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Advances in prognostic biomarkers for biliary atresia: Current insights and future directions
Ahmad Anouti1, Pavithra Sudhakara1,2, Chelsea Pratt1,2
1Department of Pediatrics, Children's Medical Center Dallas, University of Texas Southwestern Medical Center, Dallas, Texas, USA.
Insights
Prognostic biomarkers for biliary atresia (BA) are crucial for predicting outcomes. Current methods lack precision, but novel markers and models show promise for personalized treatment strategies in infants.
Area of Science:
- Pediatric Hepatology
- Gastroenterology
- Surgical Innovation
Background:
- Biliary atresia (BA) is a severe infant liver disease causing bile duct obstruction.
- Timely surgery (hepatoportoenterostomy - HPE) is vital, but predicting patient outcomes is difficult.
- Heterogeneous responses to HPE necessitate better prognostic tools.
Purpose of the Study:
- To review and synthesize current research on prognostic biomarkers for biliary atresia.
- To evaluate the potential of various biomarkers in predicting BA progression and treatment response.
- To identify gaps and future directions for improving prognostication in BA.
Main Methods:
- Comprehensive literature review of prognostic biomarkers in BA.
- Analysis of clinical indicators, laboratory tests, and histopathology.
- Evaluation of novel biomarkers including serum markers, gene expression, and imaging (e.g., ultrasound elastography).
- Assessment of in vitro and computational prognostic models.
Main Results:
- Traditional factors (age at HPE, bilirubin levels) have limited predictive power.
- Emerging biomarkers (cytokines, fibrosis markers) show potential for correlating with disease severity and survival.
- Advanced techniques like gene expression signatures and ultrasound elastography offer new prognostic avenues.
- Most novel biomarkers require further validation in large-scale studies.
Conclusions:
- Accurate prognostication in BA remains challenging despite advances in biomarker research.
- Novel biomarkers and integrated models hold promise for personalized risk stratification and treatment.
- Large-scale, multicenter validation studies are essential to translate research findings into clinical practice for biliary atresia.
Abstract:
Biliary atresia (BA) is a progressive, fibrosing cholangiopathy of infancy characterized by inflammatory obstruction of the bile ducts, ultimately leading to end-stage liver disease if untreated. Early diagnosis and timely surgical intervention via hepatoportoenterostomy (HPE) are critical for improving outcomes; however, prognostication remains challenging due to heterogeneous responses to surgery and variable clinical trajectories. This review provides a comprehensive synthesis of current research on prognostic biomarkers in BA, encompassing clinical indicators, routine laboratory parameters, novel serum biomarkers, histopathologic features, hepatic gene expression profiles, imaging modalities, and both in vitro and computational prognostic modeling systems. While traditional clinical factors, such as age at HPE and postoperative serum bilirubin levels, continue to serve as important predictors of outcome, they lack sufficient discriminatory power for individualized risk stratification. Recent advances have identified emerging biomarkers, including inflammatory cytokines, immune activation markers, and indicators of fibrosis and extracellular matrix remodeling, which show potential in correlating with disease progression and native liver survival. Imaging modalities such as ultrasound elastography have also demonstrated promise in noninvasively assessing liver stiffness and predicting clinical outcomes. Furthermore, the identification of hepatic gene expression signatures and multigene prognostic classifiers offers new avenues for precision risk assessment. However, most of these advancements have not translated into clinical practice due to small sample sizes and limited external validation. Future research efforts must focus on large-scale, multicenter studies to validate findings and establish robust, integrative prognostic models that can inform clinical decision-making and facilitate personalized therapeutic strategies in BA.
