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Building a Nomogram for Metabolic Syndrome Using Logistic Regression with a Complex Sample—A Study with 39,991,680 Cases

Metabolic syndrome can cause complications, such as stroke and cardiovascular disease. We aimed to propose a nomogram that visualizes and predicts the probability of metabolic syndrome occurrence after identifying risk factors related to metabolic syndrome for prevention and recognition. We created...

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Detalles Bibliográficos
Autores principales: Shin, Min-Seok, Lee, Jea-Young
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8871838/
https://www.ncbi.nlm.nih.gov/pubmed/35206986
http://dx.doi.org/10.3390/healthcare10020372
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author Shin, Min-Seok
Lee, Jea-Young
author_facet Shin, Min-Seok
Lee, Jea-Young
author_sort Shin, Min-Seok
collection PubMed
description Metabolic syndrome can cause complications, such as stroke and cardiovascular disease. We aimed to propose a nomogram that visualizes and predicts the probability of metabolic syndrome occurrence after identifying risk factors related to metabolic syndrome for prevention and recognition. We created a nomogram related to metabolic syndrome in this paper for the first time. We analyzed data from the Korea National Health and Nutrition Examination Survey VII. Total 17,584 participants were included in this study, and the weighted sample population was 39,991,680, which was 98.1% of the actual Korean population in 2018. We identified 14 risk factors affecting metabolic syndrome using the Rao-Scott chi-squared test. Next, logistic regression analysis was performed to build a model for metabolic syndrome and 11 risk factors were finally obtained, including BMI, marriage, employment, education, age, stroke, sex, income, smoking, family history and age* sex. A nomogram was constructed to predict the occurrence of metabolic syndrome using these risk factors. Finally, the nomogram was verified using a receiver operating characteristic curve (ROC) and a calibration plot.
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spelling pubmed-88718382022-02-25 Building a Nomogram for Metabolic Syndrome Using Logistic Regression with a Complex Sample—A Study with 39,991,680 Cases Shin, Min-Seok Lee, Jea-Young Healthcare (Basel) Article Metabolic syndrome can cause complications, such as stroke and cardiovascular disease. We aimed to propose a nomogram that visualizes and predicts the probability of metabolic syndrome occurrence after identifying risk factors related to metabolic syndrome for prevention and recognition. We created a nomogram related to metabolic syndrome in this paper for the first time. We analyzed data from the Korea National Health and Nutrition Examination Survey VII. Total 17,584 participants were included in this study, and the weighted sample population was 39,991,680, which was 98.1% of the actual Korean population in 2018. We identified 14 risk factors affecting metabolic syndrome using the Rao-Scott chi-squared test. Next, logistic regression analysis was performed to build a model for metabolic syndrome and 11 risk factors were finally obtained, including BMI, marriage, employment, education, age, stroke, sex, income, smoking, family history and age* sex. A nomogram was constructed to predict the occurrence of metabolic syndrome using these risk factors. Finally, the nomogram was verified using a receiver operating characteristic curve (ROC) and a calibration plot. MDPI 2022-02-14 /pmc/articles/PMC8871838/ /pubmed/35206986 http://dx.doi.org/10.3390/healthcare10020372 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/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 (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Shin, Min-Seok
Lee, Jea-Young
Building a Nomogram for Metabolic Syndrome Using Logistic Regression with a Complex Sample—A Study with 39,991,680 Cases
title Building a Nomogram for Metabolic Syndrome Using Logistic Regression with a Complex Sample—A Study with 39,991,680 Cases
title_full Building a Nomogram for Metabolic Syndrome Using Logistic Regression with a Complex Sample—A Study with 39,991,680 Cases
title_fullStr Building a Nomogram for Metabolic Syndrome Using Logistic Regression with a Complex Sample—A Study with 39,991,680 Cases
title_full_unstemmed Building a Nomogram for Metabolic Syndrome Using Logistic Regression with a Complex Sample—A Study with 39,991,680 Cases
title_short Building a Nomogram for Metabolic Syndrome Using Logistic Regression with a Complex Sample—A Study with 39,991,680 Cases
title_sort building a nomogram for metabolic syndrome using logistic regression with a complex sample—a study with 39,991,680 cases
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8871838/
https://www.ncbi.nlm.nih.gov/pubmed/35206986
http://dx.doi.org/10.3390/healthcare10020372
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