Exploring the association between mild behavioral impairment and plasma p‐tau217: Implications for early detection of Alzheimer's disease
Bibliographic record
Abstract
Abstract INTRODUCTION Mild behavioral impairment (MBI), marked by late‐onset persistent neuropsychiatric symptoms (NPS), may signal early dementia risk. While MBI is linked to previously established amyloid‐beta (Aβ) and tau biomarkers, its association with plasma p‐tau217, a promising blood‐based biomarker for Alzheimer's disease (AD), remains unexplored. Here, we investigated the association between MBI and plasma p‐tau217 in dementia‐free individuals from the Alzheimer's Disease Neuroimaging Initiative. METHODS MBI was defined using the Neuropsychiatric Inventory (NPI) data. Linear regression assessed the association between NPS status and continuous p‐tau217 levels, while logistic regression modeled the association between NPS status and p‐tau217 positivity, using a study‐specific cutoff. Models adjusted for age, sex, education, and cognitive diagnosis. RESULTS Among 101 participants (mean age = 72.0 ± 6.5; 44.6% female), those with MBI had higher plasma p‐tau217 levels ( β = 36.4%; 95% confidence interval [CI]: 2.2–82.0, p = 0.04) and higher odds of being p‐tau217 positive (odds ratio [OR] = 3.06, 95% CI: 1.14–8.70, p = 0.03) than MBI‐ participants. DISCUSSION Findings support the role of MBI in AD risk stratification. Highlights Mild behavioral impairment (MBI) is linked to elevated plasma p‐tau217, a specific Alzheimer's disease biomarker. MBI increases the odds of plasma p‐tau217 positivity in dementia‐free individuals. Findings support MBI as an early indicator for Alzheimer's disease risk. MBI assessment can improve biomarker‐based screening and clinical trial efficiency.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.002 | 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".