The neuro-immune pathogenesis of mild cognitive impairment
Bibliographic record
Abstract
This study aimed to delineate the relationship between clinical symptoms of aMCI and affective symptoms in older adults without major depression (MDD) or dysfunctions in activities of daily living (ADL), and to assess the cytokine network in aMCI after excluding patients with MDD, examining the immune profiles of quantitative aMCI (qMCI) and distress symptoms of old age (DSOA) scores. This case-control study recruited 61 participants with aMCI (diagnosed using Petersen’s criteria) and 59 older adults without aMCI, excluding subjects with MDD and ADL dysfunctions. Three distinct dimensions were uncovered: Distress Symptoms of Old Age (DSOA), comprising affective symptoms, perceived stress, neuroticism, and mild cognitive dysfunction (mCoDy), and qMCI, comprising episodic memory test scores, the total Mini-Mental State Examination (MMSE), and Montreal Cognitive Assessment (MoCA) scores. A large part of the variance (37.9%) in DSOA scores was explained by ACE, negative life events, a subjective feeling of cognitive decline, and education. ACE and NLE significantly impacted DSOA scores but were not associated with aMCI or its severity. Cluster analysis indicated the diagnosis of aMCI is overinclusive, as some subjects with DSOA symptoms may be incorrectly classified as having aMCI. aMCI was characterized by significant general immunosuppression and reductions in T helper 1 (Th1) and T cell growth profiles, immune-inflammatory responses, and specific cytokines (IL1β, IL6, IL7, IL12p70, IL13, GM-CSF, and MCP-1), which exhibit neuroprotective effects. Multivariate analyses identified neurotoxic chemokines (CCL11, CCL5, CXCL8) as significant predictors of aMCI. Logistic regression showed aMCI was best predicted by IL7, IL1β, MCP-1, years of education (inversely associated), and CCL5 (positively associated). 38.2% of the variance in the qMCI score was explained by IL7, IL1β, MCP-1, IL13, years of education (inversely associated), and CCL5 (positively associated). A dysbalance between lowered levels of neuroprotective cytokines and chemokines and relative increases in neurotoxic chemokines are key factors in aMCI.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".