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A Prediction Equation to Estimate Vascular Endothelial Function in Different Body Mass Index Populations

OBJECTIVE: Vascular endothelial dysfunction is considered an early predictor of endothelial injury and the initiating factor of atherosclerosis (AS). Brachial artery flow-mediated dilation (FMD) can detect endothelial injury early and provide important prognostic information beyond traditional cardi...

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Autores principales: Li, Xiao, Liu, Hanying, Zhang, Yan, Gu, Yanting, Sun, Lianjie, Yu, Haoyong, Bai, Wenkun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8960173/
https://www.ncbi.nlm.nih.gov/pubmed/35360015
http://dx.doi.org/10.3389/fcvm.2022.766565
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author Li, Xiao
Liu, Hanying
Zhang, Yan
Gu, Yanting
Sun, Lianjie
Yu, Haoyong
Bai, Wenkun
author_facet Li, Xiao
Liu, Hanying
Zhang, Yan
Gu, Yanting
Sun, Lianjie
Yu, Haoyong
Bai, Wenkun
author_sort Li, Xiao
collection PubMed
description OBJECTIVE: Vascular endothelial dysfunction is considered an early predictor of endothelial injury and the initiating factor of atherosclerosis (AS). Brachial artery flow-mediated dilation (FMD) can detect endothelial injury early and provide important prognostic information beyond traditional cardiovascular (CV) risk factors. This study aimed to find the influencing factors of FMD and develop a simple prediction model in populations with different body mass indices (BMIs). METHODS: In total, 420 volunteers with different BMIs were recruited in our study. Subjects were randomly assigned to the derivation and validation cohorts (the ratio of the two was 1:2) with simple random sampling. The former was used for influencing factors searching and model construction of FMD and the latter was used for verification and performance evaluation. RESULTS: The population was divided into two groups, i.e., 140 people in the derivation group and 280 people in the verification group. Analyzing in the training data, we found that females had higher FMD than males (p < 0.05), and FMD decreased with age (p < 0.05). In people with diabetes, hypertension or obesity, FMD was lower than that in normal individuals (p < 0.05). Through correlation analysis and linear regression, we found the main influencing factors of FMD: BMI, age, waist-to-hip radio (WHR), aspartate aminotransferase (AST) and low-density lipoprotein (LDL). And we developed a simple FMD prediction model: FMD = −0.096BMI−0.069age−4.551WHR−0.015AST−0.242LDL+17.938, where R(2) = 0.599, and adjusted R(2) = 0.583. There was no statistically significant difference between the actual FMD and the predicted FMD in the verification group (p > 0.05). The intra-class correlation coefficient (ICC) was 0.77. In a Bland-Altman plot, the actual FMD and the predicted FMD also showed good agreement. This prediction model had good hints in CV risk stratification (area under curve [AUC]: 0.780, 95 % confidence intervals [95% CI]: 0.708–0.852, p < 0.001), with a sensitivity and specificity of 73.8 and 72.1%, respectively. CONCLUSIONS: Males, older, obesity, hypertension, diabetes, smoking, etc. were risk factors for FMD, which was closely related to CV disease (CVD). We developed a simple equation to predict FMD, which showed good agreement between the training and validation groups. And it would greatly simplify clinical work and may help physicians follow up the condition and monitor therapeutic effect. But further validation and modification bears great significance.
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spelling pubmed-89601732022-03-30 A Prediction Equation to Estimate Vascular Endothelial Function in Different Body Mass Index Populations Li, Xiao Liu, Hanying Zhang, Yan Gu, Yanting Sun, Lianjie Yu, Haoyong Bai, Wenkun Front Cardiovasc Med Cardiovascular Medicine OBJECTIVE: Vascular endothelial dysfunction is considered an early predictor of endothelial injury and the initiating factor of atherosclerosis (AS). Brachial artery flow-mediated dilation (FMD) can detect endothelial injury early and provide important prognostic information beyond traditional cardiovascular (CV) risk factors. This study aimed to find the influencing factors of FMD and develop a simple prediction model in populations with different body mass indices (BMIs). METHODS: In total, 420 volunteers with different BMIs were recruited in our study. Subjects were randomly assigned to the derivation and validation cohorts (the ratio of the two was 1:2) with simple random sampling. The former was used for influencing factors searching and model construction of FMD and the latter was used for verification and performance evaluation. RESULTS: The population was divided into two groups, i.e., 140 people in the derivation group and 280 people in the verification group. Analyzing in the training data, we found that females had higher FMD than males (p < 0.05), and FMD decreased with age (p < 0.05). In people with diabetes, hypertension or obesity, FMD was lower than that in normal individuals (p < 0.05). Through correlation analysis and linear regression, we found the main influencing factors of FMD: BMI, age, waist-to-hip radio (WHR), aspartate aminotransferase (AST) and low-density lipoprotein (LDL). And we developed a simple FMD prediction model: FMD = −0.096BMI−0.069age−4.551WHR−0.015AST−0.242LDL+17.938, where R(2) = 0.599, and adjusted R(2) = 0.583. There was no statistically significant difference between the actual FMD and the predicted FMD in the verification group (p > 0.05). The intra-class correlation coefficient (ICC) was 0.77. In a Bland-Altman plot, the actual FMD and the predicted FMD also showed good agreement. This prediction model had good hints in CV risk stratification (area under curve [AUC]: 0.780, 95 % confidence intervals [95% CI]: 0.708–0.852, p < 0.001), with a sensitivity and specificity of 73.8 and 72.1%, respectively. CONCLUSIONS: Males, older, obesity, hypertension, diabetes, smoking, etc. were risk factors for FMD, which was closely related to CV disease (CVD). We developed a simple equation to predict FMD, which showed good agreement between the training and validation groups. And it would greatly simplify clinical work and may help physicians follow up the condition and monitor therapeutic effect. But further validation and modification bears great significance. Frontiers Media S.A. 2022-03-10 /pmc/articles/PMC8960173/ /pubmed/35360015 http://dx.doi.org/10.3389/fcvm.2022.766565 Text en Copyright © 2022 Li, Liu, Zhang, Gu, Sun, Yu and Bai. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Cardiovascular Medicine
Li, Xiao
Liu, Hanying
Zhang, Yan
Gu, Yanting
Sun, Lianjie
Yu, Haoyong
Bai, Wenkun
A Prediction Equation to Estimate Vascular Endothelial Function in Different Body Mass Index Populations
title A Prediction Equation to Estimate Vascular Endothelial Function in Different Body Mass Index Populations
title_full A Prediction Equation to Estimate Vascular Endothelial Function in Different Body Mass Index Populations
title_fullStr A Prediction Equation to Estimate Vascular Endothelial Function in Different Body Mass Index Populations
title_full_unstemmed A Prediction Equation to Estimate Vascular Endothelial Function in Different Body Mass Index Populations
title_short A Prediction Equation to Estimate Vascular Endothelial Function in Different Body Mass Index Populations
title_sort prediction equation to estimate vascular endothelial function in different body mass index populations
topic Cardiovascular Medicine
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8960173/
https://www.ncbi.nlm.nih.gov/pubmed/35360015
http://dx.doi.org/10.3389/fcvm.2022.766565
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