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Updated: Jul 9, 2025
Inducing and Characterizing Vesicular Steatosis in Differentiated HepaRG Cells
Published on: July 18, 2019
Proteome profiling identifies circulating biomarkers associated with hepatic steatosis in subjects with Prader-Willi
Devis Pascut1, Pablo J Giraudi2, Cristina Banfi3
1Liver Cancer Unit, Fondazione Italiana Fegato - ONLUS, Trieste, Italy.
Insights
New protein biomarkers can help detect liver steatosis in Prader-Willi syndrome (PWS). This discovery aids in personalized treatment for individuals with PWS, improving management of this rare genetic disorder.
Area of Science:
- Biochemistry
- Genetics
- Metabolomics
Background:
- Prader-Willi syndrome (PWS) is a rare genetic disorder linked to chromosome 15q11.2-q13 gene expression loss.
- Individuals with PWS often experience severe obesity and associated metabolic complications.
- Early identification of liver steatosis in PWS is challenging but crucial due to cardiovascular risks.
Purpose of the Study:
- To identify reliable circulating protein biomarkers for detecting liver steatosis in Prader-Willi syndrome.
- To assess the diagnostic potential of these biomarkers using logistic regression and ROC curve analysis.
- To explore the association of identified biomarkers with metabolic parameters and pathways.
Main Methods:
- Proteome profiling using Olink Target 96 metabolism and cardiometabolic panels in 29 PWS individuals.
- Correlation analysis between protein biomarkers and clinical variables.
- Logistic regression and ROC curve analysis for biomarker diagnostic accuracy.
Main Results:
- Differential expression of 15 proteins (e.g., CDH2, CTSO, QDPR, CANT1, ALDH1A1, TYMP, FBP1, CES1) was observed in PWS-associated liver steatosis.
- A logistic regression model combining QDPR, CANT1, TYMP, THOP1, and ALDH1A achieved high diagnostic accuracy (AUC=0.93, sensitivity=93%, specificity=80%).
- Biomarkers correlated with metabolic indicators (cholesterol, LDL, triglycerides, HbA1c) and showed involvement in seven metabolic pathways.
Conclusions:
- Novel protein biomarkers can accurately detect liver steatosis in Prader-Willi syndrome.
- These biomarkers facilitate patient stratification and personalized therapeutic strategies.
- The findings contribute to better management of metabolic complications in PWS.
Introduction:
Prader-Willi syndrome (PWS) is a rare genetic disorder characterized by loss of expression of paternal chromosome 15q11.2-q13 genes. Individuals with PWS exhibit unique physical, endocrine, and metabolic traits associated with severe obesity. Identifying liver steatosis in PWS is challenging, despite its lower prevalence compared to non-syndromic obesity. Reliable biomarkers are crucial for the early detection and management of this condition associated with the complex metabolic profile and cardiovascular risks in PWS.
Methods:
Circulating proteome profiling was conducted in 29 individuals with PWS (15 with steatosis, 14 without) using the Olink Target 96 metabolism and cardiometabolic panels. Correlation analysis was performed to identify the association between protein biomarkes and clinical variables, while the gene enrichment analysis was conducted to identify pathways linked to deregulated proteins. Receiver operating characteristic (ROC) curves assessed the discriminatory power of circulating protein while a logistic regression model evaluated the potential of a combination of protein biomarkers.
Results:
CDH2, CTSO, QDPR, CANT1, ALDH1A1, TYMP, ADGRE, KYAT1, MCFD, SEMA3F, THOP1, TXND5, SSC4D, FBP1, and CES1 exhibited a significant differential expression in liver steatosis, with a progressive increase from grade 1 to grade 3. FBP1, CES1, and QDPR showed predominant liver expression. The logistic regression model, -34.19 + 0.85 * QDPR*QDPR + 0.75 * CANT1*TYMP - 0.46 * THOP1*ALDH1A, achieved an AUC of 0.93 (95% CI: 0.63-0.99), with a sensitivity of 93% and specificity of 80% for detecting steatosis in individuals with PWS. These biomarkers showed strong correlations among themselves and were involved in an interconnected network of 62 nodes, related to seven metabolic pathways. They were also significantly associated with cholesterol, LDL, triglycerides, transaminases, HbA1c, FLI, APRI, and HOMA, and showed a negative correlation with HDL levels.
Conclusion:
The biomarkers identified in this study offer the potential for improved patient stratification and personalized therapeutic protocols.
