MO154ASSESSMENT OF MILD COGNITIVE IMPAIRMENT IN PRE-DIALYSIS CHRONIC KIDNEY DISEASE AND ITS ASSOCIATION WITH INFLAMMATION AND CHANGES IN MRI: WHAT THE EYES DON´T SEE
Bibliographic record
Abstract
Abstract Background and Aims Mild cognitive impairment (MCI) consists of a decline of one or more cognitive domains, independent of functional impairment. There are common pathophysiological factors between chronic kidney disease (CKD) and MCI. The aim of this study was to evaluate the association of MCI in pre-dialysis CKD with inflammation markers and alterations of image exams (Nuclear Magnetic Resonance-MRI). Method Cross-sectional study, were evaluated non-dialysis CKD patients in categories 1 to 5 in a secondary care clinic, from 2013 to 2015. Inclusion Criteria: age ≥ 21 and ≤ 65 years and having signed the informed consent form. Non-inclusion criteria: previous history of stroke, degenerative and infectious diseases of the CNS, presence of delirium and / or psychotic disorders, previous history of mental retardation and cranioencephalic trauma, visual and auditory disorders that prevented the tests, HIV and / or AIDS, contraindication or intolerance to MRI. The project was approved by the Research Ethics Committee. MCI was assessed using the Montreal Cognitive Assessment (MoCA) and the estimated glomerular filtration rate (eGFR), using the CKDEPI formula. Socio-demographic and clinical data were collected from medical records. Laboratory data were collected a maximum of 3 months after the MoCA evaluation, as well as the measurement of IL4, IL6, IL17, TNF alpha and hs-CRP. The brain MRI scans were performed in a Siemens Avanto high-field device (1.5 Tesla), without the use of paramagnetic contrast. Fazekas scales were used to quantify white matter lesions, MTA scale to quantify hippocampal involvement, ACG scale for global cortical atrophy. Statistical analysis a descriptive analysis was performed, followed by a comparison of abnormal vs normal MoCA among all variables with the relevant tests; MoCA and eGFR were also correlated with all variables. Finally, we performed a linear regression using MoCA as a dependent variable, adjusting for relevant confounding variables. Results 111 patients were invited, 80 were included in the neuropsychological assessment and 56 patients performed all stages of the study. The mean age was 56.3 ± 8.3 years, 51.8% had an altered MoCA. Comparing those with altered vs normal MoCA, we observed that there were fewer years of schooling (p = 0.04), greater use of diuretics (p = 0.04) and AAS (p = 0.002); in addition to higher levels of IL6 (p = 0.02) and IL17 (p = 0.05) among those with altered MoCA. There was no correlation between MoCA and eGFR. MoCA correlated with IL6 (R-0.201, p = 0.04). There was a correlation between eGFR (IL4 (R- -0.467, p = 0.005; IL6 (R- -0.652, p <0.001); IL17 (R- -0.554, p = 0.001), TNF alpha (R- -0.684, p <0,001). There was no correlation between the findings in MRI and eGFR or MoCA. In a linear regression model, the variables IL17 and IL6 were independent predictors of lower values of MoCA (CI -0.031 to -0.002, p = 0.002; CI-0.012 to -0.001, p = 0.002). Conclusion The prevalence of changes in MoCA in this population was 51.8%, the eGFR was correlated with inflammation, MoCA was negatively correlated with IL6. There was no association between MRI findings and eGFR or inflammatory variables, and eGFR was not a predictor of MoCA values, while IL17 and IL6 were.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".