Association of CSF neurogenin-1 levels with cognitive decline and structural MRI features in older adults
Bibliographic record
Abstract
Neurogenesis has been implicated in the pathogenesis of Alzheimer's disease (AD). However, the relationship between CSF neurogenin-1 levels and cognitive decline, as well as neurodegeneration in older adults, both with and without cognitive impairment, remains unclear. The current study included 666 individuals, comprising 161 cognitively unimpaired (CU) older adults and 505 cognitively impaired (CI) older adults. To examine the association of CSF neurogenin-1 levels with changes in cognitive performance and neurodegeneration over time, we performed a series of linear mixed-effects models. In these models, baseline CSF neurogenin-1 levels served as the predictor of interest, while cognitive measures, such as Mini-Mental State Examination (MMSE) scores, and hippocampal and ventricular volumes, served as the dependent variables. Higher CSF neurogenin-1 levels were associated with a slower rate of cognitive decline over time in the CI individuals but not in the CU individuals. Regarding structural MRI features, we found that higher baseline CSF neurogenin-1 levels were associated with a slower rate of ventricular enlargement in the CI individuals but not in the CU individuals. No association was observed between CSF neurogenin-1 levels and hippocampal atrophy in either group. Our findings suggest that neurogenin-1 may play a neuroprotective role in CI individuals, potentially slowing cognitive decline and structural brain changes associated with the disease.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".