An integrated germline and somatic genomic model for coronary artery disease

Domain: biology
Published: 2026-03-26
Editorial outcome: major_revision

Abstract
Multiple germline and somatic genomic factors are associated with risk of coronary artery disease, but there is no single measure of risk that integrates all information from a DNA sample. To address this gap, we develop an integrated genomic model that includes six germline and somatic genetic drivers for coronary artery disease, including polygenic risk score, genetically-proxied proteomic/metabolomic risk scores, and clonal hematopoiesis of indeterminate potential. We evaluated its predictive power in the UK Biobank (N=391,536), and validate it using data from the TOPMed program (N=34,177). The 10-year coronary artery disease risk based on the integrated genomic model profile ranges from 1.1% to 15.5% in the UK Biobank and from 3.8% to 33.0% in TOPMed, with a more pronounced gradient in males than females. The integrated genomic model captures the cumulative effect of multiple genetic drivers, identifying individuals at high risk for coronary artery disease despite lacking high-risk genetic factors, and vice-versa. In middle age, the integrated genomic model augments the performance of the Pooled Cohort Equations, a clinical risk calculator for coronary artery disease. While the integrated genomic model yields only modest incremental predictive value over polygenic risk score at the population level, it identifies approximately 13% of high-risk individuals not detected by polygenic risk score alone.

Introduction
The early identification of individuals at high risk for coronary artery disease (CAD) is a fundamental strategy in preventing disease which remains the number one cause of mortality and morbidity.1 Utilizing DNA information for CAD risk prediction has gained traction due to its ability to identify early-onset cases, predict risk earlier in life, and augment the performance of existing clinical risk measures.2,3 Significant progress has been made in understanding germline genomic risk drivers of CAD. Both monogenic drivers of risk such as pathogenic variants in familial hypercholesterolemia (FH)-related genes (LDLR, APOB, and PCSK9) and polygenic risk scores (PRS) have shown promise in CAD risk stratification, individually and in combination.4,5 Moreover, age-related somatic mutations have been associated with increased CAD risk, attributed to clonal hematopoiesis of indeterminate potential (CHIP) and shortened leukocyte telomere length (LTL).6,7 Even though germline and somatic genomic variations shape CAD risk, they remain to be studied in aggregate. A single model leveraging all available information from a single DNA biopsy could have the potential to improve risk prediction of CAD.

A comprehensive genomic risk model for CAD would integrate risk from early-life germline mutations with risk from somatic mutations occurring later in life. We previously demonstrated the interplay between monogenic and polygenic risk, highlighting that polygenic background alters the penetrance of FH variants.4 Thereafter, Zhao et al. reported that a combination of germline and somatic mutations augments the risk of CAD, as evidenced by the interaction between PRS and CHIP.8 This mounting evidence underscores that an ensemble model that integrates all known germline and somatic risk drivers and their interactions might improve genomic risk prediction of CAD.

As genomic medicine moves towards clinical adoption, it might be beneficial that a single measure of risk is communicated using the entirety of the data points available from an individual’s genome. Mixed information about risk is poised to confuse people unless integrated into a single number. This concept has long been established in clinical risk prediction. For example, a single 10-year cardiovascular risk is provided by integrating information from factors such as blood pressure, cholesterol levels, smoking, and diabetes, each of which might indicate low or high risk for an individual.9 Similarly, in genomic risk prediction, an individual might have a monogenic FH variant, a low polygenic risk score, no CHIP variant, and a short LTL, challenged by how to interpret this complex combination of genetic risk factors. It is only helpful for the individual in this context to understand the summed effect and risk estimate.

We developed an integrated genomic model (IGM) that uses a single score to maximize the precision in CAD risk prediction. We demonstrated the accuracy, calibration, and added value of this all-at-once model using half a million people from the UK Biobank, and validated its performance in studies contributing to the Trans-Omics for Precision Medicine (TOPMed) program.

Results
Developing an Integrated Genomic Risk Model for CAD
We used the UK Biobank as a discovery cohort, including 391,536 individuals (mean [SD] age, 56.5 [8.1] years; 53.8% women) including 28,346 (7.2%) participants who developed CAD over a median follow-up of 12.3 (interquartile range [IQR], 1.6) years. In the UK Biobank, 94.1% of participants were White (n = 368,296), with smaller proportions identifying as Asian (2.3%, n = 9,106), Black (1.6%, n = 6,237), and Other ancestry (2.0%, n = 7,897). In contrast, TOPMed showed greater diversity, with 68.3% White (n = 23,333), 25.9% Black (n = 8,858), 2.2% Asian (n = 737), and 3.7% Other ancestry (n = 1,250). Whole-genome sequencing data was used to curate and compute features of risk previously shown to be associated with CAD. Specifically, we included two somatic features – CHIP and LTL– and four germline features – FH variants, CAD PRS, a proteome PRS (ProPRS) and a metabolome PRS (MetPRS). Of note, ProPRS and MetPRS were constructed using a series of genetic proxies for protein and metabolite levels derived from the atlas of genetic scores that predict multi-omic traits,10 rather than relying on actual measurement of protein and metabolite levels. Using this feature matrix, we developed a somatic risk score, a germline risk score, and an integrated genomic model (IGM) (Figure 1).

We then validated the IGM in 34,177 individuals from the TOPMed program (mean [SD] age, 62.6 [10.6] years; 66.0% women) (Supplementary Table 3). Incident CAD events occurred in 3,972 (14.3%) participants who developed CAD over a median follow-up of 10.5 (IQR, 8.6) years. The IGM model provided individual 10-year risk estimates across percentiles of somatic and germline risk (Figure 1). Further details on baseline characteristics by genetic drivers are presented in Supplementary Tables 4-8.

Germline and Somatic Genomic Drivers of CAD Risk
We first estimated the individual risk of prevalent and incident CAD imparted by each of the two somatic and four germline drivers. When evaluating the association of the germline drivers with prevalent CAD in the UK Biobank, FH variant carriers had a three-fold increase in risk – odds ratio (OR) of 3.08 (95% confidence interval [CI], 2.46-3.85; p < 0.001). The CAD PRS (OR per standard deviation (SD), 2.15; 95% CI, 2.11-2.19; p < 0.001), MetPRS (OR per SD, 1.27; 95% CI, 1.25-1.29; p < 0.001), and ProPRS (OR per SD, 1.19; 95% CI, 1.17-1.21; p < 0.001) were also significantly associated with CAD (Figure 2). We combined these four germline risk factors into a single predictor called GermRisk. The OR per SD for GermRisk was 2.16 (95% CI, 2.12-2.20; p < 0.001), and individuals in the top quintile of GermRisk had 3.6-fold increase in risk compared to everyone else (95% CI, 3.47-3.73; p < 0.001) (Supplementary Table 8). The effect sizes of genetic drivers for prevalent CAD in TOPMed were overall consistent with those of UK Biobank (Figure 2B; Supplementary Table 9).

We then evaluated the association of genomic drivers with incident CAD in the UK Biobank, and demonstrated strong associations of each of the germline and somatic drivers with incident CAD (Figure 2C; Supplementary Table 10). For germline drivers, the HR for FH carriers was 1.69 (95% CI 1.41-2.03, p < 0.001) and HRs per SD for CAD PRS, MetPRS, and ProPRS were 1.56 (95% CI 1.55-1.58, p < 0.001), 1.18 (95% CI 1.17-1.19, p < 0.001), and 1.15 (95% CI 1.14-1.17, p < 0.001), respectively. For somatic drivers, CHIP and LTL were associated with CAD with HR of 1.13 (95% CI 1.06-1.20, p < 0.001) and 0.94 (95% CI 0.92-0.95, p < 0.001), respectively. We combined these two somatic risk factors into a single predictor called SomaRisk, similar to GermRisk. Both GermRisk and SomaRisk demonstrated a strong association with incident CAD – HR per SD of 1.57 (95% CI 1.55-1.59, p < 0.001) and 1.05 (95% CI 1.04-1.06, p < 0.001), respectively (Figure 2C; Supplementary Table 10). The effect sizes of genetic drivers for incident CAD in TOPMed were mostly consistent with those of UK Biobank (Figure 2D; Supplementary Table 11).

Integrated Genomic Model to Predict CAD Risk
We assessed pairwise correlations between six genetic variables from the UK Biobank and TOPMed studies using Pearson correlation coefficients to evaluate multicollinearity before combining them in a Cox proportional hazards model. This allowed us to gauge overlapping signals, particularly between CAD PRS, MetPRS, and ProPRS. The correlations among six drivers were weak (≤ 0.3) (Figure 2E and 2F), reassuring that each driver contributes distinct signals.

To obtain a comprehensive assessment of a person’s CAD risk, we used an integrated genomic model (IGM) to quantify the risk from both germline and somatic drivers – a combined predictor of GermRisk and SomaRisk (Supplementary Fig. 4). The IGM risk was significantly associated with the risk of incident CAD (HR per SD, 1.58; 95% CI, 1.56-1.59; p < 0.001), and the effect size was consistent when validated in the TOPMed external data set (HR per SD, 1.46; 95% CI, 1.40-1.53; p < 0.001) (Figure 2C and 2D; Supplementary Tables 11 and 12). Joint modeling of germline and somatic drivers indicated substantial gradients in risk of CAD, according to inherited DNA variants and variation in the rate of LTL shortening and accumulation of somatic variants leading to CHIP. For the sex-combined estimation of the 10-year risk of CAD in UK Biobank, individuals in the lowest germline and somatic risk percentile have a 10-year risk as low as 1.1%, while those in the highest germline and somatic risk percentile have a 10-year risk as high as 15.5% (Figure 3A). A similar gradient in risk across germline and somatic variation was observed in TOPMed, ranging from 3.8% to 33.0% (Figure 3B). For a sex-stratified analysis in the UK Biobank, male individuals had a 10-year risk that ranged from 1.8% to 23.0% across the germline and somatic risk spectrum. This was about 2.3 times higher than the risk spectrum for females, which spanned from 0.7% to 10.3% (Figure 3C). Large gradients in 10-year risk were consistently observed in the TOPMed for the sex-stratified analysis, with males ranging from 4.8% to 39.9% and females ranging from 3.0% and 27.0% (Figure 3D).

Heterogeneity of Genomic Risk Profiles Captured by the Integrated Genomic Model
The IGM effectively captured a range of genetic risk combinations for CAD, identifying high-risk groups (top 20% overall risk) with diverse genetic profiles in the UK Biobank (Figure 4A). High-risk IGM group included individuals at high risk in both germline and somatic factors (20.6%), those at high risk for one of the factors (78.6%), and a small proportion with moderately elevated, yet sub-threshold, risks for both factors (0.8%) (Figure 4A). Individuals at high risk for both germline and somatic factors had the highest 10-year risk (8.8%, 95% CI 8.43-9.19%) within the high-risk IGM group (Supplementary Table 12). As expected, the high-risk group identified by the IGM had a larger number of genetic risk drivers compared to the low-risk group. For example, 63.9% had two or more genetic drivers in the high-risk group, compared to only 3.4 % in the low-risk group in the UK Biobank (Figure 4B). Notably, people at low IGM risk were not without genetic risk drivers, and a non-negligible proportion of 29.3% had one or more genetic risk drivers (Figure 4B and Supplementary Table 13); however, mitigating effects from other genetic variants (e.g., low CAD PRS) seem to offset the overall risk, thus classifying these individuals as low risk. Similar proportions of breakdown and distribution patterns were in TOPMed (Figure 4C, 4D and Supplementary Table 13).

Table 1 presents the detailed genetic profiles of ten representative individuals from the high-risk IGM group. Notably, participants demonstrate diverse combinations of genetic risk factors contributing to their elevated CAD risk. For instance, Participant 1 exhibited below average PRS levels (34.4%) but had substantially elevated MetPRS (75.6%), ProPRS (73.0%), and carried an FH variant, culminating in a high overall CAD risk (83.4%). Conversely, Participant 9 had relatively modest or average genetic risk profiles in PRS (46.4%), MetPRS (21.0%), and LTL (56.1%), and this individual’s high CAD risk was primarily influenced by positive FH carrier status. These individual-level examples underscore how the cumulative and multidimensional nature of the IGM can capture high-risk individuals who might otherwise remain unrecognized when considering single genetic factors.

Integrated Genomic Model and Clinical Risk
The American College of Cardiology/American Heart Association, Pooled Cohort Equations (PCE) are a guideline-recommended clinical-risk calculator that uses clinical risk factors to identify high-risk people for initiation of preventive treatments (i.e., statin).11 Within each of the guideline-defined strata of the PCE risk, the IGM score was a strong predictor of coronary artery disease events and showed a consistent risk gradient by IGM score category (Figure 5A, B). Among participants at high PCE risk (> 20% 10-year risk), the 10-year CAD event rates were 5.4%, 9.5%, and 16.4%, for low, intermediate, and high IGM risk groups (Figure 5A). Remarkably, a 10-year CAD observed risk threshold of 7.5% for initiating statin therapy as guideline recommended was reached even among individuals in the borderline (5.0%-7.4% 10-year risk) and intermediate (7.5%-19.9% 10-year risk) PCE categories when stratified by IGM percentiles (Figure 5B) – an IGM score higher than 95th and 72nd percentile, respectively for borderline and intermediate risk (Figure 5B). A close match of the model predicted and actual observed 10-year disease risk shows the models were well calibrated (Supplementary Fig. 5). When disaggregated to individual genetic risk drivers, the FH variants and CAD PRS most prominently re-stratified the CAD risk across PCE categories, but other risk factors also have significant stratification ability (Supplementary Fig. 6).

By combining the conventional clinical risk of PCE with the genetic risk of IGM, the model showed the most potent risk stratification ability. When 20,624 individuals who experienced CAD events in the UK Biobank were used to determine reclassification by adding IGM to PCE compared to PCE alone, 1,858 were correctly classified at a higher risk, while 1,452 were incorrectly placed at a lower risk (Figure 5C), leading to a net proportion of accurate reclassifications for events is 1.97% (406/20,624). For nonevents, 22,954 individuals were correctly down-classified, and 16,708 were incorrectly up-classified, leading to a net reclassification proportion of 1.75% (6,246/356,656) for nonevents. The overall NRI combines the events and nonevents, resulting in 3.72% (95% CI, 3.15%-4.26%). Continuous NRI was 33.45% (95% CI, 32.11%-34.76%). As shown in the full stratification table, there was a lower event rate in the Low-risk category for the combined model than in the PCE-alone model (1.9% versus 2.3%), however, a higher event rate was observed for the High-risk category (27.1% versus 23.5%), demonstrating the prominent risk stratification ability of the IGM model with guideline-recommended PCE risk estimator (Supplementary Table 14).

We evaluated the discrimination of PCE, IGM, and combined model compared to a baseline model with age, sex and genetic ancestry. The C-statistic for the base model was 0.701 (95% CI, 0.698-0.704), PCE was 0.725 (95% CI 0.722-0.728), and the combination of base and IGM model was 0.734 (95% CI 0.731–0.737), respectively, in UK Biobank (Supplementary Fig. 7; Supplementary Table 15). The performance was highest when combining PCE and IGM (C-statistic, 0.750; 95% CI, 0.747-0.753). In the TOPMed data, the discrimination with IGM was further improved when limiting prediction to younger individuals (aged ≤ 45 years) (C-statistic 0.805, 95% CI, 0.699-0.913)(Supplementary Fig. 7 and Supplementary Table 15).

Finally, we compared IGM directly against a clinical biomarker model, which included clinical biomarkers– –Lp(a), CRP, and creatinine. We deliberately excluded LDL cholesterol from the clinical biomarker model, as total cholesterol and HDL cholesterol are already integral components of the PCE, which served as our primary benchmark comparator for the IGM. The result demonstrated that the clinical biomarker model (Lp(a), CRP, and creatinine; C-statistic 0.708) did not outperform the IGM (C-statistic 0.734), underscoring the incremental predictive value provided by genomic information (Supplementary Fig. 8).

Comparison of Genetically Proxied and Measurement Based Risk Scores
To compare CAD risk score based on genetically proxied and measured proteomic/metabolomic scores, we further constructed proteome risk score (ProRS) and metabolome risk score (MetRS). The ProRs and MetRS were strongly associated with CAD risk (ProRS: OR per SD, 2.77; 95% CI, 2.64-2.91; MetRS: OR per SD, 2.05; 95% CI, 2.01-2.10), recapitulated finding from Helgason et al.12 that demonstrated the effectiveness of proteomic risk scores in predicting CAD risk. We next compared the correlation between measured and genetically proxied levels for the 124 proteins that were used to develop ProPRS. The correlations were generally variable (Supplementary Fig. 9), likely influenced by several factors. Proteomic and metabolomic measurements rely on a single time point and can fluctuate according to current disease status or other temporary conditions at the time of collection. In contrast, genetically proxied scores, derived from personal genetic data, more closely reflect baseline levels that are less confounded by disease or lifestyle factors (ie, diet). Moreover, as acknowledged elsewhere,10 technical elements (serum versus plasma, batch effects, fasting state), demographic variables, and environmental factors (for example, diet) all played a role in the observed variability in proteomic measurement. While comparing measured and genetically proxied molecular levels represents an interesting approach, it is essential to recognize that these measurements derive from different biological contexts, which introduces complexity to direct comparison. The genetically proxied scores may offer unique insights into CAD risk compared to measurement based scores, as they reflect an individual's baseline levels of key molecules linked to CAD risk. Notably, the genetically proxied scores exhibited effect sizes comparable to CHIP—a well-known CAD risk factor—suggesting their potential utility as strong risk factors of CAD risk.

Methods
Ethics
The UK Biobank was approved by the UK Biobank Research Ethics Committee (reference 11/NW/0382). All UK Biobank participants provided written informed consent. Use of UK Biobank (application number 89885) data was approved by the Beijing Institute of Genomics review board. All contributing studies in TOPMed consortium obtained ethics approval from their respective local institutional review boards or ethics committees, with written informed consent from participants. Use of the TOPMed data was approved by the Massachusetts General Hospital institutional review board and TOPMed Atherosclerosis Working Group.

Dataset and Quality Control
Access to the UK Biobank data was approved with application ID 89885. Samples with discordance between self-reported sex (Field 31) and genetically inferred sex (Field 22001) were removed. Additionally, samples with individual-level genotype missing rates (Field 22005) greater than 5%, outliers for heterozygosity or missing rate (Field 22027), or sex chromosome aneuploidy (Field 22019) were excluded. To remove close relatives in the samples, we excluded one of the samples whose pairwise kinship value is greater than or equal to 0.0884 (threshold of the second-degree close relatives21) but also tried to keep as many samples as possible. Finally, 391,536 individuals were included in the final analysis (Supplementary Fig. 1A).

A total of 80,588 participants from the NHLBI’s TOPMed program with available whole genome sequencing data were considered. These studies primarily consist of observational cohorts that have been described in detail previously.6 Among 80,588 participants, we excluded 49 samples with conflicting sex information, 1 sample without principal components, and 17,237 samples with excess kinship, defined as a second-degree relationship or closer, indicated by a KING coefficient greater than 0.0884. Finally, 34,177 participants remained for analysis after excluding an additional 29,124 participants without CAD phenotype (Supplementary Fig. 1B). Cohorts contributed to this population are Amish, Atherosclerosis Risk in Communities Study[ARIC], Cardiovascular Health Study[CHS], Genetic epidemiology of COPD[COPDGene], Diabetes Heart Study[DHS], Framingham Heart Study[FHS], Genetic Study of Atherosclerosis Risk[GeneSTAR], Genetic Epidemiology Network of Arteriopathy[GENOA], Jackson Heart Study[JHS], Multi-Ethnic Study of Atherosclerosis[MESA], and Women's Health Initiative[WHI].

Curation of FH, CHIP, and LTL
To determine the carrier status of familial hypercholesterolemia (FH) for samples with available whole-exome sequencing data, a combination of variant selection criteria was applied: 1) Variants from previous publications which were manually curated by clinical geneticists;4,22–24 2) Variants in LDLR, APOB and PCSK9 from the ClinVar database (downloaded February 27th, 2023, GRCh38) annotated as pathogenic, likely pathogenic, or pathogenic/likely pathogenic without conflicts. For APOB and PCSK9 gene, only variants associated with hypercholesterolemia but not hypobetalipoproteinemia were included; 3) Variants in LDLR annotated as high-confidence loss-of-function by the VEP Loss-Of-Function Transcript Effect Estimator (LOFTEE) plugin were also included.25,26 Only variants with a net positive association with LDL cholesterol level accessed by an iterative conditional regression analysis were included in the final variant list for subsequent analysis (Supplementary Data 1).27

The carrier status of clonal hematopoiesis of indeterminate potential (CHIP) was detected following a similar procedure described in Yu Zhi et al. and others.28–30 Specifically, carrier status was determined by carrying CHIP variants from one of the genes TET2, ASXL1, JAK2, PPM1D, TP53, SRSF2, and SF3B1. Leukocyte telomere length (LTL) was log-transformed to obtain a normal distribution and then Z-standardized using the distribution of all individuals with a telomere length measurement (Field 22192). Details of processing were described in V. Codd et al.31 Unless otherwise specified, genetic drivers have been curated comparably for UK Biobank and TOPMed. More details on the curation of CHIP and LTL for TOPMed are described elsewhere.6

Development of CAD Polygenic Risk Score and Genetically Proxied Risk Scores for Metabolome and Proteome
In the UK Biobank, CAD PRS was calculated using the imputed genotype data and variant weights from the multi-ancestry and multi-trait polygenic risk score for CAD described in A. P. Patel et al.,13 implemented with PLINK2.32 Instead of relying on direct measurement of metabolite and protein levels of the limited number of samples, we utilized previously identified genetic proxies for these traits10, allowing us to leverage the vast amounts of genomic data available for large cohorts. Variant weights for the genetic proxies were downloaded from OMICSPRED resource which derived from the INTERVAL study cohort (https://www.omicspred.org/Scores/Somalogic/INTERVAL),10 including 867 metabolites (726 Metabolon, and 141 Nightingale), and 2,692 proteins (2,384 Somalogic, and 308 Olink), respectively. The 867 predicted metabolite levels and 2,692 protein levels were calculated using the imputed genotype data by PLINK2.32 Then we randomly sampled 200,000 individuals from UK Biobank as the training set, and a lasso penalty was applied to the Cox proportional hazards regression model with age, sex, and the top 10 principal components (PCs) as covariates and incident CAD as an outcome to develop the proxy risk scores for metabolome (MetPRS) and proteome (ProPRS). Five-fold cross-validation was employed to select the optimal penalization strength for hyperparameters, similar to the process described elsewhere12. The number of genetically predicted metabolome and proteome scores retained in the MetPRS and ProPRS lasso models were 142 and 124, respectively (Supplementary Data 2-3). Variant weights were further adjusted with the weights of genetic proxies determined by the cross-validation procedures, and finally applied to calculate the MetPRS and Pro PRS for all the UK Biobank samples and the TOPMed cohort samples accordingly for subsequent analysis.

CAD Definition
In the UK Biobank, CAD was defined based on self-report at enrollment, hospitalization records, or death registry records as previously described (Supplementary Table 1).4 In the TOPMed studies, CAD was defined as ischemic heart disease events, including myocardial infarction and coronary revascularization.6 Incident CAD cases were defined as those diagnosed after recruitment. The survival year was defined as the years between recruitment and diagnosis for incident CAD cases, or between the time of the recruitment and the last censoring for controls.

Covariates and Adjustment
Untreated blood pressure was estimated by adjusting the raw value for anti-hypertensive medication intake by adding 15 mmHg to the systolic blood pressure and 10 mmHg to the diastolic blood pressure.33,34 Untreated lipid levels were estimated by adjusting the raw lab-tested value according to lipid-lowering medication intake13 and detailed in Supplementary Table 2. To eliminate potential confounding effects of covariates, PRS, MetPRS, ProPRS, and LTL were regressed on recruitment age, sex, and the first 10 principal components of genetic ancestry (Supplementary Fig. 2). The scaled residuals with mean zero and standard deviation of one were then used in the subsequent analyses.

Developing the Integrated Genomic Risk Model
We evaluated pairwise correlations between six genetic variables from the UK Biobank and TOPMed studies to assess potential multicollinearity before including these variables in a regression model. The correlation matrix was computed using Pearson correlation coefficients. As germline genetic risk drivers (FH, PRS, MetPRS, and ProPRS) remain constant from birth, while CHIP accumulates and LTL shortens with age (somatic risk drivers), we systematically characterized and investigated the joint effects of germline and somatic risk drivers on CAD. Germline risks were combined into a single value termed GermRisk, and somatic risks were combined into a single value termed SomaRisk, the combination weights were estimated by a joint regression model. Specifically, for prevalent CAD, the germline risk drivers (FH, PRS, MetPRS, and ProPRS) were fitted into a logistic regression model, and the coefficients were used as weights to combine the values of the drivers into GermRisk linearly. For incident CAD, the germline risk drivers were fitted to a Cox proportional hazards model, and GermRisk was calculated as a weighted summation of germline variables and their corresponding Cox coefficients, and similar procedures were performed to obtain SomaRisk.

Next, GermRisk and SomaRisk were regressed on recruitment age, sex, and the top 10 PCs, and the residuals were scaled to have zero mean and unit standard deviation, respectively. The standardized residuals of GermRisk and SomaRisk were then fitted to a Cox proportional hazards model. A final predictor, termed IGM (integrated genomic model), was a linear summation of GermRisk and SomaRisk residual according to the Cox coefficient estimation. The weights were fixed and applied to TOPMed data for independent validation. The overall framework for developing and validating the integrated genomic model is shown in Figure 1.

Estimating Effect Sizes
To investigate the genomic factors driving the risk of prevalent and incident CAD, a logistic regression model and Cox proportional hazards regression model were employed respectively to estimate the effect sizes for individual genetic drivers. Additionally, PRS, MetPRS, ProPRS, LTL, and the ensembled ones (IGMRisk, GermRisk and SomaRisk) were binarized, with individuals in the top 20% treated as carriers (hPRS, hMetPRS, hProPRS, hIGMRisk, hGermRisk and hSomaRisk) or bottom 20% treated as a carrier (sLTL) for telomere length.

Interplay Between Genomic Drivers and Clinical Risk Score
To investigate the interplay between genomic risk and PCE, participants in the UK Biobank were divided into four groups based on guideline-defined categories of the PCE – low (estimated risk less than 5%), borderline (risk between 5% to 7.5%), intermediate (risk between 7.5% and 20%), and high (risk greater than 20%).16 For the IGM, we divide samples into three risk groups as standard in genomic analyses – high genomic risk (top quintile of the distribution), intermediate genomic risk (middle three quintiles), and low risk (bottom quintile).

Estimating 10-year risk of CAD
The 10-year risk of CAD was estimated by Cox proportional hazards regression with GermRisk and SomaRisk as predictors and the age, sex, and first 10 principal components of genetic ancestry as covariates. To investigate the stratification capacity of different models, the C-statistics from 1) a base model (sex, age, and first 10 PCs), 2) a log-transformed value of PCE, 3) a base model and IGM, and 4) a base model, IGM and log(PCE), was estimated by a Cox proportional hazards model, respectively.

To evaluate the improvement of prediction by adding the IGM to PCE, the net reclassification improvement was calculated based on a 10-year risk threshold of 7.5% for categorical reclassification and threshold 0 for continuous reclassification as demonstrated elsewhere.18,19 Confidence intervals were estimated by 100 times bootstrap. All statistical analyses were done using R v4.2.2 (R Foundation, Vienna, Austria), including the following packages: survival (v3.5-7), survminer (v0.4.9), tableone (v0.13.2), pROC(v1.18.5), nricens(v1.6), rms(v6.7-1) and glmnet (v4.1-8).

Development of Metabolome and Proteome Risk Scores Based on Measured levels
We developed the risk scores using measured metabolomic and proteomic data from 270k and 50K UK Biobank participants, respectively. We used 251 NMR metabolic biomarkers from Nightingale Health to create a metabolome risk score for CAD. After excluding participants with missing data, the remaining cohort (N=212,154) was randomly split into equal training and test sets. Using the training set, we performed five-fold cross-validated least absolute shrinkage and selection operator (LASSO) regression to select metabolite features associated with CAD, adjusting for age, sex, and the top 10 PCs of genetic ancestry, similar to our development of the genetically proxied proteomic and metabolomic risk scores (ProPRS and MetPRS). The metabolome risk score was then evaluated in the independent test set for its association with CAD risk. We used similar procedures to develop a proteome risk score with 2,923 protein measures via Olink (N=45,574).

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "The authors developed a complex integrated genomic model (IGM) with the aim to better predict future cardiovascular events. The IGM includes, in addition to a CAD polygenic risk score [CAD-PRS], further inherited (familial hypercholesterolemia [FH] variants, proteome PRS, metabolome PRS) and somatic alterations (clonal hematopoiesis of indeterminate potential [CHIP], leukocyte telomere length [LTL]). They employed population-based samples (UK Biobank and the Trans-Omics for Precision Medicine program) to test the incremental value of the IGM over a widely published CAD-PRS.\nThis rather complex “DNA biopsy” translated in a very small improvement in clinical risk prediction as indicated by an improvement of the AUC by about 0.15% or 0.02 in the OR/SD. For comparison, the effect sizes seen with (age and sex) in a clinical risk prediction model are about 100-fold stronger than the improvements observed with the measures in the complex score on top of a CAD-PRS. Nevertheless, the authors conclude that the complex genetic information has “clinically actionable value” beyond a CAD-PRS.\nMajor concern\nThis reviewer is still not convinced that the IGM is clinically actionable.\n1. The IGM has not been tested in a clinical setting but rather in two cohorts that underwent extensive genotyping and phenotyping with stringent bioinformatics QC measures. To my knowledge there are no clinical scenarios that could match this information. If I am not well informed in this respect, please clarify this in the manuscript and inform the reader how the IGM could be generated and applied clinically to add actionable value in patient care.\n2. What could be the bioinformatics approach – including QC measures – to generate a IGM in a clinical setting (in which no batches of thousands of samples are available)?\n3. In a clinical setting information on predictive parameters, that were not employed in the manuscript, is easily accessible. Thus, if the authors truly seek “clinically actionable value” in their IGM they should test this model against a prediction test refined by LDL-C, Lp(a), CRP, creatinine, and troponin (or other parameters from routine clinical routine assessment), which are already at hand for physicians and patients looking for improved risk prediction.\n4. Since the authors envision a clinical application of the IGM in a clinical setting, it would be interesting to have an estimate on the costs for generation of the model (genotyping arrays, deep sequencing of several genes and integrated bioinformatics analysis).\n5. The rational for building proxy risk scores for metabolome (MetPRS) and proteome (ProPRS) data, and the inclusion of these in an IGM, is not obvious for the reviewer. If a variant is related to CVD it should be evident by the weight of this variant that is already depicted by the CAD-PRS. If the variant increases for example the triglyceride concentration – and nothing else, this increase should lead to a respective increase in CAD risk, which is covered by the CAD-PRS. But if this variant has pleiotropic effects – and for some other mechanism lowers CAD risk – it would enter the MetPRS with a positive signal for CVD, but may be neutral overall. Indeed, the CAD-PRS+MetPRS has exactly the same OR/SD as the CAD-PRS alone. The CAD-PRS+ProPRS seems to be even weaker than the CAD-PRS by itself. Thus, it appears that the authors added complexity to the model, without adding any predictive value. This needs further explanation.\n6. Clonal hematopoiesis of indeterminate potential and shortening of leukocyte telomere length develop over time. What are the considerations of the authors on time intervals to repeat the assessment of the IGM in order to capture the onset of these risk factors clinivcally as part of an IGM.\n7. Taken together, this reviewer feels that authors should acknowledge what they did, i.e. they tested 5 further DNA-based scores in addition to a CAD-PRS in an epidemiological setting, and what the findings were.\n8. If the authors still see “clinically actionable value” in their epidemiological findings they should be discuss their data in the light of a (very small) improvement in risk prediction observed in these cohorts, the lack of consideration of established clinical risk markers and the logistic challenges to implement such IGM in a clinical setting.", "reviewer_2": "Thank you for the additional analyses that help putting the paper into context. There are two interesting results from the further analyses in the rebuttal that are worth adding to the results and discussed in the manuscript:\n- Please add confidence intervals around estimates in Figures 1 & 2 in the rebuttal letter, add the figures to the paper supplement and explain all variable acronyms in the captions\n- Many thanks for the additional metabolomic and proteomic analyses. While the focus of the paper is on genetic scores, the proteomic and metabolic scores and their correlations with ProPRS and MetPRS provide context in terms of the relative predictive impact of the “DNA biopsy”. Please add the results to the paper/supplement and discuss the relative values in the Discussion."}, "author_response": "The authors developed a complex integrated genomic model (IGM) with the aim to better predict future cardiovascular events. The IGM includes, in addition to a CAD polygenic risk score [CAD-PRS], further inherited (familial hypercholesterolemia [FH] variants, proteome PRS, metabolome PRS) and somatic alterations (clonal hematopoiesis of indeterminate potential [CHIP], leukocyte telomere length [LTL]). They employed population-based samples (UK Biobank and the Trans-Omics for Precision Medicine program) to test the incremental value of the IGM over a widely published CAD-PRS.\nThis rather complex “DNA biopsy” translated into a very small improvement in clinical risk prediction as indicated by an improvement of the AUC by about 0.15% or 0.02 in the OR/SD. For comparison, the effect sizes seen with (age and sex) in a clinical risk prediction model are about 100-fold stronger than the improvements observed with the measures in the complex score on top of a CAD-PRS. Nevertheless, the authors conclude that the complex genetic information has “clinically actionable value” beyond a CAD-PRS.\nMajor concern\nThis reviewer is still not convinced that the IGM is clinically actionable.\nAuthors’ reply: We removed the claim regarding the clinical actionability of IGM. The subsequent comments will detail the revisions made to the paper's tone and claims.\n1. The IGM has not been tested in a clinical setting but rather in two cohorts that underwent extensive genotyping and phenotyping with stringent bioinformatics QC measures. To my knowledge there are no clinical scenarios that could match this information. If I am not well informed in this respect, please clarify this in the manuscript and inform the reader how the IGM could be generated and applied clinically to add actionable value in patient care.\nAuthors’ reply: When developing a prediction model, it is not conventional for a single publication to encompass all phases—model development, internal and external validation, and clinical implementation or calibration. While model development and validation are often reported together, as we have done, the implementation and impact assessment in clinical practice are typically considered separate phases and are less commonly addressed in initial development studies. From the outset, our study aimed to develop an integrated genomic model with external validation. However, our use of the term “clinically actionable” may have unintentionally implied that clinical implementation was a core focus of this work.\nWe revised the manuscript by removing claims regarding clinical implementation, as follows:\nLines 102-104 (Abstract) We changed “We present the first model that integrates all currently available information from a single “DNA biopsy” to translate complex genetic information into a single, clinically actionable value.” to “We present the first model that integrates all currently available genomic information to help understand comprehensive genetic risk of individuals.”\nLines 132-135 (Introduction) “As genomic medicine moves towards clinical adoption, it might be beneficial that a single measure of risk is communicated using the entirety of the data points available from an individual’s genome. Mixed information about risk is poised to confuse people unless integrated into a single number that is actionable.”\nWe also clearly stated in the limitation part that this study does not address the clinical implementation aspect of the model, as follows:\nLines 406-410 (Discussion) “Lastly, while our integrated genomic model was developed using data from UKB and externally validated in TOPMed, its clinical implementation and actionability have not yet been explored. Future research should focus on understanding how the model can be effectively incorporated into clinical practice and assessing its practical benefits.”\n2. What could be the bioinformatics approach – including QC measures – to generate a IGM in a clinical setting (in which no batches of thousands of samples are available)?\nAuthors’ reply: IGM computation requires whole exome sequencing data (to obtain monogenic variants and CHIP) and low-coverage whole exome sequencing (to obtain PRSs and LTL). The combination currently exists as a highly cost-efficient product called “Blended Genome Exome (BGE)” available commercially for 120 US dollars.\nComputation would require variant curation (FH and CHIP), calculation of PRSs and LTL, and integration into a single measure for an individual using a weighted formula validated in a population. Analytic validity of IGM as a test is outside the scope of this study. Below two Nature Medicine papers [3,4] highlight examples of demonstration of analytic validity of PRS, which are often independent efforts.\nAs we also have developed a clinical translational framework for implementing genetic risk assessment in a clinical setting [5], therefore this topic is beyond the immediate focus of this study. We summarize the brief idea here for the clinical scenario when the score is to be generated on an individual basis. We first established a reference population, and the genetic score and quantitative genetic ancestry as assessed by principal components were precomputed. For a new individual for whom we need to generate the genetic score, this target individual was projected onto the quantitative ancestry space, and an ancestry-adjusted genetic score is calculated. This ancestry-adjusted genetic score is converted into a percentile rank or disease risk based on cutoffs derived from the reference distribution. For more details, please refer to our previous work [3] and the figure below adapted from the accompanying editorial comments [6].\nFigure Redacted\nReferences:\n[1] DeFelice, Matthew, et al. “Blended Genome Exome (BGE) as a Cost Efficient Alternative to Deep Whole Genomes or Arrays”. bioRxiv. 2024 Jul 30:2024.04.03.587209.\n[2] Broad Clinical Labs Blended Genome-Exome Sequenc ing. https://broadclinicallabs.org/blended-genome-exome-sequencing/\n[3] Lennon, Niall J., et al. \"Selection, optimization and validation of ten chronic disease polygenic risk scores for clinical implementation in diverse US populations.\" Nature medicine 30.2 (2024): 480-487.\n[4] Hao, Limin, et al. \"Development of a clinical polygenic risk score assay and reporting workflow.\" Nature medicine 28.5 (2022): 1006-1013.\n[5] Wang M, Menon R, Mishra S, et al. Validation of a Genome-Wide Polygenic Score for Coronary Artery Disease in South Asians. J Am Coll Cardiol. 2020;76(6):703-714.\n[6] Kullo IJ, Dikilitas O. Polygenic Risk Scores for Diverse Ancestries: Making Genomic Medicine Equitable. J Am Coll Cardiol. 2020;76(6):715-718.\n3. In a clinical setting information on predictive parameters, that were not employed in the manuscript, is easily accessible. Thus, if the authors truly seek “clinically actionable value” in their IGM they should test this model against a prediction test refined by LDL-C, Lp(a), CRP, creatinine, and troponin (or other parameters from routine clinical routine assessment), which are already at hand for physicians and patients looking for improved risk prediction.\nAuthors’ reply: We appreciate the reviewer’s suggestion. We evaluated the clinical utility of the IGM by comparing it directly against a clinical biomarker model (\"Cli,\" Supplementary Figure 8 and the below figure), which included clinical biomarkers––Lp(a), CRP, and creatinine. We deliberately excluded LDL-C from the clinical biomarker model, as total cholesterol and HDL cholesterol are already integral components of the Pooled Cohort Equations (PCE), which served as our primary benchmark comparator for the IGM. To avoid redundancy and ensure clarity of interpretation, the clinical biomarker model focused exclusively on biomarkers not included in the PCE, which are Lp(a), CRP, and creatinine.\nOur results demonstrated that the clinical biomarker model (Lp(a), CRP, and creatinine; C-statistic 0.708) did not outperform the IGM (C-statistic 0.734), underscoring the incremental predictive value provided by genomic information. This comparison is explicitly described in our revised manuscript’s Result section, as follows:\nLines 273-279 (Results): “Finally, we assessed the clinical utility of the IGM by comparing it directly against a clinical biomarker model, which included clinical biomarkers––Lp(a), CRP, and creatinine. We deliberately excluded LDL cholesterol from the clinical biomarker model, as total cholesterol and HDL cholesterol are already integral components of the PCE, which served as our primary benchmark comparator for the IGM. The result demonstrated that the clinical biomarker model (Lp(a), CRP, and creatinine; C-statistic 0.708) did not outperform the IGM (C-statistic 0.734), underscoring the incremental predictive value provided by genomic information (Figure S8).”\nFigure S8. Receiver operator characteristic curves and C statistics for different models in UK. Base model: age, sex, top 10 principal components. IGM (integrated genomic model) with six drivers including FH, PRS, MetPRS, ProPRS, CHIP, and LTL. PCE, pooled cohort equation. lnPCE: log transformed PCE. Cli: clinical biomarker model comprising Lp(a), CRP, and creatinine.\nWe also removed the claim on “clinically actionable value” from the manuscript as highlighted above.\n4. Since the authors envision a clinical application of the IGM in a clinical setting, it would be interesting to have an estimate on the costs for generation of the model (genotyping arrays, deep sequencing of several genes and integrated bioinformatics analysis).\nAuthors’ reply: As highlighted above, costs for data generation needed for IGM is 120 US dollars per sample, using Blended Genome Exome sequencing technology. Bioinformatic analysis and clinical reporting costs would be additional.\n5. The rational for building proxy risk scores for metabolome (MetPRS) and proteome (ProPRS) data, and the inclusion of these in an IGM, is not obvious for the reviewer. If a variant is related to CVD it should be evident by the weight of this variant that is already depicted by the CAD-PRS. If the variant increases for example the triglyceride concentration – and nothing else, this increase should lead to a respective increase in CAD risk, which is covered by the CAD-PRS. But if this variant has pleiotropic effects – and for some other mechanism lowers CAD risk – it would enter the MetPRS with a positive signal for CVD, but may be neutral overall. Indeed, the CAD-PRS+MetPRS has exactly the same OR/SD as the CAD-PRS alone. The CAD-PRS+ProPRS seems to be even weaker than the CAD-PRS by itself. Thus, it appears that the authors added complexity to the model, without adding any predictive value. This needs further explanation.\nAuthors’ reply: To gauge overlapping signals within the components of the IGM, we assessed pairwise correlations between the six genetic variables in the UK Biobank and TOPMed. The correlations among six drivers were weak (≤ 0.3) (Figure 2E and 2F), reassuring that each driver contributes distinct signals.\nWith regard to the additive value of MetPRS/ProPRS over CAD PRS, it is an improvement in inclusivity of risk prediction without compromise of population-level performance. For example, about 13% of individuals identified by the IGM as high-risk did not exhibit elevated CAD PRS levels (Table S19). This indicates that 13% of high-risk individuals would have gone unnoticed if only PRS was used, as their CAD risk is attributed to a combination of various other genetic risk components. A detailed individual-level inspection of the 13% subgroup (Table 1 below) underscores how combined genetic drivers—beyond PRS alone—drive CAD risk.\nTable 1. Genetic profiles for individuals in high-risk IGM group\nPRS MetPRS ProPRS FH CHIP LTL (%)* Overall risk\n(%)* (%)* (%)* carrier† variant (IGM %)\ncarrier†\nParticipant 1 34.4 75.6 73.0 1 0 14.5 83.4\nParticipant 2 44.4 91.3 79.3 1 0 54.8 89.6\nParticipant 3 51.6 93.8 91.7 0 1 28.6 84.9\nParticipant 4 75.3 23.7 25.8 0 1 96.7 81.5\nParticipant 5 77.9 30.5 62.7 1 0 93.8 95.1\nParticipant 6 65.6 94.1 93.6 0 0 0.0 86.2\nParticipant 7 73.3 3.9 87.4 0 1 71.9 85.8\nParticipant 8 95.9 26.4 85.4 1 0 83.8 99.7\nParticipant 9 46.4 21.0 64.4 1 0 56.1 83.8\nParticipant 10 88.5 99.3 97.9 0 0 63.4 94.1\n*A higher percentile corresponds to a greater risk level of the genetic driver; †A value of 1 indicates a carrier, and 0 indicates a non-carrier; High-risk IGM group indicate top 20% in IGM score in TOPMed; PRS, polygenic risk score (PRS) for CAD; MetPRS, metabolome PRS; ProPRS, proteome PRS; LTL, leukocyte telomere length; FH, familial hypercholesterolemia variants; CHIP, clonal hematopoiesis of indeterminate potential.\nTable 1 presents the detailed genetic profiles of ten representative individuals from the high-risk IGM group. Notably, participants demonstrate diverse combinations of genetic risk factors contributing to their elevated CAD risk. For instance, Participant 1 exhibited below average PRS levels (34.4%) but had substantially elevated MetPRS (75.6%), ProPRS (73.0%), and carried an FH variant, culminating in a high overall CAD risk (83.4%). Conversely, Participant 9 had relatively modest or average genetic risk profiles in PRS (46.4%), MetPRS (21.0%), and LTL (56.1%), and this individual’s high CAD risk was primarily influenced by positive FH carrier status. These individual-level examples underscore how the cumulative and multidimensional nature of the IGM can capture high-risk individuals who might otherwise remain unrecognized when considering single genetic factors, like PRS or FH variants.\n6. Clonal hematopoiesis of indeterminate potential and shortening of leukocyte telomere length develop over time. What are the considerations of the authors on time intervals to repeat the assessment of the IGM in order to capture the onset of these risk factors clinically as part of an IGM.\nAuthors’ reply: This is an important point. We agree that understanding the temporal dynamics of CHIP acquisition is crucial for framing clinical implication. According to a 21-year longitudinal study from our group[1], the prevalence of CHIP (VAF ≥ 2%) was 11% at baseline (mean age 55 years) and 25% at follow-up visits (mean age 76 years). Uddin et al. clearly demonstrated that there is a non-linear, age-dependent acceleration in the acquisition of CHIP, and the CHIP acquisition was not substantial at younger ages. Therefore, rather than proposing a fixed re-biopsy interval, we would suggest considering the individual's age and the accelerating incidence of CHIP. Re-biopsies may be less necessary, or unnecessary, for young to middle aged groups, who would most benefit from genomic risk identification.\nReference:\n[1] Uddin, Md Mesbah, et al. \"Long-term longitudinal analysis of 4,187 participants reveals insights into determinants of clonal hematopoiesis.\" Nature Communications 15.1 (2024): 7858.\nWe added this contents in the limitation section as follows:\nLines 403-406 (Discussion): “Fifth, the evolving CHIP status over time might affect the model's predictive accuracy during prolonged follow-up. Nevertheless, the low incidence of CHIP in younger and middle-aged individuals20 indicates that this limitation is unlikely to influence the performance of the model in this population for whom IGM offers the greatest potential benefit.”\n7. Taken together, this reviewer feels that authors should acknowledge what they did, i.e. they tested 5 further DNA-based scores in addition to a CAD-PRS in an epidemiological setting, and what the findings were.\nAuthors’ reply: Based on the reviewer’s suggestion, we have carefully reviewed the original statement regarding this work and have taken appropriate steps to revise its phrasing. Specifically, we have ensured that all claims related to clinical implementation have been explicitly excluded from the current iteration of the text. This modification reflects a commitment to accurately represent the scope and nature of our findings at this stage of development.\n8. If the authors still see “clinically actionable value” in their epidemiological findings they should be discuss their data in the light of a (very small) improvement in risk prediction observed in these cohorts, the lack of consideration of established clinical risk markers and the logistic challenges to implement such IGM in a clinical setting.\nAuthors’ reply: As suggested, we acknowledged and rephrased this work by excluding clinical implementation claims.\nThank you for the significant time and dedicated effort you have invested in thoroughly reviewing and providing valuable feedback to improve this work. Your insightful contributions are greatly appreciated.\nReviewer #2 (Remarks to the Author):\nThank you for the additional analyses that help putting the paper into context. There are two interesting results from the further analyses in the rebuttal that are worth adding to the results and discussed in the manuscript:\n- Please add confidence intervals around estimates in Figures 1 & 2 in the rebuttal letter, add the figures to the paper supplement and explain all variable acronyms in the captions\nAuthors’ reply: Thank you for the comments and we have added the confidence intervals for OR/SD and HR/SD, explaining all the variable acronyms in the captions. We also added the figures to the supplementary as suggested (Figures S9 and S10)\nFigure S9. Analysis of cumulative odds ratio per standard deviation and the area under the curve on the top of CAD-PRS for the prevalent CAD. Analysis of variance (ANOVA) was performed in the form of ANOVA (model with CAD-PRS only, model with CAD-PRS and additional component[s], test=”LRT”) using R v4.2.2 to test the statistical significance with other component(s) added on the top of CAD-PRS. Sex, age and top 10 principal components (PCs) were used as covariates. PRS: polygenic risk score, MetPRS: metabolome proxy risk score, ProPRS: proteome proxy risk score, FH: familial hypercholesterolemia, CHIP: clonal hematopoiesis of indeterminate potential, LTL: leukocyte telomere length.\nFigure S10. Analysis of cumulative odds ratio per standard deviation and the C-statistics on the top of CAD-PRS for the incident CAD. Analysis of variance (ANOVA) was performed in the form of anova(model with CAD-PRS only, model with CAD-PRS and additional component[s], test=”LRT”) using R v4.2.2 to test the statistical significance with other component(s) added on the top of CAD-PRS. Sex, age and top 10 PCs were used as covariates. PRS: polygenic risk score, MetPRS: metabolome proxy risk score, ProPRS: proteome proxy risk score, FH: familial hypercholesterolemia, CHIP: clonal hematopoiesis of indeterminate potential, LTL: leukocyte telomere length.\n- Many thanks for the additional metabolomic and proteomic analyses. While the focus of the paper is on genetic scores, the proteomic and metabolic scores and their correlations with ProPRS and MetPRS provide context in terms of the relative predictive impact of the “DNA biopsy”. Please add the results to the paper/supplement and discuss the relative values in the Discussion.\nAuthors’ reply: We have added supplementary figures, results descriptions, and discussion for reviewer’s suggested contents (comparison between measurement based vs genetically proxied scores), as follows:\nLines 280-301 (Results): “Comparison of Genetically Proxied and Measurement Based Risk Scores:\nTo compare CAD risk score based on genetically proxied and measured proteomic/metabolomic scores, we further constructed proteome risk score (ProRS) and metabolome risk score (MetRS). The ProRs and MetRS were strongly associated with CAD risk (ProRS: OR per SD, 2.77; 95% CI, 2.64-2.91; MetRS: OR per SD, 2.05; 95% CI, 2.01-2.10), recapitulated finding from Helgason et al.12 that demonstrated the effectiveness of proteomic risk scores in predicting CAD risk. To our knowledge, this is the first time to introduce metabolomic based risk score for CAD risk prediction. We next compared the correlation between measured and genetically proxied levels for the 124 proteins that were used to develop ProPRS. The correlations were generally variable (Figure S8), likely influenced by several factors. Proteomic and metabolomic measurements rely on a single time point and can fluctuate according to current disease status or other temporary conditions at the time of collection. In contrast, genetically proxied scores, derived from personal genetic data, more closely reflect baseline levels that are less confounded by disease or lifestyle factors (ie, diet). Moreover, as acknowledged elsewhere,10 technical elements (serum versus plasma, batch effects, fasting state), demographic variables, and environmental factors (for example, diet) all played a role in the observed variability in proteomic measurement. While comparing measured and genetically proxied molecular levels represents an interesting approach, it is essential to recognize that these measurements derive from different biological contexts, which introduces complexity to direct comparison. The genetically proxied scores may offer unique insights into CAD risk compared to measurement based scores, as they reflect an individual's baseline levels of key molecules linked to CAD risk. Notably, the genetically proxied scores exhibited effect sizes comparable to CHIP—a well-known CAD risk factor—suggesting their potential utility as novel risk factors of CAD risk.”\nLines 546-556 (Methods): “Development of Metabolome and Proteome Risk Scores Based on Measured levels: We developed the risk scores using measured metabolomic and proteomic data from 270k and 50K UK Biobank participants, respectively. We used 251 NMR metabolic biomarkers from Nightingale Health to create a metabolome risk score for CAD. After excluding participants with missing data, the remaining cohort (N=212,154) was randomly split into equal training and test sets. Using the training set, we performed five-fold cross-validated least absolute shrinkage and selection operator (LASSO) regression to select metabolite features associated with CAD, adjusting for age, sex, and the top 10 PCs of genetic ancestry, similar to our development of the genetically proxied proteomic and metabolomic risk scores (ProPRS and MetPRS). The metabolome risk score was then evaluated in the independent test set for its association with CAD risk. We used similar procedures to develop a proteome risk score with 2,923 protein measures via Olink (N=45,574).\nFigure S8. Correlations between measurement based and genetically proxied values for 124 proteins that were included in ProPRS.\nThank you for your time and effort invested to improve this work. Please accept this sincere message of gratitude for the significant effort and considerable time you have dedicated to enhancing the quality and overall impact of this work. Your contributions have been invaluable, and we deeply appreciate your commitment to its improvement."}, {"round": 2, "reviewer_comments": {"reviewer_1": "Reviewer #1 (Remarks to the Author):\nThe authors developed a complex integrated genomic model (IGM) with the aim to better predict future\ncardiovascular events. The IGM includes, in addition to a CAD polygenic risk score [CAD-PRS], further\ninherited (familial hypercholesterolemia [FH] variants, proteome PRS, metabolome PRS) and somatic\nalterations (clonal hematopoiesis of indeterminate potential [CHIP], leukocyte telomere length [LTL]). They\nemployed population-based samples (UK Biobank and the Trans-Omics for Precision Medicine program)\nto test the incremental value of the IGM over a widely published CAD-PRS.\nThis rather complex “DNA biopsy” translated into a very small improvement in clinical risk prediction as\nindicated by an improvement of the AUC by about 0.15% or 0.02 in the OR/SD. For comparison, the effect\nsizes seen with (age and sex) in a clinical risk prediction model are about 100-fold stronger than the\nimprovements observed with the measures in the complex score on top of a CAD-PRS. Nevertheless, the\nauthors conclude that the complex genetic information has “clinically actionable value” beyond a CAD-PRS.\nMajor concern\nThis reviewer is still not convinced that the IGM is clinically actionable.", "reviewer_2": "The authors have responded well to the requests. Congratulations for a fine paper."}, "author_response": "Response to Editor and Reviewers for NCOMMS-25-14393C\nREVIEWER COMMENTS\nReviewer #2 (Remarks to the Author):\nThe authors have responded well to the requests. Congratulations for a fine paper.\nResponse:\nThank you for the constructive comments that helped improve the manuscript."}]

Decision letter