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Impact of estimated glucose disposal rate for identifying prevalent ischemic heart disease: findings from a cross-sectional study
BACKGROUND: Insulin resistance is one of the major mechanisms for cardiovascular events. Estimated glucose disposal rate(eGDR) has been demonstrated as a simple, accurate, and cost-effective estimator of insulin resistance. Our study aims to evaluate the correlation between eGDR and the prevalent IH...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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BioMed Central
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9392437/ https://www.ncbi.nlm.nih.gov/pubmed/35987992 http://dx.doi.org/10.1186/s12872-022-02817-0 |
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author | Xuan, Jin Juan, Du Yuyu, Niu Anjing, Ji |
author_facet | Xuan, Jin Juan, Du Yuyu, Niu Anjing, Ji |
author_sort | Xuan, Jin |
collection | PubMed |
description | BACKGROUND: Insulin resistance is one of the major mechanisms for cardiovascular events. Estimated glucose disposal rate(eGDR) has been demonstrated as a simple, accurate, and cost-effective estimator of insulin resistance. Our study aims to evaluate the correlation between eGDR and the prevalent IHD and assess the incremental value of eGDR for identifying prevalent IHD in the rural general population. METHODS: Our study enrolled 10,895 participants from a cross-sectional survey of a metabolic management program. The survey was conducted in the rural areas of southeastern China between October 2019 and April 2020. eGDR = 21.158 − (0.09 * waist circumference) − (3.407 * hypertension) − (0.551 * HbA1c). RESULTS: The prevalence of IHD was 4.20%. After adjusting for demographic, anthropometric, laboratory, and medical history covariates, each SD increase of eGDR brought a 25.9% risk reduction for prevalent IHD. After dividing eGDR into groups, the top group had a 58.9% risk reduction than the bottom group. Furthermore, smooth curve fitting demonstrated that the correlation between eGDR and prevalent IHD was linear in the whole range of eGDR. Additionally, AUC suggested that eGDR could significantly improve the identification of prevalent IHD by adding it to cardiovascular risk factors (0.703 vs. 0.711, P for comparison = 0.041). Finally, the category-free net reclassification index and integrated discrimination index also implicated the improvement from eGDR to identify prevalent IHD. CONCLUSION: Our data demonstrated a significant, negative, and linear correlation between eGDR and prevalent IHD. Our findings could suggest the potential usefulness of eGDR to improve the identification of prevalent IHD in the rural general population. |
format | Online Article Text |
id | pubmed-9392437 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-93924372022-08-22 Impact of estimated glucose disposal rate for identifying prevalent ischemic heart disease: findings from a cross-sectional study Xuan, Jin Juan, Du Yuyu, Niu Anjing, Ji BMC Cardiovasc Disord Research BACKGROUND: Insulin resistance is one of the major mechanisms for cardiovascular events. Estimated glucose disposal rate(eGDR) has been demonstrated as a simple, accurate, and cost-effective estimator of insulin resistance. Our study aims to evaluate the correlation between eGDR and the prevalent IHD and assess the incremental value of eGDR for identifying prevalent IHD in the rural general population. METHODS: Our study enrolled 10,895 participants from a cross-sectional survey of a metabolic management program. The survey was conducted in the rural areas of southeastern China between October 2019 and April 2020. eGDR = 21.158 − (0.09 * waist circumference) − (3.407 * hypertension) − (0.551 * HbA1c). RESULTS: The prevalence of IHD was 4.20%. After adjusting for demographic, anthropometric, laboratory, and medical history covariates, each SD increase of eGDR brought a 25.9% risk reduction for prevalent IHD. After dividing eGDR into groups, the top group had a 58.9% risk reduction than the bottom group. Furthermore, smooth curve fitting demonstrated that the correlation between eGDR and prevalent IHD was linear in the whole range of eGDR. Additionally, AUC suggested that eGDR could significantly improve the identification of prevalent IHD by adding it to cardiovascular risk factors (0.703 vs. 0.711, P for comparison = 0.041). Finally, the category-free net reclassification index and integrated discrimination index also implicated the improvement from eGDR to identify prevalent IHD. CONCLUSION: Our data demonstrated a significant, negative, and linear correlation between eGDR and prevalent IHD. Our findings could suggest the potential usefulness of eGDR to improve the identification of prevalent IHD in the rural general population. BioMed Central 2022-08-20 /pmc/articles/PMC9392437/ /pubmed/35987992 http://dx.doi.org/10.1186/s12872-022-02817-0 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Xuan, Jin Juan, Du Yuyu, Niu Anjing, Ji Impact of estimated glucose disposal rate for identifying prevalent ischemic heart disease: findings from a cross-sectional study |
title | Impact of estimated glucose disposal rate for identifying prevalent ischemic heart disease: findings from a cross-sectional study |
title_full | Impact of estimated glucose disposal rate for identifying prevalent ischemic heart disease: findings from a cross-sectional study |
title_fullStr | Impact of estimated glucose disposal rate for identifying prevalent ischemic heart disease: findings from a cross-sectional study |
title_full_unstemmed | Impact of estimated glucose disposal rate for identifying prevalent ischemic heart disease: findings from a cross-sectional study |
title_short | Impact of estimated glucose disposal rate for identifying prevalent ischemic heart disease: findings from a cross-sectional study |
title_sort | impact of estimated glucose disposal rate for identifying prevalent ischemic heart disease: findings from a cross-sectional study |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9392437/ https://www.ncbi.nlm.nih.gov/pubmed/35987992 http://dx.doi.org/10.1186/s12872-022-02817-0 |
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