Poster No:
1435
Submission Type:
Abstract Submission
Authors:
Nicole Sanford1, Yuetong Yu1, Sophia Frangou1
Institutions:
1University of British Columbia, Vancouver, British Columbia
First Author:
Co-Author(s):
Yuetong Yu
University of British Columbia
Vancouver, British Columbia
Sophia Frangou
University of British Columbia
Vancouver, British Columbia
Introduction:
Major depression is intricately linked to brain health, with depressive episodes potentially contributing to accelerated brain aging (Schiweck et al., 2020; Simon et al., 2023). Further, lifestyle factors such as diet and physical exercise may contribute to psychiatric and neurological wellbeing, although relationships are complex (Lopresti et al., 2013). The present study aimed to identify comprehensive profiles distinguishing adults with a history of depression with respect to modifiable risk factors and brain age.
Methods:
The sample comprised adults from the UK Biobank (ages 44-82 years). Brain age gap estimates (brainAGE; Cole & Franke, 2017) were computed from 3T structural MRI data using support vector regression to predict biological brain age from regional cortical thicknesses, cortical surface areas, and subcortical volumes. Heterogeneity Through Discriminative Analysis (HYDRA; Varol et al., 2017) was used to identify clusters of individuals with a history of depression (n = 896; 305 males) compared to a non-psychiatric control group (n = 36,206; 17,232 males) based on 224 features encompassing diet and nutrition, alcohol use, smoking history, pastimes, relationship quality, physical exercise, and fitness, with sex and age included as covariates. The resulting clusters were compared on brainAGE and mood-related psychopathology. All group differences were identified following false discovery rate correction (p < .05).
Results:
HYDRA identified four clusters (n = 253, 178, 315, and 150, respectively) which were differentiated by 39 variables comprising social support and activities, physical exercise, fitness, smoking history, alcohol use, dietary patterns, nutritional supplements, computer and TV use, sleep quality, and outdoor exposure. These clusters varied in mood-related psychopathology but not in brainAGE. Cluster 1 exhibited the lowest psychopathology and was characterized by relatively strong social support and activities, frequent physical exercise, low BMI, high grip strength, varied diet, low computer usage, and low insomnia as compared to the other three clusters. In contrast, Cluster 3 exhibited the greatest psychopathology and was characterized by relatively low social support and activities, minimal physical exercise, high BMI, high meat and salt intake, low fruit and vegetable intake, high computer and TV use, frequent insomnia, and minimal outdoor exposure.
Conclusions:
This study underscores the potential of comprehensive interventions that could positively impact mental health. The uniformity in brainAGE among the identified clusters suggests that certain key aspects of neurological health may be resilient across individuals with varying lifestyle exposures and depressive histories. Further investigations in longitudinal cohorts are needed to determine how these relationships may evolve with clinical progression and medication exposure.
Disorders of the Nervous System:
Psychiatric (eg. Depression, Anxiety, Schizophrenia) 2
Modeling and Analysis Methods:
Classification and Predictive Modeling 1
Keywords:
Affective Disorders
Aging
Computational Neuroscience
Machine Learning
Multivariate
STRUCTURAL MRI
Other - Lifestyle
1|2Indicates the priority used for review
Provide references using author date format
Cole, J.H. (2017), ‘Predicting age using neuroimaging: Innovative brain ageing biomarkers’, Trends in Neurosciences, vol. 40, pp. 681-690.
Lopresti, A.L. (2013), ‘A review of lifestyle factors that contribute to important pathways associated with major depression: diet, sleep and exercise’, Journal of Affective Disorders, vol. 148, pp. 12-27.
Schiweck, C. (2020), ‘Depression and suicidality: A link to premature T helper cell aging and increased Th17 cells’, Brain, Behavior, and Immunity, vol. 87, pp. 603–609.
Simon, M.S. (2023), ‘Premature T cell aging in major depression: A double hit by the state of disease and cytomegalovirus infection’, Brain, Behavior, & Immunity - Health, vol. 29, pp. 100608.
Varol, E. (2017), ‘HYDRA: Revealing heterogeneity of imaging and genetic patterns through a multiple max-margin discriminative analysis framework’, NeuroImage, vol. 145, Pt B, pp. 346–364.