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[Background] Non-alcoholic fatty liver disease (NAFLD) is affecting more people globally. Indeed, NAFLD is a spectrum of metabolic dysfunctions that can progress to hepatocellular carcinoma (NAFLD-HCC). This development can occur in a non-cirrhotic liver and thus, often lack clinical surveillance. The aim of this study was to develop non-invasive surveillance method for NAFLD-HCC., [Methods] Using comprehensive ultra-high-performance liquid chromatography mass-spectrometry, we investigated 1,295 metabolites in serum from 249 patients. Area under the receiver operating characteristic curve was calculated for all detected metabolites and used to establish their diagnostic potential. Logistic regression analysis was used to establish the diagnostic score., [Findings] We show that NAFLD-HCC is characterised by a complete rearrangement of the serum lipidome, which distinguishes NAFLD-HCC from non-cancerous individuals and other HCC patients. We used machine learning to build a diagnostic model for NAFLD-HCC. We quantified predictive metabolites and developed the NAFLD-HCC Diagnostic Score (NHDS), presenting superior diagnostic potential compared to alpha-fetoprotein (AFP). Patients’ metabolic landscapes show a progressive depletion in unsaturated fatty acids and acylcarnitines during transformation. Upregulation of fatty acid transporters in NAFLD-HCC tumours contribute to fatty acid depletion in the serum., [Interpretation] NAFLD-HCC patients can be efficiently distinguished by serum metabolic alterations from the healthy population and from HCC patients related to other aetiologies (alcohol and viral hepatitis). Our model can be used for non-invasive surveillance of individuals with metabolic syndrome(s), allowing for early detection of NAFLD-HCC. Therefore, serum metabolomics may provide valuable insight to monitor patients at risk, including morbidly obese, diabetics, and NAFLD patients., We thank all funding sources: The laboratory of JBA is supported by the Novo Nordisk Foundation (14040, 0058419), Danish Cancer Society (R98-A6446, R167-A10784, R278-A16638), and the Danish Medical Research Council (4183-00118A, 1030-00070B). Data used for validation in this study provided by JMB was funded by the Spanish Ministry of Economy and Competitiveness and ’Instituto de Salud Carlos III’ grants (PI18/01075, Miguel Servet Programme CON14/00129 and CPII19/00008) co-financed by ’Fondo Europeo de Desarrollo Regional’ (FEDER); CIBERehd, Spain; IKERBASQUE, Basque foundation for Science, Spain; BIOEF (Basque Foundation for Innovation and Health Research: EiTB Maratoia BIO15/CA/016/BD); Department of Health of the Basque Country (2017111010), Euskadi RIS3 (2019222054, 2020333010); Department of Industry of the Basque Country (Elkartek: KK-2020/00008), AECC Scientific Foundation and European Commission Horizon 2020 program (ESCALON project no.: 825510). Similarly, MAJ was funded by grants from the Fondo Nacional De Ciencia y Tecnología de Chile (FONDECYT #1191145 to M.A.) and the Comisión Nacional de Investigación, Ciencia y Tecnología (CONICYT, AFB170005, CARE Chile UC).

Objectives To find plasma biomarkers prognostic of type 2 diabetes, which could also inform on pancreatic β-cell deregulations or defects in the function of insulin target tissues. Methods We conducted a systems biology approach to characterize the plasma lipidomes of C57Bl/6J, DBA/2J, and BALB/cJ mice under different nutritional conditions, as well as their pancreatic islet and liver transcriptomes. We searched for correlations between plasma lipids and tissue gene expression modules. Results We identified strong correlation between plasma triacylglycerols (TAGs) and islet gene modules that comprise key regulators of glucose- and lipid-regulated insulin secretion and of the insulin signaling pathway, the two top hits were Gck and Abhd6 for negative and positive correlations, respectively. Correlations were also found between sphingomyelins and islet gene modules that overlapped in part with the gene modules correlated with TAGs. In the liver, the gene module most strongly correlated with plasma TAGs was enriched in mRNAs encoding fatty acid and carnitine transporters as well as multiple enzymes of the β-oxidation pathway. In humans, plasma TAGs also correlated with the expression of several of the same key regulators of insulin secretion and the insulin signaling pathway identified in mice. This cross-species comparative analysis further led to the identification of PITPNC1 as a candidate regulator of glucose-stimulated insulin secretion. Conclusion TAGs emerge as biomarkers of a liver-to-β-cell axis that links hepatic β-oxidation to β-cell functional mass and insulin secretion., Highlights • Plasma triacylglycerols correlated with genes controlling β-cell mass and function. • Plasma triacylglycerols correlated with genes controlling liver β-oxidation. • In humans, triacylglycerols also correlated with key regulators of insulin secretion. • Mouse and human data identified PITPNC1 as a candidate regulator of insulin secretion. • Triacylglycerols are biomarkers of the liver-to-β-cell axis and β-cell function.