P.4.001 - Accelerated biological aging in major depressive disorder: capturing aging patterns in DNA methylation with machine learning methods

Abstract

Introduction Patients with major depressive disorder (MDD) are likely to experience stress and have a higher risk of developing aging-related diseases [1]. Globally, an estimated 350 million people of all ages suffer from a depression. At the same time, we are facing an aging society, in which manifestations of depression and aging are linked to decreased health, decreased quality of life, and increased healthcare costs. This underlines the importance of developing targeted therapeutic interventions in pursuance of a more healthy and happy society. More evidence is emerging showing that chronological age and biological age may be two different processes that can even diverge. MDD patients seem to feel older and age faster than healthy people of the same age. Current machine learning methods can accurately predict chronological age with correlations of 0.8-0.95 from epigenetics, transcriptomics, proteomics, metabolomics, brain, and other biological data [2]. By calculating the difference between one’s estimated biology-based age and chronological age, one can translate a complex multidimensional aging pattern across biology into one outcome [3]: “the age gap”. A positive age gap represents accelerated aging, whereas a negative brain age gap represents decelerated aging. Previous studies have shown biological age gap associations with physical and cognitive fitness, psychiatric and somatic diseases, health and lifestyle variables, and mortality [4], providing biologically meaningful support. Methods Within the Netherlands Study of Depression and Anxiety (NESDA) [5] we also developed such a biology-based age, specifically one based on DNA methylation patterns (DNAmAge). We selected DNA methylation levels of all 28 million CpG sites in blood as features to predict chronological age in N=1130 subjects (n=811 MDD), using ridge regression. The predictive power was estimated using 10-fold cross-validation. The correlation between chronological age and DNA methylation age estimates were 0.95. MDD diagnosis and clinical characteristics (childhood trauma, symptom duration and severity, age of onset, comorbid anxiety or alcohol dependence disorder, antidepressant use) were assessed with questionnaires and psychiatric interviews. Analyses were adjusted for sociodemographics, lifestyle, and health status. A pathway enrichment analysis was conducted using ConsensusPathDB to gain insight in the biological processes underlying DNAmAge. Results Higher DNAmAge was observed in MDD patients compared to controls (P=0.008), with a dose-effect with increasing symptom severity in the overall sample (P=0.001). Within MDD patients, DNAm age residuals were positively associated with childhood trauma scores (P=0.02). Top significantly enriched Gene Ontology terms included neuronal processes such as neurogenesis and neuron differentiation. Conclusion Our findings show that the DNAmAge for MDD patients is higher than their corresponding chronological age, as well as compared to controls. This potentially suggests an explanation for their increased risk of mortality and aging related diseases. This is an important finding as it may potentially be a therapeutic target to monitor within subjects with regards to illness progression and effectiveness of treatment. More research is needed to investigate the longitudinal dynamics and causal relationships between age-associated alterations in DNA methylation, and depression.