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

Atabaki, N.N. LU orcid ; Coral, D.E. LU orcid ; Pomares-Millan, H. LU orcid ; Behjat, H.H. LU ; Fernandez-Tajes, J.J. LU ; Kalamajski, S. LU ; Giordano, G.N. LU ; Ohlsson, M. LU orcid ; Ridderstråle, M. LU and Franks, P.W. LU (2026) In Metabolism: Clinical and Experimental 178.
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... (More)
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. © 2026 . (Less)
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Contribution to journal
publication status
published
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keywords
Basal insulin secretion, Bayesian networks, Hepatic steatosis, MASLD, Mendelian randomization, Proteomics, Type 2 diabetes
in
Metabolism: Clinical and Experimental
volume
178
article number
156552
publisher
Elsevier
external identifiers
  • scopus:105030942318
ISSN
0026-0495
DOI
10.1016/j.metabol.2026.156552
language
English
LU publication?
yes
id
319cba07-ca90-411b-8ea2-c090b7083625
date added to LUP
2026-03-24 12:41:40
date last changed
2026-03-24 12:41:53
@article{319cba07-ca90-411b-8ea2-c090b7083625,
  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. © 2026 .}},
  author       = {{Atabaki, N.N. and Coral, D.E. and Pomares-Millan, H. and Behjat, H.H. and Fernandez-Tajes, J.J. and Kalamajski, S. and Giordano, G.N. and Ohlsson, M. and Ridderstråle, M. and Franks, P.W.}},
  issn         = {{0026-0495}},
  keywords     = {{Basal insulin secretion; Bayesian networks; Hepatic steatosis; MASLD; Mendelian randomization; Proteomics; Type 2 diabetes}},
  language     = {{eng}},
  publisher    = {{Elsevier}},
  series       = {{Metabolism: Clinical and Experimental}},
  title        = {{A biological-systems-based analysis using proteomic and metabolic network inference reveals mechanistic insights into hepatic steatosis}},
  url          = {{http://dx.doi.org/10.1016/j.metabol.2026.156552}},
  doi          = {{10.1016/j.metabol.2026.156552}},
  volume       = {{178}},
  year         = {{2026}},
}