Hypertension is common among patients with type 2 diabetes mellitus (T2DM) and represents a major contributor to cardiovascular and renal morbidity. Real-world data from primary care are essential to characterise associated clinical profiles and cardiometabolic multimorbidity patterns in routine clinical practice.

To assess the prevalence of hypertension among patients with T2DM managed in primary care, to characterise associated clinical profiles, and to explore sex-related differences using real-world data.

A cross-sectional study was conducted including 680 adults with T2DM receiving routine care in primary care settings. Clinical, sociodemographic and laboratory data were obtained from electronic health records, direct clinical assessment, and structured patient interviews. Hypertension was defined as a previously recorded clinical diagnosis, current antihypertensive treatment, or blood pressure ≥ 140/90 mmHg. Comparisons were performed according to sex. Factors independently associated with hypertension were analysed using bivariate analyses and multivariable logistic regression models adjusted for age, sex, duration of T2DM, pharmacologically treated dyslipidaemia, and established cardiovascular disease.

The mean age of participants was 69.8 ± 13.3 years and the mean duration of diabetes was 9.9 ± 4.6 years; 52.1% were men. Overall hypertension prevalence was 84.3%, with no significant difference between men (85.9%) and women (82.5%). Hypertension prevalence increased with age in both sexes. Women had lower educational and socioeconomic levels, higher abdominal obesity, and higher lipid concentrations, whereas men showed higher fasting glucose, serum creatinine, and markers of renal damage. In multivariable analysis, hypertension was independently associated with older age (OR 1.04 per year; 95% CI 1.02-1.06), pharmacologically treated dyslipidaemia (OR 1.83; 95% CI 1.17-2.86), established cardiovascular disease (OR 2.03; 95% CI 1.16-3.55), and longer duration of T2DM (OR 1.06 per year; 95% CI 1.00-1.12). Sex was not independently associated with hypertension after adjustment.

Hypertension was common among patients with T2DM in primary care and was associated with older age, longer diabetes duration, and established cardiovascular disease. Sex-related differences in clinical profiles were observed but should be interpreted as descriptive. This cross-sectional secondary analysis has limitations, including the lack of blood pressure control data and the potential for residual confounding.

Diabetic ketoacidosis (DKA) is a life-threatening complication of type 1 diabetes (T1DM) and a leading cause of pediatric mortality. Prevention depends on caregivers' ability to recognize early signs and initiate proper sick-day management. However, data on DKA awareness among Iranian parents remains limited. This study aimed to evaluate both subjective self-ratings and objective calculated knowledge regarding DKA among parents of children with T1DM.

This cross-sectional study was conducted among 217 parents at a pediatric diabetes clinic in Shiraz, Iran. A validated questionnaire (Cronbach's alpha = 0.8), adapted through forward-backward translation, assessed demographics, disease duration, HbA1c, and DKA knowledge. Data were analyzed using descriptive statistics, Kruskal-Wallis tests, and Spearman's correlations (r_s) to identify predictors of awareness.

Most participants were mothers (80.6%), and 38.7% reported DKA as the initial presentation at diagnosis. Subjective knowledge scores (0-10) were 1.86 ± 3.07 for mothers and 0.89 ± 2.16 for fathers (P = 0.06). Mothers demonstrated significantly higher calculated objective awareness than fathers (P = 0.007). Higher objective knowledge significantly correlated with higher education levels (r_s=0.280, P = 0.01), longer disease duration (P = 0.01), and prior DKA admission (P = 0.02). Awareness showed no significant relationship with the child's most recent HbA1c levels (P = 0.98).

Parental DKA knowledge is critically inadequate. As DKA mortality is preventable through early detection, targeted educational interventions and accessible resources are urgently needed to empower parents and improve clinical outcomes for children with T1DM.

This study aimed to investigate the ability of an ultrasound-based risk stratification model integrating carotid intima thickness (CIT) and carotid-femoral pulse wave velocity (cfPWV) to aid in risk stratification and assessment of atherosclerotic cardiovascular disease (ASCVD) in patients with type 2 diabetes mellitus (T2DM), thereby providing an objective basis for identifying high-risk individuals and informing individualized management strategies.

A total of 105 patients with T2DM were enrolled in this study. According to the 10-year ASCVD risk score, patients were further classified into T2DM patients with low-to-moderate burden of other cardiovascular risk factors and T2DM patients with high burden of other cardiovascular risk factors. CIT was measured using high-resolution ultrasound to assess vascular structure, while cfPWV was evaluated using the automatic measurement of arterial stiffness (AMAS) system to assess vascular function. Logistic regression and least absolute shrinkage and selection operator (LASSO) regression analyses were performed to identify independent risk factors of high ASCVD risk. Based on these risk factors, individual discriminative models and a nomogram were constructed. Receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA) were used to evaluate model performance, and differences among models were assessed using the DeLong test.

CIT, cfPWV, and estimated glomerular filtration rate (eGFR) were identified as independent risk factors of high 10-year ASCVD risk in patients with T2DM. The areas under the curve (AUCs) for the CIT model, cfPWV model, eGFR model, combined CIT-cfPWV model, and the nomogram were approximately 0.781, 0.808, 0.797, 0.831, and 0.875, respectively. The constructed nomogram demonstrated excellent discrimination, calibration, and clinical applicability.

CIT and cfPWV show strong potential for identifying T2DM patients at high ASCVD risk as estimated by the China-PAR model. Incorporating these parameters into vascular evaluation may aid in risk stratification and provide a robust basis for individualized clinical intervention strategies. Prospective studies are needed to validate their prognostic value for future ASCVD events.

The number of patients taking glucagon-like peptide-1 receptor agonists (GLP-1 RAs) and sodium-glucose co-transporter 2 (SGLT 2) inhibitors presenting for elective surgery is increasing. Patients taking GLP-1 RAs with the highest risk of aspiration are non-fasters who have recently initiated the drug, have had a recent dose escalation, and/or have active gastrointestinal symptoms. Asymptomatic patients on stable doses of GLP-1 RAs can continue the medication throughout the peri-operative period. SGLT 2 inhibitor use carries a risk of euglycemic diabetic ketoacidosis, particularly in patients on insulin with prolonged fasting and should be held prior to elective procedures.

Mechanotransduction is a process converting mechanical cues into electrochemical signals. Piezo1, a mechanically sensitive cation channel protein, is widely distributed in non-sensory cells of mammals, particularly highly expressed in the endothelial cells (ECs). Piezo1 is implicated in various physiological activities, including the regulation of vascular tone, angiogenesis, and maintenance of the endothelial barrier. Under pathological mechanical forces, Piezo1 is involved in the development of endothelial dysfunction-related diseases, including atherosclerosis (AS), hypertension, heart failure (HF), myocardial infarction (MI), pulmonary arterial hypertension (PAH), acute respiratory distress syndrome (ARDS), and diabetes mellitus (DM). In this review, we focus on the underlying mechanisms of Piezo1 in different endothelial dysfunction-related diseases, highlighting its roles in regulating endothelial function. Moreover, we present some activators and inhibitors targeting Piezo1, and discuss their different effects in distinct contexts. Overall, targeting Piezo1 may open a novel avenue of therapeutic tactic for endothelial dysfunction-related diseases.

Atrial fibrillation (AF) commonly complicates myocardial infarction (MI) and is associated with adverse outcomes. However, the prognostic significance of AF timing after MI remains incompletely understood.

We conducted a retrospective cohort study of 3390 patients with myocardial infarction, stratified by MI subtype (NSTEMI and STEMI). AF was categorized as early (during index hospitalization), late (after discharge), or absent. Clinical, echocardiographic, and angiographic data were collected. Outcomes were assessed using Kaplan-Meier analyses and log-rank testing. The association between AF timing and outcomes was evaluated using multivariable time-dependent Cox proportional hazards models with pairwise comparisons, adjusting for relevant demographic and clinical covariates. Predictors of late AF were evaluated using multivariable logistic regression.

Among patients with myocardial infarction, AF developed in 451 patients (13.3%), 114 with STEMI and 337 with NSTEMI, occurring as early AF in 249 and late AF in 202. Kaplan-Meier analyses demonstrated significant in long-term major adverse cardiovascular events (MACE), defined as recurrent myocardial infarction with or without repeat revascularization, stroke, and all-cause mortality, according to AF timing, with late AF associated with the poorest outcomes (p<0.001). In multivariable time-dependent Cox models, late-onset AF was independently associated with an increased risk of MACE (HR: 1.5 [1.0-2.2], p=0.044, HR: 2.4 [1.1-5.3], p=0.032 in NSTEMI and STEMI, respectively). In contrast, early AF was not consistently associated with long-term MACE after adjustment (HR: 0.7 [0.4-1.0], p=0.060, HR: 1.3 [0.8-2.3], p=0.327 in NSTEMI and STEMI, respectively). Pairwise comparisons confirmed higher risk with late AF compared with no AF. In contrast, the late versus early AF comparison did not reach statistical significance in STEMI, likely due to limited event numbers. In multivariable logistic regression, older age and diabetes mellitus were independently associated with the development of late AF.

AF timing after myocardial infarction is prognostically relevant. Late-onset, but not early, AF was independently associated with adverse outcomes in time-dependent analyses, supporting a temporal classification of AF.

Pancreatic ductal adenocarcinoma (PDAC) is frequently preceded by new-onset diabetes mellitus (NODM), yet differentiating PDAC-associated DM from type 2 diabetes (T2D) remains clinically challenging. We investigated whether plasma proteomic profiling combined with machine learning could discriminate these conditions. Plasma samples from individuals with PDAC (with and without DM), long-standing T2D, and controls were analyzed by MALDI-TOF mass spectrometry. Spectral features were processed through a nested cross-validation framework to prevent data leakage, and model interpretability was explored using SHAP values. In parallel, low-molecular-weight proteins were characterized by GeLC-MS followed by LC-MS/MS and differential abundance analysis. Machine learning models distinguished PDAC-associated DM from T2D with a balanced accuracy of 85%. Proteomic analyses identified distinct signatures in PDAC- associated DM, including downregulation of erythrocyte-related proteins and PPBP, and upregulation of acute-phase reactants such as FGA, CP, and SERPINA3. Treatment-naïve cases displayed increased circulating epithelial and keratin-associated proteins, which were attenuated after therapy, suggesting dynamic tumor-related remodeling. These findings demonstrate that integrating MALDI-TOF profiling with machine learning can capture plasma signatures associated with PDAC-associated DM. Although exploratory, this approach supports further validation in prospective cohorts aimed at improving PDAC risk stratification among individuals with NODM. SIGNIFICANCE: Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal malignancy with a dismal 5-year survival rate, primarily due to late-stage diagnosis. The frequent occurrence of new-onset diabetes mellitus (NODM) as a paraneoplastic syndrome offers a critical window for early detection. However, the clinical challenge of distinguishing PDAC-associated diabetes (PDAC-DM) from type 2 diabetes mellitus (T2D) has hindered the implementation of effective screening strategies. This study addresses this significant clinical problem by leveraging a multi-faceted proteomics approach. We demonstrate that the integration of MALDI-TOF mass spectrometry peptide profiling with machine learning algorithms can accurately discriminate PDAC-DM from T2D with 85% accuracy. Furthermore, we used LC-MS/MS to identify specific low molecular weight proteins that are differentially regulated between these conditions, providing a molecular basis for the observed discrimination. Our work is significant as it presents a novel, high-throughput pipeline for biomarker discovery that combines the scalability of MALDI-TOF with the analytical power of LC-MS/MS and machine learning. The identified plasma signatures hold strong translational potential to improve risk stratification in patients with new-onset diabetes, ultimately enabling earlier diagnosis of PDAC and improving patient survival prospects. This research directly contributes to the field of clinical proteomics by providing a robust methodological framework and candidate biomarkers for the early detection of one of oncology's most challenging diseases.

Type 2 diabetes mellitus (T2DM) is a complex metabolic disorder that significantly predisposes individuals to delirium and dementia through multifaceted neurobiological pathways. The essence of this neurocognitive decline involves mechanisms such as central insulin resistance, chronic low-grade inflammation, and mitochondrial dysfunction. While metformin remains the cornerstone of T2DM management, its impact on the central nervous system exhibits a "double-edged sword" nature, balancing intrinsic neuroprotective properties against the potential neurotoxicity associated with vitamin B12 deficiency. This review aims to systematically synthesize epidemiological and clinical evidence linking metformin to neurocognitive outcomes, contrasting its efficacy with newer glucose-lowering agents such as GLP-1 receptor agonists and SGLT2 inhibitors. In addition, it sheds light on the reciprocal connectivity between systemic metabolic regulation and direct CNS modulation, specifically elucidating AMPK activation, the autophagy-lysosome axis, and the gut-brain and liver-brain axes. We review these molecular mechanisms to delineate the delicate trade-off between neuroprotection and risk, providing a framework for precision pharmacotherapy and biomarker-guided stratification in high-risk T2DM populations.

The prevalence and characteristics of diabetes distress (DD) in Japanese adults with type 1 diabetes mellitus (T1D) remain unclear. Therefore, this study aimed to investigate the prevalence and associated features of DD in this population. A cross-sectional study was conducted using the Type 1 Diabetes Distress Scale (T1-DDS), the Problem Areas in Diabetes (PAID) scale, and the Hypoglycemia Fear Survey (HFS). Data from 117 adults with T1D were analyzed. Group comparisons were made using t-tests and anova, and associations were assessed using Spearman's rank correlation. Confirmatory factor analysis (CFA) was used to examine the structure of the T1-DDS.

Of the 144 screened participants, 117 (mean age: 50.6 ± 12.8 years; 44.4% male; mean diabetes duration: 18.1 ± 10.7 years; mean HbA1c: 7.6% ± 0.9%) were included in the analysis. Moderate and high DD were observed in 30.8% and 16.2%, respectively. Participants with high DD had significantly higher PAID and HFS-Worry/Behavior scores, despite no significant differences in age, sex, diabetes duration, or HbA1c. Among T1-DDS subscales, Powerlessness and Eating Distress had the highest mean scores. All subscale scores were correlated with total distress (rho = 0.636-0.916) and showed good internal consistency (Cronbach's alpha = 0.784-0.875). CFA supported the seven-factor structure with marginal-to-acceptable model fit.

DD is prevalent in Japanese adults with T1D. The T1-DDS can be a useful screening tool for identifying individuals experiencing diabetes-related emotional burden and guiding personalized interventions for these individuals.