[Masked arterial hypertension in patients with type2 diabetes mellitus: Prevalence, associated factors and cardiovascular impact]

Machine Learning-Based Predictive Modeling of Diabetic Nephropathy in Type 2 Diabetes Using Integrated Biomarkers: A Single-Center Retrospective Study

Purpose

Diabetic nephropathy (DN), a major complication of diabetes mellitus, significantly impacts global health. Identifying individuals at risk of developing DN is crucial for early intervention and improving patient outcomes. This study aims to develop and validate a machine learning-based predictive model using integrated biomarkers.

Methods

A cross-sectional analysis was conducted on a baseline dataset involving 2184 participants without DN, categorized based on their development of DN over a follow-up period of 36 months: DN (n=1270) and Non-DN (n=914). Various demographic and clinical parameters were analyzed. The findings were validated using an independent dataset comprising 468 participants, with 273 developing DN and 195 remaining as Non-DN over the follow-up period. Machine learning algorithms, alongside traditional descriptive statistics and logistic regression were used for statistical analyses.

Results

Elevated levels of serum creatinine, urea, and reduced eGFR, alongside an increased prevalence of retinopathy and peripheral neuropathy, were prominently observed in those who developed DN. Validation on the independent dataset further confirmed the model's robustness and consistency. The SVM model demonstrated superior performance in the training set (AUC=0.79, F1-score=0.74) and testing set (AUC=0.83, F1-score=0.82), outperforming other models. Significant predictors of DN included serum creatinine, eGFR, presence of diabetic retinopathy, and peripheral neuropathy.

Conclusion

Integrating machine learning algorithms with clinical and biomarker data at baseline offers a promising avenue for identifying individuals at risk of developing diabetic nephropathy in type 2 diabetes patients over a 36-month period.

Management of the 'wicked' combination of heart failure and chronic kidney disease in the patient with diabetes

Patients with type 2 diabetes are at an increased risk of developing heart failure and chronic kidney disease. The presence of these co-morbidities substantially increases the risk of morbidity as well as mortality in patients with diabetes. The clinical focus has historically centred around reducing the risk of cardiovascular disease by targeting hyperglycaemia, hyperlipidaemia and hypertension. Nonetheless, patients with type 2 diabetes who have well-controlled blood glucose, blood pressure and lipid levels may still go on to develop heart failure, kidney disease or both. Major diabetes and cardiovascular societies are now recommending the use of treatments such as sodium-glucose co-transporter-2 inhibitors and non-steroidal mineralocorticoid receptor antagonists, in addition to currently recommended therapies, to promote cardiorenal protection through alternative pathways as early as possible in individuals with diabetes and cardiorenal manifestations. This review examines the most recent recommendations for managing the risk of cardiorenal progression in patients with type 2 diabetes.

Identification of sex characteristics of obesity and hypertension in the adult population of the Vladimir region

Aim. To identify sex characteristics of the prevalence of obesity and hypertension (HTN) in the adult population of the Vladimir region.

Material and methods. The study included 1350 people, men and women aged 30-69 years from 5 cities of the Vladimir region (Vladimir, Kovrov, Murom, Yuriev-Polsky and Vyazniki). The response rate was at least 80%. In total, 1174 people completed the study, including 424 men (36,1%) and 750 women (63,9%). The survey was conducted according to a standard questionnaire, including socio-demographic parameters and behavioral risk factors. In all patients, anthropometric parameters, blood pressure, heart rate and resting electrocardiography were assessed.

Results. The average body mass index among women was significantly highercompared to men (29,9±6,2 kg/m2 vs 28,3±4,7 kg/m2, p<0,001), while waist circumference among men exceeded similar parameters among women (95,5±11,7 cm vs 91,9±15,3 cm, p<0,001). Sex differences in anthropometric indicators have a number of features in different cities. Every third man is obese, while among women this figure was significantly higher (33,7% vs 42,1%, p<0,001). Every second man and 2/3 of women had abdominal obesity (53,1% and 71,6%, p<0,001). The prevalence of HTN among men and women was comparable (41,5% and 39,9%, respectively). Electrocardiographic signs of left ventricular (LV) hypertrophy are detected in every third respondent, while its prevalence between men and women did not differ statistically (30,5% and 35,5%, respectively).

Conclusion. In the surveyed population of men and women of the Vladimir region, the prevalence of obesity and HTN were comparable. The prevalence of obesity and abdominal obesity has sex differences due to the greater severity among women. Hypertension is also highly likely to be associated with LV hypertrophy. Obviously, in order to correct obesity and HTN, lifestyle changes at the population level must be combined with the use of antihypertensive therapy to achieve target blood pressure levels and regress LV hypertrophy.