MO227RELATIONSHIP BETWEEN KIDNEY DAMAGE AND COGNITIVE FUNCTIONS IN PATIENTS WITH GLOMERULOPATHIES
Bibliographic record
Abstract
Abstract Background and Aims Mild Cognitive Impairment (MCI) has been found to be highly prevalent amongst patients with Chronic Kidney Disease (CKD). In this cohort, the prevalence of MCI was estimated to be between 30% and 63%. Mild cognitive impairment is an intermediate state between normal aging and dementia. An individual suffering from MCI has difficulty in remembering, sustaining attention, or decision making which can negatively affect their daily lives. The aim of this study was to verify the role of different glomerular diseases diagnosed by kidney biopsy on the MCI through a retrospective study. Method We recruited 45 patients with bioptic diagnosis of the following glomerular diseases: Focal Segmental Glomerulo Sclerosis (FSGS), minimal change disease (MCD), membranous glomerular disease (MG), IgA nephropathy. The renal function was analyzed using clinical variables, while Cognitive functions using the MoCA test. Patients were divided into two groups based on 24h proteinuria. Results The MoCA score was directly correlated to the uric acid levels (R=0.13; p=0.03). The MoCa score in the group with higher proteinuria levels was significantly lower than those of the group with lower proteinuria levels (p = 0.03). Finally, the MoCA score in subjects with FSGS or MCD is significantly higher compared the other groups (p<0.05). Conclusion Our data suggest that serum uric acid and proteinuria in glomerular diseases influence cognitive functions. Interestingly, uric acid plays a neuroprotective role, as low levels of uric acid reduce the MoCA score. This result agrees with previous observations of a protective role of uric acid on dopamine neurons. Conversely, the extent proteinuria seems to negatively affect cognitive functions, suggesting a role of the endothelial dysfunction. Finally, glomerulopathies with a lower degree of inflammation (FSGS, MCD) have minor impact on cognitive functions.
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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 | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".