Physical activity may protect myelin via modulation of high‐density lipoprotein
Bibliographic record
Abstract
INTRODUCTION: Physical activity is associated with greater myelin content in older individuals with cerebral small vessel disease (CSVD), a condition marked by demyelination. However, potential mechanisms underlying this relationship remain understudied. METHODS: We assessed cross-sectionally whether serum high-density lipoprotein (HDL), low-density lipoprotein, and triglycerides moderated the association between physical activity and in vivo myelin in older individuals with CSVD and mild cognitive impairment. RESULTS: We included 81 highly educated, community-dwelling older individuals (mean age 74.57 years), 64% of whom were female. Regression models revealed that HDL levels significantly moderated the relationship between physical activity and myelin in the sagittal stratum, wherein higher physical activity levels were linked to greater myelin levels for those with average or high HDL (standardized B [95% CI] = 0.289 [0.087 to 0.491], p = 0.006). DISCUSSION: Physical activity may promote myelin health partly through HDL. Data from longitudinal studies are needed to confirm our findings. HIGHLIGHTS: Myelin loss is common in individuals with cerebral small vessel disease (CSVD). Physical activity was positively associated with myelin in older adults with CSVD. High-density lipoproteins (HDL) levels were also positively related to myelin. Physical activity effects on myelin were moderated by HDL levels.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".