ArticleJAMA network open2023
Analysis of Plasma Metabolic Profile on Ganglion Cell-Inner Plexiform Layer Thickness With Mortality and Common Diseases.
Article in JAMA network open, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Brain care score and retinal health: structural and metabolic insights from the UK Biobank.GeroScience · 2026Article
- A large cohort analysis of metabolic signatures underlying the liver-eye axis.Communications medicine · 2026Article
- Proteomic clocks combined with deep learning phenotypes track eye aging and diseases.NPJ digital medicine · 2026Article
- Metabolic vulnerability index and MetaboHealth score as risk factors for age-related macular degeneration in a large-scale prospective cohort.Journal of translational medicine · 2026Article
- Artificial intelligence-driven metabolomics of retinal nerve fibre layer to profile risks of mortality and cardiometabolic diseases.Nature communications · 2025Article
- Proteome atlas for mechanistic discovery and risk prediction of diabetic retinopathy.Nature communications · 2025Article
- Photoreceptor metabolic window unveils eye-body interactions.Nature communications · 2025Article
- Causal Effects Between Retinal Characteristics and Cardiovascular Diseases: Insights from Genetic Correlation, Mendelian Randomization, and Cross-Sectional Study.Global heart · 2025Article
- Initial Retinal Nerve Fiber Layer Loss and Risk of Diabetic Retinopathy Over a Four-Year Period.Investigative ophthalmology & visual science · 2024Article
- Metabolic fingerprinting on retinal pigment epithelium thickness for individualized risk stratification of type 2 diabetes mellitus.Nature communications · 2023Article
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Authors and funding
8 authors.
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Abstract
Importance: The neural retina is considered a unique window to systemic health, but its biological link with systemic health remains unknown. Objective: To investigate the independent associations of retinal ganglion cell-inner plexiform layer thickness (GCIPLT) metabolic profiles with rates of mortality and morbidity of common diseases. Design, Setting, and Participants: This cohort study evaluated UK Biobank participants enrolled between 2006 and 2010, and prospectively followed them up for multidisease diagnosis and mortality. Additional participants from the Guangzhou Diabetes Eye Study (GDES) underwent optical coherence tomography scanning and metabolomic profiling and were included for validation. Main Outcomes and Measures: Systematic analysis of circulating plasma metabolites to identify GCIPLT metabolic profiles; prospective associations of these profiles with mortality and morbidity of 6 common diseases with their incremental discriminative value and clinical utility. Results: Among 93 838 community-based participants (51 182 [54.5%] women), the mean (SD) age was 56.7 (8.1) years and mean (SD) follow-up was 12.3 (0.8) years. Of 249 metabolic metrics, 37 were independently associated with GCIPLT, including 8 positive and 29 negative associations, and most were associated with the rates of future mortality and common diseases. These metabolic profiles significantly improved the models for discriminating type 2 diabetes over clinical indicators (C statistic: 0.862; 95% CI, 0.852-0.872 vs clinical indicators only, 0.803; 95% CI, 0.792-0.814; P < .001), myocardial infarction (0.792; 95% CI, 0.775-0.808 vs 0.768; 95% CI, 0.751-0.786; P < .001), heart failure (0.803; 95% CI, 0.786-0.820 vs 0.790; 95% CI, 0.773-0.807; P < .001), stroke (0.739; 95% CI, 0.714-0.764 vs 0.719; 95% CI, 0.693-0.745; P < .001), all-cause mortality (0.747; 95% CI, 0.734-0.760 vs 0.724; 95% CI, 0.711-0.738; P < .001), and cardiovascular disease mortality (0.790; 95% CI, 0.767-0.812 vs 0.763; 95% CI, 0.739-0.788; P < .001). Additionally, the potential of GCIPLT metabolic profiles for risk stratification of cardiovascular diseases were further confirmed in the GDES cohort using a different metabolomic approach. Conclusions and Relevance: In this prospective study of multinational participants, GCIPLT-associated metabolites demonstrated the potential to inform mortality and morbidity risks. Incorporating information on these profiles may facilitate individualized risk stratification for these health outcomes.
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