Fig. 1: Overview of the study design.

a The eligible study population was categorized into three distinct groups: population-I, for identifying RNFLT metabolic states; population-II, for unravelling revelations on CMD outcomes; and population-III, for independent concept validation. b To identify RNFLT metabolic states, we conducted retinal scanning and utilized two complementary metabolomic assays. Genotyping was performed to assess genetic susceptibility in individuals. ML algorithms were employed for comprehensive model construction and evaluation. c For CMD outcome risk modelling, the study populations were randomized into training and testing sets. Dataset balancing techniques were applied before feature selection and model training. d The outcomes examined in this study include incident T2D, myocardial infarction, heart failure, stroke, all-cause mortality, and CMD mortality. e Distinct risk stratification and improved predictability and clinical utility were observed for all studied outcomes. f Special attention was given to extending the benefits to women and socially vulnerable communities. g Comprehensive sets of predictors commonly used in the CMD primary prevention were incorporated as benchmark models. Parts of panels a–d and f were created from BioRender (https://BioRender.com/yu7axft) and Flaticon (https://flaticon.com). RNFLT retinal nerve fibre layer thickness, CMD cardiometabolic disease, UKB UK Biobank, GDES Guangzhou Diabetes Eye Study, T2D type 2 diabetes, FGCRS Framingham General Cardiovascular Risk Score, SCORE2 Systematic Coronary Risk Evaluation 2, WHO-CVD World Health Organization Cardiovascular Disease, AHA-ASCVD American Heart Association-Atherosclerotic Cardiovascular Disease, UKPDS UK Perspective Diabetes Study, NZ-DCS New Zealand Diabetes Cohort Study, WAN Wan’s model, BMI body mass index, eGFR estimated glomerular filtration rate, HDL-c high-density lipoprotein cholesterol.