Metabolomics Analysis of the Aqueous Humour Uncovers Potential Metabolite Biomarkers for Early Differential Detection of Macular Edema

doi:10.21203/rs.3.rs-1621921/v1

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Metabolomics Analysis of the Aqueous Humour Uncovers Potential Metabolite Biomarkers for Early Differential Detection of Macular Edema

https://doi.org/10.21203/rs.3.rs-1621921/v1

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Background: Macular edema (ME) is a major complication of several retinal diseases, and multiple mechanisms are involved in its development. This study aims to investigate the metabolite profiles of the aqueous humour in patients with ME of different aetiologies and identify potential metabolite biomarkers allowing ME diagnosis in the very early stages of the disease.

Results: The metabolic profiles of aqueous humour differed significantly between ME patients and healthy controls, and 105 differentially expressed metabolites were identified. Pathway analysis showed that the differentially expressed metabolites are mainly involved in lipid metabolism and amino acid metabolism. Further comparison analysis also revealed significant differences in the metabolic composition of aqueous humour of ME patients derived from age-related macular degeneration (AMD), diabetic retinopathy (DR) and branch retinal vein occlusion (BRVO). In total, 39 and 79 aetiology-specific altered metabolites were identified for AMD-ME and DME, respectively. Finally, a novel aqueous humour-derived machine learning-based metabolic model was developed, which successfully validated in the test cohort with an area under the receiver operating characteristic curve of 0.79 for AMD-ME, 0.94 for DME and 0.77 for BRVO-ME.

Conclusions: Our study illustrates the underlying metabolic basis of aqueous humour for different aetiologies across ME populations and identifies aqueous humour-derived metabolite biomarkers that will improve differential diagnosis and treatment stratification for ME of different aetiologies.

macular edema

liquid chromatography-mass spectrometry

metabolic biomarkers

metabolomics

Macular edema (ME) is the final pathway shared by many intraocular and systemic injuries. ME may be associated with a variety of underlying diseases, but it is commonly seen in intraocular surgery, veno-occlusive disease, diabetic retinopathy (DR), and wet age-related macular degeneration (AMD). The pathological characteristics of ME have not been completely clarified. The diagnosis of retinal diseases depends largely on digital imaging, such as optical coherence tomography (OCT) and fluorescein angiography (FA). However, most OCT devices have a limited imaging range, with the macula and optic disc being the main imaging positions, so peripheral changes may be missed, especially in the early stage of diseases ^{[1, 2]}. In addition, the detailed disease phenotypes may fail to be detected and distinguished by current sophisticated imaging techniques ^{[3, 4]}. With the development of precision medicine, including the use of objective quantitative indices and omics methods, an important opportunity has been offered to understand the pathological characteristics and the diagnosis/treatment of ME.

Metabolomics is an emerging discipline defined as the comprehensive assessment of low-molecular-weight (< 1 kDa) endogenous metabolites produced by biochemical reactions under a given set of physiological conditions ^{[5, 6]}. It offers an opportunity to develop powerful exploratory tools to monitor the disease condition and help clarify the pathogenesis ^[7–9]. The "metabolome" represents a group of metabolites in biological tissues that consists of the end products of specific cellular processes. Cellular interactions and metabolite exchanges between the retina, retinal pigment epithelium, and choroid complexes also provide an important physiological basis for the study on the metabolomic alteration of ME ^[10]. Clinical studies have found that diet, blood glucose, serum cholesterol and triglyceride levels are risk factors for fundus diseases, stressing the potential importance of studying metabolomic alteration to identify molecular biomarkers related to these risks ^{[11, 12]}. There are some metabolomics studies on metabolite changes in the vitreous tissue of patients with DR ^{[13, 14]}, proliferative vitreoretinopathy ^{[15, 16]}, rhegmatogenous retinal detachment ^[13], and inflammatory retinal disease ^[15]. There are few studies on the metabolomics of the aqueous humour (AH) in ME patients and related topics ^[17–19]. Abundant information can be obtained from AH analysis. AH samples have been used to detect metabolites of eye diseases, and disorders of multiple metabolites are correlated with diseases such as acute/chronic glaucoma, myopia, and AMD ^[19–22].

This study aims to investigate the metabolite profiles of AH in patients with ME of different aetiologies, including ME caused by AMD (AMD-ME), DR (DME), and branch retinal vein occlusion (BRVO) (BRVO-ME). We illustrated the underlying metabolic basis of AH for different aetiologies across ME populations and identified AH-derived metabolite biomarkers by liquid chromatography-mass spectrometry (LC/MS)-based metabolomics and a series of univariate and multivariate statistical analyses. Through this study, we hope that "metabolic portraits" of different types of ME can be understood and refined to help us better understand the pathophysiological characteristics of ME and facilitate the molecular diagnosis of the disease.

Overview of data quality

The quality control (QC) samples were applied to monitor the signal and instrumental deviation to ensure data quality and reproducibility. As shown in Fig. 1A and B, the retention times and areas of the internal standard overlapped well in positive ion mode (POS) and negative ion mode (NEG) for all QC samples. The characteristics detected in samples of the disease groups (AMD-ME, DME, and BRVO-ME) and the control group (CON) were subjected to principal component analysis (PCA), and obvious clustering of the QC samples was observed in both positive and negative ion modes (Fig. 1C, D). These findings demonstrated the stability of the detection processes, the reliability of data quality, and the stability of data acquisition.

Metabolite Pathway Analysis

The important metabolite pathways were subjected to pathway analysis using MetaboAnalyst 5.0. The results showed that the identified DEMs mostly belonged to lipid metabolism (mainly including nicotinate and nicotinamide metabolism, linoleic acid and linolenic acid metabolism, sphingolipid metabolism, arachidonic acid metabolism, and glycerophospholipid metabolism), followed by amino acid metabolism (mainly including tryptophan, tyrosine, alanine, aspartate, and glutamate metabolism) and, for a few DEMs, cytochrome P450, glucose, purine, and pyrimidine metabolism. Specifically, nicotinate and nicotinamide metabolism and purine metabolism were significantly downregulated in all ME patients. Linoleic acid and linolenic acid metabolism, sphingolipid metabolism, glycerophospholipid metabolism, and steroid metabolism were significantly upregulated in all ME patients, and amino acid metabolism, especially alanine, aspartate, and glutamate metabolism, was significantly upregulated in AMD-ME and BRVO-ME patients (Fig. 3).

Differential Ah Metabolites Among Me Of Different Aetiologies

To further investigate the metabolic characteristics of AH for ME with different aetiologies, the metabolic profiles of AH were compared, and overall differences in metabolites among the AMD-ME, DME, and BRVO-ME groups were analysed using PLS-DA. Characteristics with VIP > = 1 were taken as important variables (n = 312), and the Mann-Whitney U test was performed to identify metabolite features with |Log2(fold change)|>1 and FDR < 0.05 as DEMs. Finally, a total of 40 metabolites (38 upregulated and two downregulated), 128 metabolites (81 upregulated and 47 downregulated) and 84 metabolites (27 upregulated and 57 downregulated) were identified as DEMs in BRVO-ME compared with AMD-ME, DME compared with AMD-ME and BRVO-ME compared with DME, respectively (Fig. 4A, B). Specifically, 32 DEMs were shared by the three disease groups (Fig. 4C, D). Further comparison analysis also showed significant differences in the metabolic composition of AH derived from AMD-ME, DME and BRVO-ME and identified aetiology-specific altered metabolites, including 39 for AMD-ME and 79 for DME, respectively. The enrichment results showed that carbohydrate metabolism including the citric acid cycle (TCA cycle), glyoxylate and dicarboxylate metabolism, pyruvate metabolism and glycolysis/gluconeogenesis were upregulated in BRVO-ME patients compared with DME and AMD-ME patients. The pathways upregulated in DME patients nearly overlapped the pathways downregulated in AMD-ME patients compared with the other groups. The pathways upregulated in DME patients overlapped almost completely with the downregulated pathways in AMD-ME patients compared with the other groups and mainly included lipid metabolism and carbohydrate metabolism (Fig. 4E-G).

Development And Evaluation Of A Diagnostic Metabolic Model

To search for optimal metabolites biomarker for diagnostic prediction from DEMs, an recursive feature elimination (RFE) variable selection procedure was applied to the discovery dataset, and 60 metabolites were identified as an optimal combination. The diagnostic model effectively distinguished ME from healthy controls with an AUC of 1 (Fig. 5A). For each disease group, the diagnostic model had an AUC of 1 in the discovery dataset (Fig. 5A). When tested in the test cohort, the diagnostic model distinguished ME from normal samples with an AUC of 0.83 (Fig. 5B). For different disease groups, the diagnostic model had an AUC of 0.79 for AMD-ME detection, 0.94 for DME detection, and 0.77 for BRVO-ME detection (Fig. 5B). To test the discriminatory power of the model, we applied it to each disease group (Fig. 5C). As shown in the confusion matrix, the model was extremely robust in distinguishing each disease group from the others. We found that the model consistently achieved comparable predictive performance as measured by accuracy, sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) (Fig. 5D). These results highlighted the robustness and stability of the AH-derived metabolic model for the detection and differentiation of ME.

Diet, blood glucose, serum cholesterol, and triglyceride levels are risk factors for fundus diseases, showing the importance of studying metabolomic alterations to identify molecular biomarkers related to these risks ^{[19, 23]}. Retinal diseases are increasingly associated with metabolic dysfunction, so the study of systemic and local metabolic biomarkers may offer opportunities to early detect and track disease progression. In this study, the associations between AH metabolites and disease were highlighted, including DEMs between each disease group (AMD-ME, DME, and BRVO-ME) and the CON group and DEMs between the three disease groups. Moreover, the possible pathways responsible for DEMs were analysed in depth, the disease prediction model using DEMs was established, and the potential value of the model was discussed.

Metabolites of eye diseases have been detected in AH samples in previous studies, and the disorders of phospholipids ^[24], cholesterols ^[20], sphingolipids, and ceramides ^[25] have been found in acute/chronic glaucoma studies. AH has also been used to identify myopia-related metabolic changes, and 40 metabolites might be associated with myopia ^[21], of which 20 were considered significantly different in different stages of myopia. In this study, 93, 19, and 21 DEMs were found between the DME group and the CON group, the BRVO-ME group and the CON group, and the AMD-ME group and the CON group, respectively, suggesting that the pathological metabolic disturbances in the retina may be similar among patients with different types of ME. Comparison of disease groups with the CON group found 30 DEMs, including 17 upregulated and 13 downregulated (Fig. 2a). Nine common DEMs were found in all three disease groups when compared with the CON group (Fig. 2C, 2D), indicating that ME of each aetiology has unique characteristics and an association with the primary disease.

This study found that the identified DEMs mostly belong to lipid metabolism (mainly including nicotinate and nicotinamide metabolism, linoleic acid and linolenic acid metabolism, sphingolipid metabolism, arachidonic acid metabolism, and glycerophospholipid metabolism), followed by amino acid metabolism (mainly including tryptophan, tyrosine, alanine, aspartate, and glutamate metabolism). Lipid metabolism, especially linoleic acid and linolenic acid metabolism, sphingolipid metabolism, glycerophospholipid metabolism, and steroid metabolism, was significantly upregulated in all ME patients (Fig. 3). Cholesterol is the precursor of many important steroids and was significantly upregulated in AMD-ME, DME, and BRVO-ME. Early AMD is characterized by rich deposits of extracellular cholesterol below the retinal pigment epithelium, called drusen, or in the subretinal space, called subretinal drusenoid deposits, which deposits could drive disease progression ^[26]. The structure and function of the retina rely largely on fatty acid composition ^[27]. According to evidence from epidemiological studies and animal experiments, the fatty acid composition of the retina is influenced by diet ^[28–30]. The findings in this study seem to suggest that nutrient intake and metabolic organ function may both affect ocular health through metabolism, providing new ideas for intervention strategies.

The occurrence and progression of ME has been associated with the vascular endothelial growth factor (VEGF) family ^{[31, 32]}. Inhibiting VEGF is regarded the best method for drug therapy for DME and proliferative DR and for the prevention of DR progression. Unfortunately, clinical evidence shows that intravitreal anti-VEGF therapy can only ameliorate DME in approximately 60% of patients ^[33]. Further research on the exact intraocular molecular changes in ME of different aetiologies is needed. In this study, there were 40, 128, and 84 DEMs between BRVO-ME and AMD-ME, between DME and AMD-ME, and between BRVO-ME and DME, respectively. Moreover, 32 common DEMs were found in the three disease groups (Fig. 4). Despite similar retinal structural changes in ME of different aetiologies shown on OCT, the internal molecular-biological mechanisms are different. Using the DEMs as characteristic metabolites, we input the 60 metabolites with the highest precision to establish the prediction model for aetiologies of ME (Fig. 5). The results showed that the model could predict DME, BRVO-ME, and AMD-ME, and its predictive accuracy was the highest for DME. Therefore, we tend to believe that AH can also be used for precise diagnosis, early diagnosis of the aetiology of ME, and the prediction of the aetiology of complex ME.

Taken together, our study performed metabolic analysis of AH in patients with ME of different aetiologies. Research illustrates the underlying metabolic basis of AH from ME populations which identified DEMs mostly belong to lipid metabolism, followed by amino acid metabolism. It revealed significant differences in the metabolic composition of AH of ME patients derived from AMD-ME, DME and BRVO-ME. a novel AH-derived machine learning-based metabolic model was proposed for the early detection of ME. These findings will be helpful for exploring the molecular mechanism of ME, which could provide new perspectives for early diagnosis and the search for new therapeutic targets.

Experimental vitreous sample collection

AH samples were collected from 60 ME patients and 20 age- and sex-matched healthy patients at the Eye Hospital Affiliated of Wenzhou Medical University during routine vitreous injection or anterior chamber puncture. After surface anaesthesia, the eye was prepared aseptically according to routine procedures for ophthalmic surgery. The sample (approximately 0.1 mL) was manually aspirated into a syringe using glassware. After collection, the sample was immediately cooled and stored in a low-temperature refrigerator at -80°C for later tests. All subjects provided clinical information to enrol in the study, including the patients' age, sex, diagnosis, history of eye disease, medical history, preoperative visual acuity, and date of sampling were recorded.

Ethics

The human aqueous humourused in this study were approved by of Wenzhou Medical University on September 10, 2021, and the TRN is 2021-096-k-80-01.

Metabolite extraction

Each AH sample (50 µL) was transferred to an EP tube. After adding 200 µL of extract solution (acetonitrile:methanol = 1:1, internal standard mixture containing isotopic labelling), the sample was sonicated in an ice bath for 10 min and incubated at -40°C for 1 h. The sample was then centrifuged at 4°C and 12,000 rpm (RCF = 13800 (× g), R = 8.6 cm) for 15 min. The supernatant was transferred to a new glass vial for analysis. QC sample was prepared by mixing equal volumes of supernatant from all samples.

LC-MS/MS Analysis

LC-MS/MS analysis was performed using high-performance liquid chromatography with a diode array detection system (Thermo Fisher Scientific). A UPLC BEH Amide column (2.1 mm × 100 mm, 1.7 µm) coupled to a Q Exactive HFX mass spectrometer (Thermo) was used for separation. The mobile phase consisted of 25 mmol/L ammonium acetate and 25 mmol/L aqueous ammonia hydroxide solution (pH 9.75) (A) and acetonitrile (B) The temperature of the automatic sampler was 4°C, and the injection volume was 3 µL. A QE HFX mass spectrometer was used because of its ability to collect MS/MS spectra in information correlation acquisition mode under the control of the acquisition software (Xcalibur, Thermo). In this mode, the acquisition software continuously evaluates the full-scan MS spectrum. The electrospray ionization source conditions were as follows: sheath gas flow rate 30 Arb, Aux gas flow rate 25 Arb, capillary temperature 350°C, full MS resolution 60,000, MS/MS resolution 7500, collision energy 10/30/60 in NCE mode, and spray voltage 3.6 kV (positive) or -3.2 kV (negative), respectively.

Data preprocessing and annotation

The raw data were converted to mzXML format using ProteoWizard and processed with an in-house program developed using R based on XCMS for peak detection, extraction, alignment, and integration. Then, an in-house MS2 database (BiotreeDB) was applied for metabolite annotation. The cutoff value for annotation was 0.3. Only metabolic features with a relative standard deviation of less than 30% in QC samples were retained. Next, metabolic features detected in > 50% of the samples were selected for further analysis, and missing values were recoded according to the 1/2 minimum. An internal standard-based normalization strategy was selected.

Development of the diagnostic metabolic model

DEMs were screened using univariate analysis (Mann − Whitney U test) and PLS-DA. Metabolites with VIP > = 1, |Log2(fold change)|> 1 and FDR < 0.05 were identified, where VIP was obtained from PLS-DA and FDR was obtained from the Mann-Whitney U test. RFE was applied to these DEMs to identify the optimal metabolites and distinguish a specific disease from other diseases or healthy samples. The feature selection process was performed using the R package caret. The selected optimal metabolite combination was then used to train the gradient boosting machine to predict the likelihood of a new sample being a specific disease type or healthy (i.e., “BRVO-ME”, “AMD-ME” or “DME”). The gradient boosting machine was trained using the R package caret. The samples were stratified into the training cohort and test cohort at a ratio of 6:4.

Statistical analysis

PCA and PLS-DA were performed to show the global metabolic differences among groups. Pathway enrichment analysis was performed on MetaboAnalyst (https://www.metaboanalyst.ca/) to obtain significantly enriched KEGG pathways. Venn diagrams were performed using the R packages venn and UpSetR to identify common and specific features. ROC curves were performed using the R package pROC to illustrate the predictive power. All statistical analyses were performed in R version 3.6.0.

ME	Macular edema
AH	Aqueous humour
DR	Diabetic retinopathy
AMD	Age-related macular degeneration
OCT	Optical coherence tomography
FA	Fluorescein angiography
BRVO	Branch retinal vein occlusion
LC/MS	Liquid chromatography-mass spectrometry
DEMs	Differentially expressed metabolites
PLS-DA	Partial least squares-discriminant analysis,
RFE	Recursive feature elimination
PCA	Principal component analysis
POS	Positive ion mode
NEG	Negative ion mode
PPV	Positive predictive value
NPV	Negative predictive value
CON	Control group

Acknowledgements

Special thanks to the Eye Hospital of Wenzhou Medical University for its support for the scientific research of young doctors. The authors thank the Biotree biotech Co.，Ltd.(shanghai，China) for technical support.

Contributors

Dan Jiang: designed and performed experiments, evaluated data, drafted the manuscript and provided financial support. Cong-Cong Yan: analysed and interpreted data. Li-Na Ge, Chun Yang, Ying Huang: collected clinical samples. Chong-Hua Chen: organized data and results. Wei Chen: revised the manuscript. Zhou Meng: designed experiments and revised the manuscript. Bing Lin: designed experiments and provided financial support. All authors reviewed and approved the submission.

Funding

This study was supported by The National Natural Science Foundation of China (grant number 82000909).

Availability of data and materials

All data generated or analyzed during this study are included in the article and additional files.

Ethics approval and consent to participate

The human aqueous humourused in this study were approved by of Wenzhou Medical University on September 10, 2021, and the TRN is 2021-096-k-80-01.

Consent for publication

All authors have provided consent for the manuscript to be published.

Competing interests

The authors have no competing interests to disclose.

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No competing interests reported.

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Metabolomics Analysis of the Aqueous Humour Uncovers Potential Metabolite Biomarkers for Early Differential Detection of Macular Edema

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Abstract

Figures

Background

Results

Overview of data quality

Metabolite Pathway Analysis

Differential Ah Metabolites Among Me Of Different Aetiologies

Development And Evaluation Of A Diagnostic Metabolic Model

Discussion

Conclusions

Methods

Statistical analysis

Abbreviations

Declarations

References

Additional Declarations

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