Table 1.
Summary Characteristics of Included Studies
| First Author, Publication Year | Study Design | Disease Type (Other Diseases Studied) | Country of Publication | Study Purpose | Sample Size | Classes of AI | Statistical, AI, Bioinformatics Methods | Biofluids | Biomarker(s) Analyzed | Significant Biomarker(s) and Key Pathways |
|---|---|---|---|---|---|---|---|---|---|---|
| Acar,28 2020 | Cross-sectional | AMD | Netherlands | Disease characteristics | 6608 | 2 | Unsupervised: PCA Statistical method: Univariate logistic regression, linear regression | Plasma | Metabolic profile | 146 metabolites, including those involved in large and extra-large HDL subclasses, VLDL, amino acid 73, citrate, complement activation |
| Arai,60 2020 | Prospective cohort | AMD, nAMD, PCV | Japan | Treatment decisions | 48 | 1 | Statistical method: Multiple regression analysis | Aqueous humor | Cytokines | MCP-1, IL-10 (baseline BCVA); MCP-1, CXCL13 (better BCVA in 12 months); MMP-9, CXCL12, IL-10 (increased number of injections required) |
| Boekhoorn,29 2007 | Prospective cohort | AMD | Netherlands | Risk factors | 4606 | 1 | Statistical method: Cox proportional hazards regression | Serum | CRP | CRP |
| Buch,30 2005 | Prospective cohort | AMD | Denmark | Risk factors | 359 | 1 | Statistical method: Univariate logistic regression, multivariate logistic regression | Serum | Limited lipid profile | Total cholesterol, apolipoprotein A1, apolipoprotein B |
| Chaker,31 2015 | Prospective cohort | AMD | Netherlands | Risk factors | 5573 | 1 | Statistical method: Cox proportional hazards regression | Serum | Thyroid markers | Free thyroxine |
| Cougnard-Gregoire,32 2014 | Prospective cohort | AMD | France | Risk factors | 963 | 1 | Statistical method: Generalized estimating equation logistic regression | Serum | Lipids | HDL |
| Gao,16 2020 | Prospective cohort | nAMD | Singapore | Treatment decisions | 100 | 4 | Supervised: PCA Unsupervised: OPLS-DA Bioinformatics: Pathway analysis Statistical method: Logistic regression | Serum | Metabolic profile | LysoPC (18:2), PS (18:0/20:4), glycerophosphocholine |
| Han,33 2020 | Cross-sectional | wAMD | China | Disease characteristics | 46 (20 cataract controls, 26 wAMD) | 3 | Supervised: PCA Unsupervised: OPLS-DA Bioinformatics: KEGG | Aqueous humor | Metabolic profile | Deoxycarnitine, N6, N6, N6-trimethyl-L-lysine, glycine betaine, itaconic acid, cis-aconitate, 5-aminopentanoic acid, norleucine, L-phenylalanine, carnitine, γ-glutamylglutamine, hetisine, 3-phenyllactic acid, LPC 18:2, coumaroyl agmatine, N-acetylhistidine, creatine, N-fructosyl isoleucine, L-proline (Carnitine-associated mitochondrial oxidation pathway, carbohydrate metabolism pathway, activated osmoprotection pathway) |
| Joachim,34 2015 | Prospective cohort | AMD | Australia | Risk factors | 3654 | 1 | Statistical method: Discrete logistic regression models | Serum | Limited lipid profile | None |
| Jonasson,35 2014 | Prospective cohort | AMD, GA | Iceland | Risk factors | 2868 | 1 | Statistical model: Multivariate logistic regression | Serum | Cardiovascular health profile | HDL-cholesterol |
| Kersten,36 2019 | Cross-sectional | AMD | Netherlands | Disease characteristics | 144 (72 cases, 72 control) | 2 | Supervised: sPLS-DA Statistical method: Logistic regression | Serum | Metabolic profile | Glutamine, glutamate, glutaminolysis, phosphatidylcholine diacyl C28:1 (PC aa C28.1) |
| Klein,37 2019 | Prospective cohort | AMD | USA | Risk factors | 4972 | 1 | Statistical method: Logistic regression, linear regression, multi-state Markov | Serum | Limited lipid profile | None |
| Kuiper,38 2017 | Cross-sectional | AMD (idiopathic non-infectious uveitis, primary vitreoretinal lymphoma, rhegmatogenous retinal detachment) |
Netherlands | Disease characteristics | 175 | 3 | Supervised: Decision tree Unsupervised: Hierarchical cluster analysis Statistical method: SMOTE, k-nearest neighbors | Aqueous humor | Proteomic profile | IL-10, IL-21, ACE |
| Lai,22 2009 | Randomized control trial | nAMD | Hong Kong | Treatment decisions | 50 | 1 | Statistical method: Multivariate logistic regression | Aqueous humor | VEGF, PEDF | Baseline VEGF |
| Laíns,39 2018 | Cross-sectional | AMD | USA | Disease characteristics | 120 | 2 | Unsupervised: PCA Statistical method: Multivariate logistic regression Bioinformatics: Pathway analysis | Plasma | Metabolic profile | 87 differentially expressed metabolites (48 across all AMD stages), including linoleoyl-arachidonoyl-glycerol, stearoyl-arachidonoyl-glycerol, oleoyl-arachidonoyl-glycerol, 1-Palmitoyl-2-arachidonoyl-GPC, 1-stearoyl-2-arachidonoyl-GPC, adenosine, glycerophospholipid pathway |
| Laíns,24 2019 | Cross-sectional | AMD | USA | Disease characteristics | 491 (196 with 47 controls in Boston, 295 with 53 controls in Portugal) | 3 | Unsupervised: PCA Bioinformatics: Pathway analysis, KEGG Statistical method: Multivariate logistic regression | Plasma | Proteomic profile | 28 metabolites, including those from the lycerophospholipid, purine, taurine, hypotaurine, and nitrogen metabolism pathways |
| Luo,40 2017 | Cross-sectional | wAMD | China | Disease characteristics | 40 (20 AMD, 20 controls) | 3 | Supervised: PLS-DA Unsupervised: PCA, hierarchical cluster analysis Bioinformatics: KEGG | Plasma | Metabolomic profile | N-Acetyl-L-alanine, N1-Methyl-2-pyridone-5-carboxamide, L-tyrosine, L-phenylalanine, L-palmitoylcarnitine, L-methionine, L-Arginine, isomaltose, hydrocortisone, biliverdin |
| Lynch,41 2019 | Cross-sectional | AMD | USA | Disease characteristics | 30 (10 AMD, 10 GA, 10 cataract controls) | 2 | Bioinformatics: Pathway analysis Statistical method: Linear regression | Plasma | Proteomic profile | AMD: Vinculin, CD177 AMD pathways: Cargo trafficking to the periciliary membrane, FGFR3b ligand binding and activation, VEGF binds to VEGFR leading to receptor dimerization/VEGF ligand-receptor interactions, common pathway of fibrin clot formation GA: Neuroregulin 4, soluble intercellular adhesion molecule-1 GA pathways: SHC1 events in ERBB4 signaling, PI3K events in ERBB4 signaling, SHC1 events in ERBB2 signaling, GRB2 events in ERBB2 signaling, nuclear signaling by ERBB4, NADE modulates death signaling, PI3K events in ERBB2 signaling, signaling by BMP, interleukin receptor SHC signaling, regulation of beta-cell development, regulation of commissural axon pathfinding by SLIT and ROBO, reversible hydration of carbon dioxide, tetrasaccharide linker, cooperation of PDCL (PhLP1) and TRiC/CCT in G-protein beta folding, ERBB4 |
| Lynch,42 2020 | Cross-sectional | AMD | USA | Disease characteristics | 109 | 2 | Bioinformatics: Pathway analysis Statistical method: Linear regression, Cox proportional hazards regression, univariate logistic regression | Plasma | Proteomic profile | TCL1A, CNDP1, lysozyme C, TFF3, RNAS6, SAP3 Pathways: Tumor necrosis factor binding, digestion and absorption, activin signaling, TGF-β family signaling |
| Mendez,43 2021 | Cross-sectional | AMD | USA | Disease characteristics | 71 | 1 | Statistical method: Unspecified multilevel mixed-effects linear model | Plasma | Metabolic profile | Metabolites: linolenate, mannitol, sorbitol, glycosyl ceramide (d18:2/24:1, d18:1/24:2), beta-alanine, 3-methyl-2-oxovalerate, 3-methylglutaconate, isoleucine Pathways: polyunsaturated fatty acid (n3 and n6), fructose, mannose and galactose Metabolism, fatty acid metabolism (acyl choline), hexosylceramides (HCER), pyrimidine metabolism, uracil, leucine, isoleucine and valine metabolism |
| Millen,44 2015 | Retrospective cohort | AMD | USA | Risk factors | 913 | 1 | Statistical method: Logistic regression | Serum | Vitamin D, CRP | Vitamin D |
| Millen,45 2017 | Retrospective cohort | AMD | USA | Risk factors | 9734 | 1 | Statistical method: Logistic regression | Serum | Vitamin D, lipid profile | None |
| Mitchell,52 2018 | Cross-sectional | nAMD | USA | Disease characteristics | 292 | 4 | Supervised: PLS-DA, SV_RFE, random forest Unsupervised: Hierarchical cluster analysis Bioinformatics: Pathway analysis Statistical method: Linear regression, linear models for microarray data, variable importance for projection | Plasma | Metabolic profile | 159 metabolites from the carnitine shuttle pathway (fatty acid metabolism) and bile acid biosynthesis pathway |
| Ngai,46 2011 | Prospective cohort | AMD | UK | Risk factors | 934 | 1 | Statistical method: Logistic regression, multivariable regression | Serum | Limited metabolomic and lipidomic profile | Total triglycerides, CRP |
| Nielsen,53 2019 | Prospective cohort | GA, nAMD | Denmark | Risk factors | 110 | 1 | Statistical method: Linear regression | Plasma | Cytokines, inflammatory markers | IL-6, IL-8, IL-10, TNF-R2, CRP |
| Osborn,54 2013 | Cross-sectional | nAMD | USA | Disease characteristics | 45 (26 AMD, 19 control) | 3 | Supervised: OPLS-DA, SVM Unsupervised: PCA Bioinformatics: KEGG | Serum | Metabolomic profile | 52 metabolites including those from the tyrosine, sulfur amino acid, and urea metabolism pathways |
| Robman,47 2007 | Case–control | AMD | Australia | Risk factors | 630 (197 AMD, 433 control) | 1 | Statistical method: Logistic regression | Serum | Chlamydia pneumoniae related markers | None |
| Robman,48 2010 | Case–control, retrospective cohorta | AMD | Australia | Risk factors | Case–control: 5.44 (312 AMD, 232 control) Cohort: 254 | 1 | Statistical method: Univariate and multivariate logistic regression | Serum | CRP | CRP |
| Sato,56 2018 | Prospective cohort | nAMD | Japan | Treatment decisions | 43 (21 nAMD patients, 22 cataract controls) | 1 | Statistical method: Logistic regression | Aqueous humor | Inflammatory cytokines and growth factors | IL-6, IP-10, VEGF |
| Sato,55 2019 | Cross-sectional | nAMD | Japan | Disease characteristics | 82 (62 AMD, 20 cataract control) | 2 | Unsupervised: PCA, hierarchical cluster analysis Statistical method: Binomial logistic regression, EFA | Aqueous humor | Cytokines | IL-6, IL-7, IP-10 MCP-1, MIP-1β, VEGF |
| Schori,17 2018 | Cross-sectional | AMD, nAMD (PDR, ERM) | Switzerland | Disease characteristics | 34 (6 dry AMD, 10 nAMD, 9 PDR, 9 ERM) | 2 | Unsupervised: Hierarchical Pearson clustering Bioinformatics: GO | Vitreous humor | Proteomic profile | 677 proteins including cholinesterase and oxidative stress pathway in dry AMD, focal adhesion in nAMD, and serine carboxypeptidase in all AMD |
| Subhi,57 2019 | Prospective cohort | nAMD, PCV | Denmark | Treatment decisions | 81 | 1 | Statistical method: Multiple linear regression, Pearson correlation coefficient | Plasma | CD11b+ circulating monocytes | CD11b+ circulating monocytes |
| Ueda-Consolvo,58 2017 | Retrospective cohort | nAMD | Japan | Treatment decisions | 64 | 1 | Statistical method: Multiple regression | Unknown | HbA1c | Unknown |
| Vanderbeek,49 2013 | Retrospective cohort | AMD | USA | Risk factors | 486,124 (107,007 for nonexudative AMD analysis, 113,111 for exudative AMD analysis, 10,753 for progression to exudative AMD analysis) | 1 | Statistical method: Cox proportional hazards regression | Serum | Lipid profile | HDL, LDL |
| Yao,50 2013 | Cross-sectional | wAMD | China | Disease characteristics | 12 | 1 | Bioinformatics: GO | Aqueous humor | Proteomic profile | 68 proteins, including those from the inflammation, apoptosis, angiogenesis, and oxidative stress pathways |
| Yi,59 2020 | Prospective cohort | nAMD (CRVO, DME, BRVO, pmCNV) | China | Treatment decisions | 144 | 1 | Statistical method: Multivariate linear regression | Aqueous humor | Cytokines | ICAM-1, IL-6, VEGF |
| Yip,51 2015 | Prospective cohort | AMD, GA | UK | Risk factors | 5344 | 1 | Statistical model: Multivariable logistic regression | Serum | Cardiovascular health profile | HDL-cholesterol, CRP |
Notes: aRelevant study phase.
Abbreviations: AMD, age-related macular degeneration; AI, artificial intelligence; PCA, principal component analysis; nAMD, neovascular age-related macular degeneration; PCV, polypoidal Choroidal Vasculopathy; CRP, c reactive protein; OPLS-DA, Orthogonal Projections to Latent Structures Discriminant Analysis; wAMD, wet age-related macular degeneration; KEGG, Kyoto Encyclopedia of Genes and Genomes; GA, geographic atrophy; sPLS-DA, Sparse Partial Least Squares Discriminant Analysis; SMOTE, Synthetic Minority Oversampling Technique; EFA, exploratory factor analysis; PDR, proliferative diabetic retinopathy; ERM, epiretinal membrane; GO, gene ontology; CRVO, central retinal vein occlusion; DME, diabetic macular edema; pmCNV, pathologic myopia associated choroidal neovascularization.