Depression as a risk factor for Alzheimer’s disease: A human post-mortem study
Bibliographic record
Abstract
Depression is associated with persistent low mood. In mid to late life, it has been identified as a risk factor for Alzheimer's disease (AD) with evidence that depression might be an early manifestation of AD. Although the underlying mechanisms by which depression enhances AD development remain unknown, there are several features commonly seen in both diseases such as the presence of neuroinflammation. In this study, we aimed to identify whether neuroinflammation is increased in depression as observed in the early stages of AD by examining post-mortem human brain tissue. Post-mortem human brain tissue from 54 cases with depression and 37 controls without depression were retrieved from the Douglas Bell Canada Brain Bank. Sixteen early-stage AD cases defined as a Braak stage III-IV and 15 controls were sourced from the South West Dementia Brain Bank. Frozen tissue from the dorsal prefrontal cortex was obtained for all cases in order to measure inflammatory proteins (IFN-γ, IL-1β, IL-2, IL-4, IL-6, IL-8, IL-10, IL-12p70, IL-13, and TNF-α) and endothelial markers (ICAM-1, VCAM) using ELISA and MesoScale Multiplex Assays. In the depression group, increase of IL-6 and IL-10, and decrease of IL-1β were observed compared to controls, with no changes detected for the other cytokines and the endothelial markers. In early-stage AD cases, only increased ICAM-1 expression was found compared to controls, indicating endothelial activation as an early feature of AD. None of the cytokines measured showed alteration of their expression in early-stage AD cases. Depression, but not AD, was associated with evidence of neuroinflammation. Depression may increase AD risk through different mechanism(s) than inflammation.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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".