Peripheral inflammatory markers in Alzheimer’s disease: a systematic review and meta-analysis of 175 studies
Bibliographic record
Abstract
OBJECTIVES: Increasing evidence suggests that inflammation is involved in Alzheimer's disease (AD) pathology. This study quantitatively summarised the data on peripheral inflammatory markers in patients with AD compared with healthy controls (HC). METHODS: Original reports containing measurements of peripheral inflammatory markers in AD patients and HC were included for meta-analysis. Standardised mean differences were calculated using a random effects model. Meta-regression and exploration of heterogeneity was performed using publication year, age, gender, Mini-Mental State Examination (MMSE) scores, plasma versus serum measurements and immunoassay type. RESULTS: A total of 175 studies were combined to review 51 analytes in 13 344 AD and 12 912 HC patients. Elevated peripheral interleukin (IL)-1β, IL-2, IL-6, IL-18, interferon-γ, homocysteine, high-sensitivity C reactive protein, C-X-C motif chemokine-10, epidermal growth factor, vascular cell adhesion molecule-1, tumour necrosis factor (TNF)-α converting enzyme, soluble TNF receptors 1 and 2, α1-antichymotrypsin and decreased IL-1 receptor antagonist and leptin were found in patients with AD compared with HC. IL-6 levels were inversely correlated with mean MMSE scores. CONCLUSIONS: These findings suggest that AD is accompanied by a peripheral inflammatory response and that IL-6 may be a useful biological marker to correlate with the severity of cognitive impairment. Further studies are needed to determine the clinical utility of these markers.
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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 | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.030 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".