Profiling the genome and proteome of metabolic dysfunction-associated steatotic liver disease identifies potential therapeutic targets

Abstract

Background & aims: Metabolic dysfunction-associated steatotic liver disease (MASLD) affects over 25% of the population and currently has no effective treatments. Plasma proteins with causal evidence may represent promising drug targets. We aimed to identify plasma proteins in the causal pathway of MASLD and explore their interaction with obesity.

Methods: We analysed 2,941 plasma proteins in 43,978 European participants from UK Biobank. We performed genome-wide association study (GWAS) for all MASLD-associated proteins and created the largest MASLD GWAS (109,885 cases/1,014,923 controls). We performed Mendelian Randomization (MR) and integrated proteins and their encoding genes in MASLD ranges to identify candidate causal proteins. We then validated them through independent replication, exome sequencing, liver imaging, bulk and single-cell gene expression, liver biopsies, pathway, and phenome-wide data. We explored the role of obesity by MR and multivariable MR across proteins, body mass index, and MASLD.

Results: We found 929 proteins associated with MASLD, reported five novel genetic loci associated with MASLD, and identified 17 candidate MASLD protein targets. We identified four novel targets for MASLD (CD33, GRHPR, HMOX2, and SCG3), provided protein evidence supporting roles of AHCY, FCGR2B, ORM1, and RBKS in MASLD, and validated nine previously known targets. We found that CD33, FCGR2B, ORM1, RBKS, and SCG3 mediated the association of obesity and MASLD, and HMOX2, ORM1, and RBKS had effect on MASLD independent of obesity.

Conclusions: This study identified new protein targets in the causal pathway of MASLD, providing new insights into the multi-omics architecture and pathophysiology of MASLD. These findings advise further therapeutic interventions for MASLD.

Keywords: drug target; genomics; metabolic dysfunction-associated steatotic liver disease; non-alcoholic fatty liver disease; proteomics.

Figure 1. Flow chart of the study design.

Figure 2. Association of proteins with MASLD and BMI.

A) Proteome-wide association of MASLD based on model 2. B) Comparison of the proteome-wide association between MASLD and BMI. T-statistics were obtained from logistic regression under model 2. Fitted linear regression model and Pearson’s correlation are shown. Axis labels show the number of cases and controls, or total samples used in the association analysis.

Figure 3. Association of proteins and MASLD by MR analysis.

A) Association of proteins and MASLD by MR analysis through different MASLD GWAS sources. Proteins significantly associated with at least one MASLD GWAS source are shown (p<1.2×10⁻⁴). B) Association of proteins and MASLD by different MR methods, if applicable. The figure shows the MASLD GWAS source underlying the most significant result by inverse variance weighted MR. C) Association of proteins measured by Somalogic in decode and MASLD by MR analysis through different MASLD GWAS sources. Six proteins are available and shown. D) Association of proteins and liver imaging variables. Solid point indicates p-value less than 0.05. Hollow point indicates p-value not less than 0.05. Detailed data are presented in Supplementary Table 9, 10 and 13.

Figure 4. Role of BMI in the association of candidate causal proteins and MASLD.

A) Proteins as mediators in the causal association of BMI to MASLD. B) BMI as a mediator in the causal association of proteins to MASLD. C) Proteins directly associated with MASLD after adjusted for the causal effect of BMI by multivariable MR. D) Proteins not directly associated with MASLD after adjusted for the causal effect of BMI by multivariable MR. IVs: instrumental variables. Arrows indicate direction from MR analysis, or knowledge (i.e., IVs causes exposures, and BMI causes MASLD).