Associations of Bone Turnover Markers with Cognitive Function in Patients Undergoing Hemodialysis
Bibliographic record
Abstract
Background. Patients undergoing hemodialysis experience a greater risk of cognitive impairment than the general population, but limited data elucidates the biomarkers on this. We evaluated the association of bone turnover markers on cognitive function among 251 prevalent hemodialysis enrollees in a cross-sectional study. Methods. 251 hemodialysis patients ( median age=57.8 , 55% men) and 37 control subjects ( mean age=61.2 , 56% men) without a prior stroke or dementia diagnosis were enrolled. Serum concentrations of 8 bone markers were analyzed as the association of cognitive function (Montreal Cognitive Assessment (MoCA) and Cognitive Abilities Screening Instrument (CASI)) using linear regression analysis. Results. A lower cognitive function was noted in hemodialysis patients compared to control subjects. The receptor activator of nuclear factor kappa-B ligand (RANKL) was the only bone marker found to be associated with cognitive function (MoCA and CASI tests) in hemodialysis patients without a prior stroke or dementia diagnosis. In stepwise multiple linear regression analysis, the association remained significant in MoCA ( β=1.14 , 95% CI 0.17 to 2.11) and CASI ( β=3.06 , 95% CI 0.24 to 5.88). Short-term memory ( β=0.52 , 95% CI 0.01 to 1.02), mental manipulation ( β=0.51 , 95% CI 0.05 to 0.96), and abstract thinking ( β=0.57 , 95% CI 0.06 to 1.09) were the significant subdomains in the CASI score related to RANKL. Conclusions. Serum RANKL levels were potentially associated with better cognitive function in hemodialysis patients. Further large-scale and prospective studies are needed to confirm our findings.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".