Cardiovascular benefits from SGLT2 inhibition in type 2 diabetes mellitus patients is not impaired with phosphate flux related to pharmacotherapy

Machine learning algorithms to predict major adverse cardiovascular events in patients with diabetes

BACKGROUND

Major Adverse Cardiovascular Events (MACE) are common complications of type 2 diabetes mellitus (T2DM) that include myocardial infarction (MI), stroke, and heart failure (HF). The objective of the current study was to predict MACE among T2DM patients.

METHODS

Type 2 diabetes mellitus patients above 18 years old were recruited for the study from the All of Us Research Program. Eligible participants were those who took sodium-glucose cotransporter 2 inhibitors. Different Machine learning algorithms: including RandomForest (RF), XGBoost, logistic regression (LR), and weighted ensemble model (WEM) were employed. Clinical attributes, electrolytes and biomarkers were explored in predicting MACE. The feature importance was determined using mean decrease accuracy.

RESULTS

Overall, 9, 059 subjects were included in the analyses, of which 5197 (57.4%) were females. The XGBoost Model demonstrated a prediction accuracy of 0.80 [0.78-0.82], which is higher as compared to the RF 0.78[0.76-0.80], the LR model 0.65 [0.62-0.67], and the WEM 0.75 [0.73-0.76], respectively. The classification accuracy of the models for stroke was more than 95%, which was higher than prediction accuracy for MI (∼85%), and HF (∼80%). Phosphate, blood urea nitrogen and troponin levels were the major predictors of MACE.

CONCLUSION

The ML models had shown acceptable performance in predicting MACE in T2DM patients, except the LR model. Phosphate, blood urea nitrogen, and other electrolytes were important predictors of MACE, which is consistent between the individual components of MACE, such as stroke, MI, and HF. These parameters can be calibrated as prognostic parameters of MACE events in T2DM patients.

Anti-inflammatory role of SGLT2 inhibitors as part of their anti-atherosclerotic activity: Data from basic science and clinical trials

Atherosclerosis is a progressive inflammatory disease leading to mortality and morbidity in the civilized world. Atherosclerosis manifests as an accumulation of plaques in the intimal layer of the arterial wall that, by its subsequent erosion or rupture, triggers cardiovascular diseases. Diabetes mellitus is a well-known risk factor for atherosclerosis. Indeed, Type 2 diabetes mellitus patients have an increased risk of atherosclerosis and its associated-cardiovascular complications than non-diabetic patients. Sodium-glucose co-transport 2 inhibitors (SGLT2i), a novel anti-diabetic drugs, have a surprising advantage in cardiovascular effects, such as reducing cardiovascular death in a patient with or without diabetes. Numerous studies have shown that atherosclerosis is due to a significant inflammatory burden and that SGLT2i may play a role in inflammation. In fact, several experiment results have demonstrated that SGLT2i, with suppression of inflammatory mechanism, slows the progression of atherosclerosis. Therefore, SGLT2i may have a double benefit in terms of glycemic control and control of the atherosclerotic process at a myocardial and vascular level. This review elaborates on the anti-inflammatory effects of sodium-glucose co-transporter 2 inhibitors on atherosclerosis.