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Lookup NU author(s): Donna McEvoy, Professor Mark Walker
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
© 2026 .Objective To delineate organ-specific and systemic drivers of metabolic dysfunction-associated steatotic liver disease (MASLD), we applied integrative causal inference across clinical, imaging, and proteomic domains in individuals with and without type 2 diabetes (T2D). Methods Bayesian network analyses and complementary two-sample Mendelian randomization were used to quantify causal pathways linking adipose distribution, glycemia, and insulin dynamics with liver fat in the IMI-DIRECT prospective cohort study. Data included frequently sampled metabolic challenge tests, MRI-derived abdominal and hepatic fat content, serological biomarkers, and Olink plasma proteomics from 331 adults with new-onset T2D and 964 adults without diabetes, with harmonized protocols enabling replication. Results High basal insulin secretion rate (BasalISR), estimated via C-peptide deconvolution, emerged as the primary potential causal driver of liver fat accumulation in both cohorts. BasalISR, a clearance-independent measure of β-cell insulin output distinct from peripheral insulin levels, was independently linked to hepatic steatosis. Visceral adipose tissue exhibited bidirectional associations with liver fat, suggesting a self-reinforcing metabolic loop. Of 446 analyzed proteins, 34 mapped to these metabolic networks (27 in the non-diabetes network, 18 in the T2D network, and 11 shared). Key proteins directly associated with liver fat included GUSB, ALDH1A1, LPL, IGFBP1/2, CTSD, HMOX1, FGF21, AGRP, and ACE2. Sex-stratified analyses identified GUSB in females and LEP in males as the strongest protein predictors of liver fat. Conclusions BasalISR may better capture early β-cell-driven disturbances contributing to MASLD. These findings outline a multifactorial, sex- and disease stage–specific proteo-metabolic architecture of hepatic steatosis and identify potential biomarkers or therapeutic targets.
Author(s): Atabaki NN, Coral DE, Pomares-Millan H, Smith K, Behjat HH, Koivula RW, Tura A, Miller H, Pinnick KE, Agudelo LZ, Allin KH, Brown AA, Chabanova E, Chmura PJ, Jacobsen UP, Dawed AY, Elders PJM, Fernandez-Tajes JJ, Forgie IM, Haid M, Hansen TH, Jones AG, Kokkola T, Kalamajski S, Mahajan A, McDonald TJ, McEvoy D, Muilwijk M, Tsirigos KD, Vangipurapu J, van Oort S, Vestergaard H, Adamski J, Beulens JW, Brunak S, Dermitzakis ET, Giordano GN, Gupta R, Hansen T, 't Hart LM, Hattersley AT, Hodson L, Laakso M, Loos RJF, Merino J, Ohlsson M, Pedersen O, Ridderstrale M, Ruetten H, Rutters F, Schwenk JM, Tomlinson J, Walker M, Yaghootkar H, Karpe F, McCarthy MI, Thomas EL, Bell JD, Mari A, Pavo I, Pearson ER, Vinuela A, Franks PW
Publication type: Article
Publication status: Published
Journal: Metabolism: Clinical and Experimental
Year: 2026
Volume: 178
Print publication date: 01/05/2026
Online publication date: 06/02/2026
Acceptance date: 28/01/2026
Date deposited: 09/03/2026
ISSN (print): 0026-0495
ISSN (electronic): 1532-8600
Publisher: W.B. Saunders
URL: https://doi.org/10.1016/j.metabol.2026.156552
DOI: 10.1016/j.metabol.2026.156552
PubMed id: 41655955
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