Distinct neuroinflammatory mechanisms in Alzheimer’s disease and Frontotemporal Lobar Degeneration
Bibliographic record
Abstract
Abstract Background Neuroinflammation is an important pathophysiological process in neurodegenerative diseases, such as Alzheimer’s disease (AD) and Frontotemporal Lobar Degeneration (FTLD). Limited studies have revealed distinct inflammatory markers between proteinopathies. Whether AD and FTLD have distinct neuroinflammatory profiles that relate to different clinical manifestations remains unclear. We assayed a large number of inflammatory proteins to delineate neuroinflammatory signatures across different proteinopathies. Method Using Olink Inflammation panels, we measured 737 inflammatory proteins in CSF from 22 AD [11 early‐onset (EOAD); 11 late‐onset (LOAD)], 45 FTLD patients and 5 healthy controls (HC). Multiple linear regression analyses were performed and comparison of expression between the groups was done by ANOVA F‐tests, adjusted for age, sex and age of onset, with FDR correction for multiple comparisons. The relationship between inflammatory markers and neurofilament light chain (NfL) was examined by Pearson correlation as well as linear regression analysis incorporating the interaction effect of NfL and disease groups. Result Out of 442 proteins that passed quality control, five neuroinflammatory proteins (PKLR, CAT, CXCL9, MMP1, MEPE) involved in extracellular matrix regulation, neuroprotection and chemoattractant activity were significantly downregulated in AD compared to HC (q<0.05, FDR‐corrected). Comparing AD and FTLD, 40 were significantly upregulated in AD (q<0.05, FDR‐corrected). C3, a pro‐inflammatory complement protein was significantly downregulated in AD compared to FTLD. Interestingly, seven inflammatory proteins were significantly correlated with NfL in AD following FDR correction: C7 (r = 0.79, q<0.05), LGALS9 (r = 0.72, q<0.05), LYVE1 ((r = 0.72, q<0.05), ADAMTS1 (r = 0.71, q<0.05), MRC1 (r = 0.69, q<0.05), SEMA3G (r = 0.69, q<0.05), and CCN2 (r = 0.68, q<0.05). No inflammatory proteins were correlated with NfL in FTLD. Comparing EOAD and LOAD, anti‐inflammatory cytokine TGF‐β, was upregulated in EOAD compared to LOAD (q<0.05, FDR‐corrected). A significant interaction effect of NfL and AD sub‐groups (EOAD and LOAD) was found with TNFRSF13B, CCL21, IL5RA, IL7, and MZB1 (q<0.05, FDR‐corrected), where except for IL7, all proteins were positively correlated with NfL in LOAD but not in EOAD. Conclusion AD and FTLD patients display distinct inflammatory profiles with varying relationships between inflammatory and neurodegenerative markers. EOAD and LOAD also showed distinct inflammatory patterns. Our results underscore that proteinopathy‐specific neuroinflammatory mechanisms are involved in disease pathogenesis.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".