Association of Urinary Biomarkers of Renal Tubular Injury with Cognitive Dysfunction in Older Patients with Chronic Kidney Disease: A Cross-Sectional Observational Study
Bibliographic record
Abstract
Epidemiological data suggest that individuals in all stages of chronic kidney disease (CKD) have higher risks of developing cognitive impairment. The relationship between CKD and cognition has been assessed exclusively using glomerular function markers; however, kidney tubule injury has not been assessed. We assessed the association between urinary biomarkers of renal tubular injury and cognitive dysfunction in older patients with CKD Stages 3-4. According to the Montreal Cognitive Assessment, participants were divided into cognitive dysfunction and control groups. Compared with the control group, the cognitive dysfunction group had significantly higher percentages of smokers, noticeably lower average education, and higher mitochondrial DNA (mtDNA) levels in the peripheral blood. Spearman correlation analysis showed that higher urine neutrophil gelatinase-associated lipocalin, kidney injury molecule-1, and beta-2 microglobulin (β2M) levels were significantly associated with lower cognitive scores. Multivariate logistic regression analysis showed that only increased urinary β2M levels were independently associated with cognitive worsening in CKD after adjusting for confounders. Logistic regression identified a promising role of urinary β2M combined with smoking and education for predicting cognitive impairment in CKD. Urinary β2M and cognitive function negatively correlated with mtDNA content, suggesting that mitochondrial dysfunction is a common pathophysiological mechanism linking CKD and cognitive dysfunction.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".