TY - UNPB
T1 - A Biological-Systems-Based Analyses Using Proteomic and Metabolic Network Inference Reveals Mechanistic Insights into Hepatic Lipid Accumulation
T2 - An IMI-DIRECT Study
AU - Atabaki, Natalie N
AU - Coral, Daniel E
AU - Pomares-Millan, Hugo
AU - Smith, Kieran
AU - Behjat, Harry H
AU - Koivula, Robert W
AU - Tura, Andrea
AU - Miller, Hamish
AU - Pinnick, Katherine
AU - Agudelo, Leandro
AU - Allin, Kristine H
AU - Brown, Andrew A
AU - Chabanova, Elizaveta
AU - Chmura, Piotr J
AU - Jacobsen, Ulrik P
AU - Dawed, Adem Y
AU - Elders, Petra J M
AU - Fernandez-Tajes, Juan J
AU - Forgie, Ian M
AU - Haid, Mark
AU - Hansen, Tue H
AU - Hansen, Elizaveta L
AU - Jones, Angus G
AU - Kokkola, Tarja
AU - Kalamajski, Sebastian
AU - Mahajan, Anubha
AU - McDonald, Timothy J
AU - McEvoy, Donna
AU - Muilwijk, Mirthe
AU - Tsirigos, Konstantinos D
AU - Vangipurapu, Jagadish
AU - van Oort, Sabine
AU - Vestergaard, Henrik
AU - Adamski, Jerzy
AU - Beulens, Joline W
AU - Brunak, Søren
AU - Dermitzakis, Emmanouil T
AU - Giordano, Giuseppe N
AU - Gupta, Ramneek
AU - Hansen, Torben
AU - Hart, Leen T
AU - Hattersley, Andrew T
AU - Hodson, Leanne
AU - Laakso, Markku
AU - Loos, Ruth J F
AU - Merino, Jordi
AU - Ohlsson, Mattias
AU - Pedersen, Oluf
AU - Ridderstråle, Martin
AU - Ruetten, Hartmut
AU - Rutters, Femke
AU - Schwenk, Jochen M
AU - Tomlinson, Jeremy
AU - Walker, Mark
AU - Yaghootkar, Hanieh
AU - Karpe, Fredrik
AU - McCarthy, Mark I
AU - Thomas, Elizabeth Louise
AU - Bell, Jimmy D
AU - Mari, Andrea
AU - Pavo, Imre
AU - Pearson, Ewan R
AU - Viñuela, Ana
AU - Franks, Paul W
PY - 2025/6/2
Y1 - 2025/6/2
N2 - 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).RESEARCH DESIGN AND METHODS: We used Bayesian network analyses to quantify causal pathways linking adipose distribution, glycemia, and insulin dynamics with fatty liver using data from the IMI-DIRECT prospective cohort study. Measurements were made of glucose and insulin dynamics (using frequently-sampled metabolic challenge tests), MRI-derived abdominal and liver fat content, serological biomarkers, and Olink plasma proteomics from 331 adults with new-onset T2D and 964 adults free from diabetes at enrolment. The common protocols used in these two cohorts provided the opportunity for replication analyses to be performed. When the direction of the effect could not be determined with high probability through Bayesian networks, complementary two-sample Mendelian randomization (MR) was employed.RESULTS: High basal insulin secretion rate (BasalISR) was identified as the primary causal driver of liver fat accumulation in both diabetes and non-diabetes. Excess visceral adipose tissue (VAT) was bidirectionally associated with liver fat, indicating a self-reinforcing metabolic loop. Basal insulin clearance (Clinsb) worsened as a consequence of liver fat accumulation to a greater degree before the onset of T2D. Out of 446 analysed proteins, 34 mapped to these metabolic networks and 27 were identified in the non-diabetes network, 18 in the diabetes network, and 11 were common between the two networks. Key proteins directly associated with liver fat included GUSB, ALDH1A1, LPL, IGFBP1/2, CTSD, HMOX1, FGF21, AGRP, and ACE2. Sex-stratified analyses revealed distinct proteomic drivers: GUSB and LEP were most predictive of liver fat in females and males, respectively.CONCLUSIONS: Basal insulin hypersecretion is a modifiable, causal driver of MASLD, particularly prior to glycaemic decompensation. Our findings highlight a multifactorial, sex- and disease-stage-specific proteo-metabolic architecture of hepatic steatosis. Proteins such as GUSB, ALDH1A1, LPL, and IGFBPs warrant further investigation as potential biomarkers or therapeutic targets for MASLD prevention and treatment.
AB - 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).RESEARCH DESIGN AND METHODS: We used Bayesian network analyses to quantify causal pathways linking adipose distribution, glycemia, and insulin dynamics with fatty liver using data from the IMI-DIRECT prospective cohort study. Measurements were made of glucose and insulin dynamics (using frequently-sampled metabolic challenge tests), MRI-derived abdominal and liver fat content, serological biomarkers, and Olink plasma proteomics from 331 adults with new-onset T2D and 964 adults free from diabetes at enrolment. The common protocols used in these two cohorts provided the opportunity for replication analyses to be performed. When the direction of the effect could not be determined with high probability through Bayesian networks, complementary two-sample Mendelian randomization (MR) was employed.RESULTS: High basal insulin secretion rate (BasalISR) was identified as the primary causal driver of liver fat accumulation in both diabetes and non-diabetes. Excess visceral adipose tissue (VAT) was bidirectionally associated with liver fat, indicating a self-reinforcing metabolic loop. Basal insulin clearance (Clinsb) worsened as a consequence of liver fat accumulation to a greater degree before the onset of T2D. Out of 446 analysed proteins, 34 mapped to these metabolic networks and 27 were identified in the non-diabetes network, 18 in the diabetes network, and 11 were common between the two networks. Key proteins directly associated with liver fat included GUSB, ALDH1A1, LPL, IGFBP1/2, CTSD, HMOX1, FGF21, AGRP, and ACE2. Sex-stratified analyses revealed distinct proteomic drivers: GUSB and LEP were most predictive of liver fat in females and males, respectively.CONCLUSIONS: Basal insulin hypersecretion is a modifiable, causal driver of MASLD, particularly prior to glycaemic decompensation. Our findings highlight a multifactorial, sex- and disease-stage-specific proteo-metabolic architecture of hepatic steatosis. Proteins such as GUSB, ALDH1A1, LPL, and IGFBPs warrant further investigation as potential biomarkers or therapeutic targets for MASLD prevention and treatment.
U2 - 10.1101/2025.06.02.25328773
DO - 10.1101/2025.06.02.25328773
M3 - Preprint
C2 - 40502600
T3 - medRxiv
BT - A Biological-Systems-Based Analyses Using Proteomic and Metabolic Network Inference Reveals Mechanistic Insights into Hepatic Lipid Accumulation
ER -