To explore the formation mechanism of self-perceived burden (SPB) in elderly patient with diabetes with frailty from the perspective of social ecological theory.

Using purposive sampling, 21 frail elderly patient with diabetes (aged ≥60 years, Tilburg Frailty Index ≥5, Self-Perceived Burden Scale score ≥20) were recruited from a tertiary hospital in Wuxi, China. One-on-one semi-structured interviews were conducted using an interview guide based on social ecological theory. Data were analyzed with NVivo 12.0 using theory-driven and data-driven qualitative content analysis.

Five themes with core connotations and 12 sub themes were identified: (1) Physiological disability: the loss of self-integrity caused by the progression of complications, debilitating experiences and impairment of self-care; (2) Management dilemma: management frustration and loss of life meaning caused by the conflict between subjective efforts and poor outcomes; (3) Relationship tension: the common sense of emotional debt in spousal care, intergenerational support and social withdrawal; (4) Environmental exclusion: the double marginalization experience caused by barriers to the physical environment at home and the intensification of the digital divide; (5) System imbalance: the sense of powerlessness caused by the lack of humanistic care in medical services and limited medical insurance support.

SPB is shaped by multi‑level interactions within the social ecological system. Findings can inform clinical practice and policy: stratified psychological interventions, family support optimization, age‑friendly environmental and digital inclusion, and chronic disease insurance refinement. Nurses can serve as cross‑level integrators in implementing these strategies.

The triglyceride-glucose body mass index (TyG-BMI), a novel composite measure integrating lipid-glucose metabolism and obesity, has shown promise in predicting diabetes risk. Nevertheless, its association with the onset of prediabetes remains insufficiently investigated, particularly concerning potential nonlinear relationships.

Cox proportional hazards models were used to calculate hazard ratios (HRs) and 95% confidence intervals (CIs). Nonlinear relationships were evaluated through the use of smooth curve fitting and threshold effect models. Additionally, interaction tests and subgroup analyses were undertaken to examine the robustness of the associations found.

Following comprehensive adjustment, a 10-unit increment in TyG-BMI correlated with an 11% elevated risk of developing prediabetes (Hazard Ratio [HR] 1.11, 95% Confidence Interval [CI] 1.09-1.13). A significant nonlinear association was observed, with an inflection point identified at a TyG-BMI value of 189.37. Below the threshold, each unit increase in TyG-BMI was linked to a 2% rise in risk (HR 1.02, 95% CI 1.02-1.03), whereas above this point, the risk increase diminished to 1% per unit (HR 1.01, 95% CI 1.01-1.01).

This research identifies a significant, nonlinear association between TyG-BMI and the likelihood of developing prediabetes. Additional research is required to thoroughly examine their relationship and clarify the underlying mechanisms.

Bullous pemphigoid is the most common autoimmune subepidermal blistering disease, typically diagnosed through a combination of clinical features, histopathology, and direct immunofluorescence (DIF). However, histopathological findings can be misleading when re-epithelialization shifts the blister plane from subepidermal to suprabasal, mimicking pemphigus vulgaris. We present the case of a 58-year-old woman with type 2 diabetes mellitus, diabetic neuropathy, and a sacral pressure ulcer admitted with a three-week history of generalized tense bullae in a symmetric distribution across the trunk and extremities, with subsequent oral mucosal involvement. The disease was classified as severe bullous pemphigoid. Skin biopsy revealed a suprabasal blister with eosinophilic infiltrate, a pattern classically associated with pemphigus vulgaris; however, DIF demonstrated linear immunoglobulin G and complement C3c deposition at the dermoepidermal junction, confirming bullous pemphigoid and categorically ruling out pemphigus. Initial treatment with systemic corticosteroids and immunosuppressants was complicated by septic shock secondary to a catheter-related bloodstream infection, hospital-acquired pneumonia, and urinary tract infection, requiring vasopressor support, adjustment of the immunosuppressive regimen, and antimicrobial escalation. The patient also developed progressive multifactorial anemia, sepsis-associated coagulopathy, and upper gastrointestinal bleeding. Following multidisciplinary management, she achieved near-complete resolution of skin lesions and clinical recovery, and was discharged with plans for rituximab as definitive steroid-sparing therapy.  This case highlights two critical lessons: first, a suprabasal blister on histopathology does not exclude bullous pemphigoid, as re-epithelialization can alter the apparent cleavage plane, making DIF indispensable for an accurate diagnosis; second, severe bullous pemphigoid requiring intensive immunosuppression carries a substantial risk of sequential nosocomial infections warranting close microbiological surveillance, early source control, and timely modulation of immunosuppressive regimens.

Diabetic patients face elevated risks of severe COVID-19, yet the molecular underpinnings of disease progression, particularly for Omicron subvariants, which have predominated since late 2021, remain poorly defined. This observational study leverages a cohort recruited during China's Omicron peak (December 2022-February 2023) to delineate multi-omic signatures underlying severe diabetic COVID-19.

We enrolled 55 patients across five clinical strata: non-diabetic mild/severe, diabetic mild/severe/fatal. Integrated 4D-DIA proteomics and LC/GC-MS metabolomics were applied, adjusting for confounders while retaining secondary infections as integral disease features.

Diabetic patients exhibited immune exhaustion with elevated IL-6/IL-10, blunted antiviral responses, and high secondary infection rates. We identified 62 diabetes-unique molecules associated with coordinated dysregulation across six pathways: oxidative stress, ferroptosis, glycolytic dysfunction, lipid remodeling, insulin signaling, and endothelial injury. A graded molecular signature tracked clinical deterioration: progressive depletion of GP1BB and PRG3, coupled with stepwise elevation of MMP-3/LOXL1 and Dl-Xylose. Fatal cases further revealed a metabolic substrate misalignment, glycolytic flux adduct accumulation paradoxically coexisting with glucose, lactate, ornithine depletion, suggesting terminal fuel utilization failure. Stage-dependent shifts of stress mediators (e.g., GSK3B, ALDH9A1) distinguished severe from fatal outcomes, implying transition from compensatory adaptation to homeostatic exhaustion.

Severe diabetic COVID-19 is characterized by progressive immune-metabolic collapse. GP1BB, MMP-3, and Dl-Xylose warrant evaluation as early-warning biomarkers, while ornithine and PC(18:2) may track homeostatic reserve depletion in terminal disease. Together, these findings identify candidate biomarkers and pathway nodes that, with further validation, could contribute to risk-stratification and therapeutic strategies for diabetic patients with severe viral infections.

To investigate the feasibility of applying hyperspectral imaging technology combined with machine learning algorithms for early non-invasive screening of type 2 diabetes mellitus (T2DM).

In this study, tongue images of 153 subjects were acquired using a hyperspectral imaging system. Concurrently, 53 tongue coating samples were collected for 16S rRNA sequencing. Spearman correlation analysis was performed between tongue hyperspectral data and tongue coating microbiota. Finally, machine learning was integrated to construct a T2DM disease status identification model based on tongue hyperspectral data.

The experimental results revealed that 10 genera exhibited significant differences among the three groups (P < 0.05). Among these, the relative abundance of unclassified_p_Bacillota, Pseudoleptotrichia, Xylanibacter, Gemella, norank_f_Mitochondria, and Enhydrobacter showed significant correlations with hyperspectral tongue images (P < 0.05). The T2DM disease status identification models constructed based on tongue hyperspectral data achieved favorable modeling results, with the optimal model demonstrating ACC = 0.8966 and AUC = 0.8029.

The results of this study indicate that certain correlations exist between tongue hyperspectral features and tongue coating microbial genera in T2DM patients at different disease stages. The T2DM disease status identification model constructed based on tongue hyperspectral imaging and machine learning algorithms can be used for the identification of HC, IGR, and T2DM, and this method may provide a noninvasive and rapid screening approach for early identification of T2DM.

Insulin signaling has been implicated in reward processing and motivational regulation through its modulatory effects on mesolimbic pathways. However, reward-related behavioral functioning across different diabetes subtypes and insulin-related metabolic states remains poorly understood. This study aimed to compare motivational and reward-related behavioral measures among individuals with type 1 diabetes mellitus (T1DM), type 2 diabetes mellitus (T2DM), prediabetes, and healthy controls, and to investigate their associations with diabetes self-management and glycaemic control.

This cross-sectional study included 44 individuals with T1DM, 43 with T2DM, 28 with prediabetes, and 46 healthy controls. Participants completed measures assessing behavioral inhibition/activation (BIS/BAS), anhedonia, delay discounting, depressive symptoms, and diabetes self-management. Group differences were examined using analyses of covariance controlling for age, sex, education level, and depressive symptoms. Correlation and hierarchical regression analyses were conducted to evaluate associations with diabetes self-management and HbA1c levels.

Significant group differences were observed for BIS and BAS Reward Responsiveness scores after false discovery rate correction (p = .004). Post hoc analyses demonstrated that individuals with T1DM had significantly lower BIS and BAS Reward Responsiveness scores compared to T2DM, prediabetes, and healthy controls. No significant group differences were observed for anhedonia or delay discounting measures. Reward-related behavioral measures explained additional variance in HbA1c (ΔR² = .038, p = .035), with lower BAS Drive associated with higher HbA1c after adjustment for demographic and clinical covariates (β = -.162, p = .036).

Altered reward responsiveness and behavioral inhibition may be particularly associated with T1DM rather than T2DM or prediabetes. Reward-related behavioral functioning also showed a modest association with glycaemic control, with BAS Drive demonstrating the most consistent individual association with HbA1c. These findings highlight the potential clinical relevance of motivational and reward-related processes in diabetes and suggest that considering these behavioural dimensions may help inform more individualized approaches to diabetes management and glycaemic control.

To investigate the associations of glycemic burden-adjusted urinary glucose excretion deficit (GB-UGE deficit) with serum uric acid levels and prevalent hyperuricemia in patients with type 2 diabetes mellitus (T2DM).

A total of 887 patients with T2DM were enrolled. GB-UGE deficit was defined as the inverse standardized residual from a linear regression of ln(urinary glucose excretion + 1) on glycated hemoglobin, fasting glucose, 2-hour postprandial glucose and eGFR; a higher GB-UGE deficit indicates less urinary glucose excretion than expected for a given glycemic burden. Participants were grouped by hyperuricemia status, with baseline demographic, metabolic and renal data collected. Statistical analyses included multiple linear regression, quartile-based logistic regression, stepwise-adjusted logistic regression, restricted cubic splines (RCS), ROC curves and sensitivity analyses.

Patients with hyperuricemia had a higher GB-UGE deficit (P < 0.001). In the fully adjusted linear model, each 1-SD higher GB-UGE deficit was associated with a 15.44umol/L higher serum uric acid concentration (95% CI: 9.89 to 20.98,P < 0.001). RCS analysis demonstrated an overall association between GB-UGE deficit and serum uric acid (P for overall association < 0.0001), with borderline evidence of nonlinearity (P for nonlinearity = 0.0479); the association with prevalent hyperuricemia was approximately linear. Quartile analyses showed progressively higher odds of prevalent hyperuricemia (P for trend < 0.001), and the sensitivity analyses produced estimates in the same direction. In the exploratory ROC analysis, the AUC increased by 0.021 from 0.787 for the conventional model to 0.808 for the model containing GB-UGE deficit (DeLong P = 0.0014); however, the AUC increment between the UGE-containing model and the GB-UGE deficit model was merely 0.003 and lacked statistical significance.

In this single-center cross-sectional study of hospitalized T2DM patients, GB-UGE deficit was associated with elevated serum uric acid and prevalent hyperuricemia. As an exploratory cohort-specific residual index rather than a validated clinical marker, it may signal suboptimal urinary glucose excretion, with hypothetical links to disturbed renal tubular glucose and urate co-transport. Prospective multicenter external cohorts are required to verify its reproducibility, biological mechanism and translational value.

Peak width of skeletonized mean diffusivity (PSMD) is a diffusion tensor imaging-based marker of diffuse white matter microstructural injury that has been associated with cerebral small vessel disease (CSVD). This study aimed to investigate whether patients with end-stage kidney disease (ESKD) exhibit altered PSMD compared with healthy controls.

We prospectively enrolled 38 ESKD patients and 38 age- and sex-matched healthy controls. Diffusion tensor imaging (DTI) was performed using a three-tesla MRI scanner in patients with ESKD and healthy controls. We obtained PSMD based on DTI in several steps, including preprocessing, skeletonization, application of a custom mask, and histogram analysis. We compared PSMD between the groups and performed correlation analyses between PSMD and clinical factors.

PSMD was higher in patients with ESKD than in healthy controls (2.945 ± 0.361 vs. 2.552 ± 0.458 × 10^-4 mm²/s, p < 0.001). In addition, PSMD positively correlated with age (r = 0.370, p = 0.022). In exploratory subgroup analyses, no statistically significant differences in PSMD were observed according to dialysis modality or the presence of diabetes mellitus.

We demonstrated that patients with ESKD exhibited higher PSMD than healthy controls, suggesting diffuse white matter microstructural injury that may be related to CSVD. PSMD may be a useful imaging marker for characterizing diffuse white matter abnormalities in this population, although further studies incorporating conventional CSVD markers, vascular risk-matched controls, and clinical outcome measures are warranted.

Sitagliptin is a standard therapeutic option for type 2 diabetes mellitus (T2DM), while metoprolol is an antihypertensive agent. This study aimed to assess the safety of their combination therapy in elderly patients with T2DM and hypertension.

Medical data of 52 elderly patients with T2DM and hypertension from June 2021 to March 2024 were retrospectively collected. According to different treatment methods, 52 patients were categorized into the sitagliptin + metoprolol group (n=26) and the sitagliptin group (n=26). After a 12-week treatment period, the baseline characteristics, therapeutic efficacy and biochemical indicators were analyzed between groups.

After 12-week treatment, both groups exhibited significant improvements in therapeutic efficacy, immune-inflammatory status, coagulation function, and liver-kidney function (p<0.05). Compared with the sitagliptin group, the sitagliptin + metoprolol group showed better therapeutic efficacy, with lower FPG, HbA_1c, SBP, and DBP levels (p<0.05). The combination group also achieved greater improvements in immune-inflammation, coagulation, and liver-kidney function (lower CRP, NLR, Fib, Scr and BUN, as well as higher PT and APTT) (p<0.05). There was no significant difference in incidence of adverse event between the two groups (p>0.05).

Short-term observation indicates that sitagliptin combined with metoprolol can improve blood glucose, blood pressure, inflammation, coagulation, and liver-kidney function in elderly patients with T2DM and hypertension, without a significant increase in the risk of adverse reactions during short-term follow-up. However, as a single-center, small-sample retrospective study, the present findings should be interpreted as preliminary clinical evidence.

Diabetic retinopathy (DR) progression in community-based type 2 diabetes (T2D) remains challenging to predict. This multi-site cohort study aimed to identify novel metabolic risk factors beyond glucose control and develop a resource-adaptable model for prioritizing DR risk screening.

A prospective, multi-site study was conducted among patients with T2D from 2021 to 2023 across 14 community health centers in Northwestern China (Shaanxi). Baseline and follow-up data on demographic, medical history, and laboratory data were collected. DR was assessed by trained retinal specialists through fundus photography and slit-lamp bio-microscopy. LASSO-penalized logistic regression with 10-fold cross-validation was first performed to identify candidate predictors, followed by multivariable logistic regression to construct the prediction model. Model discrimination was evaluated using receiver operating characteristic (ROC) curve analysis and bootstrap internal validation. Clinical characteristics before and after onset of DR were analyzed using the paired-sample Wilcoxon test.

Among 363 participants who completed follow-up, 50 (13.77%) patients developed DR. Patients who developed DR had longer duration of diabetes, higher HbA1c levels, poorer glycemic control, and elevated urine pH. Multivariate logistic regression identified HbA1c (OR = 1.30), urine pH (OR = 2.55), and diabetes duration (OR = 1.06) as independent risk factors. The prediction model showed moderate discrimination, with an apparent AUC of 0.72 and an optimism-corrected AUC of 0.693 after bootstrap internal validation. In paired analyses, patients exhibited significantly higher BUN and eGFR levels and lower serum creatinine after DR onset (all P < 0.05).

In this community-based cohort, a prediction model incorporating urine pH, HbA1c, and diabetes duration showed potential for identifying individuals at high risk of incident DR. Urine pH may serve as a simple and accessible metabolic marker for risk stratification, while renal functional alterations may reflect early systemic changes beyond glycemic control.