Eye Hospital and School of Ophthalmology and Optometry, Wenzhou Medical University, Wenzhou, Zhejiang, China.
National Clinical Research Center for Ocular Diseases, Wenzhou, Zhejiang, China.
BMJ Open Diabetes Res Care. 2021 Feb;9(1). doi: 10.1136/bmjdrc-2020-001443.
Despite advances in diabetic retinopathy (DR) medications, early identification is vitally important for DR administration and remains a major challenge. This study aims to develop a novel system of multidimensional network biomarkers (MDNBs) based on a widely targeted metabolomics approach to detect DR among patients with type 2 diabetes mellitus (T2DM) efficiently.
In this propensity score matching-based case-control study, we used ultra-performance liquid chromatography-electrospray ionization-tandem mass spectrometry system for serum metabolites assessment of 69 pairs of patients with T2DM with DR (cases) and without DR (controls). Comprehensive analysis, including principal component analysis, orthogonal partial least squares discriminant analysis, generalized linear regression models and a 1000-times permutation test on metabolomics characteristics were conducted to detect candidate MDNBs depending on the discovery set. Receiver operating characteristic analysis was applied for the validation of capability and feasibility of MDNBs based on a separate validation set.
We detected 613 features (318 in positive and 295 in negative ESI modes) in which 63 metabolites were highly relevant to the presence of DR. A panel of MDNBs containing linoleic acid, nicotinuric acid, ornithine and phenylacetylglutamine was determined based on the discovery set. Depending on the separate validation set, the area under the curve (95% CI), sensitivity and specificity of this MDNBs system were 0.92 (0.84 to 1.0), 96% and 78%, respectively.
This study demonstrates that metabolomics-based MDNBs are associated with the presence of DR and capable of distinguishing DR from T2DM efficiently. Our data also provide new insights into the mechanisms of DR and the potential value for new treatment targets development. Additional studies are needed to confirm our findings.
尽管糖尿病视网膜病变 (DR) 药物取得了进展,但早期识别对于 DR 的治疗至关重要,仍然是一个主要挑战。本研究旨在开发一种基于广泛靶向代谢组学方法的多维网络生物标志物 (MDNB) 新系统,以有效地检测 2 型糖尿病 (T2DM) 患者中的 DR。
在这项基于倾向评分匹配的病例对照研究中,我们使用超高效液相色谱-电喷雾电离串联质谱系统评估 69 对 T2DM 伴 DR(病例)和无 DR(对照)患者的血清代谢物。全面分析,包括主成分分析、正交偏最小二乘判别分析、广义线性回归模型和代谢组学特征的 1000 次置换检验,用于根据发现集检测候选 MDNB。基于单独的验证集,应用接收者操作特征分析来验证 MDNB 的能力和可行性。
我们检测到 613 个特征(正离子模式下 318 个,负离子模式下 295 个),其中 63 个代谢物与 DR 的存在高度相关。基于发现集确定了包含亚油酸、烟酸、鸟氨酸和苯乙酰谷氨酰胺的 MDNB 组。根据单独的验证集,该 MDNB 系统的曲线下面积(95%CI)、灵敏度和特异性分别为 0.92(0.84 至 1.0)、96%和 78%。
本研究表明,基于代谢组学的 MDNB 与 DR 的存在相关,能够有效地将 DR 与 T2DM 区分开来。我们的数据还为 DR 的发病机制提供了新的见解,并为新的治疗靶点开发提供了潜在价值。需要进一步的研究来证实我们的发现。