Can exercise benefit cerebral white matter myelin? Findings from a 12‐month randomized controlled trial in older adults with vascular cognitive impairment
Bibliographic record
Abstract
Abstract Background Cerebral small vessel disease is a major underlying cause of subcortical ischemic vascular cognitive impairment (SIVCI). Myelin loss is a feature of cerebral small vessel disease, which may underly cognitive and functional decline in older adults living with SIVCI. Resistance training (RT) is a form of exercise associated with slower progression of white matter lesions, a cerebral small vessel disease marker. However, it remains to be determined whether RT can counteract myelin loss in individuals living with SIVCI. Thus, we examined the impact of a 12‐month, twice‐weekly progressive RT program on myelin content in those with SIVCI. Method This was a sub‐study of a 12‐month single‐blinded, randomized controlled trial. Participants (n = 91) were randomized to RT or an active control group (balance and tone exercises [BAT]). Study eligibility included: 1) age 55 years and older; 2) magnetic resonance imaging (MRI) evidence of cerebral small vessel disease; 3) mild cognitive impairment; and 4) the absence of dementia. Myelin was measured using a multi‐echo gradient and spin echo T2 relaxation MRI sequence, indexed as myelin water fraction (MWF). Differences between groups at 12 months were assessed via analysis of covariance adjusting for baseline MWF and estimated intracranial volume. MWF data from fifteen white matter tracts were analyzed. Post‐hoc regressions were conducted to determine if changes in MWF were associated with changes in mobility measured with the Short Physical Performance Battery (SPPB). Result Seventy‐three participants (RT = 36) aged 74.1 (SD = 5.7), 64% females with high‐quality MRI data were included in the analyses. At 12 months, RT showed lower MWF levels compared with BAT in the genu of the corpus callosum (estimated mean difference [RT – BAT]: ‐0.610, 95% CI: ‐1.041 to ‐0.178, p = 0.006), fornix (‐0.883, 95% CI: ‐1.570 to ‐0.197, p = 0.012), and combined white matter tracts (‐0.326, 95% CI: ‐0.643 to ‐0.009, p = 0.044). Changes in MWF in the combined white matter tracs were positively associated with changes in SPPB (Unstandardized B = 0.124, SE = 0.061, p = 0.047). Conclusion Contrary to our expectations, 12 months of balance and tone exercises, but not RT, showed positive changes in myelin. These improvements may result in better mobility in older adults with SIVCI.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".