Machine learning algorithms identifying the risk of new-onset ACS in patients with type 2 diabetes mellitus: A retrospective cohort study.

Background: In recent years, the prevalence of type 2 diabetes mellitus (T2DM) has increased annually. The major complication of T2DM is cardiovascular disease (CVD). CVD is the main cause of death in T2DM patients, particularly those with comorbid acute coronary syndrome (ACS). Although risk prediction models using multivariate logistic regression are available to assess the probability of new-onset ACS development in T2DM patients, none have been established using machine learning (ML). Methods: Between January 2019 and January 2020, we enrolled 521 T2DM patients with new-onset ACS or no ACS from our institution's medical information recording system and divided them into a training dataset and a testing dataset. Seven ML algorithms were used to establish models to assess the probability of ACS coupled with 5-cross validation. Results: We established a nomogram to assess the probability of newly diagnosed ACS in T2DM patients with an area under the curve (AUC) of 0.80 in the testing dataset and identified some key features: family history of CVD, history of smoking and drinking, aspartate aminotransferase level, age, neutrophil count, and Killip grade, which accelerated the development of ACS in patients with T2DM. The AUC values of the seven ML models were 0.70-0.96, and random forest model had the best performance (accuracy, 0.89; AUC, 0.96; recall, 0.83; precision, 0.91; F1 score, 0.87). Conclusion: ML algorithms, especially random forest model (AUC, 0.961), had higher performance than conventional logistic regression (AUC, 0.801) for assessing new-onset ACS probability in T2DM patients with excellent clinical and diagnostic value.

Ononin alleviates endoplasmic reticulum stress in doxorubicin-induced cardiotoxicity by activating SIRT3.

Doxorubicin (DOX) is a potent anthracycline antineoplastic drug. However, its dose-dependent cardiotoxicity limits its clinical application. Ononin is a natural isoflavone glycoside that is crucial in modulating apoptosis-related signaling pathways. In this study, we assessed the possible cardioprotective effects of ononin in DOX-induced cardiotoxicity and elucidated the underlying molecular mechanisms. In vitro and in vivo assessments were performed using DOX-treated H9C2 cells and rats, respectively. First, DOX was injected into the tail veins of Wistar rats to induce cardiomyopathy. Next, rats in the DOX + Ononin30 and DOX + Ononin60 groups were intragastrically administered ononin two weeks before DOX treatment. H9C2 cells were treated with vehicle or DOX with or without ononin. Next, 3-TYP was used to determine the relationship between endoplasmic reticulum (ER) stress and sirtuin 3 (SIRT3) expression. Ononin treatment ameliorated DOX-induced myocardial injury as determined by echocardiography. Furthermore, ononin partially restored DOX-induced cardiac dysfunction; the left ventricular ejection fraction (LVEF) and left ventricular systolic fractional shortening (LVFS) increased after pre-treatment with ononin. Further, ononin suppressed DOX-induced ER stress and apoptosis in rat cardiomyocytes and H9C2 cells. The Bax/Bcl-2 ratio and 78-kD glucose-regulated protein (GRP78) and CCAAT enhancer-binding protein (CHOP) expression levels were higher in the DOX-treated group than in the control group but ononin treatment improved these parameters. These effects are associated with SIRT3 activity. Moreover, 3-TYP blocked the ononin-mediated protective effects. Hence, ononin positively affected DOX-induced cardiotoxicity by inhibiting ER stress and apoptosis, possibly mediated by stimulation of the SIRT3 pathway.