Bone status in adolescents and young adults with type 1 diabetes: a 10-year longitudinal study

Introduction: This study presents a 10-year longitudinal assessment of bone status in adolescents and young adults with type 1 diabetes (T1D). Material and methods: Thirty-two patients (12 female, aged 20.5 ± 3.93 years, T1D duration 13.9 ± 1.97 years) were studied using quantitative ultrasound (QUS) and dual-energy X-ray absorptiometry (DXA). Standard deviation scores (SDS) for these results were calculated. The following clinical parameters were analysed: sex, age, T1D duration, anthropometric parameters, daily insulin requirement (DIR), mean glycated haemoglobin (HbA1c) in the year preceding the examination, medication other than insulin, history of bone fractures, and comorbidities. Results: The current and past (measured 10 years earlier) QUS results did not differ and showed a significant correlation (r = 0.55, p = 0.001). We found no relation of QUS results and anthropometric parameters or gender. DXA parameters did not correlate with the present QUS measurement. DXA and QUS results were independent of HbA1c, co-morbidities, or intake of additional medicaments. Conclusions: Bone status parameters of the examined patients with currently suboptimal glycaemic control were found to be lowered in comparison to a normative reference population, both at baseline and follow-up, although no further deterioration was observed during the 10-year follow-up period.

The influence of SGLT2 inhibitors on oxidative stress in heart failure and chronic kidney disease in patients with type 2 diabetes

There is increasing interest in sodium-glucose cotransporter 2 inhibitors (SGLT2i) as not only a new oral glucose-lowering drug class but also one with cardio- and nephroprotective potential. Understanding the underlying mechanisms is therefore of great interest, and postulated benefits have included increased natriuresis, lower blood pressure, increased haematocrit, enhanced cardiac fatty acid utilization, reduced low-grade inflammation, and decreased oxidative stress. In particular, redox homeostasis seems to be crucial in the pathogenesis of heart and kidney disease in diabetes, and there is accumulating evidence that SGLT2i have beneficial effects in this perspective. In this review, we aimed to summarize the potential mechanisms of the influence of SGLT2i on oxidative stress parameters in animal and human studies, with a special focus on heart failure and chronic kidney disease in diabetes mellitus

Machine learning identification of risk factors for heart failure in patients with diabetes mellitus with metabolic dysfunction associated steatotic liver disease (MASLD): the Silesia Diabetes-Heart Project

Abstract Background Diabetes mellitus (DM), heart failure (HF) and metabolic dysfunction associated steatotic liver disease (MASLD) are overlapping diseases of increasing prevalence. Because there are still high numbers of patients with HF who are undiagnosed and untreated, there is a need for improving efforts to better identify HF in patients with DM with or without MASLD. This study aims to develop machine learning (ML) models for assessing the risk of the HF occurrence in patients with DM with and without MASLD. Research design and methods In the Silesia Diabetes-Heart Project (NCT05626413), patients with DM with and without MASLD were analyzed to identify the most important HF risk factors with the use of a ML approach. The multiple logistic regression (MLR) classifier exploiting the most discriminative patient’s parameters selected by the χ2 test following the Monte Carlo strategy was implemented. The classification capabilities of the ML models were quantified using sensitivity, specificity, and the percentage of correctly classified (CC) high- and low-risk patients. Results We studied 2000 patients with DM (mean age 58.85 ± SD 17.37 years; 48% women). In the feature selection process, we identified 5 parameters: age, type of DM, atrial fibrillation (AF), hyperuricemia and estimated glomerular filtration rate (eGFR). In the case of MASLD( +) patients, the same criterion was met by 3 features: AF, hyperuricemia and eGFR, and for MASLD(−) patients, by 2 features: age and eGFR. Amongst all patients, sensitivity and specificity were 0.81 and 0.70, respectively, with the area under the receiver operating curve (AUC) of 0.84 (95% CI 0.82–0.86). Conclusion A ML approach demonstrated high performance in identifying HF in patients with DM independently of their MASLD status, as well as both in patients with and without MASLD based on easy-to-obtain patient parameters. Graphical Abstrac

Machine Learning Predicts Cardiovascular Events in Patients With Diabetes: The Silesia Diabetes-Heart Project

We aimed to develop a machine learning (ML) model for predicting cardiovascular (CV) events in patients with diabetes (DM). This was a prospective, observational study where clinical data of patients with diabetes hospitalized in the diabetology center in Poland (years 2015-2020) were analyzed using ML. The occurrence of new CV events following discharge was collected in the follow-up time for up to 5 years and 9 months. An end-to-end ML technique which exploits the neighborhood component analysis for elaborating discriminative predictors, followed by a hybrid sampling/boosting classification algorithm, multiple logistic regression (MLR), or unsupervised hierarchical clustering was proposed. In 1735 patients with diabetes (53% female), there were 150 (8.65%) ones with a new CV event in the follow-up. Twelve most discriminative patients' parameters included coronary artery disease, heart failure, peripheral artery disease, stroke, diabetic foot disease, chronic kidney disease, eosinophil count, serum potassium level, and being treated with clopidogrel, heparin, proton pump inhibitor, and loop diuretic. Utilizing those variables resulted in the area under the receiver operating characteristic curve (AUC) ranging from 0.62 (95% Confidence Interval [CI] 0.56-0.68, P < 0.01) to 0.72 (95% CI 0.66-0.77, P < 0.01) across 5 nonoverlapping test folds, whereas MLR correctly determined 111/150 (74.00%) high-risk patients, and 989/1585 (62.40%) low-risk patients, resulting in 1100/1735 (63.40%) correctly classified patients (AUC: 0.72, 95% CI 0.66-0.77). ML algorithms can identify patients with diabetes at a high risk of new CV events based on a small number of interpretable and easy-to-obtain patients' parameters