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Cerebral White Matter Hyperintensity as a Healthcare Quotient

To better understand the risk factors and optimal therapeutic strategies of cerebral white matter hyperintensity (WMH), we examined a large population of adults with and without various vascular risk factors (VRFs) or vascular risk conditions (VRCs), such as hypertension (HT), diabetes mellitus (DM)...

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Autores principales: Park, Kaechang, Nemoto, Kiyotaka, Yamakawa, Yoshinori, Yamashita, Fumio, Yoshida, Keitaro, Tamura, Masashi, Kawaguchi, Atsushi, Arai, Tetsuaki, Sasaki, Makoto
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6912319/
https://www.ncbi.nlm.nih.gov/pubmed/31683849
http://dx.doi.org/10.3390/jcm8111823
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author Park, Kaechang
Nemoto, Kiyotaka
Yamakawa, Yoshinori
Yamashita, Fumio
Yoshida, Keitaro
Tamura, Masashi
Kawaguchi, Atsushi
Arai, Tetsuaki
Sasaki, Makoto
author_facet Park, Kaechang
Nemoto, Kiyotaka
Yamakawa, Yoshinori
Yamashita, Fumio
Yoshida, Keitaro
Tamura, Masashi
Kawaguchi, Atsushi
Arai, Tetsuaki
Sasaki, Makoto
author_sort Park, Kaechang
collection PubMed
description To better understand the risk factors and optimal therapeutic strategies of cerebral white matter hyperintensity (WMH), we examined a large population of adults with and without various vascular risk factors (VRFs) or vascular risk conditions (VRCs), such as hypertension (HT), diabetes mellitus (DM), and dyslipidemia (DLP), including the comorbidities. We assessed two participant groups having no medical history of stroke or dementia that underwent brain checkup using magnetic resonance imaging (MRI): 5541 participants (2760 men, 2781 women) without VRCs and 1969 participants (1169 men, 800 women) who had received drug treatments for VRCs and the combination of comorbidities. For data analysis, we constructed WMH-brain healthcare quotient (WMH-BHQ) based on the percentile rank of WMH volume. This metric has an inverse relation to WMH. Multiple linear regression analysis of 5541 participants without VRCs revealed that age, systolic blood pressure (SBP), Brinkman index (BI), and female sex were significant factors lowering WMH-BHQ, whereas body mass index (BMI), male sex, fasting blood sugar, and triglyceride levels were increasing factors. The Kruskal–Wallis test and Dunn tests showed that WMH-BHQs significantly increased or decreased with BMI or SBP and with BI classification, respectively. Regarding the impact of impaired fasting glucose and abnormal lipid metabolism, there were almost no significant relationships. For 1969 participants who had HT, DM, and DLP, as well as their comorbidities, we found that DLP played a substantial role in increasing WMH-BHQ for some comorbidities, whereas the presence of HT and DM alone tended to decrease it. Cerebral WMH can be used as a healthcare quotient for quantitatively evaluating VRFs and VRCs and their comorbidities.
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spelling pubmed-69123192020-01-02 Cerebral White Matter Hyperintensity as a Healthcare Quotient Park, Kaechang Nemoto, Kiyotaka Yamakawa, Yoshinori Yamashita, Fumio Yoshida, Keitaro Tamura, Masashi Kawaguchi, Atsushi Arai, Tetsuaki Sasaki, Makoto J Clin Med Article To better understand the risk factors and optimal therapeutic strategies of cerebral white matter hyperintensity (WMH), we examined a large population of adults with and without various vascular risk factors (VRFs) or vascular risk conditions (VRCs), such as hypertension (HT), diabetes mellitus (DM), and dyslipidemia (DLP), including the comorbidities. We assessed two participant groups having no medical history of stroke or dementia that underwent brain checkup using magnetic resonance imaging (MRI): 5541 participants (2760 men, 2781 women) without VRCs and 1969 participants (1169 men, 800 women) who had received drug treatments for VRCs and the combination of comorbidities. For data analysis, we constructed WMH-brain healthcare quotient (WMH-BHQ) based on the percentile rank of WMH volume. This metric has an inverse relation to WMH. Multiple linear regression analysis of 5541 participants without VRCs revealed that age, systolic blood pressure (SBP), Brinkman index (BI), and female sex were significant factors lowering WMH-BHQ, whereas body mass index (BMI), male sex, fasting blood sugar, and triglyceride levels were increasing factors. The Kruskal–Wallis test and Dunn tests showed that WMH-BHQs significantly increased or decreased with BMI or SBP and with BI classification, respectively. Regarding the impact of impaired fasting glucose and abnormal lipid metabolism, there were almost no significant relationships. For 1969 participants who had HT, DM, and DLP, as well as their comorbidities, we found that DLP played a substantial role in increasing WMH-BHQ for some comorbidities, whereas the presence of HT and DM alone tended to decrease it. Cerebral WMH can be used as a healthcare quotient for quantitatively evaluating VRFs and VRCs and their comorbidities. MDPI 2019-11-01 /pmc/articles/PMC6912319/ /pubmed/31683849 http://dx.doi.org/10.3390/jcm8111823 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Park, Kaechang
Nemoto, Kiyotaka
Yamakawa, Yoshinori
Yamashita, Fumio
Yoshida, Keitaro
Tamura, Masashi
Kawaguchi, Atsushi
Arai, Tetsuaki
Sasaki, Makoto
Cerebral White Matter Hyperintensity as a Healthcare Quotient
title Cerebral White Matter Hyperintensity as a Healthcare Quotient
title_full Cerebral White Matter Hyperintensity as a Healthcare Quotient
title_fullStr Cerebral White Matter Hyperintensity as a Healthcare Quotient
title_full_unstemmed Cerebral White Matter Hyperintensity as a Healthcare Quotient
title_short Cerebral White Matter Hyperintensity as a Healthcare Quotient
title_sort cerebral white matter hyperintensity as a healthcare quotient
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6912319/
https://www.ncbi.nlm.nih.gov/pubmed/31683849
http://dx.doi.org/10.3390/jcm8111823
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