We have identified 34 methylation loci associated with acute MI in a two-stage EWAS, analysing ~ 850,000 CpGs. All but two of these MI-associated sites (cg05575921 located in AHRR and the intergenic cg21566642) are newly reported. Of those, 12 CpGs could be studied in association with incident cases of CHD and CVD, and we identified four of them associated with incident CHD (three of them also with incident CVD). All four were also related to traditional CVRFs, supporting their role in the development of these diseases. However, their clinical utility as predictive biomarkers or drug targets was not proven.
Recently, two EWASs on incident CHD were published providing different findings from ours. Ward-Caviness et al.found nine CpGs associated with incident acute MI . Agha et al. reported 52 CpGs related to incident CHD . None of them was replicated in our study. This lack of concordance could be related to methodological differences (incident vs prevalent cases; myocardial infarction vs CHD; considered confounder variables; characteristics of the populations), and highlights the complexity of the study of these diseases.
CpG sites associated with acute MI events
The 34 identified CpGs showed similar effect sizes in the two REGICOR samples and we considered them potentially relevant. Similarly, all but three CpGs (AHRR-mapping cg05575921, F2RL3-mapping cg03636183, and the intergenic cg21566642) showed consistent effect sizes in the three models. The effect size of those three was reduced by half when adjusted for smoking, which highlights the important role of this risk factor in the MI context. In fact, all three sites are widely described to be related to smoking [17,18,19].
Differentially methylated genes were enriched in diverse molecular and physiological pathways, including lipid metabolism and metabolic and inflammatory diseases, underlining their relevance on the pathogenesis of CHD. Interestingly, the SERPINA1 locus also anchors genetic variants related to CHD , and other identified loci present with genetic variants associated with body mass index (DNMT3A, ABTB2, ZBTB16, NISCH, AHRR, DLEU1), inflammatory biomarkers or blood cell counts (AIM2, ITPKB, DNMT3A, LZTFL1, PSMB7, ZBTB16, ACTN1, SERPINA1, MPO, DNAJC5B, CPM, DLEU1, ZFPM1), blood pressure (PTCD2, PSMB7, SERPINA1, AHRR) and lipids (SERPINA1, NISCH, DLEU1, ZFPM1) .
Nonetheless, the case–control design of our initial discovery sample limits the inference of the biological sequence of the epigenetic marks, the related biological mechanisms, and the clinical event. One possible scenario could be that the identified DNA methylation marks occurred before the acute event, as potential biological mechanisms involved in MI pathogenesis. This may be the case of the three CpGs that were related to smoking. Conversely, as blood samples of MI cases were collected within the initial 24 h after hospitalization, the other possibility could be that methylation at the identified CpGs had changed as a consequence of the acute event or the therapeutic procedures. If the first scenario can be proven in further studies, these DNA methylation marks could be potential predictive biomarkers of MI or new therapeutic targets. If they are found to be post-MI marks, further studies could evaluate their potential as biomarkers of prognosis.
CpG sites consistently related to prevalent and incident CVD events
Twelve of the 34 identified CpGs could be evaluated in prospective samples and four of them were also related to incident cases of CHD. cg21566642 maps to an intergenic region, and cg05575921, cg04988978 and cg25769469 annotate to AHRR, MPO and PTCD2, respectively. To our knowledge, these CpGs were not associated with cardiovascular events in previous EWAS reports.
cg21566642 and cg05575921 were highly and inversely associated with smoking, which is supported by previous EWAS [18, 19]. We have also previously reported both CpGs as related to age-independent cardiovascular risk , and they have been related to all-cause mortality in an EWAS . cg05575921 was further associated directly with cholesterol in high-density lipoproteins (HDL-C) and inversely with cholesterol in low-density lipoproteins (LDL-C) and triglyceride levels in our study. This CpG has been related to both CHD prevalence and incidence in a candidate gene study .
cg04988978 and cg25769469 annotate to MPO and PTCD2, respectively. Both CpGs were associated directly with HDL-C and inversely with triglyceride and glucose levels. MPO encodes the myeloperoxidase, which promotes atherosclerotic lesions by enhancing APOB oxidation within low-density lipoproteins  and was causally associated with incident cardiovascular outcomes . One CpG located within PTCD2 was previously identified to be associated with hypertension in obstructive sleep apnea patients , and genetic variants in this gene have been related with blood pressure .
MRSs as predictive CVD biomarkers
To assess the value of the four identified CpGs as predictive biomarkers, we followed the AHA recommendations . However, neither we observed an independent association between the MRSs and the incidence of CVD events in the FOS, nor we observed an improvement in the predictive capacity of the Framingham risk function when including this score. This highlights the challenge of novel biomarkers to improve cardiovascular risk prediction.
Causality of the associations between methylation loci and cardiovascular outcomes
The four CpGs associated not only with acute MI, but also incident CHD, may suggest that DNA methylation changes at those loci occur prior to the event. However, this association does not guarantee whether differential DNA methylation at those loci has a causal effect on CHD. Mendelian randomization can be used to ascertain this causal relationship. However, this approach could only be undertaken for cg21566642. Although a non-causal relationship was suggested, this must be interpreted with caution as there was a single genetic instrumental variable, and we cannot discard that the meQTL is in linkage disequilibrium with the causal variant for CHD, reverse causation or horizontal pleiotropy using this framework [28, 29]. Moreover, cg21566642 showed a genetic influence in childhood and adolescence, while CHD events typically occur during adulthood.
Strengths and limitations
The main strength of our study is that it is the first two-stage EWAS on MI to be based on more than 800,000 CpGs across the genome. Moreover, we aimed to validate our findings in prospective samples of CHD and CVD as a proxy of MI. Also, we aimed to prove the clinical relevance of our findings. However, some limitations should be acknowledged. First, two thirds of the CpGs identified in the initial case–control study could not be assessed in the incident studies as the methylation arrays differed in the number of CpGs (EPIC VS 450 k, respectively). Second, we used self-reported information about cardiovascular risk factors in the case–control study, as an event such as MI modifies risk factor levels during the acute phase. Third, we cannot infer causality since changes in methylation could have occurred as a consequence of the acute phase and disease management of the MI event. We aimed to perform MR studies of the association between the identified CpGs and cardiovascular events, but available methylation Quantitative Trait Loci (meQTL) datasets are still limited. Last, our study is based on populations of European origin and the results cannot be extrapolated to other populations.