Examining the Interaction of Alzheimer's Disease and TDP‐43 Neuropathology, and Clinical Diagnosis on Neuropsychiatric Symptoms Burden
Bibliographic record
Abstract
BACKGROUND: Neuropsychiatric symptoms (NPS) are prevalent in individuals with Alzheimer's disease (AD), impacting disease progression and patient quality of life. AD pathology (amyloid and tau) and related TDP-43 neuropathology may contribute to NPS. The National Alzheimer's Coordinating Center (NACC) database was used to explore the relationship between NPS and AD, TDP-43 neuropathology and clinical diagnosis. METHOD: A total of 1391 participants from the NACC database were included in the analysis, with data available on NPS (NPI-Q domains), clinical diagnosis (normal cognition, impaired-not-MCI, MCI, and dementia) and neuropathology. Logistic and linear regression models assessed associations between NPS presence or absence, severity and interactions involving clinical diagnosis and AD and TDP-43 neuropathology, including their co-occurrence. Age, sex, education, cognitive scores, and APOE e4 status were adjusted for in all models. Odds ratios (ORs) and 95% confidence intervals (CIs) were computed to quantify the effects. RESULT: The regression models for several NPS domains were statistically significant with clinical diagnosis being the most consistent across domains (Table 1). The clinical diagnosis and neuropathology were significantly associated with apathy (OR = 2.80, 95% CI [2.13, 3.66], p < .001; OR = 1.74, 95% CI [1.09, 2.80], p = .021, respectively) and delusions (OR = 2.71, 95% CI [1.44, 5.09], p = .002; OR = 3.12, 95% CI [1.14, 8.54], p = .026, respectively). Interaction effects were also observed for apathy (OR = 0.88, 95% CI [0.77, 1.00], p = .049) and delusions (OR = 0.75, 95% CI [0.58, 0.98], p = .038). Clinical diagnosis also influenced NPS severity in nighttime behaviors (β = 1.14, 95% CI [0.02, 0.26], p = .018) and apathy (β = 0.16, 95% CI [0.01, 0.32], p = .039). Age, sex, education, cognitive scores, and APOE e4 status had domain-specific effects. CONCLUSION: The findings highlight the role of clinical diagnosis on NPS across domains, with AD and TDP-43 neuropathology and demographic factors exerting domain-specific effects, most notably in delusions and apathy. Interaction effects between neuropathology and clinical diagnosis were only seen in apathy, suggesting that pathology and clinical diagnosis may have independent effects on NPS. Future research is warranted to examine these effects longitudinally.
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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 | 0.011 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".