BACKGROUND: Obesity is characterized by low‑grade chronic inflammation and impaired insulin sensitivity. Maresin‑1 (MaR1), a specialized pro‑resolving mediator, plays a critical role in terminating inflammation and supporting metabolic homeostasis; however, interventional data in humans remain scarce. This study examined whether fisetin supplementation augments the effects of concurrent interval resistance-aerobic training on Maresin‑1, pro‑inflammatory markers, and insulin resistance in obese men. METHODS: In a 12‑week parallel‑group randomized controlled trial, 44 obese adult males (BMI > 30 kg/m²) completed one of four interventions: control-placebo (CP), fisetin (F) (200 mg/day), training-placebo (TP), or training-fisetin (TF). Training comprised eight resistance exercises at 60% 1RM with active rest followed by progressive aerobic bouts (50%-70% HRmax). Anthropometric and biochemical parameters, including plasma Maresin‑1, interleukin-6 (IL‑6), tumor necrosis factor-alpha (TNF‑α), fasting blood glucose (FBS), insulin, and HOMA‑IR, were assessed pre‑ and post‑intervention. RESULTS: Significant group × time interactions were observed for Maresin‑1 ( = 0.034), IL‑6 ( = 0.001), TNF‑α ( = 0.001), FBS ( = 0.001), insulin ( = 0.001), and HOMA‑IR ( = 0.001). Maresin‑1 increased in the TP ( = 0.001) and TF ( = 0.001) groups. IL‑6 decreased in T ( = 0.006), TF ( = 0.001), and F ( = 0.013) groups. TNF‑α decreased in all intervention groups (F, TP, and TF) ( = 0.002). FBS, insulin, and HOMA‑IR decreased significantly in all active arms ( = 0.003), with the greatest reductions in the TF group. CONCLUSION: Twelve weeks of concurrent interval resistance-aerobic training, especially when combined with fisetin, improved inflammatory resolution (↑Maresin‑1, ↓IL‑6, and ↓TNF‑α) and metabolic control (↓FBS, ↓insulin, and ↓HOMA‑IR) in obese men. The synergy between exercise‑induced adaptations and fisetin's anti‑inflammatory properties offers a promising non‑pharmacological strategy for mitigating obesity‑related metabolic risk.

BACKGROUND: Patch insulin pumps are often treated as a single class, although fully disposable and semi-reusable designs differ in cost and waste. We evaluated real-world clinical, pharmacoeconomic, and environmental outcomes of a semi-reusable tubeless insulin pump (Microtech Equil™, referred as SR-TIP). METHODS: Prospective, multicentre, open-label real-world study in adults with type 1 or type 2 diabetes transitioning from continuous subcutaneous insulin infusion (CSII) or multiple daily injections (MDI). Follow-up was 3 months. Primary endpoints were HbA1c non-inferiority and change in hypoglycaemic event frequency. Secondary endpoints included safety, device deficiencies, patient-reported outcomes (Diabetes Treatment Satisfaction Questionnaire, Device Assessment Questionnaire), monthly disposable treatment costs, and waste based on disposable component counts and material composition. RESULTS: Ninety-seven participants completed follow-up (CSII n = 79; MDI n = 18). HbA1c changes met non-inferiority criteria in both groups. Hypoglycaemic events decreased by 45% in CSII users and 86% in MDI users. Device-related adverse events were infrequent and mainly mild. Among participants previously using fully disposable tubeless pumps, mean monthly disposable treatment costs decreased by €108.6 (p < 0.001). The semi-reusable architecture eliminated routine disposal of batteries/electronic components and reduced overall waste. CONCLUSIONS: In routine care, a semi-reusable tubeless pump maintained glycaemic control while reducing hypoglycaemia and disposable costs versus fully disposable patch pumps, with measurable reductions in electronic waste. These data support value-based, sustainable diabetes technology adoption.

INTRODUCTION: Visual impairment and blindness continue to represent a substantial disease burden in Hungary. According to national epidemiological data, the combined prevalence of bilateral blindness and severe visual impairment among individuals aged 50 years and older is approximately 0.9%, and international estimates suggest that around 90% of vision loss cases could be prevented or treated with appropriate care. However, the coverage of ophthalmic screening remains low, primarily due to the lack of targeted financing, limited ophthalmology workforce capacity, and the absence of a unified national screening protocol. OBJECTIVE: The aim of our study is to review the professional, organizational, financial, legal and ethical conditions for the implementation of artificial intelligence-supported ophthalmic screening in Hungary, with a particular focus on diabetic retinopathy. METHOD: We conducted a targeted narrative literature review of national epidemiological, human resource, and cost data, as well as an analysis of international diabetic retinopathy screening models and the European Union regulatory frameworks for medical devices and artificial intelligence, using sources selected based on clinical and public health relevance. RESULTS: The level of Hungarian ophthalmological screening practice is insufficient to significantly reduce the burden of preventable vision impairment, primarily due to limited human resources and funding constraints. The current human resource capacity of the Hungarian ophthalmic care system is insufficient to provide the approximately one million diabetic fundus examinations required annually according to professional guidelines. Preventive and screening activities are not organized as dedicated services but are largely delivered as part of routine ophthalmic outpatient care, without separate financing. International experience indicates that the use of artificial intelligence as a decision-support or triage tool can reduce specialist workload while maintaining diagnostic accuracy. CONCLUSION: Artificial intelligence-supported fundus screening systems have the potential to improve access to screening, consistency, and efficiency. The introduction of artificial intelligence-based fundus screening in Hungary would require the establishment of appropriate financing mechanisms, regulation of task-sharing involving optometrists and allied health professionals, and compliance with relevant regulatory and ethical frameworks. A transitional hybrid model - combining the pilot use of an internationally validated artificial intelligence system in parallel with launch of domestic development - may offer a realistic pathway toward a structured national screening program and contribute to reducing the disease burden of preventable blindness. Orv Hetil. 2026; 167(22): 865-875.

BACKGROUND: The majority of the burden of metabolic dysfunction-associated steatohepatitis (MASH) exists in primary care; however, the prevalence of severe disease in patients with metabolic dysfunction remains poorly described. METHODS: A cross-sectional study of 84 general practices located in Victoria, Australia identified patients with metabolic dysfunction over a 12 month period from the PATRON data repository. At-risk MASH was defined using the Fibrotic NASH Index (FNI) based on laboratory data. Patients needing further liver assessment were defined by FIB-4 ≥ 1.3 (≥ 2.0 if 65+ years) and = < 2.67. Advanced liver fibrosis was defined as a FIB-4 of > 2.67. RESULTS: 110,938 individuals were identified with metabolic dysfunction. Of those with a FNI available (n = 16,586), the prevalence of At-risk MASH was 24.3%. Among those with a FIB-4 available (n = 64,948), 16.3% required further liver assessment, while the prevalence of advanced fibrosis was 4.3%. On multivariate analysis, male sex, type 2 diabetes, and hypertension were associated with At-risk MASH, whereas male sex and type 2 diabetes were associated with advanced liver fibrosis. Socio-economic disadvantage was a risk factor for both At-risk MASH and advanced liver fibrosis. CONCLUSIONS: Significant liver disease was observed in a substantial proportion of patients with metabolic dysfunction within primary care, particularly among patients with type 2 diabetes, supporting the rationale for risk stratification. Associations with socio-economic disadvantage highlight the need for societal and public health policy change to improve liver outcomes.

High-intensity interval training (HIIT) and moderate-intensity continuous training (MICT) are widely used strategies for managing type 2 diabetes mellitus (T2DM), but evidence on their comparative effects and practical feasibility is limited. A systematic search of CNKI, Wanfang, PubMed, Web of Science, and EBSCO-SPORTDiscus was conducted up to January 31, 2026, to identify randomized controlled trials enrolling adults with T2DM. Twenty studies (n = 981; 49.6% male; mean age 58.04 ± 10.08 years; mean body mass index 28.03 ± 4.43 kg/m) with interventions lasting ≥8 weeks reporting fasting blood glucose (FBG), fasting insulin (FINS), homeostatic model assessment of insulin resistance (HOMA-IR), glycated hemoglobin (HbA1c), high-density lipoprotein (HDL), low-density lipoprotein (LDL), triglycerides (TG), or total cholesterol (TC) were included. Data were extracted using standardized spreadsheets. Statistical analysis included standardized mean differences (SMDs) with 95% confidence intervals, heterogeneity assessment, and Egger's test for publication bias. Compared with routine pharmacological treatment, HIIT significantly improved FBG (SMD = -1.24), HbA1c (-1.40), HOMA-IR (-1.03), FINS (-0.97), HDL (0.86), and TG (-0.55) (all < .05), while no significant differences were observed for LDL, TC, or between HIIT and MICT. A 12-week, thrice-weekly HIIT protocol was most effective for glycemic control. High-intensity interval training's time efficiency and motivational appeal may enhance adherence and support implementation in clinical practice and public health programs.

OBJECTIVES: Children and adolescents with Down syndrome (DS) are at increased risk of cardiometabolic impairment, but this vulnerability may not be adequately captured by body mass index (BMI) alone. This study aimed to characterize anthropometric, metabolic, and hepatic alterations in pediatric DS beyond conventional BMI-based assessment. METHODS: We included 95 children and adolescents aged 5-18 years: 48 with DS and 47 controls. Anthropometric assessment included weight, height, waist circumference, BMI z-score, waist-to-height ratio (WHtR), and predicted body composition. Fasting blood samples were analyzed for glucose, insulin, homeostatic model assessment for insulin resistance (HOMA-IR), triglyceride-glucose (TyG) index, lipid profile, and liver enzymes. Between-group differences were assessed using age- and sex-adjusted analysis of covariance, while multivariate analyses identified the variables contributing most strongly to group discrimination. RESULTS: Compared with controls, children with DS showed no significant difference in BMI z-score, but had higher WHtR and % fat mass and lower % fat-free mass (p<0.05). They also exhibited a less favorable metabolic profile, with higher fasting glucose, HOMA-IR, and TyG index, lower HDL-cholesterol, higher triglycerides, and higher liver enzyme levels (p<0.05). Multivariate analyses confirmed that liver enzymes, glycemic markers, HDL-cholesterol, and WHtR were the strongest contributors to group separation. CONCLUSIONS: Children and adolescents with DS display an early high-risk cardiometabolic phenotype characterized by altered body composition, insulin resistance, dyslipidemia, and biochemical signs of hepatic involvement only partly reflected by BMI. These findings support broader metabolic surveillance in pediatric DS to improve early risk stratification and guide preventive interventions.

INTRODUCTION: Driving requires complex cognitive, motor, and sensory coordination, all of which may be adversely affected by diabetes. For individuals treated with insulin, hypoglycemia represents the principal safety concern. Although many jurisdictions provide guidance for insulin-treated drivers, adherence to these recommendations remains unclear. METHODS: An online survey was conducted among 500 licensed drivers with insulin-requiring type 2 diabetes (T2D) from the United States and the United Kingdom, not using continuous glucose monitoring (CGM). The survey included discrete and open-ended questions relating to diabetes management and driving behaviors. Descriptive and inferential statistical analyses were performed. RESULTS: Participants were predominantly male (59%, n = 293) with a mean T2D duration of eight years. Insulin regimens included basal-only therapy (54%, n = 257), multiple daily injections (39%, n = 197), and continuous subcutaneous insulin infusion (9%, n = 46). Severe hypoglycemic event(s) in the preceding 12 months were reported by 34%, and 54% had evidence of impaired awareness of hypoglycemia (Clarke score >4). Furthermore, 37% (n = 185) limited their driving due to insulin-related concerns, and 72% (n = 359) worried particularly about hypoglycemia. Only 34% (n = 168) reported checking glucose levels before driving >75% of the time. If feeling hypo before driving, 59% (n = 295) would take a snack but drive immediately. If feeling hypoglycemic while driving, 10% (n = 48) would attempt to 'get to their destination fast'. Only 27% (n = 138) carried fast-acting carbohydrates, and 42% (n = 210) felt safe to drive 'as soon as I feel better' while 32% (n = 163) would 'wait 45 minutes and recheck their glucose levels'. CONCLUSIONS: Potentially unsafe driving behaviors are common among adults with insulin-treated T2D. There appears to be a need to improve the understanding of safe driving guidance.

Sarcopenia, characterized by the age-related decline in skeletal muscle mass, strength, and function, is associated with high healthcare costs and significant health risks, including falls, fractures, functional decline, and mortality. Despite its prevalence and extensive research, there are currently no Food and Drug Administration (FDA)-approved drugs to modify its course, likely due to an incomplete understanding of its underlying mechanisms. Recent evidence highlights two key factors in sarcopenia development: (1) Disrupted circadian rhythms affecting pathways such as protein remodeling, insulin resistance, and mitochondrial function; (2) systemic chronic low-grade inflammation (SCLGI). This review focuses on circadian rhythm regulators implicated in skeletal muscle deterioration, examining their roles, potential interactions, and the impact of circadian disruption on sarcopenia progression. Additionally, we explore how clock genes reciprocally influence the inflammatory profile, which is crucial for developing treatment strategies to mitigate the detrimental effects of sarcopenia. We also examine factors that influence the clock and have the potential to restore circadian rhythm mechanisms that are deregulated in sarcopenia. Drawing from these insights, strategies aimed at restoring circadian synchrony and resolving inflammation are proposed as a novel therapeutic approach to effectively mitigate the manifestations of sarcopenia.

BACKGROUND: Type 1 autoimmune pancreatitis (AIP) is the pancreatic manifestation of IgG4-related disease (IgG4-RD) and is an uncommon cause of diabetes mellitus. CASE PRESENTATION: Here, we report the case of a 65-year-old white Australian man who was diagnosed with diabetes mellitus secondary to possible IgG4-related disease and had a good clinical and radiological response to corticosteroid therapy. CONCLUSIONS: This case report highlights that IgG4-related disease should be considered in patients who present with acute hyperglycemia, have negative GAD and IA2 antibody status and lack features of insulin resistance. Furthermore, corticosteroid therapy can lead to an improvement in glycemic control in patients with diabetes mellitus secondary to IgG4-related disease.

BACKGROUND: The prevalence of diabetes mellitus in the general population has been increasing in recent years, and diabetes-related complications contribute substantially to morbidity, mortality, and healthcare system burden. This study aimed to evaluate the relationship between glycated hemoglobin (HbA1c) levels and inflammatory markers, including the neutrophil-to-lymphocyte ratio (NLR), monocyte-to-lymphocyte ratio (MLR), and platelet-to-lymphocyte ratio (PLR), in individuals diagnosed with type 2 diabetes mellitus (T2DM). METHODS: This retrospective case-control with longitudinal follow-up study included 138 patients with T2DM followed at the Internal Medicine Outpatient Clinic of Başakşehir Çam and Sakura City Hospital (HbA1c > 7%: n = 37; HbA1c < 7%: n = 101) and 118 healthy control subjects. HbA1c, white blood cell (WBC) count, NLR, MLR, and PLR values obtained at baseline and follow-up outpatient visits were retrospectively retrieved from the hospital information system and compared between groups. RESULTS: Glucose and HbA1c levels differed significantly among the groups, with the highest values observed in the uncontrolled diabetes group (p < 0.001). WBC and neutrophil counts were also significantly higher in patients with uncontrolled diabetes (p < 0.001). Monocyte counts, as well as NLR and MLR levels, were significantly increased in this group (p < 0.05). At the third-month follow-up, patients in the uncontrolled diabetes group exhibited significant changes in glucose, HbA1c, neutrophil, lymphocyte, NLR, and PLR levels (p < 0.05), whereas the change in MLR did not reach statistical significance (p = 0.083). Correlation analysis demonstrated significant positive associations between diabetes duration, glucose, and HbA1c levels and both NLR and MLR (r = 0.316-0.354, p < 0.001). Additionally, moderate positive correlations were observed among NLR, MLR, and PLR at both baseline and third-month measurements (p < 0.001). CONCLUSION: NLR, MLR, and PLR are low-cost and easily accessible inflammatory markers that change in parallel with HbA1c levels and may be useful for monitoring glycemic control and disease progression in T2DM. These findings support the potential clinical utility of inflammation-based risk assessment in patients with type 2 diabetes mellitus. CLINICAL TRIAL NUMBER: Not applicable.

BACKGROUND: Hypoglycemia is a serious complication of diabetes. Early recognition of hypoglycemia can improve clinical prognosis, however, traditional diagnostic tools are often limited. Machine learning offers a promising approach for predicting adverse outcomes in diabetic patients. OBJECTIVE: This study aims to develop and validate machine learning-based models to predict the risk of hypoglycemia in type 2 diabetes mellitus (T2DM) patients. METHODS: A cohort study design was employed. Clinical data were collected from the electronic health record system. The dataset was randomly partitioned into training and validation subsets using a 7:3 ratio. Four machine learning algorithms, logistic regression (LR), Extreme Gradient Boosting (XGBoost), random forest (RF), and support vector machine (SVM) were implemented to develop hypoglycemia risk prediction models. Predictive performance was assessed using sensitivity, specificity, accuracy, precision, F1 score, and the area under the receiver operating characteristic curve (AUC). RESULTS: 831 T2DM patients were included, the hypoglycemia incidence was 22.0%. In the training cohort, the AUC for the LR, XGBoost, SVM, and RF models were 0.82, 0.86, 0.84, and 0.80, and corresponding AUCs were 0.76, 0.78, 0.72, and 0.75 in the validation cohort. The XGBoost demonstrated the highest overall predictive performance. Feature importance analysis based on the XGBoost model identified creatinine, triglycerides, albumin, HbA1c, C-peptide, aspartate aminotransferase, hemoglobin, and sulfonylurea use as the most influential predictors of hypoglycemia risk. CONCLUSIONS: The XGBoost model exhibited superior predictive performance for achieving the higher AUC, F1 score, greater accuracy, sensitivity and specificity. This model enables effective identification of T2DM patients who may require intensified monitoring or targeted interventions to prevent hypoglycemic events. CLINICAL TRIAL NUMBER: Not applicable.

Olfaction and its dysfunction have gained increasing interest across a broad spectrum of research areas including ageing and neurodegenerative and psychiatric disorders. In addition, olfactory dysfunction is increasingly recognised as a common feature of metabolic disorders, with mounting evidence linking an impaired sense of smell to obesity and type 2 diabetes. While the olfactory system was once considered negligible in humans, it has now emerged as a critical modulator of feeding-related behaviours, endocrine regulation, and energy and glucose homeostasis. Studies in both humans and animal models highlight the bidirectional interaction between olfactory processing and metabolic status, suggesting that olfactory changes are not merely consequences of obesity and type 2 diabetes, but may also contribute to their development and progression. This review summarises current findings on the mechanisms associated with olfactory dysfunction in obesity and type 2 diabetes in rodent models, from the initial detection of odorants in the nasal cavity to the downstream neural circuits and their physiological and behavioural outcomes. Particular emphasis is placed on the emerging concept of sensory regulation of metabolism, highlighting how food sensory cues, including food odours, influence whole-body metabolism and obesogenic responses. Finally, the therapeutic potential of targeting the olfactory system to improve olfactory performance and the olfactory regulation of whole-body metabolism is discussed. Olfactory-based therapeutics may offer novel and promising strategies for the prevention and treatment of metabolic diseases.

OBJECTIVE: Evidence on metformin's skeletal effects remains conflicting. We emulated a target trial to evaluate associations between metformin therapy and deep learning-based CT-derived vertebral bone mineral density (vBMD). We also assessed variation across prespecified subgroups. MATERIALS AND METHODS: Within the Multi-Ethnic Study of Atherosclerosis (MESA), incident metformin users (Exams 4 and 5) were compared with propensity score-matched controls. Noncontrast chest CT scans from Exams 5 and 6 were processed using a previously validated deep learning-based vertebral segmentation and calibration pipeline to quantify trabecular vBMD from T1 to T10. Median imaging follow-up was 6.4 years. Linear mixed-effects models estimated annualized Fracture Risk Assessment Tool (FRAX) absolute vBMD change, applying inverse probability of censoring weights. Prespecified subgroup analyses examined demographic, metabolic, and inflammatory modifiers. RESULTS: Among 238 trial entries (86 metformin, 152 controls), metformin was not associated with overall vBMD change (time × treatment interaction β, 0.27 mg/cm per year; 95%CI, -0.07 to 0.61; p = 0.12). Substudy-specific and per-protocol estimates were consistent. Favorable associations were observed in women, body mass index (BMI) < 30 kg/m, ASCVD risk < 0.2, hs-CRP < 2 mg/L, never-smokers, and nondrinkers. Significant effect modification was found for gender, hs-CRP, and smoking status, with borderline trends for BMI. CONCLUSIONS: Metformin use was not associated with overall CT-derived vBMD change. Subgroup analyses indicate heterogeneity of association across demographic, metabolic, and inflammatory profiles, with more favorable associations among women and participants with healthier risk profiles. Findings support metformin's skeletal safety and warrant future context-specific trials.

This study aimed to investigate glycemic variability and biochemical profiles before and after gastrectomy using continuous glucose monitoring (CGM) and to evaluate whether postoperative improvements occur in blood glucose levels and biochemical parameters. We analyzed data from patients who underwent surgery for gastric cancer. Glucose profiles were assessed using a CGM device before gastrectomy and after discharge. All patients discontinued antidiabetic medications one day before surgery and resumed them only after reassessment at outpatient follow-up. CGM-derived metrics were evaluated according to international consensus recommendations, including time above range (TAR), time below range (TBR), time in range (TIR), CV (coefficient of variation), and MAGE (mean amplitude of glycemic excursions). A total of 33 patients were included. The mean duration of diabetes was 16.9 ± 10.4 years, and 12.1% were receiving insulin therapy. Mean glucose levels and TAR significantly decreased after gastrectomy while TIR significantly increased. Although TBR did not increase significantly, adherence to hypoglycemia targets worsened postoperatively. Glucose variability, assessed by CV, remained stable. Also, most patients maintained CV values below the threshold for glycemic instability. MAGE significantly decreased after surgery, indicating a reduction in large-amplitude glucose excursions. Among 16 patients whose postoperative medication changes were evaluated, seven (43.8%) reduced their antidiabetic medication after gastrectomy. In conclusion, despite the temporary discontinuation of antidiabetic medications, hyperglycemia improved without aggravation of glycemic variability, as reflected by increased TIR, stable CV and reduced MAGE. However, the achievement of hypoglycemia targets was reduced. Therefore, instead of routinely resuming preoperative antidiabetic regimens, postoperative glucose management should be individualized based on CGM-derived glycemic profiles.

Diabetes mellitus (DM) is an escalating global public health concern, with a rapidly increasing burden in low- and middle-income countries, including Bangladesh. Despite its growing prevalence and associated complications such as cardiovascular disease, kidney failure and stroke, comprehensive evidence on its determinants and predictive modeling at the population level remains limited. This study aimed to predict the DM and identify its associated risk factors using ensemble machine learning (EML) approaches among adults in northern Bangladesh. A community-based cross-sectional study was conducted among 1408 adults in Dinajpur district between March 25 and June 5, 2025, using structured and pilot-tested questionnaires administered through face-to-face interviews. Feature selection was performed using Recursive Feature Elimination, Random Forest importance and Best First Search methods. Six machine learning models were developed, followed by a stacking ensemble model to enhance predictive performance. Model evaluation was based on accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC). Model interpretability was assessed using SHAP analysis, and findings were validated using multivariable logistic regression. The prevalence of DM was 15.1% in the study population. Among individual models, LightGBM demonstrated the highest performance (accuracy: 89.44%; AUC: 0.958 [95% CI 0.945-0.973]), followed by XGBoost (accuracy: 88.69%; AUC: 0.955 [95% CI 0.945-0.972]). The stacking ensemble model outperformed all base learners, achieving an accuracy of 91.67% and an AUC of 0.967 (95% CI 0.957-0.981). SHAP analysis identified age, family history of diabetes, BMI, weight, dietary behaviors (particularly low vegetable intake and added salt/sugar), family income, and gender as key predictors. Multivariable logistic regression confirmed these findings, showing that advancing age especially 51-60 years, female gender, family history of diabetes, hypertension, kidney disease and low vegetable consumption were independently associated with DM. Therefore, stacking-based ensemble learning significantly improves the predictive accuracy of DM while enabling robust identification of key risk factors. The consistency between machine learning and traditional statistical approaches strengthens the validity of the findings. These results highlight the importance of integrating advanced analytical methods into public health research to support early detection, targeted prevention, and evidence-based decision-making in resource-constrained settings such as northern Bangladesh.

Direct vascular aging assessment is not always feasible in routine diabetes care. We aimed to derive a control-based arterial age reference using estimated pulse wave velocity, develop a biomarker-enhanced model for predicting arterial age gap, and evaluate its ability to identify accelerated arterial aging in young-to-middle-aged adults with type 2 diabetes mellitus. This study included 300 participants (150 T2DM, 150 age- and sex-matched controls). Arterial age was derived from a control-group ePWV-age regression. Age gap was defined as estimated arterial age minus chronological age. Candidate predictors were evaluated using multivariable linear regression, and the final model was internally validated by 10-fold cross-validation. Compared with controls, participants with T2DM had higher ePWV, older estimated arterial age, larger age gap, lower adropin, and higher oxLDL (all p < 0.001). Accelerated arterial aging was more frequent in T2DM than controls (76.0% vs. 20.0%). The final model integrating HbA1c, adropin, and oxLDL explained 42% of the variance in age gap (adjusted R²=0.418), showed good discrimination for accelerated arterial aging (AUC 0.889; 95% CI 0.828-0.910), and retained acceptable internal calibration (slope 0.943). A biomarker-enhanced arterial age model integrating HbA1c, adropin, and oxLDL provided an interpretable framework for identifying accelerated arterial aging in young-to-middle-aged adults with T2DM. Although promising for translational implementation, external validation and direct pulse wave velocity benchmarking are required before clinical application. The model also enabled a three-level arterial aging classification and web-based implementation for research use, supporting its potential as a practical risk-communication tool prototype.

Coronary heart disease (CHD) and diabetes mellitus frequently co-occur through shared mechanisms such as oxidative stress and inflammation. Whether specific dietary antioxidants mitigate CHD-diabetes comorbidity remains unclear. Using National Health and Nutrition Examination Survey (NHANES) 2005-2018 data (n = 9,279), we developed an interpretable machine-learning pipeline in which standardisation and Synthetic Minority Over-sampling Technique (SMOTE) were embedded inside each fold of tenfold cross-validation to prevent data leakage. Six algorithms (Random Forest, Light Gradient Boosting Machine (LightGBM), K-nearest neighbours, Naive Bayes, support vector machine, eXtreme Gradient Boosting (XGBoost)) were compared on discrimination, calibration and decision-curve net benefit. XGBoost achieved the highest AUC-ROC (0.774, 95% CI 0.759-0.788); Random Forest showed the lowest Brier score (0.111), the calibration slope closest to unity (0.939) and the highest net benefit, and was retained for interpretation. Weighted-quantile-sum regression showed an inverse association between the antioxidant composite and comorbidity risk (OR per quantile 0.87, 95% CI 0.80-0.95; P = 0.001). In mutually adjusted logistic regression, only magnesium retained an independent protective association (per 1 SD: OR 0.80, 95% CI 0.66-0.96; P = 0.016). SHAP identified theobromine (0.020) and lycopene (0.016) as leading protective contributors. Findings support targeted dietary-antioxidant strategies as candidate modifiable factors for cardiometabolic comorbidity prevention.

Chikungunya fever (CHIKF) is a re-emerging viral disease characterized by acute systemic manifestations and debilitating musculoskeletal symptoms that can persist after viral clearance. Although typically self-limiting in healthy individuals, clinical outcomes are significantly worsened in patients with pre-existing comorbidities, particularly diabetes mellitus (DM). Epidemiological data links DM to heightened CHIKF severity and a greater risk of developing chronic arthropathy, yet the mechanism underpinning this association remains poorly understood. In this study, we established an in vivo streptozotocin (STZ)-induced diabetic C57BL/6 mice as a model to investigate the impact of DM on CHIKV pathogenesis. STZ induces selective pancreatic β-cell destruction and persistent hyperglycemia. Diabetic animals infected with CHIKV exhibited aggravated joint inflammation, increased nociceptive sensitivity, and elevated serum markers of muscle and hepatic injury, including creatine kinase (CK) and alanine aminotransferase (ALT) compared to normoglycemic infected animals. Histopathological analyses revealed that CHIKV infection alone disrupted joint architecture and initiate degenerative alterations. However, in hyperglycemic mice, CHIKV-induced lesions were markedly intensified, with pronounced inflammatory infiltrates, chondrocyte depletion, osteocyte necrosis, and fibrotic remodeling, closely resembling osteoarthritic damage. These aggravated histopathological outcomes were not associated with increased CHIKV load, but rather to the diabetic metabolic milieu. These results offer a plausible mechanistic explanation for the poorest CHIKF outcomes observed in diabetic patients. Thus, this model provides a valuable platform for exploring the molecular drivers of CHIKF severity and chronicity, especially among DM patients, as well as for development of pharmacological tools to mitigate CHIKV-associated complications in metabolically vulnerable populations.

ETHNOPHARMACOLOGICAL RELEVANCE: Diabetic osteoporosis (DOP) is a type of metabolic bone disease. Blossom of Citrus aurantium L. var. amara Engl. (CAVA), a medicinal and edible herb widely employed in China which can alleviate indigestion and replenish qi, has been demonstrated to be effective in treating metabolic disorders such as obesity and diabetes. However, its effects on DOP remains uncertain. AIM OF THE STUDY: To explore the protective effects and mechanisms of blossom of CAVA against DOP. MATERIALS AND METHODS: Ethanol extract of blossom of CAVA (CAVAE) was characterized using HPLC/MS. Network pharmacology, deep learning, molecular docking and molecular dynamics simulations were employed to predict key constituents and possible pathways. High-fat diet and streptozocin-induced type 2 diabetes mellitus (T2DM) mice were established and Micro-CT, X-ray imaging, morphological analysis and molecular biology assays were performed to investigate the effects and underlying mechanisms of CAVAE on DOP. RESULTS: The 44 compounds identified by HPLC/MS analysis suggest that CAVAE may prevent DOP via PI3K/AKT and Wnt/β-catenin pathways. Experimental validation demonstrated that CAVAE significantly improved diabetes and osteoporosis in T2DM mice, as evidenced by the decreased blood glucose content, increased bone mineral density, enhanced trabecular bone number, improved femoral bone microstructure, restored femur stability, bone metabolism and reduced serum calcium ion levels, etc. Mechanistic investigations further confirmed that CAVAE probably alleviated DOP in T2DM mice by modulating PI3K/AKT and Wnt-3a/β-catenin pathways. CONCLUSION: CAVAE might exert anti-osteoporotic effects on T2DM mice through concurrent activation of PI3K/AKT and Wnt-3a/β-catenin pathways.

BACKGROUND: Glucagon-like peptide-1 receptor agonists (GLP-1RAs) are increasingly utilized for the management of Type 2 Diabetes Mellitus (T2DM) and weight loss. While their metabolic benefits are established, their influence on bone metabolism and arthrodesis remains uncertain. PURPOSE: This study aimed to evaluate the association between GLP-1RA use and the incidence of midfoot pseudoarthrosis in diabetic patients. STUDY DESIGN: Retrospective cohort study METHODS: A retrospective analysis was performed using the TriNetX US database. Adults ≥40 years with type 2 diabetes who underwent midfoot arthrodesis were identified. The exposed group included diabetic patients with GLP-1RA exposure within six months preoperatively (n=767), while the control group comprised patients without GLP-1RA use (n=7,306). After 1:1 propensity score matching, 766 patients remained in each cohort. The primary outcome was pseudoarthrosis (ICD-10 code M96.0) within five years. Secondary outcomes included hardware infection, wound complications, and hardware removal within five years. RESULTS: After propensity matching, GLP-1RA users had no statistically significant difference in pseudoarthrosis incidence compared with non-users at 6 months (8.3% vs 6.5%, P>.05), 1 (12.4% vs 9.8%, P>.05), 2 (14.3% vs 11.3%, P>.05), and 5 years (15.0% vs 12.6%, P>.05) postoperatively. At 5 years postoperatively, GLP-1RA exposure was not associated with increased risk of hardware infection, wound complications, or hardware removal. CONCLUSION: Preoperative GLP-1RA exposure was not associated with a higher incidence of pseudoarthrosis following midfoot fusion. These findings support GLP-1RA use in patients undergoing midfoot fusion and contribute to the growing body of literature on GLP-1RA impact on bone metabolism. LEVEL OF EVIDENCE: Level III, retrospective cohort study.

TNF-α (Tumor Necrosis Factor) is a proinflammatory cytokine that amplifies inflammatory response and promotes leukocyte recruitment. TNF-α is primarily produced by activated macrophages, among others, in response to infection, inflammation, or tissue damage. Given its central role in normal and abnormal immune responses, it is the target of several therapeutics, such as adalimumab and etanercept. TNF-α is also a prognostic and diagnostic biomarker associated with rheumatoid arthritis, Alzheimer's disease, multiple sclerosis, several kidney diseases, cancers, type 2 diabetes, sepsis, and others. Because TNF-α levels change dynamically during inflammatory responses, tools capable of sensitive and spatially resolved detection could enable improved monitoring of immune activity and disease progression. Single-walled carbon nanotubes (SWCNT) are cylindrical carbon lattices that emit distinct near-infrared bandgap photoluminescence. In this work, we evaluated three aptamer-based sensor constructs, plus an additional two iterations of one aptamer sequence, and two antibody-based sensor constructs for TNF-α that use SWCNT near-infrared photoluminescence signal transduction. Several, but not all, of these aptamer and antibody-based sensors sensitively and selectively detected TNF-α in human and bovine serum in a physiologically relevant range, and we found that their sensing was impacted by both passivation and incorporating an exogenous quencher onto the aptamer sequence. This study highlights the importance and challenges of translating previously-validated molecular recognition elements to new detection conditions, in this case on the surface of SWCNT and in challenging serum conditions. It also validated a lead sensor, the VR11-SWCNT aptamer construct with or without quencher chemistry and with surface passivation, that builds upon constructs that failed in serum. These results demonstrate a strategy toward synthesis of nanoscale optical sensors capable of detecting TNF-α. We anticipate that the sensors evaluated here will have utility in both the diagnosis and study of inflammation-driven chronic disease, while the sensor assessment framework will help drive the broader field of molecularly specific diagnostics.

Psychiatric disorders frequently co-occur with endocrine, nutritional, and metabolic disorders, complicating diagnosis, treatment, and prognosis. To date, no umbrella review (UR) has summarized the corresponding evidence. We conducted a UR of meta-analyses of observational studies reporting the prevalence and outcomes of comorbid endocrine, nutritional, metabolic, and psychiatric disorders (DSM/ICD criteria, PubMed/Medline/Scopus/Embase/Web of Science, 13/10/2025). Meta-analytic prevalence and association estimates were recalculated/and graded using quantitative criteria. The methodological quality of the meta-analyses was assessed with AMSTAR-2. Subgroup and meta-regression were conducted. Eighty-one meta-analyses (records=254,154,533, k=1,780) yielded 119 meta-analytic estimates. Among 64 prevalence estimates drafted from the original meta-analyses, 10(6.4%) had strong credibility, namely (among adults unless otherwise specified): obesity in autism spectrum disorder(17.0%, 95%C.I.=13.0-22.0); Vitamin-D deficiency in schizophrenia(65.3%, 95%C.I.=46.4-84.2); major depressive disorder(MDD) in diabetic foot ulcer(47.0%, 95%C.I.=26.0-85.0), in diabetes mellitus(61.8%; 95%C.I.=56.6-66.7%), in metabolic syndrome (30.5%; 95%C.I.=26.3-35.1), and in polycystic ovary syndrome(PCOS)(37.0%; 95%C.I.=29.0-44.0%); anxiety in PCOS(48.0%, 95%C.I.=37.0-59.0), in type-1 diabetes(21.3%, 95%C.I.=17.8-26.7); type-2-diabetes in MDD(8.7%; 95%C.I.=7.3-10.2%); and mild cognitive impairment in type-2-diabetes(45.0%; 95%C.I.=36.0-54.0%). Five of 43(11.7%) outcomes met strong credibility criteria. Comorbid-MDD vs. non-comorbid-MDD posed a higher risk for all-cause mortality (relative risk/RR=1.48, 95%C.I.=1.4-1.6) and dementia (RR=2.1, 95%C.I.=1.7-2.6) among diabetic people. ADHD-comorbidity increased glycate hemoglobin-A1C levels among type-I diabetes children (SMD=0.7, 95%C.I.=0.5-0.9) and type-2 diabetic females (SMD=0.6, 95%CI=0.4-0.7). Diabetes increased the risk of 30-day hospital readmission (RR=1.2, 95%C.I.=1.1-1.3) in people with dementia. This study provides a comprehensive atlas of the prevalence and outcomes associated with multimorbidity across endocrine, nutritional, metabolic, and psychiatric diseases, assessing credibility and prompting integrated, multidisciplinary care.

BACKGROUND: CHG index, a novel metabolic biomarker calculated from total cholesterol, high-density lipoprotein cholesterol, and fasting blood glucose, is associated with type 2 diabetes and cardiovascular diseases. However, its association with the risk of new-onset sarcopenia in middle-aged and older adults remains unclear. METHODS: We included participants aged ≥50 years from the CHARLS 2011-2012, with follow-up in 2015-2016. Cumulative CHG was derived from measurements in 2011 and 2015. New-onset sarcopenia was diagnosed according to the 2025 AWGS consensus. Multivariable Cox regression and logistic regression were used to examine the associations of baseline and cumulative CHG with new-onset sarcopenia. ROC curves were employed to compare the predictive capability of CHG and TyG. RESULTS: A total of 5580 participants were included in the baseline analysis. During the 4-year follow-up, 590 (10.6%) participants developed sarcopenia. Compared with the lowest quartile (Q1) of baseline CHG, the highest quartile (Q4) was significantly associated with an increased risk of sarcopenia (HR = 1.287, 95% CI: 1.022-1.619) in the fully adjusted model. K-means clustering categorized 3287 participants into two distinct groups based on cumulative CHG trajectories. Compared with the low-stable group (Cluster 1, n = 1783), the high-declining group (Cluster 2, n = 1504) had a significantly higher risk of new-onset sarcopenia (OR = 1.25, 95% CI: 1.02-1.45). RCS analysis revealed a linear association between baseline CHG and sarcopenia risk. The predictive performance of the baseline and cumulative CHG index was modestly higher than that of the baseline and cumulative TyG index. CONCLUSION: Elevated baseline and cumulative CHG indices are independent risk factors for new-onset sarcopenia in middle-aged and older Chinese adults.

Prediabetes is a silent, common, and increasingly prevalent condition that carries a high risk of progressing to type 2 diabetes mellitus (T2DM). Obstructive sleep apnea (OSA) and prediabetes have been proposed to have a bidirectional relationship. We conducted a thorough literature search on PubMed, MEDLINE, Scopus, and Google Scholar using the keywords 'prediabetes,' 'impaired glucose tolerance,' 'OSA,' and 'sleep disordered breathing.' This narrative review aims to synthesize the epidemiology, connections, and mechanisms underlying the interaction between prediabetes and OSA. Patients with OSA experience intermittent hypoxia (IH) and sleep fragmentation due to upper airway collapse. This can lead to sympathetic nervous system activation, systemic inflammation, and changes in metabolic risk factors. Continuous positive airway pressure (CPAP) is the gold standard treatment for OSA, providing symptomatic relief and potentially improving related conditions. We present a summary of evidence indicating that treating OSA may potentially enhance glucose metabolism and insulin sensitivity. Furthermore, CPAP therapy may improve parameters of glucose metabolism, but evidence that it slows progression from prediabetes to T2DM remains limited.

OBJECTIVES: Dementia and cardiometabolic diseases are both characterized by long prodromal phases, which may complicate the assessment of their temporal relationships. Associations between cardiometabolic risk markers and cognition remain inconsistent. We examined the associations between cardiometabolic risk markers and cognitive function in middle-aged and older adults. METHODS: A longitudinal analysis was conducted using data from a population-based cohort (2007-2014) of 1255 cognitively normal Koreans aged ≥ 50 years at baseline. Cardiometabolic risk markers and Korean Mini-Mental State Examination (K-MMSE) scores were repeatedly assessed through health examinations and interviewer-administered questionnaires. Multivariable linear regression (LR) and generalized estimating equation (GEE) models were used to investigate the associations between cardiometabolic risk markers and K-MMSE scores. RESULTS: After full adjustment for covariates, increases in the homeostasis model assessment of insulin resistance (HOMA-IR) and fasting insulin levels were associated with larger percent declines in K-MMSE scores and with lower K-MMSE scores in LR and GEE models (log-transformed HOMA-IR: β = -1.03, 95% CI: -1.87 to -0.20 for LR; β = -0.25, 95% CI: -0.41 to -0.08 for GEE). By age group, increases in HOMA-IR and blood pressure were associated with declines in K-MMSE scores among adults aged ≤ 65 years. By cognitive domain, increases in blood pressure were associated with declines in memory, while increases in HOMA-IR were marginally associated with declines in visuospatial ability (p = 0.052). High-density and low-density lipoprotein cholesterol levels were not significantly associated with cognitive function across all analyses. CONCLUSIONS: Our findings suggest that longitudinal increases in insulin resistance and blood pressure are associated with cognitive decline, particularly among middle-aged adults (≤ 65 years), and may differentially influence cognitive domains.