Cerebral small vessel disease unveils a vascular pathway to motoric cognitive risk in aging
Bibliographic record
Abstract
Background Motoric cognitive risk (MCR) syndrome is characterized by subjective cognitive complaints and slow gait and confers a higher risk of dementia. Cerebral small vessel disease (CSVD) is associated with poor cognitive, functional, and survival outcomes in aging. Markers of CSVD seen on magnetic resonance imaging (MRI) include white matter hyperintensities (WMHs) and lacunes. Objective To examine associations between imaging markers of CSVD and the MCR syndrome. Methods Cross-sectional data from 4 cohorts in 4 countries were examined. WMHs and lacunes were quantified from brain MRIs manually, using a standardized grading scale. Regression models examined the associations between WMH and lacunes and MCR, gait speed, slow gait, and cognitive complaints. We also compared the prevalence of the outcomes of interest between participants with “confluent or diffuse” or “no or mild” WMH. Statistical models were adjusted for age, sex, study site, and vascular risk factors. Results Data from 1772 participants ( M Age = 71.1 years, 49.9% female) was analyzed. Higher global WMH scores were associated with MCR (aOR = 1.07, p = 0.015). Frontal and basal ganglia WMH scores were associated with MCR (aOR = 1.23, p = 0.007, aOR = 1.31, p = 0.023, respectively). Participants with “confluent-diffuse” WMH had significantly higher prevalence of MCR (30.2% versus 19.2%, p = 0.003). Basal ganglia lacunes were associated with MCR (aOR = 1.57, p = 0.018). Conclusions In this multi-cohort study of older adults without cognitive impairment, we show that WMH and lacunes independently predict increased risk of MCR, after adjusting for key confounders. Our findings, based on a large multi-ethnic cohort, reveal region-specific CSVD patterns linked to MCR and related outcomes.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".