Cerebrospinal fluid proteome profiling across the Alzheimer’s disease continuum: a step towards solving the equation for ‘X’
Bibliographic record
Abstract
Abstract Background While the temporal profile of amyloid (Aβ) and tau cerebrospinal fluid (CSF) biomarkers along the Alzheimer’s disease (AD) continuum is well-studied, chronological changes of CSF proteins reflecting other disease-relevant processes, denoted ‘X’ in the ATX(N) framework, remain poorly understood. Methods Using an untargeted mass spectrometric approach termed tandem mass tag (TMT), we quantified over 1500 CSF proteins across the AD continuum in three independent cohorts, finely staged by Aβ/tau positron emission tomography (PET), fluid biomarkers, or brain biopsy. Weighted protein co-expression network analysis identified clusters of proteins robustly correlating in all three cohorts which sequentially changed with AD progression. Obtained protein clusters were correlated with fluid biomarker measurements (phosphorylated tau (p-tau) species including p-tau 181 , p-tau 217 , and p-tau 205 , as well as Aβ), Aβ/tau PET imaging, and clinical parameters to discern disease-relevant clusters which were modelled across the AD continuum. Results Neurodegeneration-related proteins (e.g . , 14–3-3 proteins, PPIA), derived from different brain cell types, strongly correlated with fluid as well as imaging biomarkers and increased early in the AD continuum. Among them, the proteins SMOC1 and CNN3 were highly associated with Aβ pathology, while the 14–3-3 proteins YWHAZ and YWHAE as well as PPIA demonstrated a strong association with both Aβ and tau pathology as indexed by PET. Endo-lysosomal proteins (e.g., HEXB, TPP1, SIAE) increased early in abundance alongside neurodegeneration-related proteins, and were followed by increases in metabolic proteins such as ALDOA, MDH1, and GOT1 at the mild cognitive impairment (MCI) stage. Finally, later AD stages were characterized by decreases in synaptic/membrane proteins (e.g., NPTX2). Conclusions Our study identified proxies of Aβ and tau pathology, indexed by PET, (SMOC1, YWHAE, CNN3) and highlighted the dynamic fluctuations of the CSF proteome over the disease course, identifying candidate biomarkers for disease staging beyond Aβ and tau.
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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 | 0.001 | 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".