ArticleInvestigative ophthalmology & visual science2023
Metabolite and Lipid Biomarkers Associated With Intraocular Pressure and Inner Retinal Morphology: 1H NMR Spectroscopy Results From the UK Biobank.
Article in Investigative ophthalmology & visual science, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
5 citing papers in PubMed, 9 citations in OpenAlex.
- Prediagnostic Plasma Metabolomic Profiles Using NMR for Exfoliation Glaucoma Among US Health Professionals.Metabolites · 2025Article
- Pyruvate and related energetic metabolites modulate resilience against high genetic risk for glaucoma.eLife · 2025Article
- Biological Age Acceleration, Genetic Susceptibility, and Incident Glaucoma Risk.Investigative ophthalmology & visual science · 2025Article
- Pyruvate and Related Energetic Metabolites Modulate Resilience Against High Genetic Risk for Glaucoma.bioRxiv : the preprint server for biology · 2025Article
- Machine learning-based plasma metabolomics for improved cirrhosis risk stratification.BMC gastroenterology · 2025Article
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Authors and funding
10 authors at 6 institutions in 2 countries.
Funding
Abstract
Purpose: The purpose of this study was to assess metabolites associated with intraocular pressure (IOP) and inner retina structure. Methods: We cross-sectionally assessed 168 non-fasting plasma metabolites measured by nuclear magnetic resonance (NMR) spectroscopy with IOP (n = 28,195), macular retinal nerve fiber layer thickness (mRNFL; n = 10,584), and macular ganglion cell inner plexiform layer thickness (mGCIPL; n = 10,554) in the UK Biobank. We used multiple linear regression models adjusting for various covariates with probit-transformed metabolite levels as predictors for each outcome. Each estimate represents the difference in outcome variable per standard deviation increase in the probit-transformed metabolite values. We used the number of effective (NEF) tests and false discovery rate (FDR) to adjust for multiple comparisons for metabolites and metabolite classes, respectively. Results: In individual metabolite analysis, multiple amino acids, especially branched-chain amino acids, were associated with lower IOP (-0.12 mm Hg; 95% confidence interval = -0.16 to -0.07; NEF = 2.7E-05). Albumin, 3 hydroxybutyrate, lactate, and several lipids were associated with higher IOP (range = 0.07 to 0.18 mm Hg, NEF = ≤ 0.039). In IOP-adjusted analyses, five HDL-related metabolites were associated with thinner mRNFL (-0.15 microns for all metabolites, NEF = ≤ 0.027), whereas five LDL-related metabolites were associated with thicker mGCIPL (range = 0.17 to 0.20 microns; NEF = ≤ 0.044). In metabolite class analysis, the lipid components of lipoproteins (cholesterol, triglycerides, etc.) were not associated with our outcomes (FDR > 0.2 for all); yet multiple lipoproteins were significantly (FDR < 0.05) associated with all outcomes. Conclusions: Branched-chain amino acids were associated with lower IOP, HDL metabolites were associated with thinner mRNFL, and LDL metabolites were associated with thicker mGCIPL.
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