Blood-brain barrier biomarkers modulate the associations of peripheral immunity with Alzheimer’s disease
Bibliographic record
Abstract
The association between peripheral immunity and Alzheimer's disease (AD) has been increasingly recognized, but the underlying mechanisms are still unclear. We used multiple linear regression models to explore the association between peripheral immune biomarkers / blood-brain barrier (BBB)-related biomarkers and AD biomarkers. And we used causal mediation analysis with 10,000 bootstrapped iterations to investigate the functions of BBB-related biomarkers in mediating the associations between peripheral immune biomarkers and AD pathology, cerebral atrophy degree, as well as cognitive function. A total of 543 participants (38.7% female, mean age of 74.8 years) from the Alzheimer's Disease Neuroimaging Initiative (ADNI) were involved. Neutrophils percent (NEU%), lymphocytes percent (LYM%), neutrophils / lymphocytes (NLR), and chemotactic factor-3 (CCL26) were significantly associated with cerebrospinal fluid (CSF) β-amyloid-42 (Aβ-42), phosphorylated-tau (P-tau), total tau (T-tau)/Aβ-42 and P-tau/Aβ-42, the associations of NEU% with AD pathology were mediated by CCL26 (proportion: 18-24%; p < 0.05). NEU%, LYM%, NLR, CCL26, CD40 and matrix metalloproteinase-10 (MMP10) were significantly associated with whole brain, hippocampal volume, middle temporal lobe (MTL) volume, and entorhinal cortex (EC) thickness, the associations of peripheral immune biomarkers with cerebral atrophy degree were mediated by BBB-related biomarkers (proportion: 7-17%; p < 0.05). NEU%, LYM%, NLR, CCL26, CD40 and MMP10 were significantly associated with global cognition, executive function, memory function, immediate recall, and delayed recall, the associations of peripheral immune biomarkers with cognitive function were mediated by BBB-related biomarkers (proportion: 9-24%; p < 0.05). This study suggests that peripheral immunity may influence AD through influencing BBB function, providing a more robust and comprehensive evidence chain for the potential role of inflammation in AD.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".