Characterizing the relationship between neuroinflammation and neurodegeneration in AD and FTLD
Bibliographic record
Abstract
BACKGROUND: Although immune dysregulation has been reported in many neurodegenerative diseases (NDDs), our understanding of shared vs disease-specific features is still lacking. Here, we analyze a large panel of inflammatory markers in Alzheimer's disease (AD) and frontotemporal lobar degeneration (FTLD)-related syndromes. METHOD: The cohort included 26 healthy controls (HC); 90 biomarker-positive AD patients (including 57 young-onset); 25 progressive supranuclear palsy (PSP) patients; and 16 patients clinically diagnosed with semantic variant primary progressive aphasia or frontotemporal dementia with motor neuron disease (FTD+/-MND group). Their CSF samples were tested for inflammation (737 proteins, Olink proximity extension assay) and neurodegeneration biomarkers (NfL, Aβ42, ptau181, total tau). All analyses were corrected for age, sex, and plate. RESULT: ANCOVAs showed alterations in distinct subsets of proteins in NDDs compared to HC: the AD group was characterized by increased levels of inflammatory markers, while the opposite was seen in PSP. The smaller FTD+/-MND cohort only showed differences in four proteins (Figure 1). Gene-set enrichment analysis (GSEA) highlighted the implication of cell signaling pathways (including the transmembrane receptor protein tyrosine kinase signaling (q<.05) and response to growth factor (q<.10) pathways) in PSP compared to HC. Principal component analysis (PCA) revealed a limited overlap between NDDs and HC (Figure 2). Differences between diagnoses were best captured by PC2 (10% variance). Among the biomarkers, ptau181 was the strongest correlate of PC1 (24% variance) and PC2 (q<.0001). In preliminary investigations of astrocytic contributions to these differences, YKL-40 levels (astrocytic reactivity) were measured for 22 PSP subjects. YKL-40 and ptau181 were associated with the levels of distinct sets of proteins (after correcting for disease duration and age at onset). In patients with low vs high YKL40, pathways related to white blood cell function (e.g. lymphocyte and neutrophil chemotaxis, chemokine binding) were the top differentially expressed pathways (q<.01). CONCLUSION: There are distinct inflammatory patterns in AD, PSP, and FTD+/-MND. AD is characterized by increases in inflammatory marker levels, while in PSP the opposite is seen. These differences appear to be related to ptau181-related pathology. Future directions include assessing the contributions of known mediators of neuroinflammation, such as astrocytic reactivity, APOE genotype, and age at onset, to these differences.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| 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".