Shared genetic architecture between metabolic dysfunction-associated steatotic liver disease and cardiometabolic traits comorbidities: a genome-wide pleiotropic and multi-omics study

Aim: While cardiometabolic disorders and metabolic dysfunction-associated steatotic liver disease (MASLD) frequently coexist, the genetic connections and causes are not clearly understood. This study aimed to explore their shared genetic architecture to elucidate the mechanisms driving their comorbidity. Methods: Using summary statistics from genome-wide association studies (GWASs) on MASLD and 29 cardiometabolic traits (CMTs), we assessed their genetic correlation and causality, and identified shared genetic loci, genes, pathways, cell types, and tissues. Additionally, shared biological mechanisms were uncovered using single-cell RNA sequencing data. Results: Significant genetic correlations were detected between MASLD and 17 CMTs, encompassing cardiometabolic diseases, glucose, lipids, adiposity, and inflammatory markers, after adjusting for multiple testing (p .adjust < 0.05). Cross-trait analysis yielded a total of 166 shared risk SNPs (including those located in or near TRIB1 , LPL , PNPLA3 , GCKR , and PPARG ). Subsequent colocalization highlighted 73 genetic loci associated with both MASLD and CMTs, with rs429358 (APOE ) consistently prioritized in HyPrColoc. Common genes were identified (such as NPC1 , MST1R , TMBIM1 , IRAK1BP1 , L3MBTL3 , RBM6 , and RGS19 ), with significant enrichment in cholesterol metabolism, glucose metabolism, immune inflammation, and long-term depression. Shared tissue-specific heritability enrichment was identified in the liver, adipose, artery, adrenal gland, and brain tissue. Moreover, shared enrichment was observed in specific cell types (epicardial adipocytes, erythroid progenitor cells, hepatocytes, glial cells, macrophages, monocytes, and myeloid cells). The expressions of APOE and LPL , which showed colocalization between MASLD and CMTs, were significantly altered in the macrophages of patients with MASLD compared to those of controls. Causality and potential medications were also explored. Conclusion: Multiple biological pathways contribute to the comorbidity between MASLD and cardiometabolic disorders, with lipid metabolism emerging as a critical factor. This study provides valuable insight into the possible mechanisms underlying their comorbidity and offers potential directions for future therapeutic innovations.

Authors

Institutions

Publication Details

Journal
Metabolism and Target Organ Damage
Published
2025-04-21
DOI
https://doi.org/10.20517/mtod.2024.129
Citations
3
Primary Topic
Liver Disease Diagnosis and Treatment
Type
article
Field-Weighted Citation Impact
4.06
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Shared genetic architecture between metabolic dysfunction-associated steatotic liver disease and cardiometabolic traits comorbidities: a genome-wide pleiotropic and multi-omics study

Hui‐Hui Shen, Yingyu Xie, Hongjie Zhao, Yue Su et al.
3 citations
Metabolism and Target Organ Damage
Liver Disease Diagnosis and Treatment
4.06
article

Shared genetic architecture between metabolic dysfunction-associated steatotic liver disease and cardiometabolic traits comorbidities: a genome-wide pleiotropic and multi-omics study

Hui‐Hui Shen, Yingyu Xie, Hongjie Zhao, Yue Su, Xuan-Yu Wang, Qiong Lyu, Yangyang Zhang
article en
3 citations

Abstract

Aim: While cardiometabolic disorders and metabolic dysfunction-associated steatotic liver disease (MASLD) frequently coexist, the genetic connections and causes are not clearly understood. This study aimed to explore their shared genetic architecture to elucidate the mechanisms driving their comorbidity. Methods: Using summary statistics from genome-wide association studies (GWASs) on MASLD and 29 cardiometabolic traits (CMTs), we assessed their genetic correlation and causality, and identified shared genetic loci, genes, pathways, cell types, and tissues. Additionally, shared biological mechanisms were uncovered using single-cell RNA sequencing data. Results: Significant genetic correlations were detected between MASLD and 17 CMTs, encompassing cardiometabolic diseases, glucose, lipids, adiposity, and inflammatory markers, after adjusting for multiple testing (p .adjust < 0.05). Cross-trait analysis yielded a total of 166 shared risk SNPs (including those located in or near TRIB1 , LPL , PNPLA3 , GCKR , and PPARG ). Subsequent colocalization highlighted 73 genetic loci associated with both MASLD and CMTs, with rs429358 (APOE ) consistently prioritized in HyPrColoc. Common genes were identified (such as NPC1 , MST1R , TMBIM1 , IRAK1BP1 , L3MBTL3 , RBM6 , and RGS19 ), with significant enrichment in cholesterol metabolism, glucose metabolism, immune inflammation, and long-term depression. Shared tissue-specific heritability enrichment was identified in the liver, adipose, artery, adrenal gland, and brain tissue. Moreover, shared enrichment was observed in specific cell types (epicardial adipocytes, erythroid progenitor cells, hepatocytes, glial cells, macrophages, monocytes, and myeloid cells). The expressions of APOE and LPL , which showed colocalization between MASLD and CMTs, were significantly altered in the macrophages of patients with MASLD compared to those of controls. Causality and potential medications were also explored. Conclusion: Multiple biological pathways contribute to the comorbidity between MASLD and cardiometabolic disorders, with lipid metabolism emerging as a critical factor. This study provides valuable insight into the possible mechanisms underlying their comorbidity and offers potential directions for future therapeutic innovations.

Metabolism and Target Organ DamageVol. 5(2)
Tianjin University of Traditional Chinese Medicine (CN)
Openalex Percentile: Top 7%
Liver Disease Diagnosis and Treatment
4.06
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.