Sex-specific Survival Bias and Interaction Modeling in Coronary Artery Disease Risk Prediction

Abstract

Background –

The 10-year Atherosclerotic Cardiovascular Disease (ASCVD) risk score is the standard approach to predict risk of incident cardiovascular events, and recently, addition of CAD polygenic scores (PGS_CAD) have been evaluated. Although age and sex strongly predict the risk of CAD, their interaction with genetic risk prediction has not been systematically examined. This study performed an extensive evaluation of age and sex effects in genetic CAD risk prediction.

Methods –

The population-based Norwegian HUNT2 cohort of 51,036 individuals was used as the primary dataset. Findings were replicated in the UK Biobank (372,410 individuals). Models for 10-year CAD risk were fitted using Cox proportional hazards and Harrell’s concordance index, sensitivity, and specificity were compared.

Results –

Inclusion of age and sex interactions of PGS_CAD to the prediction models increased the C-index and sensitivity by accounting for non-additive effects of PGS_CAD and likely countering the observed survival bias in the baseline. The sensitivity for females was lower than males in all models including genetic information. We identified a total of 82.6% of incident CAD cases by using a two-step approach: i) ASCVD risk score (74.1%) and ii) the PGS_CAD interaction model for those in low clinical risk (additional 8.5%).

Conclusions –

These findings highlight the importance and complexity of genetic risk in predicting CAD. There is a need for modeling age and sex-interactions terms with polygenic scores to optimize detection of individuals at high-risk, those who warrant preventive interventions. Sex-specific studies are needed to understand and estimate CAD risk with genetic information.

Introduction

Coronary artery disease (CAD) is a complex disease influenced by risk factors including hypertension, hyperlipidemia, diabetes, tobacco use, age, and genetics, which leads to high morbidity and mortality¹. The American College of Cardiology / American Heart Association² recommends the 10-year Atherosclerotic Cardiovascular Disease (ASCVD) risk score [calculated with the Pooled Cohort Equation (PCE)] to estimate an individual’s risk using several demographic and cardiovascular disease risk factors. Other models include Systematic COronary Risk Evaluation (SCORE)³, QRISK⁴, Framingham risk score⁵, and NORRISK⁶. The predictive capacity of these models is moderate (C-index is between 0.6–0.8), depending on characteristics (ie., age, statin use) of the external validation dataset^7–10.

There is significant additive value of integrating genome-wide genetic data to enhance risk prediction using polygenic scores (PGS)^7–10. Additionally, individuals with a PGS in the highest 8% of score distribution have a risk of CAD comparable to having monogenic familial hypercholesterolemia (3-fold increased risk)¹⁰. To date, investigators have shown that adding PGS^11–17 to standard risk prediction algorithms enhances the power of the model to predict CAD, consistent with the estimated contribution of genetic factors responsible for 40–50% of CAD risk¹⁸.

The most predictive components of CAD prediction models are age and sex². The interplay of these two factors with the other traditional risk factors has been evaluated extensively in epidemiologic studies^2–6. However, only a limited amount of genetic risk prediction studies have evaluated the CAD risk separately for males and females^19–21. Of these three studies, two used PGSs based on significant CAD SNPs only, whereas, Huang et al. additionally evaluated the performance of polygenic score with more than one million variants. All these studies concluded that genetic prediction works better for males than for females. However, these studies did not model longitudinal time to event which is required to evaluate the usefulness of the score in the clinical setting. To systematically evaluate the age and sex effects in the genetic CAD risk prediction, we used a longitudinal population-based dataset of 51,036 samples from Norway and performed Cox proportional hazards models to explore how age and sex impact CAD genetic risk prediction (Supplemental Table 1, Supplemental Methods). The objective was to identify whether CAD genetic risk scores’ performance in the prediction of incident CAD depends on a patient’s age and sex.

Methods

Desccriptions of the used datasets and detailed statistical analysis methods can be found from Supplemental Methods. Participation in the The Trøndelag Health (HUNT) Study is based on informed consent and the study has been approved by the Data Inspectorate and the Regional Ethics Committee for Medical Research in Norway (REK: 2014/144). The summary level HUNT2 data that support the findings of this study are available from the corresponding author upon reasonable request. UK Biobank had obtained ethics approval from the North West Multi-centre Research Ethics Committee. All UK Biobank data and materials have been made public for research purposes and can be accessed at https://www.ukbiobank.ac.uk.

Results

CAD Polygenic Score and Correlations with Age and Sex

We initially tested whether the genome-wide polygenic score for CAD estimated using metaGRS weights¹² (hereafter called the PGS_CAD) was associated with sex or age in HUNT2 before examining how to optimally account for age and sex in CAD risk-stratification model. In principle, one would expect PGSs for various diseases to show equivalent distributions among males and females, provided age, sex, and ancestry are corrected in the underlying summary statistics and sex-chromosomes are excluded from the evaluation. However, the relationship between the PGS, sex, and age could be impacted by ascertainment bias, undetected population stratification, or survival bias by genotype.

Significant associations were observed between enrollment age and PGS_CAD and between sex and PGS_CAD when assessing baseline data only (Supplemental Table 2A–B). These results suggest that there is non-random selection in the cohort related to PGS_CAD, possibly ascertainment bias or survival effects. Based on the results from these two models, males at baseline had an average 0.06 SD-units lower PGS_CAD than females, and the PGS_CAD was 0.003 SD-units lower per year of age. The association of age with PGS_CAD was significant for both males and females (Supplemental Table 2C–D), although the effect for males was marginally higher (PGS_CAD 0.0032 SD-units lower per year for males, 0.0025 for females) (Figure 1A). However, when adding the interaction term to the linear regression model, age*sex term was not significant (P-value = 0.212). The sex-PGS_CAD association was partly age dependent as the effect of sex on PGS_CAD becomes non-significant (P-value = 0.404) when the interaction term is added (Supplemental Table 2E). The age and sex associations were confirmed by testing the models in the UK Biobank. In UK Biobank, the interaction term age*sex on the PGS_CAD was statistically significant (P-value = 1.0e-8; Supplemental Table 3A–C, Supplemental Figure 1A). The trends were reduced when including the prevalent cases (together with the baseline statin users in the UK Biobank) in the baseline analysis for both datasets, (N=1,455 in HUNT2, N=84,292 in UK Biobank; Figure 1B, Supplemental Table 4A–B, Supplemental Figure 1B). The slight gradual decrease in mean PGS_CAD by age, particularly in men, could be due to lower survival of older males with high PGS_CAD, who are probably absent from the cohort at baseline (and some of whom were excluded from analyses due to prevalent or earlier-onset CAD).

Figure 1.

Raw PGS_CAD by age and sex in the cohort baseline. Illustration of the selection bias in the cohort baseline using lowess curves for PGS_CAD by age for males and females separately. The plot has been zoomed in relative to the y-axis to better show the trends. Panel A shows the trends in the analysis dataset used in the Cox models (prevalent cases excluded) and panel B when prevalent cases are included. PGS-CAD = PGS_CAD = Coronary artery disease polygenic score.

CAD Polygenic Score and Age and Sex Interaction Models

The PGS_CAD performance was tested while explicitly modeling age, sex, and a comprehensive set of interaction terms to counter the survival bias observed at baseline. We used an additive PGS_CAD model (model C1, Supplemental Table 5) as the comparison model to test the effect of added interaction effects to the model performance (Supplemental Table 6). To fully capture any potential interactions, a model including all interaction terms of age, sex, and PGS_CAD was examined. Specifically, we aimed to test for the possible age-dependent (Age*PGS_CAD term) and sex-dependent (Sex*PGS_CAD term) behavior of the PGS_CAD, and age-effects of the PGS_CAD predictive performance that may differ between males and females (Age*Sex*PGS_CAD term) (model C2).

The sensitivity (78.4%) increased in the full interaction model (model C2, Supplemental Table 7) compared to the model with additive genetic effects only (model C1) (sensitivity 77.0%, Table 1), whereas, the C-index did not show a significant increase (model C2 C-index 0.839 [0.833; 0.845], model C1 C-index 0.838 [0.832; 0.844]). Similarly in the UK Biobank, the C-index did not change. The sensitivity increased after adding the interaction terms while specificity decreased. The overall sensitivity and specificity values between the two cohorts are different likely due to the well-known bias towards healthier individuals in the UK Biobank dataset (consistent with later analyses where the 7.5% risk threshold to classifies a smaller proportion of individuals into the high-risk group). However, the proportional gain in the sensitivity was consistent between the two cohorts (1.8% increase in HUNT2 and 1.2% in UK Biobank). Figure 2 illustrates the effect of the Age*Sex*PGS_CAD term in the HUNT2 dataset. The hazard ratios (HRs) for the PGS_CAD on CAD 10-year risk with a model fit separately for males and females were not significantly different. However, we observed significant differences in model performance between the age groups when stratifying the dataset into three age-bins, demonstrating an age interaction. When further stratifying both males and females separately into age bins (approximating the Age*Sex*PGS_CAD interaction term in model C2), we observed small differences in the HRs between males and females in the same age bins, which however, were not statistically significant due to the lower number of samples in the subgroups. Simultaneously, we added an age*sex interaction term to ensure the model was valid by including all lower-level effects. The positive beta of this term indicates that irrespective of the PGS_CAD, age increases the CAD risk more substantially for females compared to males. This observation may be due the effect of menopause on increased CAD risk in females²².

Table 1.

Diagnostic metrics for additive PGS_CAD model (model C1) and PGS_CAD model with all interactions (model C2) in HUNT2 and UK Biobank.

Dataset	Risk model	Incident cases with risk ⋝7.5%	Incident cases with risk <7.5%	Non-cases with risk ⋝7.5%	Non-cases with risk <7.5%	Specificity/Selectivity	Sensitivity/Recall	C-index [95% confidence interval]
HUNT2	PGSCAD additive model (model C1)	2290	684	11133	36929	76.8%	77.0%	0.838 [0.832; 0.844]
HUNT2	PGSCAD with all interactions (model C2)	2332	642	11539	36523	76.0%	78.4%	0.839 [0.833; 0.845]
UK Biobank	PGSCAD additive model (model C1)	8688	8881	52201	233948	84.6%	66.4%	0.743 [0.739; 0.746]
UK Biobank	PGSCAD with all interactions (model C2)	8996	8573	54913	231236	83.9%	67.2%	0.743 [0.740; 0.747]

PGS_CAD=Coronary artery disease polygenic score

Figure 2.

Age dependence of the sex-effect. This figure shows the hazard ratios with 95% confidence intervals for PGS_CAD in models fitted in 11 different subsets. Subsets were separated by sex (males, females), by age (<45-year-old, between 45 and 70, and more than 70-year-old) and finally stratified by both. All models have been adjusted for within-bin age and age²-effects and additionally the 3 age-bin models for sex. PGS-CAD = PGS_CAD = Coronary artery disease polygenic score.

CAD Polygenic Score with ASCVD risk score

Modeling clinical and genetic risk together

We expect that genetic risk will most likely be used in conjunction with or in addition to already existing risk estimates. With this in mind, we modeled the ASCVD risk score with the PGS_CAD. Our model with additive effects only (model C3, Supplemental Table 8) had a higher C-index (0.842 [0.836; 0.848]) and slightly lower sensitivity than model C1 (76.8%), which suggests that including the ASCVD risk score (i.e., clinical score) on top of the PGS_CAD does not increase the number of identified cases, but rather affects the specificity, which increases from 76.8% (in model C1) to 77.4% (in model C3 which in includes ASCVD risk), and is observable as an increase in the C-index. This finding could be caused by reduced transferability of PCE into Norwegian population. However, to evaluate the impact of the genetic interaction terms in a model with the clinical risk included, we tested the improvement in the model metrics by including the ASCVD risk score into the full PGS_CAD interaction model (model C4, Table 2). This model had the highest C-index (0.845 [0.839; 0.851]) and sensitivity (79.6%) of the combined prediction models. Moreover, when comparing the model with PGS_CAD and ASCVD predictors but without the interaction terms (model C3) to the same model with genetic interaction terms (model C4) the sensitivity increased from 76.8% to 79.7% while specificity decreased from 77.4% to 76.0%. Almost all of the interaction terms showed significant P-value in this model (Table 2) suggesting that the interactions between the genetic risk and age and sex should be taken into account when modeling the clinical and genetic risk together.

Table 2.

Model statistics for PGS_CAD model with all interactions and ASCVD in HUNT2 (model C4)

Variable/term (units)	Effect	SE	HR	P-value
Age2 (year2)	−2.8e-3	1.5e-4	0.997	9.4e-83
Sex (female=0, male=1)	3.24	0.255	25.47	7.1e-37
Age (year)	0.417	0.018	1.517	3.25e-119
PGSCAD (SD-unit)	1.21	0.182	3.369	2.3e-11
ASCVD (risk/100)	3.01	0.199	20.33	1.2e-51
Sex*Age (year for males compared to females)	−0.041	3.8e-3	0.960	7.3e-28
Sex*PGSCAD (SD-units for males compared to females)	−0.436	0.221	0.647	0.048
Age*PGSCAD (SD-units per year)	−0.013	2.6e-3	0.987	6.9e-7
Sex*Age*PGSCAD (SD-units per year for males compared to females)	6.2e-3	3.2e-3	1.006	0.052

SE=Standard error of the effect, HR=Hazard ratio, PGS_CAD=Coronary artery disease polygenic score, SD=Standard deviation, ASCVD=Atherosclerotic cardiovascular disease risk score

Two-step Approach

We also tested a scenario where the PGS_CAD could be added as an independent risk estimation tool to identify additional cases that were not already identified by their ASCVD risk score. This two-step case identification procedure is based on two sequential and independent risk estimates. After identifying high-risk individuals by the ASCVD risk score we applied the PGS_CAD risk model to the remaining individuals, including interaction terms (effects coming from the model C2 for the full population with full population variable distributions). This staged approach, where ASCVD is first applied and PGS_CAD with genetic interaction terms is then applied, newly classified 3,235 individuals as high-risk (8.3% of the remaining dataset or 27.2% of the total dataset) totaling to 82.6% of the cases identified with the two-step approach. Among those newly classified, we observed 253 additional future cases during the 10-year follow-up (32.9% of the cases missed by the ASCVD score; Figure 3). If we used model C1 (the model without interaction terms) in the second step instead of the model C2 with the interactions, we would identify 81.5% of the total cases instead of 82.6%, highlighting the importance of interaction modeling also when using the sequential approach. The 253 additional incident cases identified using model C2 had a mean ASCVD risk of 4.73% ranging from 1.09% to 7.49%, suggesting the PGS_CAD provides information orthogonal to the ASCVD. We identified the same number of cases when applying the PGS_CAD model first (model C2) and then the ASCVD risk score (individuals that have either high PGS_CAD model C2 risk or high ASCVD risk). However, using the ASCVD first and then applying the genetic model may be more cost efficient as the number of samples needed to be genotyped is lower (only those with low ASCVD risk), and follows the current standard clinical practice for the first stage.

Figure 3.

Illustration of the two-step approach combining ASCVD risk and the genetic risk model in HUNT2. This figure shows how combining the atherosclerotic cardiovascular disease risk score (ASCVD) and the genetic risk model with interactions in two consecutive steps allows for identification of additional cases. PGS_CAD = Coronary artery disease polygenic score.

Sex-Specific Models and Sex-Specificity of Model Metrics

The currently applied clinical risk scores are typically applied to males and females separately instead of using sex-interaction models. To test the applicability of our PGS_CAD interaction models in the similar manner, we tested the performance of models allowing for age-dependence of the PGS_CAD separately in males and females. First, we evaluated the PGS_CAD model without interactions (model S1, Supplemental Tables 9A–B). The C-indexes observed were 0.850 [0.840; 0.860] for females and 0.816 [0.808; 0.824] for males, and the magnitude of the HR for the PGS_CAD was similar for both sexes (HR females = 1.41 [1.33; 1.49], HR males = 1.43 [1.37; 1.50]).

The inclusion of the PGS_CAD*Age interaction into the models (Supplemental Tables 10A–B) did not notably change the C-indexes, even though the interaction term was significant for both sexes (P-value in females = 1.85e-6, in males = 8.91e-4). However, the sensitivity increased for both sexes in HUNT2 (Table 3A–B). In UK Biobank, the sensitivity only increased for males (Supplementary Tables 11A–B). Both the C-index and sensitivity increased for both males and females when adding the ASCVD risk score to the model (model S3, Supplemental Tables 12A–B). Lastly, we performed the two-step process described earlier for males and females separately by including i). the conventional ASCVD risk and ii) PGS_CAD with age-interaction term. Using the two-step approach, we correctly re-classified an additional 194 and 59 future cases for males and females, respectively (38.3% and 22.4% of the cases missed by the ASCVD risk assessment). We observed increased sensitivity by the two-step approach also in the UK Biobank. The corresponding numbers without the interaction terms in the second step were 183 future cases for males and 51 for females (36.1% and 19.4% of the cases missed by the ASCVD risk score).

Table 3.

Diagnostic metrics for sex-stratified models and ASCVD risk score in HUNT2

Females Risk model	Incident cases with risk ⋝7.5%	Incident cases with risk <7.5%	Non-cases with risk ⋝7.5%	Non-cases with risk <7.5%	Specificity/Selectivity	Sensitivity/Recall	C-index [95% confidence interval
ASCVD	865	263	5171	21256	80.4%	76.7%	NA *
Additive PGSCAD model (model S1)	807	321	4658	21769	82.4%	71.5%	0.850 [0.840; 0.860]
PGSCAD with age-interaction (model S2)	823	305	4792	21635	81.9%	73.0%	0.851 [0.843; 0.859]
PGSCAD with age-interaction and ASCVD (model S3)	833	295	4707	21720	82.2%	73.8%	0.859 [0.851; 0.867]
2 step: ASCVD then PGSCAD with age-interaction **	924	204	6032	20395	77.2%	81.9%	NA*
Males Risk model	Incident cases with risk ⋝7.5%	Incident cases with risk <7.5%	Non-cases with risk ⋝7.5%	Non-cases with risk <7.5%	Specificity/Selectivity	Sensitivity/Recall	C-index [95% confidence interval
ASCVD	1339	507	4808	16827	77.8%	72.5%	NA*
Additive PGSCAD model (model S1)	1504	342	6727	14908	68.9%	81.5%	0.816 [0.808; 0.824]
PGSCAD with age-interaction (model S2)	1510	336	6745	14890	68.8%	81.8%	0.816 [0.808; 0.824]
PGSCAD with age-interaction and ASCVD (model S3)	1537	309	6828	14807	68.4%	83.3%	0.822 [0.814; 0.830]
2 step: ASCVD then PGSCAD with age-interaction †	1533	313	6928	14707	68.0%	83.0%	NA*

^*C-index not available as the risk score is not based on a fitted model;

^†This is not a model but a sequential identification of high risk individuals using first ASCVD risk score and then genetic interaction model to estimate ASCVD risk.

ASCVD=Atherosclerotic cardiovascular disease risk score, PGS_CAD=Coronary artery disease polygenic score

Sex-Specific Model Metrics and the Effect of the Risk Threshold

We saw lower sensitivity and higher specificity for females compared to males in all sex stratified models that included genetic information. These two metrics are dependent on the risk-threshold. Therefore, we tested how changing the threshold would affect the risk classification. The sensitivity and specificity for males and females for varied risk-thresholds are presented in Supplemental Figures 2–5. For all of the sex-stratified models, the percentages of individuals in the high-risk group were higher for males than for females at any given risk threshold. This finding was expected given that females have a lower overall prevalence of CAD. The proportion of individuals in the high-risk group based on ASCVD risk was close between the two sexes (Supplemental Figure 5). This is most likely due to the underestimation of the ASCVD risk seen for males when applying the ASCVD risk calculation to our test dataset (Supplemental Figure 6). Supplemental Figure 7 shows the risk calibration for the model S3 as comparison. However, lower sensitivity was observed for females for models that include the PGS_CAD. To achieve the same sensitivity observed for males at the 7.5% risk threshold (81.4%), we would need to lower the risk threshold in females to 5.0% (Supplemental Figures 2,3 and 4). In all three models (S1–3), the specificity in females with the 5.0% risk threshold was better than the specificity in males with the 7.5% risk threshold.

Discussion

This study evaluated statistical approaches in two population-based datasets to fine-tune the prediction of individuals at risk for 10-year CAD events by accounting for different rates and age distributions of CAD in males and females. We found that the C-index and sensitivity of the 10-year prediction of CAD improved by including sex and age interactions when modeling PGS_CAD compared to a model without interaction effects in both datasets. Inclusion of the interaction terms accounts for the non-additive effects of age and sex that have been shown to exist for many of the complex traits²³. Additionally, including the interaction terms most likely corrects for the baseline survival bias observed. The implications of these results highlight the importance of modeling age and sex interactions in predicting CAD events with genetic information.

In our baseline correlation checks, we observed significant associations between the genetic score and both age and sex, and replicated these findings in the UK Biobank. The observed associations suggest non-random selection related to genetics in the study cohorts, and we contend that the age association is derived from the survival bias of individuals with lower genetic risk of CAD. The sex association is most likely derived from the earlier onset of CAD in males, which enhances the survival bias of those with lower genetic risk in males. We expect these biases to be present in all cross-sectional studies where the age ranges over the expected age-of-onset of the studied disease. Moreover, the same biases are most likely also present in populations where risk estimates are applied to identify high-risk individuals.

We evaluated the potential incorporation of genetic information into identifying at-risk individuals by modeling clinical and genetic risk at the same time or by applying two risk estimates (clinical and genetic) in a sequential manner. Both of these approaches showed increased number of cases identified when the age and sex dependent behavior of the genetic risk was taken into account. This suggests that the genetic risk should be evaluated in a similar manner as the other clinically relevant risk factors; separately for males and females and individuals of different ages. Additionally, with genome-wide genotyping being translated into clinical settings, CAD risk prediction may be enhanced by the sequential two-step approach we evaluate here: i.) first apply the existing clinical score (i.e., PCE/ASCVD risk score) and ii.) from those identified with a low ASCVD risk, apply a second model incorporating age, sex, and genetic information with age and sex interactions to identify additional high-risk individuals (Figure 4). Using our two-step approach with a set risk threshold of 7.5%, we identified a total of 82.6% of incident CAD diagnoses (74.1% by ASCVD risk estimation and an additional 8.5% by the PGS_CAD interaction model). The newly identified future cases in the second step suggests that incorporating genetic information including age and sex interaction modeling captures cases that do not yet show clinical signs of atherosclerosis or hypertension (which are the biggest clinical contributors to the ASCVD risk after age and sex). The implications of these results could be two-fold i.) clinicians maintain the ability to identify high-risk individuals using the ASCVD risk tool, and ii.) clinicians with access to genetic information on patients are able to more accurately discern which additional individuals may benefit from timely prevention strategies (Figure 5). Implementation of this approach will require a large study with diverse populations to tests risk factors including genetic information to ascertain population level effects that can be applied to a single patient in clinical practice.

Figure 4.

Demonstration of the different risk models implementing clinical and genetic risk with interactions in a hypothetical population of 500,000 people. The Coronary artery disease (CAD) prevalence used in the demonstration is based on the current CAD prevalence in the United States.

Figure 5.

Coronary Artery Disease Polygenic Scores Used for Risk Prediction Exhibit Age and Sex-Interactions. This figure is an illustration showing the effect of the two-step approach with the age and sex interactions included in screening of 500,000 individuals. CAD = Coronary Artery Disease, n = number of samples, PGS = Polygenic score, ASCVD = Atherosclerotic cardiovascular disease risk score

For both cohorts, the sensitivity for females was consistently lower than for males. In the HUNT2 dataset, we found that similar sensitivity to predict female cases could be achieved by lowering the risk threshold for preventive therapies from 7.5% to 5.0%. Additionally, this would not result in a higher proportion of females recommended for treatment relative to males. We suggest that the risk threshold used in the genetic screening should be independently evaluated in males and females before applying genetic information in an equal manner in the clinical setting. For example, in our dataset, if we changed the risk threshold from 7.5% to 5.0% in females when applying the two-step sequential approach, we would increase the identification of cases from 81.9% to 86.2%% without increasing the proportional amount of females suggested for treatment (25.3%) relative to males recommended for treatment (36.0%). However, as has been previously shown²⁴, the composition of CAD risk differs between males and females also in the non-genetic risk-factors suggesting that the specificity of female CAD prediction could be improved by including additional CAD risk factors (ie., c-reative protein and hormone therapies). Nonetheless, the inclusion of genetic information into clinical practice and the effect of the inclusion to the risk-thresholds needs to be comprehensively validated in further clinical studies, ideally with sex-segregated groups.

Our study has important limitations. First, the datasets used in this study, HUNT2 and UK Biobank, are sampled from different populations than the datasets in which the ASCVD score was originally created (different ancestry, country of residence, younger, and healthier). Moreover, the ASCVD score was developed to evaluate the risk of developing CAD or stroke. In our study, we used the ASCVD score to predict CAD event or death during the 10-year follow-up time. This approach may have caused the miscalibration observed in the HUNT2 study, which limits our ability to perform unbiased one-to-one comparisons between the performance of these scoring methods. However, the trends and conclusions reported herein do not rely on the exact ASCVD risk, but rather, compare the change in the metrics when modeling genetics with and without age and sex interaction terms. Second, the participants in this study are of European ancestry, and therefore, the results may not be generalizable to populations with other ancestries²⁵. Additional studies are needed to determine the importance of interaction effects in the genetic prediction of other traits and in diverse populations with different rates of clinical risk factors such as hypertension and high LDL cholesterol. Third, due to the lack of family history information available in the hospital registry linked HUNT2 dataset, we tested the performance of the interaction models against only one clinical score, albeit the one recommended by the American Heart Association(2). Lastly, our models were based on only a single PGS, although the performance of several different genome-wide PGSs (i.e. those derived from statistical methods such as metaGRS, LDpred or PRS-CS) have shown to be nearly equivalent in CAD prediction²⁶.

Conclusion

All populations screened for CAD risk are subject to survival bias that shows as a depletion of high PGS individuals. We suggest using age and sex interactions with the PGS to account for the non-additive effects in disease prediction to further increase the number of future cases identified. To predict future CAD events, the best performing models in this study utilized both clinical and genetic information including interactions -- whether applied as a single model or in a sequential two-step process. Moreover, CAD prediction studies with genetic information should focus on the sex-specific behavior of the predictors and prediction models to account for sex-specific genetic effects and differences in the incidence of CAD events between males and females.

Nonstandard Abbreviations and Acronyms:

ASCVD

Atherosclerotic cardiovascular disease

Harrell’s C-index, referred to as C-index throughout the study

Concordance index

CAD

Coronary artery disease

HRs

Hazard ratios

PCE

Pooled Cohort Equation

PGS

polygenic score

HUNT

Trøndelag Health Study

UK Biobank

United Kingdom Biobank