Objective:To construct a hypoglycemia random forest prediction model for older adults with type 2 diabetes, and assess the model′s prognostication performance through internal and external verification.Methods:From August 2022 to January 2023, 300 older adults with type 2 diabetes in Beijing Hospital were selected. The demographic characteristics, medical history, laboratory tests, and other data of the patients were collected, and the data set was randomly divided into the training set and verification set in a ratio of 7∶3. The hypoglycemia prediction model for older adults with type 2 diabetes was constructed and optimized based on the random forest algorithm. The calibration curve was used to evaluate the model′s calibration, and the ROC was used to evaluate the model′s discrimination. The clinical applicability of the model was assessed by the decision curve analysis. The risk factors for hypoglycemia in the older adults were explored by prioritizing the contributions of variables in prediction. The Bootstrap method was used for internal validation, and the validation set was used for external validation.Results:Among the 300 older adults with type 2 diabetes, 128 cases (42.67%) experienced hypoglycemia within one week. The predictive contributions of risk factors in the model were ranked as follows: the number of episodes of hypoglycemia in one month, HDL-C, heart disease, diabetes knowledge and education, combination therapy, age, duration of diabetes, staple food restriction, glycosylated hemoglobin, and gender. The internal and external calibration curves of the hypoglycemia random forest model for the older adults with type 2 diabetes fluctuated around the diagonal, indicating that the calibration degree of the predictive model is good. The AUROC of internal verification was 0.823 (95% CI 0.752-0.894), the sensitivity and specificity were 0.867 and 0.698, respectively. The external verification was 0.859 (95% CI 0.817 - 0.902), and sensitivity and specificity were 0.789 and 0.804, respectively, showing that the overall discrimination of the prediction model was good. The DCA curves were far from the all-positive line and all-negative line, which indicated that the prediction model had good clinical applicability. Conclusions:The predictive effect of this model is good, and it is suitable for predicting the risk of hypoglycemia in older adults with type 2 diabetes, and it provides a reference for early hypoglycemia screening and predictive intervention for this kind of patients.

Non-alcoholic fatty liver disease (NAFLD) is one of the most common metabolic diseases currently in the context of obesity worldwide, which contains a spectrum of chronic liver diseases, including hepatic steatosis, non-alcoholic steatohepatitis and hepatic carcinoma. In addition to the classical "Two-hit" theory, NAFLD has been recognized as a typical gut microbiota-related disease because of the intricate role of gut microbiota in maintaining human health and disease formation. Moreover, gut microbiota is even regarded as a "metabolic organ" that play complementary roles to that of liver in many aspects. The mechanisms underlying gut microbiota-mediated development of NAFLD include modulation of host energy metabolism, insulin sensitivity, and bile acid and choline metabolism. As a result, gut microbiota have been emerging as a novel therapeutic target for NAFLD by manipulating it in various ways, including probiotics, prebiotics, synbiotics, antibiotics, fecal microbiota transplantation, and herbal components. In this review, we summarized the most recent advances in gut microbiota-mediated mechanisms, as well as gut microbiota-targeted therapies on NAFLD.
Animals ; Bile Acids and Salts ; metabolism ; Choline ; metabolism ; Dietary Supplements ; Energy Metabolism ; Fecal Microbiota Transplantation ; Gastrointestinal Microbiome ; Humans ; Insulin Resistance ; Intestines ; microbiology ; Non-alcoholic Fatty Liver Disease ; microbiology ; therapy