Ibon Martínez-Arranz1, Chiara Bruzzone2, Mazen Noureddin3, Ruben Gil-Redondo2, Itziar Mincholé1, Maider Bizkarguenaga2, Enara Arretxe1, Marta Iruarrizaga-Lejarreta1, David Fernández-Ramos2, Fernando Lopitz-Otsoa2, Rebeca Mayo1, Nieves Embade2, Elizabeth Newberry4, Bettina Mittendorf5, Laura Izquierdo-Sánchez6, Vaclav Smid7, Jorge Arnold8, Paula Iruzubieta9, Ylenia Pérez Castaño10, Marcin Krawczyk11,12, Urko M Marigorta2, Martine C Morrison13, Robert Kleemann13, Antonio Martín-Duce14, Liat Hayardeny15, Libor Vitek7, Radan Bruha7, Rocío Aller de la Fuente16, Javier Crespo9, Manuel Romero-Gomez17, Jesus M Banales6,18, Marco Arrese8,19, Kenneth Cusi20, Elisabetta Bugianesi21, Samuel Klein5, Shelly C Lu3, Quentin M Anstee22,23, Oscar Millet2, Nicholas O Davidson4, Cristina Alonso1, José M Mato2. 1. OWL Metabolomics, Derio, Bizkaia, Spain. 2. CIC bioGUNE, BRTA, CIBERehd, Derio, Bizkaia, Spain. 3. Karsh Division of Gastroenterology and Hepatology, Cedars-Sinai Medical Center, Los Angeles, California, USA. 4. Department of Medicine, Washington University School of Medicine, St. Louis, Missouri, USA. 5. Center for Human Nutrition, Washington University School of Medicine, St. Louis, Missouri, USA. 6. Department of Liver and Gastrointestinal Diseases, Biodonostia Research Institute, Donostia University Hospital, Donostia, Spain. 7. First Faculty of Medicine, Charles University, Prague, Czech Republic. 8. Departamento de Gastroenterologia, Escuela de Medicina, Pontificia Universidad Católica de Chile, Santiago de Chile, Chile. 9. Marqués de Valdecilla University Hospital, Cantabria University, Santander, Spain. 10. Department of Digestive System, Osakidetza Basque Health Service, Donostia University Hospital, San Sebastian, Spain. 11. Department of Medicine II, Saarland University Medical Center, Homburg, Germany. 12. Laboratory of Metabolic Liver Diseases, Center for Preclinical Research, Department of General, Transplant and Liver Surgery, Medical University of Warsaw, Warsaw, Poland. 13. Department of Metabolic Health Research, Netherlands Organization for Applied Scientific Research, Leiden, The Netherlands. 14. Alcalá University School of Medicine and Health Sciences, University Hospital Prıncipe de Asturias, Madrid, Spain. 15. Galmed Pharmaceuticals, Tel Aviv, Israel. 16. Department of Digestive Disease, Clinic University Hospital, University Hospital of Valladolid, Valladolid, Spain. 17. Valme University Hospital, CiBERehd, Sevilla, Spain. 18. University of the Basque Country, CIBERehd, IKERBASQUE, Donostia, Spain. 19. Centro de Envejecimiento y Regeneración, Santiago, Chile. 20. Division of Endocrinology, Diabetes and Metabolism, University of Florida and Malcom Randall VAMC, Gainesville, Florida, USA. 21. Gastroenterology Department, University of Turin, Turin, Italy. 22. Translational & Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle Upon Tyne, UK. 23. Newcastle NIHR Biomedical Research Center, Newcastle Upon Tyne Hospitals NHS Trust, Newcastle Upon Tyne, UK.
Abstract
BACKGROUND AND AIMS: We previously identified subsets of patients with NAFLD with different metabolic phenotypes. Here we align metabolomic signatures with cardiovascular disease (CVD) and genetic risk factors. APPROACH AND RESULTS: We analyzed serum metabolome from 1154 individuals with biopsy-proven NAFLD, and from four mouse models of NAFLD with impaired VLDL-triglyceride (TG) secretion, and one with normal VLDL-TG secretion. We identified three metabolic subtypes: A (47%), B (27%), and C (26%). Subtype A phenocopied the metabolome of mice with impaired VLDL-TG secretion; subtype C phenocopied the metabolome of mice with normal VLDL-TG; and subtype B showed an intermediate signature. The percent of patients with NASH and fibrosis was comparable among subtypes, although subtypes B and C exhibited higher liver enzymes. Serum VLDL-TG levels and secretion rate were lower among subtype A compared with subtypes B and C. Subtype A VLDL-TG and VLDL-apolipoprotein B concentrations were independent of steatosis, whereas subtypes B and C showed an association with these parameters. Serum TG, cholesterol, VLDL, small dense LDL5,6 , and remnant lipoprotein cholesterol were lower among subtype A compared with subtypes B and C. The 10-year high risk of CVD, measured with the Framingham risk score, and the frequency of patatin-like phospholipase domain-containing protein 3 NAFLD risk allele were lower in subtype A. CONCLUSIONS: Metabolomic signatures identify three NAFLD subgroups, independent of histological disease severity. These signatures align with known CVD and genetic risk factors, with subtype A exhibiting a lower CVD risk profile. This may account for the variation in hepatic versus cardiovascular outcomes, offering clinically relevant risk stratification.
BACKGROUND AND AIMS: We previously identified subsets of patients with NAFLD with different metabolic phenotypes. Here we align metabolomic signatures with cardiovascular disease (CVD) and genetic risk factors. APPROACH AND RESULTS: We analyzed serum metabolome from 1154 individuals with biopsy-proven NAFLD, and from four mouse models of NAFLD with impaired VLDL-triglyceride (TG) secretion, and one with normal VLDL-TG secretion. We identified three metabolic subtypes: A (47%), B (27%), and C (26%). Subtype A phenocopied the metabolome of mice with impaired VLDL-TG secretion; subtype C phenocopied the metabolome of mice with normal VLDL-TG; and subtype B showed an intermediate signature. The percent of patients with NASH and fibrosis was comparable among subtypes, although subtypes B and C exhibited higher liver enzymes. Serum VLDL-TG levels and secretion rate were lower among subtype A compared with subtypes B and C. Subtype A VLDL-TG and VLDL-apolipoprotein B concentrations were independent of steatosis, whereas subtypes B and C showed an association with these parameters. Serum TG, cholesterol, VLDL, small dense LDL5,6 , and remnant lipoprotein cholesterol were lower among subtype A compared with subtypes B and C. The 10-year high risk of CVD, measured with the Framingham risk score, and the frequency of patatin-like phospholipase domain-containing protein 3 NAFLD risk allele were lower in subtype A. CONCLUSIONS: Metabolomic signatures identify three NAFLD subgroups, independent of histological disease severity. These signatures align with known CVD and genetic risk factors, with subtype A exhibiting a lower CVD risk profile. This may account for the variation in hepatic versus cardiovascular outcomes, offering clinically relevant risk stratification.
Authors: Anita M van den Hoek; Serdar Özsezen; Martien P M Caspers; Arianne van Koppen; Roeland Hanemaaijer; Lars Verschuren Journal: Int J Mol Sci Date: 2022-07-26 Impact factor: 6.208