This study aims at exploring alterations of major metabolites and metabolic pathways in retinopathy of prematurity (ROP) infants and identifying biomarkers that may merit early diagnosis of ROP. METHODS. We analyzed targeted metabolites from 81 premature infants (<34 weeks of gestational age), including 40 ROP cases (15 males and 25 females, birth weight 1.263 ± 0. 345 kg, gestational age 31.20 ± 4.62 weeks) and 41 cases (30 males, 11 females, birth weight 1.220 ± 0.293 kg, gestational age 30.96 ± 4.17 weeks) of well-matched non-ROP controls. Metabolites were measured by ultra-performance liquid chromatographytandem mass spectrometry. Standard multivariate and univariate analysis was performed to interpret metabolomic results. RESULTS. Glycine, glutamate, leucine, serine, piperidine, valine, tryptophan, citrulline, malonyl carnitine (C3DC), and homocysteine were identified as the top discriminant metabolites. In particular, discriminant concentrations of C3DC and glycine were also confirmed by univariate analysis as statistically significant different between ROP and non-ROP infants. CONCLUSIONS. This study gained an insight into the metabolomic aspects of ROP development. We suggest that higher blood levels of C3DC and glycine can be promising biomarkers to predict the occurrence, but not the severity of ROP.

What is known and Objective: Propofol provides a prominent sedation effect in gastroscopy. However, sedation with propofol alone during gastroscopy might result in circulatory and respiratory depression.

Purpose Advances in mass spectrometry have provided new insights into the role of metabolomics in the etiology of several diseases. Studies on retinopathy of prematurity (ROP), for example, overlooked the role of metabolic alterations in disease development. We employed comprehensive metabolic profiling and gold-standard metabolic analysis to explore major metabolites and metabolic pathways, which were significantly affected in early stages of pathogenesis toward ROP. Methods This was a multicenter, retrospective, matched-pair, case-control study. We collected plasma from 57 ROP cases and 57 strictly matched non-ROP controls. Non-targeted ultra-high-performance liquid chromatography–tandem mass spectroscopy (UPLC-MS/MS) was used to detect the metabolites. Machine learning was employed to reveal the most affected metabolites and pathways in ROP development. Results Compared with non-ROP controls, we found a significant metabolic perturbation in the plasma of ROP cases, which featured an increase in the levels of lipids, nucleotides, and carbohydrate metabolites and lower levels of peptides. Machine leaning enabled us to distinguish a cluster of metabolic pathways (glycometabolism, redox homeostasis, lipid metabolism, and arginine pathway) were strongly correlated with the development of ROP. Moreover, the severity of ROP was associated with the levels of creatinine and ribitol; also, overactivity of aerobic glycolysis and lipid metabolism was noted in the metabolic profile of ROP. Conclusions The results suggest a strong correlation between metabolic profiling and retinal neovascularization in ROP pathogenesis. These findings provide an insight into the identification of novel metabolic biomarkers for the diagnosis and prevention of ROP, but the clinical significance requires further validation.