Neurological and inflammatory biomarkers in CSF along the Alzheimer's Disease spectrum
Bibliographic record
Abstract
BACKGROUND: Alzheimer's disease (AD) is a heterogeneous neurodegenerative disease with a decades-long prodromal period. Monitoring of sequential pathological changes in neurodegeneration, inflammation, neurovascular dysfunction, oxidative stress and metabolic stress may provide the opportunity for intervention before symptom onset. Assessment with multiple biomarkers may also inform more tailored therapeutic intervention. METHODS: Using MULTI-ARRAY technology, 54 biomarkers were measured using less than 200 μL of CSF from individuals with AD dementia (n = 100), mild cognitive impairment (MCI) with progression to dementia during the following 3-year follow-up (n = 100), MCI non-progressors (n = 100), and subjective cognitive decline (SCD) (n = 93), collected by ACE Alzheimer Center Barcelona. Biomarkers were selected to cover multiple putative disease mechanisms such as neurovascular dysfunction, inflammation, neurodegeneration, tissue injury, and metabolic stress. One-way ANOVA with Bonferroni correction was applied to determine groupwise differences. Area under the curve (AUC) for receiver operating characteristic curves was calculated to assess biomarker utility for predicting dementia progression. RESULTS: For 43 assays, more than 80% of samples provided concentrations within the dynamic range of the assay. We found concentration differences of 30 CSF biomarkers to be statistically significant across cognitive groups, with the most significant groupwise comparisons between the dementia groups (AD and MCI progressors) and the non-dementia groups (MCI non-progressors and SCD). There were 17 analytes for which mean comparisons were statistically different between MCI progressor and non-progressor groups. Ten proteins, pTau217, total tau, neurofilament light, GFAP, MIF, MMP-10, YKL-40, neurofilament heavy, MIP-1α, and IL-15, demonstrated an AUC > 0.7 for differentiation of MCI progressors and non-progressors, showing promise for differentiating MCI individuals at risk of progressing to dementia, with ptau217 being the most significant (AUC > 0.99). CONCLUSIONS: Here we present an exploratory study with quantitative immunoassays, where we identified several CSF biomarkers indicative of dementia or progression to dementia covering multiple pathological mechanisms. Further successful integration into a biomarker panel could help personalize treatment, stratify individuals for therapeutic studies and provide a better understanding of how these early pathologies impact disease progression.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".