Evaluating the Genetic Overlap Between Congenital Heart Disease and Neuroblastoma Risk.

Demographic transition and longer life expectancies have positioned healthy aging in the spotlight. An emerging definition of aging has been proposed as the “process of accumulation of consequences of life”, encompassing insults at different levels down to molecular damage which will inevitably result in physiological decline, disease, and death (). Environmental exposures, lifestyle behaviors, and other clinical and biological factors might accelerate aging differently among individuals based on their unique experiences, suggesting that chronological age is unable to capture aging in a uniform manner. Therefore, a more accurate measure of aging named “biological aging” is thought to provide more information about lifespan and health (). Biological aging can be measured with epigenetic clocks, composite markers that integrate DNA methylation patterns across the genome to predict aging. These clocks are calculated using machine learning methods and are the most established clocks to date. First generation clocks were developed to predict chronological age being highly conserved across mammals and were commonly based on saliva or blood. Second generation clocks used a stepwise approach to link aging-relevant blood parameters to methylation patterns, and then use those correlations to predict mortality, finally calculating an estimate of biological age. Third-generation clocks were designed to estimate the rate or pace of aging (). Although organ-specific epigenetic clocks were developed and offer the advantage of capturing pathologies of the organ of interest, peripherally accessible clocks are more attractive for biomarker research relevant to clinical applications (). Psychiatric disorders and aging present a bilateral relationship, with aging increasing the risk of psychiatric disorders, and psychiatric disorders often having high comorbidity with age-related diseases, such as degenerative, metabolic, or neoplastic conditions, and early mortality (). The heritability of longevity has been estimated at only around 10 to 30%, and common variants explain just a small proportion of the variance, indicating moderate to high polygenicity with a substantial role for environmental and epigenetic factors (). Epigenetic clocks, which achieve median absolute errors of approximately 1–3 years for chronological age in blood, integrate the association of aging and mortality risk at multiple methylation sites across the genome, resulting in advantageous accuracy compared to single readouts of aging such as telomere length (). In particular, next-generation clocks (PhenoAge, GrimAge, DunedinPACE) are trained on biomarkers and endpoints, thereby integrating clinically relevant information that previously required multiple separate markers (). This capacity to condense complex ageing phenotypes into a single metric, together with the use of residual-based age acceleration measures that are independent of chronological age, makes epigenetic clocks particularly relevant to quantify the association of biological ageing with chronic psychosocial stress and risk of psychiatric disorders. In this paper, we explore briefly the state of the art of epigenetic clocks in psychiatry and the future possibilities for the use of these biomarkers of aging in the field ().