Transcriptomic Architecture of Metabolic Dysfunction-Associated Steatotic Liver Disease (MASLD) Risk in Mexican Americans

Authors

• Satish Kumar, The University of Texas Rio Grande ValleyFollow
• Miriam Aceves, The University of Texas Rio Grande ValleyFollow
• Lorena Guerra, The University of Texas Rio Grande ValleyFollow
• Jose C. Granados, The University of Texas Rio Grande ValleyFollow
• Earl Novilla, The University of Texas Rio Grande ValleyFollow
• Felicia Juarez, The University of Texas Rio Grande ValleyFollow
• Tolulope Oluwadairo, The University of Texas Rio Grande Valley
• Ana C. Leandro, The University of Texas Rio Grande ValleyFollow
• Marcelo Leandro, The University of Texas Rio Grande ValleyFollow
• Juan M. Peralta, The University of Texas Rio Grande ValleyFollow
• Sarah Williams-Blangero, The University of Texas Rio Grande ValleyFollow
• John Blangero, The University of Texas Rio Grande ValleyFollow
• Joanne E. Curran, The University of Texas Rio Grande ValleyFollow

Document Type

Article

Publication Date

8-31-2026

Abstract

Hispanics of Mexican American descent in South Texas show a very high prevalence of MASLD, with some studies reporting rates as high as 50% in adults. However, assessment of genetic risk factors underlying this prevalence is complicated by a high co-occurrence of other metabolic disorders and variable endogenous and exogenous environmental risk factors. To map the transcriptomic architecture of MASLD hepatic steatosis risk, we conducted an epidemiological-scale investigation using human induced pluripotent stem cell (iPSC)-derived hepatocyte cultures from 193 participants in our longitudinal South Texas Family Study (STFS). iPSC-based models offer greater power to map genetic risk factors by experimentally controlling for confounding organismal and environmental factors. We combined transcriptome-wide gene expression analysis with high-content cellular measurements of neutral lipids to define a core hepatic steatosis MASLD phenotype at baseline (vehicle-treated) and following a lipid challenge. The additive genetic heritability of hepatic steatosis measures was 0.44 (p-value = 0.03) at baseline and 0.42 (p-value = 0.03) at post-lipid challenge. Multivariable linear regression comparing each gene’s expression against hepatic steatosis measures identified 1070 genes at baseline and 1229 genes post-lipid challenge, whose expression showed a transcriptome-wide statistically significant association (standardized |β| ≥ 0.24; Bonferroni-corrected p-value ≤ 0.001) with baseline and post-lipid challenge hepatic steatosis measures, respectively. Functional annotation and pathway enrichment analyses of these genes implicated a broad range of hepatocellular functions, mapping an overall transcriptomic architecture of MASLD-associated steatosis risk in Mexican Americans. The genes whose expression was positively correlated with hepatic steatosis measures suggest a direct role of variation in fatty acid (FA) and cholesterol uptake, de novo lipogenesis (DNL), and carbohydrate shunts in hepatic steatosis risk, as well as a cellular stress-associated and high-turnover metabolic state marked by elevated FA-oxidation and ketogenesis. In contrast, the genes whose expression was inversely correlated with hepatic steatosis measures suggest a significant role of the cellular cytoskeleton, hepatocyte epithelial integrity, and endosomal and autophagic clearance machinery in steatosis risk.

Comments

© 2026 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.

Recommended Citation

Kumar, S., Aceves, M., Guerra, L., Granados, J., Novilla, E., Juarez, F., Oluwadairo, T., Leandro, A. C., Leandro, M., Peralta, J., Williams-Blangero, S., Blangero, J., & Curran, J. E. (2026). Transcriptomic Architecture of Metabolic Dysfunction-Associated Steatotic Liver Disease (MASLD) Risk in Mexican Americans. Cells, 15(17), 1592. https://doi.org/10.3390/cells15171592

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Publication Title

Cells

DOI

10.3390/cells15171592