A Metabolomics-Based Approach for Diagnosing NAFLD and Identifying Its Pre-Condition Along the Potential Disease Spectrum
Introduction: The significant impact of nonalcoholic fatty liver disease (NAFLD) on public health, combined with the limitations of current diagnostic approaches, demands a more comprehensive and accurate method to identify NAFLD cases in large general populations. Methods: In this cross-sectional s...
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MDPI AG
2025-03-01
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| author | Masanori Nojima Takeshi Kimura Yutaka Aoki Hirotaka Fujimoto Kuniyoshi Hayashi Junya Ohtake Mariko Kimura-Asami Kazuhiko Suzuki Kevin Urayama Masaaki Matsuura Taka-Aki Sato Katsunori Masuda |
| author_facet | Masanori Nojima Takeshi Kimura Yutaka Aoki Hirotaka Fujimoto Kuniyoshi Hayashi Junya Ohtake Mariko Kimura-Asami Kazuhiko Suzuki Kevin Urayama Masaaki Matsuura Taka-Aki Sato Katsunori Masuda |
| author_sort | Masanori Nojima |
| collection | DOAJ |
| description | Introduction: The significant impact of nonalcoholic fatty liver disease (NAFLD) on public health, combined with the limitations of current diagnostic approaches, demands a more comprehensive and accurate method to identify NAFLD cases in large general populations. Methods: In this cross-sectional study, we recruited 3733 individuals (average age 51.8 years) who underwent health check-ups between October 2015 and October 2016. NAFLD was diagnosed using ultrasound; 114 serum metabolites were measured using gas chromatography–mass spectrometry. We adopted the least absolute shrinkage and selection operator (LASSO) method to build a metabolomic-based diagnostic model. Results: NAFLD was diagnosed in 826 participants. While each metabolite exhibited a limited diagnostic ability for NAFLD when used individually, compared with BMI, the model constructed using the LASSO demonstrated adequate diagnostic power (area under the curve [AUC] 0.866, 95% confidence interval 0.847–0.885 in test set) and even for lean (BMI < 23) populations (AUC for LASSO 0.828, for BMI 0.78). Moreover, the LASSO model-derived ‘pre-NAFLD’ condition showed a potential association with insulin resistance and elevated triglycerides. Conclusions: Our metabolomic-based approach provides a comprehensive evaluation of NAFLD or ‘pre-NAFLD’, both considered parts of a hypothetical ‘NAFLD spectrum’, independent of body type. Metabolomics could offer additional diagnostic benefits and potentially expand the disease concept. |
| format | Article |
| id | doaj-art-99f00506dd1b443f9fc017de1bf2377f |
| institution | DOAJ |
| issn | 2673-4389 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Livers |
| spelling | doaj-art-99f00506dd1b443f9fc017de1bf2377f2025-08-20T02:42:25ZengMDPI AGLivers2673-43892025-03-01511210.3390/livers5010012A Metabolomics-Based Approach for Diagnosing NAFLD and Identifying Its Pre-Condition Along the Potential Disease SpectrumMasanori Nojima0Takeshi Kimura1Yutaka Aoki2Hirotaka Fujimoto3Kuniyoshi Hayashi4Junya Ohtake5Mariko Kimura-Asami6Kazuhiko Suzuki7Kevin Urayama8Masaaki Matsuura9Taka-Aki Sato10Katsunori Masuda11Center for Preventive Medicine, St. Luke’s International University, Tokyo 104-0044, JapanCenter for Preventive Medicine, St. Luke’s International University, Tokyo 104-0044, JapanLife Science Research Center, Technology Research Laboratory, Shimadzu Corporation, Tokyo 101-8448, JapanLife Science Research Center, Technology Research Laboratory, Shimadzu Corporation, Tokyo 101-8448, JapanFaculty of Data Science, Kyoto Women’s University, Kyoto 605-8501, JapanCenter for Medical Sciences, St. Luke’s International University, Tokyo 104-0044, JapanCenter for Preventive Medicine, St. Luke’s International University, Tokyo 104-0044, JapanCenter for Preventive Medicine, St. Luke’s International University, Tokyo 104-0044, JapanGraduate School of Public Health, St. Luke’s International University, Tokyo 104-0044, JapanGraduate School of Public Health, Teikyo University, Tokyo 173-8605, JapanLife Science Research Center, Technology Research Laboratory, Shimadzu Corporation, Tokyo 101-8448, JapanCenter for Preventive Medicine, St. Luke’s International University, Tokyo 104-0044, JapanIntroduction: The significant impact of nonalcoholic fatty liver disease (NAFLD) on public health, combined with the limitations of current diagnostic approaches, demands a more comprehensive and accurate method to identify NAFLD cases in large general populations. Methods: In this cross-sectional study, we recruited 3733 individuals (average age 51.8 years) who underwent health check-ups between October 2015 and October 2016. NAFLD was diagnosed using ultrasound; 114 serum metabolites were measured using gas chromatography–mass spectrometry. We adopted the least absolute shrinkage and selection operator (LASSO) method to build a metabolomic-based diagnostic model. Results: NAFLD was diagnosed in 826 participants. While each metabolite exhibited a limited diagnostic ability for NAFLD when used individually, compared with BMI, the model constructed using the LASSO demonstrated adequate diagnostic power (area under the curve [AUC] 0.866, 95% confidence interval 0.847–0.885 in test set) and even for lean (BMI < 23) populations (AUC for LASSO 0.828, for BMI 0.78). Moreover, the LASSO model-derived ‘pre-NAFLD’ condition showed a potential association with insulin resistance and elevated triglycerides. Conclusions: Our metabolomic-based approach provides a comprehensive evaluation of NAFLD or ‘pre-NAFLD’, both considered parts of a hypothetical ‘NAFLD spectrum’, independent of body type. Metabolomics could offer additional diagnostic benefits and potentially expand the disease concept.https://www.mdpi.com/2673-4389/5/1/12non-alcoholic fatty liver disease (NAFLD)metabolomicsbiomarkermachine-learningleast absolute shrinkage and selection operator (LASSO)pre-NAFLD |
| spellingShingle | Masanori Nojima Takeshi Kimura Yutaka Aoki Hirotaka Fujimoto Kuniyoshi Hayashi Junya Ohtake Mariko Kimura-Asami Kazuhiko Suzuki Kevin Urayama Masaaki Matsuura Taka-Aki Sato Katsunori Masuda A Metabolomics-Based Approach for Diagnosing NAFLD and Identifying Its Pre-Condition Along the Potential Disease Spectrum Livers non-alcoholic fatty liver disease (NAFLD) metabolomics biomarker machine-learning least absolute shrinkage and selection operator (LASSO) pre-NAFLD |
| title | A Metabolomics-Based Approach for Diagnosing NAFLD and Identifying Its Pre-Condition Along the Potential Disease Spectrum |
| title_full | A Metabolomics-Based Approach for Diagnosing NAFLD and Identifying Its Pre-Condition Along the Potential Disease Spectrum |
| title_fullStr | A Metabolomics-Based Approach for Diagnosing NAFLD and Identifying Its Pre-Condition Along the Potential Disease Spectrum |
| title_full_unstemmed | A Metabolomics-Based Approach for Diagnosing NAFLD and Identifying Its Pre-Condition Along the Potential Disease Spectrum |
| title_short | A Metabolomics-Based Approach for Diagnosing NAFLD and Identifying Its Pre-Condition Along the Potential Disease Spectrum |
| title_sort | metabolomics based approach for diagnosing nafld and identifying its pre condition along the potential disease spectrum |
| topic | non-alcoholic fatty liver disease (NAFLD) metabolomics biomarker machine-learning least absolute shrinkage and selection operator (LASSO) pre-NAFLD |
| url | https://www.mdpi.com/2673-4389/5/1/12 |
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