Objective: To investigate the association between long-term fasting blood glucose (FPG) variability and all-cause mortality in patients with type 2 diabetes. Methods: A total of 7 174 type 2 diabetic patients included in National Basic Public Health Service Program in Changshu of Jiangsu Province were recruited as participants. Long-term glucose variability was assessed using standard deviation (SD), coefficient of variation (CV), average real variability (ARV), and variability independent of the mean (VIM) across FPG measurements at the more than three visits. Death information were mainly obtained from the death registry system in Jiangsu. Then Cox proportional hazards regression models were used to estimate the associations of four variability indicators and all-cause mortality's hazard ratios (HRs) and their 95%CIs. Results: Among 55 058.50 person-years of the follow-up, the mean follow-up time was 7.67 years, and 898 deaths occurred during the follow-up period. After adjustment, compared with T1 group, the Cox regression model showed that HRs of T3 group in SD, CV, ARV and VIM were 1.24 (95%CI: 1.03-1.49), 1.20 (95%CI: 1.01-1.43), 1.28 (95%CI: 1.07-1.55) and 1.20 (95%CI:1.01-1.41), respectively. HRs of per 1 SD higher SD, CV, ARV and VIM were 1.13 (95%CI: 1.06-1.21), 1.08 (95%CI: 1.01-1.15), 1.05 (95%CI: 1.00-1.12) and 1.09 (95%CI: 1.02-1.16) for all-cause mortality, respectively. In the stratified analysis, age, gender, hypoglycemic agent and insulin uses had no effect on the above associations (all P for interaction >0.05). Conclusion: Long-term FPG glycemic variability was positively associated with the risk of all-cause mortality in type 2 diabetes patients.
Objective To explore the protective effect and mechanism of lactoferrin（Lf） on a radiation-induced intestinal injury. Methods Mice were randomly divided into the control group, 10 Gy group, and Lf+10 Gy group. On 7 d before irradiation, the Lf+10 Gy group was given 250 mg/kg Lf by gavage daily. Mice in the 10 Gy group and Lf+10 Gy group received 10 Gy abdominal irradiation. The mice were sacrificed 3.5 d after irradiation, and the samples were collected to determine the related indexes. Results Irradiation-induced intestinal injury. Lf intervention restored weight loss, hepatic oxidative stress, and pathological intestinal damage in mice. Lf significantly increased the number of Lgr5 and Ki67 positive cells, reflecting the proliferation and regeneration ability of small intestinal cells. Irradiation-induced ferroptosis in small intestinal tissue. Via the iron metabolism pathway, Lf intervention significantly downregulated the expression of intestinal divalent metal transporter（DMT1） and Fe 2+ content; via the GSH/GPX4-lipid peroxidation pathway, Lf intervention significantly upregulated the intestinal solute carrier family 7 members（SLC7A11） and glutathione peroxidase 4（GPX4） protein expression, downregulated acyl-CoA synthase long chain 4（ACSL4） protein expression, and reduced malondialdehyde（MDA） levels. Conclusions Lf ameliorated radiation-induced intestinal injury, possibly due to the regulation of ferroptosis through iron metabolism and the GSH/GPX4-lipid peroxidation pathway.
Objective In the elderly,sarcopenia is a common disease and efficient identification of sarcopenia is very important to keep health.Methods Based on the longitudinal physical examination data,a total of 2 544 subjects in a hospital from Shanghai during 2013-2019 were included.Considering the difference across annual indices,various machine learning models were constructed to predict the risk of sarcopenia in the elderly,and the decision curve analysis was applied to provide reference for clinical decision makers.Results The prediction models results showed that the prediction accuracy based on the Light Gradient Boosting Machine(LightGBM)model was relatively high(AUC=0.913 4).Decision curve analysis indicated that the net profit of the LightGBM model became larger when threshold probability(threshold for judging sarcopenia)ranged from 0.01 to 0.42 and from 0.84 to 0.92.And the net profit of the Random Forest model was larger when threshold probability ranged from 0.42 to 0.50 and from 0.60 to 0.67,while in the case of logistic regression model,the range was located in 0.50-0.60 and 0.67-0.84.Conclusions The prediction model established based on longitudinal physical examination data and machine learning methods can effectively predict the future risk of sarcopenia in the elderly,and is of great value for the early diagnosis and intervention of sarcopenia.
2023年10月28-29日,由中华预防医学会流行病学分会主办,中华流行病学杂志编辑委员会和中华疾病控制杂志编辑委员会协办,安徽理工大学公共卫生学院和北京大学公众健康与重大疫情防控战略研究中心共同承办的"2023年全国流行病学学术会议"在安徽省合肥市召开.本次大会的主题是"系统流行病学引领发展之路".
Objective The purpose of this study is to understand the factors influencing suicidal ideation among medical students and to explore the predictive effect of machine learning(ML)algorithms.Methods A random stratified whole-group sampling of medical students in Shandong Province was conducted from November 2021 to March 2022 to conduct the questionnaire survey.The Chi-square test,Fisher's exact probability method,and Logistic regression were used to explore the factors influencing suicidal ideation among medical students.The prediction models were constructed based on a machine learning algorithm in the training set.The predictive ability of the model was evaluated in the test set based on accuracy,sensitivity,and so on.Results The suicidal ideation rate in medical students was 12.80％.The results showed that living in rural areas(OR=1.523,95％CI:1.023-2.271,P=0.039),and depression symptoms(OR=3.874,95％CI:2.676-5.621,P＜0.001)were risk factors for suicidal ideation.Having not been in love in the past two years(OR=0.601,95％CI:0.427-0.841,P=0.003)and so on were protective factors for suicidal ideation.The prediction accuracy and sensitivity of the four models were higher than 0.90,the Kappa values were higher than 0.80,and the positive and negative predictive values were higher than 0.90.Conclusions All four prediction models perform well,among which the suicide ideation prediction model based on support vector machine(SVM)has more advantages.The SVM model can effectively predict the risk of suicidal ideation,which is more helpful for early identification and intervention of high-risk groups.
