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[Preprint]. 2025 Jun 2:2025.06.02.25328773.

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^{1

2

3}, Daniel E Coral², Hugo Pomares-Millan^{2

4}, Kieran Smith³, Harry H Behjat⁵, Robert W Koivula³, Andrea Tura⁶, Hamish Miller^{3

7}, Katherine Pinnick³, Leandro Agudelo^{8

9}, Kristine H Allin¹, Andrew A Brown¹⁰, Elizaveta Chabanova¹¹, Piotr J Chmura¹², Ulrik P Jacobsen¹², Adem Y Dawed¹⁰, Petra J M Elders¹³, Juan J Fernandez-Tajes², Ian M Forgie¹⁰, Mark Haid¹⁴, Tue H Hansen^{1

15}, Elizaveta L Hansen¹¹, Angus G Jones¹⁶, Tarja Kokkola¹⁷, Sebastian Kalamajski², Anubha Mahajan¹⁸, Timothy J McDonald¹⁹, Donna McEvoy²⁰, Mirthe Muilwijk^{21

22}, Konstantinos D Tsirigos²³, Jagadish Vangipurapu^{17

24}, Sabine van Oort¹³, Henrik Vestergaard^{1

25}, Jerzy Adamski^{26

27

28}, Joline W Beulens²¹, Søren Brunak¹², Emmanouil T Dermitzakis²⁹, Giuseppe N Giordano², Ramneek Gupta¹, Torben Hansen¹, Leen T Hart^{22

30

31

32}, Andrew T Hattersley¹⁶, Leanne Hodson³, Markku Laakso¹⁷, Ruth J F Loos^{1

33

34

35}, Jordi Merino¹, Mattias Ohlsson³⁶, Oluf Pedersen³⁷, Martin Ridderstråle³⁸, Hartmut Ruetten³⁹, Femke Rutters^{21

22}, Jochen M Schwenk⁴⁰, Jeremy Tomlinson³, Mark Walker⁴¹, Hanieh Yaghootkar⁴², Fredrik Karpe³, Mark I McCarthy⁴³, Elizabeth Louise Thomas⁴⁴, Jimmy D Bell⁴⁴, Andrea Mari⁶, Imre Pavo⁴⁵, Ewan R Pearson¹⁰, Ana Viñuela¹⁰, Paul W Franks^{2

46}

Affiliations

¹ Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.
² Department of Clinical Science, Genetic and Molecular Epidemiology, Lund University Diabetes Centre, Helsingborg, Sweden.
³ Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, UK.
⁴ Department of Epidemiology, Geisel School of Medicine at Dartmouth, Dartmouth-Hitchcock Medical Center, Lebanon, USA.
⁵ Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Lund, Sweden.
⁶ Institute of Neuroscience, National Research Council, Padova, Italy.
⁷ Barts Liver Centre, Queen Mary University London and Barts Health NHS Trust, London, UK.
⁸ Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, USA.
⁹ Broad Institute of MIT and Harvard, Cambridge, USA.
¹⁰ Population Health and Genomics, Ninewells Hospital and Medical School, University of Dundee, Dundee, Scotland.
¹¹ Department of Diagnostic Radiology, Copenhagen University Hospital Herlev Gentofte, Herlev, Denmark.
¹² Department of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark.
¹³ Department of General Practice, Amsterdam UMC-location Vumc, Amsterdam Public Health Research Institute, Amsterdam, The Netherlands.
¹⁴ Metabolomics and Proteomics Core, German Research Center for Environmental Health, Helmholtz Zentrum München, Neuherberg, Germany.
¹⁵ Department of Medicine, Zealand University Hospital, Køge, Denmark.
¹⁶ Department of Clinical and Biomedical Sciences, University of Exeter College of Medicine & Health, Exeter, UK.
¹⁷ Internal Medicine, Institute of Clinical Medicine, University of Eastern Finland, Kuopio, Finland.
¹⁸ Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK.
¹⁹ Blood Sciences, Royal Devon and Exeter NHS Foundation Trust, Exeter, UK.
²⁰ Diabetes Research Network, Royal Victoria Infirmary, Newcastle upon Tyne, UK.
²¹ Amsterdam UMC, Epidemiology and Data Science, Amsterdam, The Netherlands.
²² Amsterdam Public Health, Health Behaviors & Chronic Diseases, Amsterdam, The Netherlands.
²³ Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kongens Lyngby, Denmark.
²⁴ A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland.
²⁵ Steno Diabetes Center, Copenhagen, Denmark.
²⁶ Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.
²⁷ Institute of Experimental Genetics, Helmholtz Zentrum München, German Research Center for Environmental Healt, Neuherberg, Germany.
²⁸ Institute of Biochemistry, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia.
²⁹ Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, Switzerland.
³⁰ Department of Cell and Chemical Biology, Leiden University Medical Center, Leiden, The Netherlands.
³¹ Department of Biomedical Data Sciences, Section Molecular Epidemiology, Leiden University Medical Center, Leiden, The Netherlands.
³² Department of Epidemiology and Data Science, Amsterdam UMC, Amsterdam, The Netherlands.
³³ The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
³⁴ The Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
³⁵ The Novo Nordisk Foundation Center for Genomic Mechanisms of Disease, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA.
³⁶ Center for Environmental and Climate Science, Lund University, Lund, Sweden.
³⁷ Center for Clinical Metabolic Research, Herlev and Gentofte University Hospital, Copenhagen, Denmark.
³⁸ Department of Clinical Science, Lund University, Malmö, Sweden.
³⁹ Boehringer Ingelheim International GmbH, Ingelheim, Germany.
⁴⁰ Science for Life Laboratory, School of Biotechnology, Solna, Sweden.
⁴¹ Translational and Clinical Research Institute, Faculty of Medical Sciences, University of Newcastle, Newcastle upon Tyne, UK.
⁴² School of Chemistry, College of Health and Science, University of Lincoln, Lincoln, UK.
⁴³ Wellcome Trust Centre for Human Genetics, University of Oxford, UK.
⁴⁴ Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK.
⁴⁵ Eli Lilly Regional Operations Ges.m.b.H., Vienna, Austria.
⁴⁶ Precision Healthcare University Research Institute, Queen Mary University of London, London, UK.

PMID: 40502600
PMCID: PMC12155020
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 et al. medRxiv. 2025.

[Preprint]. 2025 Jun 2:2025.06.02.25328773.

doi: 10.1101/2025.06.02.25328773.

Authors

Affiliations

¹ Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.
² Department of Clinical Science, Genetic and Molecular Epidemiology, Lund University Diabetes Centre, Helsingborg, Sweden.
³ Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, UK.
⁴ Department of Epidemiology, Geisel School of Medicine at Dartmouth, Dartmouth-Hitchcock Medical Center, Lebanon, USA.
⁵ Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Lund, Sweden.
⁶ Institute of Neuroscience, National Research Council, Padova, Italy.
⁷ Barts Liver Centre, Queen Mary University London and Barts Health NHS Trust, London, UK.
⁸ Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, USA.
⁹ Broad Institute of MIT and Harvard, Cambridge, USA.
¹⁰ Population Health and Genomics, Ninewells Hospital and Medical School, University of Dundee, Dundee, Scotland.
¹¹ Department of Diagnostic Radiology, Copenhagen University Hospital Herlev Gentofte, Herlev, Denmark.
¹² Department of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark.
¹³ Department of General Practice, Amsterdam UMC-location Vumc, Amsterdam Public Health Research Institute, Amsterdam, The Netherlands.
¹⁴ Metabolomics and Proteomics Core, German Research Center for Environmental Health, Helmholtz Zentrum München, Neuherberg, Germany.
¹⁵ Department of Medicine, Zealand University Hospital, Køge, Denmark.
¹⁶ Department of Clinical and Biomedical Sciences, University of Exeter College of Medicine & Health, Exeter, UK.
¹⁷ Internal Medicine, Institute of Clinical Medicine, University of Eastern Finland, Kuopio, Finland.
¹⁸ Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK.
¹⁹ Blood Sciences, Royal Devon and Exeter NHS Foundation Trust, Exeter, UK.
²⁰ Diabetes Research Network, Royal Victoria Infirmary, Newcastle upon Tyne, UK.
²¹ Amsterdam UMC, Epidemiology and Data Science, Amsterdam, The Netherlands.
²² Amsterdam Public Health, Health Behaviors & Chronic Diseases, Amsterdam, The Netherlands.
²³ Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kongens Lyngby, Denmark.
²⁴ A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland.
²⁵ Steno Diabetes Center, Copenhagen, Denmark.
²⁶ Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.
²⁷ Institute of Experimental Genetics, Helmholtz Zentrum München, German Research Center for Environmental Healt, Neuherberg, Germany.
²⁸ Institute of Biochemistry, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia.
²⁹ Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, Switzerland.
³⁰ Department of Cell and Chemical Biology, Leiden University Medical Center, Leiden, The Netherlands.
³¹ Department of Biomedical Data Sciences, Section Molecular Epidemiology, Leiden University Medical Center, Leiden, The Netherlands.
³² Department of Epidemiology and Data Science, Amsterdam UMC, Amsterdam, The Netherlands.
³³ The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
³⁴ The Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
³⁵ The Novo Nordisk Foundation Center for Genomic Mechanisms of Disease, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA.
³⁶ Center for Environmental and Climate Science, Lund University, Lund, Sweden.
³⁷ Center for Clinical Metabolic Research, Herlev and Gentofte University Hospital, Copenhagen, Denmark.
³⁸ Department of Clinical Science, Lund University, Malmö, Sweden.
³⁹ Boehringer Ingelheim International GmbH, Ingelheim, Germany.
⁴⁰ Science for Life Laboratory, School of Biotechnology, Solna, Sweden.
⁴¹ Translational and Clinical Research Institute, Faculty of Medical Sciences, University of Newcastle, Newcastle upon Tyne, UK.
⁴² School of Chemistry, College of Health and Science, University of Lincoln, Lincoln, UK.
⁴³ Wellcome Trust Centre for Human Genetics, University of Oxford, UK.
⁴⁴ Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK.
⁴⁵ Eli Lilly Regional Operations Ges.m.b.H., Vienna, Austria.
⁴⁶ Precision Healthcare University Research Institute, Queen Mary University of London, London, UK.

PMID: 40502600
PMCID: PMC12155020
DOI: 10.1101/2025.06.02.25328773

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.

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Conflict of interest statement

DECLARATION OF INTERESTS AD works for Novo Nordisk Research Centre Oxford. SB has ownerships in Intomics A/S, Hoba Therapeutics Aps, Novo Nordisk A/S, Lundbeck A/S, and managing board memberships in Proscion A/S and Intomics A/S. MR owns stock in Novo Nordisk A/S. MMcC is an employee of Genentech and a holder of Roche stock. Within the past five years, PWF has received consulting honoraria from Eli Lilly Inc., Novo Nordisk Foundation, Novo Nordisk A/S, UBS, Qatar Foundation, and Zoe Ltd. PWF was also an employee of the Novo Nordisk Foundation (2021–2024). PWF has also received investigator-initiated grants (paid to institution) from numerous pharmaceutical companies as part of the Innovative Medicines Initiative of the European Union.

Figures

**Figure 1.. Proteins identified in Bayesian networks and their functional enrichment across metabolic pathways.**
(Top Left) Venn diagram showing the distribution of 34 proteins identified in the Bayesian networks across non-diabetes and type 2 diabetes cohorts; (Top Right) Reactome pathway enrichment highlighting associated biological processes; (Bottom Left) Molecular function enrichment analysis; (Bottom Right) Cellular component enrichment. The background gene set for the hypergeometric test included 18,731proteining-coding genes.

**Figure 2.. Bayesian network of metabolic and proteomic interactions in the IMI-DIRECT non-diabetes cohort (n = 964).**
The graph displays directed relationships among clinical and proteomic variables. Nodes are color-coded: blue (clinical/metabolic), green (proteins), peach (liver fat as outcome). Solid arrows represent directed associations with high confidence (strength and direction probability ≥ 0.8), while dashed arrows indicate less confident directionality. AGRP: agouti-related peptide; ALDH1A1: aldehyde dehydrogenase 1 family member A1; APOM: apolipoprotein M; BasalISR: basal insulin secretion rate at the beginning of the OGTT; CD4: cluster of differentiation 4; CDH5: cadherin-5; Clins: mean insulin clearance during OGTT; Clinsb: basal insulin clearance-calculated as (mean insulin secretion)/(mean insulin concentration); CTRC: chymotrypsin C; CTSD: cathepsin D; FGF21: fibroblast growth factor 21; Glucagonmin0: fasting glucagon; Glucose: fasting plasma glucose; GlucoseSens: glucose sensitivity; GUSB: β-glucuronidase; HbA1c: glycated haemoglobin A1C; HDL: high-density lipoprotein cholesterol; IGFBP1/2: insulin-like growth factor binding proteins 1 and 2; Insulin: fasting plasma insulin; KITLG: KIT ligand; LDLR: low-density lipoprotein receptor; LEP: leptin; LiverFat: hepatic fat content; LPL: lipoprotein lipase; MFGE8: milk fat globule-epidermal growth factor 8; OGIS: oral glucose insulin sensitivity index according to the method of Mari et al. [32]; PancFat: pancreas fat; PON3: paraoxonase 3; SAT: subcutaneous adipose tissue; TG: triglycerides; TotGLP1min0: fasting total GLP-1; TwoGlucose/TwoInsulin: 2-hour post-load values from OGTT; VAT: visceral adipose tissue.

**Figure 3.. Bayesian network of metabolic and proteomic interactions in the IMI-DIRECT type 2 diabetes cohort (n = 331).**
The graph displays directed relationships among clinical and proteomic variables. Nodes are color-coded: blue (clinical/metabolic), green (proteins), peach (liver fat as outcome). Solid arrows represent directed associations with high confidence (strength and direction probability ≥ 0.8), while dashed arrows indicate less confident directionality. APOM: apolipoprotein M; BasalISR: basal insulin secretion rate; CD4: cluster of differentiation 4; Clins: mean insulin clearance during OGTT; Clinsb: basal insulin clearance; CTRC: chymotrypsin C; FGF21: fibroblast growth factor 21; Glucagonmin0: fasting glucagon; Glucose: fasting plasma glucose; GlucoseSens: glucose sensitivity; HDL: high-density lipoprotein cholesterol; HOMA_IR: homeostatic model assessment of insulin resistance; Insulin: fasting plasma insulin; LDLR: low-density lipoprotein receptor; LiverFat: hepatic fat content; LPL: lipoprotein lipase; MATN2: matrilin-2; MFGE8: milk fat globule-EGF factor 8; OGIS: oral glucose insulin sensitivity index according to the method of Mari et al. [32]; PancFat: pancreas fat; PON3: paraoxonase 3; SAT: subcutaneous adipose tissue; TG: triglycerides; TGM2: transglutaminase 2; TwoGlucose: 2-hour post-load glucose (OGTT); TwoInsulin: 2-hour post-load insulin (OGTT); VAT: visceral adipose tissue.

**Figure 4.. Posterior probabilities of MASLD based on clinical predictors across IMI-DIRECT diabetes and non-diabetes cohorts.**
Radar plots display the posterior probability of MASLD (metabolic dysfunction-associated steatotic liver disease, defined as liver fat >5%) after conditioning on individual clinical variables in (Left) individuals with T2D (type 2 diabetes) and (Right) those without diabetes. Bars represent the conditional probability of MASLD given high (red bars) or low (blue bars) levels of each variable. MASLD: metabolic dysfunction-associated steatotic liver disease; T2D: type 2 diabetes; BasalISR: basal insulin secretion rate; VAT: visceral adipose tissue; SAT: subcutaneous adipose tissue; OGIS: oral glucose insulin sensitivity index; HOMA_IR: homeostatic model assessment of insulin resistance; Clinsb: basal insulin clearance; Clins: dynamic insulin clearance; TG: triglycerides; HDL: high-density lipoprotein cholesterol; HbA1c: glycated haemoglobin A1c; GLP1: glucagon-like peptide 1; OGTT: oral glucose tolerance test.

**Figure 5.. Posterior probabilities of MASLD based on proteomic predictors in IMI-DIRECT diabetes and non-diabetes cohorts.**
Radar plots display the conditional probability of MASLD after conditioning on individual proteomic variables in (Left) T2D and (Right) non-diabetes cohorts. Bars indicate high (red) or low (blue) levels of the corresponding protein. MASLD: metabolic dysfunction-associated steatotic liver disease; T2D: type 2 diabetes; GUSB: β-glucuronidase; LEP: leptin; ALDH1A1: aldehyde dehydrogenase 1 family member A1; CTSD: cathepsin D; FGF21: fibroblast growth factor 21; IGFBP1/2: insulin-like growth factor binding proteins 1 and 2; LDLR: low-density lipoprotein receptor; MFGE8: milk fat globule-EGF factor 8; AGRP: agouti-related peptide; KITLG: KIT ligand; ACE2: angiotensin-converting enzyme 2; APOM: apolipoprotein M; CD4: cluster of differentiation 4; PON3: paraoxonase 3; TGM2: transglutaminase 2; MATN2: matrilin-2.

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This is a preprint.

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

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

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Abstract

Conflict of interest statement

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