SENOLYTIC EFFECTS ON COGNITION, MOBILITY, AND BIOMARKERS IN OLDER ADULTS WITH MCI AND SLOW GAIT: A PILOT STUDY
Bibliographic record
Abstract
Abstract One aim of this single-arm pilot study, called the STAMINA Study, evaluates the preliminary effects of two senolytic drugs, Dasatinib and Quercetin (DQ), on phenotypic and biological outcomes in older adults with slow gait speed and mild cognitive impairment. Twelve participants took six cycles of 100 mg of Dasatinib and 1250 mg of Quercetin for two days every two weeks over 12 weeks. Absolute changes in cognition and mobility, and percent changes in biomarkers were calculated. Spearman correlations between changes in phenotypic and biomarker outcomes were performed. Montreal Cognitive Assessment (MoCA) scores (mean change = 1.0 point, 95%CI: -0.7, 2.7) and stride length (mean change = 0.031 meters, 95% CI: -0.003, 0.066) tended to improve following DQ, without reaching statistical significance. However, MoCA scores did improve significantly by 2 points (95%CI: 0.1, 4.0) in those with the lowest baseline MoCA scores (18-25 points). Mean percent change in tumor necrosis factor-alpha (TNF-α), a key product of the senescence associated secretory phenotype (SASP), decreased following DQ (mean change = -3.0%, 95%CI: -13.0, 7.1), which was significant when compared to biomarker controls (p=0.04). Reduction in TNF-α was significantly correlated with increases in MoCA score (r=-0.65, p=0.02).This study suggests that intermittent DQ treatment may have functional benefits in older adults with slow gait speed and mild cognitive impairment. The observed reduction in TNF-α and its correlation with increases in MoCA scores suggests that DQ may improve cognition by modulating the SASP. However, these data are preliminary and must be interpreted with caution.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| 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 | 0.001 | 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".