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

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

Additional file 1 of ADHD genetic burden associates with older epigenetic age: mediating roles of education, behavioral and sociodemographic factors among older adults

Additional file 1. Supplementary Tables and Material

Alzheimer Dis Assoc Disord

BACKGROUND: To use neuropsychological assessments for studying the underlying disease processes contributing to dementia, it is crucial that they correspond to magnetic resonance imaging (MRI)-based measures of dementia, regardless of educational level. METHODS: French 3-City Dijon MRI study cohort members (n=1782) with assessments of white matter lesion volume (WMLV), hippocampal volume (HCV), and cerebrospinal fluid volume (CSFV), and 6 waves of neuropsychological assessments over 11 years, including Mini-Mental State Examination (MMSE), plus 5 other tests combined using a Z-score or item-response theory (IRT-cognition) comprised the study cohort. We evaluated, testing interactions, whether education modified associations of MRI markers with intercept or rate of change of MMSE, Z-score composite, or IRT-cognition. RESULTS: In linear models, education modified the associations of WMLV and CSFV with MMSE and CSFV and Z-score composite. In mixed models, education modified the associations of WMLV and CSFV with level of MMSE and the association of HCV with slope of MMSE. Education also modified the association with CSFV and slope of Z-score composite decline. There was no evidence that education modified associations between MRI measures and level or slope of IRT-cognition. CONCLUSIONS: Longitudinal analysis of correctly scaled neuropsychological assessments may provide unbiased proxies for MRI-based measures of dementia risk