Growing evidence suggests that diabetes mellitus (DM) is associated with higher incidence and faster progression of Parkinson's disease (PD), but the association between DM and prodromal PD has yet to be explored.

To evaluate the effect of DM in prodromal PD, we classified 63 prodromal PD into two groups according to a prior diagnosis of DM: prodromal PD without DM (n = 51) and prodromal PD with DM (n = 12). At baseline, between-group comparisons of clinical symptoms, brain structure, and cerebral spinal fluid (CSF) pathology were performed. We then compared longitudinal changes in motor and cognitive symptoms between the two groups. Finally, the incidence of disease conversion in the two groups was compared using Cox proportional hazard regression analysis.

Compared to prodromal PD without DM, prodromal PD with DM had significantly lower global cognitive scores (p < 0.001), higher CSF p-tau (p = 0.005), decreased grey matter volume in the frontal lobe (FWE corrected, p < 0.05), and contracted shape in the bilateral putamina (TFCE FWE corrected p < 0.05). The linear mixed models demonstrated that prodromal PD with DM had significantly faster motor progression (p = 0.009) and cognitive decline (p = 0.012) than those without. Additionally, DM was associated with increased risk of disease conversion in prodromal PD (HR = 6.526, p = 0.003).

Theses findings indicate that pre-existing DM may confer a detrimental effect on disease phenotype and conversion in prodromal PD.

To investigate the impact of sleep duration on glucose homeostasis in individuals with gestational diabetes mellitus (GDM).

Cross-sectional observational study.

Secondary-level maternity care at two centres in Ningbo, China.

A total of 381 women with GDM were included (median age 30.6 years (IQR 28.2-33.1); median gestational age 26.0 weeks (IQR 25.0-27.0)). All participants underwent 2 weeks of continuous glucose monitoring (CGM), and nocturnal sleep duration was assessed using a structured questionnaire.

CGM-derived summary metrics were analysed using multivariable linear regression, and 24-hour glucose trajectories were evaluated using functional data analysis (FDA).

Long sleep duration (>9 hours/day) was associated with higher mean glucose, time above range, coefficient of variation, the pregnancy-specific glucose risk index and lower time in range (β values were 2.41 mg/dL, 1.84%, 1.04%, 1.88% and -1.85%, respectively; all p<0.05). Each additional hour of sleep increased glucose levels, variability and risk (β: mean glucose 1.29 mg/dL, time above range 0.89%, coefficient of variation 0.45%, pregnancy-specific glucose risk index 0.91%; all p<0.05), while reducing time in range (β=-0.87%; p<0.05). The FDA showed that participants with long sleep (>9 hours/day) had elevated glucose by 2.1-5.8 mg/dL from 10:55 to 00:45, lasting for 13.8 hours.

In GDM, long sleep duration was associated with less favourable glucose patterns, suggesting possible windows of greater vulnerability in glucose regulation. These findings indicate that sleep behaviours may represent an important behavioural characteristic relevant to clinical glucose management in pregnancy and warrant further investigation.

Socioeconomic inequalities significantly impact access to healthcare services for individuals with type 2 diabetes mellitus (T2DM). This study investigates these inequalities in Iran, focusing on factors such as asset, sex, urban-rural residence, age, education, employment status, and marital status.

Cross-sectional study.

This study used data from the national 'Diabetes Care (DiaCare)' study, a population-based survey conducted from 2018 to 2020 in Iran.

Socioeconomic status (SES) was assessed using Principal Component Analysis (PCA) based on assets. Socioeconomic inequalities in access to physicians, pharmacies and laboratories were measured using the Concentration Index (CI) and Erreygers Corrected Concentration Index (ECI). Decomposition analysis was performed using a probit regression model to assess the contributions of various factors to the observed inequalities.

Among 13 315 patients with T2DM, 5.8% lacked access to physicians, 6.8% to pharmacies and 8.7% to laboratories. The CI was positive and statistically significant for access to physicians (0.0614), pharmacies (0.0787) and laboratories (0.0875), indicating better access concentrated among higher SES individuals. Urban residents had the largest positive marginal effects on access to physicians (0.032), pharmacies (0.078) and laboratories (0.053), with percentage contributions of 13.21%, 23.23% and 17.39%, respectively. Higher asset quintiles showed substantial contributions to inequalities, with the highest quintile contributing 10.5% to physician access inequality, 9.68% to pharmacy access and 9.16% to laboratory access. Education level also positively impacted access, with high school education contributing 0.64% and college education 0.52% to access inequalities. Sex differences showed a negative marginal effect for women, indicating slightly lower access.

Socioeconomic factors, particularly asset, residence and education, significantly impact access to healthcare services for patients with T2DM in Iran. Policies should focus on reducing barriers to healthcare access, especially for lower SES and rural populations.

Understanding the diabetes patient journey, from awareness, screening, diagnosis, treatment, adherence and control, is crucial for improving outcomes. However, in many low- and middle-income countries, including Indonesia, data across this continuum of care remain limited due to limited health information exchange capacity. This study aimed to map and estimate the prevalence of true diabetes and key touchpoints along the patient journey to identify gaps in diabetes management in Indonesia.

We applied an evidence gap mapping approach by systematically searching relevant literature published between 1 January 2014, and 27 March 2025, in PubMed, Web of Science, Scopus and Embase, complemented by unstructured searches of government websites. Evidence was visualised using a chord diagram, bar chart and heatmaps. Random-effects meta-analyses were used to estimate pooled prevalence, stratified by study setting and sample representativeness relative to the national population.

Among 94 records, none fully applied diabetes patient journey frameworks and most of the evidence was assessed as low quality. Screening was the least studied touchpoint and awareness missed nationally representative data. Pooled prevalence estimates were: true diabetes 13% (95% confidence interval (CI): 9%-17%), awareness 71% (95% CI: 54%-85%), screening 19% (95% CI: 0%-55%), diagnosis 15% (95% CI: 6%-27%), treatment 89% (95% CI: 80%-95%), adherence 59% (95% CI: 49%-69%) and control 31% (95% CI: 25%-36%). Prevalence rates were generally lower in studies conducted in the general population using nationally representative samples. Pooled prevalence across touchpoints and subgroups decreased after excluding low-quality studies, although heterogeneity within subgroups remained high.

Evidence gap mapping offers a practical approach to generating local patient journey data in countries with limited health information exchange capacity. Specifically for Indonesia, a comprehensive consideration of the diabetes patient journey is lacking. Barriers exist across all touchpoints, particularly in the general population.

People living with Type 2 Diabetes Mellitus (T2DM) face a heightened risk of experiencing severe complications from COVID-19, underscoring the importance of vaccination. Nonetheless, the impact of COVID-19 vaccines-especially the Johnson & Johnson (Ad26.COV2.S) vaccine-on glucose regulation has not been fully elucidated. This study evaluates the impact of vaccination on glycemic parameters, including Random Blood Sugar (RBS) and Hemoglobin A1c (HbA1c), and identifies factors influencing glycemic variability.

Between May 2023 and June 2024, a prospective cohort study was carried out at Adama Hospital Medical College in Ethiopia. Adults diagnosed with Type 2 Diabetes Mellitus were divided into two cohorts based on vaccination status: those who received the vaccine and those who did not. Glycemic parameters were recorded at baseline and subsequently at three-month intervals-specifically at 3, 6, 9, and 12 months following vaccination. To evaluate trends over time and identify influencing factors, including demographic and clinical variables, longitudinal data were analyzed using Generalized Estimating Equations (GEE).

Vaccinated individuals exhibited transient elevations in RBS, peaking at three months post-vaccination before stabilizing. In contrast, HbA1c levels demonstrated a gradual increase over time. Greater glycemic variability was observed in younger individuals and females. The primary determinants of variations in glycemic levels were vaccination status, duration following immunization, and demographic characteristics. In contrast, diabetes treatments and lifestyle-related factors showed only a limited influence.

The Johnson & Johnson COVID-19 vaccine was associated with short-term RBS fluctuations and a sustained increase in HbA1c levels in T2DM patients. These findings highlight the need for personalized glycemic monitoring post-vaccination. Despite these metabolic variations, the vaccine's protective role against severe COVID-19 outweighs transient glycemic disturbances. Incorporating vaccination efforts into comprehensive diabetes management is crucial, and additional studies are warranted to investigate the underlying biological mechanisms.

Diabetic retinopathy screening in lowand middle-income countries is limited by restricted access to specialized care. Portable retinal cameras offer a practical alternative; however, image quality - affected by mydriasis - directly influences the performance of artificial intelligence models. This study evaluated the effect of mydriasis on image gradability and AI-based diabetic retinopathy detection in real-world, resource-limited settings.

The proportions of gradable images were compared between mydriatic and non-mydriatic groups. Generalized estimating equations were used to identify factors associated with image gradability, including age, sex, race, diabetes duration, and systemic hypertension. A ResNet-200d model was trained on the mobile Brazilian Ophthalmological dataset and externally validated on both mydriatic and non-mydriatic images. Model performance was evaluated using accuracy, F1 score, area under the curve, and confusion matrix metrics. Sensitivity differences were assessed using the McNemar test, and area under the curves were compared using DeLong's test. The Youden index was used to determine optimal classification thresholds. Agreement between maculaand disc-centered images was analyzed using Cohen's κ.

The mydriatic group demonstrated a higher proportion of gradable images compared with the non-mydriatic group (82.1% vs. 55.6%; p<0.001). In non-mydriatic images, lower gradability was associated with systemic hypertension, older age, male sex, and longer diabetes duration. The AI model achieved better performance in mydriatic images (accuracy, 85.15%; area under the curve, 0.94) than in non-mydriatic images (accuracy, 79.68%; area under the curve, 0.93). The McNemar test showed a significant difference in sensitivity (p=0.0001), whereas DeLong's test revealed no significant difference in area under the curve (p=0.4666). The Youden index indicated that optimal classification thresholds differed based on mydriasis status. Agreement between image fields was moderate to substantial and improved with mydriasis.

Mydriasis significantly improves image gradability and enhances AI performance in diabetic retinopathy screening. Nonetheless, in lowand middle-income countries where pharmacologic dilation may be impractical, optimizing model calibration and thresholding for non-mydriatic images is essential to ensure effective AI implementation in real-world clinical environments.

Type 1 diabetes mellitus (T1DM) can lead to severe diabetic osteopathy, largely driven by alterations in the bone marrow hypoxic microenvironment and damage to type H vessels. This study employed multimodal imaging-including DCE-MRI, Micro-CT, and USPIO-enhanced MRI-to enable early in vivo assessment of hypoxic changes and type H vessel impairment in a T1DM rabbit model. Experiments involved 20 rabbits with alloxan-induced T1DM and controls, evaluated four months post-modeling. Imaging revealed significant differences in bone marrow microcirculatory perfusion and vascular permeability in T1DM rabbits, along with elevated USPIO uptake and regional heterogeneity that correlated with type H vessel distribution. Accompanying pathological changes were confirmed via immunofluorescence, qPCR, and transmission electron microscopy, suggesting an association of the AGEs/ROS-HIF-1α-VEGF pathway with these microvascular lesions. Our findings offer visual and quantitative imaging evidence to inform future clinical strategies targeting type H vessel hypoxia in diabetic osteopathy.

Type-2 diabetes mellitus (DM) increases tuberculosis (TB) risk and can worsen treatment outcomes. Both diseases and their treatments induce significant metabolic and biochemical perturbations that influence disease progression and management. This study longitudinally evaluated clinical, metabolic, and serum biochemical changes in patients with pulmonary TB with and without DM before and during anti-TB therapy. Ninety-five adult patients newly diagnosed with pulmonary TB in Ghana were stratified into TB-Only (n = 49; HbA1c < 6.5%) and TB-DM (n = 46; HbA1c ≥ 6.5%) groups, including treated (TB-DMt) and untreated (TB-DMnt) diabetes subgroups. Serum samples collected at baseline (t₀), day 28 (t₂₈), and day 56 (t₅₆) were analyzed for electrolytes, renal function, liver enzymes, and lipid profiles using validated clinical chemistry analyzer. TB-DM cohorts exhibited significantly lower chloride levels at all time points relative to the TB-Only cohort (e.g., 98 vs. 100 mmol/L at t₀, p < 0.001). Hyponatremia (serum sodium < 136 mmol/L) was prevalent during the intensive anti-TB treatment phase, affecting 53.1% of TB-Only patients, 61.1% of TB-DMt patients, and 70.0% of TB-DMnt patients. Liver function tests revealed elevated bilirubin, gamma-glutamyl transferase (g-GT), alkaline phosphatase (ALP), and alanine aminotransferase (ALT) levels, particularly in TB-DMnt patients, with normalization over time. Lipid profiles showed a pro-atherogenic pattern with elevated triglycerides and total cholesterol (p < 0.05). High-density and low-density lipoproteins were increased at select time points. Positive correlations were noted among albumin, cholesterol fractions, and electrolytes. Primary microbiological treatment outcomes, including sputum conversion and completion rates, were similar regardless of diabetic status. The distinctive metabolic and biochemical derangements in TB-DM, especially untreated diabetes, highlight the importance of integrated clinical management. Elevated hepatic enzymes in TB-DMnt may delay metformin initiation, suggesting a need to optimize timing post hepatic recovery, while the prevalence of hyponatremia underscores the need for routine electrolyte monitoring in TB patients with diabetes. The dysregulated lipid profile highlights cardiovascular risk that warrants routine monitoring. Despite metabolic challenges, effective TB treatment outcomes are achievable with comprehensive care.

Pregnancies complicated by diabetes are at increased risk for adverse perinatal outcomes. The American College of Obstetricians and Gynecologists recommend universal screening for gestational diabetes mellitus during pregnancy at 24 to 28 weeks gestation. However, there is controversy on the utility of screening or identifying patients with early gestational diabetes mellitus. In this clinical perspective, we provide an evidence-based classification of diabetes in pregnancy (i.e., diagnostic criteria for standard gestational diabetes mellitus versus early gestational diabetes mellitus versus pregestational diabetes mellitus) to help define subgroups along the spectrum of glucose intolerance/insulin resistance to inform future outcome-based studies.

Diabetic foot ulcers (DFUs) represent one of the most debilitating and costly complications of diabetes mellitus, leading to substantial morbidity, mortality, and healthcare burden. Their global incidence continues to rise in parallel with the increasing prevalence of diabetes and aging populations.

To critically review the epidemiology, pathophysiological pathways, and contemporary management strategies of diabetic foot, with emphasis on translational advances and emerging therapeutic directions.

A systematic literature search was conducted utilising the PubMed, Scopus, and Web of Science databases for publications from 2015 to 2025 employing the terms 'diabetic foot ulcer,' 'management,' 'therapy,' 'regeneration,' and 'emerging treatment.' Only studies published in English that concentrate on clinical or translational advancements in diabetic foot care were included. Reviews, case reports, and irrelevant articles were omitted. Following the evaluation of 286 records, 132 studies were selected for synthesis.

Chronic hyperglycemia drives neuropathy, vasculopathy, and persistent inflammation, impairing the normal wound healing cascade. Standard management-including glycemic control, debridement, infection management, pressure offloading, and advanced dressings-remains essential. However, novel therapies such as bioengineered skin scaffolds, recombinant growth factors, stem cell applications, nanotechnology-based delivery systems, and negative-pressure wound therapy are transforming the field. Despite technological promise, widespread implementation remains challenged by regulatory and economic constraints.

The future of diabetic foot care lies in multidisciplinary, precision-based paradigms integrating smart biomaterials, gene therapy, artificial intelligence, and telemedicine. These convergent technologies hold the potential to revolutionize wound healing outcomes and reduce the global burden of diabetic complications.