Non-communicable diseases (NCDs) increasingly affect adolescents, particularly in low- and middle-income countries. Seven key lifestyle behaviours, alcohol and tobacco use, consumption of JUNCS foods (junk, ultra-processed, nutritionally inappropriate, caffeinated/coloured/carbonated foods/beverages, sugar-sweetened beverages); physical inactivity; inadequate sleep; excessive screen time; and stress, collectively termed the 'Big-7 Behavioural Risk Determinants', contribute to NCD onset in adolescence and persistence into adulthood.

To determine the effectiveness of the AdoHealth e-wellness initiative in reducing the Big-7 Behavioural Risk Determinants among school-going adolescents aged 14-17 years, measured as change from baseline at 6 and 12 months post-intervention compared with control schools.

This multisite, open-label, superiority cluster randomised controlled trial will include 180 high and senior secondary schools (Classes 9-12, ages 14-17 years; including public and private) in SAS Nagar (Mohali), Punjab, India, randomly assigned in a 1:1 ratio to intervention or control groups, stratified by school location (urban/rural) and school type (government/private). Participants will be recruited by trained field investigators. Eligible participants are students aged 14-17 years enrolled in participating schools, with written parental consent and adolescent assent; students with serious health conditions or planned school transfer will be excluded. The intervention comprises e-modules (10-15 min each) on the Big-7 Behavioural Risk Determinants, delivered via classroom smartboards with facilitator support, over 8 weekly sessions. A pilot phase will be conducted in one school (approximately 100 adolescents) prior to full-scale implementation. Behavioural and biochemical assessments using standardised tools will be conducted at baseline, 6 months, and 12 months post-intervention. Safety monitoring includes adverse event reporting and referral for psychological support as needed. The planned sample size is 18,000 students (9000 per arm across 90 clusters each). The primary endpoint is the change in the 'Big-7 Behavioural Risk Determinants' at 12 months post-intervention, while secondary endpoints include intermediate behavioural changes, biochemical risk markers, and overall well-being.

This study aims to evaluate the feasibility and potential effectiveness of school-based e-wellness interventions targeting multiple health behaviours simultaneously. If successful, findings may contribute to early NCD prevention strategies and inform policy development for adolescent health promotion programs in low- and middle-income countries.

Trial registration: Clinical Trials Registry of India (CTRI), CTRI/2025/01/079797. Registered on January 30, 2025. https://ctri.nic.in/Clinicaltrials/rmaindet.php?trialid=121397&EncHid=98502.11769&modid=1&compid=19. First participant recruitment commenced on August 16, 2025.

In Malaysia, one third of the adult population suffer from hypertension and literature reported poor sleep quality led to cardiovascular events. However, little study was conducted to look at the gender-specific differences in sleep quality among hypertensive patients. This study aimed to examine gender difference in sleep quality and its associated factors among hypertensive population in a primary care clinic.

This was a cross-sectional study conducted from December 2023 to February 2024 using systematic approach with validated questionnaires (PSQI, DASS-21, STOP-BANG). Multiple logistic regression and Firth's penalized regression (to address sparse data/complete separation) were used to identify the determinants of poor sleep among different gender hypertensive patients.

Three hundred thirty-five participants (62.9% female, mean age 58 years) were recruited in this study. Prevalence of poor sleep among male and female were 91.1% and 84.8% respectively. In the multivariable analysis for men, a lack of regular physical exercise (< 150 min/week) was identified as the sole significant predictor of poor sleep (aOR 6.278, p = 0.040). Among women, poor sleep was significantly associated with anxiety symptoms (aOR 11.486; 95% CI: 1.37-96.21; p = 0.024) and income, where those in the B40 (bottom 40% income) and T20 (top 20% income) groups had higher odds of poor sleep compared to the M40 group (aOR 3.46; 95% CI: 1.23-9.72; p = 0.019).

These findings highlight gender-specific determinants of sleep disturbances among those with hypertension, suggesting tailored interventions addressing physical inactivity for men and socioeconomic support or anxiety management for women to improve sleep quality.

Despite advancements in treatment, diabetic foot ulcers (DFU) are challenging to heal, and the comparative efficacy of debridement strategies is poorly understood. This study assessed the effectiveness and ranked debridement methods for DFU based on wound size reduction (WSR). We conducted a Bayesian network meta-analysis (BNMA) of randomised trials, including individuals with diabetic foot ulcers. PubMed, Embase, Scopus, Web of Science, and Cochrane were examined till August 2025. Seven debridement approaches were compared with each other and with standard wound care (SWC); trial arms were classified by the primary debridement method, with routine wound care co-interventions permitted. We estimated mean differences with 95% credible intervals, ranked treatments using the Surface Under the Cumulative Ranking curve (SUCRA) (0%-100%; higher scores indicate a greater likelihood of best effect), and assessed risk of bias and certainty of evidence using GRADE. Twenty-two RCTs (n = 1148) were incorporated. Biological debridement showed the largest reduction in WSR (MD 29.6%, 95% CrI -4.4 to 64.1), and enzymatic debridement (MD 21.8%, -11.5 to 55.6). Sensitivity analyses supported biological debridement over surgical and SWC, and enzymatic debridement over autolytic debridement. Across all interventions, SUCRA ranked autolytic (85%) and mechanical (75%) highest, whereas the largest estimated WSR were observed with biological and enzymatic debridement. Overall, certainty of evidence was low, although a few comparisons were rated as moderate certainty. Biological and enzymatic debridement seem to be the most successful for decreasing DFU size. However, SUCRA preferred autolytic and mechanical techniques. Evidence is scarce, endorsing personalised care and comprehensive multicenter trials.

The cardiometabolic index (CMI), a combination of lipid parameters and measures of adiposity, has been shown to be associated with a variety of metabolic disorders. However, its relationship with depression in diabetic individuals remains unexplored. This study aims to investigate the association between CMI and depression among adults with diabetes and determine whether a threshold effect exists in this relationship. We analyzed data from 3069 adults with diabetes in the National Health and Nutrition Examination Survey 2005 to 2018. Depression was defined as a Patient Health Questionnaire-9 score ≥ 10. Survey-weighted logistic regression models were used to examine the association between CMI and depression. Generalized additive models assessed nonlinearity, and 2-piecewise linear regression identified inflection points. In addition, sensitivity analyses were performed to determine the stability of our findings. The prevalence of depression was 11.5% among diabetic participants. A significant positive association was observed between CMI and depression (odds ratio [OR]: 1.21, 95% confidence interval [CI]: 1.02 to 1.43, P = .032) in adjusted models. Compared to participants in the lowest CMI quartile, those in the highest quartile had more than double the odds of depression (OR: 2.24, 95% CI: 1.42 to 3.53, P < .001). Two-piecewise logistic regression confirmed a threshold at CMI = 1.66. Below this threshold, each unit increase in CMI was significantly associated with higher depression risk (OR: 2.23, 95% CI: 1.36 to 3.68, P = .001), while above this threshold, no significant association was detected (OR: 0.91, 95% CI: 0.62 to 1.35, P = .649). Among U.S. adults with diabetes, a higher CMI correlates with increased odds of depression in a nonlinear fashion, with risk saturation at approximately CMI 1.66. Addressing dyslipidaemia and controlling lipid levels may lower the risk of depression in patients with diabetes.

Diabetes mellitus (DM) is known to be associated with cardiovascular disease. This study aims to determine the level of knowledge about cardiovascular diseases among DM patients in the Kingdom of Saudi Arabia. This online cross-sectional survey study was conducted between March 2024 and April 2025 in Saudi Arabia. The study population comprised patients diagnosed with DM and aged at least 18 years who were currently residents in Saudi Arabia. The questionnaire for this study was adopted from a previous study by Wagner et al, named the Heart Disease Fact Questionnaire. Logistic regression analysis was conducted to predict significant factors influencing the knowledge of heart disease. Most participants had good knowledge of heart disease risk factors. For example, 210 (94.2%) knew that smoking is a risk factor, and 202 (94.2%) agreed that quitting smoking reduces the risk. In addition, 199 (91.7%) recognized high blood pressure as a risk factor, and 204 (93.4%) knew that controlling it reduces heart disease risk. A total of 200 (94.8%) understood that high blood sugar over time increases cholesterol and heart disease risk, and 194 (92.4%) believed that good blood sugar control helps reduce this risk. Furthermore, some misconceptions were observed: 185 (78.4%) incorrectly believed a person always knows when they have heart disease, and 118 (57.3%) thought people with diabetes rarely have high cholesterol. Smokers had significantly lower odds of good knowledge compared with nonsmokers (odds ratio = 0.32, 95% confidence interval: 0.11-0.90, P = .031). Conversely, participants who adhered to their treatment were significantly more likely to have good knowledge (odds ratio = 4.63, 95% confidence interval: 1.09-19.67, P = .038). The majority of the participants had a high level of understanding of the risk factors for heart disease. There were significant misconceptions regarding diabetes-related cholesterol risk and the symptoms of heart disease. Compliant patients had a higher level of awareness, and smokers had a significantly lower level of knowledge. These findings point to the necessity for educational interventions that are specifically designed to address particular deficiencies.

Gestational diabetes mellitus (GDM) is a significant complication during pregnancy with varying prevalence across countries and ethnicities. In Taiwan, although GDM prevalence rose from 7.6% to 13.4% between 2004 and 2015, its maternal gestational and extended consequences remained underexamined. The nationwide population-based study aims to investigate GDM-related risk factors and identify the critical period during which GDM likely poses long-term health risks.

A total of 206,831 adult pregnant women from the National Health Insurance Research Database were divided into GDM (n = 8,204) and non-GDM (n = 198,627). After 1:1 matching of age and comorbidities, logistic and Cox regression was used to assess the odd and hazard ratio of maternal gestational and extended consequences of GDM. Kaplan-Meier analyses provided follow up events-free outcomes.

The incidence of preterm labor, preeclampsia, and gestational hypertension were significantly higher in the GDM group. The odd ratios of these consequences were 1.72, 2.86, and 2.85, respectively. GDM significantly affected the development of type 2 DM, chronic kidney disease (CKD), and ophthalmic disease. The hazard ratios of these diseases were 2.88, 1.54, and 1.63, respectively. Kaplan-Meier analysis revealed that GDM significantly increased these diseases during follow-up, especially within 2 years for type 2 DM and within 1 year for CKD and ophthalmic disease after delivery.

GDM was associated with higher risks of preterm labor, gestational hypertension, preeclampsia, type 2 DM, CKD, and ophthalmic disease. Postpartum GDM follow-up time is 2 years for type-2 DM and 1 year for CKD and ophthalmic disease.

Diabetic retinopathy (DR) remains a leading cause of blindness among working-age adults, yet scalable risk stratification tools tailored to primary care are lacking-particularly in underserved settings where specialized examinations are unavailable. We aimed to develop and externally validate a pragmatic, web-based nomogram for DR risk prediction using only routinely collected electronic health record (EHR) variables in community-dwelling individuals with type 2 diabetes (T2DM).

This retrospective cohort study analyzed EHR data from two independent Chinese populations. The primary cohort comprised 1,215 T2DM patients from 45 community health centers in Shenzhen, randomly split into training (n=851) and internal validation (n=364) sets. An external validation cohort of 329 patients was obtained from a center in Nanjing. Candidate predictors were screened via univariate analysis and least absolute shrinkage and selection operator (LASSO) regression within the training set. Selected variables were entered into multivariable logistic regression to construct a nomogram, which was deployed as an interactive web application. Model performance was assessed using the area under the receiver operating characteristic curve (AUC-ROC), calibration plots, decision curve analysis (DCA), and clinical impact curves (CIC).

Three predictors-diabetes duration, HbA1c, and high body mass index (BMI ≥24 kg/m², Chinese standard)-were retained in the final model. The model demonstrated robust discrimination: AUC was 0.77 (95% CI: 0.73-0.81) in the training set, 0.79 (0.73-0.85) in internal validation, and 0.81 (0.75-0.87) in external validation. Calibration was adequate, with non-significant Hosmer-Lemeshow tests (P > 0.05) and Brier scores below 0.15 across all cohorts. DCA confirmed positive net benefit over a wide range of threshold probabilities (10-95%), and CIC revealed a 1:1 ratio between predicted and observed DR cases at risk thresholds above 40%.

This three-parameter online nomogram provides a simple, readily implementable tool for DR risk stratification in primary care. Its robust external validation in an independent cohort and reliance on variables universally available in EHRs position it as a cost-effective solution to bridge the screening gap and enable timely specialist referral for high-risk T2DM patients.

Cardiovascular autonomic neuropathy (CAN) may be associated with other diabetes mellitus-related complications. In addition, lower vitamin D (VD) levels have been associated with diabetic kidney disease (DKD) and diabetic neuropathy. We evaluated the relationship between serum VD and CAN in patients with type 2 Diabetes Mellitus (T2DM) in early and advanced stages of DKD.

Seventy-six T2DM patients, 28 in early DKD stage (urine albumin to creatinine ratio (UACR)): 30 to 299 mg/g - group 1), and 48 in advanced DKD stage (UACR ≥300 mg/g - group 2), participated.

In group 1, prevalence of CAN was 46% versus 75% in group 2 (p=0.01). 25(OH)D was lower in group 2 (26.3 ± 9.8 vs 30.0 ± 8.0; p<0.05) and, in this group, those with CAN vs without CAN showed lower 25(OH)D (27.8 ± 8.3 vs 32 ± 6.3; p<0.05). Only in group 2, patients with VD deficiency (<no><20</no> ng/ml) vs normal, showed worse CAN parameters, particularly VLF (65.5 (46-104) vs 309 (106.5-682.5), p<0.01), SDNN (10.5 (8-17.5) vs 28.5 (13-48), p<0.05) and Valsalva Maneuver (1.12 ± 0.04 vs 1.30 ± 0.21, p<0.05). We have found a correlation between VD concentration and CAN prevalence (r = -0.3, p<0.05). Logistic regression showed that VD concentration <no><20</no> ng/ml increased 24 times the chance of abnormal VLF (R²: 0.38; OR: 24.1; 95% (CI: 2.6-222); p<0.01).

To our knowledge, this is the first study to demonstrate an association between lower VD and CAN in T2DM and advanced DKD.

Immune checkpoint inhibitors (ICIs) significantly improve cancer prognosis but are associated with the risk of immune checkpoint inhibitor-related cardiotoxicity (iRCs), a life-threatening complication. Type 2 Diabetes Mellitus (T2DM) further increases the risk of iRCs and worsens outcomes in these patients. Although sodium-glucose cotransporter-2 inhibitors (SGLT2i) confer cardioprotective and potential antitumor effects, their prognostic value in cancer patients with T2DM and established iRCs remains unknown.

To investigate the association of SGLT2i use with all-cause mortality, iRCs severity, and major adverse cardiovascular events (MACE) in cancer patients with T2DM who developed iRCs during ICI therapy.

In this retrospective study, we analyzed 98 cancer patients with T2DM and established iRCs between January 2019 and June 2025. Participants were categorized into an SGLT2i group (n = 26) and a non-SGLT2i group (n = 72). The primary endpoint was all-cause mortality; secondary endpoints included 40-day MACE and iRCs severity. Survival analyses were performed using Kaplan-Meier curves with the log-rank test. Independent associations were assessed via Cox proportional hazards regression.

Median follow-up was 950.5 days. SGLT2i use was independently associated with reduced all-cause mortality (adjusted HR = 0.520, 95% CI: 0.285-0.947, p = 0.033). The SGLT2i group exhibited a longer median survival time (743 days vs. 494 days) and consistently higher 1-, 2-, and 3-year survival rates (73.1% vs. 60.6%; 51.5% vs. 26.4%; 31.2% vs. 8.8%) compared to the non-SGLT2i group. Additionally, the SGLT2i group had a significantly lower proportion of high-grade iRCs (19.2% vs. 45.8%, p = 0.031). Although the incidence of MACE did not differ significantly between groups (19.2% vs. 33.3%, p = 0.271), univariate Cox regression indicated a 47% lower risk of MACE in the SGLT2i group (HR = 0.531, 95% CI: 0.202-1.391, p = 0.198), with numerical reductions observed for both overall MACE and its individual components.

SGLT2i use in cancer patients with T2DM and established iRCs was independently associated with lower all-cause mortality and linked to a reduced incidence of high-grade iRCs and favorable MACE trends. These findings warrant prospective validation to confirm cardioprotective and potential oncologic benefits of SGLT2i in this high-risk population.

Currently, numerous studies have employed machine learning (ML) methods to develop predictive models for depression risk in patients with diabetes mellitus (DM); however, the findings remain inconsistent. Therefore, this study aims to clarify the current state of research and emerging trends in this field by systematically evaluating the performance, strengths, and limitations of existing prediction models.

This systematic review evaluates the performance and clinical applicability of ML-based depression risk prediction models for patients with DM, providing reliable evidence to assist healthcare professionals in selecting and optimizing more appropriate prediction models.

We conducted a systematic search of clinical studies employing ML approaches to predict depression risk in patients with DM across the PubMed, Embase, Cochrane Library, and Web of Science databases, from their inception to January 2026. The primary performance metric for the models was the area under the receiver operating characteristic curve (AUC) along with its 95% confidence interval (95% CI). Two independent researchers screened the literature, extracted data, and used PROBAST-AI to assess the risk of bias and clinical applicability of the included studies. Pooled AUC was estimated using the Der Simonian and Laird random-effects model.

A total of 14 studies comprising 64 distinct ML models were included. All included studies were assessed as high risk of bias and high clinical applicability. A pooled analysis of the best-performing ML prediction models reported in each study showed a pooled AUC of 0.822 (95% CI, 0.789-0.858), indicating relatively good overall predictive performance. However, there was substantial heterogeneity among the studies (I² = 97.4%; P < 0.001). Subgroup analysis based on ML model types revealed the following pooled AUC values: 0.765 (95% CI 0.706-0.829) for traditional regression models, 0.789 (95% CI 0.747-0.834) for general machine learning models, and 0.802 (95% CI 0.769-0.836) for deep learning models. Notably, logistic regression (LR) (n = 10) was the most frequently employed ML method for developing depression risk prediction models in patients with DM. To evaluate model generalizability and avoid overfitting, the included studies adopted three validation strategies: 5-fold cross-validation yielded a pooled AUC of 0.913 (95% CI 0.781-1.067), 10-fold cross-validation yielded 0.819 (95% CI 0.781-0.858), and random split validation yielded 0.747 (95% CI 0.648-0.862). The most commonly used predictors in the included models were age, sex, and body mass index (BMI), which are readily available in clinical settings and strongly associated with depression risk.

ML-based depression risk prediction models for patients with DM demonstrate overall satisfactory predictive performance. However, most existing studies had relatively small sample sizes and lacked external validation. Future research should prioritize refining study design and optimizing clinical data processing to improve the generalizability and stability of these models in clinical practice.

https://www.crd.york.ac.uk/PROSPERO/view/CRD420251243343, identifier CRD420251243343.