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Risk factors for cardiovascular disease in patients with metabolic-associated fatty liver disease: a machine learning approach
BACKGROUND: Nonalcoholic fatty liver disease is associated with an increased cardiovascular disease (CVD) risk, although the exact mechanism(s) are less clear. Moreover, the relationship between newly redefined metabolic-associated fatty liver disease (MAFLD) and CVD risk has been poorly investigate...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9655870/ https://www.ncbi.nlm.nih.gov/pubmed/36371249 http://dx.doi.org/10.1186/s12933-022-01672-9 |
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author | Drożdż, Karolina Nabrdalik, Katarzyna Kwiendacz, Hanna Hendel, Mirela Olejarz, Anna Tomasik, Andrzej Bartman, Wojciech Nalepa, Jakub Gumprecht, Janusz Lip, Gregory Y. H. |
author_facet | Drożdż, Karolina Nabrdalik, Katarzyna Kwiendacz, Hanna Hendel, Mirela Olejarz, Anna Tomasik, Andrzej Bartman, Wojciech Nalepa, Jakub Gumprecht, Janusz Lip, Gregory Y. H. |
author_sort | Drożdż, Karolina |
collection | PubMed |
description | BACKGROUND: Nonalcoholic fatty liver disease is associated with an increased cardiovascular disease (CVD) risk, although the exact mechanism(s) are less clear. Moreover, the relationship between newly redefined metabolic-associated fatty liver disease (MAFLD) and CVD risk has been poorly investigated. Data-driven machine learning (ML) techniques may be beneficial in discovering the most important risk factors for CVD in patients with MAFLD. METHODS: In this observational study, the patients with MAFLD underwent subclinical atherosclerosis assessment and blood biochemical analysis. Patients were split into two groups based on the presence of CVD (defined as at least one of the following: coronary artery disease; myocardial infarction; coronary bypass grafting; stroke; carotid stenosis; lower extremities artery stenosis). The ML techniques were utilized to construct a model which could identify individuals with the highest risk of CVD. We exploited the multiple logistic regression classifier operating on the most discriminative patient’s parameters selected by univariate feature ranking or extracted using principal component analysis (PCA). Receiver operating characteristic (ROC) curves and area under the ROC curve (AUC) were calculated for the investigated classifiers, and the optimal cut-point values were extracted from the ROC curves using the Youden index, the closest to (0, 1) criteria and the Index of Union methods. RESULTS: In 191 patients with MAFLD (mean age: 58, SD: 12 years; 46% female), there were 47 (25%) patients who had the history of CVD. The most important clinical variables included hypercholesterolemia, the plaque scores, and duration of diabetes. The five, ten and fifteen most discriminative parameters extracted using univariate feature ranking and utilized to fit the ML models resulted in AUC of 0.84 (95% confidence interval [CI]: 0.77–0.90, p < 0.0001), 0.86 (95% CI 0.80–0.91, p < 0.0001) and 0.87 (95% CI 0.82–0.92, p < 0.0001), whereas the classifier fitted over 10 principal components extracted using PCA followed by the parallel analysis obtained AUC of 0.86 (95% CI 0.81–0.91, p < 0.0001). The best model operating on 5 most discriminative features correctly identified 114/144 (79.17%) low-risk and 40/47 (85.11%) high-risk patients. CONCLUSION: A ML approach demonstrated high performance in identifying MAFLD patients with prevalent CVD based on the easy-to-obtain patient parameters. |
format | Online Article Text |
id | pubmed-9655870 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-96558702022-11-15 Risk factors for cardiovascular disease in patients with metabolic-associated fatty liver disease: a machine learning approach Drożdż, Karolina Nabrdalik, Katarzyna Kwiendacz, Hanna Hendel, Mirela Olejarz, Anna Tomasik, Andrzej Bartman, Wojciech Nalepa, Jakub Gumprecht, Janusz Lip, Gregory Y. H. Cardiovasc Diabetol Research BACKGROUND: Nonalcoholic fatty liver disease is associated with an increased cardiovascular disease (CVD) risk, although the exact mechanism(s) are less clear. Moreover, the relationship between newly redefined metabolic-associated fatty liver disease (MAFLD) and CVD risk has been poorly investigated. Data-driven machine learning (ML) techniques may be beneficial in discovering the most important risk factors for CVD in patients with MAFLD. METHODS: In this observational study, the patients with MAFLD underwent subclinical atherosclerosis assessment and blood biochemical analysis. Patients were split into two groups based on the presence of CVD (defined as at least one of the following: coronary artery disease; myocardial infarction; coronary bypass grafting; stroke; carotid stenosis; lower extremities artery stenosis). The ML techniques were utilized to construct a model which could identify individuals with the highest risk of CVD. We exploited the multiple logistic regression classifier operating on the most discriminative patient’s parameters selected by univariate feature ranking or extracted using principal component analysis (PCA). Receiver operating characteristic (ROC) curves and area under the ROC curve (AUC) were calculated for the investigated classifiers, and the optimal cut-point values were extracted from the ROC curves using the Youden index, the closest to (0, 1) criteria and the Index of Union methods. RESULTS: In 191 patients with MAFLD (mean age: 58, SD: 12 years; 46% female), there were 47 (25%) patients who had the history of CVD. The most important clinical variables included hypercholesterolemia, the plaque scores, and duration of diabetes. The five, ten and fifteen most discriminative parameters extracted using univariate feature ranking and utilized to fit the ML models resulted in AUC of 0.84 (95% confidence interval [CI]: 0.77–0.90, p < 0.0001), 0.86 (95% CI 0.80–0.91, p < 0.0001) and 0.87 (95% CI 0.82–0.92, p < 0.0001), whereas the classifier fitted over 10 principal components extracted using PCA followed by the parallel analysis obtained AUC of 0.86 (95% CI 0.81–0.91, p < 0.0001). The best model operating on 5 most discriminative features correctly identified 114/144 (79.17%) low-risk and 40/47 (85.11%) high-risk patients. CONCLUSION: A ML approach demonstrated high performance in identifying MAFLD patients with prevalent CVD based on the easy-to-obtain patient parameters. BioMed Central 2022-11-12 /pmc/articles/PMC9655870/ /pubmed/36371249 http://dx.doi.org/10.1186/s12933-022-01672-9 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 Drożdż, Karolina Nabrdalik, Katarzyna Kwiendacz, Hanna Hendel, Mirela Olejarz, Anna Tomasik, Andrzej Bartman, Wojciech Nalepa, Jakub Gumprecht, Janusz Lip, Gregory Y. H. Risk factors for cardiovascular disease in patients with metabolic-associated fatty liver disease: a machine learning approach |
title | Risk factors for cardiovascular disease in patients with metabolic-associated fatty liver disease: a machine learning approach |
title_full | Risk factors for cardiovascular disease in patients with metabolic-associated fatty liver disease: a machine learning approach |
title_fullStr | Risk factors for cardiovascular disease in patients with metabolic-associated fatty liver disease: a machine learning approach |
title_full_unstemmed | Risk factors for cardiovascular disease in patients with metabolic-associated fatty liver disease: a machine learning approach |
title_short | Risk factors for cardiovascular disease in patients with metabolic-associated fatty liver disease: a machine learning approach |
title_sort | risk factors for cardiovascular disease in patients with metabolic-associated fatty liver disease: a machine learning approach |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9655870/ https://www.ncbi.nlm.nih.gov/pubmed/36371249 http://dx.doi.org/10.1186/s12933-022-01672-9 |
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