Neurofilament Light Chain and Cognitive Function in Patients Undergoing Hemodialysis: A Cross-Sectional Study
Bibliographic record
Abstract
Background: Dementia poses a global challenge for geriatric care and social welfare, including for aging dialysis patients. In recent years, non-invasive blood biomarkers have garnered increasing attention in predicting the development of Alzheimer's disease. Within the general populace, neurofilament light chain (NFL) prognosticates forthcoming cognitive function alongside amyloid-beta (Aβ). Nevertheless, investigation into this matter has yet to be extended to chronic kidney disease patients, including those undergoing hemodialysis. Methods: A cross-sectional study of hemodialysis patients investigated the association between serum Aβ (1-42) and NFL levels with cognitive function as assessed by the Montreal Cognitive Assessment (MoCA) and Mini-Mental State Examination (MMSE). Results: This study involved 362 patients whose median age was 74 (interquartile range 70-80) years and who had been receiving hemodialysis for a median of 87 (36-168) months. Aβ (1-42) exhibited a median of 2.84 (1.84-4.27) pmol/L, and NFL exhibited a median of 196.2 (146.5-262.2) pg/mL with a non-parametric distribution. The median MoCA and MMSE scores were 25 (22-26) and 28 (26-29). NFL levels transformed by the natural logarithm significantly negatively correlated with cognitive function in a multivariate linear regression analysis, including confounding factors (β coefficient [95% confidence interval], -0.98 [-1.76, -0.2]; P=0.014 for MoCA, and -0.67 [-1.3, -0.05]; P=0.034 for MMSE). No significant associations existed between cognitive function and Aβ (1-42) levels. Conclusion: Low levels of NFL were associated with preserved cognitive function in patients undergoing hemodialysis. Funding: Private Foundation Support
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".