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Artificial intelligence-driven metabolomics of retinal nerve fibre layer to profile risks of mortality and cardiometabolic diseases
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DOI：10.1038/s41467-025-66979-z.png)
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Abstract
En 中文
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. The retina offers a window into systemic health, but its molecular links to cardiometabolic disease remain unclear. Here, the authors show that metabolomic profiling on retinal nerve fibre layer combined with machine learning reveals shared metabolic states that predict cardiometabolic risk and bridge health inequities.
Keywords:
Retinal nerve fibre layer
Metabolomics
Artificial intelligence
Cardiometabolic disease
Machine learning
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