• NEWS . 21 Feb 2020
  • Chinese researchers predict the risk of new-onset ACS in patients with diabetes

  • Researchers have developed a potent risk predictive model to predict the probability of new-onset acute coronary syndrome (ACS) in patients with type 2 diabetes mellitus (T2DM), using data from the economically disadvantaged northwestern region of China.

    T2DM is prevalent and, owing to the lower rate of controlled diabetes in China compared with developed countries, leads to a higher incidence of serious cardiovascular complications, especially ACS.

    Therefore, the researchers aimed to establish a potent risk predictive model using data from 456 patients with T2DM admitted to the First Affiliated Hospital of Xi'an Jiaotong University in China. Of these, 270 patients with diabetes had no ACS, whereas 186 had newly diagnosed ACS. Overall, 32 demographic characteristics and serum biomarkers of the study patients were analysed.

    The predictive model included age, body mass index, diabetes duration, systolic blood pressure (SBP), diastolic blood pressure (DBP), low-density lipoprotein cholesterol, serum uric acid, lipoprotein(a), hypertension history and alcohol drinking status as predictors. The area under the receiver operating characteristics curve that was used to evaluate the discriminatory capacity of the model and that of the internal validation set was 0.830 (95% confidence interval [CI], 0.786–0.874) and 0.827 (95% CI, 0.756–0.899), respectively. The predictive model showed very good fitting degree, and the decision curve analysis used to evaluate its clinical validity demonstrated a clinically effective predictive model.

    In conclusion, weight loss, lowering of SBP and blood uric acid levels and appropriate control for DBP may significantly reduce the risk of new-onset ACS in T2DM patients in Northwest China.

    Reference:
    Lyu J, et al. A potent risk model for predicting new-onset acute coronary syndrome in patients with type 2 diabetes mellitus in Northwest China. Acta Diabetol 2020. doi: 10.1007/s00592-020-01484-x. [Epub ahead of print]