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Risk prediction model of dyslipidaemia over a 5-year period based on the Taiwan MJ health check-up longitudinal database
OBJECTIVE: This study aimed to provide an epidemiological model to evaluate the risk of developing dyslipidaemia within 5 years in the Taiwanese population. METHODS: A cohort of 11,345 subjects aged 35–74 years and was non-dyslipidaemia in the initial year 1996 and followed in 1997–2006 to derive a...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6240269/ https://www.ncbi.nlm.nih.gov/pubmed/30447693 http://dx.doi.org/10.1186/s12944-018-0906-2 |
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author | Yang, Xinghua Xu, Chaonan Wang, Yunfeng Cao, Chunkeng Tao, Qiushan Zhan, Siyan Sun, Feng |
author_facet | Yang, Xinghua Xu, Chaonan Wang, Yunfeng Cao, Chunkeng Tao, Qiushan Zhan, Siyan Sun, Feng |
author_sort | Yang, Xinghua |
collection | PubMed |
description | OBJECTIVE: This study aimed to provide an epidemiological model to evaluate the risk of developing dyslipidaemia within 5 years in the Taiwanese population. METHODS: A cohort of 11,345 subjects aged 35–74 years and was non-dyslipidaemia in the initial year 1996 and followed in 1997–2006 to derive a risk score that could predict the occurrence of dyslipidaemia. Multivariate logistic regression was used to derive the risk functions using the check-up centre of the overall cohort. Rules based on these risk functions were evaluated in the remaining three centres as the testing cohort. We evaluated the predictability of the model using the area under the receiver operating characteristic (ROC) curve (AUC) to confirm its diagnostic property on the testing sample. We also established the degrees of risk based on the cut-off points of these probabilities after transforming them into a normal distribution by log transformation. RESULTS: The incidence of dyslipidaemia over the 5-year period was 19.1%. The final multivariable logistic regression model includes the following six risk factors: gender, history of diabetes, triglyceride level, HDL-C (high-density lipoprotein cholesterol), LDL-C (low-density lipoprotein cholesterol) and BMI (body mass index). The ROC AUC was 0.709 (95% CI: 0.693–0.725), which could predict the development of dyslipidaemia within 5 years. CONCLUSION: This model can help individuals assess the risk of dyslipidaemia and guide group surveillance in the community. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12944-018-0906-2) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-6240269 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-62402692018-11-23 Risk prediction model of dyslipidaemia over a 5-year period based on the Taiwan MJ health check-up longitudinal database Yang, Xinghua Xu, Chaonan Wang, Yunfeng Cao, Chunkeng Tao, Qiushan Zhan, Siyan Sun, Feng Lipids Health Dis Research OBJECTIVE: This study aimed to provide an epidemiological model to evaluate the risk of developing dyslipidaemia within 5 years in the Taiwanese population. METHODS: A cohort of 11,345 subjects aged 35–74 years and was non-dyslipidaemia in the initial year 1996 and followed in 1997–2006 to derive a risk score that could predict the occurrence of dyslipidaemia. Multivariate logistic regression was used to derive the risk functions using the check-up centre of the overall cohort. Rules based on these risk functions were evaluated in the remaining three centres as the testing cohort. We evaluated the predictability of the model using the area under the receiver operating characteristic (ROC) curve (AUC) to confirm its diagnostic property on the testing sample. We also established the degrees of risk based on the cut-off points of these probabilities after transforming them into a normal distribution by log transformation. RESULTS: The incidence of dyslipidaemia over the 5-year period was 19.1%. The final multivariable logistic regression model includes the following six risk factors: gender, history of diabetes, triglyceride level, HDL-C (high-density lipoprotein cholesterol), LDL-C (low-density lipoprotein cholesterol) and BMI (body mass index). The ROC AUC was 0.709 (95% CI: 0.693–0.725), which could predict the development of dyslipidaemia within 5 years. CONCLUSION: This model can help individuals assess the risk of dyslipidaemia and guide group surveillance in the community. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12944-018-0906-2) contains supplementary material, which is available to authorized users. BioMed Central 2018-11-17 /pmc/articles/PMC6240269/ /pubmed/30447693 http://dx.doi.org/10.1186/s12944-018-0906-2 Text en © The Author(s). 2018 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Yang, Xinghua Xu, Chaonan Wang, Yunfeng Cao, Chunkeng Tao, Qiushan Zhan, Siyan Sun, Feng Risk prediction model of dyslipidaemia over a 5-year period based on the Taiwan MJ health check-up longitudinal database |
title | Risk prediction model of dyslipidaemia over a 5-year period based on the Taiwan MJ health check-up longitudinal database |
title_full | Risk prediction model of dyslipidaemia over a 5-year period based on the Taiwan MJ health check-up longitudinal database |
title_fullStr | Risk prediction model of dyslipidaemia over a 5-year period based on the Taiwan MJ health check-up longitudinal database |
title_full_unstemmed | Risk prediction model of dyslipidaemia over a 5-year period based on the Taiwan MJ health check-up longitudinal database |
title_short | Risk prediction model of dyslipidaemia over a 5-year period based on the Taiwan MJ health check-up longitudinal database |
title_sort | risk prediction model of dyslipidaemia over a 5-year period based on the taiwan mj health check-up longitudinal database |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6240269/ https://www.ncbi.nlm.nih.gov/pubmed/30447693 http://dx.doi.org/10.1186/s12944-018-0906-2 |
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