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A prediction model for the impact of environmental and genetic factors on cardiovascular events: development in a salt substitutes population

BACKGROUND: Cardiovascular disease (CVD) has evolved into a serious public health issue that demands the use of suitable methods to estimate the risk of the disease. As a result, in a sample of individuals who completed a 3-year low-sodium salt or conventional salt intervention in a hypertensive env...

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Autores principales: Zhao, Dan, Sun, Hao, Li, Huamin, Li, Chaoxiu, Zhou, Bo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9887817/
https://www.ncbi.nlm.nih.gov/pubmed/36717874
http://dx.doi.org/10.1186/s12967-023-03899-w
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author Zhao, Dan
Sun, Hao
Li, Huamin
Li, Chaoxiu
Zhou, Bo
author_facet Zhao, Dan
Sun, Hao
Li, Huamin
Li, Chaoxiu
Zhou, Bo
author_sort Zhao, Dan
collection PubMed
description BACKGROUND: Cardiovascular disease (CVD) has evolved into a serious public health issue that demands the use of suitable methods to estimate the risk of the disease. As a result, in a sample of individuals who completed a 3-year low-sodium salt or conventional salt intervention in a hypertensive environment, we constructed a 13-year cardiovascular (CV) event risk prediction model with a 10-year follow-up. METHODS: A Cox proportional hazards model was used to build a prediction model based on data from 306 participants who matched the inclusion criteria. Both the discriminating power and the calibration of the prediction models were assessed. The discriminative power of the prediction model was measured using the area under the curve (AUC). Brier scores and calibration plots were used to assess the prediction model's calibration. The model was internally validated using the tenfold cross-validation method. The nomogram served as a tool for visualising the model. RESULTS: Among the 306 total individuals, there were 100 cases and 206 control. In the model, there were six predictors including age, smoking, LDL-C (low-density lipoprotein cholesterol), baseline SBP (systolic blood pressure), CVD (cardiovascular history), and CNV (genomic copy number variation) nsv483076. The fitted model has an AUC of 0.788, showing strong model discrimination, and a Brier score of 0.166, indicating that it was well-calibrated. According to the results of internal validation, the prediction model utilised in this study had a good level of repeatability. According to the model integrating the interaction of CNVs and baseline blood pressure, the effect of baseline SBP on CV events may be greater when nsv483076 was normal double copies than when nsv483076 was copy number variation. CONCLUSIONS: The efficacy of risk prediction models for CV events that include environmental and genetic components is excellent, and they may be utilised as risk assessment tools for CV events in specific groups to offer a foundation for tailored intervention strategies. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12967-023-03899-w.
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spelling pubmed-98878172023-02-01 A prediction model for the impact of environmental and genetic factors on cardiovascular events: development in a salt substitutes population Zhao, Dan Sun, Hao Li, Huamin Li, Chaoxiu Zhou, Bo J Transl Med Research BACKGROUND: Cardiovascular disease (CVD) has evolved into a serious public health issue that demands the use of suitable methods to estimate the risk of the disease. As a result, in a sample of individuals who completed a 3-year low-sodium salt or conventional salt intervention in a hypertensive environment, we constructed a 13-year cardiovascular (CV) event risk prediction model with a 10-year follow-up. METHODS: A Cox proportional hazards model was used to build a prediction model based on data from 306 participants who matched the inclusion criteria. Both the discriminating power and the calibration of the prediction models were assessed. The discriminative power of the prediction model was measured using the area under the curve (AUC). Brier scores and calibration plots were used to assess the prediction model's calibration. The model was internally validated using the tenfold cross-validation method. The nomogram served as a tool for visualising the model. RESULTS: Among the 306 total individuals, there were 100 cases and 206 control. In the model, there were six predictors including age, smoking, LDL-C (low-density lipoprotein cholesterol), baseline SBP (systolic blood pressure), CVD (cardiovascular history), and CNV (genomic copy number variation) nsv483076. The fitted model has an AUC of 0.788, showing strong model discrimination, and a Brier score of 0.166, indicating that it was well-calibrated. According to the results of internal validation, the prediction model utilised in this study had a good level of repeatability. According to the model integrating the interaction of CNVs and baseline blood pressure, the effect of baseline SBP on CV events may be greater when nsv483076 was normal double copies than when nsv483076 was copy number variation. CONCLUSIONS: The efficacy of risk prediction models for CV events that include environmental and genetic components is excellent, and they may be utilised as risk assessment tools for CV events in specific groups to offer a foundation for tailored intervention strategies. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12967-023-03899-w. BioMed Central 2023-01-30 /pmc/articles/PMC9887817/ /pubmed/36717874 http://dx.doi.org/10.1186/s12967-023-03899-w Text en © The Author(s) 2023 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
Zhao, Dan
Sun, Hao
Li, Huamin
Li, Chaoxiu
Zhou, Bo
A prediction model for the impact of environmental and genetic factors on cardiovascular events: development in a salt substitutes population
title A prediction model for the impact of environmental and genetic factors on cardiovascular events: development in a salt substitutes population
title_full A prediction model for the impact of environmental and genetic factors on cardiovascular events: development in a salt substitutes population
title_fullStr A prediction model for the impact of environmental and genetic factors on cardiovascular events: development in a salt substitutes population
title_full_unstemmed A prediction model for the impact of environmental and genetic factors on cardiovascular events: development in a salt substitutes population
title_short A prediction model for the impact of environmental and genetic factors on cardiovascular events: development in a salt substitutes population
title_sort prediction model for the impact of environmental and genetic factors on cardiovascular events: development in a salt substitutes population
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9887817/
https://www.ncbi.nlm.nih.gov/pubmed/36717874
http://dx.doi.org/10.1186/s12967-023-03899-w
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