Depression, dementia and immune dysregulation
Bibliographic record
Abstract
Major depression is a prevalent illness that increases the risk of several neurological conditions. These include stroke, cardiovascular disease, and dementia including Alzheimer's disease. In this review we ask whether certain types of depression and associated loneliness may be a harbinger of cognitive decline and possibly even dementia. We propose that chronic stress and inflammation combine to compromise vascular and brain function. The resulting increases in proinflammatory cytokines and microglial activation drive brain pathology leading to depression and mild cognitive impairment, which may progress to dementia. We present evidence that by treating the inflammatory changes, depression can be reversed in many cases. Importantly, there is evidence that anti-inflammatory and antidepressant treatments may reduce or prevent dementia in people with depression. Thus, we propose a model in which chronic stress and inflammation combine to increase brain permeability and cytokine production. This leads to microglial activation, white matter damage, neuronal and glial cell loss. This is first manifest as depression and mild cognitive impairment, but can eventually evolve into dementia. Further research may identify clinical subgroups with inflammatory depression at risk for dementia. It would then be possible to address in clinical trials whether effective treatment of the depression can delay the onset of dementia.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".