White Matter Hyperintensities Predict Cognitive Decline: A Community-Based Study
Bibliographic record
Abstract
INTRODUCTION: White matter hyperintensities (WMHs) were commonly seen in brain magnetic resonance imaging (MRI) of the elderly. Many studies found that WMHs were associated with cognitive decline and dementia. However, the association between WMHs in different brain regions and cognitive decline remains debated. METHODS: We explored the association of the severity of WMHs and cognitive decline in 115 non-demented elderly (≥50 years old) sampled from the Wuliqiao Community located in urban area of Shanghai. MRI scans were done during 2009-2011 at the beginning of the study. Severity of WMHs in different brain regions was scored by Improved Scheltens Scale and Cholinergic Pathways Hyperintensities Scale (CHIPS). Cognitive function was evaluated by Mini-Mental State Examination (MMSE) every 2 to 4 years during 2009-2018. RESULTS: After adjusting for confounding factors including age, gender, education level, smoking status, alcohol consumption, depression, hypertension, diabetes, hyperlipidemia, brain infarcts, brain atrophy, apoE4 status, and baseline MMSE score, periventricular and subcortical WMH lesions as well as WMHs in cholinergic pathways were significantly associated with annual MMSE decline ( p < 0.05), in which the severity of periventricular WMHs predicted a faster MMSE decline (-0.187 points/year, 95% confidence interval: -0.349, -0.026, p = 0.024). CONCLUSIONS: The severity of WMHs at baseline was associated with cognitive decline in the non-demented elderly over time. Interventions on WMH lesions may offer some benefits for cognitive deterioration.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".