AI-based multiomics profiling reveals complementary omics contributions to personalized prediction of cardiovascular disease

Domain: neuroscience
Published: 2026-02-02
Editorial outcome: minor_revision

Abstract
Genomics, metabolomics, and proteomics offer complementary insights into cardiovascular disease (CVD) risk. Leveraging UK Biobank data, we introduce the CardiOmicScore, a multitask deep learning framework, to learn disease-specific proteomic (ProScore) and metabolomic (MetScore) risk scores for the six most common CVDs by profiling 2920 proteins and 168 metabolites. Experiments demonstrate that ProScore and MetScore are strong sole CVD risk predictors (C-index range: 0.69–0.82 for ProScore and 0.64–0.74 for MetScore), and can significantly enhance risk prediction across CVDs up to 15 years prior to disease onset when combined with clinical data, increasing the C-index by 0.005–0.102. These findings suggest that incorporating multiomics profiling into clinical practice can improve personalized risk assessments at early stages. CardiOmicScore also identifies important CVD-related proteins and metabolites, which represent promising data-driven pathways, calling for further external validation, to develop novel biomarkers and targeted therapies, facilitating precision medicine for primary prevention of CVDs.

Introduction
Cardiovascular diseases (CVDs) are the leading cause of mortality worldwide, claiming an estimated 19.8 million lives in 2022 alone1,2. This staggering figure is projected to rise further with the aging global population, underscoring the urgent need for innovative strategies to identify individuals at high risk and intervene early3–5.

Accurate CVD risk stratification is challenging since cardiovascular risk is determined by the complex interplay of genetic, environmental, and lifestyle factors6. Several approaches, such as the ASCVD and SCORE2, though widely used7–9, rely heavily on conventional clinical risk factors and thus exhibit limited discrimination and calibration10. Recent endeavors to incorporate polygenic risk scores (PRSs) have yielded only modest improvements and fail to capture the pathophysiological processes underlying disease progression11–14. Blood-based biomarkers offer a promising, non-invasive, and cost-effective alternative, but their full potential remains untapped11.

We are now entering the era of high-throughput metabolomics and proteomics. These cutting-edge technologies further enable the measurement of a wider range of proteins and metabolites in the blood, offering a comprehensive snapshot of the molecular mechanisms driving CVDs11,15. While recent studies have suggested the potential of metabolomics16–19 and proteomics20–33 data for risk prediction, a holistic integration of genetics, omics-based biomarkers, and traditional clinical risk factors has yet to be fully realized due to the scarcity of longitudinal multiomics cohort data and the limitations in scalable computational frameworks to handle heterogeneous data modalities13,14,16,17,20–30.

Metabolomics and proteomics offer complementary insights into CVD risk, with metabolomics providing a broad profile of metabolites largely involved in lipoprotein metabolism34, while proteomics focuses on a detailed set of proteins related to coagulation, inflammation, oxidative stress, and vascular remodeling35,36. Given the demonstrated effectiveness of multiomics approaches in improving risk identification for diseases such as diabetes and osteoarthritis37,38, integrating these multiomics data with genetic risk and clinical information may further enhance the predictive performance for cardiovascular risk. Moreover, existing prediction models often directly input high-dimensional omics data into linear regression to predict disease risk, which are constrained by assumptions of non-correlation between predictors and linear exposure-outcome associations16,18–20,23,25,27–32. In fact, high-order correlations exist within and across different omics data, highlighting the need for artificial intelligence (AI) algorithms to capture these complex, nonlinear relationships and interactions, and to generate informative low-dimensional representations for downstream disease risk prediction. Additionally, previous studies incorporating multiomics data have focused on a narrow set of CVDs, such as myocardial infarction and atrial fibrillation18,19,31–33, restricting the understanding of the shared pathways at the metabolomic and proteomic levels across a broad spectrum of cardiovascular outcomes.

Our study aims to bridge this gap by proposing a groundbreaking AI-based CardiOmicScore framework. Leveraging the unparalleled depth of data in the UK Biobank (UKB), we introduced two advanced deep learning models, MetNet and ProNet, designed to simultaneously predict the risk of six CVDs using 168 metabolites and 2920 proteins, respectively (Fig. 1). Unlike traditional models, both MetNet and ProNet utilize a multitask architecture to learn high-dimensional representations from metabolomics and proteomics data, generating disease-specific risk scores, namely MetScore and ProScore, for a comprehensive range of CVDs. These CVDs include coronary artery disease (CAD), stroke, heart failure (HF), atrial fibrillation (AF), peripheral artery disease (PAD), and venous thromboembolism (VTE)—conditions with the highest global burden1. We further developed Cox proportional hazards (CPH) models on various combinations of PRS, MetScore, ProScore, and clinical risk factors to predict each CVD onset. We evaluated the predictive performance, as well as the potential clinical utility of all the models to investigate the additive value of multiomics profiling in CVD risk prediction.

Results
Studypopulation
A two-stage, individual-level data partitioning strategy was employed to rigorously evaluate our model’s performance and generalizability (see sub-section “Data partition and imputation” and Supplementary Fig. 1). First, we excluded participants who had neither metabolomics nor proteomics data available. Then, we divided the remaining participants into the Metabolomics-only (N=220,859; those with metabolomics but not proteomics data), Proteomics-only (N=19,086; those with proteomics but not metabolomics data), and Multiomics (N=24,287; those with genomics, metabolomics, and proteomics data) cohorts based on omics availability. The Metabolomics-only and Proteomics-only cohorts were used within our CardiOmicScore framework to develop two deep learning models, MetNet and ProNet, respectively. The Multiomics cohort was reserved as an untouched validation set to assess the added predictive value of integrating different omics data.

Following best practices in recent UKB studies39–41, each of these development cohorts was further split into training/validation sets (England and Wales) and a geographic testing set (Scotland). Specifically, the Metabolomics-only cohort included 187,272 participants in the training set, 20,808 in the validation set, and 12,779 in the geographic testing set, while the Proteomics-only cohort comprised 15,579, 1732, and 1775 participants in the training, validation, and geographic testing sets, respectively. Baseline characteristics were comparable between training and validation sets in both cohorts, whereas significant differences were observed between training and geographic testing sets (Supplementary Data 2 and 3), thus allowing a robust assessment of the models’ regional generalizability.

Baseline characteristics and incident cases during follow-up were broadly consistent across all study datasets. The median age of participants at baseline was between 56.0 and 58.0 years. The proportion of males ranged from 42.8% to 45.8%. The prevalence of baseline medication use was comparable, with 15.8–18.1% of participants receiving lipid-lowering medication and 10.5–11.2% on antihypertensive medication. The distribution of outcomes was also similar, with 83.2–85.7% of participants remaining free of any incident CVD, 10.6–11.8% developing one CVD, and 3.7–5.0% developing multiple CVDs. Detailed baseline characteristics and follow-up information for each cohort are provided in Supplementary Data 1–3 and Supplementary Fig. 2.

Omics information is the key driver of cardiovascular risk stratification
We derived the MetScore and ProScore for each CVD using the trained MetNet and ProNet with 168 metabolic and 2920 proteomic biomarkers as input. We observed strong correlations among the MetScores for the six different CVDs (Spearman correlation coefficients=0.78–0.96) and among the ProScores (Spearman correlation coefficients=0.87–0.94) (Supplementary Fig. 3). Additionally, we constructed PRSs for all CVDs.

Participants were stratified into three risk groups (low, medium, and high) based on the tertiles of the PRS, MetScore, and ProScore. Kaplan-Meier survival curves showed significant differences across risk groups defined by MetScore and ProScore for all CVDs (all log-rank P<0.001) (Fig. 2a and b). In contrast, PRS risk groups exhibited significant differences in survival curves only for CAD, stroke, AF, and VTE (all log-rank P<0.001) (Supplementary Fig. 5a). Similar patterns were observed in the correlation between observed event rates and the percentiles of the three omics scores (Supplementary Fig. 4).

After adjusting for demographic information, healthy lifestyles, family history of disease, disease and medication history, physical measurements, and blood count, both categorical and continuous analyses of ProScore, MetScore, and PRS showed significant associations with incident CVDs (Fig. 2c and Supplementary Fig. 5b). Participants in the top tertile of ProScore had over a 1.68-fold higher risk of all CVDs ranging from stroke (hazard ratio [HR]=1.68, 95% confidence interval [CI]=1.40–2.01) to CAD (HR=2.18, 95% CI=1.88–2.52) compared with those in the bottom tertile. The top tertile group of MetScore was associated with an increased risk of all CVDs, with the highest HRs observed in HF (HR=1.64, 95% CI=1.39–1.92) and CAD (HR=1.70, 95% CI=1.49–1.94). Participants in the high PRS risk group had a higher risk of stroke (HR=1.25, 95% CI=1.08–1.46), VTE (HR=1.54, 95% CI=1.33–1.78), CAD (HR=1.65, 95% CI=1.49–1.83), and AF (HR=1.89, 95% CI=1.69–2.11) than those in the low PRS risk group. When modeled as continuous variables (per 1-standard deviation [SD] increase), ProScore, MetScore, and PRS remained significantly associated with CVD risk, with HRs per SD increase ranging from 1.07 (95% CI=1.01–1.13) for PRS of stroke to 1.57 (95% CI=1.49–1.66) for ProScore of AF.

Advancing cardiovascular risk prediction with the power of omics information
We investigated the predictive performance of three omics scores, two published algorithms for CVD risk assessment (ASCVD and SCORE2)7–9, and three clinical predictor sets (AgeSex, Clin, and PANEL). The Clin set contained 25 questionnaire-collected variables and physical measurements relevant to cardiovascular risk, while the PANEL set built upon the Clin set by additionally incorporating blood count information17,26. ASCVD and SCORE2 were calculated using the published regression equations (see sub-section “Clinical predictors and cardiovascular risk scores”). Omics scores were treated as continuous variables, while clinical predictors were included as either continuous or categorical variables. Discriminative performance of all models, evaluated by Harrell’s C-index, and differences in the C-index across models are presented in Fig. 3. Among the three omics scores, ProScore showed the highest performance for all CVDs with the C-index ranging from 0.69 (95% CI=0.67–0.71) for VTE to 0.82 (95% CI=0.80–0.84) for PAD (Fig. 3a). The C-index of MetScore spanned from 0.64 (95% CI=0.61–0.66) for VTE to 0.74 (95% CI=0.71–0.76) for PAD. Importantly, the strong performance of ProScore and MetScore was replicated in their respective geographic testing sets, underscoring their generalizability across distinct UK populations (Supplementary Fig. 6). In contrast, PRS provided the most limited predictive capacity (C-index range=0.52–0.60). Finally, the discriminative performance increased with more clinical predictors included in the CPH models for all diseases (Fig. 3a).

Compared with baseline clinical-based models (i.e., AgeSex, Clin, and PANEL), these models had significantly greater performance (i.e., 95% CIs of delta C-index not including zero) in all CVDs after adding omics scores (Fig. 3b). For the AgeSex set, a significant increase in the C-index was observed with a range of 0.005 (95% CI=0.001–0.013, +PRS for stroke) to 0.102 (95% CI=0.081–0.124, +PRS+ProScore for PAD). The addition of ProScore achieved the greatest improvement in model performance (0.040 for AF to 0.101 for PAD), followed by MetScore (0.009 for AF to 0.051 for PAD). Although the improvements were more modest than those observed with the AgeSex-based model, adding omics scores to the Clin- and PANEL-based models still resulted in significant performance improvements, with the delta C-index ranging from 0.003 (95% CI=0.001–0.006) to 0.049 (95% CI=0.036–0.061) for the Clin set and 0.003 (95% CI=0.001–0.005) to 0.049 (95% CI=0.035–0.061) for the PANEL set. Even for the PANEL set that already contained predictive information, the inclusion of omics signatures resulted in better discriminative performance for all CVDs.

We then used models combining the PANEL set with one omics score (i.e., +PRS, +MetScore, or +ProScore) as baselines to demonstrate the added value of incorporating additional omics features. Model performance improved for all CVDs when additional omics features were integrated into models already containing PRS (i.e., +PRS) or MetScore (i.e., +MetScore) (Supplementary Fig. 7). Furthermore, when using +ProScore as the baseline, adding PRS (either alone or in combination with MetScore) improved prediction for CAD, stroke, AF, and VTE, while incorporating MetScore specifically improved performance for CAD and stroke.

PANEL-based models achieve optimal calibration and net benefit
We assessed the potential clinical utility of our models by two indicators: calibration and net benefit. Calibration measures the extent to which predicted risks align with observed risks42, while net benefit quantifies the trade-off between true positives and false positives at various decision thresholds43. To provide a summary measure of overall clinical utility, we further calculated the area under the net benefit curve (A-NBC)44. Models based on the PANEL set were well calibrated for all outcomes as their calibration curves closely followed the diagonal line (Fig. 4a). The consistency between the observed risks and the predicted risks was greater in the PANEL-based models than in the AgeSex- and Clin-based models (Fig. 4a, Supplementary Fig. 8a, and Supplementary Fig. 9a). The inclusion of omics information, especially ProScore, improved standardized net benefits across a range of decision thresholds compared with clinical-based models. Specifically, the maximum improvement in A-NBC, achieved by the full multiomics model (i.e., +PRS+MetScore+ProScore), ranged from 35.39 for stroke to as high as 212.36 for AF. The incremental benefits diminished as the number of clinical predictors in the baseline models increased (Fig. 4b, Supplementary Figs. 8b and 9b).

AI identifies disease-specific metabolites and proteins
We further utilized the Shapley Additive exPlanations (SHAP) method to examine the relationship of metabolites and proteins with each CVD. To capture global importance, we first calculated the mean absolute SHAP value for each feature and normalized these values within each disease. Figure 5a and b display the top 65 metabolites and 77 proteins ranked by their global importance.

We focused on the top five metabolites and proteins to identify the most impactful biomarkers for each disease. For metabolites, we observed a partial overlap across the six CVDs. The commonly impactful metabolites included creatinine, albumin, glutamine, fatty acids such as linoleic acids (LA) and monounsaturated fatty acids (MUFA), and lipoprotein components such as free cholesterol in intermediate-density lipoprotein (IDL_FC), free cholesterol in very large high-density lipoprotein (XL_HDL_FC), cholesteryl esters in chylomicrons, and extremely large very-low-density lipoprotein (XXL_VLDL_CE). Meanwhile, other biomarkers were important for specific diseases, such as tyrosine and leucine for HF and AF, and glycoprotein acetyls (GlycA) for PAD and VTE. For proteins, the impactful biomarkers were more distinct among CVDs. We found that NT-proBNP and NPPB were important for CAD, HF, and AF, while GDF15 was important for CAD, HF, AF, and VTE. Other key associations included MMP12 for CAD and PAD, CDCP1 for CAD, HF, and VTE, EDA2R for CAD and stroke, NEFL for stroke and PAD, WFDC2 for HF and PAD, and FASLG for stroke and VTE. The most important disease-specific proteins also included KLK4 and CRIP2 for stroke; BCAN and ELN for AF; PLB1 and ENDOU for PAD; and HPGDS and ADGRG2 for VTE.

The SHAP analysis also revealed that while the direction of the top biomarker-disease associations was highly consistent across all six CVDs, the magnitude of their contributions varied. We used CAD as an example to illustrate the associations between plasma biomarkers and disease risks. Among metabolites, higher plasma levels of creatinine, GlycA, MUFA, tyrosine, glutamine, IDL_FC, and XL_HDL_FC were associated with an increased risk of CAD, while lower levels of albumin, LA, leucine, and XXL_VLDL_CE were associated with CAD risk (Fig. 6a). For proteins, NT-proBNP, NPPB, GDF15, MMP12, CDCP1, EDA2R, NEFL, WFDC2, KLK4, CRIP2, and ELN were positively associated with disease risk, while a negative association was observed between CAD risk and elevated levels of FASLG, BCAN, PLB1, ENDOU, HPGDS, and ADGRG2 (Fig. 6b). SHAP beeswarm plots of metabolites and proteins for other diseases are provided in Supplementary Figs. 10 and 11. A summary of the top-ranking metabolites and proteins is provided in Supplementary Data 4.

Sensitivity analyses
We evaluated the performance of 25 key individual biomarkers (8 metabolites and 17 proteins) that were selected for either their high global importance in our CardiOmicScore framework (see sub-section “AI identifies disease-specific metabolites and proteins”) or their routine use in clinical practice. Overall, whether used individually or combined with clinical risk factors, these key biomarkers’ discriminative ability was much weaker than that of MetScore or ProScore (Supplementary Fig. 12). Additionally, the incremental benefit of adding omics scores to clinical models was consistent across age groups and sexes (Supplementary Fig. 13), as well as among participants stratified by lipid-lowering and antihypertensive therapy (Supplementary Fig. 14). We also found that the predictive value of the omics scores remained unchanged after excluding incident cases that occurred within the first two years of follow-up (Supplementary Fig. 15) or accounting for the competing risk of death (Supplementary Fig. 16).

Moreover, our CardiOmicScore framework proved superior to conventional machine learning methods for generating omics scores. Specifically, MetScore and ProScore derived from MetNet and ProNet demonstrated generally superior performance across multiple CVDs compared to analogous scores developed using XGBoost, LightGBM, random forest, and logistic regression (Supplementary Fig. 17). Finally, after refitting the MetNet model in the subgroup of participants not taking lipid-lowering medications, the most impactful metabolites included MUFA, glycine, creatinine, albumin, extra-large high-density lipoprotein free cholesterol, glutamine, IDL_FC, tyrosine, LA, and GlycA (Supplementary Fig. 18). Seven of these metabolites overlapped with those highlighted in the main analysis. Furthermore, although the individual predictive performance of some traditional lipid-related biomarkers for CAD improved slightly after excluding participants on lipid-lowering medication, their addition still provided only limited improvement in predictive performance to the PANEL-based model. This finding was consistent in both the full Multiomics cohort and the subgroup excluding baseline users of lipid-lowering medication (Supplementary Figs. 19 and 20).

Methods
Study population
The UKB is a prospective cohort study that has collected extensive genetic and phenotypic data from ~500,000 participants who were recruited from 22 centers across the UK during 2006–201064. Metabolomics and proteomics profiling were performed in plasma samples collected at baseline from a randomly selected subset of ~280,000 and ~54,000 UKB participants. UKB has approval from the North West Multi-Centre Research Ethics Committee as a Research tissue biobank (REC reference 11/NW/0382). Participants provided written informed consent. This study received approval from the UKB under application number 79146.

Data partition and imputation
To rigorously evaluate model performance and ensure generalizability, we adopted a two-stage individual-level data partition strategy, designed to approximate external validation while fully leveraging UKB data (Supplementary Fig. 1). First, we excluded participants who lacked both metabolomics and proteomics data. The remaining participants were then split into three cohorts based on omics data availability: a Metabolomics-only cohort (N=220,859; those with metabolomics but not proteomics data), a Proteomics-only cohort (N=19,086; those with proteomics but not metabolomics data), and a Multiomics cohort (N=24,287; those with genomics, metabolomics, and proteomics data). In our CardiOmicScore framework, we used the Metabolomics-only and Proteomics-only cohorts to develop the respective deep learning models (MetNet and ProNet) and to generate the corresponding omics scores (MetScore and ProScore). To enable geographic validation, we further split each of these two cohorts into a training/validation set (England and Wales) and a geographic testing set (Scotland) according to the recruitment regions, following approaches commonly used in recent UKB studies39–41. The Multiomics cohort was kept untouched throughout model training and was subsequently used as an additional validation cohort to assess the incremental benefit of integrating multiple omics data types for risk prediction.

Categorical variables were one-hot encoded, and continuous variables were standardized by the mean and standard deviation. We used the K-nearest neighbors algorithm (scikit-learn v1.3.2 package)65, setting the number of neighbors to five, to impute missing values for continuous variables. Categorical variables were imputed with the mode. For metabolomics or proteomics data, imputation and standardization of continuous variables were performed using parameters derived exclusively from the training set. The fitted preprocessing models were then applied to the corresponding validation and geographic testing sets, as well as the Multiomics cohort. To derive imputation models and standardization parameters for subsequent application to the Multiomics cohort, we constructed two cohorts from all available individuals with relevant data: one for clinical information (N=427,225) and one for genomics (N=412,797).

Ascertainment of cardiovascular diseases
The six CVDs analyzed in this study included coronary artery disease (CAD), stroke, heart failure (HF), atrial fibrillation (AF), peripheral artery disease (PAD), and venous thromboembolism (VTE). All diseases were ascertained based on self-reported diagnoses and operations, hospital episode statistics, and death records. Self-reported information was used only to determine the presence of CVDs at baseline. Detailed definitions are provided in Supplementary Data 5. Follow-up duration was calculated from the date of baseline assessment to the date of incident outcomes, death, loss to follow-up, or end of available registry follow-up (November 30, 2023 for England & Wales and December 31, 2023 for Scotland66, whichever came first).

Multiomics data
Genetic data. Central quality control and imputation of genotypic data are available in previous literature64. In brief, genotypes were generated based on two closely related purpose-designed arrays (UK BiLEVE Axiom and UK Biobank Axiom). Genotypes were imputed using the Haplotype Reference Consortium and the merged UK10K and 1000 Genomes as reference panels.

PRSs were calculated for all CVDs based on the following equation, implemented via PLINK 1.9 software within the ukbrapR v0.3.7 R package67,68.

PRSj = \sum_{i=1}^{n} \beta i * dosageij,

where n is the number of single-nucleotide polymorphisms (SNPs), \beta i is the effect size of SNP i, and dosageij is the number of risk alleles of SNP i for individual j. Effect sizes of SNP-disease associations were collected from published PRSs available in the PGS Catalog13,69–73. We included 235 SNPs for CAD (PGS003438)13, 63 SNPs for stroke (PGS005230)69, 38 SNPs for HF (PGS003969)70, 154 SNPs for AF (PGS004905)71, 19 SNPs for PAD (PGS005158)72, and 297 SNPs for VTE (PGS000753)73. Full lists of SNPs are provided in Supplementary Data 6–11.

Metabolomics data. Metabolite biomarkers from EDTA-plasma samples were measured using Nightingale’s high-throughput NMR-based platform34,74. A total of 249 metabolites were quantified, with 168 in absolute concentrations and 81 ratios, covering lipoprotein lipids in 14 subclasses, apolipoproteins, fatty acids, cholesterol, cholesteryl esters, amino acids, ketone bodies, glycolysis metabolites, fluid balance-related metabolites, and inflammation biomarkers. Technical variation in these NMR metabolic biomarkers was removed using the ukbnmr v2.2 R package75. We finally included 168 metabolic biomarkers in absolute concentrations in the present study (Supplementary Data 12)17.

Proteomics data. Detailed information on sample selection, data processing, and quality control is provided in published documents76. Briefly, proteomic profiling was performed in EDTA-plasma samples using the Olink Explore 1536 and Explore Expansion platforms, which measured 2923 unique proteins targeted by 2941 assays across four panels containing cardiometabolic, inflammation, neurology, and oncology. Protein measurements were expressed as normalized protein expression values on a log2 scale. After excluding three proteins with over 50% missingness across samples (GLIPR1, NPM1, PCOLCE), we included 2920 proteins in this study (Supplementary Data 13).

Clinical predictors and cardiovascular risk scores
Clinical predictors (n=34) were collected at baseline, including demographic information (age, sex, ethnicity, and Townsend deprivation index), healthy lifestyles (current smoking, daily alcohol intake, sleep duration, physical activity, diet, and social connection), family disease history (family history of heart disease, stroke, hypertension, and diabetes), disease and medication history (hypertension, diabetes, lipid-lowering medication, and antihypertensive medication), physical measurements (height, weight, waist circumference, waist-hip ratio, body mass index, and systolic/diastolic blood pressure), and blood count (hemoglobin concentration, hematocrit percentage, and the count of leukocyte, lymphocyte, monocyte, neutrophil, eosinophil, basophil, and platelet). We defined three clinical predictor sets—AgeSex, Clin, and PANEL. Detailed definitions are listed in Supplementary Data 14. The Clin set consisted of 25 clinical predictors, covering sociodemographic factors, lifestyle, health history, and physical measurements, and the PANEL set extended the Clin set by adding blood count data17,26. Additionally, we calculated two sex-specific cardiovascular risk scores for primary prevention, ASCVD and SCORE2, that are recommended by the American College of Cardiology and European Society of Cardiology7–9 (Supplementary Methods).

Deep learning models
Model development. We developed two multitask deep neural networks, named MetNet and ProNet, to derive the MetScore and ProScore for six CVDs, with 168 metabolites and 2920 proteins as the input, respectively. MetNet was trained on the training set from the Metabolomics-only cohort, and ProNet on the training set from the Proteomics-only cohort, while their respective validation sets were used to monitor model fitting and select the optimal checkpoints. The final model was first evaluated on the geographic testing set to assess generalizability across regions. Subsequently, the Multiomics cohort, which remained untouched throughout model development, was used for a further performance evaluation.

MetNet and ProNet had similar model architectures, consisting of a shared network and six parallel, disease-specific networks (one for each CVD) (Supplementary Fig. 21). The shared neural network (denoted as “shared multilayer perceptron [MLP]”) included multiple fully connected layers, each with a nonlinear activation function, dropout, and batch normalization. The output of the shared network (i.e., shared representation) is a high-dimensional representation, capturing the common features for all CVDs. The disease-specific network, comprising a disease-specific MLP and a predictor MLP, was designed to learn disease-specific features and predict the risk of individual diseases (i.e., whether the disease will occur or not). The original metabolomic/proteomic biomarkers were fed into the disease-specific MLP with nonlinear activation functions, dropout, and batch normalization to obtain a disease-specific representation. The shared and disease-specific representations were then concatenated and passed on to the predictor MLP that included linear layers followed by nonlinear activation functions, dropout, and batch normalization before the final single-output layer to generate the MetScore or ProScore for each disease. These scores were subsequently used as predictors in CPH models to assess their utility for cardiovascular risk prediction.

Binary cross-entropy was used as the loss function for each disease. To address the class imbalance in multitask prediction, we applied both sample-level and task-level weighting (Supplementary Methods). At the sample level, the pos_weight parameter was calculated as the ratio of the number of negative samples to positive samples for each task, increasing the penalty for misclassifying positive cases and encouraging the model to pay more attention to rare events. At the task level, each disease was assigned a weight equal to the inverse of its number of positive samples, ensuring that tasks with fewer positive cases contribute proportionally more to the overall loss. The final loss was computed as the mean of the weighted losses across all tasks.

Our multitask design enables the simultaneous modeling of multiple CVDs, accounting for individuals who experience several events over time (e.g., CAD followed by stroke or HF). In this setup, each participant simultaneously contributes to the prediction tasks for all six diseases. If a participant experiences multiple incident diseases, they generate multiple non-zero losses but are only included once in the dataset. This design avoids data duplication while fully leveraging all available incident disease information17,26. It also reflects the clinical reality in which multimorbidity is increasingly common, and where the most useful risk prediction models are those capable of discriminating the outcome of interest in the presence of co-occurring diseases27.

We performed random hyperparameter searches separately for MetNet and ProNet using their respective training and validation sets. Each model was trained and evaluated across 100 randomly sampled hyperparameter configurations. Hyperparameters included the hidden layer architecture, dropout rate, and activation function for all MLPs (Supplementary Data 15). The optimal hyperparameter combination for each model, provided in Supplementary Data 15, was selected based on the highest average Harrell’s C-index across all six CVDs on the validation set. Once the optimal architecture was determined, we trained the final models five times using different random seeds to account for stochastic variability in the training process. The ultimate MetScore and ProScore for each participant represent the average of the predictions from these five independent runs, ensuring the robustness of the final scores. The final models were trained using the Adam optimizer. All deep learning models were developed in Python v3.8.5 using PyTorch v1.11.0 package with hyperparameter tuning using Optuna v4.0.0 package77,78.

Model interpretation. Since our deep learning models, MetNet and ProNet, are black-box models, we further employed the SHAP method (shap v0.46.0 package), a post-hoc explanation algorithm, to explore the relationships of metabolomic and proteomic biomarkers with each outcome. SHAP values, computed based on a game-theoretical approach, can quantify the contribution of each predictor to the prediction for each participant and explain the final prediction as the sum of the SHAP value of each predictor79. Higher (or lower) SHAP values imply large positive (or negative) contributions to the MetScore or ProScore, where both scores were positively related to CVD risk. To ensure stable and reliable interpretations, we generated SHAP values for each of the five models trained with different random seeds. The final SHAP value for each biomarker was calculated by averaging these results across the five runs. We then calculated the mean absolute SHAP values across all participants to obtain the global impact of each predictor. We used all participants in the Multiomics cohort to explain our models.

Statistical analyses
Baseline characteristics were summarized using median (IQR) for continuous variables or numbers (percentages) for categorical variables. Chi-squared tests were used for categorical variables, and Mann–Whitney U tests were used for non-normally distributed continuous variables to compare baseline characteristics between the training and validation sets, and between the training and geographic testing sets. Incidence proportion for each specific CVD was calculated as the number of new cases for that disease divided by the total number of participants at risk at baseline. All statistical analyses were conducted using Python v3.8.5, and visualizations were performed in R v4.3.3. A two-sided P<0.05 was considered statistically significant.

We conducted model performance evaluations on the Multiomics cohort, including risk stratification, Harrell’s C-index, calibration plot, and net benefit curve. For the risk stratification, we classified the participants into tertile groups (low, medium, and high) based on PRS, MetScore, and ProScore, respectively. Then, we used the Kaplan–Meier curve (lifelines v0.27.8 package)80 to visualize the cumulative survival probability over time and compared the survival functions across groups using the log-rank test (lifelines v0.27.8 package). Finally, we fitted CPH models for each score to estimate HRs with 95% CIs. Each score was analyzed both as a categorical variable (tertiles, with the low group as the reference) and as a continuous variable (per 1-SD increase). All models were adjusted for the following covariates from the PANEL set: demographic information, healthy lifestyle factors, family history of disease, disease and medication history, physical measurements, and blood count.

CPH models were fitted on various predictor sets. Specifically, we first developed models with only PRS, MetScore, or ProScore as continuous variables; next, we fitted models using clinical predictor sets of AgeSex, Clin, and PANEL; finally, we added omics information to clinical predictor-based models to assess the combined predictive power of PRS, MetScore, and ProScore when integrated with traditional clinical variables. For example, AgeSex +PRS +MetScore +ProScore means we included age, sex, and three omics scores as the predictors in the CPH model. All CPH models were fitted on the Multiomics cohort using the lifelines v0.27.8 package, with a penalizer of 0.03 to facilitate model convergence. We calculated the Harrell’s C-index to assess model discriminative performance. The C-index was reported with a point estimate and 95% confidence intervals (CIs) calculated via 1000 bootstrap resamples of the Multiomics cohort. Discriminative performance was considered significantly different when the 95% CIs of the C-index deltas did not include zero17,27.

Additionally, we predicted the probability of incident diseases at 15 years to draw calibration curves and net benefit curves, two recommended metrics for calibration and clinical utility of prediction models42,43. Calibration curves can measure the concordance between observed event rates and predicted risks by CPH models, while net benefit curves (dcurves v0.5.0 R package) are used to evaluate the net benefit of prediction models compared with two strategies of treat all patients and treat no patients. Since the net benefit was difficult to interpret, we further calculated the standardized net benefit by dividing it by its maximum value81. We also computed the area under the net benefit curve (A-NBC) using trapezoidal numerical integration, providing a summary metric of model performance44.

To test the robustness of our findings, we conducted six sensitivity analyses. First, to evaluate the performance of key individual biomarkers, we developed separate CPH models based on clinical predictors in combination with one biomarker at a time. The biomarkers selected for this analysis included those with high global importance in our deep learning models (i.e., the top five non-overlapping biomarkers by mean absolute SHAP value for each CVD, resulting in 8 metabolites and 17 proteins) or routinely used in clinical practice. Second, since age, sex, and lipid-lowering and antihypertensive therapy may influence metabolomic or proteomic profiles82–84, we fitted CPH models by age, sex, and baseline use of lipid-lowering or antihypertensive medication to assess whether the predictive performance of the models was consistent across subgroups. Third, to mitigate the potential for reverse causation, we repeated the primary analyses after excluding incident cases that occurred within the first two years of follow-up for each respective CVD. Fourth, to account for the competing risk of death, we fitted Fine–Gray subdistribution hazard models, treating all-cause death as a competing event (cmprsk v2.2.11 R package). Fifth, to demonstrate the advantage of our CardiOmicScore framework for high-dimensional omics data, we developed alternative MetScore and ProScore using several statistical machine learning algorithms, including Extreme Gradient Boosting (XGBoost, xgboost v2.0.3 package), LightGBM (lightgbm v4.4.0 package), random forest (scikit-learn v1.3.2 package), and logistic regression (scikit-learn v1.3.2 package), following an identical development pipeline (Supplementary Methods and Supplementary Data 16). We then compared the predictive utility of these scores against those generated by MetNet and ProNet. Finally, to investigate the potential effect of lipid-lowering treatment, we refitted the MetNet using the same architecture but trained only on participants not taking lipid-lowering medication. We then calculated the mean absolute SHAP values to identify the important metabolites within this subgroup. Additionally, using the same analytical procedure as our first sensitivity analysis, we evaluated the predictive performance of traditional lipid-related biomarkers in the Multiomics cohort after excluding baseline users of lipid-lowering medication, and compared it with their performance in the full set.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "The authors introduce a deep learning framework with a multitask objective to predict risks of nine common cardiovascular diseases. The model uses multilayer perceptron (MLP) based architecture and predicts binary outcome of each individual disease. It takes proteomic and metabolic biomarkers of 24,308 participants as inputs and is trained and evaluated through a 10-fold cross validation schema. By evaluations including risk stratification, Harrel’s C-index, calibration plot and net benefit curve, the authors show that multiomics signatures can not only be applied as individual predictors, but enhance CVD risk predictions when combined with other clinical predictors.\nMajor comments:\n1. The proposed deep learning model is trained using 24,308 total participants data on 9 cardiovascular diseases, where the number of all cardiovascular outcomes accounts for 4,213 and some diseases have very limited data points (e.g. 123 for AAA). It is very likely that training with such imbalanced data will lead to a biased model. The limited sample size for selected outcomes also raises concerns about the potential risk of overfitting especially if high embedding dimensions are applied.\n2. Although the authors showed the superior performance of their deep learning derived multiomics scores in comparison with combinations of other predictors, additional analysis is needed to prove the necessity of using neural networks to process multiomic data since simpler machine learning models (e.g. XGBoost, random forest) can also introduce non-linearity of input features. The authors may consider building simpler machine learning models to generate alternative multiomic scores and compare with the deep learning derived scores.\n3. Some details of data composition and processing need to be added to verify the validity of model development and improve the clarity of the manuscript. The composition of the study population needs to be clarified. For example, the reported total number of CVD outcomes does not match the sum of outcomes of all individual CVD. Additional clarification is needed to explain the proportion of individuals with multiple CVDs and how the incidence rates are calculated. In addition, the authors didn’t mention how dataset splitting was performed in training and testing and how individuals with multiple CVDs are handled in the model training and evaluation. If such cases were treated as independent data points, a single participant with multiple CVDs may appear in both training and testing sets, resulting in data leakage and inflation of model performance.\nMinor comments:\n1. Additional details of the deep learning model architecture and loss function need to be added. For example, does the model output a unique score for each CVD (9 scores in total)? How is the overall loss calculated? Also, the authors should include their final choice of model hyperparameters to improve reproducibility of the model.\n2. In Figure 3, it would be clearer if all subplots use the same x-axis range in each sub-figure.\n3. There are some redundant texts in the manuscript. For example, the authors currently repeatedly explain meanings of all CVD abbreviations and population breakdown in all figure legends. Instead, it may be better if the authors use a separate section (e.g. in Methods) to explain their evaluation setup.\n4. Some figures need to be adjusted. For example, the texts in Figure 5 c & d are too small to read. Supplementary figures 13 & 14 are not completely displayed.", "reviewer_2": "The authors undertake Genomic, Transcriptomic, and Proteomic profiling of a large cohort of patients from the UK biobank and through AI deep learning models have developed genomic, transcriptomic, and proteomic risk scores for the prediction of 9 cardiovascular disease outcomes. This study reports that such these multiomic risk scores are superior to existing risk scores based on clinical and biochemical data both individually and in combination. This is a great pilot dataset, but it needs true external validation in an independent cohort, in order to be mature for presentation as definitive.\nMajor Comments\n- The authors helpfully provide a list of proteins and metabolites whose levels increase and decrease the risk of cardiovascular disease. However with NTpro-BNP coming out as the top hit in all 9 cardiovascular diseases is there risk that these results have been confounded? High NTProBNP contributes towards increased CAD risk for example, but does this merely represent the interaction or comorbidity of different types of CVD such as CAD and HF? On closer inspection, the directionality and effect sizes of the proteins are quite similar visually between CVD conditions – are the authors therefore suggesting that the multiomic signatures of 9 different cardiovascular diseases are largely the same?\n- Similarly, LDL metabolite fractions and total lipids / cholesterol are not strongly predictive of CAD (which is unusual in a healthy cohort, most of whom are not taking statins). Does this trend persist when the 20% of the cohort on lipid-lowering therapies are removed from the cohort? It would be helpful if the authors could comment on this observation and why this might be the case.\n- The authors report that “the genetic risk of CVDeath was measured using the PRS for CAD since there is no available GWAS summary statistics for CVDeath”. Is there any reason the author’s couldn’t run their own CVDeath GWAS rather than applying the CAD GWAS results to a non-identical outcome.\n- The authors describe the demographics of the whole cohort but not the demographics of the individual training and validation cohorts. Could the authors please provide these along with statistical testing to show that such cohorts are not statistically different. This is especially important given the absence of an external validation cohort.\n- On trying to access the online demo of the model, the weblink appeared to be broken. Please could the authors provide a working link to allow reviewers and readers to access the online demo.\n-The main issue with this work is that is used 10% of the cohort for validation, so this is considered internal (not external) validation, and the models are expected to suffer from overfitting.\nMinor Comments\n- In the demographics it would also be helpful to know the numbers of participants on statin / antihypertensive therapies\n- Minor grammatical error in supplementary figure 1 caption, lines 115-116. “Here, we defined the follow-up duration as the period from the date of baseline assessment to the date of incident any cardiovascular diseases…” doesn’t make sense grammatically. Perhaps the intended line is “the date of any incident cardiovascular diseases”\n- Minor grammatical error in 2.3, paragraph 2, line 2: “(i.e., 95% CIs of delta C-index not included zero)”. “Included” should be changed to “including”\n- Supplementary figure 10 appears to have a light blue figure legend marker for ‘clinical scores’ which seems to be redundant as it does not feature in the figure outside the legend."}, "author_response": "Dear Editor and Reviewers,\nThank you so much for reviewing our paper and the opportunity to revise our paper! We\nreally appreciate the helpful comments and constructive suggestions! We have substantially\nrevised the manuscript according to the review comments. All revised portions are marked in\nred in the track-change version of the manuscript.\nSpecific comments and our responses are detailed below.\nREVIEWER 1’S COMMENTS\nRemarks to the Author:\nCOMMENT 1-1. The proposed deep learning model is trained using 24,308 total\nparticipants data on 9 cardiovascular diseases, where the number of all cardiovascular\noutcomes accounts for 4,213 and some diseases have very limited data points (e.g. 123 for\nAAA). It is very likely that training with such imbalanced data will lead to a biased model.\nThe limited sample size for selected outcomes also raises concerns about the potential risk of\noverfitting especially if high embedding dimensions are applied.\nRESPONSE 1-1. Thank you very much for the insightful comments. We agree with the\nreviewer that class imbalance and potential overfitting are challenges when developing\nclinical prediction models. To address these concerns, we significantly revamped the study by\nincorporating more data and introducing sample- and task-level weighting in the loss function\nof our CardiOmicScore framework.\nFigure R1: Comparison of study framework before and after revision.\nSpecifically, at the data level, we revised our data partitioning strategy (Figure R1) to\nleverage a much larger portion of the UK Biobank data for model development (i.e., training\nand validation sets in the Metabolomics-only and Proteomics-only cohorts), followed by a\ngeographic validation on their respective testing sets, while keeping the Multiomics cohort as\na completely non-overlapping dataset to assess the added predictive value of integrating\ndifferent omics data (Figure 1 and Supplementary Figure 1). For our two deep learning\nmodels within our CardiOmicScore framework, MetNet and ProNet, the development\ndatasets (training + validation sets) now include 208,080 and 17,311 participants,\nrespectively. This has substantially increased the number of incident cardiovascular diseases\n(CVDs) for model training.\nDespite this increase, the proportion of incident ventricular arrhythmias and abdominal aortic\naneurysm remains very low (e.g., 0.8% and 0.4% in our training cohort, respectively).\nTraining reliable models for such rare events is indeed challenging. Therefore, we removed\nthese two diseases and cardiovascular death from our study to focus on the six most common\nCVDs: coronary artery disease (CAD), stroke, heart failure (HF), atrial fibrillation (AF),\nperipheral artery disease (PAD), and venous thromboembolism (VTE).\nAt the modeling level, we implemented two technical improvements in our CardiOmicScore\nframework to address the remaining class imbalance among the six CVDs:\n• Sample-level weighting: For each disease prediction task, we calculated a pos_weight\nparameter as the ratio of negative samples to positive samples. This parameter was\nused in the loss function to apply a heavier penalty for misclassifying positive cases\n(i.e., individuals who develop a disease), forcing the model to pay closer attention to\nthese rare events.\n• Task-level weighting: We assigned a unique weight to each of the six disease\nprediction tasks, where the weight was equal to the inverse of the number of positive\nsamples for that disease. This strategy ensures that tasks with fewer positive cases\n(e.g., PAD) contribute more proportionally to the overall loss function, preventing the\nmodel from being dominated by the most common diseases.\nTo provide strong evidence against overfitting and to rigorously assess the generalizability of\nour models, we implemented a multi-stage evaluation process that goes beyond standard\ninternal validation (Figure 1). First, we evaluated the performance of MetScore and ProScore\ngenerated by our CardiOmicScore framework on their respective geographic testing sets (N =\n12,779 for MetNet and N = 1,775 for ProNet), following best practices from recent large-\nscale UKB studies (Argentieri et al., 2024; Zhang et al., 2024; Julkunen & Rousu, 2025).\nSubsequently, the Multiomics cohort was used to evaluate the incremental benefit of multi-\nomics integration. We found that despite significant differences in several clinical\ncharacteristics between the training and geographic testing sets, both MetScore and ProScore\ndemonstrated strong predictive performance in the geographic testing sets, confirming their\ngeneralizability across distinct UK populations (Supplementary Figure 6). Furthermore, this\nsuperior performance was replicated in the Multiomics cohort. This two-stage strategy\nprovides a robust assessment of model performance and external validity.\nThe revised parts are copied below:\nIn the main text:\nIntroduction Section, Fifth Paragraph: “These CVDs include coronary artery disease\n(CAD), stroke, heart failure (HF), atrial fibrillation (AF), peripheral artery disease (PAD),\nand venous thromboembolism (VTE)—conditions with the highest global burden1.”\nResults Section, Study population Part: “A two-stage, individual-level data partitioning\nstrategy was employed to rigorously evaluate our model’s performance and generalizability\n(see sub-section 4.2 and Supplementary Figure 1). First, we excluded participants who had\nneither metabolomics nor proteomics data available. Then, we divided the remaining\nparticipants into the Metabolomics-only (N = 220,859; those with metabolomics but not\nproteomics data), Proteomics-only (N = 19,086; those with proteomics but not metabolomics\ndata), and Multiomics (N = 24,287; those with genomics, metabolomics, and proteomics\ndata) cohorts based on omics availability. The Metabolomics-only and Proteomics-only\ncohorts were used within our CardiOmicScore framework to develop two deep learning\nmodels, MetNet and ProNet, respectively. The Multiomics cohort was reserved as an\nuntouched validation set to assess the added predictive value of integrating different omics\ndata.\nFollowing best practices in recent UKB studies39–41, each of these development cohorts was\nfurther split into training/validation sets (England and Wales) and a geographic testing set\n(Scotland). Specifically, the Metabolomics-only cohort included 187,272 participants in the\ntraining set, 20,808 in the validation set, and 12,779 in the geographic testing set, while the\nProteomics-only cohort comprised 15,579, 1,732, and 1,775 participants in the training,\nvalidation, and geographic testing sets, respectively. Baseline characteristics were\ncomparable between training and validation sets in both cohorts, whereas significant\ndifferences were observed between training and geographic testing sets (Supplementary\nTables 2–3), thus allowing a robust assessment of the models’ regional generalizability.\nBaseline characteristics and incident cases during follow-up were broadly consistent across\nall study datasets. The median age of participants at baseline was between 56.0 and 58.0\nyears. The proportion of males ranged from 42.8% to 45.8%. The prevalence of baseline\nmedication use was comparable, with 15.8% to 18.1% of participants receiving lipid-\nlowering medication and 10.5% to 11.2% on antihypertensive medication. The distribution of\noutcomes was also similar, with 83.2–85.7% of participants remaining free of any incident\nCVD, 10.6–11.8% developing one CVD, and 3.7–5.0% developing multiple CVDs. Detailed\nbaseline characteristics and follow-up information for each cohort are provided in\nSupplementary Tables 1–3 and Supplementary Figure 2.”\nResults Section, Advancing cardiovascular risk prediction with the power of omics\ninformation Part: “Among the three omics scores, ProScore showed the highest performance\nfor all CVDs with the C-index ranging from 0.69 (95% CI: 0.67–0.71) for VTE to 0.82 (95%\nCI: 0.80–0.84) for PAD (Figure 3a). The C-index of MetScore spanned from 0.64 (95% CI:\n0.61–0.66) for VTE to 0.74 (95% CI: 0.71–0.76) for PAD. Importantly, the strong\nperformance of ProScore and MetScore was replicated in their respective geographic testing\nsets, underscoring their generalizability across distinct UK populations (Supplementary\nFigure 6). In contrast, PRS provided the most limited predictive capacity (C-index range:\n0.52–0.60). Finally, the discriminative performance increased with more clinical predictors\nincluded in the CPH models for all diseases (Figure 3a).”\nMethods Section, Data partition and imputation Part: “To rigorously evaluate model\nperformance and ensure generalizability, we adopted a two-stage individual-level data\npartition strategy, designed to approximate external validation while fully leveraging UKB\ndata (Supplementary Figure 1). First, we excluded participants without both metabolomics\nand proteomics data. The remaining participants were then split into three cohorts based on\nomics data availability: a Metabolomics-only cohort (N = 220,859; those with metabolomics\nbut not proteomics data), a Proteomics-only cohort (N = 19,086; those with proteomics but\nnot metabolomics data), and a Multiomics cohort (N = 24,287; those with genomics,\nmetabolomics, and proteomics data). In our CardiOmicScore framework, we used the\nMetabolomics-only and Proteomics-only cohorts to develop the respective deep learning\nmodels (MetNet and ProNet) and to generate the corresponding omics scores (MetScore and\nProScore). To enable geographic validation, we further split each of these two cohorts into a\ntraining/validation set (England and Wales) and a geographic testing set (Scotland) according\nto the recruitment regions, following approaches commonly used in recent UKB studies39–41.\nThe Multiomics cohort was kept untouched throughout model training and was subsequently\nused as an additional validation cohort to assess the incremental benefit of integrating\nmultiple omics data types for risk prediction.\nCategorical variables were one-hot encoded and continuous variables were standardized by\nthe mean and standard deviation. We used the K-nearest neighbors algorithm (scikit-learn\nv1.3.2 package)65, setting the number of neighbors to five, to impute missing values for\ncontinuous variables. Categorical variables were imputed with mode. For metabolomics or\nproteomics data, imputation and standardization of continuous variables were performed\nusing parameters derived exclusively from the training set. The fitted preprocessing models\nwere then applied to the corresponding validation and geographic testing sets, as well as the\nMultiomics cohort. To derive imputation models and standardization parameters for\nsubsequent application to the Multiomics cohort, we constructed two cohorts from all\navailable individuals with relevant data: one for clinical information (N = 427,225) and one\nfor genomics (N = 412,797).”\nMethods Section, Ascertainment of cardiovascular diseases Part: “The six CVDs analyzed\nin this study included coronary artery disease (CAD), stroke, heart failure (HF), atrial\nfibrillation (AF), peripheral artery disease (PAD), and venous thromboembolism (VTE).”\nMethods Section, Deep learning models Part: “We developed two multitask deep neural\nnetworks, named MetNet and ProNet, to derive the MetScore and ProScore for six CVDs,\nwith 168 metabolites and 2,920 proteins as the input, respectively. MetNet was trained on the\ntraining set from the Metabolomics-only cohort, and ProNet on the training set from the\nProteomics-only cohort, while their respective validation sets were used to monitor model\nfitting and select the optimal checkpoints. The final model was first evaluated on the\ngeographic testing set to assess generalizability across regions. Subsequently, the Multiomics\ncohort, which remained untouched throughout model development, was used for a further\nperformance evaluation.”\nMethods Section, Deep learning models Part: “To address the class imbalance in multitask\nprediction, we applied both sample-level and task-level weighting (Supplementary Methods).\nAt the sample level, the pos_weight parameter was calculated as the ratio of the number of\nnegative samples to positive samples for each task, increasing the penalty for misclassifying\npositive cases and encouraging the model to pay more attention to rare events. At the task\nlevel, each disease was assigned a weight equal to the inverse of its number of positive\nsamples, ensuring that tasks with fewer positive cases contribute proportionally more to the\noverall loss. The final loss was computed as the mean of the weighted losses across all tasks.”\nIn the Supplementary Materials:\nSupplementary Methods Section, Sample-level and task-level weighting Part: “In our\nmulti-task learning framework, the loss function incorporates both sample-level weighting\nand task-level weighting to account for variations in data distribution and task importance.\nAt the sample level, we applied the positive class weight used in PyTorch’s\nBCEWithLogitsLoss:\n𝑁 −𝑃\n! !\n𝑝𝑜𝑠_𝑤𝑒𝑖𝑔ℎ𝑡 =\n! 𝑃\n!\nwhere 𝑁 and 𝑃 denote the number of samples and positive samples in task 𝑡 (𝑡 = 1,...,𝑇),\n! !\nrespectively. The weighted binary cross-entropy loss for task 𝑡 is:\n+!\n1\n\"#$%&' (!) (!) (!) (!)\nℒ = − ./𝑝𝑜𝑠_𝑤𝑒𝑖𝑔ℎ𝑡 ∙𝑦 ∙log𝜎(𝑥 )+(1−𝑦 )∙log81−𝜎(𝑥 )9:\n! 𝑁 ! ( ( ( (\n!\n!,- \nwhere 𝑥 (!) is the model’s predicted logit of sample 𝑖 for task 𝑡; 𝑦 (!) ∈ {0,1} is the binary\n( ( \n-\nlabel of sample 𝑖 in task 𝑡; 𝜎(𝑧) = is the sigmoid function. This increases the penalty\n-.'\"#\nfor misclassifying positive samples, encouraging the model to focus on rare events.\nAt the task level, we assigned each task 𝑡 a weight:\n1/𝑃\n!\n𝜔 = \n! ∑0 (1/𝑃 )\n/,- /\nwhere 𝑇 is the number of tasks. Tasks with fewer positive samples thus contribute\nproportionally more to the total loss, preventing them from being dominated by more\nprevalent diseases. For the ProNet, the task-level weights are 0.06 for coronary artery disease\n(CAD), 0.19 for stroke, 0.14 for heart failure (HF), 0.07 for atrial fibrillation (AF), 0.38 for\nperipheral artery disease (PAD), and 0.15 for venous thromboembolism (VTE). The MetNet\nmodel uses a comparable weighting scheme with values of 0.06 for CAD, 0.20 for stroke,\n0.15 for HF, 0.07 for AF, 0.36 for PAD, and 0.16 for VTE.\nThe total weighted loss is:\nℒ = ∑0 𝜔 ∙ℒ \"#$%&' .”\n!1!#& !,- ! !\nThe updated figures are copied below:\nFigure 1: Study design. a. We extracted data from the UK Biobank, including clinical,\ngenomic, metabolomic, and proteomic predictors, as well as six cardiovascular diseases\n(CVDs) defined by self-reported diagnoses, hospital episode statistics, and death records. b.\nA two-stage data partition strategy was applied: the Multiomics cohort, which remained\nuntouched throughout model training and was used as an independent validation set to\nevaluate the added value of integrating multiple omics data types, and separate training,\nvalidation, and geographic testing sets within the Metabolomics-only and Proteomics-only\ncohorts for model development. Our CardiOmicScore framework utilized two multitask deep\nneural networks to predict the risk of six CVDs from 168 metabolites and 2,920 proteins\nseparately, generating MetScore and ProScore for each CVD. We trained Cox proportional\nhazard (CPH) models on various combinations of polygenic risk score (PRS), MetScore,\nProScore, and three predefined clinical predictor sets (i.e., AgeSex, Clin, and PANEL). c.\nModel performance was first evaluated for MetScore and ProScore in their respective\ngeographic testing sets, and subsequently for all feature combinations in the Multiomics\ncohort. Performance metrics included Harrell’s C-index, calibration plots, and net benefit\ncurves, with confidence intervals estimated using 1,000 bootstrap resamples.\nSupplementary Figure 1: Two-stage individual-level data partitioning strategy. a. Sample\nexclusion flowchart. Genotype data quality control excluded participants with discordant self-\nreported and genetic sex, sex chromosome aneuploidy, and individuals having more than ten\nthird-degree genetic relatives. b. Study framework.\n1“Participants not considered for the Multiomics cohort” in the Metabolomics-only,\nProteomics-only, Genomics-only, and Clinical-only cohorts refer to those with available\nmetabolomics, proteomics, genomics, or clinical data, respectively, after excluding the\n30,167 individuals with all three omics data, who were eligible for inclusion in the\nMultiomics cohort.\nCAD Stroke HF AF PAD VTE\nProScore\nMetScore\n0.6 0.7 0.8 0.90.6 0.7 0.8 0.90.6 0.7 0.8 0.90.6 0.7 0.8 0.90.6 0.7 0.8 0.90.6 0.7 0.8 0.9\nC-index (95% CI)\nHold-out testing Geographic testing\nSupplementary Figure 6: Discriminative performance of MetScore and ProScore.\nDiscriminative performance was evaluated separately for MetScore and ProScore in both the\ngeographic testing and the Multiomics cohort. The Multiomics cohort included 24,287\nparticipants, while the geographic testing sets included 12,779 for MetScore and 1,775 for\nProScore. Performance was assessed using Harrell’s C-index with 95% confidence intervals\nestimated by 1,000 bootstrap resamples.\nReferences:\nArgentieri, M. A., Xiao, S., Bennett, D., Winchester, L., Nevado-Holgado, A. J., Ghose, U.,\nAlbukhari, A., Yao, P., Mazidi, M., Lv, J., Millwood, I., Fry, H., Rodosthenous, R. S.,\nPartanen, J., Zheng, Z., Kurki, M., Daly, M. J., Palotie, A., Adams, C. J., … van\nDuijn, C. M. (2024). Proteomic aging clock predicts mortality and risk of common\nage-related diseases in diverse populations. Nature Medicine, 1–11.\nhttps://doi.org/10.1038/s41591-024-03164-7\nJulkunen, H., & Rousu, J. (2025). Comprehensive interaction modeling with machine\nlearning improves prediction of disease risk in the UK Biobank. Nature\nCommunications, 16(1), 6620. https://doi.org/10.1038/s41467-025-61891-y\nZhang, S., Wang, Z., Wang, Y., Zhu, Y., Zhou, Q., Jian, X., Zhao, G., Qiu, J., Xia, K., Tang,\nB., Mutz, J., Li, J., & Li, B. (2024). A metabolomic profile of biological aging in\n250,341 individuals from the UK Biobank. Nature Communications, 15(1), 8081.\nhttps://doi.org/10.1038/s41467-024-52310-9\nCOMMENT 1-2. Although the authors showed the superior performance of their deep\nlearning derived multiomics scores in comparison with combinations of other predictors,\nadditional analysis is needed to prove the necessity of using neural networks to process\nmultiomic data since simpler machine learning models (e.g. XGBoost, random forest) can\nalso introduce non-linearity of input features. The authors may consider building simpler\nmachine learning models to generate alternative multiomic scores and compare with the deep\nlearning derived scores.\nRESPONSE 1-2. Thank you so much for the valuable suggestion. To benchmark our\nCardiOmicScore framework, we conducted a sensitivity analysis comparing it to several\nmachine learning algorithms, including XGBoost, LightGBM, random forest, and logistic\nregression. We followed an identical pipeline to develop alternative MetScore and ProScore\nversions for a fair comparison. The results show that the omics scores generated by the\nCardiOmicScore framework consistently outperformed those derived from the alternative\nalgorithms (Supplementary Figure 17). This finding suggests that while traditional machine\nlearning models can capture non-linear features, the CardiOmicScore framework’s deep\nlearning architecture, as a model with high representational capacity, was more effective at\nextracting predictive signals from the high-dimensional metabolomic and proteomic data.\nWe copied the modifications in the revised manuscript below:\nIn the main text:\nResults Section, Sensitivity analyses Part: “Moreover, our CardiOmicScore framework\nproved superior to conventional machine learning methods for generating omics scores.\nSpecifically, MetScore and ProScore derived from MetNet and ProNet consistently\noutperformed analogous scores developed using XGBoost, LightGBM, random forest, and\nlogistic regression (Supplementary Figure 17).”\nDiscussion Section, AI-identified biomarkers offer biological insights Part: “Moreover,\nour CardiOmicScore framework outperformed conventional machine learning algorithms,\nunderscoring the unique strength of the multitask deep learning architecture to model\ncomplex, non-linear interactions in high-dimensional omics data.”\nMethods Section, Statistical analyses Part: “Fifth, to demonstrate the advantage of our\nCardiOmicScore framework for high-dimensional omics data, we developed alternative\nMetScore and ProScore using several statistical machine learning algorithms, including\nExtreme Gradient Boosting (XGBoost, xgboost v2.0.3 package), LightGBM (lightgbm v4.4.0\npackage), random forest (scikit-learn v1.3.2 package), and logistic regression (scikit-learn\nv1.3.2 package), following an identical development pipeline (Supplementary Methods and\nSupplementary Table 16). We then compared the predictive utility of these scores against\nthose generated by MetNet and ProNet.”\nIn the Supplementary Materials:\nSupplementary Methods Section, Development of machine learning models Part: “We\ndeveloped alternative MetScore and ProScore using four widely-used machine learning\nalgorithms: Extreme Gradient Boosting (XGBoost), LightGBM, random forest, and logistic\nregression. We trained a separate, independent model for each of the six cardiovascular\ndiseases (CVDs) using all individual metabolomic or proteomic biomarkers as input features.\nData splitting and preprocessing followed the same procedures as in the main analysis. For\neach of the 24 models (4 algorithms × 6 CVDs), we conducted a separate hyperparameter\noptimization using a random search of 50 combinations. The optimal hyperparameter set was\nselected based on the combination that achieved the highest area under the receiver operating\ncharacteristic curve (AUC) on the validation set. Key hyperparameters were tuned for each\nmodel (Supplementary Table 15). For XGBoost and LightGBM, this included the number of\nestimators, learning rate, maximum tree depth, and subsampling ratios, while for random\nforest, we tuned the number of estimators, maximum tree depth, maximum features per split,\nand minimum samples for splits and leaves. For logistic regression, we tuned the inverse of\nregularization strength. To address class imbalance, we employed a sample weighting\nstrategy. For XGBoost and LightGBM, we used the scale_pos_weight parameter;\nspecifically, this was calculated as the ratio of the number of negative cases to positive cases\nin the training data. For random forest and logistic regression, the class_weight='balanced'\nsetting was used. After identifying the optimal hyperparameters, a final model was retrained\non the complete training dataset. To prevent overfitting in XGBoost and LightGBM, early\nstopping was applied during the tuning phase, halting training if the validation AUC did not\nimprove for 20 consecutive rounds. The resulting risk predictions from these models\nconstituted the alternative MetScores and ProScores used for comparison.”\na\nCAD Stroke HF AF PAD VTE\nMetScore\nProScore\nPANEL\n+MetScore\n+ProScore\n+PRS+MetScore\n+PRS+ProScore\n+MetScore+ProScore\n+PRS+MetScore+ProScore\n0.5 0.75 1.0 0.5 0.75 1.0 0.5 0.75 1.0 0.5 0.75 1.0 0.5 0.75 1.0 0.5 0.75 1.0\nC-index (95% CI)\nb\nCAD Stroke HF AF PAD VTE\nPANEL Baseline Baseline Baseline Baseline Baseline Baseline\n+MetScore\n+ProScore\n+PRS+MetScore\n+PRS+ProScore\n+MetScore+ProScore\n+PRS+MetScore+ProScore\n0 0.03 0.06 0 0.03 0.06 0 0.03 0.06 0 0.03 0.06 0 0.03 0.06 0 0.03 0.06\nΔ C-index (95% CI)\nLogistic regression Random forest LightGBM XGBoost MetNet/ProNet\nSupplementary Figure 17: Performance comparison of CardiOmicScore framework against\nother statistical machine learning models. a. Discriminative performance trained on various\npredictor sets. MetScore and ProScore were estimated using different algorithms, including\nlogistic regression, random forest, LightGBM, XGBoost, and MetNet/ProNet. Discriminative\nperformance was evaluated by the Harrell’s C-index. b. Differences in discriminative\nperformance by adding omics information to clinical predictor sets. Data are presented as\npoint estimates with 95% confidence intervals estimated from 1,000 bootstrap resamples.\nSupplementary Table 15: Hyperparameters for machine learning models.\nParameters Values\nXGBoost\nMaximum depth 3, 4, 5, 6, 7, 8\nMinimum child weight 1, 3, 5, 7\nPercentage of subsample per tree 0.6, 0.7, 0.8, 0.9, 1.0\nPercentage of features per tree 0.6, 0.7, 0.8, 0.9, 1.0\nGamma 0, 0.1, 0.2, 0.5, 1.0\nLearning rate 0.01, 0.05, 0.1, 0.2\nNumber of boosting rounds 1000\nLightGBM\nMaximum depth 3, 5, 7, -1\nMinimum child samples 20, 30, 50\nPercentage of subsample per tree 0.6, 0.8, 1.0\nPercentage of features per tree 0.6, 0.8, 1.0\nNumber of leaves 15, 31, 63\nLearning rate 0.01, 0.05, 0.1\nNumber of boosting rounds 1000\nRandom forest\nMaximum depth 5, 7, 10, None\nMaximum features per split Square root, Logarithm (base 2)\nMinimum samples for splits 2, 5, 10\nMinimum samples for leaves 1, 2, 4\nNumber of trees 100, 300, 500\nLogistic regression\nInverse of regularization strength 0.001, 0.01, 0.1, 1, 10, 100\nCOMMENT 1-3. Some details of data composition and processing need to be added to\nverify the validity of model development and improve the clarity of the manuscript. The\ncomposition of the study population needs to be clarified. For example, the reported total\nnumber of CVD outcomes does not match the sum of outcomes of all individual CVD.\nAdditional clarification is needed to explain the proportion of individuals with multiple CVDs\nand how the incidence rates are calculated. In addition, the authors didn’t mention how\ndataset splitting was performed in training and testing and how individuals with multiple\nCVDs are handled in the model training and evaluation. If such cases were treated as\nindependent data points, a single participant with multiple CVDs may appear in both\ntraining and testing sets, resulting in data leakage and inflation of model performance.\nRESPONSE 1-3. Thank you very much for the thoughtful comments and suggestions. We\nrevised the Methods section to improve clarity and provide the requested details on data\nprocessing methods. First, we clarified that our individual-level data partition strategy\nensures that a single participant cannot appear in both training and testing sets, thus\npreventing data leakage (also refer to RESPONSE 1-1). Furthermore, we expanded our\ndescription of the multitask design, emphasizing that each participant is included only once in\nthe dataset but may contribute to the loss function for multiple diseases if they experience\nseveral events, following the methodology of recent UKB studies (Buergel et al., 2022; You\net al., 2023). This approach also explains that the difference between the total number of\nevents and the sum of individual disease events arises because one person can contribute as a\ncase to multiple diseases. Finally, we added details on how incidence rates were calculated in\nthe Methods section, reported the specific proportion of individuals with multiple CVDs to\nthe Results section, and updated the Supplementary Figure 2 to present the follow-up\nduration and distribution of incident CVD cases.\nThe updated parts and figures are copied below:\nIn the main text:\nResults Section, Study population Part: “Baseline characteristics and incident cases during\nfollow-up were broadly consistent across all study datasets. The median age of participants at\nbaseline was between 56.0 and 58.0 years. The proportion of males ranged from 42.8% to\n45.8%. The prevalence of baseline medication use was comparable, with 15.8% to 18.1% of\nparticipants receiving lipid-lowering medication and 10.5% to 11.2% on antihypertensive\nmedication. The distribution of outcomes was also similar, with 83.2–85.7% of participants\nremaining free of any incident CVD, 10.6–11.8% developing one CVD, and 3.7–5.0% \ndeveloping multiple CVDs. Detailed baseline characteristics and follow-up information for\neach cohort are provided in Supplementary Tables 1–3 and Supplementary Figure 2.”\nMethods Section, Deep learning models Part: “Our multitask design enables the\nsimultaneous modeling of multiple CVDs, accounting for individuals who experience several\nevents over time (e.g., CAD followed by stroke or HF). In this setup, each participant\ncontributes simultaneously to the prediction tasks for all six diseases. If a participant\nexperiences multiple incident diseases, they generate multiple non-zero losses but are only\nincluded once in the dataset. This design avoids data duplication while fully leveraging all\navailable incident disease information17,26. It also reflects the clinical reality in which\nmultimorbidity is increasingly common, and where the most useful risk prediction models are\nthose capable of discriminating the outcome of interest in the presence of co-occurring\ndiseases27.”\nMethods Section, Statistical analyses Part: “Incidence proportion for each specific CVD\nwas calculated as the number of new cases for that disease divided by the total number of\nparticipants at risk at baseline.”\nIn the Supplementary Materials:\nTrain Validation Geographic testing Multiomics cohort\nMedian:14.5yrs Median:14.5yrs Median:15.9yrs Median:14.5yrs\nN=187,272 N=20,808 N=12,779 N=24,287\n1.0\nMedian:14.3yrs Median:14.3yrs Median:15.8yrs\nN=15,579 N=1,732 N=1,775\n0.5\n0.0\n0 5 10 15 0 5 10 15 0 5 10 15 0 5 10 15\nObservation time (years)\nSupplementary Figure 2: Follow-up duration and distribution of incident cardiovascular\ndisease (CVD) cases across datasets. a. Distribution of follow-up duration. Here, we defined\nthe follow-up duration as the period from the date of baseline assessment to the date of any\nincident cardiovascular diseases, death, loss to follow-up, or end of available registry follow-\nup (November 30 2023 for England & Wales and December 31 2023 for Scotland),\nwhichever came first. Text annotations provide the median observation time and the total\nnumber of participants for each dataset. b. Proportion of participants with incident cases of\neach CVD. c. Proportion of participants grouped by the number of incident CVD.\nReferences:\nBuergel, T., Steinfeldt, J., Ruyoga, G., Pietzner, M., Bizzarri, D., Vojinovic, D., Upmeier zu\nBelzen, J., Loock, L., Kittner, P., Christmann, L., Hollmann, N., Strangalies, H.,\nBraunger, J. M., Wild, B., Chiesa, S. T., Spranger, J., Klostermann, F., van den\nAkker, E. B., Trompet, S., … Landmesser, U. (2022). Metabolomic profiles predict\nytisneD\na\nTrain Validation Geographic testing Multiomics cohort\n8\n6\n4\n2\n)%\n(\nnoitroporP\nb\nTrain Validation Geographic testing Multiomics cohort\n75\n50\n25\n0\n0 1 >=2 0 1 >=2 0 1 >=2 0 1 >=2\nNumber of incident diseases\n)%\n(\nnoitroporP\nc\nMetabolomics-only cohort Proteomics-only cohort\nindividual multidisease outcomes. Nature Medicine, 28(11), 2309–2320.\nhttps://doi.org/10.1038/s41591-022-01980-3\nYou, J., Guo, Y., Zhang, Y., Kang, J.-J., Wang, L.-B., Feng, J.-F., Cheng, W., & Yu, J.-T.\n(2023). Plasma proteomic profiles predict individual future health risk. Nature\nCommunications, 14(1), 7817. https://doi.org/10.1038/s41467-023-43575-7\nCOMMENT 1-4. Additional details of the deep learning model architecture and loss\nfunction need to be added. For example, does the model output a unique score for each CVD\n(9 scores in total)? How is the overall loss calculated? Also, the authors should include their\nfinal choice of model hyperparameters to improve reproducibility of the model.\nRESPONSE 1-4. Thank you so much for these valuable suggestions. We revised the “Model\ndevelopment” subsection in the manuscript and added a “Sample-level and task-level\nweighting” part in the Supplementary Methods to improve the clarity and reproducibility of\nour CardiOmicScore framework. First, we clarified the model architecture and outputs in the\nMethods section, specifying that the model consists of a shared network and six parallel,\ndisease-specific networks (one for each CVD), which generate a unique MetScore or\nProScore for each CVD. Second, we expanded the description of the loss function in the\nMethods section, detailing our sample- and task-level weighting strategy to address class\nimbalance and clarifying how the final loss is computed, with the full mathematical formulas\nprovided in the Supplementary Methods (also refer to RESPONSE 1-1). Finally, to\nenhance reproducibility, the optimal hyperparameter combinations are provided in the\nSupplementary Table 15, and a diagram of the model architecture is available in\nSupplementary Figure 21. All code used for model development and analysis has been\nmade publicly available at Github: https://github.com/YanLuoCityU/cardiomicscore.\nThe revised parts are copied below:\nMethods Section, Deep learning models Part: “We developed two multitask deep neural\nnetworks, named MetNet and ProNet, to derive the MetScore and ProScore for six CVDs,\nwith 168 metabolites and 2,920 proteins as the input, respectively. MetNet was trained on the\ntraining set from the Metabolomics-only cohort, and ProNet on the training set from the\nProteomics-only cohort, while their respective validation sets were used to monitor model\nfitting and select the optimal checkpoints. The final model was first evaluated on the\ngeographic testing set to assess generalizability across regions. Subsequently, the Multiomics\ncohort, which remained untouched throughout model development, was used for a further\nperformance evaluation.\nMetNet and ProNet had similar model architectures, consisting of a shared network and six\nparallel, disease-specific networks (one for each CVD) (Supplementary Figure 21). The\nshared neural network (denoted as “shared multilayer perceptron [MLP]”) included multiple\nfully connected layers, each with a nonlinear activation function, dropout, and batch\nnormalization. The output of the shared network (i.e., shared representation) is a high-\ndimension representation, capturing the common features for all CVDs. The disease-specific\nnetwork, comprising a disease-specific MLP and a predictor MLP, was designed to learn\ndisease-specific features and predict the risk of individual diseases (i.e., whether the disease\nwill occur or not). The original metabolomic/proteomic biomarkers were fed into the disease-\nspecific multi-layer MLP with nonlinear activation functions, dropout, and batch\nnormalization to obtain a disease-specific representation. The shared and disease-specific\nrepresentations were then concatenated and passed on to the predictor MLP that included\nlinear layers followed by nonlinear activation functions, dropout, and batch normalization\nbefore the final single-output layer to generate the MetScore or ProScore for each disease.\nThese scores were subsequently used as predictors in CPH models to assess their utility for\ncardiovascular risk prediction. Binary cross entropy was used as the loss function for each\ndisease. To address the class imbalance in multitask prediction, we applied both sample-level\nand task-level weighting (Supplementary Methods). At the sample level, the pos_weight\nparameter was calculated as the ratio of the number of negative samples to positive samples\nfor each task, increasing the penalty for misclassifying positive cases and encouraging the\nmodel to pay more attention to rare events. At the task level, each disease was assigned a\nweight equal to the inverse of its number of positive samples, ensuring that tasks with fewer\npositive cases contribute proportionally more to the overall loss. The final loss was computed\nas the mean of the weighted losses across all tasks.\nOur multitask design enables the simultaneous modeling of multiple CVDs, accounting for\nindividuals who experience several events over time (e.g., CAD followed by stroke or HF). In\nthis setup, each participant contributes simultaneously to the prediction tasks for all six\ndiseases. If a participant experiences multiple incident diseases, they generate multiple non-\nzero losses but are only included once in the dataset. This design avoids data duplication\nwhile fully leveraging all available incident disease information17,26. It also reflects the\nclinical reality in which multimorbidity is increasingly common, and where the most useful\nrisk prediction models are those capable of discriminating the outcome of interest in the\npresence of co-occurring diseases27.”\nWe performed random hyperparameter searches separately for MetNet and ProNet using their\nrespective training and validation sets. Each model was trained and evaluated across 100\nrandomly sampled hyperparameter configurations. Hyperparameters included the hidden\nlayer architecture, dropout rate, and activation function for all MLPs (Supplementary Table\n15). The optimal hyperparameter combination for each model, provided in Supplementary\nTable 15, was selected based on the highest average Harrell’s C-index across all six CVDs on\nthe validation set. Once the optimal architecture was determined, we trained the final models\nfive times using different random seeds to account for stochastic variability in the training\nprocess. The ultimate MetScore and ProScore for each participant represent the average of\nthe predictions from these five independent runs, ensuring the robustness of the final scores.\nThe final models were trained using the Adam optimizer. All deep learning models were\ndeveloped in Python v3.8.5 using Pytorch v1.11.0 package with hyperparameter tuning using\nOptuna v4.0.0 package77,78.”\nThe updated figures and tables are copied below:\nSupplementary Table 15: Hyperparameters for ProNet and MetNet.\nParameters Search space Final hyperparameters\nShare MLP ProNet MetNet\nHidden dimensions1 • [128, 64, 32] [512, 256, [512, 256,\n• [256, 128, 64, 32] 128] 128]\n• [512, 256, 128, 64, 32]\n• [256, 128, 64]\n• [512, 256, 128, 64]\n• [512, 256, 128]\nDropout 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7 0.5 0.1\nDisease-specific\nMLP\nHidden dimensions1 • [128, 64, 32] [512, 256, [128, 64, 32]\n• [256, 128, 64, 32] 128]\n• [512, 256, 128, 64, 32]\n• [256, 128, 64]\n• [512, 256, 128, 64]\n• [512, 256, 128]\nDropout 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7 0.6 0.5\nPredictor MLP\nHidden dimensions2 • [32] [256] [64, 32]\n• [64, 32]\n• [128, 64, 32]\n• [256, 128, 64, 32]\n• [512, 256, 128, 64, 32]\n• [64]\n• [128, 64]\n• [256, 128, 64]\n• [512, 256, 128, 64]\n• [128]\n• [256, 128]\n• [512, 256, 128]\n• [256]\n• [512, 256]\nDropout 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7 0.1 0.5\nActivation ReLU, LeakyReLU, ELU, Tanh ELU ELU\nBatch size 128 for ProNet, 1024 for 128 1024\nMetNet\nLearning rate 1×1023 for ProNet, 1×1023 1×1024\n1×1024 for MetNet\nEpochs 50 50 50\n1Each list defines the neuron counts for successive hidden layers. The final value in each list\nrepresents the output dimension for that specific MLP block.\n2The Predictor MLP was designed with a single output neuron (output dimension = 1) to\ngenerate the final scalar risk score.\nSupplementary Figure 21: The architecture of MetNet (a) and ProNet (b). MLP =\nMultilayer perceptron, CAD = Coronary artery disease, VTE = Venous thromboembolism.\nCOMMENT 1-5. In Figure 3, it would be clearer if all subplots use the same x-axis range in\neach sub-figure.\nRESPONSE 1-5. Thank you for pointing this out. We updated Figure 3 to use the same x-\naxis range for all subplots as requested. The revised figure is copied below:\na\nCAD Stroke HF AF PAD VTE\nASCVD\nSCORE2\nPRS\nMetScore\nProScore\nAgeSex\n+PRS\n+MetScore\n+ProScore\n+PRS+MetScore\n+PRS+ProScore\n+MetScore+ProScore\n+PRS+MetScore+ProScore\nClin\n+PRS\n+MetScore\n+ProScore\n+PRS+MetScore\n+PRS+ProScore\n+MetScore+ProScore\n+PRS+MetScore+ProScore\nPANEL\n+PRS\n+MetScore\n+ProScore\n+PRS+MetScore\n+PRS+ProScore\n+MetScore+ProScore\n+PRS+MetScore+ProScore\n0.5 0.75 1.0 0.5 0.75 1.0 0.5 0.75 1.0 0.5 0.75 1.0 0.5 0.75 1.0 0.5 0.75 1.0\nC-index (95% CI)\nb\nCAD Stroke HF AF PAD VTE\nAgeSex Baseline Baseline Baseline Baseline Baseline Baseline\n+PRS\n+MetScore\n+ProScore\n+PRS+MetScore\n+PRS+ProScore\n+MetScore+ProScore\n+PRS+MetScore+ProScore\nClin Baseline Baseline Baseline Baseline Baseline Baseline\n+PRS\n+MetScore\n+ProScore\n+PRS+MetScore\n+PRS+ProScore\n+MetScore+ProScore\n+PRS+MetScore+ProScore\nPANEL Baseline Baseline Baseline Baseline Baseline Baseline\n+PRS\n+MetScore\n+ProScore\n+PRS+MetScore\n+PRS+ProScore\n+MetScore+ProScore\n+PRS+MetScore+ProScore\n0 0.04 0.08 0.12 0 0.04 0.08 0.12 0 0.04 0.08 0.12 0 0.04 0.08 0.12 0 0.04 0.08 0.12 0 0.04 0.08 0.12\nΔC-index(95%CI)\nOmics-based AgeSex-based Clinical-based PANEL-based\nFigure 3: Predictive performance of multiomics for cardiovascular diseases. a.\nDiscriminative performance of models trained on various predictor sets. Vertical dashed lines\nindicate the performance of the three clinical predictor sets. Discriminative performance was\nevaluated by the Harrell’s C-index. b. Differences in discriminative performance by adding\nomics information to clinical predictor sets. Data are presented as point estimates with 95%\nconfidence intervals estimated from 1,000 bootstrap resamples.\nCOMMENT 1-6. There are some redundant texts in the manuscript. For example, the\nauthors currently repeatedly explain meanings of all CVD abbreviations and population\nbreakdown in all figure legends. Instead, it may be better if the authors use a separate section\n(e.g. in Methods) to explain their evaluation setup.\nRESPONSE 1-6. Thank you for this valuable feedback. We followed your suggestion and\nremoved the redundant text from the figure legends. The definitions of the CVD\nabbreviations and the number of incident cases and incidence rates for each cardiovascular\ndisease are described in the Methods and Results sections, respectively, to avoid repetition.\nWe copied the updated paragraphs in the main text below:\nResults Section, Study population Part: “A two-stage, individual-level data partitioning\nstrategy was employed to rigorously evaluate our model’s performance and generalizability\n(see sub-section 4.2 and Supplementary Figure 1). First, we excluded participants who had\nneither metabolomics nor proteomics data available. Then, we divided the remaining\nparticipants into the Metabolomics-only (N = 220,859; those with metabolomics but not\nproteomics data), Proteomics-only (N = 19,086; those with proteomics but not metabolomics\ndata), and Multiomics (N = 24,287; those with genomics, metabolomics, and proteomics\ndata) cohorts based on omics availability. The Metabolomics-only and Proteomics-only\ncohorts were used within our CardiOmicScore framework to develop two deep learning\nmodels, MetNet and ProNet, respectively. The Multiomics cohort was reserved as an\nuntouched validation set to assess the added predictive value of integrating different omics\ndata.”\nFollowing best practices in recent UKB studies39–41, each of these development cohorts was\nfurther split into training/validation sets (England and Wales) and a geographic testing set\n(Scotland). Specifically, the Metabolomics-only cohort included 187,272 participants in the\ntraining set, 20,808 in the validation set, and 12,779 in the geographic testing set, while the\nProteomics-only cohort comprised 15,579, 1,732, and 1,775 participants in the training,\nvalidation, and geographic testing sets, respectively. Baseline characteristics were\ncomparable between training and validation sets in both cohorts, whereas significant\ndifferences were observed between training and geographic testing sets (Supplementary\nTables 2–3), thus allowing a robust assessment of the models’ regional generalizability.\nBaseline characteristics and incident cases during follow-up were broadly consistent across\nall study datasets. The median age of participants at baseline was between 56.0 and 58.0\nyears. The proportion of males ranged from 42.8% to 45.8%. The prevalence of baseline\nmedication use was comparable, with 15.8% to 18.1% of participants receiving lipid-\nlowering medication and 10.5% to 11.2% on antihypertensive medication. The distribution of\noutcomes was also similar, with 83.2–85.7% of participants remaining free of any incident\nCVD, 10.6–11.8% developing one CVD, and 3.7–5.0% developing multiple CVDs. Detailed\nbaseline characteristics and follow-up information for each cohort are provided in\nSupplementary Tables 1–3 and Supplementary Figure 2.”\nMethods Section, Ascertainment of cardiovascular diseases Part: “The six CVDs analyzed\nin this study included coronary artery disease (CAD), stroke, heart failure (HF), atrial\nfibrillation (AF), peripheral artery disease (PAD), and venous thromboembolism (VTE). All\ndiseases were ascertained based on self-reported diagnoses and operations, hospital episode\nstatistics, and death records. Self-reported information was used only to determine the\npresence of CVDs at baseline. Detailed definitions are provided in Supplementary Table 5.\nFollow-up duration was calculated from the date of baseline assessment to the date of\nincident outcomes, death, loss to follow-up, or end of available registry follow-up (November\n30 2023 for England & Wales and December 31 2023 for Scotland)66, whichever came first.”\nCOMMENT 1-7. Some figures need to be adjusted. For example, the texts in Figure 5 c & d\nare too small to read. Supplementary figures 13 & 14 are not completely displayed.\nRESPONSE 1-7. Thank you for the comment. We corrected all the issues you pointed out:\nthe content from the former Figure 5c–5d was moved to a new Figure 6, and the\nsupplementary figures you mentioned (renumbered as Supplementary Figures 10 and 11)\nwere reformatted for full and clear display. Due to their large size, Supplementary Figures 10\nand 11 are not shown here. The updated Figures 5–6 is copied below:\na b\nMUFA MMP12\nXL_HDL_FC NTproBNP\nLA C G D D C F P 15 1\nCreatinine EDA2R\nAlbumin HAVCR1\nGly CXCL17\nXXL_VLDL_CE LTBP2\nIDL_FC DCN\nTNFRSF11B\nGln NPPB\nS_LDL_PL ACRV1\nTotal_FA FASLG\nXS_VLDL_FC ELN\nTyr BCAN\nS_HDL_P W SP F IN DC K6 2\nM_VLDL_CE PGF\nIDL_P CTHRC1\nCholines SPOCK1\nSFA CCL11\nGlycA CRIP2\nIGDCC4\nL_LDL_TG KLK4\nAcetone CHGA\nGlucose ADGRG2\nLactate EGFR\nL_HDL_TG CHRDL2\nXS_VLDL_CE GA C L D N 1 T C 5\nPyruvate NEFL\nS_LDL_TG SLITRK1\nL_HDL_C TNXB\nXXL_VLDL_C MYL3\nVLDL_CE AGER\nPIGR\nVLDL_size CEACAM5\nL_LDL_FC PHOSPHO1\nHis RGMA\nM_HDL_TG FABP9\nS_VLDL_TG ACE2\nM_HDL_PL ADAM M T S S M 1 B 3\nM_LDL_TG ACTA2\nXXL_VLDL_FC SUSD2\nAcetoacetate GALNT10\nDHA ADM\nS_LDL_CE A R R E T T 3\nM_VLDL_PL PTPRR\nLDL_FC HPGDS\nL_HDL_FC LRRN1\nPhosphatidylc DPP4\nHDL_C EDN1\nC19orf12\nXL_VLDL_TG CLEC4A\nApoA1 IGFBP7\nM_LDL_PL NPL\nS_VLDL_L ODAM\nLeu ACVRL1\nVal A L L Y P 7 P 5\nL_VLDL_CE TSHB\nL_LDL_CE FCAR\nS_LDL_C NCAN\nS_HDL_PL SEZ6L\nXXL_VLDL_L CRIM1\nPLB1\nHDL_P SLAMF1\nS_LDL_P SFTPD\nIDL_TG ACP5\nSphingomyelins TNFRSF13C\nLDL_size ENDOU\nCitrate C1Q L T TA N 4 F H 9\nIle TIMD4\nPhe DLK1\nCAD Stroke HF AF PAD VTE CAD Stroke HF AF PAD VTE\nNormalized mean |SHAP|\n0 0.5 1\nFigure 5: Importance of biomarkers in cardiovascular disease-specific MetScore (a) and\nProScore (b). Importance was measured by mean absolute SHAP values. For each disease,\nthe 50 most important metabolites and the 25 most important proteins were identified. The\nunion of these features across all diseases yielded a total of 65 metabolites and 77 proteins.\nAlaGlnGlyHisIleLeuTotal_B V C a A l A PheTyrAlbu C m r i e n atin G in l e ycA DHL AAMUO FmO AemP g Uea S g_ FFT a3 AAo _ Ut 6 anls_ C aF Gi t t Au L rlau P r a a tcc e y t o A i tr o a A s u c n A te v c e e bc a et O H a t et L e o T t Ht D V D e oa o CH b L L L n R c L TV t n l u a D D _ e D e i _ o o e L n t l L t y L_ t L LL a n i m Da c r L_ t __ _ a l e a L n_ L CC H t l _ e aC _ DC n L t L D _ _ C L C _C XL_VLDL_PL XL_VLDL_P XL_VLDL_L XL_VLDL_FC XL_VLDL_C X E L_VLDL_C XXL_VL X D X L L _ _ T V G X L X D L L_ _ P V X L L X D L L _ _ X V P X LD L_ L X V _ X L L L D X _ L V X _ L F L D C _ L V _ L C V D E L L D _ T C L o _ ta P L l D _P H L_ D PL_ V P LL DD L H L _ D_ sisL zi S _ e zes pP i h z hP i e nohC gso op hs mh o V p o l y T ih L n ge o a L D ll e y i t t HD n is La cd s D l_ L y V _T l _ T L c T L G L T o _ G H D D G t T a V D L G T Ll L __ L H L o _ P V D P D T P t _ LH D a L o P L L L D L L l D L tD ___ L aL _CC L C Ll_ C _ _ EEE _ F E F F F CC C C\nFigure 6: Metabolites (a) and proteins (b) are associated with cardiovascular disease risk.\nSHAP beeswarm plots are shown here for coronary artery disease as an example. Each\nindividual is assigned a SHAP value for every protein and metabolite. To simplify the\npresentation, SHAP values for each biomarker were divided into percentiles, and the mean\nSHAP value and biomarker level were calculated for each percentile. Each dot represents one\npercentile. All 168 metabolites and the top-ranked 157 proteins are shown. Dark grey lines\nindicate zero SHAP value (i.e., no contribution). The farther a dot from this line, the stronger\nthe absolute contribution to the MetScore or ProScore. Deviations toward the center and\nperiphery represent negative and positive contributions. Red and blue colors indicate high\nand low levels of metabolites and proteins, respectively.\nAlaGlnGlyHisIleLeuTotal_B V C a A l A PheTyrAlbu C m r i e n atin G in l e ycA DHL AAMUO FmO AemP g Uea S g_ FFT a3 AAo _ Ut 6 anls_ C aF Gi t t Au L rlau P r a a tcc e y t o A i tr o a A s u c n A te v c e e bc a et O H a t et L e o T t Ht D V D e oa o CH b L L L n R c L TV t n l u a D D _ e D e i _ o o e L n t l L t y L_ t L LL a n i m Da c r L_ t __ _ a l e a L n_ L CC H t l _ e aC _ DC n L t L D _ _ C L C _C XL_VLDL_PL XL_VLDL_P XL_VLDL_L XL_VLDL_FC XL_VLDL_C X E L_VLDL_C XXL_VL X D X L L _ _ T V G X L X D L L_ _ P V X L L X D L L _ _ X V P X LD L_ L X V _ X L L L D X _ L V X _ L F L D C _ L V _ L C V D E L L D _ T C L o _ ta P L l D _P H L_ D PL_ V P LL DD L H L _ D_ sisL zi S _ e zes pP i h z hP i e nohC gso op hs mh o V p o l y T ih L n ge o a L D ll e y i t t HD n is La cd s D l_ L y V _T l _ T L c T L G L T o _ G H D D G t T a V D L G T Ll L __ L H L o _ P V D P D T P t _ LH D a L o P L L L D L L l D L tD ___ L aL _CC L C Ll_ C _ _ EEE _ F E F F F CC C C\nREVIEWER 2’S COMMENTS\nRemarks to the Author:\nCOMMENT 2-1. The authors undertake Genomic, Transcriptomic, and Proteomic profiling\nof a large cohort of patients from the UK biobank and through AI deep learning models have\ndeveloped genomic, transcriptomic, and proteomic risk scores for the prediction of 9\ncardiovascular disease outcomes. This study reports that such these multiomic risk scores are\nsuperior to existing risk scores based on clinical and biochemical data both individually and\nin combination. This is a great pilot dataset, but it needs true external validation in an\nindependent cohort, in order to be mature for presentation as definitive.\nRESPONSE 2-1. Thank you very much for the positive feedback and for raising the crucial\npoint about model validation. We agree that robust validation is essential for demonstrating\nthe clinical potential of the risk model. Given the limited availability of external cohorts with\ncomparable multi-omics data, we implemented a two-stage individual-level data partition\nstrategy that goes beyond standard internal validation to rigorously assess the generalizability\nof our models (Figure 1, Supplementary Figure 1, and also refer to RESPONSE 1-1). This\napproach, consistent with best practices for large-scale studies within the UK Biobank\n(Argentieri et al., 2024; Zhang et al., 2024; Julkunen & Rousu, 2025), is intended to\napproximate the conditions of external validation. We first evaluated the performance of the\nMetScore and ProScore generated by our CardiOmicScore framework on a geographically\ndistinct testing set (participants from Scotland) and subsequently performed a final evaluation\nof added value of multiomics integration on the Multiomics cohort, which was kept entirely\nseparate from all development stages. Notably, even with significant differences in baseline\nclinical characteristics between the training and geographic testing sets, both MetScore and\nProScore showed strong and reproducible predictive performance, which was further\nconfirmed in the Multiomics cohort (Supplementary Figure 6). We believe this multi-stage\napproach provides robust evidence of our models’ ability to generalize.\nThe revised parts are copied below:\nIn the main text:\nResults Section, Study population Part: “A two-stage, individual-level data partitioning\nstrategy was employed to rigorously evaluate our model’s performance and generalizability\n(see sub-section 4.2 and Supplementary Figure 1). First, we excluded participants who had\nneither metabolomics nor proteomics data available. Then, we divided the remaining\nparticipants into the Metabolomics-only (N = 220,859; those with metabolomics but not\nproteomics data), Proteomics-only (N = 19,086; those with proteomics but not metabolomics\ndata), and Multiomics (N = 24,287; those with genomics, metabolomics, and proteomics\ndata) cohorts based on omics availability. The Metabolomics-only and Proteomics-only\ncohorts were used within our CardiOmicScore framework to develop two deep learning\nmodels, MetNet and ProNet, respectively. The Multiomics cohort was reserved as an\nuntouched validation set to assess the added predictive value of integrating different omics\ndata.\nFollowing best practices in recent UKB studies39–41, each of these development cohorts was\nfurther split into training/validation sets (England and Wales) and a geographic testing set\n(Scotland). Specifically, the Metabolomics-only cohort included 187,272 participants in the\ntraining set, 20,808 in the validation set, and 12,779 in the geographic testing set, while the\nProteomics-only cohort comprised 15,579, 1,732, and 1,775 participants in the training,\nvalidation, and geographic testing sets, respectively. Baseline characteristics were\ncomparable between training and validation sets in both cohorts, whereas significant\ndifferences were observed between training and geographic testing sets (Supplementary\nTables 2–3), thus allowing a robust assessment of the models’ regional generalizability.\nBaseline characteristics and incident cases during follow-up were broadly consistent across\nall study datasets. The median age of participants at baseline was between 56.0 and 58.0\nyears. The proportion of males ranged from 42.8% to 45.8%. The prevalence of baseline\nmedication use was comparable, with 15.8% to 18.1% of participants receiving lipid-\nlowering medication and 10.5% to 11.2% on antihypertensive medication. The distribution of\noutcomes was also similar, with 83.2–85.7% of participants remaining free of any incident\nCVD, 10.6–11.8% developing one CVD, and 3.7–5.0% developing multiple CVDs. Detailed\nbaseline characteristics and follow-up information for each cohort are provided in\nSupplementary Tables 1–3 and Supplementary Figure 2.”\nResults Section, Advancing cardiovascular risk prediction with the power of omics\ninformation Part: “Among the three omics scores, ProScore showed the highest performance\nfor all CVDs with the C-index ranging from 0.69 (95% CI: 0.67–0.71) for VTE to 0.82 (95%\nCI: 0.80–0.84) for PAD (Figure 3a). The C-index of MetScore spanned from 0.64 (95% CI:\n0.61–0.66) for VTE to 0.74 (95% CI: 0.71–0.76) for PAD. Importantly, the strong\nperformance of ProScore and MetScore was replicated in their respective geographic testing\nsets, underscoring their generalizability across distinct UK populations (Supplementary\nFigure 6). In contrast, PRS provided the most limited predictive capacity (C-index range:\n0.52–0.60). Finally, the discriminative performance increased with more clinical predictors\nincluded in the CPH models for all diseases (Figure 3a).”\nMethods Section, Data partition and imputation Part: “To rigorously evaluate model\nperformance and ensure generalizability, we adopted a two-stage individual-level data\npartition strategy, designed to approximate external validation while fully leveraging UKB\ndata (Supplementary Figure 1). First, we excluded participants without both metabolomics\nand proteomics data. The remaining participants were then split into three cohorts based on\nomics data availability: a Metabolomics-only cohort (N = 220,859; those with metabolomics\nbut not proteomics data), a Proteomics-only cohort (N = 19,086; those with proteomics but\nnot metabolomics data), and a Multiomics cohort (N = 24,287; those with genomics,\nmetabolomics, and proteomics data). In our CardiOmicScore framework, we used the\nMetabolomics-only and Proteomics-only cohorts to develop the respective deep learning\nmodels (MetNet and ProNet) and to generate the corresponding omics scores (MetScore and\nProScore). To enable geographic validation, we further split each of these two cohorts into a\ntraining/validation set (England and Wales) and a geographic testing set (Scotland) according\nto the recruitment regions, following approaches commonly used in recent UKB studies39–41.\nThe Multiomics cohort was kept untouched throughout model training and was subsequently\nused as an additional validation cohort to assess the incremental benefit of integrating\nmultiple omics data types for risk prediction.\nCategorical variables were one-hot encoded and continuous variables were standardized by\nthe mean and standard deviation. We used the K-nearest neighbors algorithm (scikit-learn\nv1.3.2 package)65, setting the number of neighbors to five, to impute missing values for\ncontinuous variables. Categorical variables were imputed with mode. For metabolomics or\nproteomics data, imputation and standardization of continuous variables were performed\nusing parameters derived exclusively from the training set. The fitted preprocessing models\nwere then applied to the corresponding validation and geographic testing sets, as well as the\nMultiomics cohort. To derive imputation models and standardization parameters for\nsubsequent application to the Multiomics cohort, we constructed two cohorts from all\navailable individuals with relevant data: one for clinical information (N = 427,225) and one\nfor genomics (N = 412,797).”\nThe updated figures are copied below:\nFigure 1: Study design. a. We extracted data from the UK Biobank, including clinical,\ngenomic, metabolomic, and proteomic predictors, as well as six cardiovascular diseases\n(CVDs) defined by self-reported diagnoses, hospital episode statistics, and death records. b.\nA two-stage data partition strategy was applied: the Multiomics cohort, which remained\nuntouched throughout model training and was used as an independent validation set to\nevaluate the added value of integrating multiple omics data types, and separate training,\nvalidation, and geographic testing sets within the Metabolomics-only and Proteomics-only\ncohorts for model development. Our CardiOmicScore framework utilized two multitask deep\nneural networks to predict the risk of six CVDs from 168 metabolites and 2,920 proteins\nseparately, generating MetScore and ProScore for each CVD. We trained Cox proportional\nhazard (CPH) models on various combinations of polygenic risk score (PRS), MetScore,\nProScore, and three predefined clinical predictor sets (i.e., AgeSex, Clin, and PANEL). c.\nModel performance was first evaluated for MetScore and ProScore in their respective\ngeographic testing sets, and subsequently for all feature combinations in the Multiomics\ncohort. Performance metrics included Harrell’s C-index, calibration plots, and net benefit\ncurves, with confidence intervals estimated using 1,000 bootstrap resamples.\nSupplementary Figure 1: Two-stage individual-level data partitioning strategy. a. Sample\nexclusion flowchart. Genotype data quality control excluded participants with discordant self-\nreported and genetic sex, sex chromosome aneuploidy, and individuals having more than ten\nthird-degree genetic relatives. b. Study framework.\n1“Participants not considered for the Multiomics cohort” in the Metabolomics-only,\nProteomics-only, Genomics-only, and Clinical-only cohorts refer to those with available\nmetabolomics, proteomics, genomics, or clinical data, respectively, after excluding the\n30,167 individuals with all three omics data, who were eligible for inclusion in the\nMultiomics cohort.\nCAD Stroke HF AF PAD VTE\nProScore\nMetScore\n0.6 0.7 0.8 0.90.6 0.7 0.8 0.90.6 0.7 0.8 0.90.6 0.7 0.8 0.90.6 0.7 0.8 0.90.6 0.7 0.8 0.9\nC-index (95% CI)\nHold-out testing Geographic testing\nSupplementary Figure 6: Discriminative performance of MetScore and ProScore.\nDiscriminative performance was evaluated separately for MetScore and ProScore in both the\ngeographic testing and the Multiomics cohort. The Multiomics cohort included 24,287\nparticipants, while the geographic testing sets included 12,779 for MetScore and 1,775 for\nProScore. Performance was assessed using Harrell’s C-index with 95% confidence intervals\nestimated by 1,000 bootstrap resamples.\nReferences:\nArgentieri, M. A., Xiao, S., Bennett, D., Winchester, L., Nevado-Holgado, A. J., Ghose, U.,\nAlbukhari, A., Yao, P., Mazidi, M., Lv, J., Millwood, I., Fry, H., Rodosthenous, R. S.,\nPartanen, J., Zheng, Z., Kurki, M., Daly, M. J., Palotie, A., Adams, C. J., … van\nDuijn, C. M. (2024). Proteomic aging clock predicts mortality and risk of common\nage-related diseases in diverse populations. Nature Medicine, 1–11.\nhttps://doi.org/10.1038/s41591-024-03164-7\nJulkunen, H., & Rousu, J. (2025). Comprehensive interaction modeling with machine\nlearning improves prediction of disease risk in the UK Biobank. Nature\nCommunications, 16(1), 6620. https://doi.org/10.1038/s41467-025-61891-y\nZhang, S., Wang, Z., Wang, Y., Zhu, Y., Zhou, Q., Jian, X., Zhao, G., Qiu, J., Xia, K., Tang,\nB., Mutz, J., Li, J., & Li, B. (2024). A metabolomic profile of biological aging in\n250,341 individuals from the UK Biobank. Nature Communications, 15(1), 8081.\nhttps://doi.org/10.1038/s41467-024-52310-9\nCOMMENT 2-2. The authors helpfully provide a list of proteins and metabolites whose\nlevels increase and decrease the risk of cardiovascular disease. However with NTpro-BNP\ncoming out as the top hit in all 9 cardiovascular diseases is there risk that these results have\nbeen confounded? High NTProBNP contributes towards increased CAD risk for example, but\ndoes this merely represent the interaction or comorbidity of different types of CVD such as\nCAD and HF? On closer inspection, the directionality and effect sizes of the proteins are\nquite similar visually between CVD conditions – are the authors therefore suggesting that the\nmultiomic signatures of 9 different cardiovascular diseases are largely the same?\nRESPONSE 2-2. Thank you very much for the insightful comments. We agree that, in our\ninitial analysis, the multiomic signatures were overly similar across different CVDs. Upon\nfurther investigation, we found that this issue was primarily driven by class imbalance,\nwhereby model training was dominated by the most prevalent diseases (e.g., CAD, AF, and\nHF). To address these concerns, we re-ran our analyses after implementing two significant\nchanges. First, we refined our study’s scope to the six most common CVDs, removing the\ntwo rarest outcomes (ventricular arrhythmias and abdominal aortic aneurysm) as their\nextremely low incidence rates (0.8% and 0.4%, respectively) were a primary source of model\ninstability. Second, we incorporated both sample-level and task-level weighting strategies\ninto CardiOmicScore framework to ensure that less frequent diseases contributed\nproportionally to model training (also refer to RESPONSE 1-1). In addition, to improve the\nstability and reliability of feature-importance estimates, we trained five models per score with\ndifferent random seeds and calculated the final SHAP value for each biomarker by averaging\nthe results across these five runs. These revisions have led to a much more nuanced and\nbiologically plausible set of findings. For metabolites, there is a partial overlap in the top-\nranking biomarkers across the six CVDs, reflecting shared metabolic pathways. In contrast,\nthe important proteins are now more disease-specific. For example, GDF15 was important for\nCAD, HF, AF, and VTE, whereas KLK4 was specific for stroke and BCAN for AF (Figures\n5–6). To more clearly illustrate these shared and disease-specific associations, we\nsummarized the top-ranking metabolites and proteins for all six CVDs in a new table\n(Supplementary Table 4). Collectively, these results suggest that the revised model captures\nboth common and disease-specific biology in a more reliable manner.\nResults Section, AI identifies disease-specific metabolites and proteins Part: “We further\nutilized the Shapley Additive exPlanations (SHAP) method to examine the relationship of\nmetabolites and proteins with each CVD. To capture global importance, we first calculated\nthe mean absolute SHAP value for each feature and normalized these values within each\noutcome. Figure 5a-b display the top 65 metabolites and 77 proteins ranked by their global\nimportance.\nWe observed a partial overlap in the top-ranking metabolites across the six CVDs. The most\nimpactful metabolites across all CVDs included creatinine, albumin, glutamine, fatty acids\nsuch as linoleic acids (LA) and monounsaturated fatty acids (MUFA), and lipoprotein\ncomponents such as free cholesterol in intermediate-density lipoprotein (IDL_FC), free\ncholesterol in very large high-density lipoprotein (XL_HDL_FC), cholesteryl esters in\nchylomicrons and extremely large very-low-density lipoprotein (XXL_VLDL_CE). \nMeanwhile, other biomarkers were important for specific diseases, such as tyrosine and\nleucine for HF and AF, and glycoprotein acetyls (GlycA) for PAD and VTE. For proteins, the\nimportant biomarkers were more distinct between CVDs. We found that NT-proBNP and\nNPPB were important for CAD, HF, and AF, while GDF15 was important for CAD, HF, AF,\nand VTE. Other key associations included MMP12 for CAD and PAD, CDCP1 for CAD,\nHF, and VTE, EDA2R for CAD and stroke, NEFL for stroke and PAD, WFDC2 for HF and\nPAD, and FASLG for stroke and VTE. The most important disease-specific proteins also\nincluded KLK4 and CRIP2 for stroke; BCAN and ELN for AF; PLB1 and ENDOU for PAD;\nand HPGDS and ADGRG2 for VTE.\nThe SHAP analysis also revealed that while the direction of the top biomarker-disease\nassociations was highly consistent across all six CVDs, the magnitude of their contributions\nvaried. We used CAD as an example to illustrate the associations between plasma biomarkers\nand disease risks. Among metabolites, higher plasma levels of creatinine, GlycA, MUFA,\ntyrosine, glutamine, IDL_FC, and XL_HDL_FC were associated with an increased risk of\nCAD, while higher levels of albumin, LA, leucine, and XXL_VLDL_CE were associated\nwith lower CAD risk (Figure 6a). For proteins, NT-proBNP, NPPB, GDF15, MMP12,\nCDCP1, EDA2R, NEFL, WFDC2, KLK4, CRIP2, ELN, positively influenced disease risk,\nwhile a negative association was observed between CAD risk and elevated levels of FASLG,\nBCAN, PLB1, ENDOU, HPGDS, and ADGRG2 (Figure 6b). SHAP beeswarm plots of\nmetabolites and proteins for other diseases are provided in Supplementary Figure 10–11.\nSummary of top-ranking metabolites and proteins are presented in Supplementary Table 4.”\nDiscussion Section, AI-identified biomarkers offer biological insights Part: “Evaluating\nthe contributions of important proteins and metabolites to each CVD reveals underlying\ndisease mechanisms and potential therapeutic targets. We confirmed the role of proteins such\nas NT-proBNP and NPPB, as well as metabolites such as albumin and creatinine, in CVD\nrisk assessment. These biomarkers are well-established in epidemiological studies and are\nalready part of routine clinical care47–50. However, our analyses revealed that ProScore\noutperformed any single proteins such as NT-proBNP and NPPB both as an individual\npredictor and when added to clinical models. Similarly, individual metabolic biomarkers such\nas creatinine, albumin, and traditional lipid-related biomarkers (e.g., total lipids or\ncholesterol), yielded lower predictive performance compared to MetScore. These findings\nsuggest that the MetScore and ProScore may provide a more comprehensive representation of\ndisease-related molecular mechanisms than single biomarkers, and thus highlight the\nimportance of high-throughput metabolomics and proteomics in personalized CVD risk\nprediction. Moreover, our CardiOmicScore framework outperformed conventional machine\nlearning algorithms, underscoring the unique strength of the multitask deep learning\narchitecture to model complex, non-linear interactions in high-dimensional omics data.\nIn addition to the established biomarkers, we identified several less-studied circulating\nmetabolites and proteins associated with CVD risk, which were consistent with previous\nomics-based modeling studies17,22–33. We observed a positive association between future\nCVD and GlycA, a composite NMR-based signal and biomarker of systemic inflammation51.\nIn line with earlier findings, increasing levels of MUFA52 and tyrosine53 were associated with\na higher risk of CVDs, while we also confirmed the protective role of LA52. Interestingly,\nwhile remnant cholesterol is recognized as a causal risk factor for atherosclerotic\ncardiovascular disease (ASCVD)54, we found that its components may have inconsistent\nassociations with CVD risk: higher IDL_FC levels were associated with increased risk,\nwhereas higher XXL_VLDL_CE levels were associated with lower risk. These opposing\nassociations warrant future investigation, as they suggest that measuring IDL_FC and\nXXL_VLDL_CE may further stratify risk beyond traditional LDL-cholesterol levels.\nRegarding proteins, our analysis identified a diverse spectrum of biomarkers, revealing\npathways that are shared across CVDs and highly specific to individual diseases. Several\nproteins were associated with multiple CVDs, suggesting common underlying pathological\nprocesses. For example, the stress- and inflammation-induced cytokine GDF15 showed\nstrong predictive value for CAD, HF, AF, and VTE, acting as a general marker of systemic\nstress and broad cardiovascular pathology55,56. Similarly, MMP12, an enzyme involved in\nextracellular matrix degradation and inflammatory responses, was associated with both CAD\nand PAD57,58. The apoptosis-related ligand FASLG, a key mediator of extrinsic cell death and\nvascular inflammation, was important for stroke and VTE59. In addition, the neuronal damage\nmarker NEFL, reflecting systemic neuroaxonal injury, was predictive of both stroke and\nPAD, suggesting a mechanistic link between neural injury and peripheral vascular events60.\nBeyond these shared pathways, we also found several disease-specific proteins, underscoring\nthe distinct biology of each condition. Key specific associations included such as CRIP2, a\nkey regulator of cellular responses to ischemia, with stroke61, and PLB1, involved in\nmetabolic regulation, with PAD62. Taken together, the identification of biomarkers from such\ndiverse pathways—covering cardiac stress, inflammation, neuronal injury, and matrix\nremodeling—suggests that our CardiOmicScore framework can capture a multi-system\nbiological signature of cardiovascular risk that extends far beyond traditional risk factors.\nThese key molecules not only serve as potential targets for new therapeutic interventions to\nprevent or reverse CVD progression, but also illuminate druggable pathways for repurposing\nexisting medications—such as anti-inflammatory colchicine63. Our findings also suggest that\nbased on individual omics profiling, clinicians may be able to tailor treatments targeting the\nunderlying biological mechanisms of specific CVDs. For example, patients with high levels\nof inflammation markers like GlycA may benefit from treatments aimed at reducing systemic\ninflammation, while those with elevated proteins like MMP12, which is involved in\nextracellular matrix degradation, could be targeted with therapies focused on preventing the\nvascular remodeling that lead to atherosclerosis. Notably, our study provides only\npreliminary evidence for the predictive importance of circulating biomarkers, and further\nlarge cohort studies and clinical trials are needed to validate our findings.”\nMethods Section, Deep learning models Part: “To ensure stable and reliable interpretations,\nwe generated SHAP values for each of the five models trained with different random seeds.\nThe final SHAP value for each biomarker was calculated by averaging these results across\nthe five runs.”\na b\nMUFA MMP12\nXL_HDL_FC NTproBNP\nLA C G D D C F P 15 1\nCreatinine EDA2R\nAlbumin HAVCR1\nGly CXCL17\nXXL_VLDL_CE LTBP2\nIDL_FC DCN\nTNFRSF11B\nGln NPPB\nS_LDL_PL ACRV1\nTotal_FA FASLG\nXS_VLDL_FC ELN\nTyr BCAN\nS_HDL_P W SP F IN DC K6 2\nM_VLDL_CE PGF\nIDL_P CTHRC1\nCholines SPOCK1\nSFA CCL11\nGlycA CRIP2\nIGDCC4\nL_LDL_TG KLK4\nAcetone CHGA\nGlucose ADGRG2\nLactate EGFR\nL_HDL_TG CHRDL2\nXS_VLDL_CE GA C L D N 1 T C 5\nPyruvate NEFL\nS_LDL_TG SLITRK1\nL_HDL_C TNXB\nXXL_VLDL_C MYL3\nVLDL_CE AGER\nPIGR\nVLDL_size CEACAM5\nL_LDL_FC PHOSPHO1\nHis RGMA\nM_HDL_TG FABP9\nS_VLDL_TG ACE2\nM_HDL_PL ADAM M T S S M 1 B 3\nM_LDL_TG ACTA2\nXXL_VLDL_FC SUSD2\nAcetoacetate GALNT10\nDHA ADM\nS_LDL_CE A R R E T T 3\nM_VLDL_PL PTPRR\nLDL_FC HPGDS\nL_HDL_FC LRRN1\nPhosphatidylc DPP4\nHDL_C EDN1\nC19orf12\nXL_VLDL_TG CLEC4A\nApoA1 IGFBP7\nM_LDL_PL NPL\nS_VLDL_L ODAM\nLeu ACVRL1\nVal A L L Y P 7 P 5\nL_VLDL_CE TSHB\nL_LDL_CE FCAR\nS_LDL_C NCAN\nS_HDL_PL SEZ6L\nXXL_VLDL_L CRIM1\nPLB1\nHDL_P SLAMF1\nS_LDL_P SFTPD\nIDL_TG ACP5\nSphingomyelins TNFRSF13C\nLDL_size ENDOU\nCitrate C1Q L T TA N 4 F H 9\nIle TIMD4\nPhe DLK1\nCAD Stroke HF AF PAD VTE CAD Stroke HF AF PAD VTE\nNormalized mean |SHAP|\n0 0.5 1\nFigure 5: Importance of biomarkers in cardiovascular disease-specific MetScore (a) and\nProScore (b). Importance was measured by mean absolute SHAP values. For each disease,\nthe 50 most important metabolites and the 25 most important proteins were identified. The\nunion of these features across all diseases yielded a total of 65 metabolites and 77 proteins.\nAlaGlnGlyHisIleLeuTotal_B V C a A l A PheTyrAlbu C m r i e n atin G in l e ycA DHL AAMUO FmO AemP g Uea S g_ FFT a3 AAo _ Ut 6 anls_ C aF Gi t t Au L rlau P r a a tcc e y t o A i tr o a A s u c n A te v c e e bc a et O H a t et L e o T t Ht D V D e oa o CH b L L L n R c L TV t n l u a D D _ e D e i _ o o e L n t l L t y L_ t L LL a n i m Da c r L_ t __ _ a l e a L n_ L CC H t l _ e aC _ DC n L t L D _ _ C L C _C XL_VLDL_PL XL_VLDL_P XL_VLDL_L XL_VLDL_FC XL_VLDL_C X E L_VLDL_C XXL_VL X D X L L _ _ T V G X L X D L L_ _ P V X L L X D L L _ _ X V P X LD L_ L X V _ X L L L D X _ L V X _ L F L D C _ L V _ L C V D E L L D _ T C L o _ ta P L l D _P H L_ D PL_ V P LL DD L H L _ D_ sisL zi S _ e zes pP i h z hP i e nohC gso op hs mh o V p o l y T ih L n ge o a L D ll e y i t t HD n is La cd s D l_ L y V _T l _ T L c T L G L T o _ G H D D G t T a V D L G T Ll L __ L H L o _ P V D P D T P t _ LH D a L o P L L L D L L l D L tD ___ L aL _CC L C Ll_ C _ _ EEE _ F E F F F CC C C\nFigure 6: Metabolites (a) and proteins (b) are associated with cardiovascular disease risk.\nSHAP beeswarm plots are shown here for coronary artery disease as an example. Each\nindividual is assigned a SHAP value for every protein and metabolite. To simplify the\npresentation, SHAP values for each biomarker were divided into percentiles, and the mean\nSHAP value and biomarker level were calculated for each percentile. Each dot represents one\npercentile. All 168 metabolites and the top-ranked 157 proteins are shown. Dark grey lines\nindicate zero SHAP value (i.e., no contribution). The farther a dot from this line, the stronger\nthe absolute contribution to the MetScore or ProScore. Deviations toward the center and\nperiphery represent negative and positive contributions. Red and blue colors indicate high\nand low levels of metabolites and proteins, respectively.\nAlaGlnGlyHisIleLeuTotal_B V C a A l A PheTyrAlbu C m r i e n atin G in l e ycA DHL AAMUO FmO AemP g Uea S g_ FFT a3 AAo _ Ut 6 anls_ C aF Gi t t Au L rlau P r a a tcc e y t o A i tr o a A s u c n A te v c e e bc a et O H a t et L e o T t Ht D V D e oa o CH b L L L n R c L TV t n l u a D D _ e D e i _ o o e L n t l L t y L_ t L LL a n i m Da c r L_ t __ _ a l e a L n_ L CC H t l _ e aC _ DC n L t L D _ _ C L C _C XL_VLDL_PL XL_VLDL_P XL_VLDL_L XL_VLDL_FC XL_VLDL_C X E L_VLDL_C XXL_VL X D X L L _ _ T V G X L X D L L_ _ P V X L L X D L L _ _ X V P X LD L_ L X V _ X L L L D X _ L V X _ L F L D C _ L V _ L C V D E L L D _ T C L o _ ta P L l D _P H L_ D PL_ V P LL DD L H L _ D_ sisL zi S _ e zes pP i h z hP i e nohC gso op hs mh o V p o l y T ih L n ge o a L D ll e y i t t HD n is La cd s D l_ L y V _T l _ T L c T L G L T o _ G H D D G t T a V D L G T Ll L __ L H L o _ P V D P D T P t _ LH D a L o P L L L D L L l D L tD ___ L aL _CC L C Ll_ C _ _ EEE _ F E F F F CC C C\nCOMMENT 2-3. Similarly, LDL metabolite fractions and total lipids / cholesterol are not\nstrongly predictive of CAD (which is unusual in a healthy cohort, most of whom are not\ntaking statins). Does this trend persist when the 20% of the cohort on lipid-lowering\ntherapies are removed from the cohort? It would be helpful if the authors could comment on\nthis observation and why this might be the case?\nRESPONSE 2-3. We thank the reviewer for the thoughtful comments. Following the\nreviewer’s suggestion, we conducted a two-part sensitivity analysis among participants not\ntaking lipid-lowering medication at baseline. We first refitted the MetNet model and re-\ncalculated the SHAP-based feature importance rankings within this subgroup. Second, we\nspecifically evaluated the predictive performance of traditional lipid-related biomarkers in\nCPH models for this subgroup and compared it to the full cohort. In the refitted MetNet\nmodel, we found that the top-ranked metabolites still included a mix of non-lipid and lipid\nmarkers (e.g., glycine, creatinine, GlycA) (Supplementary Figure 18), with seven of the top\nmetabolites overlapping with our primary analysis, consistent with previous findings in the\nXL_VLDL_TG M a e S d m iu a m L ll a V L L r e V g D D r e e y L r L I y l L D a s D L rg m M L SS M e S M S M a S_ _ _ M SS _ M l __ _ H L L L _ _ l M L L _ _ _ LL L D D _ D L L L V D D _ L L L D _ D DL D D _ I L L L L D L D D L D L L I L D L I L L L LD D _ _ L _ D L D L L _ _ X D L L X L _ X _ L _ D _ C L _ T F _ _ L L _ I P I L X L L _ L _ _ L S P _ I T I L D D X L FC _ L L C D G C L L E D _ L _ D D P _ _ P _ L C L L _ _ G S C L L _ D D L L _ a E F T H C L L _ C V H L X L X E P _ _ L _ r C G H L L _ _ X D _ g _ L X SV D L P D XX S L L _ P _ T P D D L X _ F L e L _L SS L m C X L L L G _H C V _ _ L L DL _ __ S _ H H E H C _ a _D _ L V C V L C _ H l X D D F D D T E l L _ LV L S D C L G L P D V _ L LD L L _ L _ L_ L _ D L L L _ L S V _ PP_ P H _ D L _ L H _ L TL F _ D _ L SV D L D G H CC _L LL L LD VD E __ _ S _ L L C L C L C H _ D _ _ _ L M V E S D F H_ LT S L_ e H L CL _ D G _ V D d _ _ P D V S L i H L L L u L L L _ _ D D _ m V D _P PL L M P L L S _ M _ H D e L _ _ L T _ d M D F L V G H i C _ _ u L L D H C M D m M L D E _ L _ _ V V L _ H C M _ L L C D D C _ M D L V E L _ L _ L _ H F M D M T D C _ G L _ L V H _ M _ L P M D _ L D L H _ L L V _ M D M _ P L _ L _ P D V H _P L L D M D _ L L _ L L _ V _ T L F G D C L M S _ _ _ C V H E S L D S D m L _ L _ a H C _ ll D C H L_ D S L C L _ _ E H V D LD L_ L F _ L C S T _ L G _ V D H L L D D V L L e _ _ g L P S r L L a _ _ H L V D L L D C _ L P _ L P _ L L P D _ L L V D _ H L _S CF_ G LD T L _L V D _L H_S EC_LDLV_L snieto C rp _ o L p D il L o V pA _L 1AopA A BopA D Ve A ry m la in rg o e a V c L id D s L Branch a e m d i - n ch o e a x i a n t c r i e d m C s h el y y lo l A a m r r g o ic e m r V o L a n D t s i L c an a d m F in lu o i a d ci c b d o a s L I n l n a i c p n fl e o c a n e p m tr ro m at t i a e o s i t n n i i o z s L p n ei a p s rt o F ic p a le r t o ty t O e a in t c h i p d e a s r rt l G i i T c p m l l r e i y d ig ec s t l o y a K P lb c y e h e o s t o r T l o i C i i s d s t o n h e p F r e C t e s e o s h a r l h b l l e a o e o o t l e l e i s i l d p p d e t c i i i e e h s d d r s t o s s y e l l r e o e s s l t t e e L r N r o s o l e w P g B o a i s t o i i v m ti e v a e c r o c k n o e t n r r i t b r v i u b a t u l i u o ti n e o H n igh N b eu N ro e l u o r g o y lo I g I y TN P F H S S R O PS CS S L R S R OU AF P T M F P G O S PG SCR N K R PT 1 O D H P ND N I M DKP I P M X0 L G F E RM D R E OA C O A B B R B 12 12 A F R A R I A S I R 1 FA A n M B L 1 S 1R I D E L R G L 1 C c R 4 3 N L C P A R L o M C E 1 F E R C C C A C P 2 l _ BA A D o D G R 2 D D 1 D PP C g 1 3 I 4 E 3 CP A 4 C y 0 C 0 F P2 0 M 1 2 BDE L1 C 9 5 S4 G D o A B B E G r A f 6 C G 1 4 B 2 G A 2A RF CC A F L NAV L L 7 N I M R T n S T 3 f L G L l 1 a 1 I F G R 0 T m A H R P N m P A A K A a V 3 S 1 t C H 3 i E P o P R G 1 n M G 1 H A Y I D S I O 4 S 6 M S H 3 T S L 1 E P P P A B H 6 K C A L R K 4 I L S 4 T P A C 3 4 M H H M A P D 12 C C R 1 7 Q E T T N SE F9 Z6 A L M SP O IN T K6 AH W S F G DC2 ACRV1 C 4A4L I O I A y 9 C g T o N lo W cnO UODNE 2MERT 22LI 01FSFNT A 1POT C31FSRFNTTNFRSF11B TFF2 ACE2 D SLAM A F C 1 P5 SCG A 3 CTA2 PO A N D 3 AMTS1 P 3 G A F DGRG2 M A LN NGPT M L A E 3 P PE LP1 LY A 7 P 5 OM L A R R RN T3 L 1 C A D M H P I 2 3 T C G H A R I 6 L C D 1 O L A 2 I L L C 1 1 A X 5 1 C H D S L C 1 D G 6 N D 1 Z 1 L B M F K D 1 C A 1 P R F E P I C L n D 4 6 A F f EI l A L R a G S 3 F m G A F L D m E R B G G P F P a P C 1 G t C 9 i 2 X 5 o C A ZH n C M M TC SI LS G HP S 1 C K C FB 7 FRC IL BT 1 3 P I CL L P M C E C TL C M 7 1 a B C CC M L M r B P N 4 C d 2L S A N A P 4 4 2 P i 2L P o M GG S 7 T P 31 P N m L R B E 1A p R S M B TX L P T E r I e RL T P P G o F YEG B N FE I C t E C T L D N S B M X R a A T A CL B P D 1 L H D b H TM N P S D 3 BL N 2 I TC o H N G B P F D P NP 4 2 l C B i RA 11 c 3T4 2 C 5 1 CardiometabolicII\nUK Biobank (Buergel et al., 2022). Furthermore, while the predictive performance of some\nlipid markers for CAD did improve slightly in the participants not taking lipid-lowering\nmedication, their added value to the comprehensive PANEL-based clinical model remained\nlimited (Supplementary Figures 19–20). Taken together, these findings suggest that the\nconfounding effect of lipid-lowering therapy is not the primary reason for the lower-than-\nexpected ranking of traditional lipid markers in our model. Instead, it highlights the ability of\nour CardiOmicScore framework to capture more powerful predictive signals that go beyond\nwhat traditional lipid measures alone can offer.\nResults Section, Sensitivity analyses Part: “Finally, after refitting the MetNet model in the\nsubgroup of participants not taking lipid-lowering medications, the most important\nmetabolites included MUFA, glycine, creatinine, albumin, extra-large high-density\nlipoprotein free cholesterol, glutamine, IDL_FC, tyrosine, LA, and GlycA (Supplementary\nFigure 18). Seven of these metabolites overlapped with those highlighted in the main\nanalysis. Furthermore, although the individual predictive performance of some traditional\nlipid-related biomarkers for CAD improved slightly after excluding participants on lipid-\nlowering medication, their addition still provided only limited improvement in predictive\nperformance to the PANEL-based model. This finding was consistent in both the full\nMultiomics cohort and the subgroup excluding baseline users of lipid-lowering medication\n(Supplementary Figures 19–20).”\nDiscussion Section, AI-identified biomarkers offer biological insights Part: “Evaluating\nthe contributions of important proteins and metabolites to each CVD reveals underlying\ndisease mechanisms and potential therapeutic targets. We confirmed the role of proteins such\nas NT-proBNP and NPPB, as well as metabolites such as albumin and creatinine, in CVD\nrisk assessment. These biomarkers are well-established in epidemiological studies and are\nalready part of routine clinical care47–50. However, our analyses revealed that ProScore\noutperformed any single proteins such as NT-proBNP and NPPB both as an individual\npredictor and when added to clinical models. Similarly, individual metabolic biomarkers such\nas creatinine, albumin, and traditional lipid-related biomarkers (e.g., total lipids or\ncholesterol), yielded lower predictive performance compared to MetScore. These findings\nsuggest that the MetScore and ProScore may provide a more comprehensive representation of\ndisease-related molecular mechanisms than single biomarkers, and thus highlight the\nimportance of high-throughput metabolomics and proteomics in personalized CVD risk\nprediction. Moreover, our CardiOmicScore framework outperformed conventional machine\nlearning algorithms, underscoring the unique strength of the multitask deep learning\narchitecture to model complex, non-linear interactions in high-dimensional omics data.”\nMethods Section, Statistical analyses Part: “Finally, to investigate the potential effect of\nlipid-lowering treatment, we refitted the MetNet using the same architecture but trained only\non participants not taking lipid-lowering medication. We then calculated the mean absolute\nSHAP values to identify the important metabolites within this subgroup. Additionally, using\nthe same analytical procedure as our first sensitivity analysis, we evaluated the predictive\nperformance of traditional lipid-related biomarkers in the Multiomics cohort after excluding\nbaseline users of lipid-lowering medication, and compared it with their performance in the\nfull set.”\nThe added figures are copied below:\nXL_HDL_FC\nAlbumin\nCreatinine\nGly\nMUFA\nIDL_FC\nGln\nTyr\nXXL_VLDL_CE\nGlycA\nS_LDL_PL\nLA\nS_HDL_P\nCholines\nXS_VLDL_FC\nL_HDL_TG\nTotal_FA\nLactate\nM_VLDL_CE\nAcetone\nPyruvate\nL_LDL_TG\nL_LDL_FC\nHis\nSFA\nAcetoacetate\nL_HDL_C\nS_HDL_PL\nIDL_P\nGlucose\nXS_VLDL_CE\nS_LDL_TG\nApoA1\nUnsaturation\nLDL_FC\nXXL_VLDL_C\nM_LDL_TG\nL_LDL_CE\nLeu\nXXL_VLDL_TG\nHDL_C\nXXL_VLDL_L\nXS_VLDL_TG\nPhosphatidylc\nM_VLDL_PL\nL_VLDL_CE\nM_HDL_TG\nHDL_CE\nM_HDL_PL\nL_HDL_FC\nM_LDL_PL\nL_VLDL_C\nXXL_VLDL_FC\nPhosphoglyc\nL_VLDL_FC\nXL_VLDL_TG\nHDL_P\nM_VLDL_FC\nXL_VLDL_CE\nIle\nL_LDL_C\nS_HDL_L\nDHA\nLDL_size\nOmega_3\nVal\nCitrate\nPhe\nPUFA\nOmega_6\nCAD Strok\nN\ne\norma\nH\nli\nF\nzed m\nA\ne\nF\nan |\nP\nS\nA\nH\nD\nAP|\nVTE\n0 0.5 1\nSupplementary Figure 18: Feature importance from MetNet refitted after excluding after\nexcluding participants on lipid-lowering medication. The MetNet was refitted using an\nidentical architecture on a subgroup of the Metabolomics-only cohort that excluded\nindividuals on baseline lipid-lowering medication (N = 185,086). Feature importance was\nmeasured by calculating mean absolute SHAP values for the participants in the Multiomics\ncohort who were also not receiving lipid-lowering medication (N = 19,895). For each disease,\nthe 50 most important metabolites were identified. The union of these features across all\ndiseases yielded a total of 70 metabolites.\na\nCAD Stroke HF AF PAD VTE\nRemnant_C\nTotal_C\nTotal_CE\nTotal_FC\nTotal_L\nTotal_PL\nTotal_TG\nnon_HDL_C\nClinical_LDL_C\nLDL_C\nLDL_CE\nLDL_FC\nLDL_L\nLDL_P\nLDL_PL\nLDL_TG\nLDL_size\nPANEL\n+Remnant_C\n+Total_C\n+Total_CE\n+Total_FC\n+Total_L\n+Total_PL\n+Total_TG\n+non_HDL_C\n+Clinical_LDL_C\n+LDL_C\n+LDL_CE\n+LDL_FC\n+LDL_L\n+LDL_P\n+LDL_PL\n+LDL_TG\n+LDL_size\n0.5 0.75 1.0 0.5 0.75 1.0 0.5 0.75 1.0 0.5 0.75 1.0 0.5 0.75 1.0 0.5 0.75 1.0\nC-index (95% CI)\nb\nCAD Stroke HF AF PAD VTE\nPANEL Baseline Baseline Baseline Baseline Baseline Baseline\n+Remnant_C\n+Total_C\n+Total_CE\n+Total_FC\n+Total_L\n+Total_PL\n+Total_TG\n+non_HDL_C\n+Clinical_LDL_C\n+LDL_C\n+LDL_CE\n+LDL_FC\n+LDL_L\n+LDL_P\n+LDL_PL\n+LDL_TG\n+LDL_size\n0 0.015 0.03 0 0.015 0.03 0 0.015 0.03 0 0.015 0.03 0 0.015 0.03 0 0.015 0.03\nΔC-index(95%CI)\nIndividual biomarker PANEL-based\nSupplementary Figure 19: Predictive performance of traditional lipid-related biomarkers for\ncardiovascular diseases. a. Discriminative performance for 17 lipid-related biomarkers.\nVertical dashed lines indicate the performance of the PANEL set. Discriminative\nperformance was evaluated by the Harrell’s C-index. b. Differences in discriminative\nperformance by adding each individual biomarker to the PANEL set. Data are presented as\npoint estimates with 95% confidence intervals estimated from 1,000 bootstrap resamples.\na\nCAD Stroke HF AF PAD VTE\nRemnant_C\nTotal_C\nTotal_CE\nTotal_FC\nTotal_L\nTotal_PL\nTotal_TG\nnon_HDL_C\nClinical_LDL_C\nLDL_C\nLDL_CE\nLDL_FC\nLDL_L\nLDL_P\nLDL_PL\nLDL_TG\nLDL_size\nPANEL\n+Remnant_C\n+Total_C\n+Total_CE\n+Total_FC\n+Total_L\n+Total_PL\n+Total_TG\n+non_HDL_C\n+Clinical_LDL_C\n+LDL_C\n+LDL_CE\n+LDL_FC\n+LDL_L\n+LDL_P\n+LDL_PL\n+LDL_TG\n+LDL_size\n0.5 0.75 1.0 0.5 0.75 1.0 0.5 0.75 1.0 0.5 0.75 1.0 0.5 0.75 1.0 0.5 0.75 1.0\nC-index (95% CI)\nb\nCAD Stroke HF AF PAD VTE\nPANEL Baseline Baseline Baseline Baseline Baseline Baseline\n+Remnant_C\n+Total_C\n+Total_CE\n+Total_FC\n+Total_L\n+Total_PL\n+Total_TG\n+non_HDL_C\n+Clinical_LDL_C\n+LDL_C\n+LDL_CE\n+LDL_FC\n+LDL_L\n+LDL_P\n+LDL_PL\n+LDL_TG\n+LDL_size\n0 0.015 0.03 0 0.015 0.03 0 0.015 0.03 0 0.015 0.03 0 0.015 0.03 0 0.015 0.03\nΔC-index(95%CI)\nIndividual biomarker PANEL-based\nSupplementary Figure 20: Predictive performance of traditional lipid-related biomarkers for\ncardiovascular diseases among participants not receiving lipid-lowering medication (N =\n19,895). a. Discriminative performance for 17 lipid-related biomarkers. Vertical dashed lines\nindicate the performance of the PANEL set. Discriminative performance was evaluated by\nthe Harrell’s C-index. b. Differences in discriminative performance by adding each\nindividual biomarker to the PANEL set. Data are presented as point estimates with 95%\nconfidence intervals estimated from 1,000 bootstrap resamples.\nCOMMENT 2-4. The authors report that “the genetic risk of CVDeath was measured using\nthe PRS for CAD since there is no available GWAS summary statistics for CVDeath”. Is\nthere any reason the author’s couldn’t run their own CVDeath GWAS rather than applying\nthe CAD GWAS results to a non-identical outcome.\nRESPONSE 2-4. Thank you very much for this important methodological point. We refined\nour analytical framework to focus on six common cardiovascular diseases (coronary artery\ndisease, stroke, heart failure, atrial fibrillation, peripheral artery disease, and venous\nthromboembolism) and removed CVDeath from our primary endpoints. Running a GWAS\nfor CVDeath within our cohort was not feasible due to the limited number of events, which\nwould result in insufficient power and unstable estimates. Similarly, ventricular arrhythmias\nand abdominal aortic aneurysm were excluded given their low incidence (0.8% and 0.4%,\nrespectively). To still account for the influence of mortality, we performed competing risk\nanalyses using Fine-Gray subdistribution hazard models for each of the six CVDs, treating\nall-cause death as a competing event, which confirmed that the predictive value of the omics\nscores was consistent with our main findings (Supplementary Figure 16). Finally, we added\nthe PGS Catalog IDs for all six PRS to the Methods section to enhance reproducibility. The\nrevised manuscript contains the following change:\nIntroduction Section, Fifth Paragraph: “Unlike traditional models, both MetNet and ProNet\nutilize a multitask architecture to learn high-dimensional representations from metabolomics\nand proteomics data, generating disease-specific risk scores, termed MetScore and ProScore,\nfor a comprehensive range of CVDs. These CVD include coronary artery disease (CAD),\nstroke, heart failure (HF), atrial fibrillation (AF), peripheral artery disease (PAD), and venous\nthromboembolism (VTE)—conditions with the highest global burden1.”\nResults Section, Statistical analyses Part: “We also found that the predictive value of the\nomics scores remained unchanged after excluding incident cases that occurred within the first\ntwo years of follow-up (Supplementary Figure 15) or accounting for the competing risk of\ndeath (Supplementary Figure 16).”\nMethods Section, Ascertainment of cardiovascular diseases Part: “The six CVDs analyzed\nin this study included coronary artery disease (CAD), stroke, heart failure (HF), atrial\nfibrillation (AF), peripheral artery disease (PAD), and venous thromboembolism (VTE).”\nMethods Section, Multiomics data Part: “Effect sizes of SNP-disease associations were\ncollected from published PRSs available in the PGS Catalog13,69–73. We included 235 SNPs\nfor CAD (PGS003438)13, 63 SNPs for stroke (PGS005230)69, 38 SNPs for HF\n(PGS003969)70, 154 SNPs for AF (PGS004905)71, 19 SNPs for PAD (PGS005158)72, and\n6–11.”\nMethods Section, Statistical analyses Part: “Fourth, to account for the competing risk of\ndeath, we fitted Fine-Gray subdistribution hazard models, treating all-cause death as a\ncompeting event (cmprsk v2.2.11 R package).”\nThe added figure is copied below:\na\nCAD Stroke HF AF PAD VTE\nPRS\nMetScore\nProScore\nPANEL\n+PRS\n+MetScore\n+ProScore\n+PRS+MetScore\n+PRS+ProScore\n+MetScore+ProScore\n+PRS+MetScore+ProScore\n0.5 0.75 1.0 0.5 0.75 1.0 0.5 0.75 1.0 0.5 0.75 1.0 0.5 0.75 1.0 0.5 0.75 1.0\nC-index (95% CI)\nb\nCAD Stroke HF AF PAD VTE\nPANEL Baseline Baseline Baseline Baseline Baseline Baseline\n+PRS\n+MetScore\n+ProScore\n+PRS+MetScore\n+PRS+ProScore\n+MetScore+ProScore\n+PRS+MetScore+ProScore\n0 0.03 0.06 0 0.03 0.06 0 0.03 0.06 0 0.03 0.06 0 0.03 0.06 0 0.03 0.06\nΔC-index(95%CI)\nOmics-based PANEL-based\nSupplementary Figure 16: Predictive performance of multiomics for cardiovascular\ndiseases after considering competing risk of death. a. Discriminative performance trained on\nvarious predictor sets. Vertical dashed lines indicate the performance of the PANEL set.\nDiscriminative performance was evaluated by the Harrell’s C-index. b. Differences in\ndiscriminative performance by adding omics information to the PANEL set. All-cause death\nwas treated as a competing event. The number of primary (CVD-specific) and competing\n(death) events were as follows: CAD (1,788 primary, 1,592 competing), stroke (584 primary,\n1,791 competing), HF (798 primary, 1,694 competing), AF (1,524 primary, 1,627\ncompeting), PAD (324 primary, 1,879 competing), and VTE (679 primary, 1,727 competing).\nData are presented as point estimates with 95% confidence intervals estimated from 1,000\nbootstrap resamples.\nCOMMENT 2-5. The authors describe the demographics of the whole cohort but not the\ndemographics of the individual training and validation cohorts. Could the authors please\nprovide these along with statistical testing to show that such cohorts are not statistically\ndifferent. This is especially important given the absence of an external validation cohort.\nRESPONSE 2-5. Thank you for this valuable suggestion to provide a more detailed\ncomparison of the study cohorts. We performed the requested analyses and added the results\nto the manuscript (Supplementary Tables 1–3, Supplementary Figure 2). The statistical\ncomparisons confirm the need of our splitting strategy. As expected, there were no significant\ndifferences in baseline characteristics between the training and validation sets. In contrast, we\nobserved significant differences between the training and geographic testing sets. This\nheterogeneity was by design, as the geographic split (England/Wales vs. Scotland) was\nintended to provide a robust test of our models’ regional generalizability before the final\nevaluation. We have copied the relevant methods and results below (Supplementary Tables\n2–3 are omitted due to their length):\nIn the main text:\nResults Section, Study population Part: “A two-stage, individual-level data partitioning\nstrategy was employed to rigorously evaluate our model’s performance and generalizability\n(see sub-section 4.2 and Supplementary Figure 1). We first divided participants into the\nMetabolomics-only (N = 220,859), Proteomics-only (N = 19,086), and Multiomics (N =\n24,287) cohorts based on omics availability. The Metabolomics-only and Proteomics-only\ncohorts were used within our CardiOmicScore framework to develop two deep learning\nmodels, MetNet and ProNet, respectively. The Multiomics cohort was reserved as an\nuntouched validation set to assess the added predictive value of integrating different omics\ndata.\nFollowing best practices in recent UKB studies39–41, each of these development cohorts was\nfurther split into training/validation sets (England and Wales) and a geographic testing set\n(Scotland). Specifically, the Metabolomics-only cohort included 187,272 participants in the\ntraining set, 20,808 in the validation set, and 12,779 in the geographic testing set, while the\nProteomics-only cohort comprised 15,579, 1,732, and 1,775 participants in the training,\nvalidation, and geographic testing sets, respectively. Baseline characteristics were\ncomparable between training and validation sets in both cohorts, whereas significant\ndifferences were observed between training and geographic testing sets (Supplementary\nTables 2–3), thus allowing a robust assessment of the models’ regional generalizability.\nBaseline characteristics and incident cases during follow-up were broadly consistent across\nall study datasets. The median age of participants at baseline was between 56.0 and 58.0\nyears. The proportion of males ranged from 42.8% to 45.8%. The prevalence of baseline\nmedication use was comparable, with 15.8% to 18.1% of participants receiving lipid-\nlowering medication and 10.5% to 11.2% on antihypertensive medication. The distribution of\noutcomes was also similar, with 83.2–85.7% of participants remaining free of any incident\nCVD, 10.6–11.8% developing one CVD, and 3.7–5.0% developing multiple CVDs. Detailed\nbaseline characteristics and follow-up information for each cohort are provided in\nSupplementary Tables 1–3 and Supplementary Figure 2.”\nMethods Section, Statistical analyses Part: “Baseline characteristics were summarized using\nmedian (IQR) for continuous variables or numbers (percentages) for categorical variables.\nChi-squared tests were used for categorical variables, and Mann–Whitney U tests were used\nfor non-normally distributed continuous variables to compare baseline characteristics\nbetween the training and validation sets, and between the training and geographic testing\nsets.”\nThe updated figures and tables are copied below:\nTrain Validation Geographic testing Multiomics cohort\nMedian:14.5yrs Median:14.5yrs Median:15.9yrs Median:14.5yrs\nN=187,272 N=20,808 N=12,779 N=24,287\n1.0\nMedian:14.3yrs Median:14.3yrs Median:15.8yrs\nN=15,579 N=1,732 N=1,775\n0.5\n0.0\n0 5 10 15 0 5 10 15 0 5 10 15 0 5 10 15\nObservation time (years)\nSupplementary Figure 2: Follow-up duration and distribution of incident cardiovascular\ndisease (CVD) cases across datasets. a. Distribution of follow-up duration. Here, we defined\nthe follow-up duration as the period from the date of baseline assessment to the date of any\nincident cardiovascular diseases, death, loss to follow-up, or end of available registry follow-\nup (November 30 2023 for England & Wales and December 31 2023 for Scotland),\nwhichever came first. Text annotations provide the median observation time and the total\nnumber of participants for each dataset. b. Proportion of participants with incident cases of\neach CVD. c. Proportion of participants grouped by the number of incident CVD.\nSupplementary Table 1: Baseline characteristics of participants in the Multiomics cohort.\nCharacteristics1 Multiomics cohort (N = 24,287)\nDemographic information\nAge (years) 58.0 (50.0, 63.0)\nMale 10,666 (43.9%)\nEthnicity\nWhite 22,735 (93.7%)\nBlack 521 (2.1%)\nAsian 421 (1.7%)\nOthers 583 (2.4%)\nTownsend deprivation index -2.1 (-3.7, 0.5)\nHealthy lifestyles\nCurrent smoking 2,467 (10.2%)\nDaily alcohol intake 4,876 (20.1%)\nHealthy sleep 17,785 (73.3%)\nPhysical activity 15,259 (81.3%)\nHealthy diet 13,775 (56.8%)\nytisneD\na\nTrain Validation Geographic testing Multiomics cohort\n8\n6\n4\n2\n)%\n(\nnoitroporP\nb\nTrain Validation Geographic testing Multiomics cohort\n75\n50\n25\n0\n0 1 >=2 0 1 >=2 0 1 >=2 0 1 >=2\nNumber of incident diseases\n)%\n(\nnoitroporP\nc\nMetabolomics-only cohort Proteomics-only cohort\nSocial connection 22,124 (91.2%)\nFamily disease history\nFamily history of heart disease 10,179 (41.9%)\nFamily history of stroke 6,361 (26.2%)\nFamily history of hypertension 11,551 (47.6%)\nFamily history of diabetes 5,236 (21.6%)\nDisease and medication history\nHypertension 6,446 (26.5%)\nDiabetes 1,161 (4.8%)\nLipid-lowering medication 4,392 (18.1%)\nAntihypertensive medication 2,717 (11.2%)\nPhysical measurements\nSystolic blood pressure (mmHg) 136.5 (124.5, 149.5)\nDiastolic blood pressure (mmHg) 82.0 (75.5, 89.0)\nHeight (cm) 167.5 (161.0, 175.0)\nWeight (kg) 75.7 (66.0, 86.4)\nWaist circumference (cm) 89.0 (80.0, 98.0)\nWaist-hip ratio 0.9 (0.8, 0.9)\nBody mass index (kg/m2) 26.6 (24.1, 29.6)\nBlood count\nLeukocyte count (109 cells/Litre) 6.6 (5.6, 7.8)\nLymphocyte count (109 cells/Litre) 1.9 (1.5, 2.3)\nMonocyte count (109 cells/Litre) 0.5 (0.4, 0.6)\nNeutrophill count (109 cells/Litre) 4.0 (3.2, 4.9)\nEosinophill count (109 cells/Litre) 0.1 (0.1, 0.2)\nBasophill count (109 cells/Litre) 0.02 (0.00, 0.04)\nPlatelet count (109 cells/Litre) 249.3 (214.8, 288.7)\nHaemoglobin concentration (grams/decilitre) 14.1 (13.3, 15.0)\nHaematocrit percentage (%) 40.9 (38.6, 43.4)\nCardiovascular disease\nCoronary artery disease 1,795 (7.4%)\nStroke 584 (2.4%)\nHeart failure 800 (3.3%)\nAtrial fibrillation 1,525 (6.3%)\nPeripheral artery disease 325 (1.3%)\nVenous thromboembolism 679 (2.8%)\n1Data are presented as median (interquartile range) or frequency (%).\nCOMMENT 2-6. On trying to access the online demo of the model, the weblink appeared to\nbe broken. Please could the authors provide a working link to allow reviewers and readers to\naccess the online demo.\nRESPONSE 2-6. We thank the reviewer for identifying the issue with the weblink to our\nonline demo and apologize for the inconvenience. The previous hosting platform experienced\na technical issue that was beyond our control. To ensure stable, long-term access, we have\nnow migrated the online demo to GitHub Pages. The restored link is now available at:\nhttps://yanluocityu.github.io/cardiomicscore-website/. The manuscript has been updated with\nthis new address.\nData and code availability Section: “The data was obtained from UK Biobank\n(https://www.ukbiobank.ac.uk/). Code associated with our paper is available at\nhttps://github.com/YanLuoCityU/cardiomicscore. An online demo is available at\nhttps://yanluocityu.github.io/cardiomicscore-website/.\nCOMMENT 2-7. The main issue with this work is that is used 10% of the cohort for\nvalidation, so this is considered internal (not external) validation, and the models are\nexpected to suffer from overfitting.\nRESPONSE 2-7. Thank you so much for the comment. To provide strong evidence against\noverfitting and to rigorously assess generalizability, we implemented a two-stage individual-\nlevel data partition strategy, which is designed to approximate the conditions of external\nvalidation (also refer to RESPONSE 2-1). First, we tested our models on a geographic\ntesting set (participants from Scotland), which was completely separate from our training data\n(from England and Wales). Subsequently, we performed a final evaluation on a completely\nuntouched Multiomics cohort to demonstrate the improvement in predictive performance by\nintegrating different omics data types. Although the training and geographic testing sets\nexhibited significant differences in several clinical characteristics, both MetScore and\nProScore achieved strong predictive performance, and this performance was highly consistent\nwith the results from the Multiomics cohort, providing robust evidence for their\ngeneralizability (Supplementary Figure 6).\nWe copied the Supplementary Figure 6 below:\nCAD Stroke HF AF PAD VTE\nProScore\nMetScore\n0.6 0.7 0.8 0.90.6 0.7 0.8 0.90.6 0.7 0.8 0.90.6 0.7 0.8 0.90.6 0.7 0.8 0.90.6 0.7 0.8 0.9\nC-index (95% CI)\nHold-out testing Geographic testing\nSupplementary Figure 6: Discriminative performance of MetScore and ProScore.\nDiscriminative performance was evaluated separately for MetScore and ProScore in both the\ngeographic testing and the Multiomics cohort. The Multiomics cohort included 24,287\nparticipants, while the geographic testing sets included 12,779 for MetScore and 1,775 for\nProScore. Performance was assessed using Harrell’s C-index with 95% confidence intervals\nestimated by 1,000 bootstrap resamples.\nCOMMENT 2-8. In the demographics it would also be helpful to know the numbers of\nparticipants on statin / antihypertensive therapies.\nRESPONSE 2-8. Thank you for this helpful suggestion. We added the proportion of\nparticipants on lipid-lowering and antihypertensive therapies to the Result section: “The\nprevalence of baseline medication use was comparable, with 15.8% to 18.1% of participants\nreceiving lipid-lowering medication and 10.5% to 11.2% on antihypertensive medication.”\nCOMMENT 2-9. Minor grammatical error in supplementary figure 1 caption, lines 115-\n116. “Here, we defined the follow-up duration as the period from the date of baseline\nassessment to the date of incident any cardiovascular diseases…” doesn’t make sense\ngrammatically. Perhaps the intended line is “the date of any incident cardiovascular\ndiseases”.\nRESPONSE 2-9. Thank you so much for pointing out this error. We corrected the sentence\nin the figure caption, and the figure is now Supplementary Figure 2. The revised sentence is:\n“Here, we defined the follow-up duration as the period from the date of baseline assessment\nto the date of any incident cardiovascular diseases, death, loss to follow-up, or end of\navailable registry follow-up (November 30 2023 for England & Wales and December 31\n2023 for Scotland), whichever came first.”\nCOMMENT 2-10. Minor grammatical error in 2.3, paragraph 2, line 2: “(i.e., 95% CIs of\ndelta C-index not included zero)”. “Included” should be changed to “including”.\nRESPONSE 2-10. We thank the reviewer for spotting this error. We have corrected the\nsentence in the Methods section, changing “included” to “including”.\nCOMMENT 2-11. Supplementary figure 10 appears to have a light blue figure legend\nmarker for ‘clinical scores’ which seems to be redundant as it does not feature in the figure\noutside the legend.\nRESPONSE 2-11. Thank you for the suggestion. We removed the redundant “clinical\nscores” marker from the figure legend as requested. The revised figure is shown below:\na\nCAD Stroke HF AF PAD VTE\nASCVD\nSCORE2\nPRS\nMetScore\nProScore\nAgeSex\n+PRS\n+MetScore\n+ProScore\n+PRS+MetScore\n+PRS+ProScore\n+MetScore+ProScore\n+PRS+MetScore+ProScore\nClin\n+PRS\n+MetScore\n+ProScore\n+PRS+MetScore\n+PRS+ProScore\n+MetScore+ProScore\n+PRS+MetScore+ProScore\nPANEL\n+PRS\n+MetScore\n+ProScore\n+PRS+MetScore\n+PRS+ProScore\n+MetScore+ProScore\n+PRS+MetScore+ProScore\n0.5 0.75 1.0 0.5 0.75 1.0 0.5 0.75 1.0 0.5 0.75 1.0 0.5 0.75 1.0 0.5 0.75 1.0\nC-index (95% CI)\nb\nCAD Stroke HF AF PAD VTE\nAgeSex Baseline Baseline Baseline Baseline Baseline Baseline\n+PRS\n+MetScore\n+ProScore\n+PRS+MetScore\n+PRS+ProScore\n+MetScore+ProScore\n+PRS+MetScore+ProScore\nClin Baseline Baseline Baseline Baseline Baseline Baseline\n+PRS\n+MetScore\n+ProScore\n+PRS+MetScore\n+PRS+ProScore\n+MetScore+ProScore\n+PRS+MetScore+ProScore\nPANEL Baseline Baseline Baseline Baseline Baseline Baseline\n+PRS\n+MetScore\n+ProScore\n+PRS+MetScore\n+PRS+ProScore\n+MetScore+ProScore\n+PRS+MetScore+ProScore\n0 0.04 0.08 0.12 0 0.04 0.08 0.12 0 0.04 0.08 0.12 0 0.04 0.08 0.12 0 0.04 0.08 0.12 0 0.04 0.08 0.12\nΔC-index(95%CI)\nOmics-based AgeSex-based Clinical-based PANEL-based\nFigure 3: Predictive performance of multiomics for cardiovascular diseases. a.\nDiscriminative performance of models trained on various predictor sets. Vertical dashed lines\nindicate the performance of the three clinical predictor sets. Discriminative performance was\nevaluated by the Harrell’s C-index. b. Differences in discriminative performance by adding\nomics information to clinical predictor sets. Data are presented as point estimates with 95%\nconfidence intervals estimated from 1,000 bootstrap resamples.\nDear Editor and Reviewers,\nThank you so much for taking the time to review this manuscript. We really appreciate all\nyour comments and suggestions! We hope that the revised manuscript could meet the\nrequirements for Nature Communications. All revised portions are marked in red in the\nrevised manuscript.\nThe main comments and our specific responses are detailed below.\nREVIEWER 2’S COMMENTS\nRemarks to the Author:\nCOMMENT 2-1. The authors have made some reasonable changes to the manuscript\ngenerally. The inclusion of a geographically separate cohort, whilst an improvement on the\noriginal study design, still does not offer quite the same level of robust validation as a true\nexternal validation cohort. On balance, I feel that the manuscript has sufficient merrit to\nwarrant publication of the existing material with the following further revisions:\nThe manuscript states, under the limitations section, 'Finally, since the majority of UKB\npopulation are European ancestry and white ethnicity, the generalizability of our findings\nneeds to be validated in ethnically diverse populations to ensure broader applicability.\nPlease would the authors kindely submit a revision that mentions explicitly the lack of a\nproper external validation cohort (rather than just citing a need for ethnic diverse\npopulations).\nRESPONSE 2-1. Thank you very much for the positive feedback. We fully agree that a\ngeographically separated cohort does not provide the same level of robustness as a true\nindependent external validation cohort. Following your recommendation, we have revised the\nlimitations section to explicitly acknowledge the lack of a proper external validation cohort.\nWe copied the updated parts in the main text below:\nDiscussion Section, Twelfth Paragraph: “Finally, although we conducted geographic\nvalidation to assess model generalizability, our study still lacks an independent external\nvalidation.”\nCOMMENT 2-2. The authors discuss the clinical utility and application of the model in\ntheir discussion section. As some point in this discussion it should be clearly stated that the\nsignatures are not clinically generalisable or applicable until they have undergone external\nvalidation.\nRESPONSE 2-2. We thank the reviewer for this important and insightful comment.\nFollowing your suggestion, we have revised the Discussion section to clearly state that the\nidentified molecular signatures are not clinically generalizable or applicable until external\nvalidation has been performed. The updated paragraph is copied below:\nDiscussion Section, Tenth Paragraph: “Notably, our study provides only preliminary\nevidence for the predictive importance of circulating biomarkers; therefore, it must be clearly\nstated that these signatures are not clinically generalizable or applicable until they have\nundergone external validation.”"}, {"round": 2, "reviewer_comments": {"reviewer_1": "The authors have addressed all our concerns. We have no more comments.", "reviewer_2": "The authors have made some reasonable changes to the manuscript generally. The inclusion of a geographically separate cohort, whilst an improvement on the original study design, still does not offer quite the same level of robust validation as a true external validation cohort. On balance, I feel that the manuscript has sufficient merrit to warrant publication of the existing material with the following further revisions:\nThe manuscript states, under the limitations section, 'Finally, since the majority of UKB population are European ancestry and white ethnicity, the generalizability of our findings needs to be validated in ethnically diverse populations to ensure broader applicability. Please would the authors kindely submit a revision that mentions explicitly the lack of a proper external validation cohort (rather than just citing a need for ethnic diverse populations).\nThe authors discuss the clinical utility and application of the model in their discussion section. As some point in this discussion it should be clearly stated that the signatures are not clinically generalisable or applicable until they have undergone external validation.\nWith these revisions I would be satisfied to recommend publication."}, "author_response": "Dear Editor and Reviewers,\nThank you so much for taking the time to review this manuscript. We really appreciate all\nyour comments and suggestions! We hope that the revised manuscript could meet the\nrequirements for Nature Communications. All revised portions are marked in red in the\nrevised manuscript.\nThe main comments and our specific responses are detailed below.\nREVIEWER 2’S COMMENTS\nRemarks to the Author:\nCOMMENT 2-1. The authors have made some reasonable changes to the manuscript\ngenerally. The inclusion of a geographically separate cohort, whilst an improvement on the\noriginal study design, still does not offer quite the same level of robust validation as a true\nexternal validation cohort. On balance, I feel that the manuscript has sufficient merrit to\nwarrant publication of the existing material with the following further revisions:\nThe manuscript states, under the limitations section, 'Finally, since the majority of UKB\npopulation are European ancestry and white ethnicity, the generalizability of our findings\nneeds to be validated in ethnically diverse populations to ensure broader applicability.\nPlease would the authors kindely submit a revision that mentions explicitly the lack of a\nproper external validation cohort (rather than just citing a need for ethnic diverse\npopulations).\nRESPONSE 2-1. Thank you very much for the positive feedback. We fully agree that a\ngeographically separated cohort does not provide the same level of robustness as a true\nindependent external validation cohort. Following your recommendation, we have revised the\nlimitations section to explicitly acknowledge the lack of a proper external validation cohort.\nWe copied the updated parts in the main text below:\nDiscussion Section, Twelfth Paragraph: “Finally, although we conducted geographic\nvalidation to assess model generalizability, our study still lacks an independent external\nvalidation.”\nCOMMENT 2-2. The authors discuss the clinical utility and application of the model in\ntheir discussion section. As some point in this discussion it should be clearly stated that the\nsignatures are not clinically generalisable or applicable until they have undergone external\nvalidation.\nRESPONSE 2-2. We thank the reviewer for this important and insightful comment.\nFollowing your suggestion, we have revised the Discussion section to clearly state that the\nidentified molecular signatures are not clinically generalizable or applicable until external\nvalidation has been performed. The updated paragraph is copied below:\nDiscussion Section, Tenth Paragraph: “Notably, our study provides only preliminary\nevidence for the predictive importance of circulating biomarkers; therefore, it must be clearly\nstated that these signatures are not clinically generalizable or applicable until they have\nundergone external validation.”"}]

Decision letter