Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Advertisement

Nature Communications
  • View all journals
  • Search
  • My Account Login
  • Content Explore content
  • About the journal
  • Publish with us
  • Sign up for alerts
  • RSS feed
  1. nature
  2. nature communications
  3. articles
  4. article
Clinically distinct metabotypes of pediatric MASLD identified through unsupervised clustering of NASH CRN data
Download PDF
Download PDF
  • Article
  • Open access
  • Published: 24 February 2026

Clinically distinct metabotypes of pediatric MASLD identified through unsupervised clustering of NASH CRN data

  • Helaina E. Huneault  ORCID: orcid.org/0000-0003-3866-23871,2,
  • Pradeep Tiwari3,4,
  • Zachery R. Jarrell  ORCID: orcid.org/0000-0003-4670-14975,
  • Matthew Ryan Smith  ORCID: orcid.org/0000-0002-8889-34774,5,
  • Chih-Yu Chen6,
  • Ana Ramirez Tovar1,7,
  • Cristian Sanchez-Torres7,
  • Scott Gillespie8,
  • Shasha Bai8,
  • Rodrigo M. Carrillo-Larco3,
  • Ajay K. Jain9,
  • Katherine P. Yates  ORCID: orcid.org/0000-0001-6138-219410,
  • Brent A. Neuschwander-Tetri  ORCID: orcid.org/0000-0002-8520-739811,
  • Jeffrey B. Schwimmer12,13,
  • Stavra A. Xanthakos14,
  • Jean P. Molleston15,
  • Cynthia A. Behling16,
  • Mark H. Fishbein17,
  • Terryl J. Hartman1,18,
  • Francisco J. Pasquel  ORCID: orcid.org/0000-0002-3845-670319,
  • Rishikesan Kamaleswaran20,21,
  • Dean P. Jones  ORCID: orcid.org/0000-0002-2090-06775,
  • Jean A. Welsh1,7,22 &
  • …
  • Miriam B. Vos1,2,7,22,23 

Nature Communications , Article number:  (2026) Cite this article

We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and all legal disclaimers apply.

Subjects

  • Endocrine system and metabolic diseases
  • Non-alcoholic fatty liver disease
  • Paediatrics

Abstract

Metabolic dysfunction-associated steatotic liver disease (MASLD) is the most common liver disease worldwide, yet treatment remains “one size fits all,” despite phenotypic heterogeneity. We analyzed clinical and metabolomics data from 514 children (ages 5-18, 73% male) with biopsy-proven MASLD across three NASH Clinical Research Network studies. Unsupervised clustering of clinical data identified three distinct metabotypes: early-mild (49.4%, youngest, lowest lipids, liver enzymes, insulin resistance), cardiometabolic (36.8%, highest waist circumference, lipids, uric acid, SBP), and inflammatory-fibrotic (13.8%, highest liver enzymes, steatohepatitis, advanced fibrosis). Integrative network and pathway enrichment analyses revealed alterations in tryptophan metabolism within the inflammatory-fibrotic group, including elevated kynurenine pathway metabolites, which were significantly correlated with fibrosis stage. Branched-chain amino acid degradation, butanoate, and purine metabolism demonstrated greater enrichment in the cardiometabolic group. Here, we show that pediatric MASLD subtypes differ in clinical and metabolic features, providing a framework for targeted interventions, with validation needed in independent cohorts.

Data availability

Clinical and metabolomics data from the NAFLD Pediatric Database 2 (DB2) NASH CRN study are available through the Human Health Exposure Analysis Resource (HHEAR) Data Center (Project ID: 2017-1593) and the Metabolomics Workbench (Study ID: ST001428). Clinical data from the Treatment of NAFLD in Children (TONIC) trial and the NAFLD Pediatric Database (DB1) NASH CRN studies are available through the NIDDK Central Repository. Metabolomics datasets generated from the TONIC and DB1 studies are available under controlled access due to NASH CRN data use agreements and participant privacy protections, and may be requested through the NIDDK Central Repository (https://repository.niddk.nih.gov/pages/for_requestors). All processed data supporting the findings of this study, including metabolite feature tables, statistical outputs, pathway enrichment results, and figure source data, are provided in the accompanying Source Data files. Raw, identifiable clinical data cannot be shared due to NIDDK data use policies and participant privacy regulations. Source data for Table 1 and Supplementary Data 1 are not publicly available to protect participant privacy due to the presence of indirect identifiers, but may be obtained through request to the corresponding author for purposes of interpreting, verifying, and extending the research described in this article, subject to institutional approvals and data use agreements. Requests will be reviewed within 30 days. Source data are provided with this paper.

Code availability

The code used to perform the analyses in this study is publicly available at: (https://github.com/HHuneault/NASH_CRN_pediatric_metabotypes_R). This repository includes scripts for the unsupervised clustering, metabolomics analysis, and associated figures described in the manuscript. DOI: Huneault, H. Code for Clinically Distinct Metabotypes of Pediatric MASLD. Zenodo. https://doi.org/10.5281/zenodo.17807234 (2025).

References

  1. Rinella, M. E. et al. A multisociety Delphi consensus statement on new fatty liver disease nomenclature. Hepatology 78, 1966–1986 (2023).

    Google Scholar 

  2. Goldner, D. & Lavine, J. E. Nonalcoholic fatty liver disease in children: unique considerations and challenges. Gastroenterology 158, 1967–1983.e1961 (2020).

    Google Scholar 

  3. Mischel, A. K. et al. Prevalence of elevated ALT in adolescents in the US 2011–2018. J. Pediatr. Gastroenterol. Nutr. 77, 103–109 (2023).

    Google Scholar 

  4. Yu, E. L. et al. Prevalence of nonalcoholic fatty liver disease in children with obesity. J. Pediatr. 207, 64–70 (2019).

    Google Scholar 

  5. Vittorio, J. & Lavine, J. E. Recent advances in understanding and managing pediatric nonalcoholic fatty liver disease. F1000Res 9, F1000 Faculty Rev–F1000 Faculty1377 (2020).

    Google Scholar 

  6. Karaivazoglou, K., Kalogeropoulou, M., Assimakopoulos, S. & Triantos, C. Psychosocial issues in pediatric nonalcoholic fatty liver disease. Psychosomatics 60, 10–17 (2019).

    Google Scholar 

  7. Kistler, K. D. et al. Symptoms and quality of life in obese children and adolescents with non-alcoholic fatty liver disease. Aliment Pharm. Ther. 31, 396–406 (2010).

    Google Scholar 

  8. Grønbæk, H. et al. Effect of a 10-week weight loss camp on fatty liver disease and insulin sensitivity in obese Danish children. J. Pediatr. Gastroenterol. Nutr. 54, 223–228 (2012).

    Google Scholar 

  9. Ramon-Krauel, M. et al. A low-glycemic-load versus low-fat diet in the treatment of fatty liver in obese children. Child Obes. 9, 252–260 (2013).

    Google Scholar 

  10. Pozzato, C. et al. Liver fat change in obese children after a 1-year nutrition-behavior intervention. J. Pediatr. Gastroenterol. Nutr. 51, 331–335 (2010).

    Google Scholar 

  11. Koot, B. G. et al. Intensive lifestyle treatment for non-alcoholic fatty liver disease in children with severe obesity: inpatient versus ambulatory treatment. Int J. Obes. (Lond.) 40, 51–57 (2016).

    Google Scholar 

  12. Draijer, L., Benninga, M. & Koot, B. Pediatric NAFLD: an overview and recent developments in diagnostics and treatment. Expert Rev. Gastroenterol. Hepatol. 13, 447–461 (2019).

    Google Scholar 

  13. Pal, P., Palui, R. & Ray, S. Heterogeneity of non-alcoholic fatty liver disease: Implications for clinical practice and research activity. World J. Hepatol. 13, 1584–1610 (2021).

    Google Scholar 

  14. Arrese, M. et al. Insights into nonalcoholic fatty-liver disease heterogeneity. Semin Liver Dis. 41, 421–434 (2021).

    Google Scholar 

  15. Lonardo, A. Separating the apples from the oranges: from NAFLD heterogeneity to personalized medicine. Exploration Med. 2, 435–442 (2021).

    Google Scholar 

  16. Chen, Y. et al. Genome-wide association meta-analysis identifies 17 loci associated with nonalcoholic fatty liver disease. Nat. Genet 55, 1640–1650 (2023).

    Google Scholar 

  17. Valenzuela-Vallejo, L., Sanoudou, D. & Mantzoros, C. S. Precision medicine in fatty liver disease/non-alcoholic fatty liver disease. J. Personalized Med. 13, 830 (2023).

    Google Scholar 

  18. Francque, S. M. Towards precision medicine in non-alcoholic fatty liver disease. Rev. Endocr. Metab. Disord. 24, 885–899 (2023).

    Google Scholar 

  19. Stefan, N., Yki-Järvinen, H. & Neuschwander-Tetri, B. A. Metabolic dysfunction-associated steatotic liver disease: heterogeneous pathomechanisms and effectiveness of metabolism-based treatment. Lancet Diab Endocrinol. 13, 109–121 (2025).

    Google Scholar 

  20. Vandromme, M. et al. Automated phenotyping of patients with non-alcoholic fatty liver disease reveals clinically relevant disease subtypes. Pac. Symp. Biocomput 25, 91–102 (2020).

    Google Scholar 

  21. Kim, H. Y. Recent advances in nonalcoholic fatty liver disease metabolomics. Clin. Mol. Hepatol. 27, 553–559 (2021).

    Google Scholar 

  22. Sartini, A. et al. Non-alcoholic fatty liver disease phenotypes in patients with inflammatory bowel disease. Cell Death Dis. 9, 87 (2018).

    Google Scholar 

  23. Chung, S. et al. Computable phenotypes for NAFLD and NASH Identify patients with significant comorbidities yet most remain undiagnosed. Am. J. Gastroenterol. 116, S564 (2021).

    Google Scholar 

  24. Saffo, S. & Do, A. Clinical Phenotyping and the application of precision medicine in MAFLD. Clin. Liver Dis. 19, 227–233 (2022).

    Google Scholar 

  25. Singh, S. P. et al. Non-alcoholic fatty liver disease: not time for an obituary just yet!. J. Hepatol. 74, 972–974 (2021).

    Google Scholar 

  26. Wruck, W. et al. Multi-omic profiles of human non-alcoholic fatty liver disease tissue highlight heterogenic phenotypes. Sci. Data 2, 150068 (2015).

    Google Scholar 

  27. Raverdy, V. et al. Data-driven cluster analysis identifies distinct types of metabolic dysfunction-associated steatotic liver disease. Nat. Med. 30, 3624–3633 (2024).

    Google Scholar 

  28. Lonardo, A., Arab, J. P. & Arrese, M. Perspectives on precision medicine approaches to NAFLD diagnosis and management. Adv. Ther. 38, 2130–2158 (2021).

    Google Scholar 

  29. Sarría-Santamera, A., Orazumbekova, B., Maulenkul, T., Gaipov, A. & Atageldiyeva, K. The identification of diabetes mellitus subtypes applying cluster analysis techniques: a systematic review. Int. J. Environ. Res. Public Health 17, 9523 (2020).

    Google Scholar 

  30. Ferro, S. et al. Phenomapping of patients with primary breast cancer using machine learning-based unsupervised cluster analysis. J. personalized Med. 11, 272 (2021).

    Google Scholar 

  31. Ahlqvist, E. et al. Novel subgroups of adult-onset diabetes and their association with outcomes: a data-driven cluster analysis of six variables. Lancet Diab Endocrinol. 6, 361–369 (2018).

    Google Scholar 

  32. Liu, K. H. et al. Reference standardization for quantification and harmonization of large-scale metabolomics. Anal. Chem. 92, 8836–8844 (2020).

    Google Scholar 

  33. Giles, G. I. & Jacob, C. Reactive sulfur species: an emerging concept in oxidative stress. Biol. Chem. 383, 375–388 (2002).

    Google Scholar 

  34. Swain, R. M., Sanchez, A., Gutierrez, D. A., Varela-Ramirez, A. & Aguilera, R. J. Thiophene derivative inflicts cytotoxicity via an intrinsic apoptotic pathway on human acute lymphoblastic leukemia cells. PLoS One 18, e0295441 (2023).

    Google Scholar 

  35. Schwimmer, J. B. et al. Histopathology of pediatric nonalcoholic fatty liver disease. Hepatology 42, 641–649 (2005).

    Google Scholar 

  36. Africa, J. A. et al. In children with nonalcoholic fatty liver disease, zone 1 steatosis is associated with advanced fibrosis. Clin. Gastroenterol. Hepatol. 16, 438–446.e431 (2018).

    Google Scholar 

  37. Borén, J. et al. Effects of PNPLA3 I148M on hepatic lipid and very-low-density lipoprotein metabolism in humans. J. Intern. Med. 291, 218–223 (2022).

    Google Scholar 

  38. Carrillo-Larco, R. M. et al. Phenotypes of non-alcoholic fatty liver disease (NAFLD) and all-cause mortality: unsupervised machine learning analysis of NHANES III. BMJ Open 12, e067203 (2022).

    Google Scholar 

  39. Yi, J., Wang, L., Guo, J. & Ren, X. Novel metabolic phenotypes for extrahepatic complication of nonalcoholic fatty liver disease. Hepatol. Commun. 7, e0016 (2023).

    Google Scholar 

  40. Ye, J. et al. Novel metabolic classification for extrahepatic complication of metabolic associated fatty liver disease: a data-driven cluster analysis with international validation. Metabolism 136, 155294 (2022).

    Google Scholar 

  41. Kelsey, M. M. & Zeitler, P. S. Insulin resistance of puberty. Curr. Diab Rep. 16, 64 (2016).

    Google Scholar 

  42. Jamialahmadi, O. et al. Partitioned polygenic risk scores identify distinct types of metabolic dysfunction-associated steatotic liver disease. Nat. Med. 30, 3614–3623 (2024).

    Google Scholar 

  43. Roe, J. D., Garcia, L. A., Klimentidis, Y. C. & Coletta, D. K. Association of PNPLA3 I148M with liver disease biomarkers in Latinos. Hum. Heredity 86, 21–27 (2021).

    Google Scholar 

  44. Dludla, P. V. et al. Pancreatic β-cell dysfunction in type 2 diabetes: implications of inflammation and oxidative stress. World J. Diab 14, 130–146 (2023).

    Google Scholar 

  45. Zhou, Q., Shi, Y., Chen, C., Wu, F. & Chen, Z. A narrative review of the roles of indoleamine 2, 3-dioxygenase and tryptophan-2, 3-dioxygenase in liver diseases. Ann. Transl. Med. 9, 174 (2021).

    Google Scholar 

  46. Teunis, C., Nieuwdorp, M. & Hanssen, N. Interactions between tryptophan metabolism, the gut microbiome and the immune system as potential drivers of non-alcoholic fatty liver disease (NAFLD) and metabolic diseases. Metabolites 12, 514 (2022).

  47. Arto, C. et al. Metabolic profiling of tryptophan pathways: Implications for obesity and metabolic dysfunction-associated steatotic liver disease. Eur. J. Clin. Investig. 54, e14279 (2024).

    Google Scholar 

  48. Osawa, Y. et al. L-tryptophan-mediated enhancement of susceptibility to nonalcoholic fatty liver disease is dependent on the mammalian target of rapamycin. J. Biol. Chem. 286, 34800–34808 (2011).

    Google Scholar 

  49. Oxenkrug, G. F. Tryptophan kynurenine metabolism as a common mediator of genetic and environmental impacts in major depressive disorder: the serotonin hypothesis revisited 40 years later. Isr. J. Psychiatry Relat. Sci. 47, 56–63 (2010).

    Google Scholar 

  50. Mangge, H. et al. Disturbed tryptophan metabolism in cardiovascular disease. Curr. Med Chem. 21, 1931–1937 (2014).

    Google Scholar 

  51. Machado, M. V. et al. Vitamin B5 and N-acetylcysteine in nonalcoholic steatohepatitis: a preclinical study in a dietary mouse model. Dig. Dis. Sci. 61, 137–148 (2016).

    Google Scholar 

  52. Chen, J. et al. Distinct changes in serum metabolites and lipid species in the onset and progression of NAFLD in obese chinese. Comput. Struct. Biotechnol. J. 23, 791–800 (2024).

    Google Scholar 

  53. Gabr, S. A., Alghadir, A. H., Sherif, Y. E. & Ghfar, A. A. Hydroxyproline as a Biomarker in Liver. Biomark. Liver Dis. 1, 21 (2016).

    Google Scholar 

  54. Garibay-Nieto, N. et al. Metabolomic phenotype of hepatic steatosis and fibrosis in mexican children living with obesity. Medicina 59, 1785 (2023).

    Google Scholar 

  55. Yang, X. et al. Serum uric acid levels and prognosis of patients with non-alcoholic fatty liver disease. Sci. Rep. 14, 5923 (2024).

    Google Scholar 

  56. Faienza, M. F. et al. Dietary fructose: from uric acid to a metabolic switch in pediatric metabolic dysfunction-associated steatotic liver disease. Crit. Rev. Food Sci. Nutr. 65, 4583–4598 (2025).

    Google Scholar 

  57. van den Berg, E. H. et al. Non-alcoholic fatty liver disease and risk of incident type 2 diabetes: role of circulating branched-chain amino acids. Nutrients 11, 705 (2019).

    Google Scholar 

  58. Vanweert, F., Schrauwen, P. & Phielix, E. Role of branched-chain amino acid metabolism in the pathogenesis of obesity and type 2 diabetes-related metabolic disturbances BCAA metabolism in type 2 diabetes. Nutr. Diab 12, 35 (2022).

    Google Scholar 

  59. Lo, E. K. K. et al. The emerging role of branched-chain amino acids in liver diseases. Biomedicines 10, 6 (2022).

  60. Sookoian, S. et al. Intrahepatic bacterial metataxonomic signature in non-alcoholic fatty liver disease. Gut 69, 1483–1491 (2020).

    Google Scholar 

  61. Pirola, C. J. & Sookoian, S. The lipidome in nonalcoholic fatty liver disease: actionable targets. J. Lipid Res 62, 100073 (2021).

    Google Scholar 

  62. Vallianou, N. et al. Understanding the role of the gut microbiome and microbial metabolites in non-alcoholic fatty liver disease: current evidence and perspectives. Biomolecules 12, 56 (2022).

    Google Scholar 

  63. Subramanian, M. et al. Precision medicine in the era of artificial intelligence: implications in chronic disease management. J. Transl. Med. 18, 1–12 (2020).

    Google Scholar 

  64. Patton, H. M. et al. Clinical correlates of histopathology in pediatric nonalcoholic steatohepatitis. Gastroenterology 135, 1961–1971.e1962 (2008).

    Google Scholar 

  65. Lavine, J. E. et al. Effect of Vitamin E or metformin for treatment of nonalcoholic fatty liver disease in children and adolescents: the tonic randomized controlled trial. JAMA 305, 1659–1668 (2011).

    Google Scholar 

  66. Lavine, J. E. et al. Treatment of nonalcoholic fatty liver disease in children: TONIC trial design. Contemp. Clin. Trials 31, 62–70 (2010).

    Google Scholar 

  67. Harrington, D. M., Staiano, A. E., Broyles, S. T., Gupta, A. K. & Katzmarzyk, P. T. BMI percentiles for the identification of abdominal obesity and metabolic risk in children and adolescents: evidence in support of the CDC 95th percentile. Eur. J. Clin. Nutr. 67, 218–222 (2013).

    Google Scholar 

  68. Friedewald, W. T., Levy, R. I. & Fredrickson, D. S. Estimation of the concentration of low-density lipoprotein cholesterol in plasma, without use of the preparative ultracentrifuge. Clin. Chem. 18, 499–502 (1972).

    Google Scholar 

  69. Wallace, T. M., Levy, J. C. & Matthews, D. R. Use and abuse of HOMA modeling. Diab Care 27, 1487–1495 (2004).

    Google Scholar 

  70. Force, U. P. S. T. Screening for high blood pressure in children and adolescents: US preventive services task force recommendation statement. JAMA 324, 1878–1883 (2020).

    Google Scholar 

  71. Kleiner, D. E. et al. Design and validation of a histological scoring system for nonalcoholic fatty liver disease. Hepatology 41, 1313–1321 (2005).

    Google Scholar 

  72. Kleiner, D. E. et al. Comparison of adult and pediatric NAFLD—confirmation of a second pattern of progressive fatty liver disease in children: 189. Hepatology 44, 259A–260A (2006).

    Google Scholar 

  73. Kleiner, D. E. et al. Association of Histologic Disease Activity With Progression of Nonalcoholic Fatty Liver Disease. JAMA Netw. Open 2, e1912565 (2019).

    Google Scholar 

  74. Kronthaler, F. & Zöllner, S. Data analysis with RStudio. Data Analysis with RStudio, (2021).

  75. Charrad, M., Ghazzali, N., Boiteau, V. & Niknafs, A. NbClust: an R package for determining the relevant number of clusters in a data set. J. Stat. Softw. 61, 1–36 (2014).

    Google Scholar 

  76. R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria https://R-project.org/ (2020).

  77. Kassambara, A. & Mundt, F. factoextra: Extract and visualize the results of multivariate data analyses. R package version 1.0.7 https://CRAN.R-project.org/package=factoextra (2017).

  78. Hartigan, J. A. & Wong, M. A. Algorithm AS 136: A k-means clustering algorithm. J. R. Stat. Soc. Ser. c. (Appl. Stat.) 28, 100–108 (1979).

    Google Scholar 

  79. Walker, D. I. et al. Metabolome-wide association study of anti-epileptic drug treatment during pregnancy. Toxicol. Appl Pharm. 363, 122–130 (2019).

    Google Scholar 

  80. Liu, K. H. et al. High-resolution metabolomics assessment of military personnel: evaluating analytical strategies for chemical detection. J. Occup. Environ. Med. 58, S53–S61 (2016).

    Google Scholar 

  81. Yu, T., Park, Y., Johnson, J. M. & Jones, D. P. apLCMS-adaptive processing of high-resolution LC/MS data. Bioinformatics 25, 1930–1936 (2009).

    Google Scholar 

  82. Uppal, K. et al. xMSanalyzer: automated pipeline for improved feature detection and downstream analysis of large-scale, non-targeted metabolomics data. BMC Bioinforma. 14, 15 (2013).

    Google Scholar 

  83. Johnson, W. E., Li, C. & Rabinovic, A. Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics 8, 118–127 (2007).

    Google Scholar 

  84. Tugizimana, F., Steenkamp, P. A., Piater, L. A. & Dubery, I. A. A conversation on data mining strategies in LC-MS untargeted metabolomics: pre-processing and pre-treatment steps. Metabolites 6, 40 (2016).

  85. Go, Y.-M. et al. Reference Standardization for Mass Spectrometry and High-resolution Metabolomics Applications to Exposome Research. Toxicol. Sci. 148, 531–543 (2015).

    Google Scholar 

  86. Uppal, K., Walker, D. I. & Jones, D. P. xMSannotator: An R Package for Network-Based Annotation of High-Resolution Metabolomics Data. Anal. Chem. 89, 1063–1067 (2017).

    Google Scholar 

  87. Li, S. et al. Predicting network activity from high throughput metabolomics. PLoS Comput Biol. 9, e1003123 (2013).

    Google Scholar 

  88. Pang, Z. et al. MetaboAnalyst 6.0: towards a unified platform for metabolomics data processing, analysis and interpretation. Nucleic Acids Res 52, W398–w406 (2024).

    Google Scholar 

  89. Kolde, R. & Kolde, M. R. Package ‘pheatmap’. R. package 1, 790 (2015).

    Google Scholar 

  90. Wickham, H. ggplot2. Wiley Interdiscip. Rev.: Comput. Stat. 3, 180–185 (2011).

    Google Scholar 

  91. Uppal, K., Ma, C., Go, Y. M., Jones, D. P. & Wren, J. xMWAS: a data-driven integration and differential network analysis tool. Bioinformatics 34, 701–702 (2018).

    Google Scholar 

  92. González, I., Cao, K. A., Davis, M. J. & Déjean, S. Visualising associations between paired ‘omics’ data sets. BioData Min. 5, 19 (2012).

    Google Scholar 

  93. Blondel, V., Guillaume, J.-L., Lambiotte, R. & Lefebvre, E. Fast Unfolding of Communities in Large Networks. J. Stat. Mech. Theory Exp. 2008, P10008 (2008).

  94. Lichtblau, Y. et al. Comparative assessment of differential network analysis methods. Brief. Bioinforma. 18, 837–850 (2016).

    Google Scholar 

  95. Odibat, O. & Reddy, C. K. Ranking differential hubs in gene co-expression networks. J. Bioinforma. Comput. Biol. 10, 1240002 (2012).

    Google Scholar 

  96. Harrell, F. E. Jr & Harrell, M. F. E. Jr Package ‘hmisc’. CRAN2018 2019, 235–236 (2019).

    Google Scholar 

Download references

Acknowledgements

This work was supported by the National Center for Advancing Translational Sciences of the National Institutes of Health under Award Numbers UL1TR002378 (MBV), UL1TR000454 (MBV), and UL1TR000448 (MBV); by NIH grants R01DK125701 (MBV), K23DK080953 (MBV), and NR019083 (MBV); by the US Department of Veterans Affairs Career Development Award 5IK2BX005913-02 (MRS); and by the NIDDK NASH CRN cooperative agreements U01DK061731 (MBV), U01DK061718 (MBV), U01DK061730(MBV), and U24DK061730 (MBV). Additional support was provided through the Human Health Exposure Analysis Resource (HHEAR) program under Award Numbers R01DK131136 (MBV) and R21AI169487 (MBV). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health or the U.S. Department of Veterans Affairs. We gratefully acknowledge the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) for supporting the NASH Clinical Research Network (NASH CRN) and its collaboration with the Human Health Exposure Analysis Resource (HHEAR), developed by the National Institute of Environmental Health Sciences (NIEHS). We thank the NASH CRN investigators and the Ancillary Studies Committee for access to clinical samples and data from the NAFLD Database, NAFLD Pediatric Database 2 (NCT01061684), and the TONIC trial (NCT00063635). Biospecimens reported here were supplied by the NIDDK Central Repository. This manuscript was not prepared in collaboration with the NIDDK Central Repository and does not necessarily reflect its opinions or official views. We also thank Ken Liu, Mary Nellis, James Zhan, Joshua Preston, ViLinh Tran, William Crandall, and Jaclyn Weinberg of the Emory Clinical Biomarkers Laboratory for their technical support. Finally, we extend our deepest appreciation to the NASH CRN study participants and their families for their invaluable contributions.

Author information

Authors and Affiliations

  1. Nutrition & Health Sciences Program, Laney Graduate School, Emory University, Atlanta, GA, USA

    Helaina E. Huneault, Ana Ramirez Tovar, Terryl J. Hartman, Jean A. Welsh & Miriam B. Vos

  2. Department of Pediatrics and Human Development, Michigan State University, College of Human Medicine, Grand Rapids, MI, USA

    Helaina E. Huneault & Miriam B. Vos

  3. Hubert Department of Global Health, Rollins School of Public Health, Emory University, Atlanta, GA, USA

    Pradeep Tiwari & Rodrigo M. Carrillo-Larco

  4. VA Healthcare System of Atlanta, Decatur, GA, USA

    Pradeep Tiwari & Matthew Ryan Smith

  5. Division of Pulmonary, Allergy, Critical Care, and Sleep Medicine, Department of Medicine, Emory University School of Medicine, Atlanta, GA, USA

    Zachery R. Jarrell, Matthew Ryan Smith & Dean P. Jones

  6. Emory Integrated Metabolomics and Lipidomics Core, School of Medicine, Emory University, Atlanta, GA, USA

    Chih-Yu Chen

  7. Department of Pediatrics, Division of Gastroenterology, Hepatology, and Nutrition, Emory University, Atlanta, GA, USA

    Ana Ramirez Tovar, Cristian Sanchez-Torres, Jean A. Welsh & Miriam B. Vos

  8. Pediatric Biostatistics Core, Department of Pediatrics, School of Medicine, Emory University, Atlanta, GA, USA

    Scott Gillespie & Shasha Bai

  9. Department of Pediatrics, Saint Louis University School of Medicine, Saint Louis, MO, USA

    Ajay K. Jain

  10. Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA

    Katherine P. Yates

  11. Department of Internal Medicine, Saint Louis University, St. Louis, Missouri, USA

    Brent A. Neuschwander-Tetri

  12. Department of Gastroenterology, Rady Children’s Hospital San Diego, San Diego, CA, USA

    Jeffrey B. Schwimmer

  13. Department of Pediatrics, School of Medicine, University of California, San Diego, La Jolla, CA, USA

    Jeffrey B. Schwimmer

  14. Division of Gastroenterology, Hepatology and Nutrition, Cincinnati Children’s Hospital Medical Center, Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA

    Stavra A. Xanthakos

  15. Department of Pediatrics, Indiana University/Riley Hospital for Children, Indianapolis, IN, USA

    Jean P. Molleston

  16. Sharp Memorial Hospital, San Diego, California, USA

    Cynthia A. Behling

  17. Department of Pediatrics, Feinberg School of Medicine at Northwestern University, Chicago, IL, USA

    Mark H. Fishbein

  18. Department of Epidemiology, Rollins School of Public Health and Winship Cancer Institute, Emory University, Atlanta, GA, USA

    Terryl J. Hartman

  19. Division of Endocrinology, Department of Medicine, Emory University School of Medicine, Atlanta, GA, USA

    Francisco J. Pasquel

  20. Department of Surgery, Duke University School of Medicine, Durham, NC, USA

    Rishikesan Kamaleswaran

  21. Department of Anesthesiology, Duke University School of Medicine, Durham, NC, USA

    Rishikesan Kamaleswaran

  22. Children’s Healthcare of Atlanta, Atlanta, GA, USA

    Jean A. Welsh & Miriam B. Vos

  23. Helen DeVos Children’s Hospital, Corewell Health, Grand Rapids, MI, USA

    Miriam B. Vos

Authors
  1. Helaina E. Huneault
    View author publications

    Search author on:PubMed Google Scholar

  2. Pradeep Tiwari
    View author publications

    Search author on:PubMed Google Scholar

  3. Zachery R. Jarrell
    View author publications

    Search author on:PubMed Google Scholar

  4. Matthew Ryan Smith
    View author publications

    Search author on:PubMed Google Scholar

  5. Chih-Yu Chen
    View author publications

    Search author on:PubMed Google Scholar

  6. Ana Ramirez Tovar
    View author publications

    Search author on:PubMed Google Scholar

  7. Cristian Sanchez-Torres
    View author publications

    Search author on:PubMed Google Scholar

  8. Scott Gillespie
    View author publications

    Search author on:PubMed Google Scholar

  9. Shasha Bai
    View author publications

    Search author on:PubMed Google Scholar

  10. Rodrigo M. Carrillo-Larco
    View author publications

    Search author on:PubMed Google Scholar

  11. Ajay K. Jain
    View author publications

    Search author on:PubMed Google Scholar

  12. Katherine P. Yates
    View author publications

    Search author on:PubMed Google Scholar

  13. Brent A. Neuschwander-Tetri
    View author publications

    Search author on:PubMed Google Scholar

  14. Jeffrey B. Schwimmer
    View author publications

    Search author on:PubMed Google Scholar

  15. Stavra A. Xanthakos
    View author publications

    Search author on:PubMed Google Scholar

  16. Jean P. Molleston
    View author publications

    Search author on:PubMed Google Scholar

  17. Cynthia A. Behling
    View author publications

    Search author on:PubMed Google Scholar

  18. Mark H. Fishbein
    View author publications

    Search author on:PubMed Google Scholar

  19. Terryl J. Hartman
    View author publications

    Search author on:PubMed Google Scholar

  20. Francisco J. Pasquel
    View author publications

    Search author on:PubMed Google Scholar

  21. Rishikesan Kamaleswaran
    View author publications

    Search author on:PubMed Google Scholar

  22. Dean P. Jones
    View author publications

    Search author on:PubMed Google Scholar

  23. Jean A. Welsh
    View author publications

    Search author on:PubMed Google Scholar

  24. Miriam B. Vos
    View author publications

    Search author on:PubMed Google Scholar

Contributions

H.E.H. performed the statistical and metabolomics analysis, and P.T., S.G., S.B., R.M.C.L., Z.R.J., M.R.S., and C.Y.C. provided guidance. H.E.H. wrote the manuscript, and P.T., Z.R.J., C.Y.C., and M.B.V. provided guidance. P.T., Z.R.J., M.R.S., C.Y.C., A.R.T., C.S.T., S.G., S.B., R.M.C.L., A.K.J., K.P.Y., B.A.N.T., J.B.S., S.A.X., J.P.M., C.Y.B., M.H.F., T.J.H., F.J.P., R.K., D.P.J., J.A.W., and M.B.V. reviewed the final manuscript and provided feedback.

Corresponding author

Correspondence to Helaina E. Huneault.

Ethics declarations

Competing interests

MBV serves as a consultant to Boehringer Ingelheim, Novo Nordisk, Eli Lilly, Intercept, Takeda, and Alberio. She has stock or stock options in Thiogenesis and Tern Pharmaceuticals. Her institution has received research grants (or in-kind research services) from Target Real World Evidence, Quest, Labcorp, and Sonic Incytes Medical Corp. JPM has research grant funding from Gilead, Abbvie, Albireo, and Mirum. BANT: Advisor or consultant: Abbvie, Akero, Aldeyra, Aligos, Arrowhead, Corcept, Galectin, GSK, Hepion, HistoIndex, Madrigal, Merck, Mirum, Pfizer, Sagimet, Senseion; Stock options: HepGene, HeptaBio; Institutional research grants: Madrigal. SAX: Institutional research grants: Target Real World Evidence. The remaining authors declare no competing interests.

Peer review

Peer review information

Nature Communications thanks Svetlana Kutuzova, Mojgan Masoodi and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. [A peer review file is available.]

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information

Description of Additional Supplementary Files

Supplementary Data

Reporting Summary

Transparent Peer Review file

Source data

Source Data

Rights and permissions

Open Access This 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/.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Huneault, H.E., Tiwari, P., Jarrell, Z.R. et al. Clinically distinct metabotypes of pediatric MASLD identified through unsupervised clustering of NASH CRN data. Nat Commun (2026). https://doi.org/10.1038/s41467-026-69735-z

Download citation

  • Received: 12 February 2025

  • Accepted: 28 January 2026

  • Published: 24 February 2026

  • DOI: https://doi.org/10.1038/s41467-026-69735-z

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

Download PDF

Advertisement

Explore content

  • Research articles
  • Reviews & Analysis
  • News & Comment
  • Videos
  • Collections
  • Subjects
  • Follow us on Facebook
  • Follow us on X
  • Sign up for alerts
  • RSS feed

About the journal

  • Aims & Scope
  • Editors
  • Journal Information
  • Open Access Fees and Funding
  • Calls for Papers
  • Editorial Values Statement
  • Journal Metrics
  • Editors' Highlights
  • Contact
  • Editorial policies
  • Top Articles

Publish with us

  • For authors
  • For Reviewers
  • Language editing services
  • Open access funding
  • Submit manuscript

Search

Advanced search

Quick links

  • Explore articles by subject
  • Find a job
  • Guide to authors
  • Editorial policies

Nature Communications (Nat Commun)

ISSN 2041-1723 (online)

nature.com sitemap

About Nature Portfolio

  • About us
  • Press releases
  • Press office
  • Contact us

Discover content

  • Journals A-Z
  • Articles by subject
  • protocols.io
  • Nature Index

Publishing policies

  • Nature portfolio policies
  • Open access

Author & Researcher services

  • Reprints & permissions
  • Research data
  • Language editing
  • Scientific editing
  • Nature Masterclasses
  • Research Solutions

Libraries & institutions

  • Librarian service & tools
  • Librarian portal
  • Open research
  • Recommend to library

Advertising & partnerships

  • Advertising
  • Partnerships & Services
  • Media kits
  • Branded content

Professional development

  • Nature Awards
  • Nature Careers
  • Nature Conferences

Regional websites

  • Nature Africa
  • Nature China
  • Nature India
  • Nature Japan
  • Nature Middle East
  • Privacy Policy
  • Use of cookies
  • Legal notice
  • Accessibility statement
  • Terms & Conditions
  • Your US state privacy rights
Springer Nature

© 2026 Springer Nature Limited

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing