White matter hyperintensities may be an early marker for age-related cognitive decline
Bibliographic record
Abstract
Abstract Background Research suggests that cerebral small vessel disease (CSVD), amyloid, and pTau contribute to age-related cognitive decline. It remains unknown how these factors relate to one another, nor how they jointly contribute to cognitive decline in normal aging. This project examines the association between these pathologies and their relationship to cognitive decline in cognitively normal older adults without subjective cognitive decline. Methods A total of 230 subjects with CSF Aß42, CSF pTau181, white matter hyperintensities (WMHs) used as a proxy of CSVD and cognitive scores from the Alzheimer’s Disease Neuroimaging Initiative were included. Associations between each pathology and cognitive score were investigated using regression models. Furthermore, relationships between the three pathologies were also examined using regression models. Results At baseline, there was an inverse association between WMH load and Aß42 ( t =-4.20, p <.001). There was no association between WMH load and pTau (t=0.32, p=0.75), nor with Aß42 and pTau ( t =0.51, p =.61). Correcting for age, sex and education, baseline WMH load was associated with baseline ADAS-13 scores ( t =2.59, p =.01) and lower follow-up executive functioning ( t = -2.84, p =.005). Baseline Aß42 was associated with executive function at baseline ( t =3.58, p< .004) but not at follow-up ( t =1.05, p =0.30), nor with ADAS-13 at baseline (t=-0.24, p0.81) or follow-up ( t =0.09, p =0.93). Finally, baseline pTau was not associated with any cognitive measure at baseline or follow-up. Conclusion Both baseline Aß42 and WMH load are associated with some baseline cognition scores, but only baseline WMH load is associated with follow-up executive functioning, indicating that it may be one of the earliest pathologies that contributes to future cognitive decline, in cognitively healthy older adults. Given that healthy older adults with WMH pathology exhibit declines in cognitive functioning, they may be less resilient to future pathology increasing their risk for cognitive impairment due to dementia than those without WMHs.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".