Depression is a common psychiatric disorder frequently associated with type 2 diabetes mellitus (T2DM) and negatively impacts quality of life and disease outcomes.

 The objective of this study is to compare the frequency and severity of Patient Health Questionnaire-9 (PHQ-9)-screened clinically significant depressive symptoms among adults with T2DM and non-diabetic adults.

This cross-sectional comparative study was conducted from August 2025 to April 2026. A total of 114 participants were enrolled using non-probability consecutive sampling and divided equally into T2DM (n = 57) and control (n = 57) groups. Depression was assessed using the PHQ-9 questionnaire, with a score ≥10 indicating clinically significant depression. IBM SPSS Statistics for Windows, Version 25 (Released 2017; IBM Corp., Armonk, New York, United States) was used to analyze the data, and the chi-square test was used for categorical variables.

Depression was significantly higher in the T2DM group compared to controls. In diabetic patients, 61.40% (n = 35) were depressed, while 38.60% (n = 22) were not depressed. In the control group, 33.33% (n = 19) were depressed, and 66.67% (n = 38) were not depressed (p = 0.003). Depression was more frequent in participants aged 46-50 years and showed significant associations with gender and duration of diabetes. However, no significant difference was observed in depression severity distribution between the two groups.

Depression is significantly more prevalent among patients with T2DM compared to non-diabetic individuals.

This study systematically evaluating temporal trends of type 2 diabetes (T2DM) prevalence, mortality, and DALY estimates from countries in MENA (Middle East and North Africa) region.

The involved a secondary analysis of Global Burden of Disease (GBD) 2023 data regarding age-standardized prevalence, mortality, and DALY rates are calculated for men and women by country and sub-region using 1990 and 2023.

Overall, prevalence of T2DM increased from 5.6% in 1990 to 11.5% in 2023. Diabetes-related deaths, peaked between 2010 and 2015, declined to approximately 30 per 100,000 by 2023, disability burdens (DALY rates) rose, among men. Regional variations include rapid increases in Turkey, high rates in Levant, upward trends in North Africa (notably Egypt and Tunisia), and highest DALYs in Saudi Arabia and Bahrain (2,600 to 3,400 per 100,000). Iran and Yemen reported low but increasing disability burdens.

T2DM has been consistently increasing over last 30 years in MENA region and has presented itself as a more debilitating and lethal chronic disease over this period. There is a wide range of variation between countries within the MENA region.

Type 2 diabetes mellitus (T2DM) imposes a growing global burden, and glycemic control remains central to the prevention of long-term microvascular and cardiovascular complications. Yoga has been proposed as an adjunctive mind-body intervention, but existing meta-analytic evidence has been constrained by pooling approaches that ignore within-study dependency, dichotomized dose comparisons, and limited reporting of prediction intervals.

PubMed, Embase, Web of Science, and the Cochrane Central Register of Controlled Trials were systematically searched through May 2026 for randomized controlled trials (RCTs) comparing structured yoga interventions with usual care or waiting-list control in adults with T2DM. The primary outcomes were glycated hemoglobin (HbA1c) and fasting plasma glucose (FBG). A Bayesian three-level random-effects model was fitted to accommodate within-study dependency. Categorical subgroup analyses, spline-based meta-regression, sensitivity analyses, publication bias assessments, and GRADE certainty ratings were also performed.

Twenty-eight RCTs comprising 2, 241 adults were included. Yoga reduced HbA1c by 0.64% (95% credible interval [CrI] -0.87 to -0.41) and FBG by 1.36 mmol/L (95% CrI -1.75 to -1.00), with direction probabilities of 100% and extreme Bayes factor evidence against the null hypothesis. The 95% prediction intervals remained below zero for both outcomes, although the upper bound for FBG approached the null. No clear evidence of effect modification was found by country, supervision modality, or the WHO threshold of 600 MET-min per week, although these subgroup comparisons were underpowered and evidence from outside India was limited. GRADE certainty was low for both HbA1c and FBG.

In adults with T2DM, yoga was associated with clinically meaningful reductions in HbA1c and fasting glucose, with the largest benefits in patients with poorer baseline control, although the certainty of this evidence was low. These findings support consideration of yoga as a feasible adjunct to standard diabetes care rather than a fixed prescription.

https://www.crd.york.ac.uk/PROSPERO/view/CRD420261391309, identifier CRD420261391309.

Metabolic dysfunction-associated fatty liver disease (MAFLD) is associated with an increased risk of coronary heart disease (CHD), which is one of the leading causes of chronic disease-related mortality worldwide. Early identification of CHD risk in patients with MAFLD is essential for risk stratification and timely intervention. Although radiomics and deep learning (DL) have shown promising performance in medical image analysis, their application for CHD risk assessment in patients with MAFLD remains limited. Therefore, this study aimed to develop and validate an AI-assisted segmentation-based deep learning radiomics-clinical (DLRC) model for identifying CHD risk in patients with MAFLD.

A total of 1,515 patients with MAFLD, including 440 with concomitant CHD, were retrospectively enrolled between January 2023 and December 2025. Patients were randomly divided into a training cohort (n = 1,060) and a test cohort (n = 455). AI-assisted liver segmentation was performed using a deep learning-assisted framework integrated into ITK-SNAP. Radiomics features were extracted using PyRadiomics, and DL features were extracted using a pre-trained DenseNet-121 network. After feature selection using Pearson correlation analysis, minimum redundancy maximum relevance (mRMR), principal component analysis (PCA), and least absolute shrinkage and selection operator (LASSO) regression, radiomics and DL features were integrated to construct a deep learning radiomics (DLR) model. Clinical predictors were identified using multivariable logistic regression. Clinical, radiomics, DLR, and combined DLRC models were developed and evaluated using machine learning algorithms. Model performance was assessed by receiver operating characteristic (ROC) analysis, calibration curves, DeLong tests, and decision curve analysis (DCA).

Multivariable analysis identified older age, hypertension, diabetes mellitus, hyperlipidemia, male sex, and lower BMI as independent predictors of CHD in patients with MAFLD. Eleven radiomics features and fifteen DLR features were selected for model construction. The DLRC model achieved the best predictive performance, with AUCs of 0.917 in the training cohort and 0.878 in the test cohort, outperforming the DLR model (0.895 and 0.854), radiomics model (0.821 and 0.784), clinical model (0.768 and 0.751), respectively. DeLong tests demonstrated significant superiority of the DLRC model over the other models (all P < 0.05). Calibration and DCA analyses further confirmed its excellent calibration and clinical utility.

An AI-assisted segmentation-based fusion model integrating clinical factors, radiomics features, and DL features demonstrated excellent performance for identifying CHD risk in patients with MAFLD. This non-invasive and efficient approach may facilitate early risk stratification and personalized management of MAFLD patients.

Machine learning-based risk classification of type 2 diabetes mellitus (T2DM) has become a prevailing research direction in intelligent healthcare. Nevertheless, current studies suffer from two key limitations. First, most existing models are built on cross-sectional data and cannot capture the dynamic progression characteristics of T2DM. Second, traditional longitudinal time-series models are commonly adopted without sufficiently exploring the deep interactive relationships among multi-temporal physical examination features, limiting model predictive performance.

To tackle the above drawbacks, this study proposes a novel multi-dimensional feature extraction and fusion network (MFFNet) for T2DM early risk prediction based on sequential annual physical examination data. The proposed architecture comprehensively mines latent feature information from three complementary dimensions: the horizontal interaction of multiple examination indicators within a single period, the longitudinal dynamic evolution pattern of individual indicators across consecutive years, and the cross-temporal synergistic correlations among different indicators across multiple periods. Experiments were conducted on a real-world longitudinal dataset consisting of three consecutive years of physical examination records from the Health Management Center of Beijing Hospital, which presents an extremely imbalanced sample distribution with a positive-negative ratio of 1:8.34.

Comparative experiments with state-of-the-art time-series models (RNN, LSTM, GRU, and Transformer) demonstrate the superiority of MFFNet. The proposed model achieved a sensitivity of 0.8766, a specificity of 0.7082, a negative predictive value (NPV) of 0.9799, and a PR-AUC of 0.4322. Furthermore, SHAP interpretability analysis identified core predictive features for T2DM risk assessment, including age, creatinine (Cr), waist circumference (WC), alanine aminotransferase (ALT), triglycerides (TG), and systolic blood pressure (SBP).

The proposed MFFNet can effectively implement accurate T2DM classification and early risk prediction using longitudinal physical examination sequences. This multi-dimensional feature fusion strategy substantially improves the mining capability of temporal healthcare data. The lightweight and cost-effective MFFNet provides a reliable artificial intelligence-assisted solution for large-scale early pre-screening and risk intervention of T2DM in primary clinical healthcare.

Alpha-lipoic acid (ALA) is widely utilized for diabetic peripheral neuropathy (DPN); however, optimal administration routes (oral, intravenous [IV], or sequential) remain debated. This study evaluates and ranks their efficacy and safety through a Bayesian network meta-analysis (NMA) equipped with rigorous bias-exclusion frameworks.

The PubMed, Embase, Web of Science, Cochrane Library, and Scopus databases were systematically searched (up to 2025) to identify randomized controlled trials (RCTs) comparing oral, intravenous, and sequential ALA therapies, as well as placebo, encompassing varying dosages (600-1,800 mg/d) and treatment durations (3 to 208 weeks), for DPN. Core outcome measures included the Total Symptom Score (TSS), Neuropathy Impairment Score (NIS), Neuropathy Impairment Score in the Lower Limbs (NIS-LL), and Global Satisfaction (GS). The NMA was conducted using a Bayesian framework in R software. Interventions were ranked by calculating the surface under the cumulative ranking curve (SUCRA), and a dual-outcome plot was constructed to evaluate the benefit-risk ratio. Evidence certainty was evaluated via CINeMA, and sensitivity analyses were executed by excluding high-bias studies.

Nine high-quality RCTs were included. Initially, sequential ALA therapy demonstrated overwhelming superiority in improving TSS and NIS. However, CINeMA evaluations revealed severe within-study bias, and sensitivity analyses exposed this initial superiority as an artifact. In the unbiased network, IV ALA emerged as the absolute optimal intervention for rapidly alleviating subjective symptoms (TSS) and overall objective signs (NIS), achieving a "dual-optimal" efficacy-safety profile that completely bypasses gastrointestinal risks. Confronting the most refractory distal impairment (NIS-LL), oral ALA stood alone as the sole surviving intervention supported by robust, completely homogeneous evidence (I² = 0%, SUCRA: 97.4%) for structural repair. Both standalone IV and oral routes significantly enhanced global patient satisfaction.

The optimal ALA administration is stage-dependent. IV ALA delivers unmatched acute neurovascular rescue, while oral ALA serves as the indispensable cornerstone for long-term distal structural repair. Importantly, rather than negating sequential therapy, these distinct phase-specific benefits fundamentally validate its core "induction-maintenance" clinical rationale. While unbiased evidence for the integrated sequential regimen remains sparse-necessitating future large-scale, double-blinded RCTs-the sequential framework itself is robustly justified by the verified strengths of its constituent phases.

CRD420261411001.

To examine the correlation among pre-pregnancy body mass index (BMI), BMI increments during different gestational periods, and the occurrence of macrosomia in singleton full-term pregnant women without complications. Additionally, we aimed to assess the predictive performance of these BMI-related indicators for macrosomia, thereby identifying key time windows for targeted gestational weight management.

An analytical approach was adopted, entailing a retrospective evaluation of clinical data from 4,218 pregnant women without complications who underwent regular prenatal examinations and singleton full-term delivery during the period between January 2019 and December 2025 at the Jinshan Branch of Shanghai Sixth People's Hospital. Based on neonatal birth weight, a case-control design was employed, selecting 200 cases of macrosomia (macrosomia group) and 250 cases of normal birth weight infants (control group). Data including pre-pregnancy BMI, baseline BMI at the first prenatal visit (10-13 weeks), and BMI at various stages of mid- and late-pregnancy (20, 24, 28, 32, 36 weeks, and before delivery) were collected to calculate BMI increments for each stage. The predictive efficacy of individual BMI indicators and a combined model for macrosomia was evaluated using independent sample t-tests, Pearson correlation analysis (PCA), and receiver operating characteristic (ROC) curves.

(1) Increased pre-pregnancy BMI was significantly and positively correlated with an increased macrosomia risk. Pre-pregnancy overweight (OR = 2.63) and obesity (OR = 9.06) emerged as independent risk contributors for macrosomia (P < 0.01). (2) Baseline BMI at 10-13 weeks, along with BMI increments during the 32-36 weeks period and the period from 36 weeks to delivery, exhibited a significant increase in the macrosomia group in comparison to the control group (P < 0.05). (3) The analysis of correlation demonstrated a moderate positive correlation between baseline BMI at 10-13 weeks and neonatal birth weight (r = 0.386, P < 0.001). However, no significant correlation was identified between mid-pregnancy BMI increments and birth weight (P > 0.05). (4) Utilising ROC curve analysis, it was ascertained that baseline BMI at 10-13 weeks exhibited the most optimal predictive efficacy among the individual indicators (AUC = 0.73), with the late-pregnancy increment indicators following in succession. The combined predictive model, integrating early-pregnancy BMI and late-pregnancy BMI increments, achieved a superior AUC of 0.84.

Pre-pregnancy BMI, early-pregnancy (10-13 weeks) BMI, and late-pregnancy (32 weeks to delivery) BMI increments are critical predictors of macrosomia in pregnant women without complications. Specifically, baseline BMI at 10-13 weeks serves as a core indicator for early risk identification. Clinical interventions should focus on these two critical stages-early and late pregnancy-to implement individualized weight management, thereby effectively reducing the incidence of macrosomia and improving maternal and neonatal outcomes.

Type 2 diabetes (T2D) has emerged as a major global public health challenge, with dyslipidemia playing a pivotal role in its pathogenesis. Body fat percentage (BFP), a direct measure of adiposity, surpasses body mass index (BMI) in identifying metabolic risk; however, its prospective association with incident T2D remains unclear.

To systematically investigate the association between BFP and the risk of incident T2D and to identify potential nonlinear thresholds.

A large-scale retrospective cohort study was conducted involving 15,464 Japanese adults with a mean follow-up of 6.05 years. Cox proportional hazards regression models were employed to estimate hazard ratios (HRs), and restricted cubic spline analyses were performed to assess nonlinear relationships.

BFP was identified as an independent risk factor for T2D. After comprehensive adjustment for confounders, each 1% increase in BFP corresponded to a 6% increase in T2D risk (HR = 1.06, 95% CI: 1.04-1.09). Crucially, restricted cubic spline analysis revealed a significant nonlinear "hockey-stick" pattern, identifying an inflection point at 21.65%. Below this threshold, the risk rose sharply with increasing BFP (HR = 1.18, 95% CI: 1.03-1.36); above it, the upward trend plateaued. This association remained robust in subgroups with normal waist circumference and absence of fatty liver disease, underscoring BFP's unique utility in detecting normal-weight obesity.

BFP serves as a crucial predictor of T2D. Maintaining BFP below 21.65% may represent a novel target for personalized diabetes prevention strategies.

To identify hemodynamic phenotypes based on arterial stiffness index (ASI) and pulse pressure (PP) and examine their associations with incident diabetic microvascular complications (DMC) in individuals with type 2 diabetes (T2D).

A total of 9,163 participants with T2D free of DMC at baseline were included from the UK Biobank. K-means clustering based on ASI and PP was used to identify hemodynamic phenotypes. Associations with incident DMC were evaluated using cause-specific Cox and Fine-Gray models. Restricted cubic spline analyses assessed nonlinear associations. Secondary analyses examined diabetic kidney disease (DKD), diabetic retinopathy (DR), and diabetic neuropathy (DN).

During a median follow-up of 12.9 years, 2,349 participants developed DMC. Three phenotypes were identified. Compared with the low ASI-low PP phenotype, the high PP phenotype was associated with a higher risk of DMC in both cause-specific Cox models (hazard ratio [HR] 1.16, 95% CI 1.05-1.28) and Fine-Gray models (subdistribution hazard ratio [SHR] 1.16, 95% CI 1.05-1.29), whereas no significant association was observed for the high ASI phenotype. ASI and PP showed L-shaped and J-shaped associations with DMC, respectively. ASI was primarily associated with DKD, whereas PP was associated with both DKD and DR.

Hemodynamic phenotypes defined by ASI and PP were associated with differential risks of DMC in T2D. Elevated PP, rather than elevated ASI, was consistently associated with increased microvascular risk. Distinct associations of ASI and PP suggest heterogeneous hemodynamic pathways underlying DMC.

Previous research has shown that cardiovascular events are more prevalent among type 2 diabetes mellitus (T2DM) patients; therefore, it is vital to reduce cardiovascular risk factors in this population. Randomized controlled trials (RCTs) investigating the effects of coenzyme Q10 (CoQ10) supplementation on cardiovascular risk factors have reported inconsistent findings. As a result, we intended to assess the effects of CoQ10 supplementation on lipid and glucose profiles, blood pressure, oxidative stress, and inflammation in patients with prediabetes and T2DM.

A systematic literature search was carried out using electronic databases, including PubMed, Web of Science, and Scopus, from inception to May 2025to identify eligible RCTs evaluating the effect of CoQ10 supplementation on cardiovascular risk factors. We used STATA software to combine the individual study results for all outcomes studied. Heterogeneity tests of the selected trials were performed using the I2 statistic. All analyses were performed using random-effects models to account for potential between-study heterogeneity, and pooled data were determined as the weighted mean difference with a 95% confidence interval.

Of 4894 records, 20 eligible RCTs were included in the current meta-analysis. Our meta-analysis of the pooled findings showed that CoQ10 supplementation significantly reduced triglycerides (p=0.001), fasting blood sugar (p<0.001), hemoglobin A1c (p=0.004), Homeostatic Model Assessment for Insulin Resistance (p=0.028) and C-reactive protein (P<0.001), while increasing high-density lipoprotein (p= 0.003). However, CoQ10 supplementation did not significantly affect total cholesterol, low-density lipoprotein, systolic and diastolic blood pressure, and malondialdehyde.

Overall, the results demonstrated that CoQ10 supplementation may improve several cardiovascular risk factors in patients with prediabetes and T2DM. However, further research with long-term interventions and diverse dosages is required.