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Atabaki, N.N.* ; Coral, D.E.* ; Pomares-Millan, H.* ; Smith, K.* ; Behjat, H.H.* ; Koivula, R.W.* ; Tura, A.* ; Miller, H.* ; Pinnick, K.* ; Agudelo, L.* ; Allin, K.H.* ; Brown, A.A.* ; Chabanova, E.* ; Chmura, P.J.* ; Jacobsen, U.P.* ; Dawed, A.Y.* ; Elders, P.J.M.* ; Fernandez-Tajes, J.J.* ; Forgie, I.M.* ; Haid, M. ; Hansen, T.H.* ; Hansen, E.L.* ; Jones, A.G.* ; Kokkola, T.* ; Kalamajski, S.* ; Mahajan, A.* ; McDonald, T.J.* ; McEvoy, D.* ; Muilwijk, M.* ; Tsirigos, K.D.* ; Vangipurapu, J.* ; van Oort, S.* ; Vestergaard, H.* ; Adamski, J. ; Beulens, J.W.* ; Brunak, S.* ; Dermitzakis, E.T.* ; Giordano, G.N.* ; Gupta, R.* ; Hansen, T.* ; Hart, L.T.* ; Hattersley, A.T.* ; Hodson, L.* ; Laakso, M.* ; Loos, R.J.F.* ; Merino, J.* ; Ohlsson, M.* ; Pedersen, O.* ; Ridderstråle, M.* ; Ruetten, H.* ; Rutters, F.* ; Schwenk, J.M.* ; Tomlinson, J.* ; Walker, M.* ; Yaghootkar, H.* ; Karpe, F.* ; McCarthy, M.I.* ; Thomas, E.L.* ; Bell, J.D.* ; Mari, A.* ; Pavo, I.* ; Pearson, E.R.* ; Viñuela, A.* ; Franks, P.W.*

A biological-systems-based analysis using proteomic and metabolic network inference reveals mechanistic insights into hepatic steatosis.

Metabolism 178:156552 (2026)
Verlagsversion Forschungsdaten DOI PMC
Open Access Hybrid
Creative Commons Lizenzvertrag
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.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Basal Insulin Secretion ; Bayesian Networks ; Hepatic Steatosis ; Masld ; Mendelian Randomization ; Proteomics ; Type 2 Diabetes
ISSN (print) / ISBN 0026-0495
e-ISSN 1532-8600
Zeitschrift Metabolism: clinical and experimental
Quellenangaben Band: 178, Heft: , Seiten: , Artikelnummer: 156552 Supplement: ,
Verlag Elsevier
Begutachtungsstatus Peer reviewed
Institut(e) CF Metabolomics & Proteomics (CF-MPC)
Institute of Experimental Genetics (IEG)