Factors associated with brain volume in major depression in older adults without dementia: results from a large autopsy study
Bibliographic record
Abstract
OBJECTIVE: We examined brain volume and atrophy in individuals with major depressive disorder (MDD) without dementia that were referred to a large autopsy service. We also examined potential risk factors for brain atrophy, including demographics and clinical variables. METHODS: In this study, 1373 participants (787 male) aged 50 years or older who died from natural causes were included. Participants with no reliable informant, with cognitive impairment or dementia, with a medical history of severe chronic disease, or with prolonged agonal state were excluded. Presence of MDD at least once in their lifetime was defined according to the Structured Clinical Interview for DSM. Brain volume was measured immediately after removal from the skull. RESULTS: Mean age at death was 68.6 ± 11.6, and MDD was present in 185 (14%) individuals. Smaller brain volume was associated with older age (p < 0.001), lower education (years; p < 0.001), hypertension (p = 0.001), diabetes (p = 0.006), and female gender (p < 0.001). In the multivariate analysis adjusted for sociodemographics and cardiovascular risk factors, smaller brain volume was not associated with major depression (β = -0.86, 95% CI = -26.50 to 24.77, p = 0.95). CONCLUSIONS: In this large autopsy study of older adults, MDD was not associated with smaller brain volumes. Regardless of the presence of MDD, in this sample of older adults without dementia, we found that smaller brain volumes were associated with risk factors for brain neurodegeneration such as older age, diabetes, hypertension, and lower education. Copyright © 2017 John Wiley & Sons, Ltd.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".