A Metabolomics-Based Approach for Diagnosing NAFLD and Identifying Its Pre-Condition Along the Potential Disease Spectrum
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Design and Population
2.2. Data Collection and Fatty Liver Diagnosis
2.3. Sample Collection
2.4. Metabolome Analysis
2.5. Statistical Analysis
3. Results
3.1. The Association Bewteen Metabolites and NAFLD
3.2. MSEA
3.3. Diagnostic Model Development Using a Machine Learning Method
3.4. Predictive Advantage of LASSO Model Independent from BMI
3.5. What Does the LASSO Score Reflect Clinically?
4. Discussion
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Metabolites Most Upregulated in Participants with NAFLD | Univariate Analysis (Welch’s t-Test) | ROC Analysis | |||
---|---|---|---|---|---|
p Value | Difference NAFLD- non-NAFLD a | 95% CI | AUC | 95% CI | |
Glutamic acid | 6.4 × 10−89 | 0.914 | 0.996–0.833 | 0.759 | 0.740–0.778 |
2-Oxoglutaric acid | 3.5 × 10−64 | 0.887 | 0.982–0.791 | 0.729 | 0.709–0.750 |
Valine | 1.6 × 10−57 | 0.696 | 0.777–0.615 | 0.699 | 0.678–0.719 |
Tyrosine | 4.4 × 10−53 | 0.662 | 0.743–0.582 | 0.685 | 0.664–0.705 |
2-Aminoadipic acid | 3.3 × 10−47 | 0.433 | 0.491–0.376 | 0.729 | 0.710–0.748 |
Phenylalanine | 2.1 × 10−43 | 0.597 | 0.678–0.516 | 0.667 | 0.646–0.688 |
Pyruvic acid | 2.7 × 10−42 | 0.632 | 0.719–0.544 | 0.671 | 0.649–0.692 |
Uric acid | 1.6 × 10−39 | 0.599 | 0.685–0.513 | 0.666 | 0.645–0.687 |
2-Oxoisocaproic acid | 5.8 × 10−38 | 0.565 | 0.648–0.482 | 0.659 | 0.638–0.680 |
Alanine | 2.6 × 10−36 | 0.549 | 0.632–0.466 | 0.654 | 0.633–0.675 |
BMI | 5.0 × 10−164 | 1.264 | 1.199–1.330 | 0.853 | 0.840–0.867 |
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Nojima, M.; Kimura, T.; Aoki, Y.; Fujimoto, H.; Hayashi, K.; Ohtake, J.; Kimura-Asami, M.; Suzuki, K.; Urayama, K.; Matsuura, M.; et al. A Metabolomics-Based Approach for Diagnosing NAFLD and Identifying Its Pre-Condition Along the Potential Disease Spectrum. Livers 2025, 5, 12. https://doi.org/10.3390/livers5010012
Nojima M, Kimura T, Aoki Y, Fujimoto H, Hayashi K, Ohtake J, Kimura-Asami M, Suzuki K, Urayama K, Matsuura M, et al. A Metabolomics-Based Approach for Diagnosing NAFLD and Identifying Its Pre-Condition Along the Potential Disease Spectrum. Livers. 2025; 5(1):12. https://doi.org/10.3390/livers5010012
Chicago/Turabian StyleNojima, Masanori, Takeshi Kimura, Yutaka Aoki, Hirotaka Fujimoto, Kuniyoshi Hayashi, Junya Ohtake, Mariko Kimura-Asami, Kazuhiko Suzuki, Kevin Urayama, Masaaki Matsuura, and et al. 2025. "A Metabolomics-Based Approach for Diagnosing NAFLD and Identifying Its Pre-Condition Along the Potential Disease Spectrum" Livers 5, no. 1: 12. https://doi.org/10.3390/livers5010012
APA StyleNojima, M., Kimura, T., Aoki, Y., Fujimoto, H., Hayashi, K., Ohtake, J., Kimura-Asami, M., Suzuki, K., Urayama, K., Matsuura, M., Sato, T.-A., & Masuda, K. (2025). A Metabolomics-Based Approach for Diagnosing NAFLD and Identifying Its Pre-Condition Along the Potential Disease Spectrum. Livers, 5(1), 12. https://doi.org/10.3390/livers5010012