Search

Objectives: To assess the value of National Metabolic Management Centers (MMC) specialized online equipment for the maintenance of metabolic control among patients with type 2 diabetes (T2DM). Patients: Between June 2017 and April 2021, patients with T2DM with at least one follow-up visit with HbA1c detected from 10 MMCs were enrolled and divided into non and application of online equipment (non-AOE and AOE) groups. Measurements: Propensity score matching (PSM) was used to balance the characteristics of patients between groups. The change in glycated haemoglobin (HbA1c) was used to evaluate the effect of online equipment on diabetic management. Further stratified analyses were performed to assess the metabolic outcomes in subgroups. Results: After PSM, 12528 patients with T2DM were finally enrolled. After a median (IQR) follow-up of 15.88 (7.10, 24.27) months, the AOE group showed better control of HbA1c than non-AOE group (-0.90 [-2.60, 0.00] % vs. -0.70 [-2.20, 0.10] %, P < 0.0001) and high frequency AOE group showed better control of HbA1c than low frequency AOE group (-1.50 [-3.60, -0.30] % vs. -0.80 [-2.38, 0.10] %, P < 0.0001) after adjustment. More decrease in body mass index (BMI) was observed in AOE group. Stratification analyses showed that significant between-group change in HbA1c was observed in those with lower education level or poor control of HbA1c at baseline. Conclusions: Applying online equipment promotes better metabolic improvement and offers an alternative to out-of-hospital patient care in patients with T2DM, especially with lower education level or poorer control of HbA1c at baseline.

To determine whether the follow-up frequency for type 2 diabetes mellitus (T2DM) patients in the National Metabolic Management Centers (MMCs) leads to different clinical outcomes.A total of 19 908 T2DM patients with at least 6 months of facility-based follow-up were recruited in MMCs between June 2017 and April 2021 and divided into lower-frequency and higher-frequency follow-up (LFF and HFF) groups according to the median follow-up frequency of 2.0 (interquartile range 1.2) times per year. Metabolic parameters at baseline and at the last follow-up visit were analyzed. Multivariable linear regression models were performed to assess the relationship between follow-up frequency and between-group percentage changes, adjusting for the major covariables. Additional stratified analyses were conducted to evaluate the metabolic outcomes in the subgroups.The characteristics of the participants in the LFF and HFF groups were significantly different at baseline. Participants had significant improvements in multiple metabolic parameters after follow-up. Patients with HFF showed significantly greater decrease in percentage changes of fasting blood glucose (-4.95% ± 37.96% vs -2.21% ± 43.08%, P .0001) and glycosylated hemoglobin (HbA1c) (-12.14% ± 19.78% vs -9.67% ± 20.29%, P .0001) after adjustments compared to those with LFF. Furthermore, stratification analyses showed that significant between-group percentage changes of HbA1c were observed in those with younger age (55 years) and higher HbA1c (9%) at baseline (P for interaction.001).HFF is associated with better metabolic outcomes. Participants, especially with younger age or worse HbA1c at baseline in the HFF group achieved better glycemic control than those in the LFF group.背景: 本研究旨在确定国家代谢管理中心(MMC)对2型糖尿病(T2D)患者的随访频率是否会导致不同的临床结果。 方法: 选择2017年6月至2021年4月在MMC进行至少6个月随访的2型糖尿病患者19,908例, 根据每年2.0次(IQR, 1.2)的中位数随访频率, 分为低频组(LFF)和高频组(HFF)。分析基线和末次随访时的代谢参数。采用多变量线性回归模型评估经主要协变量校正后的随访率和组间百分比变化之间的关系。另外还进行了分层分析, 以评估各亚组的代谢结果。 结果: LFF组和HFF组的受试者在基线时的特征有显著差异。随访后, 受试者的多项代谢参数均有显著改善。校正后HFF组的空腹血糖(-4.95±37.96% vs -2.21±43.08%, P0.0001)和糖化血红蛋白(HbA1c) (-12.14±19.78% vs -9.67±20.29%, P0.0001)比LFF组显著下降。此外, 分层分析显示, 在基线时年龄较小(55岁)和HbA1c较高(9%)的人群中, HbA1c的组间百分比变化显著(P0.001)。 结论: HFF与较好的代谢结局有关。HFF组较年轻或基线HbA1c较差的受试者, 其血糖控制效果好于LFF组。.

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.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.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.In summary, higher WST appears to be associated with higher prevalence of carotid plaque in patients with T2DM, especially in aged populations.背景: 动脉粥样硬化是2型糖尿病患者的常见并发症。动脉粥样硬化的发生发展涉及多种因素。我们评估了每周久坐时间(WST)与颈动脉斑块形成的关系。 方法: 数据清理后, 纳入2017年6月至2021年4月10个国家代谢管理中心(MMC)的26664例2型糖尿病患者。自报生活方式资料包括WST, 睡眠时间, 吸烟和饮酒情况, 颈动脉超声和生化指标。用多变量Logistic回归模型评估久坐与其他生活方式行为之间的独立关联, 并报告95%可信区间(CI)的优势比(OR)。此外, 还对混杂因素进行了分层分析, 论证了混杂因素的影响。 结果: 受试者的平均年龄为54.0(11.6)岁, WST的中位数(四分位数范围)为35.0(21.0, 42.0)小时。与WST前3分位数的受试者相比, WST第二或第三3分位数的受试者年龄更小, 糖尿病病程更短。调整后久坐时间与动脉斑块形成的OR值呈正相关, 调整后2, 3分位数的OR值分别为1.40(95%CI:1.31~1.50)和1.67(95%CI:1.56~1.79)。而WST对18~40岁患者斑块的影响无统计学意义, 第三3分位数P值为0.163。 结论: 高WST似乎与T2 DM患者颈动脉斑块的高患病率有关, 尤其是在老年人群中。.

IntroductionEarly 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 methodsThe 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.ResultsIn 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.ConclusionThis 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 numberNCT04240652.