MO204CHRONIC KIDNEY DISEASE, SLEEPINESS, MILD COGNITIVE IMPAIRMENT AND FINE MOTOR CONTROL
Bibliographic record
Abstract
Abstract Background and Aims Chronic kidney disease (CKD) is a systemic condition because it modifies all organs' function due to an imbalance in plasma volume, electrolytes, hormones, and proteins. Indeed, at the nervous system level, mild cognitive impairment (MCI), sleep disorders and depression often accompany CKD. MCI partially explains the low quality of life of CKD patients, comparable to that of metastatic cancer patients. Mild Cognitive Impairment (MCI) has a high prevalence in this cohort (27-62%). Nevertheless, scattered literature data suggest that CKD patients can also have poor motor control, evidenced by a higher risk of falls, postural instability, reduced gait speed. In this cohort, few data are available regarding the motor circuits called central pattern generators, which control physiological tremor. Specifically, uraemic encephalopathy accentuates physiological tremor, which is regulated by central and peripheral oscillators. Overall, subtle changes in motor control often accompany other forms of MCI. Therefore, this study aimed at evaluating the effects of chronic kidney disease on cognitive and motor functions using up-to-date technologies to record physiological tremor and innovative data analysis. Method This retrospective case-control study enrolled 313 patients (139 controls, 79 CKD patients stage III-IV, 35 kidney transplant (Tx), 60 dialysis (HD) patients). These groups were comparable for age and weight. Creatininemia, azotemia, LDL, HDL, hemoglobin, and proteinuria were used for correlative analyses. We evaluated the chronotype using the Morningness-Eveningness Questionnaire (MEQ) and the degree of sleepiness using the Epworth Sleepiness Scale (ESS). Cognitive impairment was assessed by the Montreal Cognitive Assessment test (MoCA). Cognitive domains of the MoCA score were projected onto brain regions using CerebroViz library in R and a new transformation matrix derived from fMRI literature data. UMAP algorithm was used to identify patients' subgroups. The physiological tremor was recorded on patients maintaining the dominant arm extended using the smartphone App Phyphox. The tremor frequency spectrum was extracted by Fourier analysis. Results The sleepiness score (ESS) was significantly increased in HD (ESS = 5±0.4) compared to the healthy controls (ESS= 4±0.41) whereas was not significantly modified in CKD patients (3.24± 0.32). The chronotype was also not significantly different among the various groups. The mean score of the MoCA test was significantly lower in CKD, Tx, and HD groups (CKD MoCA =24.5±0.3; Tx MoCA =25.4±0.6; HD MoCA =24.6±0.7) than controls (MoCA score=28±0.1). A different pattern of impairment in the cognitive domains of MoCA was evidenced in the various groups using the CerebroViz projection and UMAP tools. MoCA score was inversely correlated with proteinuria (Pearson coefficient=-0.47; p<0.05). The higher frequencies of the physiological tremor (11-13 Hz) were significantly more represented in Tx patients compared to controls (p<0.05). Conversely, the lower frequencies (1-4 Hz) were significantly less represented in the HD group compared to controls (p<0.05). The peak frequency was inversely correlated with age in all patients (Pearson coefficient= -0.45; p<0.05) and inversely associated with azotemia levels, particularly in HD patients (Pearson coefficient=0.43; p<0.05). Conclusion Our results suggest that CKD patients present altered cognitive and motor control patterns, linked in part to the proteinuria level, suggesting a pathogenetic role of endothelial dysfunction. The characteristic motor, sleepiness and cognitive patterns of HD patients might be due to the arteriovenous fistula or the other peculiarities of these patients. These results might help identify new early markers of brain dysfunction in these patients, with the possibility of delaying or reversing cognitive decay.
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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 | 0.001 |
| 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".