A Biological-Systems-Based Analyses Using Proteomic and Metabolic Network Inference Reveals Mechanistic Insights into Hepatic Lipid Accumulation: An IMI-DIRECT Study

Natalie N Atabaki; Daniel E Coral; Hugo Pomares-Millan; Kieran Smith; Harry H. Behjat; Robert W Koivula; Andrea Tura; Hamish Miller; Katherine Pinnick; Leandro Agudelo; Kristine H Allin; Andrew A Brown; Elizaveta Chabanova; Piotr J Chmura; Ulrik P Jacobsen; Petra J M Elders; Juan J Fernandez-Tajes; Ian M Forgie; Mark Haid; Tue H Hansen; Elizaveta L Hansen; Angus G Jones; Tarja Kokkola; Sebastian Kalamajski; Anubha Mahajan; Timothy J McDonald; Donna McEvoy; Mirthe Muilwijk; Konstantinos D Tsirigos; Jagadish Vangipurapu; Sabine van Oort; Henrik Vestergaard; Jerzy Adamski; Joline W Beulens; Søren Brunak; Emmanouil T Dermitzakis; Giuseppe N Giordano; Ramneek Gupta; Torben Hansen; Leen T Hart; Andrew T Hattersley; Leanne Hodson; Markku Laakso; Ruth J F Loos; Jordi Merino; Mattias Ohlsson; Oluf Pedersen; Martin Ridderstråle; Hartmut Ruetten; Femke Rutters; Jochen M Schwenk; Jeremy Tomlinson; Mark Walker; Hanieh Yaghootkar; Fredrik Karpe; Mark I McCarthy; Elizabeth Louise Thomas; Jimmy D Bell; Andrea Mari; Imre Pavo; Ewan R Pearson; Ana Viñuela; Paul W Franks

doi:10.1101/2025.06.02.25328773

A Biological-Systems-Based Analyses Using Proteomic and Metabolic Network Inference Reveals Mechanistic Insights into Hepatic Lipid Accumulation: An IMI-DIRECT Study

Natalie N Atabaki
, Daniel E Coral
, Hugo Pomares-Millan
, Kieran Smith
, Harry H. Behjat
, Robert W Koivula
, Andrea Tura
, Hamish Miller
, Katherine Pinnick
, Leandro Agudelo
, Kristine H Allin
, Andrew A Brown
, Elizaveta Chabanova
, Piotr J Chmura
, Ulrik P Jacobsen
, Petra J M Elders
, Juan J Fernandez-Tajes
, Ian M Forgie
, Mark Haid
, Tue H Hansen

Elizaveta L Hansen, Angus G Jones, Tarja Kokkola, Sebastian Kalamajski, Anubha Mahajan, Timothy J McDonald, Donna McEvoy, Mirthe Muilwijk, Konstantinos D Tsirigos, Jagadish Vangipurapu, Sabine van Oort, Henrik Vestergaard, Jerzy Adamski, Joline W Beulens, Søren Brunak, Emmanouil T Dermitzakis, Giuseppe N Giordano, Ramneek Gupta, Torben Hansen, Leen T Hart, Andrew T Hattersley, Leanne Hodson, Markku Laakso, Ruth J F Loos, Jordi Merino, Mattias Ohlsson, Oluf Pedersen, Martin Ridderstråle, Hartmut Ruetten, Femke Rutters, Jochen M Schwenk, Jeremy Tomlinson, Mark Walker, Hanieh Yaghootkar, Fredrik Karpe, Mark I McCarthy, Elizabeth Louise Thomas, Jimmy D Bell, Andrea Mari, Imre Pavo, Ewan R Pearson, Ana Viñuela, Paul W Franks

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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).

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.

Original language	English
Publisher	medRxiv
Number of pages	29
DOIs	https://doi.org/10.1101/2025.06.02.25328773
Publication status	Published - 2 Jun 2025

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Name	medRxiv : the preprint server for health sciences
Publisher	Cold Spring Harbor Laboratory Press

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

endocrinology

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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., Elders, P. J. M., Fernandez-Tajes, J. J., Forgie, I. M., Haid, M., ... Franks, P. W. (2025). A Biological-Systems-Based Analyses Using Proteomic and Metabolic Network Inference Reveals Mechanistic Insights into Hepatic Lipid Accumulation: An IMI-DIRECT Study. (medRxiv : the preprint server for health sciences). medRxiv. https://doi.org/10.1101/2025.06.02.25328773

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title = "A Biological-Systems-Based Analyses Using Proteomic and Metabolic Network Inference Reveals Mechanistic Insights into Hepatic Lipid Accumulation: An IMI-DIRECT Study",

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). 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.",

keywords = "endocrinology",

author = "Atabaki, \{Natalie N\} and Coral, \{Daniel E\} and Hugo Pomares-Millan and Kieran Smith and Behjat, \{Harry H.\} and Koivula, \{Robert W\} and Andrea Tura and Hamish Miller and Katherine Pinnick and Leandro Agudelo and Allin, \{Kristine H\} and Brown, \{Andrew A\} and Elizaveta Chabanova and Chmura, \{Piotr J\} and Jacobsen, \{Ulrik P\} and Elders, \{Petra J M\} and Fernandez-Tajes, \{Juan J\} and Forgie, \{Ian M\} and Mark Haid and Hansen, \{Tue H\} and Hansen, \{Elizaveta L\} and Jones, \{Angus G\} and Tarja Kokkola and Sebastian Kalamajski and Anubha Mahajan and McDonald, \{Timothy J\} and Donna McEvoy and Mirthe Muilwijk and Tsirigos, \{Konstantinos D\} and Jagadish Vangipurapu and \{van Oort\}, Sabine and Henrik Vestergaard and Jerzy Adamski and Beulens, \{Joline W\} and S{\o}ren Brunak and Dermitzakis, \{Emmanouil T\} and Giordano, \{Giuseppe N\} and Ramneek Gupta and Torben Hansen and Hart, \{Leen T\} and Hattersley, \{Andrew T\} and Leanne Hodson and Markku Laakso and Loos, \{Ruth J F\} and Jordi Merino and Mattias Ohlsson and Oluf Pedersen and Martin Ridderstr{\aa}le and Hartmut Ruetten and Femke Rutters and Schwenk, \{Jochen M\} and Jeremy Tomlinson and Mark Walker and Hanieh Yaghootkar and Fredrik Karpe and McCarthy, \{Mark I\} and Thomas, \{Elizabeth Louise\} and Bell, \{Jimmy D\} and Andrea Mari and Imre Pavo and Pearson, \{Ewan R\} and Ana Vi{\~n}uela and Franks, \{Paul W\}",

note = "The copyright holder for this preprint is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.",

year = "2025",

month = jun,

day = "2",

doi = "10.1101/2025.06.02.25328773",

language = "English",

series = "medRxiv : the preprint server for health sciences",

publisher = "medRxiv",

type = "WorkingPaper",

institution = "medRxiv",

}

Atabaki, NN, Coral, DE, Pomares-Millan, H, Smith, K, Behjat, HH, Koivula, RW, Tura, A, Miller, H, Pinnick, K, Agudelo, L, Allin, KH, Brown, AA, Chabanova, E, Chmura, PJ, Jacobsen, UP, Elders, PJM, Fernandez-Tajes, JJ, Forgie, IM, Haid, M, Hansen, TH, Hansen, EL, 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, Hart, LT, Hattersley, AT, Hodson, L, Laakso, M, Loos, RJF, Merino, J, Ohlsson, M, Pedersen, O, Ridderstråle, 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 , Viñuela, A & Franks, PW 2025 'A Biological-Systems-Based Analyses Using Proteomic and Metabolic Network Inference Reveals Mechanistic Insights into Hepatic Lipid Accumulation: An IMI-DIRECT Study' medRxiv : the preprint server for health sciences, medRxiv. https://doi.org/10.1101/2025.06.02.25328773

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 - 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

N1 - The copyright holder for this preprint is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.

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.

KW - endocrinology

U2 - 10.1101/2025.06.02.25328773

DO - 10.1101/2025.06.02.25328773

M3 - Preprint

C2 - 40502600

T3 - medRxiv : the preprint server for health sciences

BT - A Biological-Systems-Based Analyses Using Proteomic and Metabolic Network Inference Reveals Mechanistic Insights into Hepatic Lipid Accumulation

PB - medRxiv

ER -

A Biological-Systems-Based Analyses Using Proteomic and Metabolic Network Inference Reveals Mechanistic Insights into Hepatic Lipid Accumulation: An IMI-DIRECT Study

Abstract

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UN SDGs

Keywords

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