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Association between cognitive capacity and metabolic indices in patients with neuropsychiatric disorders

BACKGROUND: Although previous studies suggested the relationship between metabolic indices and cognitive capacity, results have been conflicting. The prevalence of metabolic and cognitive disorders is high in patients with neuropsychiatric disorders. We aimed to assess the relationship between labor...

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Autores principales: Rajabi, Fatemeh, Rahimi, Somayyeh, Maracy, Mohammad R., Barekatain, Majid
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Wolters Kluwer - Medknow 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10578545/
https://www.ncbi.nlm.nih.gov/pubmed/37849862
http://dx.doi.org/10.4103/jehp.jehp_82_23
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author Rajabi, Fatemeh
Rahimi, Somayyeh
Maracy, Mohammad R.
Barekatain, Majid
author_facet Rajabi, Fatemeh
Rahimi, Somayyeh
Maracy, Mohammad R.
Barekatain, Majid
author_sort Rajabi, Fatemeh
collection PubMed
description BACKGROUND: Although previous studies suggested the relationship between metabolic indices and cognitive capacity, results have been conflicting. The prevalence of metabolic and cognitive disorders is high in patients with neuropsychiatric disorders. We aimed to assess the relationship between laboratory metabolic indices and specific areas of cognitive capacity. MATERIALS AND METHODS: This was a retrospective review of the medical records of 423 from 452 patients with neuropsychiatric disorders who were admitted to the neuropsychiatry unit, Ayatollah Kashani Hospital, Isfahan, Iran, from September 1, 2018, to September 30, 2022. We extracted demographic factors, laboratory metabolic indices, and scores of the Neuropsychiatry Unit Cognitive Assessment tool (NUCOG). We utilized a generalized linear model (GLM) to demonstrate the effect of metabolic indices on the risk of reduction in cognitive domains. Due to the presence of missing data in the metabolic indices, we used the multiple imputation method. RESULTS: The regression coefficient of NUCOG total score and subscale scores for metabolic indices using GLM after multiple imputation method demonstrated that among the metabolic indicators, fasting blood sugar (FBS) had the reverse relationship with the total score of NUCOG (β = -.05). Among the NUCOG subscales, executive functioning had the strongest relationship with FBS (β = -.01). Also, there was a negative relationship between patients’ age and the total score of NUCOG (β = -.38). Educational level had a positive relationship with the total NUCOG score (β =10.2). CONCLUSIONS: The main metabolic factors that might reduce cognitive capacity were higher FBS.
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spelling pubmed-105785452023-10-17 Association between cognitive capacity and metabolic indices in patients with neuropsychiatric disorders Rajabi, Fatemeh Rahimi, Somayyeh Maracy, Mohammad R. Barekatain, Majid J Educ Health Promot Original Article BACKGROUND: Although previous studies suggested the relationship between metabolic indices and cognitive capacity, results have been conflicting. The prevalence of metabolic and cognitive disorders is high in patients with neuropsychiatric disorders. We aimed to assess the relationship between laboratory metabolic indices and specific areas of cognitive capacity. MATERIALS AND METHODS: This was a retrospective review of the medical records of 423 from 452 patients with neuropsychiatric disorders who were admitted to the neuropsychiatry unit, Ayatollah Kashani Hospital, Isfahan, Iran, from September 1, 2018, to September 30, 2022. We extracted demographic factors, laboratory metabolic indices, and scores of the Neuropsychiatry Unit Cognitive Assessment tool (NUCOG). We utilized a generalized linear model (GLM) to demonstrate the effect of metabolic indices on the risk of reduction in cognitive domains. Due to the presence of missing data in the metabolic indices, we used the multiple imputation method. RESULTS: The regression coefficient of NUCOG total score and subscale scores for metabolic indices using GLM after multiple imputation method demonstrated that among the metabolic indicators, fasting blood sugar (FBS) had the reverse relationship with the total score of NUCOG (β = -.05). Among the NUCOG subscales, executive functioning had the strongest relationship with FBS (β = -.01). Also, there was a negative relationship between patients’ age and the total score of NUCOG (β = -.38). Educational level had a positive relationship with the total NUCOG score (β =10.2). CONCLUSIONS: The main metabolic factors that might reduce cognitive capacity were higher FBS. Wolters Kluwer - Medknow 2023-08-31 /pmc/articles/PMC10578545/ /pubmed/37849862 http://dx.doi.org/10.4103/jehp.jehp_82_23 Text en Copyright: © 2023 Journal of Education and Health Promotion https://creativecommons.org/licenses/by-nc-sa/4.0/This is an open access journal, and articles are distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 License, which allows others to remix, tweak, and build upon the work non-commercially, as long as appropriate credit is given and the new creations are licensed under the identical terms.
spellingShingle Original Article
Rajabi, Fatemeh
Rahimi, Somayyeh
Maracy, Mohammad R.
Barekatain, Majid
Association between cognitive capacity and metabolic indices in patients with neuropsychiatric disorders
title Association between cognitive capacity and metabolic indices in patients with neuropsychiatric disorders
title_full Association between cognitive capacity and metabolic indices in patients with neuropsychiatric disorders
title_fullStr Association between cognitive capacity and metabolic indices in patients with neuropsychiatric disorders
title_full_unstemmed Association between cognitive capacity and metabolic indices in patients with neuropsychiatric disorders
title_short Association between cognitive capacity and metabolic indices in patients with neuropsychiatric disorders
title_sort association between cognitive capacity and metabolic indices in patients with neuropsychiatric disorders
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10578545/
https://www.ncbi.nlm.nih.gov/pubmed/37849862
http://dx.doi.org/10.4103/jehp.jehp_82_23
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