Inflammation‐related proteomic changes in response to amyloid and tau pathologies
Bibliographic record
Abstract
Abstract Background Emerging evidence underscores the importance of neuroinflammation in the progression of Alzheimer’s disease (AD) pathophysiology. Recent studies indicate the involvement of inflammatory mechanisms in amyloid‐β (Aβ) and tau deposition in the brain. Due to the complexity of the immune responses and the intricate interplay between the peripheral and the central nervous systems, identifying biomarkers that reflect the brain inflammatory changes in AD has been challenging. With this study, we sought to characterize immune‐related proteins in cerebrospinal fluid (CSF) and plasma, in relation to Aβ and tau PET. Methods Participants from the Translational Biomarker for Aging and Dementia Cohort (TRIAD) incorporating within the AD spectrum, and with available Aβ PET ([ 18 F]AZD4694) and tau PET ([ 18 F]MK6240), had plasma ( n = 376) and CSF ( n = 277) samples analyzed with the NULISA technology (Alamar Biosciences®). A total of 309 inflammation‐related proteins were selected and included in our analysis. Validation cohorts were used to generalize the results: ADNI had CSF samples ( n = 384) analyzed with the SomaScan technology (SomaLogic ® ); and BBDP had plasma samples ( n = 253) analyzed with NULISA technology (Alamar Biosciences ® ). Standardized (Std) β coefficients from linear models relating Aβ‐ and tau‐PET SUVR (both included as independent variables) to the CSF and plasma protein levels. Models included age and sex and as covariates. Results Several proteins exhibit distinct patterns in relation to Aβ and tau pathologies. Notably, specific proteins show strong positive associations with global Aβ‐PET levels, independent of tau‐PET levels, while others are positively associated with global tau‐PET levels, independent of Aβ‐PET levels (Figure 1). Validation in ADNI (plasma data) and BBDP cohort (CSF data) confirmed these findings. A Venn diagram illustrates unique and shared significant proteins across different groups and matrices (Figure 2a). Furthermore, LOESS curves revealed that specific proteins increase or decrease along the disease pseudo‐time, highlighting their potential roles in disease progression (Figure 2b, 2c). Conclusion Overall, using a multi‐omics approach, this preliminary analysis provided new insights on key proteins and molecular inflammatory pathways that co‐occur with, and follow the accumulation of, Aβ and tau load along the AD continuum. This analysis will be further expanded and detailed in more cohorts.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".