large-scale multicenter studies are warranted to validate these findings and establish ethnicity-specific thresholds.

Epilepsy is among the most prevalent neurological disorders in children, with nearly half of all cases remaining idiopathic. The global prevalence of maternal diabetes-encompassing both pregestational types (type 1 diabetes mellitus (T1DM) and type 2 diabetes mellitus (T2DM)) and gestational diabetes mellitus (GDM)-has been steadily increasing, raising growing concerns regarding its impact on offspring neurodevelopment. While accumulating evidence suggests that maternal diabetes is associated with an elevated risk of various neurodevelopmental disorders in children, its specific relationship with the risk of epilepsy remains unclear, with available data being both limited and conflicting. To address this gap, we conducted a meta-analysis to comprehensively evaluate the association between distinct types of maternal diabetes (pregestational and gestational) and the subsequent risk of epilepsy in offspring.

We systematically searched the PubMed, Embase, Web of Science, and the Cochrane Library for observational studies examining the association between maternal diabetes and offspring epilepsy risk, covering the period from database inception to July 2026. Two independent reviewers performed literature screening, data extraction, and risk-of-bias assessment. Statistical analyses were performed using Stata version 14.0.

A total of three studies, encompassing 5,287,837 participants, met the inclusion criteria. Pregestational diabetes-both T1DM and T2DM-was significantly associated with an increased risk of epilepsy in offspring (T1DM: HR = 1.33, 95% CI [1.17, 1.52], P < 0.001; T2DM: HR = 1.38, 95% CI [1.25, 1.51], P < 0.001). Nevertheless, owing to the observational design of the included studies, these associations should not be interpreted as causal. Regarding GDM, the pooled estimate did not reach conventional statistical significance (HR = 1.04, 95% CI [0.93, 1.16], P = 0.469). Furthermore, sensitivity analyses revealed that this null finding lacked robustness, with the pooled estimate being substantially influenced by individual studies. Consequently, the current evidence is insufficient to either support or refute a conclusive association between GDM and offspring epilepsy, and any positive findings must be interpreted with considerable caution.

This meta-analysis suggests that pregestational T1DM and T2DM may increase the risk of epilepsy in offspring. In contrast, for GDM, the available analytical evidence remains inconclusive and displays notable between-study heterogeneity; although the summary effect was not statistically significant, it proved highly unstable under sensitivity testing. Given the limited number of included studies and the inability to fully exclude residual confounding, the above findings-particularly those pertaining to GDM-should be regarded as exploratory rather than definitive causal inferences. Interpretation of these results warrants prudence. Future large-scale, high-quality prospective cohort studies are urgently needed to validate these observations, with special emphasis on stringent glycemic management in pregnant women with diabetes and long-term neurodevelopmental surveillance of their offspring.

https://www.crd.york.ac.uk/PROSPERO/view/CRD420261465624, identifier CRD420261465624.

Chronic low-grade meta-inflammation contributes to insulin resistance and metabolic dysfunction in type 2 diabetes (T2D). γδ T cells have emerged as important mediators of metabolic inflammation, yet their responsiveness to metabolic improvement in humans remains poorly understood. We investigated whether successful antidiabetic therapy is associated with alterations in γδ T cell-mediated inflammation in patients with T2D.

Twenty-five patients with poorly controlled T2D initiating SGLT2i therapy (n=15) or SGLT2i plus GLP-1RA (n=10) were prospectively followed for 12 months. Age- and sex-matched non-diabetic controls (n=30) were included at baseline. Peripheral blood γδ T cell phenotype and cytokine production were assessed by multiparametric flow cytometry. Metabolic, hepatic, and renal parameters were evaluated longitudinally.

At baseline, patients with T2D exhibited increased interferon-γ (IFN-γ) production by both Vδ1⁺ and Vδ2⁺ γδ T-cell subsets compared with controls, while subset frequencies remained unchanged. During follow-up, successful antidiabetic therapy was associated with a progressive reduction in IFN-γ production, reaching approximately 40% after 12 months (p<0.01). This reduction did not correlate with changes in HbA1c and was not enhanced by the addition of GLP-1 receptor agonist therapy. In contrast, lower IFN-γ production was associated with improved insulin sensitivity, reduced fat mass, and markers of improved metabolic health. The largest reductions were observed in individuals with mild hepatic or renal impairment and correlated with indices of organ involvement.

This real-world study shows that γδ T cell-mediated meta-inflammation decreases during successful antidiabetic therapy independently of glycemic control. IFN-γ production by γδ T cells is closely associated with insulin resistance, adiposity, and metabolic organ dysfunction, supporting its potential utility as a biomarker of metabolic tissue stress and therapeutic response in T2D.