Association of sedentary time and carotid atherosclerotic plaques in patients with type 2 diabetes.

BACKGROUND: Atherosclerosis is a common complication in patients with type 2 diabetes (T2DM). Multiple factors are involved in the development and progress of atherosclerosis. We evaluated the association of weekly sedentary time (WST) with carotid plaque formation. METHODS: After data cleaning, a total of 26 664 participants with T2DM from 10 National Metabolic Management Centers (MMCs) from June 2017 to April 2021 were enrolled. Self-reported lifestyle data including WST, sleeping time, smoking and drinking information, carotid artery ultrasound, and biochemical parameters were obtained. The independent association of carotid plaue with sedentary and other lifestyle behaviors was evaluated using multivariable logistic regression models, and odds ratio (OR) with 95% confidence interval (CI) were reported. Moreover, stratified analysis was conducted to demonstrate the influence of confounding factors. RESULTS: The mean (SD) age of the participants was 54.0 (11.6) years, and the median (interquartile range) WST was 35.0 (21.0, 42.0) h. Comparing with participants in the first tertile of WST, those in the second or third tertile of WST were younger and with a shorter duration of diabetes. There were positive associations between longer sedentary time and odds of artery plaque after adjustment, with corresponding ORs in the second and third tertile were 1.40 (95% CI: 1.31-1.50) and 1.67 (95% CI: 1.56-1.79), respectively. However, the effect of WST on plaque in patients aged 18-40 years old had no statistical significance; the p value in the third tertile was 0.163. CONCLUSIONS: In summary, higher WST appears to be associated with higher prevalence of carotid plaque in patients with T2DM, especially in aged populations.

Artificial intelligence-enabled screening for diabetic retinopathy: a real-world, multicenter and prospective study.

INTRODUCTION: Early screening for diabetic retinopathy (DR) with an efficient and scalable method is highly needed to reduce blindness, due to the growing epidemic of diabetes. The aim of the study was to validate an artificial intelligence-enabled DR screening and to investigate the prevalence of DR in adult patients with diabetes in China. RESEARCH DESIGN AND METHODS: The study was prospectively conducted at 155 diabetes centers in China. A non-mydriatic, macula-centered fundus photograph per eye was collected and graded through a deep learning (DL)-based, five-stage DR classification. Images from a randomly selected one-third of participants were used for the DL algorithm validation. RESULTS: In total, 47 269 patients (mean (SD) age, 54.29 (11.60) years) were enrolled. 15 805 randomly selected participants were reviewed by a panel of specialists for DL algorithm validation. The DR grading algorithms had a 83.3% (95% CI: 81.9% to 84.6%) sensitivity and a 92.5% (95% CI: 92.1% to 92.9%) specificity to detect referable DR. The five-stage DR classification performance (concordance: 83.0%) is comparable to the interobserver variability of specialists (concordance: 84.3%). The estimated prevalence in patients with diabetes detected by DL algorithm for any DR, referable DR and vision-threatening DR were 28.8% (95% CI: 28.4% to 29.3%), 24.4% (95% CI: 24.0% to 24.8%) and 10.8% (95% CI: 10.5% to 11.1%), respectively. The prevalence was higher in female, elderly, longer diabetes duration and higher glycated hemoglobin groups. CONCLUSION: This study performed, a nationwide, multicenter, DL-based DR screening and the results indicated the importance and feasibility of DR screening in clinical practice with this system deployed at diabetes centers. TRIAL REGISTRATION NUMBER: NCT04240652.