ObjectiveThis study employed data derived from a nationally representative panel survey to investigate the impact of comorbidities on the health-related quality of life (HRQoL) of individuals living with diabetes mellitus.MethodsA retrospective, cross-sectional, population-based analysis was conducted using data from 10,049 participants in the Korea Health Panel Survey (KHPS) in 2020. The participants were divided into three groups: individuals without diabetes, individuals with diabetes only, and individuals with diabetes and comorbidities. HRQoL was assessed using the European Quality of Life-5 Dimensions (EQ-5D) tool. Multiple regression analyses were performed to examine the contribution of comorbidities to HRQoL scores.ResultsComorbidities were associated with lower scores in physiological, psychological, and overall HRQoL. A significant reduction in HRQoL was observed with comorbidities, and a negative association was established between the number of comorbidities and physiological and overall HRQoL (P < .001). Demographic factors such as age, education, and disability significantly influenced HRQoL. In addition, psychological factors such as high stress had a substantial impact on HRQoL, particularly in individuals with diabetes and comorbidities (P < .01).ConclusionComorbidities considerably impair HRQoL among patients with diabetes. Health providers should consider the presence of comorbidities and diseases in diabetes when developing intervention methods to effectively manage diabetes and patients' well-being.

To identify effective interventions to lower cardiovascular disease (CVD) risk factors among Asian Indian (AI) immigrants.As the second largest immigrant group in the USA, AI immigrants experience CVD prevalence rates as high as 13% among males and 4.4% among females; these rates are significantly higher than other Asian subgroups and the general US population. Despite extensive knowledge of CVD risk factors, there have been few cardioprotective interventions in this population.

Interventional studies of first-generation AI immigrants aged 18-70 years, conducted between 2000 and 2025, will be identified, including randomised controlled trials, factorial and cross-over designs, and cluster randomised trials. We will include AIs with and without a specific family history of CVD, or personal history of type 2 diabetes mellitus, hyperlipidaemia or obesity. Exclusion criteria include AIs born in the USA or visiting.The Arksey and O'Malley methodology framework and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews will be used for analysis. A preliminary review of interventions to lower the CVD risk in AIs will guide the research questions (stage 1). Relevant articles published between 2000 and 2025 will be retrieved using electronic databases and search terms (stage 2). Two independent reviewers will select studies based on eligibility criteria (stage 3). Reviewers will determine eligibility status, screen full texts and retrieve relevant publications. Reviewers will extract data, capturing study design, sample characteristics, types of interventions, outcomes and key findings (stage 4). A summary of results will be presented (stage 5). The review will identify the most effective interventions, potential areas for future research and practical recommendations to improve CVD outcomes among AI immigrants.

Included studies will meet ethical standards in research. Findings will be disseminated through manuscripts, presentations at relevant conferences and community outreach programmes to promote effective interventions.

Most research on the relationship between diabetes and cognitive health has used data from high-income countries. This study described this relationship in India, the world's most populous country.

Cross-sectional analysis of the baseline wave of the nationally representative Longitudinal Ageing Study in India, conducted from 2017 to 2019.

All 36 Indian states and union territories.

57 905 adults aged 45 years or older.

Scaled cognitive scores (mean of 0 and SD of 1) and cognitive impairment defined as a cognitive score 1.5 SD or below the age-matched and education-matched mean. Diabetes was defined as a self-report of a prior diabetes diagnosis made by a health professional or having a measured haemoglobin A1c ≥6.5%.

In age-adjusted and sex-adjusted models, people with diabetes had cognitive scores that were 0.24 SD higher (95% CI 0.22 to 0.26) and had a 1.2% (95% CI 0.6% to 1.7%) lower prevalence of cognitive impairment than people without diabetes. Differences persisted even when adjusting for demographic, socioeconomic and geographical characteristics. Rural versus urban residence modified the relationships of diabetes with cognitive score (p=0.001) and cognitive impairment (p=0.003). In fully adjusted models, rural respondents with diabetes had 0.05 SD (95% CI 0.03 to 0.07) greater cognitive scores and 1.6% (95% CI 0.9% to 2.4%) lower prevalence of cognitive impairment than those without diabetes. In urban areas, respondents with and without diabetes had similar cognitive scores and prevalence of cognitive impairment.

Middle-aged and older adults with diabetes living in India had better cognitive health than those without diabetes. Rural versus urban area of residence modified this relationship. Urban-rural differences, the nutrition transition and social conditions likely influenced the cross-sectional relationship between diabetes and cognitive health in India, leading to different associations than reported in other countries.

Diabetic kidney disease (DKD) is characterized by progressive fibrosis, oxidative stress, and mitochondrial dysfunction, contributing to renal dysfunction. EphrinB2, a cell surface protein, has been implicated in tissue repair and fibrosis, but its role in DKD remains poorly understood. This study investigates the impact of EphrinB2 expression on renal fibrosis, mitochondrial dynamics, and cellular signaling pathways in DKD.

EphrinB2 expression and function were investigated in renal tissues from DKD patients, STZ-induced diabetic mice, and HG-treated HK-2 cells. EphrinB2 overexpression was achieved using AAV in vivo and lentiviral vectors in vitro. Functional assessments included histological and biochemical evaluations, while mechanistic studies utilized siRNA knockdown, pathway-specific inhibitors and activators, and co-immunoprecipitation to explore the role of the Epac1-Rap1 signaling pathway in EphrinB2-mediated antifibrotic and mitochondrial protective effects.

EphrinB2 expression was significantly downregulated in the kidneys of DKD patients and STZ-induced diabetic mice, correlating with increased fibrosis and tubular injury. Overexpression of EphrinB2 (EphrinB2-OE) in diabetic mice restored renal function, reduced fibrosis, alleviated oxidative stress, and preserved mitochondrial structure. In HK-2 cells, EphrinB2-OE mitigated HG-induced fibrosis, reduced ROS levels, and restored MMP and ATP production. Mechanistically, EphrinB2-OE enhanced the Epac1-Rap1 pathway, stabilizing Epac1 protein and promoting mitochondrial biogenesis via PGC-1α. Additionally, EphrinB2-OE modulated the E-cadherin/β-catenin complex and preventing β-catenin nuclear translocation, and preserving epithelial integrity and epithelial-to-mesenchymal transition (EMT).

EphrinB2 exerts protective effects against renal fibrosis and dysfunction in diabetic conditions by regulating fibrosis pathways, mitochondrial dynamics, and epithelial stability. Targeting EphrinB2 signaling presents a promising therapeutic strategy for diabetic kidney disease.

Pancreatic cellular heterogeneity is fundamental to systemic metabolic regulation, yet its pathological remodeling in diabetes remains poorly characterized.

We integrated single-cell RNA sequencing with machine learning frameworks to decode pancreatic heterogeneity. Novel tools included PanSubPred (two-stage feature selection/XGBoost classifier) for multi-lineage annotation and PSC-Stat (XGBoost/Gini optimization) for stellate cell activation analysis.

By establishing PanSubPred, we systematically decoded pancreatic cellular diversity, identifying 64 cell-type-specific markers (38 novel) that maintained cross-dataset accuracy (AUC > 0.970) even after excluding known canonical markers. Building on this annotation precision, we developed PSC-Stat to quantify stellate cell activation dynamics, revealing their progressive activation from diabetes to pancreatic cancer (activated/quiescent ratio: control: 1.44 ± 1.02, diabetes: 4.72 ± 4.01, pancreatic cancer: 18.67 ± 18.70). Diabetes reorganized intercellular communication into ductal-centric hubs via FGF7-FGFR2/3, EFNB3-EPHB2/4/6 and EFNA5-EPHA2 axes, from which we derived a 15-gene signature for diabetic ductal cells (AUC = 0.846). Beta cell heterogeneity analysis uncovered diabetes-associated depletion of mature insulin-secretory clusters (INS + NKX6-1+), expansion of immature (CD81 + RBP4+) and endoplasmic reticulum stress-adapted subtypes (DDIT3 + HSPA5+). Moreover, non-beta lineages exhibited parallel dysfunction: acinar cells shifted toward inflammatory states (CCL2 + CXCL17+), while ductal cells adopted secretory phenotypes (MUC1 + CFTR+).

This study presents a machine learning-based single-cell framework that systematically maps pancreatic cellular alterations in diabetes. The identified novel signatures, stellate activation dynamics, and beta cell maturation trajectories may serve as potential targets for diabetic management and pancreatic cancer risk stratification.

The increasing prevalence of obesity among individuals with type 1 diabetes mellitus (T1DM) presents a significant clinical challenge, as it exacerbates insulin resistance, impairs glycemic control, and increases cardiometabolic risk. While obesity in T1DM is influenced by both genetic and environmental factors, recent evidence highlights the role of the gut-brain axis in metabolic regulation. This review explores the complex relationship between obesity and T1DM, the role of the gut-brain axis in metabolic dysregulation, and current weight management strategies, highlighting the need for further research to optimize treatment outcomes in this unique patient population.

Key gastrointestinal hormones, including glucagon-like peptide-1 (GLP-1), glucose-dependent insulinotropic peptide (GIP), and amylin, play essential roles in appetite control, energy balance, and glucose metabolism, yet their dysregulation in T1DM remains poorly understood. Addressing obesity requires a multifaceted approach, including lifestyle modifications, pharmacotherapy with GLP-1 receptor agonists (GLP-1RAs), and bariatric surgery (BS). Although limited, accumulating evidence regarding the use of liraglutide, semaglutide and tirzepatide in T1DM begin to highlight the safety and effectiveness of these molecules in this subset of patients as well. Lifestyle modifications, GLP-1 RAs based pharmacotherapy and BS have emerged as potential strategies to address obesity in patients with T1DM. Initial findings point to potential improvements in both metabolic health and glycemic control, but further exploration of their role in the co-occurrence of obesity and T1DM remains limited. Ongoing research is crucial to better understand how the gut-brain axis influences weight regulation in T1DM and to determine the sustained benefits and risks of these emerging therapies.

Pancreatic β-cell dysfunction and mass loss are core pathologies of type 2 diabetes mellitus (T2DM), which are closely related to intense autoinflammation. However, the molecular mechanisms regulating β-cell autoinflammation remain unclear. Here, we show that STING is significantly elevated in T2DM β cells. We also clarify the key role of uncoupling protein 2 (UCP2), and reveal that interleukin-1β (IL-1β) drives β cells to produce autoinflammation through the UCP2/mtDNA/STING axis in T2DM. To inhibit UCP2 activity in vivo, we design a tailored nanomedicine, Mito-G, with sequential targeting from islets to β-cell mitochondria. Mito-G is a negatively charged ultra-small nanomedicine synthesized by polymerization of genipin (a potent UCP2 inhibitor) and glycine. It can specifically reach β cells and have a natural mitochondrial targeting. In this work, Mito-G effectively eliminates β-cell auto-inflammation by specifically inhibiting β-cell UCP2 activity in vivo, providing a paradigm for targeting autoinflammation of β cells to treat T2DM.

Empagliflozin and dapagliflozin, frequently prescribed Sodium-Glucose- Cotransporter2-inhibitors (SGLT2i), are now also recommended for nondiabetic patients with heart failure (HF). Since concerns exist about myopathy and sarcopenia as adverse effects of SGLT2i, the review summarizes effects of dapagliflozin and empagliflozin on skeletal muscle. PubMed was searched for "SGLT2" OR "SGLT2 inhibitors" OR "SGLT2i" OR "dapagliflozin" OR "empagliflozin" AND "myopathy" OR "sarcopenia" OR "skeletal muscle" OR "muscle loss" OR "muscle weakness" OR "muscle wasting" OR "myotoxicity" OR "muscle atrophy" OR "rhabdomyolysis". Excluded were reviews, editorials, study protocols, letters to the editor, in vitro and in vivo studies. Myopathy/rhabdomyolysis were reported in 5 patients, 3 with concomitant statins, whereas 3 clinical/pharmacovigilance studies found no increased myopathy/rhabdomyolysis-risk with a SGLT2i-statin-combination. SGLT2i-associated sarcopenia was studied in small cohorts (<100 patients in 65 % of the studies), with diabetes-mellitus (68 %) and Japanese (53 %). Observation time was >6 months in only 32 % of the studies, HF-patients were included in 26 %. Controversial data are reported for muscle-mass, which decreased in 15 and remained unchanged in 6 studies. Physical-performance-tests remained unchanged under SGLT2i. Muscle-biopsies found SGLT2i-assosciated metabolic changes and myofiber-atrophy, but no correlation with functional tests were reported. Exercise or dietary measures did not influence SGLT2i-associated loss of muscle-mass. Two pharmacovigilance studies identified SGLT2i as risk factors for sarcopenia. Data on SGLT2i-associated effects on skeletal muscle are limited and controversial. There is an urgent need for research, especially in nondiabetic, non-Japanese and HF-patients. Since sarcopenia develops over time, observation periods of >12 months are necessary.

Hyperglycaemia after noncardiac surgery is rarely detected outside of the critical care environment, yet occurs commonly and is associated with excess complications including infections and myocardial injury. Systematic, prospectively collected data regarding the accuracy of continuous glucose monitoring commenced immediately before surgery and throughout the early perioperative period are lacking.

We prospectively enrolled patients aged >50 yr undergoing noncardiac surgery who required at least 24 h of hospital stay. We used real-time continuous glucose monitoring (Dexcom G7 sensor, placed in the upper outer arm) (Dexcom, San Diego, CA, USA) with reference values from arterial blood glucose measurements by amperometry. The primary outcome was the overall mean difference (bias) before surgery, at end of surgery, and 24 h after surgery (Bland-Altman analysis). Secondary outcomes included the mean absolute relative difference and surveillance error grid analyses.

We compared paired blood (73% arterial) and continuous glucose monitoring glucose values at each prespecified timepoint in 118 participants (64/118 [54%] female; mean age: 66 [range: 51-89] yr; 25% with diabetes mellitus). The overall bias between continuous glucose monitoring and blood glucose from measurements at each of the three timepoints in the first 24 h after induction of anaesthesia was 0.38 mM (95% confidence interval [95% CI]: 0.23-0.53; n=340 paired readings). Bias decreased from before the start of surgery (1.08 mM [95% CI: 0.87-1.29]; n=116) to 0.15 mM at the end of surgery (95% CI: -0.15 to 0.46; n=113). Mean absolute relative difference ranged from 12.0% to 18.3%. Error grid analyses found that >98% continuous glucose monitoring values were within acceptable risk ranges.

The accuracy of state-of-the-art continuous glucose monitoring is sufficient for perioperative use and could enhance perioperative surveillance of dysglycaemia.

ISRCTN46862025.

The patient was a 41-year-old man with a history of diabetes mellitus since the age of 22 years and a family history of diabetes in his mother. He had repeated headaches, cerebellar symptoms, and bilateral cerebellar lesions on brain MRI (DWI, T₂ FLAIR hyperintensities). He was previously diagnosed with cerebellitis and treated with steroids at another hospital. Upon his third relapse, he was referred to our department. Clinical findings included short stature, diabetes, elevated CSF lactate and pyruvate levels, and increased lactate levels on aerobic exercise testing. Muscle biopsy revealed mitochondrial pathological abnormalities, and genetic testing of muscle tissue identified the mitochondrial DNA m.3243 mutation. These findings led to the diagnosis of mitochondrial encephalomyopathy, lactic acidosis, and stroke-like episodes (MELAS). Stroke-like lesions in MELAS can involve not only the cerebrum but also the cerebellum. When cortical-predominant lesions with associated vascular dilatation are observed, MELAS should be considered in the differential diagnosis.