Diabetology

Background/Objectives: Youth with type 1 diabetes (T1DM) face unique challenges in balancing dietary choices, physical health outcomes, and social–emotional well-being in school settings. This cross-sectional exploratory pilot study examined the associations of diet with physical health and teacher-reported social–emotional functioning in students with T1DM. Methods: Students with T1DM (mean age = 13.42; 47 female, 50 male; 50% White, Non-Hispanic, 50% minority) self-reported their nutritional habits using the KBlock Dietary Screener for Children when school was in session. Teacher-rated school-related behaviors were assessed through the Behavior Assessment Scale for Children-2nd Edition (BASC-2). Canonical correlation analysis was conducted to determine whether the variable sets (diet with physical health and school-related behavioral health) shared a significant multivariate relationship. Results: Youth with lower glycemic loads and consuming more sugar, dairy, and meat/poultry/fish but fewer legumes, fruit, and less saturated fat exhibited fewer externalizing symptoms and higher BMI. Diet uniquely accounted for modest variance in combined social–emotional and physical health, controlling for demographics and T1DM duration. Findings support increasing the availability of whole, nutrient-rich foods, integrating comprehensive nutrition education into curricula, and ensuring access for all students, regardless of socioeconomic status. Conclusions: Comprehensive dietary assessments and school-based randomized control trials are needed to enact more evidence-based dietary recommendations or interventions for youth, aiming for a balanced approach that addresses both mental and physical health outcomes.

Background/Objectives: This study examined the metabolic, oxidative, immunological, and histomorphological effects of the multicomponent fermented biological product derived from camel milk, Inullact-Fito, in comparison to metformin in a rat model of alloxan-induced diabetes resulting from insulin insufficiency. The model was chosen as an experimental system that replicates pancreatic β-cell damage induced by oxidative stress rather than insulin resistance. Methods: Alloxan-induced diabetes was used to evaluate metabolic, oxidative, immunological, and histomorphological alterations. Metformin was utilized as a pharmacological comparator. Blood glucose levels, circulating insulin concentrations, markers of oxidative stress and lipid peroxidation, immunoglobulin levels, CD4⁺/CD8⁺ T cell balance, and pancreatic histostructure were assessed. Results: Alloxan administration led to substantial hyperglycemia, oxidative stress, immunological imbalance, and structural damage to pancreatic tissue. Following therapy with Inullact-Fito, blood glucose levels reduced dramatically (from 21.9 ± 0.22 to 9.85 ± 0.10 mmol/L, p < 0.05), circulating insulin concentrations were largely corrected, oxidative stress and lipid peroxidation markers decreased. Immunological evaluation revealed decreased serum immunoglobulin M and IgG levels (p < 0.05) and partial normalization of the CD4⁺/CD8⁺ T cell balance. Metformin showed comparative effects; however, its activity in this model is limited by its primary mechanism related to insulin resistance. Conclusions: Overall, the data reveal that Inullact-Fito combines metabolic, antioxidant, and immunomodulatory actions under experimental oxidative and metabolic stress conditions. Further research using models of insulin resistance and type 2 diabetes, as well as long-term clinical trials, is needed to fully evaluate the therapeutic potential, safety profile, and translational importance of this fermented dairy product as a functional nutritional intervention.

Background: Dysglycemia remains a persistent challenge in hospital care. Despite advances in outpatient diabetes technology, inpatient insulin management largely depends on intermittent point-of-care glucose testing, static insulin dosing protocols and rule-based decision support systems. Artificial intelligence (AI) offers potential to transform this care through predictive modeling and adaptive insulin control. Methods: Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) guidelines, a scoping review was conducted to characterize AI algorithms for insulin dosing and glycemic management in hospitalized patients. An interdisciplinary team of clinicians and engineers reached consensus on AI definitions to ensure inclusion of machine learning, deep learning, and reinforcement learning approaches. A librarian-assisted search of five databases identified 13,768 citations. After screening and consensus review, 26 studies (2006–2025) met the inclusion criteria. Data were extracted on study design, population, AI methods, data inputs, outcomes, and implementation findings. Results: Studies included ICU (N = 13) and general ward (N = 9) patients, including patients with diabetes and stress hyperglycemia. Early randomized trials of model predictive control demonstrated improved mean glucose (5.7–6.2 mmol/L) and time in target range compared with standard care. Later machine learning models achieved strong predictive accuracy (AUROC 0.80–0.96) for glucose forecasting or hypoglycemia risk. Most algorithms used data from Medical Information Mart for Intensive Care (MIMIC) databases; few incorporated continuous glucose monitoring (CGM). Implementation and usability outcomes were seldom reported. Conclusions: Hospital AI-driven models showed strong algorithmic performance but limited clinical validation. Future co-designed, interpretable systems integrating CGM and real-time workflow testing are essential to advance safe, adaptive insulin management in hospital settings.