Quantification of Butyrylcholinesterase Activity as a Sensitive and Specific Biomarker of Alzheimer’s Disease
Bibliographic record
Abstract
Amyloid-β (Aβ) plaques are a neuropathological hallmark of Alzheimer's disease (AD); however, a significant number of cognitively normal older adults can also have Aβ plaques. Thus, distinguishing AD from cognitively normal individuals with Aβ plaques (NwAβ) based on Aβ plaque detection is challenging. It has been observed that butyrylcholinesterase (BChE) accumulates in plaques preferentially in AD. Thus, detecting BChE-associated plaques has the potential as an improved AD biomarker. We present Aβ, thioflavin-S, and BChE quantification of 26 postmortem brain tissues; AD (n = 8), NwAβ (n = 6), cognitively normal without plaques (n = 8), and other common dementias including corticobasal degeneration, frontotemporal dementia with tau, dementia with Lewy bodies, and vascular dementia. Pathology burden in the orbitofrontal cortex, entorhinal cortex, amygdala, and hippocampal formation was determined and compared. The predictive value of Aβ and BChE quantification was determined, via receiver-operating characteristic plots, to evaluate their AD diagnostic performance using sensitivity, specificity, and area under curve (AUC) metrics. In general, Aβ and BChE-associated pathology were greater in AD, particularly in the orbitofrontal cortex. In this region, the largest increase (9.3-fold) was in BChE-associated pathology, observed between NwAβ and AD, due to the virtual absence of BChE-associated plaques in NwAβ brains. Furthermore, BChE did not associate with pathology of the other dementias. In this sample, BChE-associated pathology provided better diagnostic performance (AUC = 1.0, sensitivity/specificity = 100% /100%) when compared to Aβ (AUC = 0.98, 100% /85.7%). These findings highlight the predictive value of BChE as a biomarker for AD that could facilitate timely disease diagnosis and management.
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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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".