A biological-systems-based analysis using proteomic and metabolic network inference reveals mechanistic insights into hepatic steatosis
Metabolism, 2026
Atabaki N., Coral D., Pomares-Millan H., Smith K., Behjat H., Koivula R., Tura A., Miller H., Pinnick K., Agudelo L., Allin K., Brown A., Chabanova E., Chmura P., Jacobsen U., Dawed A., Elders P., Fernandez-Tajes J., Forgie I., Haid M., Hansen T., Jones A., Kokkola T., Kalamajski S., Mahajan A., McDonald T., McEvoy D., Muilwijk M., Tsirigos K., Vangipurapu J., van Oort S., Vestergaard H., Adamski J., Beulens J., Brunak S., Dermitzakis E., Giordano G., Gupta R., Hansen T., ‘t Hart L., Hattersley A., Hodson L., Laakso M., Loos R., Merino J., Ohlsson M., Pedersen O., Ridderstråle M., Ruetten H., Rutters F., Schwenk J., Tomlinson J., Walker M., Yaghootkar H., Karpe F., McCarthy M., Thomas E., Bell J., Mari A., Pavo I., Pearson E., Viñuela A., Franks P.
| Disease area | Application area | Sample type | Products |
|---|---|---|---|
Metabolic Diseases Hepatology | Pathophysiology | Plasma |  Olink Target 96 |
Abstract
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.