A serum metabolomics study of patients with nAMD in response to anti-VEGF therapy

Metabolomics facilitates differential diagnosis in common inherited retinal degenerations by exploring their profiles of serum metabolites

The diagnosis of inherited retinal degeneration (IRD) is challenging owing to its phenotypic and genotypic complexity. Clinical information is important before a genetic diagnosis is made. Metabolomics studies the entire picture of bioproducts, which are determined using genetic codes and biological reactions. We demonstrated that the common diagnoses of IRD, including retinitis pigmentosa (RP), cone-rod dystrophy (CRD), Stargardt disease (STGD), and Bietti's crystalline dystrophy (BCD), could be differentiated based on their metabolite heatmaps. Hundreds of metabolites were identified in the volcano plot compared with that of the control group in every IRD except BCD, considered as potential diagnosing markers. The phenotypes of CRD and STGD overlapped but could be differentiated by their metabolomic features with the assistance of a machine learning model with 100% accuracy. Moreover, EYS-, USH2A-associated, and other RP, sharing considerable similar characteristics in clinical findings, could also be diagnosed using the machine learning model with 85.7% accuracy. Further study would be needed to validate the results in an external dataset. By incorporating mass spectrometry and machine learning, a metabolomics-based diagnostic workflow for the clinical and molecular diagnoses of IRD was proposed in our study.

Applications of artificial intelligence and bioinformatics methodologies in the analysis of ocular biofluid markers: a scoping review

PURPOSE

This scoping review summarizes the applications of artificial intelligence (AI) and bioinformatics methodologies in analysis of ocular biofluid markers. The secondary objective was to explore supervised and unsupervised AI techniques and their predictive accuracies. We also evaluate the integration of bioinformatics with AI tools.

METHODS

This scoping review was conducted across five electronic databases including EMBASE, Medline, Cochrane Central Register of Controlled Trials, Cochrane Database of Systematic Reviews, and Web of Science from inception to July 14, 2021. Studies pertaining to biofluid marker analysis using AI or bioinformatics were included.

RESULTS

A total of 10,262 articles were retrieved from all databases and 177 studies met the inclusion criteria. The most commonly studied ocular diseases were diabetic eye diseases, with 50 papers (28%), while glaucoma was explored in 25 studies (14%), age-related macular degeneration in 20 (11%), dry eye disease in 10 (6%), and uveitis in 9 (5%). Supervised learning was used in 91 papers (51%), unsupervised AI in 83 (46%), and bioinformatics in 85 (48%). Ninety-eight papers (55%) used more than one class of AI (e.g. > 1 of supervised, unsupervised, bioinformatics, or statistical techniques), while 79 (45%) used only one. Supervised learning techniques were often used to predict disease status or prognosis, and demonstrated strong accuracy. Unsupervised AI algorithms were used to bolster the accuracy of other algorithms, identify molecularly distinct subgroups, or cluster cases into distinct subgroups that are useful for prediction of the disease course. Finally, bioinformatic tools were used to translate complex biomarker profiles or findings into interpretable data.

CONCLUSION

AI analysis of biofluid markers displayed diagnostic accuracy, provided insight into mechanisms of molecular etiologies, and had the ability to provide individualized targeted therapeutic treatment for patients. Given the progression of AI towards use in both research and the clinic, ophthalmologists should be broadly aware of the commonly used algorithms and their applications. Future research may be aimed at validating algorithms and integrating them in clinical practice.

Integrating genetics and metabolomics from multi-ethnic and multi-fluid data reveals putative mechanisms for age-related macular degeneration

Age-related macular degeneration (AMD) is a leading cause of blindness in older adults. Investigating shared genetic components between metabolites and AMD can enhance our understanding of its pathogenesis. We conduct metabolite genome-wide association studies (mGWASs) using multi-ethnic genetic and metabolomic data from up to 28,000 participants. With bidirectional Mendelian randomization analysis involving 16,144 advanced AMD cases and 17,832 controls, we identify 108 putatively causal relationships between plasma metabolites and advanced AMD. These metabolites are enriched in glycerophospholipid metabolism, lysophospholipid, triradylcglycerol, and long chain polyunsaturated fatty acid pathways. Bayesian genetic colocalization analysis and a customized metabolome-wide association approach prioritize putative causal AMD-associated metabolites. We find limited evidence linking urine metabolites to AMD risk. Our study emphasizes the contribution of plasma metabolites, particularly lipid-related pathways and genes, to AMD risk and uncovers numerous putative causal associations between metabolites and AMD risk.

The Interplay between Metabolites and MicroRNAs in Aqueous Humor to Coordinate Corneal Endothelium Integrity

Purpose

The purpose of the study was to clarify the interplay between metabolites and microRNAs (miRs) in the aqueous humor (AqH) of bullous keratopathy (BK) patients to retain human corneal endothelium (HCE) integrity.

Design

Prospective, comparative, observational study.

Participants

A total of 55 patients with BK and 31 patients with cataract (Cat) as control.

Methods

A biostatic analysis of miRs and metabolites in the AqH, hierarchical clustering, and a least absolute shrinkage and selection operator (Lasso) analysis were employed. The miR levels in AqH of BK (n = 18) and Cat (n = 8) patients were determined using 3D-Gene human miR chips. Hierarchical clusters of metabolites detected by liquid chromatography-mass spectrometry or gas chromatography-mass spectrometry in AqH specimens from 2 disease groups, BK (total n = 55) and Cat (total n = 31), were analyzed twice to confirm the reproducibility. The analytical procedure applied for investigating the association between metabolites and miRs in AqH was the exploratory data analysis of biostatistics to avoid any kind of prejudice. This research procedure includes a heat-map, cluster analysis, feature extraction techniques by principal component analysis, and a regression analysis method by Lasso. The cellular and released miR levels were validated using reverse transcription polymerase chain reaction and mitochondria membrane potential was assessed to determine the functional features of the released miRs.

Main Outcome Measures

Identification of interacting metabolites and miRs in AqH attenuating HCE degeneration.

Results

The metabolites that decreased in the AqH of BK patients revealed that 3-hydroxyisobutyric acid (HIB), 2-aminobutyric acid (AB) and branched-chain amino acids, and serine were categorized into the same cluster by hierarchical clustering of metabolites. The positive association of HIB with miR-34a-5p was confirmed (P = 0.018), and the Lasso analysis identified the interplay between miR-34a-5p and HIB, between miR-24-3p and AB, and between miR-34c-5p and serine (P = 0.041, 0.027, and 0.009, respectively). 3-hydroxyisobutyric acid upregulated the cellular miR-34a expression, mitochondrial membrane potential, and release of miR-184 in dedifferentiated cultured HCE cells.

Conclusions

Metabolites and miRs in AqH may synchronize in ensuring the integrity of the HCE to maintain efficient dehydration from the stroma.

Financial Disclosures

Proprietary or commercial disclosure may be found after the references.

New insight of metabolomics in ocular diseases in the context of 3P medicine

Metabolomics refers to the high-through untargeted or targeted screening of metabolites in biofluids, cells, and tissues. Metabolome reflects the functional states of cells and organs of an individual, influenced by genes, RNA, proteins, and environment. Metabolomic analyses help to understand the interaction between metabolism and phenotype and reveal biomarkers for diseases. Advanced ocular diseases can lead to vision loss and blindness, reducing patients' quality of life and aggravating socio-economic burden. Contextually, the transition from reactive medicine to the predictive, preventive, and personalized (PPPM / 3P) medicine is needed. Clinicians and researchers dedicate a lot of efforts to explore effective ways for disease prevention, biomarkers for disease prediction, and personalized treatments, by taking advantages of metabolomics. In this way, metabolomics has great clinical utility in the primary and secondary care. In this review, we summarized much progress achieved by applying metabolomics to ocular diseases and pointed out potential biomarkers and metabolic pathways involved to promote 3P medicine approach in healthcare.

Metabolomics study of treatment response to conbercept of patients with neovascular age-related macular degeneration and polypoidal choroidal vasculopathy

Background: Neovascular age-related macular degeneration (nAMD) and polypoidal choroidal vasculopathy (PCV) are major causes of blindness in aged people. 30% of the patients show unsatisfactory response to anti-vascular endothelial growth factor (anti-VEGF) drugs. This study aims to investigate the relationship between serum metabolome and treatment response to anti-VEGF therapy.

Methods: A prospective longitudinal study was conducted between March 2017 and April 2019 in 13 clinical sites in China. The discovery group were enrolled from Shanghai General Hospital. The validation group consisted of patients from the other 12 sites. Participants received at least one intravitreal injection of 0.5 mg anti-VEGF drug, conbercept, and were divided into two groups - responders and non-responders. Serum samples of both groups were processed for UHPLC-MS/MS analysis. We constructed principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) models to investigate the metabolic differences between two groups using SIMCA-P. Area under curve (AUC) was calculated to screen the biomarkers to predict treatment response. Metabolites sub-classes and enriched pathways were obtained using MetaboAnalyst5.0.

Results: 219 eyes from 219 patients (nAMD = 126; PCV = 93) were enrolled. A total of 248 metabolites were detected. PCA and PLS-DA models of the discovery group demonstrated that the metabolic profiles of responders and non-responders clearly differed. Eighty-five differential metabolites were identified, including sub-classes of diacylglycerophosphocholines, lysophosphatidylcholine (LPC), fatty acids, phosphocholine, etc. Responders and non-responders differed most significantly in metabolism of LPC (p = 7.16 × 10^-19) and diacylglycerophosphocholine (p = 6.96 × 10^-17). LPC 18:0 exhibited the highest AUC, which is 0.896 with 95% confidence internal between 0.833 and 0.949, to discriminate responders. The predictive accuracy of LPC 18:0 was 72.4% in the validation group.

Conclusions: This study suggests that differential metabolites may be useful for guiding treatment options for nAMD and PCV. Metabolism of LPC and diacylglycerophosphocholine were found to affect response to conbercept treatment. LPC 18:0 was a potential biomarker to discriminate responders from non-responders.

Artificial Intelligence Analysis of Biofluid Markers in Age-Related Macular Degeneration: A Systematic Review

This systematic review explores the use of artificial intelligence (AI) in the analysis of biofluid markers in age-related macular degeneration (AMD). We detail the accuracy and validity of AI in diagnostic and prognostic models and biofluid markers that provide insight into AMD pathogenesis and progression. This review was conducted in accordance with the Preferred Reporting Items for a Systematic Review and Meta-analysis guidelines. A comprehensive search was conducted across 5 electronic databases including Cochrane Central Register of Controlled Trials, Cochrane Database of Systematic Reviews, EMBASE, Medline, and Web of Science from inception to July 14, 2021. Studies pertaining to biofluid marker analysis using AI or bioinformatics in AMD were included. Identified studies were assessed for risk of bias and critically appraised using the Joanna Briggs Institute Critical Appraisal tools. A total of 10,264 articles were retrieved from all databases and 37 studies met the inclusion criteria, including 15 cross-sectional studies, 15 prospective cohort studies, five retrospective cohort studies, one randomized controlled trial, and one case–control study. The majority of studies had a general focus on AMD (58%), while neovascular AMD (nAMD) was the focus in 11 studies (30%), and geographic atrophy (GA) was highlighted by three studies. Fifteen studies examined disease characteristics, 15 studied risk factors, and seven guided treatment decisions. Altered lipid metabolism (HDL-cholesterol, total serum triglycerides), inflammation (c-reactive protein), oxidative stress, and protein digestion were implicated in AMD development and progression. AI tools were able to both accurately differentiate controls and AMD patients with accuracies as high as 87% and predict responsiveness to anti-VEGF therapy in nAMD patients. Use of AI models such as discriminant analysis could inform prognostic and diagnostic decision-making in a clinical setting. The identified pathways provide opportunity for future studies of AMD development and could be valuable in the advancement of novel treatments.

Metabolomics in Retinal Diseases: An Update

Retinal diseases are a leading cause of visual loss and blindness, affecting a significant proportion of the population worldwide and having a detrimental impact on quality of life, with consequent economic burden. The retina is highly metabolically active, and a number of retinal diseases are associated with metabolic dysfunction. To better understand the pathogenesis underlying such retinopathies, new technology has been developed to elucidate the mechanism behind retinal diseases. Metabolomics is a relatively new "omics" technology, which has developed subsequent to genomics, transcriptomics, and proteomics. This new technology can provide qualitative and quantitative information about low-molecular-weight metabolites (M.W. < 1500 Da) in a given biological system, which shed light on the physiological or pathological state of a cell or tissue sample at a particular time point. In this article we provide an extensive review of the application of metabolomics to retinal diseases, with focus on age-related macular degeneration (AMD), diabetic retinopathy (DR), retinopathy of prematurity (ROP), glaucoma, and retinitis pigmentosa (RP).

Metabolomics in Age-Related Macular Degeneration: A Systematic Review

Purpose

Age-related macular degeneration (AMD) is one of the leading causes of blindness among the elderly, and the exact pathogenesis of the AMD remains unclear. The purpose of this review is to summarize potential metabolic biomarkers and pathways of AMD that might facilitate risk predictions and clinical diagnoses of AMD.

Methods

We obtained relevant publications of metabolomics studies of human beings by systematically searching the MEDLINE (PubMed) database before June 2020. Studies were included if they performed mass spectrometry-based or nuclear magnetic resonance-based metabolomics approach for humans. In addition, AMD was assessed from fundus photographs based on standardized protocols. The metabolic pathway analysis was performed using MetaboAnalyst 3.0.

Results

Thirteen studies were included in this review. Repeatedly identified metabolites including phenylalanine, adenosine, hypoxanthine, tyrosine, creatine, citrate, carnitine, proline, and maltose have the possibility of being biomarkers of AMD. Validation of the biomarker panels was observed in one study. Dysregulation of metabolic pathways involves lipid metabolism, carbohydrate metabolism, nucleotide metabolism, amino acid metabolism, and translation, which might play important roles in the development and progression of AMD.

Conclusions

This review summarizes the potential metabolic biomarkers and pathways related to AMD, providing opportunities for the construction of diagnostic or predictive models for AMD and the discovery of new therapeutic targets.