4.7 Article

Plasma Metabolomics Reveals Metabolic Profiling For Diabetic Retinopathy and Disease Progression

Journal

FRONTIERS IN ENDOCRINOLOGY
Volume 12, Issue -, Pages -

Publisher

FRONTIERS MEDIA SA
DOI: 10.3389/fendo.2021.757088

Keywords

diabetic retinopathy; plasma metabolomics; biomarkers; diabetes mellitus; machine learning

Funding

  1. Suqian Key Research and Development Program [S201807]

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This study identified 22 differentially expressed metabolites associated with the occurrence of DR, with pseudouridine levels strongly linked to DR. Additionally, four circulating plasma metabolites were found to be differentially expressed in PDR patients, and a risk score formula based on these metabolites was significantly related to PDR.
Backgrounds Diabetic retinopathy (DR), the main retinal vascular complication of DM, is the leading cause of visual impairment and blindness among working-age people worldwide. The aim of this study was to investigate the difference of plasma metabolic profiles in patients with DR to better understand the mechanism of this disease and disease progression. Methods We used ultrahigh-performance liquid Q-Exactive mass spectrometry and multivariate statistical analyses to conduct a comprehensive analysis of plasma metabolites in a population with DR and proliferative DR (PDR). A risk score based on the level of the selected metabolite was established and evaluated using the least absolute shrinkage and selection operator regularization logistic regression (LASSO-LR) based machine learning model. Results 22 differentially expressed metabolites which belonged to different metabolic pathway were identified and confirmed to be associated with the occurrence of DR. A risk score based on the level of the selected metabolite pseudouridine was established and evaluated to strongly associated with the occurrence of DR. Four circulating plasma metabolites (pseudouridine, glutamate, leucylleucine and N-acetyltryptophan) were identified to be differentially expressed between patients with PDR and other patients, and a risk score formula based on these plasma metabolites was developed and assessed to be significantly related to PDR. Conclusions Our work highlights the possible use of the risk score assessment based on the plasma metabolites not only reveal in the early diagnosis of DR and PDR but also assist in enhancing current therapeutic strategies in the clinic.

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