Mild cognitive impairment is associated with effect of uremic metabolites on gray matter structural changes in end-stage kidney disease
Bibliographic record
Abstract
To investigate the grey matter volume (GMV) changes and uremic metabolites in end-stage kidney disease (ESKD) patients with mild cognitive impairment (MCI) (ESKD-MCI) and further examine the classification and diagnostic efficacy of these features for ESKD-MCI patients. A total of 65 patients with ESKD, including 34 ESKD-MCI and 31 with non-cognitive impairment (ESKD-NCI), and 55 health controls (HCs) were enrolled. All participants underwent brain structural magnetic resonance imaging (MRI) scanning and Montreal cognitive assessment test. Clinical characteristics and GMV differences among these three groups were analyzed. In addition, mediation analysis was performed to determine the mediating effect of GMV changes on the association between clinical risk factors and MCI. Finally, support vector machine were employed to examine the classification and diagnostic efficacy of GMV changes and clinical features for MCI. Both patient groups exhibited widespread structural brain injury compared with the HCs. Moreover, compared with ESKD-NCI, ESKD-MCI patients demonstrated reduced GMV specifically in the left middle temporal gyrus and inferior temporal gyrus. Notably, these GMV changes completely mediates the effect of serum phosphorus levels on MCI. Furthermore, imaging features rather than serum phosphorus levels had good classification and diagnostic efficacy for ESKD-MCI. Our findings underscore the significance of the left temporal gyrus as a pivotal brain region in ESKD-MCI patients, fully mediating the link between uremic metabolite and MCI. GMV alterations presents a promising avenue for effectively detecting MCI in individuals with ESKD.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".