Plasma <i>p</i> ‐tau217 in relation to mild behavioral impairment: implications for early detection of Alzheimer's disease
Bibliographic record
Abstract
BACKGROUND: The 2024 NIA-AA revised criteria define Alzheimer's disease (AD) biologically through core biomarkers amyloid-beta (Aβ) and tau. While cognitive function remains central to the clinical evaluation of early-stage dementia, mild neurobehavioral changes may coexist and even precede cognitive decline. Mild behavioral impairment (MBI), marked by late-onset persistent neuropsychiatric symptoms (NPS), has emerged as a potential early indicator of dementia risk. While MBI is associated with well-established Aβ and tau biomarkers, the association with plasma p-tau217, a novel blood-based biomarker with high accuracy for AD-related pathology, remains unexplored. Here, we investigated the association between MBI and plasma p-tau217 levels in older adults with normal cognition or mild cognitive impairment (MCI) from the Alzheimer's Disease Neuroimaging Initiative. METHOD: NPS scores were obtained from the Neuropsychiatric Inventory, with MBI status (MBI+/-) determined over two consecutive visits to operationalize the MBI symptom persistence criterion. Participants without plasma ptau-217 data prior to dementia diagnosis were excluded. Linear regression modeled the association between NPS status and p-tau217 level as a continuous variable outcome. Additionally, logistic regression modeled the association between NPS status and p-tau217 positivity status, using a study-specific cut-off derived using Gaussian mixture modeling. Models adjusted for age, sex, education, and cognitive diagnosis. RESULT: Plasma p-tau217 levels and NPS status were available in 101 participants (50.5% MCI; mean age 72.0±6.5; 44.6% female). Participants with MBI had significantly higher plasma p-tau217 levels (Beta=36.4%; 95%CI: 2.2-82.0, p = 0.04) (Table 1) and higher odds of being p-tau217 positive (OR=3.06, 95%CI: 1.14-8.70, p = 0.03), relative to MBI- participants (Table 2). CONCLUSION: Findings add to the evidence base that appropriately measured behavioural symptoms can represent AD proteinopathies, supporting the role of MBI in AD risk stratification. The link between MBI and elevated plasma p-tau217 levels highlights the potential utility of MBI for early AD detection and more efficient clinical trial design by utilizing MBI assessment at screening to identify high-risk individuals for biomarker positivity.
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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 | 0.000 |
| 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 | 0.001 | 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".