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

Domain: physics
Published: 2025-12-11
Editorial outcome: major_revision

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.

Introduction
The eye with the retina as part of the central nervous system offers an anatomically unique opportunity for a non-invasive endoscopic view into cardiometabolic health1–3. Taking advantage of the optical transparency of the eye, state-of-the-art non-invasive in vivo retinal scanning now permits detection of subtle changes within the various retinal layers at an unprecedented micrometre-level resolution4,5, generating new evidence suggesting the relevance of the retinal nerve fibre layer (RNFL) to cardiometabolic diseases (CMDs) proposed by us and others6–11. As these scans are risk-free, easy to administer, and require only minimal expertise and time, they are increasingly being conducted routinely in primary care settings and communities, poising RNFL as a scalable surrogate for monitoring cardiometabolic health12–15.

Despite this promise, the biological underpinnings of the retina–cardiometabolic connections have remained unclear. Elucidating the molecular mediators of this link is crucial not only for establishing RNFL as a valid biomarker but also for revealing early systemic perturbations that may be actionable. Previous research has shown that metabolic factors, such as serum concentrations of triglycerides, low-density lipoprotein (LDL) and high-density lipoprotein (HDL) cholesterol, and creatinine, play a significant role in the pathogenesis of CMDs16–21. These metabolic components exert harmful or protective effects on cardiometabolic health by modulating lipid metabolism or suggesting renal impairment18,21. Interestingly, these same circulating metabolic factors have also been found to affect retinal health, as evidenced by their associations with RNFL thickness (RNFLT)22–27. Given the reciprocal relationships observed between systemic metabolism and both RNFLT degeneration and CMD outcomes, we hypothesize that a specific metabolic basis may underlie the connection between RNFLT and CMDs, and that characterizing these intrinsic metabolic states may enable early risk stratification across CMD outcomes.

Recent advances in high-throughput metabolomics and machine learning have offered opportunities to characterize systemic biological states and uncover shared disease mechanisms28,29. Building on prior work decoding the metabolic basis underlying (1) pigment epithelium degeneration and type 2 diabetes (T2D) and (2) retinal aging and age-related multisystem disorders30,31, we extended this framework to interrogate retinal–cardiometabolic connections through systematically profiling RNFLT-associated metabolic states and their links with CMD risk (Fig. 1). To explore the potential of the shared metabolic basis in informing CMD risk, we constructed predictive models integrating these metabolic insights and evaluated their performance and clinical utility across varying genetic susceptibility. Of significant interest is our focus on the benefits for socially vulnerable communities, aiming to mitigate entrenched health disparities in CMD management. Finally, this integrated framework was validated in an independent cohort with distinct ethnic and clinical backgrounds, highlighting the translational potential of RNFLT metabolic states as early indicators of CMD risk.

Results
Participants’ characteristics
Atotalof7824participantswhounderwentbothopticalcoherencetomography(OCT)scanningandmetabolomicprofiling(population-I),alongwith86,014whounderwentmetabolomicprofilingaloneatbaseline(population-II),wereincludedinthisstudy(seethe“Methods”section).Thestudypopulationhadamedianageof56.8(8.1)years, of whom 54.6% were women, 10.8% were ever or current smokers, and 21.6% were on antihypertensive medication. Participants who underwent OCT scanning at baseline demonstrated a younger age, male sex, higher educational attainment, lower smoking prevalence, and a decreased likelihood of being on antihypertensive medications compared to those who did not (Table S1). The data of an additional 1286 participants who underwent both OCT scanning and metabolomic profiling from the Guangzhou Diabetic Eye Study (GDES) (population-III) are described in detail below (see the “Methods” section).

RNFLT-associated metabolites and incident CMD risk
After adjusting for age, sex, ethnicity, assessment centre, household income, Townsend deprivation, education attainment, smoking status, alcohol consumption, body mass index, use of lipid-lowering medications, enzymatic HDL cholesterol, systolic blood pressure and HbA1c level, and applying multiple testing corrections, a total of 26 nuclear magnetic resonance (NMR)-based biomarkers were independently associated with the RNFLT in population-I (Fig. 2a and Table S2). Among these biomarkers, 24 exhibited negative associations, encompassing diverse constituents, such as total lipids, phospholipids, cholesterol, free cholesterol, and cholesteryl esters in various subclasses of HDL particles, as well as phosphatidylcholines, total cholines, and apolipoprotein A1. The adjusted β for these associations spanned a range from −0.201 (95% CI: −0.309 to −0.093) to −0.117 (95% CI: −0.229 to −0.005) per-standard deviation (SD) change. Two biomarkers exhibited positive associations with RNFLT, specifically, the phospholipids-to-total-lipids ratio in very low-density lipoprotein (β=0.120; 95% CI: 0.015–0.224) and intermediate-density lipoprotein (β=0.126; 95% CI: 0.026–0.227) subfractions. Sensitivity analyses yielded consistent results when further adjusting for prevalent CMDs (Table S3).

After a median time of 12.2 (interquartile range: 11.5–12.9) years of follow-up in population-II, a total of 6071 participants developed T2D, 2866 developed myocardial infarction, 2537 developed heart failure, 1578 developed stroke, and 6254 died, 1544 of whom succumbed to cardiovascular events. Following adjustment for the same covariates as above, most of the 26 RNFLT-associated metabolites were also associated with CMD outcomes (24 for T2D; 26 for myocardial infarction; 26 for heart failure; 19 for stroke; 19 for all-cause mortality; and 25 for cardiovascular mortality) (Table S4). These included 126 negative associations, with the adjusted hazard ratios (HRs) ranging from 0.688 (95% CI: 0.663–0.714) to 0.969 (95% CI: 0.942–0.996) per-SD increment, as well as 13 positive associations, with the adjusted HRs ranging from 1.048 (95% CI: 1.020–1.077) to 1.345 (95% CI: 1.308–1.383) per-SD increment. Stratified analyses by sex, Townsend deprivation, and educational attainment yielded consistent results (Tables S5–S7).

To assess whether these metabolic biomarkers mediate the relationship between RNFLT and CMD risk, we conducted a mediation analysis comparing the mediation effect of traditional risk factors and RNFL-associated metabolites (see the “Methods” section and Table 1). After accounting for traditional risk factors (same as above), most of the associations between RNFLT and CMD outcomes remained significant, suggesting that these factors explained only a proportion of the observed associations (e.g., 19.4% for myocardial infarction and 7.7% for all-cause mortality). In contrast, when RNFL-associated metabolites were included in the models, the direct associations between RNFLT and CMD outcomes were substantially attenuated toward the null. This change suggested that these metabolic biomarkers accounted for a major proportion of the relationship between RNFLT and CMD risk. The estimated proportions were 63.7% for T2D, 44.7% for myocardial infarction, 49.3% for heart failure, 35.8% for stroke, 17.0% for all-cause mortality, and 38.8% for CMD mortality.

RNFLT metabolic states for CMD prediction
The participants in population-II were split randomly into training and testing sets at an 8:2 ratio to evaluate the utility of RNFL-associated biomarkers in characterizing CMD risk (see the “Methods” section). Baseline characteristics were comparable between the two subsets (all P>0.05) (Table S1). We first modelled RNFLT metabolic states and observed an increase in event rate trajectories over RNFLT metabolic states across all CMD endpoints (Fig. 2 and S1, S2). For T2D (Fig. 2b), participants in the top decile of RNFLT metabolic states exhibited event rates over 20-fold higher compared to those in the bottom decile (HR=21.77, 95% CI: 14.18–33.42), suggesting substantial information encapsulated within RNFLT metabolic states for assessing the risk of T2D. For myocardial infarction (Fig. 2d), heart failure (Fig. 2f), and cardiovascular mortality (Fig. 2g), the ratios were greater than five (myocardial infarction, HR=8.04; 95% CI: 5.15–12.54; heart failure, HR=5.15; 95% CI: 3.38–7.85; cardiovascular mortality, HR=5.72; 95% CI: 3.24–10.11). Distinct separation in risk trajectories was also evident for stroke (Fig. 2c) and all-cause mortality (Fig. 2e), with HRs of 4.23 (95% CI: 2.40–7.44) and 2.84 (95% CI: 2.22–3.63), respectively.

We then examined whether RNFLT metabolic states predict CMD risk (see the “Methods” section). Given the substantial class imbalance in CMD outcomes (Table S8), we quantified model performance using the area under the precision-recall curve (AUPRC) as it provides an unbiased assessment in imbalanced settings. C-statistics were additionally reported as a complementary metric to facilitate intuitive interpretability and cross-outcome comparison with differing event rates. In general, RNFLT metabolic states exhibited variable performance across CMD outcomes as assessed by C-statistics (Fig. 3a–f). For T2D, myocardial infarction, heart failure, and CMD mortality, the model fit solely on the RNFLT metabolic states outperformed the Age&Sex model and all individual conventional predictors, including age, blood pressure, enzymatic HDL cholesterol, and LDL cholesterol (Table S9). In the case of other CMD outcomes, the states also outperform most conventional predictors, demonstrating predictive performance competitive with or second only to age. When combined with minimal demographic characteristics (age and sex), RNFLT metabolic states already performed comparably to most established models (Fig. 3a–f). These findings were largely consistent when evaluated using C-statistics (Table S10 and Fig. S3).

To assess the ability of RNFLT metabolic states to complement conventional models, we calculated the percent change of AUPRC (ΔAUPRC%) to unbiasedly quantify the predictive improvement achieved by incorporating RNFLT-associated metabolic information into established models (see the “Methods” section). Our findings showed that including these states resulted in a significant predictive improvement for the six CMD outcomes in all 30 models (Fig. 3a–f and Table S11). The most substantial improvement was observed for T2D, with a 117.2% improvement over Age&Sex model and an average improvement of 43.8% over all conventional models. However, the improvement became comparable to that observed for other hard cardiovascular outcomes when benchmarking against a model designated for predicting T2D risk (ΔAUPRC%=9.7%). The average improvements over conventional models were 16.8% for myocardial infarction, 18.6% for heart failure, 17.2% for stroke, 10.6% for all-cause mortality, and 20.1% for CMD mortality. Findings from C-statistics (Fig. S3 and Table S12) and across alternative modelling approaches were largely consistent (Supplementary Results and Table S13).

By calculating polygenic risk scores (PRSs) using genotyped data and meta-analysed external genome-wide association study (GWAS) summary statistics, participants were stratified by genetic susceptibility to CMDs (see the “Methods” section). We prioritized C-statistics to facilitate cross-outcome comparison. In general, models integrating RNFLT metabolic states shared consistent improvement across varying risk categories (Table 2). In populations at high genetic risk, RNFLT metabolic states improved the predictability of most CMD outcomes over FGCRS, except for myocardial infarction (Fig. 3g). While these states provided greater incremental value for heart failure in high genetic risk populations, lower genetic risk populations typically experienced greater improvement in CMD outcome prediction. Additional analyses stratified by PRS for T2D generated from two external GWAS datasets32,33 yielded consistent findings in the T2D assessment (Table S14).

While improvement in discriminatory power is pivotal, it is imperative to evaluate the practical clinical utility of these models (Fig. 4). We observed that all models were well-calibrated for each CMD outcome (Fig. 4a–f). Decision curve analyses demonstrated that improvements in predictability generally lead to greater enhancements in clinical utility (Fig. 4g–l). The integration of RNFLT metabolic states either reduced or eradicated the discrepancy between the Age&Sex model and the established model (FGCRS). Furthermore, the integration of these RNFL metabolic states into the FGCRS contributed to additional improvements in clinical utility across all six CMD outcomes, spanning a wide range of clinical decision thresholds (Fig. 4g–l). Net reclassification improvement for integrating RNFLT metabolic states was moderate yet consistent for all CMD outcomes and models in Table 3.

Benefits to populations experiencing health inequalities
As health disparities exist in CMD outcomes and access to healthcare across diverse population groups34–37, we were interested in the potential benefits of incorporating RNFLT metabolic states for populations experiencing health inequalities. To address this, we quantified the disparities in discriminatory benefits across different groups with a particular focus on social health determinants, including sex, deprivation, and educational attainment (Fig. 5).

In line with previous reports34,35, we observed sexual disparities in CMD risk prediction, as the models fitted on FGCRS predictors exhibited stronger discriminatory power for men compared to women across most CMD outcomes (Fig. 5a–f). However, with the inclusion of RNFLT metabolic states, we observed significantly greater improvements in women’s risk prediction across all CMD outcomes, even in cases where women did not exhibit a pre-existing disadvantage (Fig. 5c and d). This resulted in the eradication or narrowing of the performance gap for CMDs across sexes. In the case of T2D (Fig. 5a) and CMD mortality (Fig. 5f), the substantial improvements observed in women even led to a reversal of the performance gap, where women surpassed men in terms of discriminatory power. Furthermore, integrating RNFLT metabolic states also led to the eradication or narrowing of the prediction gap experienced by socioeconomically disadvantaged populations and those with reduced educational attainment (Fig. 5g–r and Supplementary Results). After employing data balancing techniques38 to address potential biases in the dataset, the main results were consistent (Tables S15 and S16). Taken together, RNFLT metabolic states may represent the common basis of early CMD pathogenesis across demographic strata, narrowing the prediction gap experienced by socially disadvantaged populations.

Extrapolation in an independent cohort
Leveraging data from the GDES39 (Table S17), we extrapolated our analytic framework to an ethnically distinct cohort of 1286 participants who underwent both OCT scanning and metabolomic profiling at baseline (population-III) (Fig. 6). Our findings demonstrated significant associations among 673 metabolites with RNFLT (Table S18), with the vast majority (n=457 and 279) also associated with incident cardiovascular disease (CVD) (Fig. 6a and Table S19) and mortality (Table S20), indicating their relevance between RNFLT and CMD outcomes across multiple biological processes (Fig. S4). The panorama of these revelations encompassed the replication of phospholipids, phosphoglycerides, cholines and phosphatidylcholines across assays, while offering additional insights spanning a rich spectrum, including aminos, benzene derivatives, alcohols, amines, coenzymes, nucleotides, carbohydrates, organic acids, fatty acids, and heterocyclic compounds (Fig. 6a), which fell beyond the scope of assays in the UKB cohort. Enrichment analyses implicated several biological pathways related to these metabolites, such as aminoacyl-tRNA biosynthesis, arginine and phenylalanine metabolism, glutathione metabolism, as well as nicotinate and pantothenate metabolism (Fig. S4).

RNFLT metabolic states remained the most powerful predictors for incident CVD and mortality (Tables S21 and S22), and their integration into both Age&Sex models and FGCRS yielded significant improvements across the overall population (Fig. 6b and Tables S23 and S24) and those with high genetic susceptibility (Fig. 6d and Table 3). Since the GDES cohort represents a diabetic population39, we included three additional benchmark models (UKPDS, NZ-DCS and WAN)40–42 customized for the diabetic population, alongside FGCRS for general population risk assessment, where consistent improvements were observed across all models (Fig. 6b, S5 and Tables S25, S26). The decision curve analysis revealed further improvement in clinical utility with the addition of these states (Fig. 6c and e). The finding of comparison analyses across sexes, social classes and education attainment were replicated, where prediction models for women, individuals with low income and those without university education yielded more substantial improvements compared to their respective counterparts (Fig. 6f–h and Table 4). As observed in the UKB, the substantial improvements observed in the case of women even led to a reversal of the performance gap, where women outperformed their men counterparts in terms of discriminative performance (Fig. 6f).

Methods
Study population
The UKB study is a prospective, multicentre cohort study that recruited over 500,000 participants aged 40–69 years from 22 assessment centres across the United Kingdom54. Baseline assessments, including questionnaires, medical interviews, physical examinations and blood tests, were conducted between 2006 and 2010. The GDES is a community-based cohort study that recruited over 2500 patients with diabetes aged 35–85 in Guangzhou, China30. Baseline assessments were conducted from 2017 to 2019. The study was conducted in accordance with the principles of the Declaration of Helsinki and approved by the Northwest Multicenter Research Ethics Committee (11/NW/0382) and the Ethics Committee of Zhongshan Ophthalmic Center (2017KYPJ094). Prior to any engagement, written informed consent was obtained from all participants. The study adhered to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) and Standards for Reporting of Diagnostic Accuracy Studies (STARD) guidelines for reporting.

The UKB participants were categorized into three non-overlapping groups: (1) population-I comprised individuals who underwent both OCT scanning and metabolomic profiling (n=7824); (2) population-II underwent metabolomic profiling only, without OCT scanning (n=86,014); and (3) the remaining individuals were excluded from the analysis15. Participants with prevalent glaucoma, retinal disorders (including retinal detachment and break, retinal vascular occlusion, and others), and neurodegenerative diseases were further excluded from population-I because of the potential for secondary retinal destruction. Further information on the specific eligibility criteria for each population group can be found in the Supplementary Methods. The UKB population-I was used to identify RNFLT metabolic states, while population-II was used for downstream analyses. Replication in an independent cohort was conducted in a subset of the population that underwent both eligible OCT scanning and metabolomic profiling in the GDES cohort (population-III).

Metabolomic profiling
High-throughput proton NMR (1H-NMR) (Nightingale Health, Finland) was employed to quantify the metabolite concentrations from the plasma samples of the participants29,55–57. Cryopreserved EDTA plasma samples were thawed and subjected to centrifugation to separate the supernatant. This supernatant was then mixed with phosphate buffer to create the sample mixture. The prepared samples were loaded onto a specialized cooled sample changer. Two distinct NMR spectra were recorded for each plasma sample using a high-resolution 500 MHz NMR spectrometer (Bruker AVANCE III HD, USA). One of these spectra characterized resonances originating from proteins and lipid lipoprotein particles, while the other spectrum detected resonances from low-molecular-weight metabolites. Following quality control processes, metabolic biomarkers were quantified using the Nightingale Health Biomarker Quantification Library 2020. Detailed information can be found in the Supplementary Methods.

OCT imaging
Spectral-domain OCT was performed in an enclosed darkroom with a Topcon 3D OCT-1000 Mk II system (Topcon, Japan)58–60. The system had an axial resolution of 6 μm and an image acquisition rate of 18,000 A-scans/s. The imaging was performed using a 3D 6×6 mm macular volume scan mode centred at the fovea, with a scan density of 512 A-scans × 128 B-scans within 3.7 s. The Topcon Advanced Boundary Model Segmentation algorithm automatically segmented the retinal layers and determined the average RNFLT as the distance from the internal limiting membrane to the nerve fibre layer-ganglion cell layer junction61. Image quality scores, internal limiting membrane indicators, validity counts, and motion indicators were recorded for quality control (Supplementary Methods). Images with low signal strength (<45) or poor segmentation or centration (the worst 20% for each metric) were excluded from analysis. In cases in which both eyes of a participant met the eligibility criteria, the right eyes were selected for further analysis.

Outcomes and covariates
The Hospital Episode Statistics database, Scottish Morbidity Record and Patient Episode Database were used to record inpatient hospital records for England, Scotland, and Wales. Mortality data were obtained from national datasets with the National Health System (NHS) Digital and the NHS Central Register. The assessment of CMD outcomes and the recording of the primary causes of death were based on the International Classification of Diseases-10. The follow-up period was from 16 March 2006 to 31 March 2021. Person days for each participant were calculated from the date of baseline assessment to the date of disease onset, death, or the end of follow-up, whichever came first. Details of the covariate assessment are available in Supplementary Methods.

Genotyping and outcome-specific PRS
The PRSs for both T2D and CVD were computed to identify genetically high-risk individuals. Genotyping was conducted using the UK BiLEVE Axiom Array or the UKB Axiom Array. Quality control processes and imputation were conducted based on the Haplotype Reference Consortium and UK10K haplotype resources54. External GWAS summary statistic data were meta-analysed via fixed-effect inverse variance meta-analysis, with a correction for sample overlap where required62. The PRS algorithms were constructed based on outcome-specific meta-analyses using a Bayesian approach. Per-individual PRSs were computed as the genome-wide summation of per-variant posterior effect sizes multiplied by allele dosage. After generating raw PRSs using the derived weights, centring and standardization were applied to produce corrected PRSs (Supplementary Methods).

RNFLT-associated metabolites and their linkage to CMD outcomes
The associations of metabolic biomarkers with RNFLT were assessed using multilevel linear regression models after adjusting for age, sex, ethnicity, assessment centre, household income, Townsend deprivation index, education, smoking, alcohol consumption, body mass index, use of lipid-lowering medications, enzymatic assay-based HDL cholesterol, systolic blood pressure and HbA1c level. Sensitivity analyses were performed by further adjusting for prevalent CMDs. The Benjamini–Hochberg method was employed to reduce the false-positive rate. Biomarkers that reached statistical significance after multiple testing were identified as RNFLT metabolic states.

The RNFLT-associated metabolites were further analysed using Cox proportional hazards models for the risk of six CMD outcomes, accounting for the same covariates while accounting for both time-event data and censored observations63. Participants with a baseline CMD diagnosis were excluded from the corresponding analysis (e.g., in the case of the T2D endpoint, participants with baseline T2D were excluded). A mediation analysis was conducted to estimate the mediation effect of traditional risk factors and RNFLT-associated metabolites on the association between RNFLT and CMD risk. We assessed it as the percentage change in adjusted Cox proportional hazards coefficients for each CMD outcome after adjusting for the metabolites of interest. The proportional hazard assumption was tested using the Schoenfeld residual method and was satisfied for each model.

Model development and evaluation
We employed Random Forest and several other traditional machine learning (extreme gradient boosting, support vector machine, least absolute shrinkage and selection operator, and ridge) and deep learning (multilayer perception and convolutional neural network) approaches to model CMD outcome risk. The datasets were randomly split into training and testing sets at an 8:2 ratio. In the case of deep learning modelling, an additional subset of the training data (20%) was set aside for validation, during which loss reduction monitoring was performed to ensure optimal model performance. Hyperparameters were fine-tuned through a grid search for optimal model configurations64. Detailed modelling procedures can be found in the Supplementary Methods.

Model evaluation was performed in the fully withheld testing set. Participants were stratified based on the predicted RNFLT metabolic states, which were derived from the respective RNFLT metabolic models for each CMD outcome, and cumulative event rates were compared across the top, middle and bottom 10% of the states for each CMD outcome15,29. The AUPRC and Harrell’s C-statistic were calculated to assess the predictive value of the RNFLT metabolic states for each CMD outcome and were also benchmarked against individual conventional predictors. While the C-statistic provides an overall measure of discrimination, it assigns equal weight to false positives and false negatives and therefore distorts performance in imbalanced datasets. Given that the positive-to-negative ratios across CMD outcomes ranged from 1:99 to 7:93 (Table S10), AUPRC was prioritized to evaluate absolute and incremental performance for each individual outcome, as it provides an unbiased assessment in imbalanced settings. Notably, the baseline (no-skill) AUPRC varies by outcome and corresponds to the event rates in the dataset: 0.06 for T2D, 0.03 for myocardial infarction, 0.03 for heart failure, 0.02 for stroke, 0.07 for all-cause mortality, and 0.02 for CMD mortality. As such, interpretation of AUPRC values should be made relative to these outcome-specific baselines rather than on absolute magnitude alone. C-statistics were additionally reported as a complementary metric to facilitate intuitive interpretability and cross-outcome comparison with differing event rates.

We evaluated the incremental predictability of these states compared to the Age&Sex model and a spectrum of conventional algorithms for CMD prediction (FGCRS65, SCORE266, AHA/ASCVD67, WHO-CVD68, FRS for T2D69, NZ-DCS40, UKPDS41 and Wan’s model42), where applicable. Calibration plots were built to assess the goodness of model fit, and decision curve analyses were performed to estimate net benefits in clinical utility. The reclassification improvement was calculated by adding the percentage of patients with an event correctly reclassified to the percentage of patients without an event correctly reclassified, at a risk category of <5%, 5–10%, and >10%. Further subgroup analyses were conducted on different genetic susceptibility, sexes, social classes, and educational attainment.

Extrapolation in an independent cohort
At baseline, all GDES participants underwent retinal OCT scanning (SS-OCT, DRI OCT Triton; Topcon, Japan) using a 3D Macula Cube 7×7 mm scan mode centred on the fovea15,30. A built-in software (IMAGEnet 6, v1.22) was employed for automatic retinal segmentation (Supplementary Methods). Furthermore, a subset of the GDES participants underwent liquid chromatography tandem triple quadrupole mass spectrometry (LC, Exion LC AD, SCIEX, USA; MS, QTRAP System, SCIEX, USA) for metabolomic profiling with stringent quality control processes employed (Supplementary Methods)70,71. Incident CVD was defined as the development of coronary heart disease, heart failure, atrial fibrillation, stroke or related mortality during the follow-up period, determined by a combination of medical records, questionnaires, and verbal interviews. Fasting blood samples were collected to assess the serum concentration of HbA1c, total cholesterol, and HDL cholesterol (enzymatic), and to assess the glomerular filtration rate. The modelling details and analytical processes are as above.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "NCOMMS Review 555255 Machine-learning Enhanced Plasma Metabolomics of Retinal Nerve Fiber Layer to Characterize\nRisks of Mortality and Cardiometabolic Diseases: AMulti-Cohort Study\nGeneral Comments: The objective of this well designed and clearly written manuscript is to elucidate relationships between\nmetabolites associated with RNFLT and cardiovascular metabolic diseases (CMD). The manuscript provides a\ncomprehensive assessment of both metabolites associated with macula RNFLT and CMD in distinct ethnic populations of\nvarying genetic susceptibility. Their results suggest that there is a relationship between RNFLT and CMD metabolic\nfingerprints and that including RNFLT associated metabolic information into existing CMD models significantly improved\ntheir discriminatory power of the models. One concern is that the metabolic fingerprints were identified based on their\nassociation with RNFLT – which is affected by many ocular diseases; adjustment for these ocular conditions (glaucoma for\nexample) is encouraged and mention of this issue in the discussion section of the manuscript. Although the authors used\nexternal datasets, the population was generally of White and Chinese ethnicity.\nSpecific Comments:\nAbstract: This concluding sentence in the abstract may be overstating the results; the authors should consider revising a bit.\n“Our integrated approach, validated across cohorts of diverse ethnic backgrounds and genetic risks, highlights the possibility\nthat RNFLT metabolic fingerprints as a share basis underlying retinal–cardiometabolic connections and as early indicators\nthat advance equitable cardiometabolic disease management, positioning the retina as a valuable window into\ncardiometabolic health.”\nMethods:\n1) RNFLT metabolic states are based on associations with RNFLT. However numerous eye diseases can also affect\nRNFLT. Were these eye diseases considered in the models?\n2) More detail is needed in describing the OCT methods used:\na. For the UKB dataset, RNFLT was measured from macula volume scans centered on the fovea\ni. Which specific scans were acquired in the independent cohort and how was RNFLT measured?\n3) The authors are commended for using several population groups in their analysis. The datasets include mainly Chinese\nand White individuals. The lack of representation of Black individuals should be mentioned in the discussion as a limitation\nto this study.\nResults\n4) Results / tables β and HR should indicate the specific amount of change related to the coefficient of interest; “per 1-SD\nincrement” as well as the value of the 1-SD increment should be included in the tables.\n5) It should be clearly stated in tables and figures which datasets the results represent.", "reviewer_2": "The manuscript titled \"Machine-learning Enhanced Plasma Metabolomics of Retinal Nerve Fiber Layer to Characterize\nRisks of Mortality and Cardiometabolic Diseases: A Multi-Cohort Study\" investigates the relationship between retinal nerve\nfiber layer thickness (RNFLT) and cardiometabolic diseases (CMDs) using advanced metabolomics and machine learning\ntechniques. The study identifies RNFLT metabolic fingerprints as biomarkers of CMD risk and integrates them into predictive\nmodels, demonstrating improved risk prediction across multiple CMD outcomes. These findings are validated in both the UK\nBiobank and an independent, ethnically distinct cohort, highlighting the potential for RNFLT as a non-invasive tool for\nsystemic health profiling and CMD management.\nThe study has several strengths. It leverages large, well-characterized cohorts with advanced imaging and metabolomics\ndata, providing robust validation across populations. The integration of machine learning significantly enhances the\npredictive power of traditional models, showcasing the utility of RNFLT as a bridge between ophthalmology and\ncardiometabolic health. The study also highlights key use cases, including personalized risk prediction, early detection of\nCMDs, and potential applications in addressing health inequities. The framework paves the way for translational\napplications, particularly in integrating retinal imaging into routine primary care for systemic health assessments.\nSuggestions for Improvement\n1) The study's biggest limitation is lack of reproducibility. Without data and source code, the value of this study is\nsignificantly diminished and evaluation of the methodology is impossible. It's understandable that UKBB data can only be\naccessed through the established protocols. The validation data appears to be not accessible at all. At the very least, a\nworking source code and simulated data should be made available.\n2) Given the class imbalance in CMD outcomes, including Area Under the Precision-Recall Curve (AUPRC) as a\ncomplementary metric (and associated baselines including a completely random/no-skill predictor) is critical.\n3) How should the magnitude of the improvement in predictive power (compared to baseline) be interpreted from an\napplication/clinical standpoint? Are these improvements clinically relevant? A simulation study of benefits from such an\nimprovement would be very valuable.\n4) Future steps are focused on making the model more accurate and analyzing other diseases. But what is next for this\nparticular model? An RCT? A clinical dashboard / risk predictor? I am struggling with understanding the algorithm's real-\nworld potential.\n5) [minor] Please Elaborate on the criteria used to select retinal scans and how imaging quality issues were addressed to\nminimize bias.", "reviewer_3": "This manuscript details a study of metabolomics from UK Biobank to find associations between metabolites and retinal\nnerve fiber layer thickness and then see if these metabolites are associated with incident cardiometabolic diseases (RNFLT;\nN=7824 with OCT and metabolomics; N=86000 for just metabolomics to look at incident cardiometabolic diseases). This is\nbased on the premise that RNFLT may have implications for cardiometabolic diseases. They find: (1) 26 metabolites were\nassociated with RNFLT after adjustment for multiple comparisons; (2) majority of metabolites were also associated with\nincident cardiometabolic diseases; (2) metabolites mediate a significant portion of RNFLT-CMD association (up to 35.1%);\n(3) metabolite “fingerprints” added to clinical models of risk for many of the cardiometabolic diseases as evidenced by a\nstatistically (and often clinically) meaningful increase in the c-statistic; (4) they do analyses of “health disparities” looking at\nsex- and social determinants of health stratified models which suggest that the metabolite “fingerprint” improves risk\nprediction even more so in the group in whom the clinical model performs less well; (5) they do similar analyses in a more\nethnically diverse cohort (Guangzhou Diabetes Eye Study) with a more comprehensive metabolomics platform and show\nthat most RNFLT associated metabolites are also associated with incident CVD and mortality.\nOverall, the concept of linking ocular findings with biology through circulating analytes/omics is of interest and the link to\nincident cardiometabolic diseases in that context is interesting. The strengths of the study are the good use of the UKB\ndataset, the incorporation of genetic risk to show that the metabolite signature adds to clinical risk prediction even in high\ngenetic risk, and the inclusion of an external validation cohort (GDES).\nEnthusiasm for the manuscript is significantly tempered due to the following major concerns. Firstly, the impact of the\nmanuscript is significantly decreased due to an already published manuscript (PMID: 39581638, including the senior author\nof this manuscript) detailing these associations; the fact that a large proportion of the metabolites converge on HDL\ncholesterol which is a known risk factor; and relatedly, that the clinical implications of this metabolite score are unclear given\nthat the clinical models that were used are not robust and the analyses were not adjusted for HDL cholesterol; and that many of\nthe models other than DM have a very small increase in the C-statistic (0.02) for most outcomes. Secondly, there are\nvalidity concerns as detailed below, the primary of which is the lack of accounting for confounders in the original analyses,\nthus any link between RNFLT metabolites and incident CMD more likely reflects residual confounding related to the\npresence of CMD risk factors (i.e. pre-DM), as opposed to a direct link between the metabolite and the incident CMD\n(relatedly, the mediation analyses do not appear to account for confounders).\nMajor issues:\n(1) Link between RNFLT is tenuous since all metabolites are associated with CMD (and they don’t show that RNFLT is\nassociated with incident CMD); this likely reflects confounders that are not accounted for in the models (HTN, BP, individual\nprevalent CMD, HgA1c, HDL).\n(2) Clinical models need to include more granular data like HgA1C that may reflect residual confounding; also need to\ninclude traditional cholesterol parameters including HDL); Framingham risk score not a very comprehensive risk score (need\nto compare to the clinical model from the discovery dataset)\n(3) How was prevalent CVD/DM addressed?\n(4) Not clear that Cox models and sex/SDoH stratified models are adjusted for covariates?\n(5) The majority of the increase in c-statistics are minimal (0.02) except for DM when compared to more robust models like\nFHS, so not clinically meaningful, but it’s not even clear from Fig 2 if they are statistically significant changes?\n(6) Not clear how metabolic “fingerprints” were created; would devote more of main text methods to this rather than\nregurgitating UKB methods for metabolomics and RNFLT measurements that have already been published\n(7) How does this study compare/differ with a previously conducted similar study under the same UKB proposal number\n(PMID: 39581638)? That study does not appear to be referenced in this study?\n(8) The biological interpretation in discussion is too speculative; also need to provide more details and context around\nindividual metabolites and talk about prior literature\n(9) Nightingale platform is not comprehensive\n(10) Need to adjust for robust risk factors in all analyses (not just RNFLT associations) including in mediation analyses\nMore minor issues:\n*Language in abstract is too expansive and not specific enough\n*Throughout, need to be more specific about the metabolite score (i.e. shouldn’t call them fingerprints…fingerprints don’t\nchange over a lifetime like metabolites do)\n*spell out all acronyms in main text first time they are used\n*should use “European ancestry” instead of “Caucasian ethnicity” (as the latter was not specifically addressed in UKB);\nshouldn’t use “diabetic patients” (instead should use “patients with diabetes”)\n*data use statement only covers UKB, not GDES\n*fig 2: all Y-axes need to be same range of values\n*Table 2 should be a supplementary table\n**need to adjust for multiple testing for incident CMD including the number of tests at the level of number of CMD tested"}, "author_response": "Responds to the reviewers’ comments\nWe would like to express our sincere gratitude for all your invaluable comments\nand constructive suggestions. We are truly honoured to have the opportunity to\naddress all your concerns and incorporate your feedback into our manuscript.\nWe firmly believe that the valuable input you offered has significantly enhanced\nthe overall quality of our work. In the following section, we provide a\ncomprehensive point-by-point response to each specific comment, which is\nhighlighted in BLUE for your easy reference.\nResponds to Reviewer #1\nComment #1. The objective of this well designed and clearly written manuscript\nis to elucidate relationships between metabolites associated with RNFLT and\ncardiovascular metabolic diseases (CMD). The manuscript provides a\ncomprehensive assessment of both metabolites associated with macula RNFLT\nand CMD in distinct ethnic populations of varying genetic susceptibility. Their\nresults suggest that there is a relationship between RNFLT and CMD metabolic\nfingerprints and that including RNFLT associated metabolic information into\nexisting CMD models significantly improved their discriminatory power of the\nmodels. One concern is that the metabolic fingerprints were identified based on\ntheir association with RNFLT – which is affected by many ocular diseases;\nadjustment for these ocular conditions (glaucoma for example) is encouraged\nand mention of this issue in the discussion section of the manuscript. Although\nthe authors used external datasets, the population was generally of White and\nChinese ethnicity.\nResponse: We sincerely appreciate the time and effort you have dedicated to\nreviewing our manuscript and for your thoughtful and constructive feedback. We\nare also truly grateful for your positive assessment of our work. Your insightful\ncomments have been invaluable in refining our study. We have carefully\nexamined all your specific comments, clarified the population eligibility criteria\nand the OCT scanning methodology, refined the abstract, improved the legends\n1\nfor tables and figures, and revised the manuscript accordingly. We hope that\nthese modifications have strengthened the manuscript and addressed your\nconcerns effectively.\nWe totally agree with your concern about the potential influence of ocular\nconditions on RNFLT and the importance of accounting for these confounders. To\nminimize their impact, we implemented a predefined procedure to exclude\nindividuals with glaucoma, retinal disease (including retinal detachments and\nbreaks, retinal vascular occlusions, and other structural abnormalities),\nneurodegenerative disease, and other exclusion criteria (see Response to\nComment #3), given their potential to confound RNFLT measurements. This\napproach ensured that our analysis was conducted in a population free from\nknown retinal pathology, thereby allowing us to identify RNFLT-associated\nbiomarkers more reliably. The limited representation of Black individuals has\nalso been acknowledged in the study limitations, in accordance with your\nsuggestions (see Response to Comment #5). A detailed point-by-point response\nto each of your specific suggestions is provided below. We believe these revisions\nimprove the clarity, accuracy, and overall impact of our manuscript.\nComment #2. Abstract: This concluding sentence in the abstract may be\noverstating the results; the authors should consider revising a bit. “Our\nintegrated approach, validated across cohorts of diverse ethnic backgrounds and\ngenetic risks, highlights the possibility that RNFLT metabolic fingerprints as a\nshare basis underlying retinal–cardiometabolic connections and as early\nindicators that advance equitable cardiometabolic disease management,\npositioning the retina as a valuable window into cardiometabolic health.”\nResponse: We sincerely apologize for any overstatement caused by our initial\nphrasing in the Abstract conclusion. In the revised manuscript, we have removed\nthe extended claim that \"the retina is positioned as a valuable window into\ncardiometabolic health\" and instead emphasize that further studies in broader\n2\npopulations are required to validate our findings (Page 4, Line 98). In addition,\nwe have reworded the whole sentence to reflect a more cautious interpretation\nof our results. We believe this revised phrasing more accurately conveys the\ntentative nature of our conclusions while highlighting the significance of future\nresearch to validate these findings in diverse cohorts.\nComment #3. Methods: RNFLT metabolic states are based on associations with\nRNFLT. However numerous eye diseases can also affect RNFLT. Were these eye\ndiseases considered in the models?\nResponse: We sincerely appreciate your insightful comment regarding the\npotential influence of ocular conditions on RNFLT. We fully agree that factors\nsuch as glaucoma and other retinal disorders may affect RNFLT and should be\naccounted for. To address this concern, we implemented a predefined procedure\nto exclude individuals with known ocular conditions that could confound the\nRNFLT measurements.\nSpecifically, when identifying RNFLT-associated biomarkers, we excluded\nparticipants without eligible metabolomics profiling (391,763 participants) and\nOCT scanning (96,068 participants), as well as those with missing thickness\nvalues (294 right eyes and 407 left eyes), low signal strength (1,272 right eyes\nand 1,067 left eyes), poor centration or segmentation (4,291 right eyes and 4,309\nleft eyes), high refractive error (spherical equivalent > 6 or < −6 dioptres; 341\nright eyes and 404 left eyes), visual impairment (> 0.1 logMAR; 2,054 right eyes\nand 2,014 left eyes), or abnormal intraocular pressure (IOP ≥ 22 or ≤ 5 mmHg;\n701 right eyes and 752 left eyes). Furthermore, patients with glaucoma (82\nparticipants), retinal disease (including retinal detachments and breaks, retinal\nvascular occlusions, and others) (162 participants), and neurodegenerative\ndisease (39 participants) were excluded, given their potential to confound RNFLT\nmeasurements. This approach ensured that our analysis was conducted in a\n3\npopulation free from known retinal pathology, thereby allowing us to identify\nRNFLT-associated biomarkers more reliably.\nOriginally, these detailed eligibility criteria were provided in the Supplementary\nMaterial. However, we recognize that it is crucial for readers to have immediate\naccess to this information in the main text. Accordingly, we have revised the\nMethods section (Page 16, Line 459) to emphasize the exclusion of prevalent\nconditions that may confound RNFLT measurements in the population used for\nidentifying RNFLT-associated biomarkers, rather than simply referring to the\ncorresponding section in the Supplementary Material. We hope this clarification\naddresses your concern.\nComment #4. More detail is needed in describing the OCT methods used: a. For\nthe UKB dataset, RNFLT was measured from macula volume scans centered on\nthe fovea. Which specific scans were acquired in the independent cohort and how\nwas RNFLT measured?\nResponse: We apologize for any lack of clarity regarding the OCT methods used\nin the independent cohort (GDES). In this population, all participants underwent\nretinal OCT scanning using a swept-source OCT (SS-OCT; DRI OCT Triton, Topcon,\nJapan) to obtain structural images of the macula. Each scan was performed with\nan internal fixation system, and the fixation was monitored by the instrument’s\nbuilt-in fundus camera. The retina was imaged using a 3D Macula Cube 7×7 mm\nscan mode centred on the fovea, with a scan density of 512 A-scans × 512 B-\nscans. The built-in SS-OCT software (IMAGEnet 6, Version 1.22) automatically\nsegmented the retinal layers and measured the RNFLT as the distance from the\ninternal limiting membrane to the nerve fibre layer–ganglion cell layer junction.\nWhere segmentation errors were identified, manual adjustments were\nperformed. This information has been added to the Methods section (Page 21,\nLine 588) and further elaborated in the Supplementary Methods . We hope this\nclarification addresses your concern.\ntheir analysis. The datasets include mainly Chinese and White individuals. The\nlack of representation of Black individuals should be mentioned in the discussion\nas a limitation to this study.\nResponse: We sincerely appreciate your generous comment and your\nrecognition of our efforts to validate our findings across multiple ethnic groups.\nWe agree that the underrepresentation of Black individuals is a limitation of our\nstudy. Accordingly, we have now explicitly mentioned this limitation in both the\nDiscussion section (Page 15, Line 420) and the Abstract conclusion (Page 4,\nLine 98). We hope that this acknowledgment, along with the suggestion for\nfuture work to include more diverse populations, adequately addresses your\nconcern.\nComment #6. Results: Results / tables β and HR should indicate the specific\namount of change related to the coefficient of interest; “per 1-SD increment” as\nwell as the value of the 1-SD increment should be included in the tables.\nResponse: We sincerely appreciate your valuable feedback regarding the clarity\nof our results presentation. We have carefully reviewed the entire manuscript\nand supplementary materials to ensure that the effect sizes are clearly defined.\nWe have updated the captions of Table 1, Supplementary Tables S2–S9, and\nS20–S22 to explicitly indicate that the reported β and HR values are per 1-SD\nincrement of the exposures. The corresponding value of the 1-SD increment has\nbeen provided where applicable. We hope these revisions improve the\ninterpretability of our results and address your concern.\nComment #7. It should be clearly stated in tables and figures which datasets the\nresults represent.\nResponse: We apologize for this oversight. We have reviewed and updated the\nlegends of all tables and figures (Figs. 1–6, Tables 1–3, Supplementary Figs. S1–\nrepresent. These revisions ensure that readers can immediately identify the\nsource dataset for each result.\nFinally, we would like to express our deepest gratitude for your kind words and\nconstructive comments. We truly appreciate the time and effort you have\ndedicated to reviewing our work, and we hope that our revisions have\nsuccessfully addressed all your concerns. Your valuable feedback has been\ninstrumental in improving the clarity and rigor of our manuscript.\nComment #1. The manuscript titled \"Machine-learning Enhanced Plasma\nMetabolomics of Retinal Nerve Fiber Layer to Characterize Risks of Mortality and\nCardiometabolic Diseases: A Multi-Cohort Study\" investigates the relationship\nbetween retinal nerve fiber layer thickness (RNFLT) and cardiometabolic\ndiseases (CMDs) using advanced metabolomics and machine learning techniques.\nThe study identifies RNFLT metabolic fingerprints as biomarkers of CMD risk and\nintegrates them into predictive models, demonstrating improved risk prediction\nacross multiple CMD outcomes. These findings are validated in both the UK\nBiobank and an independent, ethnically distinct cohort, highlighting the potential\nfor RNFLT as a non-invasive tool for systemic health profiling and CMD\nmanagement.\nResponse: We would like to express our deepest gratitude for your thoughtful\ncomments and positive assessment of our work. Your insightful feedback has\nbeen invaluable in refining our manuscript, and we sincerely appreciate the time\nand effort you dedicated to reviewing our study. We have carefully examined all\nyour specific comments, reviewed relevant literature, conducted additional\nanalyses including AUPRC analysis that addresses data imbalance, and\nreclassification and decision analyses that simulate clinical scenarios, and made\nrevisions throughout the manuscript. Through comparisons with previous\nliterature and simulation analyses, we have further highlighted the clinical\nrelevance of our findings. We have also provided synthetic data for the validation\ncohort, along with the accompanying codes used for data analysis. We firmly\nbelieve that these improvements have strengthened the clarity, rigor, and overall\nquality of our work, and we hope that our revisions have adequately addressed\nyour concerns.\nComment #2. The study has several strengths. It leverages large, well-\ncharacterized cohorts with advanced imaging and metabolomics data, providing\nrobust validation across populations. The integration of machine learning\n7\nsignificantly enhances the predictive power of traditional models, showcasing the\nutility of RNFLT as a bridge between ophthalmology and cardiometabolic health.\nThe study also highlights key use cases, including personalized risk prediction,\nearly detection of CMDs, and potential applications in addressing health\ninequities. The framework paves the way for translational applications,\nparticularly in integrating retinal imaging into routine primary care for systemic\nhealth assessments.\nResponse: We sincerely appreciate your generous and encouraging comments.\nYour recognition of our study’s strengths and its potential translational impact is\ntruly gratifying. We are especially grateful for your acknowledgment of our\nefforts in leveraging large, well-characterized cohorts, integrating machine\nlearning, and highlighting the broader clinical implications of our findings. Your\nkind words reinforce our commitment to advancing the role of retinal imaging in\nsystemic health assessments, and we are deeply appreciative of your thoughtful\nreview.\nComment #3. Suggestions for Improvement: The study's biggest limitation is lack\nof reproducibility. Without data and source code, the value of this study is\nsignificantly diminished and evaluation of the methodology is impossible. It's\nunderstandable that UKBB data can only be accessed through the established\nprotocols. The validation data appears to be not accessible at all. At the very\nleast, a working source code and simulated data should be made available.\nResponse: We sincerely apologize for not providing the source code and data in\nour initial submission. To enhance the transparency and reproducibility of our\nstudy, we have now made all source code used in our analyses publicly available\nat a GitHub repository (github.com/zocskl/RNFLT-metabolic-states-predict-\nCMD-outcomes). In addition, as per your suggestion, we have generated and\nprovided simulated data for the validation cohort (GDES). The code and data are\nprovided in a .zip file with a readme.txt file along with the revised manuscript.\nwhich can only be obtained through established protocols. The primary dataset\nused in this study is available via the UKB Research Analysis Platform (RAP)\n(https://ukbiobank.dnanexus.com/). The UKB operates as a non-profit charity,\nallowing global access to approved researchers via a secure cloud-based platform\nenabled by DNAnexus. However, over the last few years, the UKB has\nprogressively restricted the download of raw data, requiring all analyses to be\nconducted within the UKB RAP platform. As a result, the original dataset cannot\nbe directly shared between researchers. However, any researcher can apply for\naccess by submitting a proposal outlining their study objectives, feasibility, and\npublic health relevance (http://www.ukbiobank.ac.uk). Given that the UKB data\nis publicly available under these terms and that we have provided the complete\nsource code, all results presented in our manuscript can be independently\nvalidated.\nRaw data from the GDES cannot be made publicly available due to the presence\nof sensitive information that could compromise participant privacy and violate\nlocal privacy laws. Although the data can be anonymized by removing apparently\nidentifiable features and perturbing them, it may still contain enough\ninformation to identify specific individuals once linked to other datasets.1 To\nbalance data accessibility with privacy protection, we have generated a synthetic\ndataset that preserves key statistical properties of the original dataset as per\nyour suggestion. This was achieved using the Distribution-Based Random\nGeneration approach, initially introduced by Rubin et al.2 and later refined by\nRaghunathan et al.3, which has been widely adopted for privacy-preserving data\nsharing.1,4,5 This approach constructs a synthetic dataset that preserves the\nstatistical distribution of the original data while ensuring individual participant\ninformation cannot be inferred. We have also provided the source code for\nanalysing the sample data and the expected output. To further facilitate\ntransparency, we have also provided the source code for analysing the synthetic\n9\ndataset, along with the expected output. Below, we briefly introduce the\nmethodology used to generate the data, as well as detailed instructions on how to\nrun the code provided.\nSpecifically, we assume that the original dataset 𝑋 follows an underlying\nprobability distribution 𝑃(𝑋) and generate a synthetic dataset 𝑋 by\n𝑠𝑦𝑛\nestimation its parameters. We first extracted the statistical characteristics of each\nvariable from the original dataset, including the mean vector 𝜇 and the\ncovariance matrix Σ for continuous variables, while also computing conditional\nprobabilities for categorical variables. To prevent the reconstruction of individual\nrecords, Gaussian noise was injected into both the mean vector 𝜇 and the\ncovariance matrix Σ to ensure differential privacy compliance. The privacy\nbudget parameters 𝜀 and 𝛿 were set to 1.0 and 1×10−5, respectively, while the\nglobal sensitivity of the estimated parameters is denoted by 𝛥 and 𝛥 ,\n𝜇 Σ\nrespectively. The standard deviation of the added noise 𝜎 is given by:\n𝛥√2𝑙𝑛(1.25/𝛿)\n𝜎 =\n𝜀\nwhere 𝛥 corresponds to either 𝛥 or 𝛥 . For each component 𝜇 in the mean\n𝜇 Σ 𝑖\nvector, independent Gaussian noise was added:\n𝜇′ = 𝜇 +𝜂 , 𝜂 ~ 𝑁(0,𝜎2)\n𝑖 𝑖 𝑖 𝑖 𝜇\nwhere the noise standard deviation for 𝜇 is given by 𝜎 =\n𝛥𝜇√2𝑙𝑛(1.25/𝜀) \n. For\n𝜇\n𝜀\neach element 𝜎 in the covariance matrix, noise was added as:\n𝑖𝑗\n𝜎′ = 𝜎 +𝜁 , 𝜁 ~ 𝑁(0,𝜎2)\n𝑖𝑗 𝑖𝑗 𝑖𝑗 𝑖𝑗 Σ\nwhere the noise standard deviation for Σ is 𝜎 =\n𝛥Σ√2𝑙𝑛(1.25/𝛿)\n. \nΣ\n𝜀\nFor continuous variables (e.g., physical measurements, laboratory tests, and\nmetabolite concentrations), a multivariate normal distribution was assumed:\n𝑋 ~ 𝑁(𝜇′,Σ′)\n𝑠𝑦𝑛\n10\nwhere 𝑋 represents the synthetic data matrix, and 𝑁 denotes the\n𝑠𝑦𝑛\nmultivariate normal distribution, which preserves both the marginal\ndistributions and inter-variable dependencies observed in the original dataset.\nFor variables exhibiting skewed distributions, we used a log-normal distribution\ninstead to better retain their statistical properties:\n𝑋 ~ 𝐿𝑜𝑔𝑛𝑜𝑟𝑚𝑎𝑙(𝜇′,𝜎′2)\n𝑠𝑦𝑛\nwhere 𝜇′ and 𝜎′2 represent the log-transformed mean and variance estimated\nfrom the original dataset. To ensure the realism of the generated variables, a\npost-processing step was applied to constrain all values within the observed\nranges of the original dataset. All numeric variables have been normalised to a\nrange of 0–1. For categorical variables (e.g., sex and disease status), synthetic\n synthetic\nvalues were generated by sampling from the observed frequency distribution in\nthe original data:\n𝑃(𝑌 = 𝑘) = 𝑛 /𝑛\n𝑘\nwhere 𝑛 represents the number of occurrences of category 𝑘 in the original\n𝑘\ndataset, and 𝑛 is the total sample size. For outcome labels, rather than directly\nassigning deterministic values, we employed a probabilistic approach to capture\nthe dependencies between predictor variables and outcomes. Specifically, we\nfirst trained a model to estimate the conditional probability of the outcome given\nthe predictors:\n𝑃(𝑌 = 1|𝑋) = 𝑓(𝑋)\nwhere 𝑓(𝑋) represents the estimated probability function derived from the\nmodel. Synthetic outcome values were then sampled from a Bernoulli\ndistribution:\n𝑌 ~ 𝐵𝑒𝑟𝑛𝑜𝑢𝑙𝑙𝑖(𝑃(𝑌 = 1|𝑋))\n𝑠𝑦𝑛\nwhere the synthetic label 𝑌 was generated probabilistically based on the\n𝑠𝑦𝑛\nlearned conditional probabilities, ensuring that the synthetic outcomes\nrealistically reflect their dependence on the predictor variables while\nincorporating inherent variability to avoid overfitting and determinism.\nrepeated the analyses from the original study using the synthetic datasets (as\ndetailed below). Our results show that RNFL metabolic states significantly\noutperform traditional predictors, including systolic blood pressure, HbA1c, BMI,\nand other biomarkers, in predicting cardiovascular disease. This observation\naligns with the findings observed in the original dataset. Further comparisons of\nmodel performance show that integrating RNFLT metabolic states into multiple\nbaseline clinical models consistently leads to significant improvements.\nSpecifically, the C-statistic for the Age&Sex model improved from 0.664 to 0.741,\nwith a gain of 0.077 after integrating RNFLT metabolic states; the FGCRS model\nimproved from 0.677 to 0.753, with a gain of 0.076; the Wan model improved\nfrom 0.674 to 0.750, with a gain of 0.076; the UKPDS model improved from 0.678\nto 0.754, with a gain of 0.076; and the DCS model improved from 0.680 to 0.754,\nwith a gain of 0.074. All improvements were statistically significant (all P < 0.05),\nreaffirming the findings from the original dataset.\nIt is important to highlight that the primary purpose of providing synthetic data\nis not to assess the absolute predictive performance of the models, but rather to\nvalidate the robustness of our analytical methodology and to compare the\nrelative performance of different models. Our analysis of the synthetic dataset\neffectively supports the findings derived from the original data. To further\nenhance transparency and reproducibility, we have made available our code for\nthe analyses in a GitHub repository (https://github.com/zocskl/RNFLT-\nmetabolic-states-predict-CMD-outcomes). In addition, we have included a\nJupyter notebook and a sample dataset as a .zip file in this submission. Below, we\nprovide step-by-step instructions for running the analysis script.\n1. Setup\nit contains the necessary library imports and function definitions:\n(1) Import essential libraries such as `pandas’, `numpy’, `sklearn’, and `scipy’ for\ndata processing and model training; and `matplotlib’ and `seaborn’ for\nvisualization.\n(2) Define custom functions for data pre-processing and model evaluation.\n2. Comparison of individual predictors\nThis section evaluates the predictive power of individual predictors in the\nsynthetic dataset:\n(1) Load the synthetic dataset using `pd.read_csv(path)’. The `path’ should be the\ndirectory on your local file system.\n(2) Define the outcome label `event = 'cvd'’.\n(3) Specify a list `convar’ of predictors of 13 variables, including age, sex, smoking\nstatus, diabetes duration, systolic blood pressure, diastolic blood pressure,\nHbA1c, total cholesterol, HDL cholesterol, eGFR, insulin use, BMI, and RNFLT\nmetabolic states.\n(4) Initialize an empty DataFrame to store the results.\n(5) Iterate through each predictor and perform the following steps:\n① Shuffle the dataset: `sample(frac = 1, random_state = 100)’ to ensure\nreproducibility.\n② Split the dataset into training and test sets in an 8:2 ratio.\n③ Extract data for each predictor, ensuring that RNFLT metabolic states\n(which involve multiple columns) are handled correctly.\n④ Convert the data into NumPy arrays for model training.\n⑤ Train a `RandomForestRegressor’ model using:\n`RandomForestRegressor(n_estimators = 200, max_depth = 10, oob_score\n= True, random_state = 100)’. This process sets 200 decision trees with a\nmaximum depth of 10, initializes the out-of-bag (OOB) error estimation\nand sets the random seed to 100.\n13\n⑥ Fit the model to the training set.\n⑦ Make predictions on the test set.\n⑧ Compute the C-statistic and 95% confidence intervals using the\n`delong_roc_ci’ function (note that the C-statistic is equivalent to the AUC\nin binary classification tasks).\n⑨ Store the results in the initialized DataFrame .\n⑩ Save predictions and true labels together using `np.save’ for further\nanalysis.\n(6) Export the final results as a .csv file.\n3. Comparison of model performance\nThis section evaluates how the integration of RNFLT metabolic states improves\nthe predictive performance of multiple clinical models:\n(1) Define the `prepare_model_data’ function, which specifies relevant clinical\npredictors for each of the models, including AgeSex, FGCRS, UKPDS, DCS, and\nWan.\n(2) Create a list of models and initialize an empty DataFrame to store the results.\n(3) For each model, perform the following steps:\n① Prepare the dataset with `prepare_model_data()’.\n② Shuffle the dataset with the same random seed and split it into training\nand test sets in an 8:2 ratio.\n③ Extract features and labels for both the baseline model (clinical\npredictors only) and the integrated model (clinical predictors + RNFLT\nmetabolic states).\n④ Convert the data into NumPy arrays.\n⑤ Train a `RandomForestRegressor’ model with the same parameters as\nabove: `RandomForestRegressor(n_estimators = 200, max_depth = 10,\noob_score = True, random_state = 100)’.\n⑥ Fit the model to the training set.\n⑦ Make predictions on the test set.\n⑧ Repeat the same process for each baseline clinical model.\n⑨ Statistically compare the performance of the baseline clinical model and\nthe model incorporating RNFLT metabolic states.\n⑩ Compute 95% confidence intervals for performance differences.\n⑪ Store the results in the initialized DataFrame.\n⑫ Save predictions and true labels together using `np.save’.\n(4) Export the final comparison results as a .csv file.\nTo ensure full reproducibility, we have fixed the random seed `random_state =\n100’ across all analyses. Consequently, our results can be fully replicated by\nrunning the provided Jupyter notebook with the same parameters and random\nseed. These steps further strengthen the transparency and reproducibility of our\nmanuscript.\nFurthermore, we are also open to facilitating access for interested researchers\nthrough a formal request process for applying the GDES data. This information\nhas been added to the Data Availability section under Methods, as per Nature\nCommunication’s policy. All requests for access to in-house data should be\naddressed to the corresponding authors, Dr. Wei Wang (email:\nwangwei@gzzoc.com), and will be processed in accordance with Zhongshan\nOphthalmic Centre policies. The GDES group will evaluate all requests based on\nthe purpose of the data request. A material-transfer and data-usage agreement\nwill be required between Zhongshan Ophthalmic Centre and the receiving\norganization, and the requesting organization must state the intended purpose of\nthe data transfer and provide assurances that the transferred data will only be\nused for non-commercial academic and educational purposes in compliance with\nZhongshan Ophthalmic Centre institutional policies.\nand ethical research conduct while enabling transparency and reproducibility of\nthe findings in our manuscript. We hope this addresses your concerns.\nReferences\n1. Goncalves, A. et al. Generation and evaluation of synthetic patient data. BMC\nMed. Res. Methodol. 20, 108 (2020).\n2. Rubin, D. B. Statistical disclosure limitation. J. Off. Stat. 9, 461-468 (1993).\n3. Raghunathan, T. E., Reiter, J. P. & Rubin, D. B. Multiple Imputation for\nStatistical Disclosure Limitation. J. Off. Stat. 19, 1 (2003).\n4. Murtaza, H. et al. Synthetic data generation: State of the art in health care\ndomain. Comput. Sci. Rev. 48, 100546 (2023).\n5. Murray, J. S. Multiple Imputation: A Review of Practical and Theoretical\nFindings. Stat. Sci. 33, 142-159, 18 (2018).\nComment #4. Given the class imbalance in CMD outcomes, including Area Under\nthe Precision-Recall Curve (AUPRC) as a complementary metric (and associated\nbaselines including a completely random/no-skill predictor) is critical.\nResponse: We fully agree with your suggestion regarding the importance of\nincluding AUPRC as a complementary evaluation metric, given the class\nimbalance in CMD outcomes. We have taken your suggestion and conducted\nAUPRC analyses for all CMD outcomes, including comparisons with a\nrandom/no-skill predictor. Our results consistently show an improvement in\nAUPRC after integrating RNFLT metabolic states across all outcomes, indicating\nthat our conclusions are robust and not biased by class imbalance.\nClass imbalance is an important consideration in our manuscript, with positive\nsample rates ranging from 0.018 for stroke to 0.074 for all-cause mortality. While\nthe C-statistic provides an overall measure of discrimination, it risks\noverestimating performance in imbalanced datasets as it gives equal weight to\n16\nfalse positives and false negatives. In contrast, the AUPRC is more informative in\nunbalanced datasets, as it reflects the model’s ability to identify true positives\nwhile accounting for the high rates of negative cases. For each CMD outcome, we\ncalculated the AUPRC for a random/no-skill predictor, which corresponds to the\nproportion of positives in the dataset (0.070 for T2D; 0.033 for MI; 0.031 for HF;\n0.018 for stroke; 0.074 for all-cause mortality; and 0.019 for cardiovascular\nmortality). These serve as the baseline values that any useful predictive model\nshould exceed.\nOur results show that all models, including clinical baseline models and those\nintegrated with RNFLT metabolic states, outperformed the no-skill predictor\nacross all outcomes (Supplementary Table S13, shown below for your easy\nreference). For T2D, the AUPRC of the model integrating RNFLT metabolic states\nwas 0.533, which is 7.6 times higher than the no-skill predictor. For\ncardiovascular mortality, the AUPRC for the integrated model was 0.070, which is\n3.7 times higher than the no-skill predictor. The gaps between the models\nintegrating RNFLT metabolic states and the no-skill predictor for other outcomes\nare similarly substantial, ranging from 2.5 times for stroke to 3.2 times for\nmyocardial infarction. Furthermore, all clinical baseline models also showed\nsuperior performance compared to the no-skill predictor, demonstrating their\nrobust ability to identify true positives in the minority class.\nWhen compared to the clinical baseline, integrating RNFLT metabolic states\nconsistently improved AUPRC across all CMD outcomes (Supplementary Table\nS13). For T2D, the integration of RNFLT metabolic states improved the AUPRC of\nthe clinical baseline model from 0.480 to 0.533 (ΔAUPRC = +0.053 [+11.0%]; P <\n0.001). For stroke, the AUPRC of the integrated model improved from 0.036 to\n0.044 (ΔAUPRC = +0.009 [+25.0%]; P < 0.001). The improvement was also\nsignificant for other outcomes, ranging from +6.2% for myocardial infarction to\n+21.3% for cardiovascular mortality (all P < 0.001). These improvements can be\n17\ninterpreted from a Bayesian perspective as an enhancement in the model’s ability\nto translate prior probabilities into posterior probabilities for identifying high-\nrisk individuals.1 For instance, for all-cause mortality, setting a threshold 𝜆 to\ndivide the population into high-risk and low-risk groups, we are interested in\n𝑃(𝑀𝑜𝑟𝑡𝑎𝑙𝑖𝑡𝑦 | 𝑝𝑟𝑒𝑑𝑖𝑐𝑡𝑖𝑜𝑛 > 𝜆).2 The posterior probability for the traditional\nFramingham model is 2.3 times higher than the prior (0.170/0.074), while the\nposterior probability for model integrating RNFLT metabolic states is 2.5 times\nhigher (0.185/0.074). This represents an 8.7% relative improvement [(2.5 −\n2.3)/2.3] in the model’s capacity to stratify risk at the same recall rate. The\nimprovement in AUPRC aligns with the improvement in C-statistics, reinforcing\nthe robustness of the model across different evaluation metrics.\nIn terms of clinical decision-making, the practical significance of these\nimprovements becomes evident. In resource-limited screening scenarios, models\nwith higher AUPRC can allocate resources more efficiently by reducing the\nproportion of false positives among those flagged for intervention. For instance,\nfor cardiovascular mortality, the integrated RNFLT metabolic states model\nachieved an AUPRC of 0.069, compared to 0.019 for the no-skill predictor. At the\nsame recall rate, the no-skill predictor would misclassify 9,814 false positives in\norder to identifying 186 true positives out of 10,000 samples (precision = 186 /\n[186 + FP] = 0.0186). In contrast, integrating RNFLT metabolic states would\navoid 7,312 of these false positives, leading to a 74.5% reduction in unnecessary\ninterventions. Furthermore, compared to the traditional Framingham model, the\nintegrated RNFLT metabolic states model would reduce false positives from\n3,077 to 2,502 cases (ΔFP = 575), thereby reducing unnecessary interventions by\n18.7%. These findings align with and complement our decision curve analysis,\nwhich quantifies the net benefit for clinical decision-making, while AUPRC\nreflects the model’s efficiency in optimizing resource allocation by improving the\nidentification of true positive cases.\nsignificant class imbalance, integrating RNFLT metabolic states significantly\nimproves the prediction of all CMD outcomes studied. This consistency of\nimprovement observed in both AUPRC, C-statistic and area under the decision\ncurve further supports the clinical relevance of integrating RNFLT metabolic\nstates into CMD prediction. We hope this adequately addresses your concerns.\nReferences\n1. Comparison of Alternative Approaches to Inference. Bayesian Approaches to\nClinical Trials and Health‐Care Evaluation; 2003. pp. 121-137.\n2. Saito, T. & Rehmsmeier, M. The precision-recall plot is more informative than\nthe ROC plot when evaluating binary classifiers on imbalanced datasets. Plos\nOne. 10, e0118432 (2015).\nImprovement in the AUPRC of integrating RNFLT metabolic states for CMD outcome prediction beyond established predictors in the UKB\npopulation-II.\nAUPRC\nOutcomes Performance gaps ‡ P §\nFDR\nNo-skill predictor Integrated models * Basic models †\nType 2 diabetes 0.070 0.533 (0.503–0.562) 0.480 (0.448–0.512) +0.053 <0.001\nMyocardial infarction 0.033 0.106 (0.091–0.123) 0.100 (0.085–0.115) +0.006 <0.001\nHeart failure 0.031 0.094 (0.080–0.111) 0.088 (0.075–0.102) +0.007 <0.001\nStroke 0.018 0.044 (0.036–0.056) 0.036 (0.029–0.043) +0.009 <0.001\nAll-cause mortality 0.074 0.185 (0.166–0.203) 0.170 (0.154–0.185) +0.015 <0.001\nCardiovascular mortality 0.019 0.069 (0.055–0.087) 0.057 (0.046–0.070) +0.012 <0.001\n* Models that integrated RNFLT metabolic states.\n† Basic models were based on Framingham General Cardiovascular Risk Score (FGCRS).\n‡ Performance gaps between integrated models and basic models.\n§ P values were calculated through two-sided non-parametric bootstrap tests with 1,000 iterations followed by controlling FDR for\nmultiple testing.\nAUPRC = area under the precision-recall curve; CMD = cardiometabolic disease; UKB = UK Biobank; FDR = false discovery rate.\npower (compared to baseline) be interpreted from an application/clinical\nstandpoint? Are these improvements clinically relevant? A simulation study of\nbenefits from such an improvement would be very valuable.\nResponse: We greatly appreciate your insightful comments regarding the clinical\nsignificance of our findings, particularly your observation that the improvement\nin the C-statistic was generally less than 0.05 (average 0.031). To better interpret\nthe clinical relevance of our study"}, {"round": 2, "reviewer_comments": {"reviewer_1": "The authors have extensively revised the original submission and have adequately addressed the reviewer comments.\nThe numerous supplementary tables provide critical analyses that strengthen their conclusions.", "reviewer_2": "The authors have done a reasonable job addressing my comments. However, I am concerned that the new Supplementary Table S13 demonstrates very limited improvements compared to baseline. In fact, in 5 out of 6 diseases listed the baseline performance is within the confidence interval of the proposed models. Even if \"significant\" I am not sure how meaningful this is in the real world.", "reviewer_3": "This manuscript details an analysis in UKB and the GDES cohort to evaluate association between metabolites (NMR\nNightingale in UKB; more comprehensive MS metabolomics in GDES) and retinal nerve fiber layer thickness, and to\ndetermine if metabolites mediate the association between RNFL with incident cardiometabolic events. They find HDL\nparameters (more granular than composite HDL, as measured by NMR) are independently and incrementally associated\nwith RNFL; mediate the association between RNFL with incident cardiometabolic diseases; and are themselves associated\nwith incident CMD events.\nThe authors attempted to address this reviewer’s concerns but impact and interpretation concerns remain. Specifically:\n(1) I appreciate the careful inclusion of potential confounders across all the models, and that they continue to demonstrate\nstatistically significant increases in c-statistic, newly added AUPRC, and in the mediation analyses. These appear to be well\ndone, however, these results do not appear to be included as primary analyses but instead are buried as supplemental\ntables as sensitivity analyses. This will make it very confusing for the reader and as such, these fully adjusted models should\nbe the primary analyses reported throughout;\n(2) I continue to disagree with the interpretation of the robustness of the increase in c-statistics (and the newly included\nAUPRC)…while it may be true that they are comparable to other papers reporting new findings, that does not mean that they\nhave clinical utility. The main increase is really around DM and not particularly robust for hard incident CVD outcomes. The\naddition of the NRI is, however, a major strength of the paper, but again highlight the results primarily around DM and not\nincident CVD. Further, the clinical models for incident DM discrimination should be compared to DM prediction models (not\nCVD prediction);\n(3) Relatedly, the authors argue that the paper is about metabolic mechanisms linking RNFL and CMD but most of the paper\nis devoted to assessing clinical utility, and not really digging into these mechanisms (in fact the abstract doesn’t even\nmention that the metabolite states are reporting primarily on HDL subclasses)\n(4) Thank you for clarifying the difference between this manuscript and your prior manuscript published in the Br J. Ophtho,\nwith the primary difference between using OCT techniques in this paper. Having “split” the ocular imaging analyses into\nseparate papers also reduces impact of this paper, and further, there is no comparison of the two papers to provide context to\nthe reader."}, "author_response": "Responds to the reviewers’ comments\nWe would like to express our sincere gratitude for all your invaluable comments\nand constructive suggestions. We are truly honoured to have the opportunity to\naddress all your concerns and incorporate your feedback into our manuscript.\nWe firmly believe that the valuable input you offered has significantly enhanced\nthe overall quality of our work. In the following section, we provide a\ncomprehensive point-by-point response to each specific comment, which is\nhighlighted in BLUE for your easy reference.\nResponds to Reviewer #1\nComment #1. The objective of this well designed and clearly written manuscript\nis to elucidate relationships between metabolites associated with RNFLT and\ncardiovascular metabolic diseases (CMD). The manuscript provides a\ncomprehensive assessment of both metabolites associated with macula RNFLT\nand CMD in distinct ethnic populations of varying genetic susceptibility. Their\nresults suggest that there is a relationship between RNFLT and CMD metabolic\nfingerprints and that including RNFLT associated metabolic information into\nexisting CMD models significantly improved their discriminatory power of the\nmodels. One concern is that the metabolic fingerprints were identified based on\ntheir association with RNFLT – which is affected by many ocular diseases;\nadjustment for these ocular conditions (glaucoma for example) is encouraged\nand mention of this issue in the discussion section of the manuscript. Although\nthe authors used external datasets, the population was generally of White and\nChinese ethnicity.\nResponse: We sincerely appreciate the time and effort you have dedicated to\nreviewing our manuscript and for your thoughtful and constructive feedback. We\nare also truly grateful for your positive assessment of our work. Your insightful\ncomments have been invaluable in refining our study. We have carefully\nexamined all your specific comments, clarified the population eligibility criteria\nand the OCT scanning methodology, refined the abstract, improved the legends\n1\nfor tables and figures, and revised the manuscript accordingly. We hope that\nthese modifications have strengthened the manuscript and addressed your\nconcerns effectively.\nWe totally agree with your concern about the potential influence of ocular\nconditions on RNFLT and the importance of accounting for these confounders. To\nminimize their impact, we implemented a predefined procedure to exclude\nindividuals with glaucoma, retinal disease (including retinal detachments and\nbreaks, retinal vascular occlusions, and other structural abnormalities),\nneurodegenerative disease, and other exclusion criteria (see Response to\nComment #3), given their potential to confound RNFLT measurements. This\napproach ensured that our analysis was conducted in a population free from\nknown retinal pathology, thereby allowing us to identify RNFLT-associated\nbiomarkers more reliably. The limited representation of Black individuals has\nalso been acknowledged in the study limitations, in accordance with your\nsuggestions (see Response to Comment #5). A detailed point-by-point response\nto each of your specific suggestions is provided below. We believe these revisions\nimprove the clarity, accuracy, and overall impact of our manuscript.\nComment #2. Abstract: This concluding sentence in the abstract may be\noverstating the results; the authors should consider revising a bit. “Our\nintegrated approach, validated across cohorts of diverse ethnic backgrounds and\ngenetic risks, highlights the possibility that RNFLT metabolic fingerprints as a\nshare basis underlying retinal–cardiometabolic connections and as early\nindicators that advance equitable cardiometabolic disease management,\npositioning the retina as a valuable window into cardiometabolic health.”\nResponse: We sincerely apologize for any overstatement caused by our initial\nphrasing in the Abstract conclusion. In the revised manuscript, we have removed\nthe extended claim that \"the retina is positioned as a valuable window into\ncardiometabolic health\" and instead emphasize that further studies in broader\n2\npopulations are required to validate our findings (Page 4, Line 98). In addition,\nwe have reworded the whole sentence to reflect a more cautious interpretation\nof our results. We believe this revised phrasing more accurately conveys the\ntentative nature of our conclusions while highlighting the significance of future\nresearch to validate these findings in diverse cohorts.\nComment #3. Methods: RNFLT metabolic states are based on associations with\nRNFLT. However numerous eye diseases can also affect RNFLT. Were these eye\ndiseases considered in the models?\nResponse: We sincerely appreciate your insightful comment regarding the\npotential influence of ocular conditions on RNFLT. We fully agree that factors\nsuch as glaucoma and other retinal disorders may affect RNFLT and should be\naccounted for. To address this concern, we implemented a predefined procedure\nto exclude individuals with known ocular conditions that could confound the\nRNFLT measurements.\nSpecifically, when identifying RNFLT-associated biomarkers, we excluded\nparticipants without eligible metabolomics profiling (391,763 participants) and\nOCT scanning (96,068 participants), as well as those with missing thickness\nvalues (294 right eyes and 407 left eyes), low signal strength (1,272 right eyes\nand 1,067 left eyes), poor centration or segmentation (4,291 right eyes and 4,309\nleft eyes), high refractive error (spherical equivalent > 6 or < −6 dioptres; 341\nright eyes and 404 left eyes), visual impairment (> 0.1 logMAR; 2,054 right eyes\nand 2,014 left eyes), or abnormal intraocular pressure (IOP ≥ 22 or ≤ 5 mmHg;\n701 right eyes and 752 left eyes). Furthermore, patients with glaucoma (82\nparticipants), retinal disease (including retinal detachments and breaks, retinal\nvascular occlusions, and others) (162 participants), and neurodegenerative\ndisease (39 participants) were excluded, given their potential to confound RNFLT\nmeasurements. This approach ensured that our analysis was conducted in a\n3\npopulation free from known retinal pathology, thereby allowing us to identify\nRNFLT-associated biomarkers more reliably.\nOriginally, these detailed eligibility criteria were provided in the Supplementary\nMaterial. However, we recognize that it is crucial for readers to have immediate\naccess to this information in the main text. Accordingly, we have revised the\nMethods section (Page 16, Line 459) to emphasize the exclusion of prevalent\nconditions that may confound RNFLT measurements in the population used for\nidentifying RNFLT-associated biomarkers, rather than simply referring to the\ncorresponding section in the Supplementary Material. We hope this clarification\naddresses your concern.\nComment #4. More detail is needed in describing the OCT methods used: a. For\nthe UKB dataset, RNFLT was measured from macula volume scans centered on\nthe fovea. Which specific scans were acquired in the independent cohort and how\nwas RNFLT measured?\nResponse: We apologize for any lack of clarity regarding the OCT methods used\nin the independent cohort (GDES). In this population, all participants underwent\nretinal OCT scanning using a swept-source OCT (SS-OCT; DRI OCT Triton, Topcon,\nJapan) to obtain structural images of the macula. Each scan was performed with\nan internal fixation system, and the fixation was monitored by the instrument’s\nbuilt-in fundus camera. The retina was imaged using a 3D Macula Cube 7×7 mm\nscan mode centred on the fovea, with a scan density of 512 A-scans × 512 B-\nscans. The built-in SS-OCT software (IMAGEnet 6, Version 1.22) automatically\nsegmented the retinal layers and measured the RNFLT as the distance from the\ninternal limiting membrane to the nerve fibre layer–ganglion cell layer junction.\nWhere segmentation errors were identified, manual adjustments were\nperformed. This information has been added to the Methods section (Page 21,\nLine 588) and further elaborated in the Supplementary Methods . We hope this\nclarification addresses your concern.\ntheir analysis. The datasets include mainly Chinese and White individuals. The\nlack of representation of Black individuals should be mentioned in the discussion\nas a limitation to this study.\nResponse: We sincerely appreciate your generous comment and your\nrecognition of our efforts to validate our findings across multiple ethnic groups.\nWe agree that the underrepresentation of Black individuals is a limitation of our\nstudy. Accordingly, we have now explicitly mentioned this limitation in both the\nDiscussion section (Page 15, Line 420) and the Abstract conclusion (Page 4,\nLine 98). We hope that this acknowledgment, along with the suggestion for\nfuture work to include more diverse populations, adequately addresses your\nconcern.\nComment #6. Results: Results / tables β and HR should indicate the specific\namount of change related to the coefficient of interest; “per 1-SD increment” as\nwell as the value of the 1-SD increment should be included in the tables.\nResponse: We sincerely appreciate your valuable feedback regarding the clarity\nof our results presentation. We have carefully reviewed the entire manuscript\nand supplementary materials to ensure that the effect sizes are clearly defined.\nWe have updated the captions of Table 1, Supplementary Tables S2–S9, and\nS20–S22 to explicitly indicate that the reported β and HR values are per 1-SD\nincrement of the exposures. The corresponding value of the 1-SD increment has\nbeen provided where applicable. We hope these revisions improve the\ninterpretability of our results and address your concern.\nComment #7. It should be clearly stated in tables and figures which datasets the\nresults represent.\nResponse: We apologize for this oversight. We have reviewed and updated the\nlegends of all tables and figures (Figs. 1–6, Tables 1–3, Supplementary Figs. S1–\nrepresent. These revisions ensure that readers can immediately identify the\nsource dataset for each result.\nFinally, we would like to express our deepest gratitude for your kind words and\nconstructive comments. We truly appreciate the time and effort you have\ndedicated to reviewing our work, and we hope that our revisions have\nsuccessfully addressed all your concerns. Your valuable feedback has been\ninstrumental in improving the clarity and rigor of our manuscript.\nComment #1. The manuscript titled \"Machine-learning Enhanced Plasma\nMetabolomics of Retinal Nerve Fiber Layer to Characterize Risks of Mortality and\nCardiometabolic Diseases: A Multi-Cohort Study\" investigates the relationship\nbetween retinal nerve fiber layer thickness (RNFLT) and cardiometabolic\ndiseases (CMDs) using advanced metabolomics and machine learning techniques.\nThe study identifies RNFLT metabolic fingerprints as biomarkers of CMD risk and\nintegrates them into predictive models, demonstrating improved risk prediction\nacross multiple CMD outcomes. These findings are validated in both the UK\nBiobank and an independent, ethnically distinct cohort, highlighting the potential\nfor RNFLT as a non-invasive tool for systemic health profiling and CMD\nmanagement.\nResponse: We would like to express our deepest gratitude for your thoughtful\ncomments and positive assessment of our work. Your insightful feedback has\nbeen invaluable in refining our manuscript, and we sincerely appreciate the time\nand effort you dedicated to reviewing our study. We have carefully examined all\nyour specific comments, reviewed relevant literature, conducted additional\nanalyses including AUPRC analysis that addresses data imbalance, and\nreclassification and decision analyses that simulate clinical scenarios, and made\nrevisions throughout the manuscript. Through comparisons with previous\nliterature and simulation analyses, we have further highlighted the clinical\nrelevance of our findings. We have also provided synthetic data for the validation\ncohort, along with the accompanying codes used for data analysis. We firmly\nbelieve that these improvements have strengthened the clarity, rigor, and overall\nquality of our work, and we hope that our revisions have adequately addressed\nyour concerns.\nComment #2. The study has several strengths. It leverages large, well-\ncharacterized cohorts with advanced imaging and metabolomics data, providing\nrobust validation across populations. The integration of machine learning\n7\nsignificantly enhances the predictive power of traditional models, showcasing the\nutility of RNFLT as a bridge between ophthalmology and cardiometabolic health.\nThe study also highlights key use cases, including personalized risk prediction,\nearly detection of CMDs, and potential applications in addressing health\ninequities. The framework paves the way for translational applications,\nparticularly in integrating retinal imaging into routine primary care for systemic\nhealth assessments.\nResponse: We sincerely appreciate your generous and encouraging comments.\nYour recognition of our study’s strengths and its potential translational impact is\ntruly gratifying. We are especially grateful for your acknowledgment of our\nefforts in leveraging large, well-characterized cohorts, integrating machine\nlearning, and highlighting the broader clinical implications of our findings. Your\nkind words reinforce our commitment to advancing the role of retinal imaging in\nsystemic health assessments, and we are deeply appreciative of your thoughtful\nreview.\nComment #3. Suggestions for Improvement: The study's biggest limitation is lack\nof reproducibility. Without data and source code, the value of this study is\nsignificantly diminished and evaluation of the methodology is impossible. It's\nunderstandable that UKBB data can only be accessed through the established\nprotocols. The validation data appears to be not accessible at all. At the very\nleast, a working source code and simulated data should be made available.\nResponse: We sincerely apologize for not providing the source code and data in\nour initial submission. To enhance the transparency and reproducibility of our\nstudy, we have now made all source code used in our analyses publicly available\nat a GitHub repository (github.com/zocskl/RNFLT-metabolic-states-predict-\nCMD-outcomes). In addition, as per your suggestion, we have generated and\nprovided simulated data for the validation cohort (GDES). The code and data are\nprovided in a .zip file with a readme.txt file along with the revised manuscript.\nwhich can only be obtained through established protocols. The primary dataset\nused in this study is available via the UKB Research Analysis Platform (RAP)\n(https://ukbiobank.dnanexus.com/). The UKB operates as a non-profit charity,\nallowing global access to approved researchers via a secure cloud-based platform\nenabled by DNAnexus. However, over the last few years, the UKB has\nprogressively restricted the download of raw data, requiring all analyses to be\nconducted within the UKB RAP platform. As a result, the original dataset cannot\nbe directly shared between researchers. However, any researcher can apply for\naccess by submitting a proposal outlining their study objectives, feasibility, and\npublic health relevance (http://www.ukbiobank.ac.uk). Given that the UKB data\nis publicly available under these terms and that we have provided the complete\nsource code, all results presented in our manuscript can be independently\nvalidated.\nRaw data from the GDES cannot be made publicly available due to the presence\nof sensitive information that could compromise participant privacy and violate\nlocal privacy laws. Although the data can be anonymized by removing apparently\nidentifiable features and perturbing them, it may still contain enough\ninformation to identify specific individuals once linked to other datasets.1 To\nbalance data accessibility with privacy protection, we have generated a synthetic\ndataset that preserves key statistical properties of the original dataset as per\nyour suggestion. This was achieved using the Distribution-Based Random\nGeneration approach, initially introduced by Rubin et al.2 and later refined by\nRaghunathan et al.3, which has been widely adopted for privacy-preserving data\nsharing.1,4,5 This approach constructs a synthetic dataset that preserves the\nstatistical distribution of the original data while ensuring individual participant\ninformation cannot be inferred. We have also provided the source code for\nanalysing the sample data and the expected output. To further facilitate\ntransparency, we have also provided the source code for analysing the synthetic\n9\ndataset, along with the expected output. Below, we briefly introduce the\nmethodology used to generate the data, as well as detailed instructions on how to\nrun the code provided.\nSpecifically, we assume that the original dataset 𝑋 follows an underlying\nprobability distribution 𝑃(𝑋) and generate a synthetic dataset 𝑋 by\n𝑠𝑦𝑛\nestimation its parameters. We first extracted the statistical characteristics of each\nvariable from the original dataset, including the mean vector 𝜇 and the\ncovariance matrix Σ for continuous variables, while also computing conditional\nprobabilities for categorical variables. To prevent the reconstruction of individual\nrecords, Gaussian noise was injected into both the mean vector 𝜇 and the\ncovariance matrix Σ to ensure differential privacy compliance. The privacy\nbudget parameters 𝜀 and 𝛿 were set to 1.0 and 1×10−5, respectively, while the\nglobal sensitivity of the estimated parameters is denoted by 𝛥 and 𝛥 ,\n𝜇 Σ\nrespectively. The standard deviation of the added noise 𝜎 is given by:\n𝛥√2𝑙𝑛(1.25/𝛿)\n𝜎 =\n𝜀\nwhere 𝛥 corresponds to either 𝛥 or 𝛥 . For each component 𝜇 in the mean\n𝜇 Σ 𝑖\nvector, independent Gaussian noise was added:\n𝜇′ = 𝜇 +𝜂 , 𝜂 ~ 𝑁(0,𝜎2)\n𝑖 𝑖 𝑖 𝑖 𝜇\nwhere the noise standard deviation for 𝜇 is given by 𝜎 =\n𝛥𝜇√2𝑙𝑛(1.25/𝜀) \n. For\n𝜇\n𝜀\neach element 𝜎 in the covariance matrix, noise was added as:\n𝑖𝑗\n𝜎′ = 𝜎 +𝜁 , 𝜁 ~ 𝑁(0,𝜎2)\n𝑖𝑗 𝑖𝑗 𝑖𝑗 𝑖𝑗 Σ\nwhere the noise standard deviation for Σ is 𝜎 =\n𝛥Σ√2𝑙𝑛(1.25/𝛿)\n. \nΣ\n𝜀\nFor continuous variables (e.g., physical measurements, laboratory tests, and\nmetabolite concentrations), a multivariate normal distribution was assumed:\n𝑋 ~ 𝑁(𝜇′,Σ′)\n𝑠𝑦𝑛\n10\nwhere 𝑋 represents the synthetic data matrix, and 𝑁 denotes the\n𝑠𝑦𝑛\nmultivariate normal distribution, which preserves both the marginal\ndistributions and inter-variable dependencies observed in the original dataset.\nFor variables exhibiting skewed distributions, we used a log-normal distribution\ninstead to better retain their statistical properties:\n𝑋 ~ 𝐿𝑜𝑔𝑛𝑜𝑟𝑚𝑎𝑙(𝜇′,𝜎′2)\n𝑠𝑦𝑛\nwhere 𝜇′ and 𝜎′2 represent the log-transformed mean and variance estimated\nfrom the original dataset. To ensure the realism of the generated variables, a\npost-processing step was applied to constrain all values within the observed\nranges of the original dataset. All numeric variables have been normalised to a\nrange of 0–1. For categorical variables (e.g., sex and disease status),n synthetic\nvalues were generated by sampling from the observed frequency distribution in\nthe original data:\n𝑃(𝑌 = 𝑘) = 𝑛 /𝑛\n𝑘\nwhere 𝑛 represents the number of occurrences of category 𝑘 in the original\n𝑘\ndataset, and 𝑛 is the total sample size. For outcome labels, rather than directly\nassigning deterministic values, we employed a probabilistic approach to capture\nthe dependencies between predictor variables and outcomes. Specifically, we\nfirst trained a model to estimate the conditional probability of the outcome given\nthe predictors:\n𝑃(𝑌 = 1|𝑋) = 𝑓(𝑋)\nwhere 𝑓(𝑋) represents the estimated probability function derived from the\nmodel. Synthetic outcome values were then sampled from a Bernoulli\ndistribution:\n𝑌 ~ 𝐵𝑒𝑟𝑛𝑜𝑢𝑙𝑙𝑖(𝑃(𝑌 = 1|𝑋))\n𝑠𝑦𝑛\nwhere the synthetic label 𝑌 was generated probabilistically based on the\n𝑠𝑦𝑛\nlearned conditional probabilities, ensuring that the synthetic outcomes\nrealistically reflect their dependence on the predictor variables while\nincorporating inherent variability to avoid overfitting and determinism.\nrepeated the analyses from the original study using the synthetic datasets (as\ndetailed below). Our results show that RNFL metabolic states significantly\noutperform traditional predictors, including systolic blood pressure, HbA1c, BMI,\nand other biomarkers, in predicting cardiovascular disease. This observation\naligns with the findings observed in the original dataset. Further comparisons of\nmodel performance show that integrating RNFLT metabolic states into multiple\nbaseline clinical models consistently leads to significant improvements.\nSpecifically, the C-statistic for the Age&Sex model improved from 0.664 to 0.741,\nwith a gain of 0.077 after integrating RNFLT metabolic states; the FGCRS model\nimproved from 0.677 to 0.753, with a gain of 0.076; the Wan model improved\nfrom 0.674 to 0.750, with a gain of 0.076; the UKPDS model improved from 0.678\nto 0.754, with a gain of 0.076; and the DCS model improved from 0.680 to 0.754,\nwith a gain of 0.074. All improvements were statistically significant (all P < 0.05),\nreaffirming the findings from the original dataset.\nIt is important to highlight that the primary purpose of providing synthetic data\nis not to assess the absolute predictive performance of the models, but rather to\nvalidate the robustness of our analytical methodology and to compare the\nrelative performance of different models. Our analysis of the synthetic dataset\neffectively supports the findings derived from the original data. To further\nenhance transparency and reproducibility, we have made available our code for\nthe analyses in a GitHub repository (https://github.com/zocskl/RNFLT-\nmetabolic-states-predict-CMD-outcomes). In addition, we have included a\nJupyter notebook and a sample dataset as a .zip file in this submission. Below, we\nprovide step-by-step instructions for running the analysis script.\n1. Setup\nit contains the necessary library imports and function definitions:\n(1) Import essential libraries such as `pandas’, `numpy’, `sklearn’, and `scipy’ for\ndata processing and model training; and `matplotlib’ and `seaborn’ for\nvisualization.\n(2) Define custom functions for data pre-processing and model evaluation.\n2. Comparison of individual predictors\nThis section evaluates the predictive power of individual predictors in the\nsynthetic dataset:\n(1) Load the synthetic dataset using `pd.read_csv(path)’. The `path’ should be the\ndirectory on your local file system.\n(2) Define the outcome label `event = 'cvd'’.\n(3) Specify a list `convar’ of predictors of 13 variables, including age, sex, smoking\nstatus, diabetes duration, systolic blood pressure, diastolic blood pressure,\nHbA1c, total cholesterol, HDL cholesterol, eGFR, insulin use, BMI, and RNFLT\nmetabolic states.\n(4) Initialize an empty DataFrame to store the results.\n(5) Iterate through each predictor and perform the following steps:\n① Shuffle the dataset: `sample(frac = 1, random_state = 100)’ to ensure\nreproducibility.\n② Split the dataset into training and test sets in an 8:2 ratio.\n③ Extract data for each predictor, ensuring that RNFLT metabolic states\n(which involve multiple columns) are handled correctly.\n④ Convert the data into NumPy arrays for model training.\n⑤ Train a `RandomForestRegressor’ model using:\n`RandomForestRegressor(n_estimators = 200, max_depth = 10, oob_score\n= True, random_state = 100)’. This process sets 200 decision trees with a\nmaximum depth of 10, initializes the out-of-bag (OOB) error estimation\nand sets the random seed to 100.\n13\n⑥ Fit the model to the training set.\n⑦ Make predictions on the test set.\n⑧ Compute the C-statistic and 95% confidence intervals using the\n`delong_roc_ci’ function (note that the C-statistic is equivalent to the AUC\nin binary classification tasks).\n⑨ Store the results in the initialized DataFrame .\n⑩ Save predictions and true labels together using `np.save’ for further\nanalysis.\n(6) Export the final results as a .csv file.\n3. Comparison of model performance\nThis section evaluates how the integration of RNFLT metabolic states improves\nthe predictive performance of multiple clinical models:\n(1) Define the `prepare_model_data’ function, which specifies relevant clinical\npredictors for each of the models, including AgeSex, FGCRS, UKPDS, DCS, and\nWan.\n(2) Create a list of models and initialize an empty DataFrame to store the results.\n(3) For each model, perform the following steps:\n① Prepare the dataset with `prepare_model_data()’.\n② Shuffle the dataset with the same random seed and split it into training\nand test sets in an 8:2 ratio.\n③ Extract features and labels for both the baseline model (clinical\npredictors only) and the integrated model (clinical predictors + RNFLT\nmetabolic states).\n④ Convert the data into NumPy arrays.\n⑤ Train a `RandomForestRegressor’ model with the same parameters as\nabove: `RandomForestRegressor(n_estimators = 200, max_depth = 10,\noob_score = True, random_state = 100)’.\n⑥ Fit the model to the training set.\n⑦ Make predictions on the test set.\n⑧ Repeat the same process for each baseline clinical model.\n⑨ Statistically compare the performance of the baseline clinical model and\nthe model incorporating RNFLT metabolic states.\n⑩ Compute 95% confidence intervals for performance differences.\n⑪ Store the results in the initialized DataFrame.\n⑫ Save predictions and true labels together using `np.save’.\n(4) Export the final comparison results as a .csv file.\nTo ensure full reproducibility, we have fixed the random seed `random_state =\n100’ across all analyses. Consequently, our results can be fully replicated by\nrunning the provided Jupyter notebook with the same parameters and random\nseed. These steps further strengthen the transparency and reproducibility of our\nmanuscript.\nFurthermore, we are also open to facilitating access for interested researchers\nthrough a formal request process for applying the GDES data. This information\nhas been added to the Data Availability section under Methods, as per Nature\nCommunication’s policy. All requests for access to in-house data should be\naddressed to the corresponding authors, Dr. Wei Wang (email:\nwangwei@gzzoc.com), and will be processed in accordance with Zhongshan\nOphthalmic Centre policies. The GDES group will evaluate all requests based on\nthe purpose of the data request. A material-transfer and data-usage agreement\nwill be required between Zhongshan Ophthalmic Centre and the receiving\norganization, and the requesting organization must state the intended purpose of\nthe data transfer and provide assurances that the transferred data will only be\nused for non-commercial academic and educational purposes in compliance with\nZhongshan Ophthalmic Centre institutional policies.\nand ethical research conduct while enabling transparency and reproducibility of\nthe findings in our manuscript. We hope this addresses your concerns.\nReferences\n1. Goncalves, A. et al. Generation and evaluation of synthetic patient data. BMC\nMed. Res. Methodol. 20, 108 (2020).\n2. Rubin, D. B. Statistical disclosure limitation. J. Off. Stat. 9, 461-468 (1993).\n3. Raghunathan, T. E., Reiter, J. P. & Rubin, D. B. Multiple Imputation for\nStatistical Disclosure Limitation. J. Off. Stat. 19, 1 (2003).\n4. Murtaza, H. et al. Synthetic data generation: State of the art in health care\ndomain. Comput. Sci. Rev. 48, 100546 (2023).\n5. Murray, J. S. Multiple Imputation: A Review of Practical and Theoretical\nFindings. Stat. Sci. 33, 142-159, 18 (2018).\nComment #4. Given the class imbalance in CMD outcomes, including Area Under\nthe Precision-Recall Curve (AUPRC) as a complementary metric (and associated\nbaselines including a completely random/no-skill predictor) is critical.\nResponse: We fully agree with your suggestion regarding the importance of\nincluding AUPRC as a complementary evaluation metric, given the class\nimbalance in CMD outcomes. We have taken your suggestion and conducted\nAUPRC analyses for all CMD outcomes, including comparisons with a\nrandom/no-skill predictor. Our results consistently show an improvement in\nAUPRC after integrating RNFLT metabolic states across all outcomes, indicating\nthat our conclusions are robust and not biased by class imbalance.\nClass imbalance is an important consideration in our manuscript, with positive\nsample rates ranging from 0.018 for stroke to 0.074 for all-cause mortality. While\nthe C-statistic provides an overall measure of discrimination, it risks\noverestimating performance in imbalanced datasets as it gives equal weight to\n16\nfalse positives and false negatives. In contrast, the AUPRC is more informative in\nunbalanced datasets, as it reflects the model’s ability to identify true positives\nwhile accounting for the high rates of negative cases. For each CMD outcome, we\ncalculated the AUPRC for a random/no-skill predictor, which corresponds to the\nproportion of positives in the dataset (0.070 for T2D; 0.033 for MI; 0.031 for HF;\n0.018 for stroke; 0.074 for all-cause mortality; and 0.019 for cardiovascular\nmortality). These serve as the baseline values that any useful predictive model\nshould exceed.\nOur results show that all models, including clinical baseline models and those\nintegrated with RNFLT metabolic states, outperformed the no-skill predictor\nacross all outcomes (Supplementary Table S13, shown below for your easy\nreference). For T2D, the AUPRC of the model integrating RNFLT metabolic states\nwas 0.533, which is 7.6 times higher than the no-skill predictor. For\ncardiovascular mortality, the AUPRC for the integrated model was 0.070, which is\n3.7 times higher than the no-skill predictor. The gaps between the models\nintegrating RNFLT metabolic states and the no-skill predictor for other outcomes\nare similarly substantial, ranging from 2.5 times for stroke to 3.2 times for\nmyocardial infarction. Furthermore, all clinical baseline models also showed\nsuperior performance compared to the no-skill predictor, demonstrating their\nrobust ability to identify true positives in the minority class.\nWhen compared to the clinical baseline, integrating RNFLT metabolic states\nconsistently improved AUPRC across all CMD outcomes (Supplementary Table\nS13). For T2D, the integration of RNFLT metabolic states improved the AUPRC of\nthe clinical baseline model from 0.480 to 0.533 (ΔAUPRC = +0.053 [+11.0%]; P <\n0.001). For stroke, the AUPRC of the integrated model improved from 0.036 to\n0.044 (ΔAUPRC = +0.009 [+25.0%]; P < 0.001). The improvement was also\nsignificant for other outcomes, ranging from +6.2% for myocardial infarction to\n+21.3% for cardiovascular mortality (all P < 0.001). These improvements can be\n17\ninterpreted from a Bayesian perspective as an enhancement in the model’s ability\nto translate prior probabilities into posterior probabilities for identifying high-\nrisk individuals.1 For instance, for all-cause mortality, setting a threshold 𝜆 to\ndivide the population into high-risk and low-risk groups, we are interested in\n𝑃(𝑀𝑜𝑟𝑡𝑎𝑙𝑖𝑡𝑦 | 𝑝𝑟𝑒𝑑𝑖𝑐𝑡𝑖𝑜𝑛 > 𝜆).2 The posterior probability for the traditional\nFramingham model is 2.3 times higher than the prior (0.170/0.074), while the\nposterior probability for model integrating RNFLT metabolic states is 2.5 times\nhigher (0.185/0.074). This represents an 8.7% relative improvement [(2.5 −\n2.3)/2.3] in the model’s capacity to stratify risk at the same recall rate. The\nimprovement in AUPRC aligns with the improvement in C-statistics, reinforcing\nthe robustness of the model across different evaluation metrics.\nIn terms of clinical decision-making, the practical significance of these\nimprovements becomes evident. In resource-limited screening scenarios, models\nwith higher AUPRC can allocate resources more efficiently by reducing the\nproportion of false positives among those flagged for intervention. For instance,\nfor cardiovascular mortality, the integrated RNFLT metabolic states model\nachieved an AUPRC of 0.069, compared to 0.019 for the no-skill predictor. At the\nsame recall rate, the no-skill predictor would misclassify 9,814 false positives in\norder to identifying 186 true positives out of 10,000 samples (precision = 186 /\n[186 + FP] = 0.0186). In contrast, integrating RNFLT metabolic states would\navoid 7,312 of these false positives, leading to a 74.5% reduction in unnecessary\ninterventions. Furthermore, compared to the traditional Framingham model, the\nintegrated RNFLT metabolic states model would reduce false positives from\n3,077 to 2,502 cases (ΔFP = 575), thereby reducing unnecessary interventions by\n18.7%. These findings align with and complement our decision curve analysis,\nwhich quantifies the net benefit for clinical decision-making, while AUPRC\nreflects the model’s efficiency in optimizing resource allocation by improving the\nidentification of true positive cases.\nsignificant class imbalance, integrating RNFLT metabolic states significantly\nimproves the prediction of all CMD outcomes studied. This consistency of\nimprovement observed in both AUPRC, C-statistic and area under the decision\ncurve further supports the clinical relevance of integrating RNFLT metabolic\nstates into CMD prediction. We hope this adequately addresses your concerns.\nReferences\n1. Comparison of Alternative Approaches to Inference. Bayesian Approaches to\nClinical Trials and Health‐Care Evaluation; 2003. pp. 121-137.\n2. Saito, T. & Rehmsmeier, M. The precision-recall plot is more informative than\nthe ROC plot when evaluating binary classifiers on imbalanced datasets. Plos\nOne. 10, e0118432 (2015).\nImprovement in the AUPRC of integrating RNFLT metabolic states for CMD outcome prediction beyond established predictors in the UKB\npopulation-II.\nAUPRC\nOutcomes Performance gaps ‡ P §\nFDR\nNo-skill predictor Integrated models * Basic models †\nType 2 diabetes 0.070 0.533 (0.503–0.562) 0.480 (0.448–0.512) +0.053 <0.001\nMyocardial infarction 0.033 0.106 (0.091–0.123) 0.100 (0.085–0.115) +0.006 <0.001\nHeart failure 0.031 0.094 (0.080–0.111) 0.088 (0.075–0.102) +0.007 <0.001\nStroke 0.018 0.044 (0.036–0.056) 0.036 (0.029–0.043) +0.009 <0.001\nAll-cause mortality 0.074 0.185 (0.166–0.203) 0.170 (0.154–0.185) +0.015 <0.001\nCardiovascular mortality 0.019 0.069 (0.055–0.087) 0.057 (0.046–0.070) +0.012 <0.001\n* Models that integrated RNFLT metabolic states.\n† Basic models were based on Framingham General Cardiovascular Risk Score (FGCRS).\n‡ Performance gaps between integrated models and basic models.\n§ P values were calculated through two-sided non-parametric bootstrap tests with 1,000 iterations followed by controlling FDR for\nmultiple testing.\nAUPRC = area under the precision-recall curve; CMD = cardiometabolic disease; UKB = UK Biobank; FDR = false discovery rate.\npower (compared to baseline) be interpreted from an application/clinical\nstandpoint? Are these improvements clinically relevant? A simulation study of\nbenefits from such an improvement would be very valuable.\nResponse: We greatly appreciate your insightful comments regarding the clinical\nsignificance of our findings, particularly your observation that the improvement\nin the C-statistic was generally less than 0.05 (average 0.031). To better interpret\nthe clinical relevance of our study"}]

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