Serum levels of insulin-like growth factor-1 and brain-derived neurotrophic factor as potential recovery biomarkers in stroke
Bibliographic record
Abstract
Objectives: Our objectives were: 1) to determine whether maximal aerobic exercise increased serum neurotrophins in chronic stroke and 2) to determine the factors that predict resting and exercise-dependent levels.Methods: We investigated the potential predictors of resting and exercise-dependent serum insulin-like growth factor-1 and brain-derived neurotrophic factor among 35 chronic stroke patients. Predictors from three domains (demographic, disease burden, and cardiometabolic) were entered into 4 separate stepwise linear regression models with outcome variables: resting insulin-like growth factor, resting brain-derived neurotrophic factor, exercise-dependent change in insulin-like growth factor, and exercise-dependent change brain-derived neurotrophic factor.Results: Insulin-like growth factor decreased after exercise (p = 0.001) while brain-derived neurotrophic factor did not change (p = 0.38). Greater lower extremity impairment predicted higher resting brain-derived neurotrophic factor (p = 0.004, r2 = 0.23). Higher fluid intelligence predicted greater brain-derived neurotrophic factor response to exercise (p = 0.01, r2 = 0.18). There were no significant predictors of resting or percent change insulin-like growth factor-1.Discussion: Biomarkers have the potential to characterize an individual’s potential for recovery from stroke. Neurotrophins such as insulin-like growth factor-1 and brain-derived neurotrophic factor are thought to be important in neurorehabilitation; however, the factors that modulate these biomarkers are not well understood. Resting brain-derived neurotrophic factor and percent change in brain-derived neurotrophic factor were related to physical and cognitive recovery in chronic stroke, albeit weakly. Insulin-like growth factor-1 was not an informative biomarker among chronic stroke patients. The novel finding that fluid intelligence positively correlated with exercise-induced change in brain-derived neurotrophic factor warrants further research.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".