Comparative Analysis Reveals Novel Changes in Plasma Metabolites and Metabolomic Networks of Infants With Retinopathy of Prematurity

Abstract

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.

Figure 1.

Pie chart showing fractions of main classes of metabolites. Categories of metabolites were ordered from the highest to lowest fraction as follows: lipids (47.04%), amino acids (29.11%), nucleotides (5.12%), cofactors and vitamins (4.99%), peptides (4.58%), carbohydrates (4.04%), xenobiotics (3.77%), and energy (1.35%).

Figure 2.

Heatmap illustrating categories of metabolites in control and ROP groups. The color scales range from bright blue (low ratio) to bright red (high ratio) and represent the relative ratio of Y = log(Y)(intensity) between the two groups. In general, the levels of metabolites in the ROP group were slightly higher than those in the control group, especially in lipids.

Figure 3.

OPLS-DA scatterplot of samples from ROP and control groups. Samples from the ROP and control groups clearly separated clusters in the OPLS-DA analysis (R²Ycum = 0.908, Q²cum = 0.523). Green dots in the figure represent ROP (n = 57); blue dots represent non-ROP (n = 57).

Figure 4.

Results of metabolite set enrichment analysis. Pathway impact was determined by topological analysis (x-axis), and the enrichment logP value was adjusted by the original P value (y-axis). Node color is based on the P value, and the node size is based on pathway impact values. The top nine most significantly affected metabolic pathways (impact > 0.3 and P < 0.05) are inside the green box.

Figure 5.

Illustration of a random forest graph. The vertical axis shows the top 30 metabolites, and the horizontal axis represents the corresponding importance of the selected feature. Different colors indicate different classes of metabolites. Isocitric lactone had the highest feature importance.

Figure 6.

Correlation heatmap of the top 30 metabolites and pathways selected from random forest analysis. The green # symbols on the left denote metabolites corresponding to enriched metabolic pathways (on the right with green colors 1, 2, 4, 5, 8, and 9). Statistically significant differences are indicated by purple triangles on the left and right sides (adjusted P < 0.05). Additionally, results before and after adjustment for arginine biosynthesis, FAO, and redox homeostasis pathway enrichment analysis are shown.

Figure 7.

Box plot of potential biomarkers of ROP. Creatinine, ribitol, and glutamic acid gamma-methyl ester showed statistically significant differences (P < 0.05); ornithine, 10-undecenoate (11:1n1), and picolinoylglycine exhibited relatively significant differences (0.05 < P < 0.1). Light red represents elevated levels of the metabolites, and light blue indicates reduced levels of the metabolites. Circles represent outliers, and the plus signs represent the means.

Figure 8.

Nonlinear regression analysis was used to assess correlation between PMA (days) and biomarkers. The curve was obtained by fitting to a two-parameter model in the nonlinear regression analysis. The potential biomarkers—glutamic acid gamma-methyl ester, ornithine, creatinine, ribitol, 10-undecenoate (11:1n1), and picolinoylglycine—would presumably vary with PMA; however, the analysis did not verify the predicted linear relationship. In the figure, red lines represent the ROP group, and black lines indicate the control group.

Figure 9.

Schematics of potential pathways associated with ROP development. Red words indicate increased metabolites in the ROP group; green words indicate decreased metabolites in the ROP group. Red arrows indicate pathways that had more active reactions, and green arrows represent a less-active reactions. The purple background denotes key enzymes of relevant pathways. CPT1, carnitine palmitoyltransferase 1; PEP, phosphoenolpyruvic acid; ASNS, asparagine synthase; GLS1, glutaminase 1; GPD, glucose-6-phosphate dehydrogenase; GSH, glutathione; GGT, gamma-glutamyl transferase; SAM, S-adenosylmethionine; SAH, S-adenosylhomocysteine; ASS, argininosuccinate synthase; ASL, argininosuccinate lyase; NOS, nitric oxide synthase; IDO, indoleamine-2,3-dioxygenases.