Evaluating the Predictive Value of Frontal White Matter Hyperintensity Burden on Cognitive Response to NAC and Exercise Therapy in Vascular Mild Cognitive Impairment: Findings from the MOVE‐IT Trial
Bibliographic record
Abstract
Abstract Background White matter hyperintensities (WMHs) in persons with vascular mild cognitive impairment (vMCI), particularly frontal WMHs, have been associated with decreased executive function (EF). While N‐acetylcysteine (NAC) and exercise therapy may improve EF in patients with vMCI, identifying predictors of response could help personalize treatment. We hypothesized that lower baseline frontal WMH volume in vMCI patients treated with NAC and exercise will correlate with increasing improvement in EF over 6 months of treatment. Method Participants received 24 weeks of NAC or placebo in combination with exercise therapy ( n = 60) as part of the Efficacy and Safety of N‐acetylcysteine in patients with mild vascular cognitive impairment (MOVE‐IT) clinical trial. Composite z scores for EF were calculated using Trail Making Part Test B, F‐A‐S Test, and Animal Naming Test administered at baseline, week 12, and week 24. Frontal WMH volumes were captured from baseline MRI scans. Linear mixed models were used that accounted for sex differences, age, years of education, and baseline executive function. Result Both treatment groups had a significant improvement in EF at midpoint (β = 0.194, SE = 0.059, p = 0.002) and endpoint (β = 0.342, SE = 0.058, p < 0.001). Lower baseline frontal WMH and treatment group was not associated with greater improvement in EF at midpoint (β = ‐0.260, SE = 0.144, p = 0.075) or at endpoint (β = ‐0.162, SE = 0.144, p = 0.266). Lower baseline frontal WMH volume was significantly associated with greater improvement in performance on the Trail Making Test Part B at midpoint (β = ‐0.597, SE = 0.197, p = 0.003) but not at endpoint (β = ‐0.328, SE = 0.197, p = 0.099) among participants randomized to the NAC and exercise group, but not the placebo and exercise group. Conclusion This study found a predictive value of frontal WMH burden for novel interventions aiming to improve cognitive outcomes in patients with vMCI. WMH burden could help guide the development of targeted, effective therapies that address the complexities of cognitive impairment in aging populations.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".