Fig. 6: Extrapolation in the GDES. | Nature Communications

Fig. 6: Extrapolation in the GDES.

From: Artificial intelligence-driven metabolomics of retinal nerve fibre layer to profile risks of mortality and cardiometabolic diseases

Fig. 6: Extrapolation in the GDES.The alternative text for this image may have been generated using AI.

a Landscape of the RNFLT metabolic state profile for cardiovascular disease captured by complementary LC–MS assays (n = 1286). Individual metabolite attributions are aggregated by percentiles, with each dot representing one percentile. The distance of a dot from the circular baseline reflects the strength of the absolute attribution for that percentile. Deviations towards the centre and periphery signify negative and positive contributions. Dot colours indicate the normalized values for each metabolite. b–e Comparison of predictability (b and d) and clinical utility (c and e) between established models and models incorporating RNFLT metabolic states for predicting cardiovascular disease across varying genetic susceptibility (n = 1286) (d and e). Data are presented as estimated performance for different models and genetic susceptibility contexts with 95% CIs indicated by error bars. Shaded areas illustrate the incremental net benefit of incorporating RNFLT metabolic states into established models. f–h Comparison of performance for predicting cardiovascular diseases across different demographic groups (n = 1286): sex (f), income (g), and educational attainment (h). Colours denote the absolute performance and benefits of various demographic groups. Source data are provided as a Source Data file. GDES Guangzhou Diabetic Eye Study, RNFLT MET RNFLT metabolic state, FGCRS Framingham General Cardiovascular Risk Score, UKPDS UK Perspective Diabetes Study, NZ-DCS New Zealand Diabetes Cohort Study, WAN Wan’s model.

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