Gliflozin derivatives, a class of Sodium-Glucose Cotransporter (SGLT) inhibitors, have emerged as effective antidiabetic agents offering notable cardiovascular and renal protection. This class of compounds inhibits glucose reabsorption in the kidneys, thereby improving overall glycaemic control. This review provides a comprehensive overview of gliflozin derivatives, emphasizing their chemical characterization, safety, therapeutic efficacy, pharmacokinetic analysis, and potential future applications, focusing on clinical trials and new developments released between 2010 and 2026. Gliflozin derivatives slow the progression of chronic kidney disease and reduce the risk of heart failure. Some preliminary experimental evidence has reported the antibacterial activity of canagliflozin against MRSA. The Gliflozin derivatives dapagliflozin (IC50 =1.1nM), ertugliflozin (IC50 =0.9nM), sotagliflozin (IC50 =1.8nM), and bexagliflozin (IC50 =2.3nM), which has core motif, 1-chloro-2-(4-alkoxy benzyl) benzene as an aglycon unit and a glycon unit bind with SGLT2 receptor and lowered the HbA1c by 1.2%, 1.7%, 0.41% and 0.80%, respectively. Its selectivity, pharmacokinetics, and toxicity profiles are also optimal. Recent studies have demonstrated their involvement in conditions other than diabetes, including obesity, non-alcoholic fatty liver disease, Cardioprotective, Nephroprotective, Anti-inflammatory, and antihypertensive effects. Gliflozin derivatives represent a paradigm shift in the management of diabetes and have broad therapeutic potential. Future studies should emphasize creative structural changes to increase safety and efficacy.
Diabetes mellitus is one of the world’s leading health problems, and standard approaches to therapy do not take into consideration the impact of circadian misalignment on metabolic health. Chronobiological processes are involved in regulating glucose homeostasis, insulin release, hepatic glucose production, lipids, and energy metabolism; hence, disruptions such as those caused by non-rhythmic sleep patterns, shifts at work, irregular meal times, and light exposure could cause insulin resistance and even type 2 diabetes. The current critical review discusses the light-melatonin interaction and personalized chronobiological medicine. Instead of giving a descriptive account of particular treatments, the paper introduces the integrative importance of chronobiology for the management of diabetes by considering data about chrononutrition, time-restricted feeding, chronopharmacology, chrono-exercise, and an approach to chronometabolism based on the integration of the timing of meals, drugs, exercise, sleep, and light exposure with chronotype, metabolic phenotype, and the feasibility of the therapy. Circadian timing can be a promising adjuvant in the treatment of diabetes; however, the available evidence does not suggest an ideal time for intervention. Further studies should focus on measuring time, well-powered randomized controlled trials, long-term outcomes, and individualization of chronotherapy.
Abstract: Diabetes mellitus is a global health issue with escalating metabolic dysfunction, clinical heterogeneity, and rising microvascular and macrovascular consequences. Traditional diabetes diagnosis, risk assessment, treatment selection, and complication surveillance rely on periodic clinical measures and population-level risk models, which may not fully capture dynamic, individualized disease pathways. Precision diabetes treatment is being transformed by Artificial intelligence (AI), including machine learning (ML), deep learning (DL), and multimodal data analytics. This review critically examines AI-driven approaches to diabetes management, including early disease prediction, diagnostic support, glycemic monitoring, treatment-response prediction, and the prevention and detection of diabetes-associated complications. The US Food and Drug Administration (USFDA) has sanctioned multiple AI/ML-based medical devices for automated retinal screening, clinical diagnostic assistance, and patient self-management applications. For clinically meaningful patterns and disease progression prediction, AI-enabled analysis of continuous glucose monitoring data, retinal images, electronic health records, wearable device signals, laboratory parameters, and multi-omics datasets is prioritized. The use of AI for detecting diabetic retinopathy, kidney disease, cardiovascular disease, neuropathy, and foot disorders is also highlighted.Data quality, algorithmic bias, interpretability, privacy, cybersecurity, model drift, regulatory monitoring, clinical validation, and equitable access prevent widespread application despite promising results. This study offers a thorough and critical assessment of the developing roles of AI, specifically ML and DL, in the prediction, diagnosis, and management of diabetes and its related complications. Real-world validation, standardized evaluation frameworks, trustworthy AI, and effective human- AI collaboration will drive advancement. AI could transform diabetes management into predictive, tailored, continuous, and complication-preventive care.
Introduction/Objective: Self-management is essential for chronic disease control. This study aimed to explore patients’ perspectives on self-management behaviors and the factors influencing these behaviors in chronic disease management. Methods: A descriptive analytical cross-sectional study was conducted in primary healthcare centers affiliated with Jeddah’s Second Health Cluster. Using simple random sampling, 123 adults with controlled type 2 diabetes, controlled hypertension, or both conditions were recruited. Data were collected through telephone interviews using a validated questionnaire. Results: Overall self-management was high (mean = 2.65; 82.5%). The highest-rated behaviors were medication adherence (93%), adherence to treatment recommendations (91.5%), and appointment attendance (90%). Symptom monitoring, contact with the healthcare team, and monitoring at primary healthcare centers were also high (≥82.5%). Dietary adherence and physical activity were moderate (72% each). Influencing factors were high for knowledge/awareness (84%-89%), intrinsic motivation (81.5%), self-efficacy (83.5%), problem-solving ability (80.5%), and social/family support (84.5%). Stress-management coping capacity (69.5%) and post-appointment follow-up (61%) were moderate. Self-management was higher among participants without comorbidities, non-smokers, and those living with family (p < 0.05), positively associated with disease duration and education, and negatively associated with age. Discussion: Self-management was strongest when embedded in treatment routines and supported by personal capability and family support, whereas sustaining lifestyle change and psychological coping remained more challenging. Conclusion: Among adults with controlled type 2 diabetes, controlled hypertension, or both conditions, self-management was generally high and supported by enabling factors, but with limited generalizability. Targeted, patient-centered interventions are needed to strengthen lifestyle self-management, psychological coping, and structured follow-up.
Introduction: Acrylamide has been shown to induce oxidative stress and β-cell toxicity in animal studies; however, population-based evidence remains limited and inconsistent. Therefore, this study aimed to evaluate the associations between acrylamide (AA) hemoglobin biomarkers and the risk of prediabetes and diabetes in the US population. Methods: Data were obtained from the 2013-2016 National Health and Nutrition Examination Survey (NHANES). Participants were classified into diabetes, prediabetes, and healthy control groups by their glycometabolic status. The associations between prediabetes and diabetes with AA biomarkers, specifically hemoglobin adducts of acrylamide (HbAA), glycidamide (HbGA), the Hb- GA/HbAA ratio, and their combined levels (HbAA + HbGA), were investigated using multivariate logistic regression and restricted cubic spline models. Results: The fully adjusted logistic regression analyses indicated that participants in the highest HbGA quartile had a significantly elevated risk of prediabetes (odd ratio [OR] = 2.47, 95% confidence interval [CI]: 1.52-4.02, P = 0.007; Ptrend = 0.005) and diabetes (OR = 2.46, 95% CI: 1.27-4.73, P = 0.019; Ptrend = 0.043). The HbGA/HbAA ratio showed a positive correlation with prediabetes (OR = 2.10, 95% CI: 1.05-4.20, P = 0.042; Ptrend = 0.024) and diabetes (OR = 5.58, 95% CI: 2.10-14.79, P = 0.008; Ptrend < 0.001). In contrast, HbAA levels were not significantly associated with either prediabetes (OR = 1.49, 95% CI: 0.86-2.61, P = 0.116; Ptrend = 0.088) or diabetes (OR = 0.92, 95% CI: 0.47-1.81, P = 0.744; Ptrend = 0.351). Discussion: The divergent patterns observed for HbAA and the HbGA/HbAA ratio may reflect a shift from exogenous exposure to intrinsic metabolic capacity across different stages of disease progression. Conclusion: Higher HbGA levels and elevated HbGA/HbAA ratios were associated with an increased prevalence of prediabetes and diabetes. Further research is needed to clarify the causal role of acrylamide exposure in abnormal glucose metabolism.
