![]() The study shows that advanced biological aging could be a potential risk factor for incident depression/anxiety in midlife and older adults and a potential target for risk assessment and intervention. In this work, we tested the associations of the two biological ages with the risks of depression/anxiety in the UK Biobank cohort. Following our approach in past projects, we used both algorithms to establish the robustness of findings to assumptions about the correct way to model biological aging 20, 21. This model assumes biological age increases exponentially with time. ![]() PhenoAge models biological age as the average biological state associated with a particular level of mortality risk in a reference population. This model assumes biological age increases linearly with time. KDM-BA models biological age as the average biological state associated with a particular chronological age in a reference population. We computed biological age values for participants using the Klemera-Doubal method Biological Age (KDM-BA) and the PhenoAge algorithms 16, 17, both of which have been validated in multi-ethnic cohorts of older adults to predict disease, disability, and mortality 18, 19. To test the hypothesis that accelerated biological aging could increase the risk for depression/anxiety, we applied two published and validated clinical-parameter biological-age algorithms to blood chemistries collected from ~0.4 million UK Biobank participants at their baseline assessment and linked the computed biological age data with health records compiled over ~nine years of follow-up. Among these, algorithms that combine information from standard clinical parameters have proven to be among the most accurate for predicting morbidity and mortality 14, 15. Several measurements of biological aging have been proposed, ranging from individual biomarkers such as telomere length to algorithms that integrate information across epigenetic, proteomic, metabolomic, and other molecular levels of analysis 12, 13. Ideal measurements of biological aging should thus reflect the landscape of aging in multiple biological systems 11. A complementary, but less-studied hypothesis is that accelerated processes of biological aging may, themselves, pose risks to depression/anxiety disorders of older adults 9.Īging is a complex biological process that progressively undermines the integrity and resilience capacity of cells, tissues, and organs 10. However, nearly all work to date had focused on the hypothesis that poor mental health accelerates processes of biological aging 8. Recent reports from multiple cohorts reveal poor mental health as a risk factor for more advanced and faster biological aging, including self-reported unsuccessful aging 3, accelerated brain aging 4, shorter leukocyte telomere length 5, epigenetic aging measured from blood DNA methylation profiles 6, and older biological age and faster pace of aging as measured from blood chemistries and other clinical traits 7. Identification of risk factors and mechanisms of vulnerability to mental disorders is a public health priority. Prevention of depression and anxiety in older adults therefore has potential to mitigate disease burden in an aging population 2. Advanced biological aging may represent a potential risk factor for incident depression/anxiety in midlife and older adults and a potential target for risk assessment and intervention.ĭepression and anxiety are common mental disorders that often co-occur and are associated with increased disability and mortality, especially in older adults 1. Biological-aging-associated risk of depression/anxiety was independent of and additive to genetic risk measured by genome-wide-association-study-based polygenic scores. During a median of 8.7 years of follow-up, participants with older biological age were at increased risk of incident depression/anxiety (5.9% increase per standard deviation of KDM-BA acceleration, 95% confidence intervals : 3.3%–8.5% 11.3% increase per SD of PhenoAge acceleration, 95% CI: 9.%–13.0%). At baseline, participants who were biologically older more often experienced depression/anxiety. We measured biological age from clinical traits using the KDM-BA and PhenoAge algorithms. To test this hypothesis, we evaluated prospective associations between biological age and incident depression and anxiety in 424,299 UK Biobank participants. Theory predicts that biological processes of aging may contribute to poor mental health in late life.
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