Association Between Neuropsychiatric Symptom Trajectory and Conversion to Alzheimer Disease
Bibliographic record
Abstract
INTRODUCTION: Neuropsychiatric symptoms (NPS) are both common in mild cognitive impairment and Alzheimer disease (AD). Studies have shown that some NPS such as apathy and depression are a key indicator for progression to AD. METHODS: We compared Neuropsychiatric Inventory (NPI) total score and NPI subdomain score between mild cognitive impairment-converters (MCI-C) and mild cognitive impairment-nonconverters (MCI-NC) longitudinally for 6 years using the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. In addition to the NPI, Mini-Mental State Examination (MMSE) scores were also compared to find out if MMSE scores would differ between different NPI groups. Lastly, a linear regression model was done on MMSE and NPI total score to establish a relationship between MMSE and NPI total score. RESULTS: The results in this study showed that NPI total scores between MCI-C and MCI-NC differed significantly throughout 6 years. MCI-C subjects had a higher mean NPI total score and lower MMSE score compared with MCI-NC subjects. In addition, MMSE scores were significantly different between the 3 groups of NPI total score. Subjects who have a high NPI score have the lowest mean MMSE score, thus demonstrating that NPI scores do indeed affect MMSE scores. Further analyses using a regression model revealed that a unit change in NPI total score lead to 0.1 to 0.3 decrease in MMSE. DISCUSSION: On the basis of the findings, this study showed evidence that increase in NPS burden (reflected by increase in NPI) over time predicts conversion to AD, whereas stability of symptoms (reflected by stable NPI score) favors nonconversion. Further study should investigate the underlying mechanisms that drive both NPS burden and cognitive decline.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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; both teacher heads agree on what is shown here.
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".