Association of Human Plasma Metabolomics with Delayed Dark Adaptation in Age-Related Macular Degeneration

Mendez, Kevin; Kim, Janice; Nigalye, Archana; Katz, Raviv; Pundik, Shrinivas; Kim, Ivana K.; Liang, Liming; Vavvas, Demetrios G.; Miller, John B.; Miller, Joan W.; Lasky‐Su, Jessica; Husain, Deeba

doi:10.3390/metabo11030183

Cited by 6 publications

(9 citation statements)

References 71 publications

(104 reference statements)

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“…Our results consistently showed that glutamine was associated with changes in AUDAC at 3 years, with pathway analysis pointing to an enrichment of metabolites in alanine, aspartate and glutamate pathways. Interestingly, our group has recently published a cross-sectional study [30] that also showed that amino acids related to glutamate are among those with the most significant associations with AUDAC (and also RIT).…”

Section: Discussionmentioning

confidence: 99%

Plasma Metabolomic Profiles Associated with Three-Year Progression of Age-Related Macular Degeneration

et al. 2022

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Plasma metabolomic profiles have been shown to be associated with age-related macular degeneration (AMD) and its severity stages. However, all studies performed to date have been cross-sectional and have not assessed progression of AMD. This prospective, longitudinal, pilot study analyzes, for the first time, the association between plasma metabolomic profiles and progression of AMD over a 3-year period. At baseline and 3 years later, subjects with AMD (n = 108 eyes) and controls (n = 45 eyes) were imaged with color fundus photos for AMD staging and tested for retinal function with dark adaptation (DA). Fasting plasma samples were also collected for metabolomic profiling. AMD progression was considered present if AMD stage at 3 years was more advanced than at baseline (n = 26 eyes, 17%). Results showed that, of the metabolites measured at baseline, eight were associated with 3-year AMD progression (p < 0.01) and 19 (p < 0.01) with changes in DA. Additionally, changes in the levels (i.e., between 3 years and baseline) of 6 and 17 metabolites demonstrated significant associations (p < 0.01) with AMD progression and DA, respectively. In conclusion, plasma metabolomic profiles are associated with clinical and functional progression of AMD at 3 years. These findings contribute to our understanding of mechanisms of AMD progression and the identification of potential therapeutics for this blinding disease.

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Section: Discussionmentioning

confidence: 99%

Plasma Metabolomic Profiles Associated with Three-Year Progression of Age-Related Macular Degeneration

et al. 2022

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“…Their results showed that the levels of leucine, isoleucine, and valine in BCAA metabolism were enhanced in the serum of DR patients. In an analysis of the relationship between metabolite levels of plasma and dark adaptation in AMD, Mendez et al [40] revealed that BCAA metabolism was associated with rod-intercept time, one measure of dark adaptation. Moreover, Valle et al [30] found that the levels of BCAAs were diminished in the plasma of four patients with gyrate atrophy (GA) of the choroid and retina.…”

Section: The Alterations Of Bcaas In Retinopathymentioning

confidence: 99%

Branched-Chain Amino Acids Metabolism and Their Roles in Retinopathy: From Relevance to Mechanism

Zhang

Xia

et al. 2023

Nutrients

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Retinopathy is one of the leading causes of irreversible blindness and vision loss worldwide. Imbalanced nutrients play important roles in the pathogenesis and pathophysiology of retinal diseases. Branched-Chain Amino Acids (BCAAs), as essential amino acids, perform a variety of biological functions, including protein synthesis, glucose metabolism, lipid metabolism, inflammation, and oxidative stress in metabolic tissues of diabetes and aging-related diseases. Recently, it has been shown that BCAAs are highly related to neuroprotection, oxidative stress, inflammatory and glutamate toxicity in the retina of retinopathy. Therefore, this review summarizes the alterations of BCAA levels in retinopathy, especially diabetic retinopathy and aging-related macular disease, and the genetics, functions, and mechanisms of BCAAs in the retina as well as other metabolic tissues for reference. All of these efforts aim to provide fundamental knowledge of BCAAs for further discoveries and research on retina health based on the sensing and signaling of essential amino acids.

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“…Twenty-two papers exclusively used AI statistical methods to identify independent factors related to an outcome of interest or control for known risk factors. 22,[29][30][31][32]34,35,37,[43][44][45][46][47][48][49]51,53,[56][57][58][59][60] Regression was typically used to determine longitudinal association between a biomarker and a clinical outcome or condition, with the goal of risk factor identification or identifying if an intervention was effective in preventing an outcome or disease progression. 22,[29][30][31][32]34,35,37,[43][44][45][46][47][48][49]51,53,[56][57][58][59][60] As regression is less useful in the study of highly dimensional data, studies using only regression analysis tended to focus on specific risk factor analysis rather than entire biomarker profiles; for AMD, these studies often examined the influence of lipids, CRP, VEGF, inflammatory marke...…”

Section: Pucchio Et Almentioning

confidence: 99%

“…22,[29][30][31][32]34,35,37,[43][44][45][46][47][48][49]51,53,[56][57][58][59][60] As regression is less useful in the study of highly dimensional data, studies using only regression analysis tended to focus on specific risk factor analysis rather than entire biomarker profiles; for AMD, these studies often examined the influence of lipids, CRP, VEGF, inflammatory markers such as cytokines, cardiovascular health profile, and clinical characteristics on AMD development or progression. 22,[29][30][31][32]34,35,37,[43][44][45][46][47][48][49]51,53,[56][57][58][59][60] No studies using only AI statistical methods used validation procedures such as test and validation sets or bootstrapping in their analysis.…”

Section: Pucchio Et Almentioning

confidence: 99%

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

Pucchio¹,

Krance²,

Pur³

et al. 2022

OPTH

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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.

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Association of Human Plasma Metabolomics with Delayed Dark Adaptation in Age-Related Macular Degeneration

Cited by 6 publications

References 71 publications

Plasma Metabolomic Profiles Associated with Three-Year Progression of Age-Related Macular Degeneration

Plasma Metabolomic Profiles Associated with Three-Year Progression of Age-Related Macular Degeneration

Branched-Chain Amino Acids Metabolism and Their Roles in Retinopathy: From Relevance to Mechanism

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

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