ArticleNature communications2025
Artificial intelligence-driven metabolomics of retinal nerve fibre layer to profile risks of mortality and cardiometabolic diseases.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
3 citing papers in PubMed.
- Proteomic clocks combined with deep learning phenotypes track eye aging and diseases.NPJ digital medicine · 2026Article
- Artificial intelligence-driven metabolomics of the retinal nerve fiber layer to profile risks of mortality and cardiometabolic diseases.Annals of medicine and surgery (2012) · 2026Article
- Artificial intelligence-integrated multimodal retinal imaging for early detection and risk stratification of systemic vascular and neurodegenerative diseases.Frontiers in neurology · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
14 authors.
Funding
Abstract
Retinal nerve fibre layer (RNFL) is a non-invasive structural biomarker of cardiometabolic health, yet its biological underpinnings remain unknown. Here, we integrate advanced retinal optical biopsy and artificial intelligence (AI) algorithms with two complementary metabolomic assays across ethnically diverse cohorts to elucidate the metabolic basis underlying RNFL degeneration and its link to cardiometabolic disease (CMD) in Western cohort and Eastern cohort (Guangzhou Diabetic Eye Study, GDES). We identify 26 metabolic biomarkers significantly associated with RNFL thickness, most of which (ranging from 19 to 26) are linked to HDL composition and lipid transport, mediating a substantial proportion of the RNFL-CMD association (e.g., 63.7% for type 2 diabetes and 44.7% for myocardial infarction). AI-driven RNFL metabolic state model stratifies CMD risk with up to 21.8-fold enrichment between risk deciles and augments prediction while translating into clinical utility across genetic and demographic strata, particularly within socially vulnerable populations. This integrated approach highlights RNFL metabolic states as a shared basis underlying retinal-cardiometabolic connections and as early indicators that inform equitable CMD management.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.