Godoy-Matos, A. F., Silva Júnior, W. S. & Valerio, C. M. NAFLD as a continuum: From obesity to metabolic syndrome and diabetes. Diabetol. Metab. Syndr. https://doi.org/10.1186/s13098-020-00570-y (2020).ArticleÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Schuster, S., Cabrera, D., Arrese, M. & Feldstein, A. E. Triggering and resolution of inflammation in NASH. Nat. Rev. Gastroenterol. Hepatol. 15, 349–364. https://doi.org/10.1038/s41575-018-0009-6 (2018).ArticleÂ
CASÂ
PubMedÂ
Google ScholarÂ
Zhu, C., Tabas, I., Schwabe, R. F. & Pajvani, U. B. Maladaptive regeneration—the reawakening of developmental pathways in NASH and fibrosis. Nat. Rev. Gastroenterol. Hepatol. 18, 131–142. https://doi.org/10.1038/s41575-020-00365-6 (2021).ArticleÂ
PubMedÂ
Google ScholarÂ
Schonmann, Y., Yeshua, H., Bentov, I. & Zelber-Sagi, S. Liver fibrosis marker is an independent predictor of cardiovascular morbidity and mortality in the general population. Dig. Liver Dis. 53, 79–85 (2021).ArticleÂ
CASÂ
PubMedÂ
Google ScholarÂ
Vieira Barbosa, J. et al. Fibrosis-4 index as an independent predictor of mortality and liver-related outcomes in NAFLD. Hepatol. Commun. 6, 2022 (2021).
Google ScholarÂ
Keam, S. J. Resmetirom: First approval. Drugs 84, 729–735 (2024).ArticleÂ
CASÂ
PubMedÂ
Google ScholarÂ
Stiglund, N., Hagström, H., StÃ¥l, P., Cornillet, M. & Björkström, N. K. Dysregulated peripheral proteome reveals NASH-specific signatures identifying patient subgroups with distinct liver biology. Front. Immunol. 14, 1186097 (2023).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Jorge, A. S. B. et al. Body mass index and the visceral adipose tissue expression of IL-6 and TNF-alpha are associated with the morphological severity of non-alcoholic fatty liver disease in individuals with class III obesity. Obes. Res. Clin. Pract. 12, 1–8 (2018).ArticleÂ
PubMedÂ
Google ScholarÂ
Adolph, T. E., Grander, C., Grabherr, F. & Tilg, H. Adipokines and non-alcoholic fatty liver disease: Multiple interactions. Int. J. Mol. Sci. https://doi.org/10.3390/ijms18081649 (2017).ArticleÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Kisseleva, T. & Brenner, D. Molecular and cellular mechanisms of liver fibrosis and its regression. Nat. Rev. Gastroenterol. Hepatol. 18, 151–166. https://doi.org/10.1038/s41575-020-00372-7 (2021).ArticleÂ
PubMedÂ
Google ScholarÂ
Arrese, M. et al. Insights into nonalcoholic fatty-liver disease heterogeneity. Semin. Liver Dis. 41, 421–434. https://doi.org/10.1055/s-0041-1730927 (2021).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Suppli, M. P. et al. Hepatic transcriptome signatures in patients with varying degrees of nonalcoholic fatty liver disease compared with healthy normal-weight individuals. Am. J. Physiol. Gastrointest. Liver Physiol. 316, 462–472 (2019).ArticleÂ
Google ScholarÂ
MartÃnez-Arranz, I. et al. Metabolic subtypes of patients with NAFLD exhibit distinctive cardiovascular risk profiles. Hepatology 76, 1121–1134 (2022).ArticleÂ
PubMedÂ
Google ScholarÂ
Ratziu, V. & Friedman, S. L. Why do so many NASH trials fail?. Gastroenterologyhttps://doi.org/10.1053/j.gastro.2020.05.046 (2020).ArticleÂ
PubMedÂ
Google ScholarÂ
Guan, Y. et al. Characterization of pro-fibrotic signaling pathways using human hepatic organoids. https://doi.org/10.1101/2023.04.25.538102.Alonso, C., Noureddin, M., Lu, S. C. & Mato, J. M. Biomarkers and subtypes of deranged lipid metabolism in nonalcoholic fatty liver disease. World J. Gastroenterol. 25, 3009–3020. https://doi.org/10.3748/wjg.v25.i24.3009 (2019).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Ahlqvist, E. et al. Novel subgroups of adult-onset diabetes and their association with outcomes: A data-driven cluster analysis of six variables. Lancet Diabetes Endocrinol. 6, 361 (2018).ArticleÂ
PubMedÂ
Google ScholarÂ
Govaere, O. et al. Transcriptomic profiling across the nonalcoholic fatty liver disease spectrum reveals gene signatures for steatohepatitis and fibrosis. Sci. Transl. Med. 12http://stm.sciencemag.org/ (2020).Hoang, S. A. et al. Gene expression predicts histological severity and reveals distinct molecular profiles of nonalcoholic fatty liver disease. Sci. Rep. 9, 12541 (2019).ArticleÂ
ADSÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Verschuren, L. et al. Development of a novel non-invasive biomarker panel for hepatic fibrosis in MASLD. Nat. Commun. 15, 4564 (2024).ArticleÂ
ADSÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Langfelder, P. & Horvath, S. WGCNA: An R package for weighted correlation network analysis. BMC Bioinform. 9, 1–13 (2008).ArticleÂ
Google ScholarÂ
Package ‘Pheatmap’ (2022).Liu, H. et al. Entropy-based consensus clustering for patient stratification. Bioinformatics 33, 2691–2698 (2017).ArticleÂ
CASÂ
PubMedÂ
Google ScholarÂ
Brooks-Warburton, J. et al. A systems genomics approach to uncover patient-specific pathogenic pathways and proteins in ulcerative colitis. Nat. Commun. 13, 2299 (2022).ArticleÂ
ADSÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Shu, Z. et al. Symptom-based network classification identifies distinct clinical subgroups of liver diseases with common molecular pathways. Comput. Methods Programs Biomed. 174, 41–50 (2019).ArticleÂ
PubMedÂ
Google ScholarÂ
Li, L. et al. Identification of type 2 diabetes subgroups through topological analysis of patient similarity. https://www.science.org.Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 1–21 (2014).ArticleÂ
Google ScholarÂ
Chawla, N. V., Bowyer, K. W., Hall, L. O. & Kegelmeyer, W. P. SMOTE: Synthetic minority over-sampling technique. J. Artif. Intell. Res. 16, 321–357 (2002).ArticleÂ
Google ScholarÂ
He, H., Bai, Y., Garcia, E. A. & Li, S. ADASYN: Adaptive synthetic sampling approach for imbalanced learning. In Proceedings of the International Joint Conference on Neural Networks 1322–1328 (2008). https://doi.org/10.1109/IJCNN.2008.4633969.Lemaitre, G., Nogueira, F. & Aridas, C. K. Imbalanced-learn: A Python toolbox to tackle the curse of imbalanced datasets in machine learning (2016).Pedregosa, F. et al. Scikit-learn: Machine learning in Python (2012).Marschner, I., Donoghoe, M. W., glm2: Fitting Generalized Linear Models, R package version 1.2.1, https://cran.rproject.org/web/packages/glm2/index.html (2022).Ampuero, J. & Romero-Gomez, M. Stratification of patients in NASH clinical trials: A pitfall for trial success. JHEP Rep. https://doi.org/10.1016/j.jhepr.2020.100148 (2020).ArticleÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Arguello, G., Balboa, E., Arrese, M. & Zanlungo, S. Recent insights on the role of cholesterol in non-alcoholic fatty liver disease. Biochimica et Biophysica Acta Molecular Basis of Disease 1852, 1765–1778. https://doi.org/10.1016/j.bbadis.2015.05.015 (2015).ArticleÂ
CASÂ
Google ScholarÂ
Steinberg, G. R. & Hardie, D. G. New insights into activation and function of the AMPK. Nat. Rev. Mol. Cell Biol.https://doi.org/10.1038/s41580-022-00547-x (2022).ArticleÂ
PubMedÂ
Google ScholarÂ
Gerhard, G. S. et al. AEBP1 expression increases with severity of fibrosis in NASH and is regulated by glucose, palmitate, and miR-372–3p. PLoS One 14, e0219764 (2019).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Wang, Z. Y. et al. Single-cell and bulk transcriptomics of the liver reveals potential targets of NASH with fibrosis. Sci. Rep. 11, 19396 (2021).ArticleÂ
ADSÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ