Global trends in diabetes-related mortality with regard to lifestyle modifications, risk factors, and affordable management: A preliminary analysis

A Comprehensive Review of Emerging Therapies for Type 2 Diabetes and Their Cardiovascular Effects

The discovery of inhibitors for sodium-glucose cotransporter 2 (SGLT2) and glucagon-like peptide-1 receptor agonists (GLP-1 RA) has significantly improved type 2 diabetes management. Large-scale clinical studies have shown that both SGLT2 inhibitors and GLP-1 RA enhance cardiovascular health. Benefits include reduced cardiovascular disease risk, lower mortality, fewer heart failure hospitalizations (SGLT2 inhibitors), and stroke prevention (GLP-1 RA). Additionally, these drugs slow chronic kidney disease progression. This comprehensive treatment targets vascular events. Despite differences, both drug classes are crucial. GLP-1 RA mainly reduce stroke risk, while SGLT2 inhibitors alleviate heart failure. Our findings, based on a literature review, will address the renal and cardiac effects of SGLT2 inhibitors and GLP-1 RA in both diabetics and non-diabetics, highlighting their combined benefits for heart conditions.

A personalized multitasking framework for real-time prediction of blood glucose levels in type 1 diabetes patients

Real-time prediction of blood glucose levels (BGLs) in individuals with type 1 diabetes (T1D) presents considerable challenges. Accordingly, we present a personalized multitasking framework aimed to forecast blood glucose levels in patients. The patient data was initially categorized according to gender and age and subsequently utilized as input for a modified GRU network model, creating five prediction sub-models. The model hyperparameters were optimized and tuned after introducing the decay factor and incorporating the TCN network and attention mechanism into the GRU model. This step was undertaken to improve the capability of feature extraction. The Ohio T1DM clinical dataset was used to train and evaluate the performance of the proposed model. The metrics, including Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Clark Error Grid Analysis (EGA), were used to evaluate the performance. The results showed that the average RMSE and the MAE of the proposed model were 16.896 and 9.978 mg/dL, respectively, over the prediction horizon (PH) of 30 minutes. The average RMSE and the MAE were 28.881 and 19.347 mg/dL, respectively, over the PH of 60 min. The proposed model demonstrated excellent prediction accuracy. In addition, the EGA analysis showed that the proposed model accurately predicted 30-minute and 60-minute PH within zones A and B, demonstrating that the framework is clinically feasible. The proposed personalized multitask prediction model in this study offers robust assistance for clinical decision-making, playing a pivotal role in improving the outcomes of individuals with diabetes.

Efficient Utilization of Fruit Peels for the Bioproduction of D-Allulose and D-Mannitol

Currently, the demand for low-calorie sweeteners has grown dramatically because consumers are more mindful of their health than they used to be. Therefore, bioproduction of low-calorie sweeteners from low-cost raw materials becomes a hot spot. In this study, a two-stage strategy was established to efficiently utilize D-fructose from fruit and vegetable wastes. Firstly, ketose 3-epimerase was used to produce D-allulose from D-fructose of pear peels. Secondly, the residual D-fructose was converted to D-mannitol by the engineered strain co-expression of D-mannitol 2-dehydrogenase and formate dehydrogenase. Approximately 29.4% D-fructose of pear peels was converted to D-allulose. Subsequently, under optimal conditions (35 °C, pH 6.5, 1 mM Mn²⁺, 2 g/L dry cells), almost all the residual D-fructose was transformed into D-mannitol with a 93.5% conversion rate. Eventually, from 1 kg fresh pear peel, it could produce 10.8 g of D-allulose and 24.6 g of D-mannitol. This bioprocess strategy provides a vital method to biosynthesize high-value functional sugars from low-cost biomass.

Health and economic effects on patients with type 2 diabetes mellitus in the long run: predictions for the Chilean population

BACKGROUND

Diabetes is associated to a high financial and disease burden, explaining a large proportion of expenditure of the health system in one year. The purpose of this study was to estimate long-term costs and health outcomes of recently diagnosed patients with type 2 diabetes in Chile.

METHODS

Cost and consequence study based on mathematical discrete event simulation (DES) model. We modelled expected costs (USD) and quality-adjusted life-years (QALYs) from diagnosis to death (or the age of 95) of a hypothetical cohort of 100,000 incident cases, simulated based on the Chilean National Health Survey 2018. The incidence of twelve complications was estimated assuming the hazard functions provided by the United Kingdom Prospective Diabetes Study. We explore heterogeneity across patients based on their baseline risk covariates and their impact on costs and QALYs.

RESULTS

The expected cost and QALY of a recently diagnosed type 2 diabetes patient in Chile were USD 8660 and 12.44 QALYs. Both costs and QALYs were independently determined by baseline risk and the patient's life expectancy from the diagnosis. Length of life since diagnosis showed the major impact on costs (5.2% increase for every additional year). Myocardial infarction was the most frequent complication (47.4%) and the most frequent cause of death.

CONCLUSION

Diabetes type 2 determines a significant expenditure of the health system and substantial health losses. Although the control of cardiovascular risk factors and the metabolic control of the disease, both have an important impact on costs and outcomes, the main impact is achieved by postponing the age of onset of the disease.

Supporting the emotional well-being of patients with diabetes mellitus in primary care

Diabetes mellitus is a condition characterised by elevated blood glucose levels that can lead to significant acute and long-term complications. Alongside these physical complications, the condition can have substantial effects on people's emotional well-being, potentially resulting in diabetes distress and/or major depressive disorder. Therefore, timely assessment and referral of patients with diabetes who display signs and symptoms of diabetes distress or other mental health conditions are essential. This article discusses emotional well-being in patients with diabetes, and outlines some diabetes-specific validated assessment tools that can be used in practice. It also discusses the management and appropriate onward referral of people with diabetes who require support.