Epigenetic Contributions to Clinical Risk Prediction of Cardiovascular Disease

Epigenetic Clocks and EpiScore for Preventive Medicine: Risk Stratification and Intervention Models for Age-Related Diseases

Aging is the primary risk factor for chronic diseases such as cardiovascular disease, cancer, and dementia. However, chronological age alone fails to capture individual variability in aging trajectories and disease susceptibility. Recent advances in epigenetic clocks-DNA methylation-based models that estimate biological age-have opened new possibilities for personalized and preventive medicine. This review explores the clinical potential of epigenetic clocks and EpiScores, composite biomarkers that predict health risks and physiological status. We present a comparative evaluation of widely used epigenetic clocks, including Horvath, GrimAge, PhenoAge, and DunedinPACE, and summarize their predictive performance for mortality, cognitive decline, and cardiovascular outcomes. EpiScores linked to inflammation, glycemic control, and immunosenescence are highlighted as tools for stratified risk assessment. When integrated with multi-omics data and electronic health records, these measures enhance the precision of population health management. Special emphasis is placed on applications in longevity clinics and anti-aging clinics, community-based care, and national health checkup systems. We also explore global standardization efforts and ethical considerations, as well as Japan's unique initiatives-including the "Aging Measurement" project at the Osaka-Kansai Expo 2025. Furthermore, we propose the development of a Global Health and Aging Index that integrates the biological, functional, and subjective dimensions of aging, aligned with the WHO concept of Intrinsic Capacity. In conclusion, epigenetic clocks and EpiScores represent transformative tools for shifting from reactive treatment to proactive health optimization, and from chronological to biological metrics in aging science and public health policy.

The predictive power of profiling the DNA methylome in human health and disease

Early and accurate diagnosis significantly improves the chances of disease survival. DNA methylation (5mC), the major DNA modification in the human genome, is now recognized as a biomarker of immense clinical potential. This is due to its ability to delineate precisely cell-type, quantitate both internal and external exposures, as well as tracking chronological and biological components of the aging process. Here, we survey the current state of DNA methylation as a biomarker and predictor of traits and disease. This includes Epigenome-wide association study (EWAS) findings that inform Methylation Risk Scores (MRS), EpiScore long-term estimators of plasma protein levels, and machine learning (ML) derived DNA methylation clocks. These all highlight the significant benefits of accessible peripheral blood DNA methylation as a surrogate measure. However, detailed DNA methylation biopsy analysis in real-time is also empowering pathological diagnosis. Furthermore, moving forward, in this multi-omic and biobank scale era, novel insights will be enabled by the amplified power of increasing sample sizes and data integration.

Early-life smoking, cardiovascular disease risk, and the mediating role of DNA methylation biomarkers of aging

BACKGROUND

Early-life smoking is linked to biological aging and chronic diseases, yet its specific relationship with cardiovascular disease (CVD) risk and the role of DNA methylation biomarkers of aging as potential mediators of that relationship remain underexplored.

METHODS

In this study, we analyzed data from 2345 participants in the National Health and Nutrition Examination Survey (NHANES; 1999-2002). Early-life smoking status was assessed on the basis of the age of smoking initiation (ASI) and categorized into three smoking initiation periods (SIPs): childhood (5-14 years), adolescence/adulthood (> 14 years), and never smoked. DNA methylation biomarkers of aging (DNAm PhenoAge, DunedinPoAm, HorvathTelo) were measured, and CVD outcomes were determined via self-reported, physician-confirmed diagnoses. Multivariate logistic regression and causal mediation analyses were performed to assess the associations between SIP and CVD outcomes and explore the mediating effects of DNA methylation biomarkers on those associations.

RESULTS

Earlier smoking initiation was more strongly associated with an increased risk of developing CVD, with childhood smoking showing the highest risk (OR = 1.95, 95% CI: 1.15-3.29; P = 0.013). Furthermore, DNA methylation biomarkers of aging were independently associated with increased CVD risk (1-year increase in DNAm PhenoAge: OR = 1.03, 95% CI: 1.01-1.05, P < 0.001; 0.1-unit increase in DunedinPoAm: OR = 1.19, 95% CI: 1.00-1.40, P < 0.05; 1-kb increase in HorvathTelo: OR = 0.57, 95% CI: 0.34-0.96, P < 0.05). Subgroup analysis revealed that the association between early-life smoking status and the risk of developing CVD was stronger among individuals without household smoking exposure (P for interaction = 0.035). Moreover, compared with early-life smoking status, later smoking initiation status was correlated with delayed epigenetic aging, as indicated by lower DNAm PhenoAge (β=-0.02, 95% CI: -0.03--0.01, P < 0.01), slower DunedinPoAm (β=-0.01, 95% CI: -0.01--0.01, P < 0.001), and longer HorvathTelo (β = 0.01, 95% CI: 0.01-0.01, P < 0.001). Mediation analysis revealed that DNAm PhenoAge significantly mediated the relationship between early-life smoking status and CVD risk, accounting for 6% of the total effect (ASI: ACME=-0.000100, P = 0.010; SIP: ACME = 0.004796, P = 0.022).

CONCLUSION

Early-life smoking status is associated with significantly increased CVD risk. DNAm PhenoAge partially mediates this relationship, suggesting its potential as a target for prevention. Moreover, these findings highlight the need for early smoking prevention to reduce CVD risk.

DNA methylation-predicted plasma protein levels and breast cancer risk

BACKGROUND

Blood DNA methylation (DNAm) profiles have been used to show that changes in circulating leukocyte composition occur during breast cancer development, suggesting that peripheral immune system alterations are markers of breast cancer risk. Blood DNAm profiles have recently been used to predict plasma protein concentrations ("Protein EpiScores"), but their associations with breast cancer risk have not been examined in detail.

METHODS

Whole blood DNAm profiles were obtained for a case-cohort sample of participants in the Sister Study and used to calculate 109 Protein EpiScores. Of the 4,479 women included, 2,151 (48%) were diagnosed with breast cancer within 15 years of their baseline blood draw (median time to diagnosis: 8.6 years; 1,673 invasive cancer and 478 ductal carcinomas in situ). Protein EpiScores associations with breast cancer incidence were estimated using weighted Cox regression models, overall and stratified by time and participant characteristics.

RESULTS

Protein EpiScores for RARRES2, IGFBP4, and CCL21 were positively associated with invasive breast cancer risk (hazard ratios from 1.17 to 1.24), while those for F7, SELL, CXCL9, CD48, and IL19 were inversely associated (hazard ratios from 0.82 to 0.86) (all FDR < 0.10). Eight immune response-related Protein EpiScores (CXCL9, CD48, FCGR3B, CXCL11, CCL21, CRTAM, VCAM1, GZMA) were associated with invasive cancers diagnosed within five years of enrollment. Protein EpiScore associations were consistently stronger for estrogen receptor-negative tumors.

CONCLUSIONS

Several Protein EpiScores, including many related to immune response, were associated with breast cancer risk, highlighting novel changes to the peripheral immune system that occur during breast cancer development.

Epigenomic biomarkers insights in PBMCs for prognostic assessment of ECMO-treated cardiogenic shock patients

OBJECTIVE

As the global use of extracorporeal membrane oxygenation (ECMO) treatment increases, survival rates have not correspondingly improved, emphasizing the need for refined patient selection to optimize resource allocation. Currently, prognostic markers at the molecular level are limited.

METHODS

Thirty-four cardiogenic shock (CS) patients were prospectively enrolled, and peripheral blood mononuclear cells (PBMCs) were collected at the initiation of ECMO (t0), two-hour post-installation (t2), and upon removal of ECMO (tr). The PBMCs were analyzed by comprehensive epigenomic assays. Using the Wilcoxon signed-rank test and least absolute shrinkage and selection operator (LASSO) regression, 485,577 DNA methylation features were analyzed and selected from the t0 and tr datasets. A random forest classifier was developed using the t0 dataset and evaluated on the t2 dataset. Two models based on DNA methylation features were constructed and assessed using receiver operating characteristic (ROC) curves and Kaplan-Meier survival analyses.

RESULTS

The ten-feature and four-feature models for predicting in-hospital mortality attained area under the curve (AUC) values of 0.78 and 0.72, respectively, with LASSO alpha values of 0.2 and 0.25. In contrast, clinical evaluation systems, including ICU scoring systems and the survival after venoarterial ECMO (SAVE) score, did not achieve statistical significance. Moreover, our models showed significant associations with in-hospital survival (p < 0.05, log-rank test).

CONCLUSIONS

This study identifies DNA methylation features in PBMCs as potent prognostic markers for ECMO-treated CS patients. Demonstrating significant predictive accuracy for in-hospital mortality, these markers offer a substantial advancement in patient stratification and might improve treatment outcomes.

DNA methylation in cardiovascular disease and heart failure: novel prediction models?

BACKGROUND

Cardiovascular diseases (CVD) affect over half a billion people worldwide and are the leading cause of global deaths. In particular, due to population aging and worldwide spreading of risk factors, the prevalence of heart failure (HF) is also increasing. HF accounts for approximately 36% of all CVD-related deaths and stands as the foremost cause of hospitalization. Patients affected by CVD or HF experience a substantial decrease in health-related quality of life compared to healthy subjects or affected by other diffused chronic diseases.

MAIN BODY

For both CVD and HF, prediction models have been developed, which utilize patient data, routine laboratory and further diagnostic tests. While some of these scores are currently used in clinical practice, there still is a need for innovative approaches to optimize CVD and HF prediction and to reduce the impact of these conditions on the global population. Epigenetic biomarkers, particularly DNA methylation (DNAm) changes, offer valuable insight for predicting risk, disease diagnosis and prognosis, and for monitoring treatment. The present work reviews current information relating DNAm, CVD and HF and discusses the use of DNAm in improving clinical risk prediction of CVD and HF as well as that of DNAm age as a proxy for cardiac aging.

CONCLUSION

DNAm biomarkers offer a valuable contribution to improving the accuracy of CV risk models. Many CpG sites have been adopted to develop specific prediction scores for CVD and HF with similar or enhanced performance on the top of existing risk measures. In the near future, integrating data from DNA methylome and other sources and advancements in new machine learning algorithms will help develop more precise and personalized risk prediction methods for CVD and HF.

Epigenetic scores derived in saliva are associated with gestational age at birth

BACKGROUND

Epigenetic scores (EpiScores), reflecting DNA methylation (DNAm)-based surrogates for complex traits, have been developed for multiple circulating proteins. EpiScores for pro-inflammatory proteins, such as C-reactive protein (DNAm CRP), are associated with brain health and cognition in adults and with inflammatory comorbidities of preterm birth in neonates. Social disadvantage can become embedded in child development through inflammation, and deprivation is overrepresented in preterm infants. We tested the hypotheses that preterm birth and socioeconomic status (SES) are associated with alterations in a set of EpiScores enriched for inflammation-associated proteins.

RESULTS

In total, 104 protein EpiScores were derived from saliva samples of 332 neonates born at gestational age (GA) 22.14 to 42.14 weeks. Saliva sampling was between 36.57 and 47.14 weeks. Forty-three (41%) EpiScores were associated with low GA at birth (standardised estimates |0.14 to 0.88|, Bonferroni-adjusted p-value < 8.3 × 10-3). These included EpiScores for chemokines, growth factors, proteins involved in neurogenesis and vascular development, cell membrane proteins and receptors, and other immune proteins. Three EpiScores were associated with SES, or the interaction between birth GA and SES: afamin, intercellular adhesion molecule 5, and hepatocyte growth factor-like protein (standardised estimates |0.06 to 0.13|, Bonferroni-adjusted p-value < 8.3 × 10-3). In a preterm subgroup (n = 217, median [range] GA 29.29 weeks [22.14 to 33.0 weeks]), SES-EpiScore associations did not remain statistically significant after adjustment for sepsis, bronchopulmonary dysplasia, necrotising enterocolitis, and histological chorioamnionitis.

CONCLUSIONS

Low birth GA is substantially associated with a set of EpiScores. The set was enriched for inflammatory proteins, providing new insights into immune dysregulation in preterm infants. SES had fewer associations with EpiScores; these tended to have small effect sizes and were not statistically significant after adjusting for inflammatory comorbidities. This suggests that inflammation is unlikely to be the primary axis through which SES becomes embedded in the development of preterm infants in the neonatal period.

Epigenomic insights into common human disease pathology

The epigenome-the chemical modifications and chromatin-related packaging of the genome-enables the same genetic template to be activated or repressed in different cellular settings. This multi-layered mechanism facilitates cell-type specific function by setting the local sequence and 3D interactive activity level. Gene transcription is further modulated through the interplay with transcription factors and co-regulators. The human body requires this epigenomic apparatus to be precisely installed throughout development and then adequately maintained during the lifespan. The causal role of the epigenome in human pathology, beyond imprinting disorders and specific tumour suppressor genes, was further brought into the spotlight by large-scale sequencing projects identifying that mutations in epigenomic machinery genes could be critical drivers in both cancer and developmental disorders. Abrogation of this cellular mechanism is providing new molecular insights into pathogenesis. However, deciphering the full breadth and implications of these epigenomic changes remains challenging. Knowledge is accruing regarding disease mechanisms and clinical biomarkers, through pathogenically relevant and surrogate tissue analyses, respectively. Advances include consortia generated cell-type specific reference epigenomes, high-throughput DNA methylome association studies, as well as insights into ageing-related diseases from biological 'clocks' constructed by machine learning algorithms. Also, 3rd-generation sequencing is beginning to disentangle the complexity of genetic and DNA modification haplotypes. Cell-free DNA methylation as a cancer biomarker has clear clinical utility and further potential to assess organ damage across many disorders. Finally, molecular understanding of disease aetiology brings with it the opportunity for exact therapeutic alteration of the epigenome through CRISPR-activation or inhibition.