Effects of Exercise on Urinary AD7c-NTP (Alzheimer-Associated Neuronal Thread Protein) Levels and Cognitive Function Among Active Korean Elderly: A Randomized Controlled Trial
Bibliographic record
Abstract
Background: Alzheimer-associated neuronal thread protein (AD7c-NTP) has been demonstrated to have high diagnostic accuracy in differentiating Alzheimer's disease (AD) patients from healthy individuals. However, it is yet unclear whether exercise can lower the level of AD7c-NTP in urine among active Korean elderly. Objective: To assess the effect of exercise on AD7c-ntp levels in urine and cognitive function among active Korean elderly. Methods: In total, 40 Korean elderly (≥65 years) were divided into Active Control group (CG, n = 10), Aerobic exercise group (AG, n = 18), and combined Resistance/Aerobic exercise group (RAG, n = 12). A total of 12 weeks of exercise intervention was implemented. At week 0 and 12, cognitive performance (Korean Mini-Mental State Examination, Korean-Color Word Stroop test), grip strength, and body composition (muscle mass and body fat percentage) were measured. Also, a morning urine sample was obtained from each subject. The level of AD7c-NTP was measured using competitive enzyme-linked immunosorbent assay (ELISA). Results: After 12 weeks of exercise intervention, there was a significant difference of AD7c-NTP levels between RAG and CG (p = 0.026), AG and CG (p = 0.032), respectively. Furthermore, the AD7c-NTP levels in urine showed negative correlation with K-MMSE scores (r = -0.390, p = 0.013) and grip strength (r = -0.376, p = 0.017), among all participants after exercise intervention. Conclusions: This is the first study to investigate urine biomarker through exercise intervention. In future stuides, participants who have low cognitive function and low activity levels need to be recruited to observe more significant 'Exercise' effect.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".