A blood DNA methylation biomarker for predicting short-term risk of cardiovascular events

Abstract

Background: Recent evidence highlights the epidemiological value of blood DNA methylation (DNAm) as surrogate biomarker for exposure to risk factors for non-communicable diseases (NCD). DNAm surrogate of exposures predicts diseases and longevity better than self-reported or measured exposures in many cases. Consequently, disease prediction models based on blood DNAm surrogates may outperform current state-of-the-art prediction models. This study aims to develop novel DNAm surrogates for cardiovascular diseases (CVD) risk factors and develop a composite biomarker predictive of CVD risk. We compared the prediction performance of our newly developed risk score with the state-of-the-art DNAm risk scores for cardiovascular diseases, the 'next-generation' epigenetic clock DNAmGrimAge, and the prediction model based on traditional risk factors SCORE2.

Results: Using data from the EPIC Italy cohort, we derived novel DNAm surrogates for BMI, blood pressure, fasting glucose and insulin, cholesterol, triglycerides, and coagulation biomarkers. We validated them in four independent data sets from Europe and the USA. Further, we derived a DNAmCVDscore predictive of the time-to-CVD event as a combination of several DNAm surrogates. ROC curve analyses show that DNAmCVDscore outperforms previously developed DNAm scores for CVD risk and SCORE2 for short-term CVD risk. Interestingly, the performance of DNAmGrimAge and DNAmCVDscore was comparable (slightly lower for DNAmGrimAge, although the differences were not statistically significant).

Conclusions: We described novel DNAm surrogates for CVD risk factors useful for future molecular epidemiology research, and we described a blood DNAm-based composite biomarker, DNAmCVDscore, predictive of short-term cardiovascular events. Our results highlight the usefulness of DNAm surrogate biomarkers of risk factors in epigenetic epidemiology to identify high-risk populations. In addition, we provide further evidence on the effectiveness of prediction models based on DNAm surrogates and discuss methodological aspects for further improvements. Finally, our results encourage testing this approach for other NCD diseases by training and developing DNAm surrogates for disease-specific risk factors and exposures.

Keywords: Cardiovascular risk; DNA methylation; Epigenetics; Molecular epidemiology; Risk scores; Surrogate biomarkers.

Fig. 1

Flow chart for development and validation of DNAmCVDscore. Step 1: We train prediction models for developing DNAm surrogates for 13 CVD risk factors/biomarkers using data from the EPIC Italy study (n = 1803). We tested the validity of DNAm surrogates in four independent studies (n = 2107). Nine out of 13 DNAm biomarkers were validated in the testing set. Step 2: 60 candidate DNAm surrogates (nine newly developed + 51 from the literature) were regressed against the time from study recruitment to cardiovascular event in EPIC Italy (n = 1803). The elastic net regression model selected ten DNAm surrogates as components of the DNAmCVDscore. Step 3: In EXPOsOMICS CVD data set (N = 315), NICOLA (N = 1728), and HRS (N = 2146) we evaluated the prediction performance of DNAmCVDscore at different time points (right-censoring follow-up time) using logistic regression models adjusted for chronological age, sex, and recruitment centre (matching variables in EXPOsOMICS CVD) or Cox regression models (in NICOLA and HRS). DNAmCVDscore has a higher AUC for short-term cardiovascular events than for long-term CVD. Step 4: We compared the prediction performance of DNAmCVDscore with previously developed composite biomarkers: MRS, DNAmGrimAge, SCORE2 and SCORE2 + DNAmCVDscore. SCORE2 outperforms epigenetic predictors for long-term CVD risk (occurred more than 8 years after recruitment), whereas DNAmCVDscore predicts short-term events (occurred within 7 years after recruitment) better than other biomarkers. The enriched SCORE2 + DNAmCVDscore model outperformed all the competitors for the entire time horizon considered in the study

Fig. 2

Prediction performance of DNAmCVDscore, MRS, DNAmGrimAge, SCORE2 and SCORE2 + DNAmCVDscore. Area under the ROC curve (AUC), on the y-axis, as a function of the follow-up length (x-axis) for the five composite biomarkers investigated in this study. MRS has the worst prediction performance at each time point. SCORE2 outperforms epigenetic predictors for long-term CVD risk (occurred more than 8 years after recruitment), whereas DNAmCVDscore and DNAmGrimAge predict short-term risk (CVD events within 7 years after recruitment or less) better than the other biomarkers. The enriched SCORE2 + DNAmCVDscore model outperformed all the competitors for the entire time horizon considered in the study