Cargando…
Prediction of subclinical atherosclerosis in low Framingham risk score individuals by using the metabolic syndrome criteria and insulin sensitivity index
BACKGROUND: Subclinical atherosclerosis can be present in individuals with an optimal cardiovascular risk factor profile. Traditional risk scores such as the Framingham risk score do not adequately capture risk stratification in low-risk individuals. The aim of this study was to determine if markers...
Autores principales: | , , , , , , , , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9639788/ https://www.ncbi.nlm.nih.gov/pubmed/36352897 http://dx.doi.org/10.3389/fnut.2022.979208 |
_version_ | 1784825710146224128 |
---|---|
author | Huang, Benjamin Huang, Weiting Allen, John Carson Sun, Lijuan Goh, Hui Jen Kong, Siew Ching Lee, Dewaine Ding, Cherlyn Bosco, Nabil Egli, Leonie Actis-Goretta, Lucas Magkos, Faidon Arigoni, Fabrizio Leow, Melvin Khee-Shing Tan, Swee Yaw Yeo, Khung Keong |
author_facet | Huang, Benjamin Huang, Weiting Allen, John Carson Sun, Lijuan Goh, Hui Jen Kong, Siew Ching Lee, Dewaine Ding, Cherlyn Bosco, Nabil Egli, Leonie Actis-Goretta, Lucas Magkos, Faidon Arigoni, Fabrizio Leow, Melvin Khee-Shing Tan, Swee Yaw Yeo, Khung Keong |
author_sort | Huang, Benjamin |
collection | PubMed |
description | BACKGROUND: Subclinical atherosclerosis can be present in individuals with an optimal cardiovascular risk factor profile. Traditional risk scores such as the Framingham risk score do not adequately capture risk stratification in low-risk individuals. The aim of this study was to determine if markers of metabolic syndrome and insulin resistance can better stratify low-risk individuals. METHODS: A cross-sectional study of 101 healthy participants with a low Framingham risk score and no prior morbidities was performed to assess prevalence of subclinical atherosclerosis using computed tomography (CT) and ultrasound. Participants were compared between groups based on Metabolic Syndrome (MetS) and Insulin-Sensitivity Index (ISI-cal) scores. RESULTS: Twenty three individuals (23%) had subclinical atherosclerosis with elevated CT Agatston score ≥1. Presence of both insulin resistance (ISI-cal <9.23) and fulfillment of at least one metabolic syndrome criterion denoted high risk, resulting in significantly improved AUC (0.706 95%CI 0.588–0.822) over the Framingham risk score in predicting elevated CT Agatston score ≥1, with net reclassification index of 50.9 ± 23.7%. High-risk patients by the new classification also exhibited significantly increased carotid intima thickness. CONCLUSIONS: The overlap of insulin resistance and presence of ≥1 criterion for metabolic syndrome may play an instrumental role in identifying traditionally low-risk individuals predisposed to future risk of atherosclerosis and its sequelae. |
format | Online Article Text |
id | pubmed-9639788 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-96397882022-11-08 Prediction of subclinical atherosclerosis in low Framingham risk score individuals by using the metabolic syndrome criteria and insulin sensitivity index Huang, Benjamin Huang, Weiting Allen, John Carson Sun, Lijuan Goh, Hui Jen Kong, Siew Ching Lee, Dewaine Ding, Cherlyn Bosco, Nabil Egli, Leonie Actis-Goretta, Lucas Magkos, Faidon Arigoni, Fabrizio Leow, Melvin Khee-Shing Tan, Swee Yaw Yeo, Khung Keong Front Nutr Nutrition BACKGROUND: Subclinical atherosclerosis can be present in individuals with an optimal cardiovascular risk factor profile. Traditional risk scores such as the Framingham risk score do not adequately capture risk stratification in low-risk individuals. The aim of this study was to determine if markers of metabolic syndrome and insulin resistance can better stratify low-risk individuals. METHODS: A cross-sectional study of 101 healthy participants with a low Framingham risk score and no prior morbidities was performed to assess prevalence of subclinical atherosclerosis using computed tomography (CT) and ultrasound. Participants were compared between groups based on Metabolic Syndrome (MetS) and Insulin-Sensitivity Index (ISI-cal) scores. RESULTS: Twenty three individuals (23%) had subclinical atherosclerosis with elevated CT Agatston score ≥1. Presence of both insulin resistance (ISI-cal <9.23) and fulfillment of at least one metabolic syndrome criterion denoted high risk, resulting in significantly improved AUC (0.706 95%CI 0.588–0.822) over the Framingham risk score in predicting elevated CT Agatston score ≥1, with net reclassification index of 50.9 ± 23.7%. High-risk patients by the new classification also exhibited significantly increased carotid intima thickness. CONCLUSIONS: The overlap of insulin resistance and presence of ≥1 criterion for metabolic syndrome may play an instrumental role in identifying traditionally low-risk individuals predisposed to future risk of atherosclerosis and its sequelae. Frontiers Media S.A. 2022-10-24 /pmc/articles/PMC9639788/ /pubmed/36352897 http://dx.doi.org/10.3389/fnut.2022.979208 Text en Copyright © 2022 Huang, Huang, Allen, Sun, Goh, Kong, Lee, Ding, Bosco, Egli, Actis-Goretta, Magkos, Arigoni, Leow, Tan and Yeo. 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 | Nutrition Huang, Benjamin Huang, Weiting Allen, John Carson Sun, Lijuan Goh, Hui Jen Kong, Siew Ching Lee, Dewaine Ding, Cherlyn Bosco, Nabil Egli, Leonie Actis-Goretta, Lucas Magkos, Faidon Arigoni, Fabrizio Leow, Melvin Khee-Shing Tan, Swee Yaw Yeo, Khung Keong Prediction of subclinical atherosclerosis in low Framingham risk score individuals by using the metabolic syndrome criteria and insulin sensitivity index |
title | Prediction of subclinical atherosclerosis in low Framingham risk score individuals by using the metabolic syndrome criteria and insulin sensitivity index |
title_full | Prediction of subclinical atherosclerosis in low Framingham risk score individuals by using the metabolic syndrome criteria and insulin sensitivity index |
title_fullStr | Prediction of subclinical atherosclerosis in low Framingham risk score individuals by using the metabolic syndrome criteria and insulin sensitivity index |
title_full_unstemmed | Prediction of subclinical atherosclerosis in low Framingham risk score individuals by using the metabolic syndrome criteria and insulin sensitivity index |
title_short | Prediction of subclinical atherosclerosis in low Framingham risk score individuals by using the metabolic syndrome criteria and insulin sensitivity index |
title_sort | prediction of subclinical atherosclerosis in low framingham risk score individuals by using the metabolic syndrome criteria and insulin sensitivity index |
topic | Nutrition |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9639788/ https://www.ncbi.nlm.nih.gov/pubmed/36352897 http://dx.doi.org/10.3389/fnut.2022.979208 |
work_keys_str_mv | AT huangbenjamin predictionofsubclinicalatherosclerosisinlowframinghamriskscoreindividualsbyusingthemetabolicsyndromecriteriaandinsulinsensitivityindex AT huangweiting predictionofsubclinicalatherosclerosisinlowframinghamriskscoreindividualsbyusingthemetabolicsyndromecriteriaandinsulinsensitivityindex AT allenjohncarson predictionofsubclinicalatherosclerosisinlowframinghamriskscoreindividualsbyusingthemetabolicsyndromecriteriaandinsulinsensitivityindex AT sunlijuan predictionofsubclinicalatherosclerosisinlowframinghamriskscoreindividualsbyusingthemetabolicsyndromecriteriaandinsulinsensitivityindex AT gohhuijen predictionofsubclinicalatherosclerosisinlowframinghamriskscoreindividualsbyusingthemetabolicsyndromecriteriaandinsulinsensitivityindex AT kongsiewching predictionofsubclinicalatherosclerosisinlowframinghamriskscoreindividualsbyusingthemetabolicsyndromecriteriaandinsulinsensitivityindex AT leedewaine predictionofsubclinicalatherosclerosisinlowframinghamriskscoreindividualsbyusingthemetabolicsyndromecriteriaandinsulinsensitivityindex AT dingcherlyn predictionofsubclinicalatherosclerosisinlowframinghamriskscoreindividualsbyusingthemetabolicsyndromecriteriaandinsulinsensitivityindex AT bosconabil predictionofsubclinicalatherosclerosisinlowframinghamriskscoreindividualsbyusingthemetabolicsyndromecriteriaandinsulinsensitivityindex AT eglileonie predictionofsubclinicalatherosclerosisinlowframinghamriskscoreindividualsbyusingthemetabolicsyndromecriteriaandinsulinsensitivityindex AT actisgorettalucas predictionofsubclinicalatherosclerosisinlowframinghamriskscoreindividualsbyusingthemetabolicsyndromecriteriaandinsulinsensitivityindex AT magkosfaidon predictionofsubclinicalatherosclerosisinlowframinghamriskscoreindividualsbyusingthemetabolicsyndromecriteriaandinsulinsensitivityindex AT arigonifabrizio predictionofsubclinicalatherosclerosisinlowframinghamriskscoreindividualsbyusingthemetabolicsyndromecriteriaandinsulinsensitivityindex AT leowmelvinkheeshing predictionofsubclinicalatherosclerosisinlowframinghamriskscoreindividualsbyusingthemetabolicsyndromecriteriaandinsulinsensitivityindex AT tansweeyaw predictionofsubclinicalatherosclerosisinlowframinghamriskscoreindividualsbyusingthemetabolicsyndromecriteriaandinsulinsensitivityindex AT yeokhungkeong predictionofsubclinicalatherosclerosisinlowframinghamriskscoreindividualsbyusingthemetabolicsyndromecriteriaandinsulinsensitivityindex |