National Clinical Research Center for Ocular Diseases, Eye Hospital, Wenzhou Medical University, Wenzhou, China.
School of Optometry and Vision Science, University of New South Wales, Sydney, NSW, Australia.
Front Endocrinol (Lausanne). 2024 Sep 20;15:1420948. doi: 10.3389/fendo.2024.1420948. eCollection 2024.
The long-term glucose monitoring is essential to the risk assessment of diabetic retinopathy (DR), the aim of this study was to investigate the predictive ability of visit-to-visit fasting blood glucose (FBG) indices on the risk of DR.
This was a community-based, cohort study conducted from 2013 to 2021. DR was diagnosed by digital fundus photography. The FPG indices included FBG, var. Associations of each FBG indices and DR were estimated using multinomial logistic regression models adjusting for confounders, and discrimination was determined by area under the curve (AUC). Predictive utility of different models was compared by changes in AUC, integrated discrimination improvement (IDI), and net reclassification index (NRI).
This study analyzed 5054 participants, the mean age was 46.26 ± 11.44 years, and 2620 (51.84%) were women. After adjustment for confounders, the adjusted odds ratios (ORs) with 95% confidence intervals (CIs) for FBG, SD, CV, VIM, ARV, M-FBG, and cumulative FBG load were 1.62 (1.52-1.73), 2.74 (2.38-3.16), 1.78 (1.62-1.95), 1.11 (0.95-1.29), 1.72 (1.56-1.91), 2.15 (1.96-2.36), and 2.57 (2.31-2.85), respectively. The AUC of the model with separate cumulative FBG load and classical risk factors was 0.9135 (95%CI 0.8890-0.9380), and no substantive improvement in discrimination was achieved with the addition of other FBG indices once cumulative FBG load was in the model.
Cumulative FBG load is adequate for capturing the glucose-related DR risk, and the predictive utility of cumulative FBG load is not significantly improved by adding or replacing other FBG indices in the assessment of DR risk.
长期血糖监测对于糖尿病视网膜病变（DR）的风险评估至关重要，本研究旨在探讨随访空腹血糖（FBG）指标对 DR 风险的预测能力。
这是一项 2013 年至 2021 年进行的基于社区的队列研究。DR 通过数字眼底摄影诊断。FBG 指标包括 FBG、变异性等。使用多变量逻辑回归模型调整混杂因素后，估计每个 FBG 指标与 DR 的关联，并通过曲线下面积（AUC）确定判别能力。通过 AUC、综合判别改善（IDI）和净重新分类指数（NRI）的变化比较不同模型的预测效用。
本研究分析了 5054 名参与者，平均年龄为 46.26±11.44 岁，2620 名（51.84%）为女性。调整混杂因素后，FBG、SD、CV、VIM、ARV、M-FBG 和累积 FBG 负荷的调整比值比（OR）及其 95%置信区间（CI）分别为 1.62（1.52-1.73）、2.74（2.38-3.16）、1.78（1.62-1.95）、1.11（0.95-1.29）、1.72（1.56-1.91）、2.15（1.96-2.36）和 2.57（2.31-2.85）。单独累积 FBG 负荷和经典危险因素模型的 AUC 为 0.9135（95%CI 0.8890-0.9380），而在模型中加入其他 FBG 指标后，并未显著提高判别能力。
累积 FBG 负荷足以捕捉与血糖相关的 DR 风险，在评估 DR 风险时，添加或替换其他 FBG 指标并不能显著提高累积 FBG 负荷的预测效用。
