Lifestyle and BrainAGE in Adult Depression
Bibliographic record
Abstract
Background: This study tested whether lifestyle and fitness features that influence brain health in the general population differentially affect adults with a history of depression. Brain health was assessed using the brain-age-gap-estimate (brainAGE), a personalized index of the brain's biological age. Methods: Medically healthy adults (44-82 years) from the UK Biobank with a history of depression (n=896) or no psychiatric history (n=36,206) were included. Heterogeneity Through Discriminative Analysis was used to cluster the depression group based on 224 lifestyle and fitness features. Global and local (voxel-based) brainAGE were computed from structural neuroimaging data. The study design and implementation involved input from the position of related lived experience. Outcomes: Four depression clusters were identified. The "balanced moderates" cluster (n=253) had good health and balanced lifestyle habits. The "optimal diet and activity" cluster (n=178) had good health, healthy diets, and regular physical activity. The "metabolic risk-sedentary" cluster (n=315) had higher body mass index, poor diet, and sedentary behaviour. The "frailty-low activity" cluster (n=150) had a varied diet coupled with physical frailty. Mood symptoms were lowest in the "balanced moderates" cluster and highest in the "metabolic risk-sedentary" cluster. The presence of a history of depression was associated with older global brainAGE and with local brainAGE in ventromedial prefrontal regions regardless of cluster assignment. The "metabolic risk-sedentary" cluster also exhibited elevated local brainAGE in the hippocampal complex and thalamus. Interpretation: This study highlights the heterogeneity in lifestyle and fitness factors among adults with a history of depression, underscoring the detrimental influence of depression as well as poor diet and physical inactivity on the biological age of the brain.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".