The association of enlarged perivascular space with microglia-related inflammation and Alzheimer's pathology in cognitively normal elderly
Bibliographic record
Abstract
Glymphatic dysfunction may contribute to the accumulation of Alzheimer's disease (AD) pathologies. Conversely, AD pathologic change might also cause neuroinflammation and aggravate glymphatic dysfunction, forming a loop that accelerates AD progression. In vivo validations are needed to confirm their relationships. In this study, we included 144 cognitively normal participants with AD pathological biomarker data (baseline CSF Aβ1–42, T-Tau, P-Tau181; plasma P-Tau181 at baseline and at least one follow-up) from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Each subject had completed structural MRI scans. Among them, 117 subjects have available neuroinflammatory biomarker (soluble triggering receptor expressed on myeloid cells 2 (sTREM2), and 123 subjects have completed two times [18F]-florbetapir PET. The enlarged PVS (EPVS) visual rating scores in basal ganglia (BG) and centrum semiovale (CS) were assessed on T1-weighted images to reflect glymphatic dysfunction. Intracranial volume and white matter hyperintensities (WMH) volume were also calculated for further analysis. We performed stepwise linear regression models and mediation analyses to estimate the association between EPVS severity, sTREM2, and AD biomarkers. CS-EPVS degree was associated with CSF sTREM2, annual change of plasma P-tau181 and total WMH volume, whereas BG-EPVS severity was associated with age, gender and intracranial volume. The sTREM2 mediated the association between CSF P-tau181 and CS-EPVS. Impaired glymphatic dysfunction could contribute to the accumulation of pathological tau protein. The association between tauopathy and glymphatic dysfunction was mediated by the microglia inflammatory process. These findings may provide evidence for novel treatment strategies of anti-neuroinflammation therapy in the early stage.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".