Metabolomic analysis of aqueous humor reveals potential metabolite biomarkers for differential detection of macular edema
Tóm tắt
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. 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. 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. 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.
Tài liệu tham khảo
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.
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.
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.
Bekkers A, Borren N, Ederveen V, Fokkinga E, Andrade De Jesus D, Sánchez Brea L, et al. Microvascular damage assessed by optical coherence tomography angiography for glaucoma diagnosis: a systematic review of the most discriminative regions. Acta Ophthalmol. 2020;98(6):537–58.
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.
Joussen AM, Poulaki V, Qin W, Kirchhof B, Mitsiades N, Wiegand SJ, et al. Retinal vascular endothelial growth factor induces intercellular adhesion molecule-1 and endothelial nitric oxide synthase expression and initiates early diabetic retinal leukocyte adhesion in vivo. Am J Pathol. 2002;160(2):501–9.
Figueira J, Fletcher E, Massin P, Silva R, Bandello F, Midena E, et al. Ranibizumab plus panretinal photocoagulation versus panretinal photocoagulation alone for high-risk proliferative diabetic retinopathy (PROTEUS Study). Ophthalmology. 2018;125(5):691–700.
Bressler SB, Beaulieu WT, Glassman AR, Gross JG, Melia M, Chen E, et al. Panretinal photocoagulation versus ranibizumab for proliferative diabetic retinopathy: factors associated with vision and edema outcomes. Ophthalmology. 2018;125(11):1776–83.
Snowden S, Dahlén SE, Wheelock CE. Application of metabolomics approaches to the study of respiratory diseases. Bioanalysis. 2012;4(18):2265–90.
Wang H, Fang J, Chen F, Sun Q, Xu X, Lin SH, et al. Metabolomic profile of diabetic retinopathy: a GC-TOFMS-based approach using vitreous and aqueous humor. Acta Diabetol. 2020;57(1):41–51.
Xie S, Zhang H, Liu Y, Gao K, Zhang J, Fan R, et al. The role of serum metabolomics in distinguishing chronic rhinosinusitis with nasal polyp phenotypes. Front Mol Biosci. 2020;7:593976.
Li X, Luo X, Lu X, Duan J, Xu G. Metabolomics study of diabetic retinopathy using gas chromatography-mass spectrometry: a comparison of stages and subtypes diagnosed by western and Chinese medicine. Mol Biosyst. 2011;7(7):2228–37.
Campochiaro PA, Iftikhar M, Hafiz G, Akhlaq A, Tsai G, Wehling D, et al. Oral N-acetylcysteine improves cone function in retinitis pigmentosa patients in phase I trial. J Clin Invest. 2020;130(3):1527–41.
Berber E, Rouse BT. Controlling herpes simplex virus-induced immunoinflammatory lesions using metabolic therapy: a comparison of 2-deoxy-d-glucose with metformin. J Virol. 2022;96(14): e0068822.
Haines NR, Manoharan N, Olson JL, D’Alessandro A, Reisz JA. Metabolomics analysis of human vitreous in diabetic retinopathy and rhegmatogenous retinal detachment. J Proteome Res. 2018;17(7):2421–7.
Young SP, Nessim M, Falciani F, Trevino V, Banerjee SP, Scott RA, et al. Metabolomic analysis of human vitreous humor differentiates ocular inflammatory disease. Mol Vis. 2009;15:1210–7.
Chen L, Gao Y, Wang LZ, Cheung N, Tan GSW, Cheung GCM, et al. Recent advances in the applications of metabolomics in eye research. Anal Chim Acta. 2018;1037:28–40.
Tan SZ, Begley P, Mullard G, Hollywood KA, Bishop PN. Introduction to metabolomics and its applications in ophthalmology. Eye (Lond). 2016;30(6):773–83.
Laíns I, Gantner M, Murinello S, Lasky-Su JA, Miller JW, Friedlander M, et al. Metabolomics in the study of retinal health and disease. Prog Retin Eye Res. 2019;69:57–79.
Aribindi K, Guerra Y, Piqueras Mdel C, Banta JT, Lee RK, Bhattacharya SK. Cholesterol and glycosphingolipids of human trabecular meshwork and aqueous humor: comparative profiles from control and glaucomatous donors. Curr Eye Res. 2013;38(10):1017–26.
Hou XW, Wang Y, Pan CW. Metabolomics in age-related macular degeneration: a systematic review. Invest Ophthalmol Vis Sci. 2020;61(14):13.
Chakravarthy U, Wong TY, Fletcher A, Piault E, Evans C, Zlateva G, et al. Clinical risk factors for age-related macular degeneration: a systematic review and meta-analysis. BMC Ophthalmol. 2010;10:31.
Jian Q, Wu Y, Zhang F. Metabolomics in diabetic retinopathy: from potential biomarkers to molecular basis of oxidative stress. Cells. 2022;11(19):3005.
Edwards G, Aribindi K, Guerra Y, Lee RK, Bhattacharya SK. Phospholipid profiles of control and glaucomatous human aqueous humor. Biochimie. 2014;101:232–47.
Aljohani AJ, Edwards G, Guerra Y, Dubovy S, Miller D, Lee RK, et al. Human trabecular meshwork sphingolipid and ceramide profiles and potential latent fungal commensalism. Invest Ophthalmol Vis Sci. 2014;55(6):3413–22.
Ji Y, Rao J, Rong X, Lou S, Zheng Z, Lu Y. Metabolic characterization of human aqueous humor in relation to high myopia. Exp Eye Res. 2017;159:147–55.
Hioki T, Kamiya K, Tsuda H, Maekawa T, Komine M, Murata S, et al. Acute generalized exanthematous pustulosis induced by amoxicillin/clavulanic acid, manifesting as severe laryngeal edema. J Dermatol. 2019;46(11):e443–4.
Ban N, Lee TJ, Sene A, Choudhary M, Lekwuwa M, Dong Z, et al. Impaired monocyte cholesterol clearance initiates age-related retinal degeneration and vision loss. JCI Insight. 2018;3(17): 120824.
Albouery M, Buteau B, Grégoire S, Martine L, Gambert S, Bron AM, et al. Impact of a high-fat diet on the fatty acid composition of the retina. Exp Eye Res. 2020;196:108059.
Picklo MJ, Murphy EJ. A high-fat, high-oleic diet, but not a high-fat, saturated diet, reduces hepatic alpha-linolenic acid and eicosapentaenoic acid content in mice. Lipids. 2016;51(5):537–47.
Kamegawa M, Nakanishi-Ueda T, Iwai S, Ueda T, Kosuge S, Ogura H, et al. Effect of lipid-hydroperoxide-induced oxidative stress on vitamin E, ascorbate and glutathione in the rabbit retina. Ophthalmic Res. 2007;39(1):49–54.
Wilkinson-Berka JL, Suphapimol V, Jerome JR, Deliyanti D, Allingham MJ. Angiotensin II and aldosterone in retinal vasculopathy and inflammation. Exp Eye Res. 2019;187:107766.
Tomita Y, Lee D, Miwa Y, Jiang X, Ohta M, Tsubota K, et al. Pemafibrate protects against retinal dysfunction in a murine model of diabetic retinopathy. Int J Mol Sci. 2020;21(17):6243.
Li X, Cai S, He Z, Reilly J, Zeng Z, Strang N, et al. Metabolomics in retinal diseases: an update. Biology (Basel). 2021;10(10):944.
Ye P, Zhang X, Xu Y, Xu J, Song X, Yao K. Alterations of the gut microbiome and metabolome in patients with proliferative diabetic retinopathy. Front Microbiol. 2021;12:667632.