Mental Illness is Associated with Signs of Accelerated Brain Aging in the UK Biobank
Maame Forson
Mood disorders are mental health conditions that primarily affect your emotional state and cause severe lows called depression or highs called hypomania or mania (Sekhon & Gupta, 2023). Thought disorders, on the other hand, are a disturbance in cognition that affects language, thought, and communication (Sass & Parnas, 2017). In this study, we attempted to determine the differences between how mood and thought disorders affect brain aging. Because thought disorders are often more severe and more chronic than mood disorders, we hypothesized that the brains of people with thought disorders age faster than those of people with mood disorders. The significance of this study is mental health is a modifiable risk factor for age-related diseases later in life. Suppose mental illness is linked to an accelerated rate of biological aging. In that case, it raises the possibility that treating mental illness could have downstream effects across the lifespan to prevent age-related diseases. Also, delineating the differences between how thought and mood disorders influence brain aging could help inform interventions specific to each disorder.
UK Biobank is a cohort study of over 500,000 participants established to identify the determinants of common health conditions. A mental health questionnaire was developed for UK Biobank participants called MHQ-9, and we utilized two main methods from this questionnaire to identify mental health diagnoses: self-reported symptoms and self-reported physician diagnosis. To understand brain aging, we utilized BrainAge, an algorithmic tool that uses brain imaging measures to predict biological brain age. (Biondo et al., 2022). BrainAge works by estimating how old someone's brain appears based on neuroimaging data, which can differ from their actual chronological age, indicating whether their brain is aging faster or slower than expected for their age. We applied BrainAge to MRI scans in UK Biobank to predict an individual’s biological brain age. Then, we estimated the BrainAge gap, which is the difference between an individual’s biological brain age and their chronological age. Aging causes the cortex to thin (Burzynska et al., 2012), subcortical structures like the hippocampus to atrophy (Erickson et al., 2010), and ventricles to get larger (Barron et al., 1976). The BrainAge gap, a general indicator of brain health, is an estimate of how much older or younger someone’s brain looks than expected. A BrainAge gap of 0 means the participant’s biological and chronological brain age are the same, while a BrainAge gap of 10 means a difference of 10 years between the participant’s biological and chronological brain age. The final data set for this study was 26,395 individuals in UK Biobank took the MHQ and also had brain imaging data.
The demographics of the final dataset were as follows: of the four disorders chosen (psychosis and bipolar as thought disorders, and depression and anxiety as mood disorders), the smallest diagnosed sample size was that of psychosis, containing 0.4% of participants, and the largest was depression, with over 31% of participants. Women were more represented in each diagnosis, and the average age overall was about 63 years. Lastly, the racial makeup of the participants was majority white, at about 97%.
When analyzing the data to determine the correlation between BrainAge predictions and actual biological age, the coefficient of determination was found to be 0.648, meaning there is a positive correlation and the BrainAge is a moderately accurate tool for approximating biological brain age. We then created a histogram for the BrainAge gap, where over 80% of participants had a BrainAge gap between -10 to 10, meaning their biological and chronological brain ages were within 10 years of each other.
The original hypothesis was that mental illness is associated with signs of accelerated brain aging, and thought disorders cause faster brain aging than mood disorders. We found evidence consistent with these hypotheses: all diagnoses were associated with older brain age gaps and the most severe disorders (thought) had larger effect sizes than the more prevalent but less severe mood disorders. We found this by creating a barplot as a visual representation of the data, where each bar represents the effect size (in years) of how a specific disorder influences your brain’s age. Out of the 4 mental illnesses we tested, depression, anxiety, and bipolar disorder had significant effects. Anxiety and depression had an effect size of about 0.25 years, meaning the data shows having a mood disorder could cause you to age about 0.25x your chronological age. With bipolar, it was about 0.75x your chronological age, and psychosis had an effect size of over a year. However, psychosis had a very small sample size, so the confidence interval was below 0. Overall, it seems the most severe mental illnesses are associated with the most brain aging.
This study’s findings are consistent with the idea that mental illness may cause accelerated aging, which suggests an exciting possibility: treating mental illness could help slow down age-related diseases later in life, especially for those with thought disorders. Our results suggest that there could be a link between mental illness and biological aging and it should be studied more in a longitudinal dataset.
Some of the limitations of this study lie in the fact that the findings might not be as generalizable to the general public due to UK Biobank’s participants’ selection bias of being mostly white, cisgender, and higher-income. Also, because it was a correlational study, we cannot establish causation. The next steps of this study would be to replicate the research with a more diverse sample, conduct another research study that is longitudinal and causal, and increase the sample size for thought disorders.
About the Author
Maame Forson (‘25) is a senior at Harvard College concentrating in neuroscience and evolutionary psychology.
References
UK Biobank is a cohort study of over 500,000 participants established to identify the determinants of common health conditions. A mental health questionnaire was developed for UK Biobank participants called MHQ-9, and we utilized two main methods from this questionnaire to identify mental health diagnoses: self-reported symptoms and self-reported physician diagnosis. To understand brain aging, we utilized BrainAge, an algorithmic tool that uses brain imaging measures to predict biological brain age. (Biondo et al., 2022). BrainAge works by estimating how old someone's brain appears based on neuroimaging data, which can differ from their actual chronological age, indicating whether their brain is aging faster or slower than expected for their age. We applied BrainAge to MRI scans in UK Biobank to predict an individual’s biological brain age. Then, we estimated the BrainAge gap, which is the difference between an individual’s biological brain age and their chronological age. Aging causes the cortex to thin (Burzynska et al., 2012), subcortical structures like the hippocampus to atrophy (Erickson et al., 2010), and ventricles to get larger (Barron et al., 1976). The BrainAge gap, a general indicator of brain health, is an estimate of how much older or younger someone’s brain looks than expected. A BrainAge gap of 0 means the participant’s biological and chronological brain age are the same, while a BrainAge gap of 10 means a difference of 10 years between the participant’s biological and chronological brain age. The final data set for this study was 26,395 individuals in UK Biobank took the MHQ and also had brain imaging data.
The demographics of the final dataset were as follows: of the four disorders chosen (psychosis and bipolar as thought disorders, and depression and anxiety as mood disorders), the smallest diagnosed sample size was that of psychosis, containing 0.4% of participants, and the largest was depression, with over 31% of participants. Women were more represented in each diagnosis, and the average age overall was about 63 years. Lastly, the racial makeup of the participants was majority white, at about 97%.
When analyzing the data to determine the correlation between BrainAge predictions and actual biological age, the coefficient of determination was found to be 0.648, meaning there is a positive correlation and the BrainAge is a moderately accurate tool for approximating biological brain age. We then created a histogram for the BrainAge gap, where over 80% of participants had a BrainAge gap between -10 to 10, meaning their biological and chronological brain ages were within 10 years of each other.
The original hypothesis was that mental illness is associated with signs of accelerated brain aging, and thought disorders cause faster brain aging than mood disorders. We found evidence consistent with these hypotheses: all diagnoses were associated with older brain age gaps and the most severe disorders (thought) had larger effect sizes than the more prevalent but less severe mood disorders. We found this by creating a barplot as a visual representation of the data, where each bar represents the effect size (in years) of how a specific disorder influences your brain’s age. Out of the 4 mental illnesses we tested, depression, anxiety, and bipolar disorder had significant effects. Anxiety and depression had an effect size of about 0.25 years, meaning the data shows having a mood disorder could cause you to age about 0.25x your chronological age. With bipolar, it was about 0.75x your chronological age, and psychosis had an effect size of over a year. However, psychosis had a very small sample size, so the confidence interval was below 0. Overall, it seems the most severe mental illnesses are associated with the most brain aging.
This study’s findings are consistent with the idea that mental illness may cause accelerated aging, which suggests an exciting possibility: treating mental illness could help slow down age-related diseases later in life, especially for those with thought disorders. Our results suggest that there could be a link between mental illness and biological aging and it should be studied more in a longitudinal dataset.
Some of the limitations of this study lie in the fact that the findings might not be as generalizable to the general public due to UK Biobank’s participants’ selection bias of being mostly white, cisgender, and higher-income. Also, because it was a correlational study, we cannot establish causation. The next steps of this study would be to replicate the research with a more diverse sample, conduct another research study that is longitudinal and causal, and increase the sample size for thought disorders.
About the Author
Maame Forson (‘25) is a senior at Harvard College concentrating in neuroscience and evolutionary psychology.
References
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