Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2023 Apr 1;10(1):14.
doi: 10.1186/s40662-023-00331-8.

Metabolomic analysis of aqueous humor reveals potential metabolite biomarkers for differential detection of macular edema

Dan Jiang #  1 , Congcong Yan #  1 , Lina Ge  1 , Chun Yang  1 , Ying Huang  1 , Yau Kei Chan  2 , Chonghua Chen  1 , Wei Chen  1 , Meng Zhou  3 , Bing Lin  4
Affiliations

Metabolomic analysis of aqueous humor reveals potential metabolite biomarkers for differential detection of macular edema

Dan Jiang et al. Eye Vis (Lond). .

Abstract

Background: Macular edema (ME) is a major complication of retinal disease with multiple mechanisms involved in its development. This study aimed to investigate the metabolite profile of aqueous humor (AH) in patients with ME of different etiologies and identify potential metabolite biomarkers for early diagnosis of ME.

Methods: Samples of AH were collected from 60 patients with ME and 20 age- and sex-matched controls and analyzed by liquid chromatography-mass spectrometry (LC/MS)-based metabolomics. A series of univariate and multivariate statistical analyses were performed to identify differential metabolites and enriched metabolite pathways.

Results: The metabolic profile of AH differed significantly between ME patients and healthy controls, and differentially expressed metabolites were identified. Pathway analysis revealed that these differentially expressed metabolites are mainly involved in lipid metabolism and amino acid metabolism. Moreover, significant differences were identified in the metabolic composition of AH from patients with ME due to different retinal diseases including age-related macular degeneration (AMD-ME), diabetic retinopathy (DME) and branch retinal vein occlusion (BRVO-ME). In total, 39 and 79 etiology-specific altered metabolites were identified for AMD-ME and DME, respectively. Finally, an AH-derived machine learning-based diagnostic model was developed and successfully validated in the test cohort with an area under the receiver operating characteristic (ROC) curve of 0.79 for AMD-ME, 0.94 for DME and 0.77 for BRVO-ME.

Conclusions: Our study illustrates the potential underlying metabolic basis of AH of different etiologies across ME populations. We also identify AH-derived metabolite biomarkers that may improve the differential diagnosis and treatment stratification of ME patients with different etiologies.

Keywords: Liquid chromatography-mass spectrometry; Macular edema; Metabolic biomarkers; Metabolomics.

PubMed Disclaimer

Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
Quality control (QC) assessment of liquid chromatography-tandem mass spectrometry (LC-MS) analysis. Total ion chromatogram (TIC) of QC samples in negative ion mode (NEG, a) and positive ion mode (POS, b). Principle component analysis score plots for all aqueous humor samples in NEG (c) and POS (d) with different colored dots representing different groups of samples. AMD-ME, macular edema of age-related macular degeneration; BRVO-ME, macular edema of branch retinal vein occlusion; CON, control group; DME, diabetic retinopathy
Fig. 2
Fig. 2
Identification of differentially expressed metabolites (DEMs) of aqueous humor from macular edema (ME) and control group (CON). Volcano plot (a) and heatmap (b) of differentially expressed metabolites for different disease groups and CON. c Venn diagram showing the overlap among DEMs between different disease groups and CON. AMD-ME, macular edema of age-related macular degeneration; BRVO-ME, macular edema of branch retinal vein occlusion; DME, diabetic retinopathy; FDR, false discovery rate
Fig. 3
Fig. 3
Functional characterization of differentially expressed metabolites (DEMs) of aqueous humor from ME (a), DME (b), AMD-ME (c), and BRVO-ME (d) compared with the CON group. DME diabetic macular edema, AMD-ME macular edema of age-related macular degeneration, BRVO-ME macular edema of branch retinal vein occlusion, CON control group, ME macular edema
Fig. 4
Fig. 4
Identification and functional characterization of differentially expressed metabolites (DEMs) of aqueous humor among macular edema (ME) of different etiologies. Volcano plot (a) and heatmap (b) of differentially expressed metabolites for ME of different etiologies. Venn diagram visualizing the overlap among differentially expressed metabolites for ME of different etiologies (c). Functional characterization of DEMs of aqueous humor from DME and AMD-ME (d), BRVO-ME and AMD-ME (e), and BRVO-ME and DME (f). AMD-ME, macular edema of age-related macular degeneration; BRVO-ME, macular edema of branch retinal vein occlusion; CON, control group; DME, diabetic macular edema; FDR, false discovery rate
Fig. 5
Fig. 5
Performance evaluation of the diagnostic metabolic model. Receiver operating characteristic (ROC) curves of the diagnostic metabolic model in the training cohort (a) and test cohort (b). c Confusion matrices showing predicted outcomes generated by the diagnostic metabolic model in the test cohort. d Performance measurements of the diagnostic metabolic model illustrated by the five indices. AMD-ME, macular edema of age-related macular degeneration; BRVO-ME, macular edema of branch retinal vein occlusion; DME, diabetic macular edema; ME, macular edema; NPV, negative predictive value; PPV, positive predictive value
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

See all "Cited by" articles

References

    1. Reznicek L, Kolb JP, Klein T, Mohler KJ, Wieser W, Huber R, et al. Wide-field Megahertz OCT imaging of patients with diabetic retinopathy. J Diabetes Res. 2015;2015:305084. doi: 10.1155/2015/305084. - DOI - PMC - PubMed
    1. De Pretto LR, Moult EM, Alibhai AY, Carrasco-Zevallos OM, Chen S, Lee B, et al. Controlling for artifacts in widefield optical coherence tomography angiography measurements of non-perfusion area. Sci Rep. 2019;9(1):9096. doi: 10.1038/s41598-019-43958-1. - DOI - PMC - PubMed
    1. Frizziero L, Midena G, Longhin E, Berton M, Torresin T, Parrozzani R, et al. Early retinal changes by OCT angiography and multifocal electroretinography in diabetes. J Clin Med. 2020;9(11):3514. doi: 10.3390/jcm9113514. - DOI - PMC - PubMed
    1. Bekkers A, Borren N, Ederveen V, Fokkinga E, De Jesus DA, Brea LS, Klein S, van Walsum T, Barbosa-Breda J, Stalmans I. Microvascular damage assessed by OCT angiography for glaucoma diagnosis: a systematic review of the most discriminative regions. Acta Ophthalmol. 2020;98(6):537–558. doi: 10.1111/aos.14392. - DOI - PMC - PubMed
    1. Mitchell SL, Ma C, Scott WK, Agarwal A, Pericak-Vance MA, Haines JL, et al. Plasma metabolomics of intermediate and neovascular age-related macular degeneration patients. Cells. 2021;10(11):3141. doi: 10.3390/cells10113141. - DOI - PMC - PubMed

LinkOut - more resources