Impact of sarcosine on diabetic retinopathy: Findings based on weighted gene co‐expression network analysis and machine learning techniques

Abstract

AbstractAimTo quantify the association between serum sarcosine and diabetic retinopathy (DR) using weighted gene co‐expression network analysis (WGCNA).MethodsWe measured serum metabolites in 69 pairs of type 2 diabetes (T2D) patients with and without DR matched by age, gender, body mass index（BMI and HbA1c, using a propensity score matching‐based approach. To identify modules and metabolites linked to DR, pathway analysis was performed using WGCNA, the Kyoto Encyclopedia of Genes and Genomes and Small‐Molecule Pathway Database. The association of sarcosine with DR was estimated by restricted cubic spline and conditional logistic regression models. Its joint effects with covariates on DR were also extensively examined.ResultsWith per interquartile range elevation of sarcosine, the adjusted odds ratio (AOR) of DR significantly decreased by 67% (AOR: 0.33, 95% confidence interval [CI]: 0.19‐0.58). Similar results were also found in the tertile analysis. Compared with those in the first tertile of sarcosine, the AOR significantly decreased by 54% (AOR: 0.46, 95% CI: 0.18‐1.17) and 78% (AOR: 0.22, 95% CI: 0.08‐0.59) for subjects in the second and third tertiles, respectively. Compared with subjects with lower sarcosine and lower HDL‐C levels, those with higher sarcosine and lower HDL‐C levels had the lowest odds of DR (OR: 0.13, 95% CI: 0.04, 0.43).ConclusionsSerum sarcosine was inversely related to DR, especially in T2D patients with insufficient HDL‐C. This study provides insights on a possible novel target for DR precision prevention and control, as well as a better understanding of the DR mechanism.