Plasma Metabolomics Identifies Key Metabolites and Improves Prediction of Diabetic Retinopathy: Development and Validation across Multinational Cohorts

Purpose: To identify longitudinal metabolomic fingerprints of diabetic retinopathy (DR) and to evaluate their usefulness in predicting DR development and progression. Design: Multicenter, multiethnic cohort study. Participants: This study included 17 675 participants from the UK Biobank (UKB) who had baseline prediabetes or diabetes, identified in accordance with the 2021 American Diabetes Association guidelines, and were free of baseline DR and an additional 638 participants with type 2 diabetes mellitus from the Guangzhou Diabetic Eye Study (GDES) for external validation. Diabetic retinopathy was determined by ICD-10 codes in the UKB cohort and revised ETDRS grading criteria in the GDES cohort. Methods: Longitudinal DR metabolomic fingerprints were identified through nuclear magnetic resonance (NMR) assay in UKB participants. The predictive value of these fingerprints for predicting DR development were assessed in a fully withheld test set. External validation and extrapolation analyses of DR progression and microvascular damage were conducted in the GDES cohort using NMR technology. Model assessments included the concordance (C) statistic, net classification improvement (NRI), integrated discrimination improvement (IDI), calibration, and clinical usefulness in both cohorts. Main Outcome Measures: DR development and progression and retinal microvascular damage. Results: Of 168 metabolites, 118 were identified as candidate metabolomic fingerprints for future DR development. These fingerprints significantly improved the predictability for DR development beyond traditional indicators (C statistic, 0.802 [95% confidence interval (CI), 0.760–0.843] vs. 0.751 [95% CI, 0.706–0.796]; P = 5.56 × 10−4). Glucose, lactate, and citrate were among the fingerprints validated in the GDES cohort. Using these parsimonious and replicable fingerprints yielded similar improvements for predicting DR development (C statistic, 0.807 [95% CI, 0.711–0.903] vs. 0.617 [95% CI, 0.494–0.740]; P = 1.68 × 10−4) and progression (C statistic, 0.797 [95% CI, 0.712–0.882] vs. 0.665 [95% CI, 0.545–0.784]; P = 0.003) in the external GDES cohort. Improvements in NRIs, IDIs, and clinical usefulness also were evident in both cohorts (all P < 0.05). In addition, lactate and citrate were associated with microvascular damage across macular and optic nerve head regions among Chinese GDES (all P < 0.05). Conclusions: Metabolomic profiling may be effective in identifying robust fingerprints for predicting future DR development and progression, providing novel insights into the early and advanced stages of DR pathophysiology. Financial Disclosure(s): The author(s) have no proprietary or commercial interest in any materials discussed in this article.