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
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,002 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».