Association between cognitive function and skeletal muscle in patients undergoing maintenance hemodialysis
Bibliographic record
Abstract
Background: Patients on hemodialysis have a higher burden of cognitive impairment than individuals of the same age in the general population. Studies have found a link between cognition and skeletal muscle function. However, few studies have investigated these associations and the underlying mechanisms in patients on hemodialysis. Methods: A total of 166 patients on hemodialysis were enrolled in this longitudinal study. Cognitive function was assessed by Montreal Cognitive Assessment (MoCA) scores. Skeletal muscle indicators were evaluated using Inbody S10. Plasma brain-derived neurotrophic factor (BDNF) concentrations were measured by enzyme-linked immunosorbent assay. The primary outcome was a change in the MoCA scores. A mediation analysis was performed to examine the indirect effect of skeletal muscle on cognitive decline through BDNF. Results: Among the 166 patients, the average age was 49.9 ± 11.2 years. Of these patients with a median follow-up of 1,136 days, 133 participated in the study. We defined MoCA scores decreased by ≥2 points at 3 years from the baseline measurement as cognitive decline (CD). Compared to the cognitively unchanged group, patients with CD had significantly lower fat-free mass, soft lean mass, skeletal muscle mass, and skeletal muscle index (all P<0.05). After adjusting for potential confounders, skeletal muscle indicators were protective predictors of CD. A significant increase in plasma BDNF levels was observed in the CD group. Mediation analysis suggested that BDNF played a mediating role of 20-35% between cognitive impairment and skeletal muscle. Conclusion: Skeletal muscle is a protective predictor of CD in patients undergoing dialysis. BDNF mediates the relationship between cognitive impairment and skeletal muscle function.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".