Machine learning-derived gut microbiome signature predicts fatty liver disease in the presence of insulin resistance
Abstract A simple predictive biomarker for fatty liver disease is required for individuals with insulin resistance. Here, we developed a supervised machine learning-based classifier for fatty liver disease using fecal 16S rDNA sequencing data. Based on the Kangbuk Samsung Hospital cohort (n = 777),...
| Main Authors: | , , , , , , , , , , , , |
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| Format: | Article |
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Nature Portfolio
2022-12-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-022-26102-4 |
| _version_ | 1828265286851100672 |
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| author | Baeki E. Kang Aron Park Hyekyung Yang Yunju Jo Tae Gyu Oh Seung Min Jeong Yosep Ji Hyung‐Lae Kim Han‐Na Kim Johan Auwerx Seungyoon Nam Cheol-Young Park Dongryeol Ryu |
| author_facet | Baeki E. Kang Aron Park Hyekyung Yang Yunju Jo Tae Gyu Oh Seung Min Jeong Yosep Ji Hyung‐Lae Kim Han‐Na Kim Johan Auwerx Seungyoon Nam Cheol-Young Park Dongryeol Ryu |
| author_sort | Baeki E. Kang |
| collection | DOAJ |
| description | Abstract A simple predictive biomarker for fatty liver disease is required for individuals with insulin resistance. Here, we developed a supervised machine learning-based classifier for fatty liver disease using fecal 16S rDNA sequencing data. Based on the Kangbuk Samsung Hospital cohort (n = 777), we generated a random forest classifier to predict fatty liver diseases in individuals with or without insulin resistance (n = 166 and n = 611, respectively). The model performance was evaluated based on metrics, including accuracy, area under receiver operating curve (AUROC), kappa, and F1-score. The developed classifier for fatty liver diseases performed better in individuals with insulin resistance (AUROC = 0.77). We further optimized the classifiers using genetic algorithm. The improved classifier for insulin resistance, consisting of ten microbial genera, presented an advanced classification (AUROC = 0.93), whereas the improved classifier for insulin-sensitive individuals failed to distinguish participants with fatty liver diseases from the healthy. The classifier for individuals with insulin resistance was comparable or superior to previous methods predicting fatty liver diseases (accuracy = 0.83, kappa = 0.50, F1-score = 0.89), such as the fatty liver index. We identified the ten genera as a core set from the human gut microbiome, which could be a diagnostic biomarker of fatty liver diseases for insulin resistant individuals. Collectively, these findings indicate that the machine learning classifier for fatty liver diseases in the presence of insulin resistance is comparable or superior to commonly used methods. |
| first_indexed | 2024-04-13T04:38:13Z |
| format | Article |
| id | doaj.art-c07fe36604554929b95943d14016c913 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-13T04:38:13Z |
| publishDate | 2022-12-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-c07fe36604554929b95943d14016c9132022-12-22T03:02:07ZengNature PortfolioScientific Reports2045-23222022-12-0112111210.1038/s41598-022-26102-4Machine learning-derived gut microbiome signature predicts fatty liver disease in the presence of insulin resistanceBaeki E. Kang0Aron Park1Hyekyung Yang2Yunju Jo3Tae Gyu Oh4Seung Min Jeong5Yosep Ji6Hyung‐Lae Kim7Han‐Na Kim8Johan Auwerx9Seungyoon Nam10Cheol-Young Park11Dongryeol Ryu12Department of Molecular Cell Biology, Sungkyunkwan University School of MedicineDepartment of Health Sciences and Technology, Gachon Advanced Institute for Health Sciences and Technology, Gachon UniversityMedical Research Institute, School of Medicine, Kangbuk Samsung Hospital, Sungkyunkwan UniversityDepartment of Molecular Cell Biology, Sungkyunkwan University School of MedicineGene Expression Laboratory, Salk Institute for Biological StudiesDepartment of Molecular Cell Biology, Sungkyunkwan University School of MedicineHEM Inc.Department of Biochemistry, College of Medicine, Ewha Womans UniversityMedical Research Institute, School of Medicine, Kangbuk Samsung Hospital, Sungkyunkwan UniversityInstitute of Bioengineering, Faculty of Life Sciences, Ecole Polytechnique Fédérale de LausanneDepartment of Health Sciences and Technology, Gachon Advanced Institute for Health Sciences and Technology, Gachon UniversityDivision of Endocrinology and Metabolism, Department of Internal Medicine, Kangbuk Samsung Hospital, Sungkyunkwan University School of MedicineDepartment of Molecular Cell Biology, Sungkyunkwan University School of MedicineAbstract A simple predictive biomarker for fatty liver disease is required for individuals with insulin resistance. Here, we developed a supervised machine learning-based classifier for fatty liver disease using fecal 16S rDNA sequencing data. Based on the Kangbuk Samsung Hospital cohort (n = 777), we generated a random forest classifier to predict fatty liver diseases in individuals with or without insulin resistance (n = 166 and n = 611, respectively). The model performance was evaluated based on metrics, including accuracy, area under receiver operating curve (AUROC), kappa, and F1-score. The developed classifier for fatty liver diseases performed better in individuals with insulin resistance (AUROC = 0.77). We further optimized the classifiers using genetic algorithm. The improved classifier for insulin resistance, consisting of ten microbial genera, presented an advanced classification (AUROC = 0.93), whereas the improved classifier for insulin-sensitive individuals failed to distinguish participants with fatty liver diseases from the healthy. The classifier for individuals with insulin resistance was comparable or superior to previous methods predicting fatty liver diseases (accuracy = 0.83, kappa = 0.50, F1-score = 0.89), such as the fatty liver index. We identified the ten genera as a core set from the human gut microbiome, which could be a diagnostic biomarker of fatty liver diseases for insulin resistant individuals. Collectively, these findings indicate that the machine learning classifier for fatty liver diseases in the presence of insulin resistance is comparable or superior to commonly used methods.https://doi.org/10.1038/s41598-022-26102-4 |
| spellingShingle | Baeki E. Kang Aron Park Hyekyung Yang Yunju Jo Tae Gyu Oh Seung Min Jeong Yosep Ji Hyung‐Lae Kim Han‐Na Kim Johan Auwerx Seungyoon Nam Cheol-Young Park Dongryeol Ryu Machine learning-derived gut microbiome signature predicts fatty liver disease in the presence of insulin resistance Scientific Reports |
| title | Machine learning-derived gut microbiome signature predicts fatty liver disease in the presence of insulin resistance |
| title_full | Machine learning-derived gut microbiome signature predicts fatty liver disease in the presence of insulin resistance |
| title_fullStr | Machine learning-derived gut microbiome signature predicts fatty liver disease in the presence of insulin resistance |
| title_full_unstemmed | Machine learning-derived gut microbiome signature predicts fatty liver disease in the presence of insulin resistance |
| title_short | Machine learning-derived gut microbiome signature predicts fatty liver disease in the presence of insulin resistance |
| title_sort | machine learning derived gut microbiome signature predicts fatty liver disease in the presence of insulin resistance |
| url | https://doi.org/10.1038/s41598-022-26102-4 |
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