Abstract / Summary
Abstract Purpose Despite technical advances, the expansion of minimally invasive approaches and the development of novel biomaterials, recurrence and complications following abdominal wall hernia (AWH) repair remain frequent. This variability likely reflects anatomical and technical factors and host biological heterogeneity. Precision herniology is emerging as a translational paradigm integrating biomarkers, genetics, quantitative imaging, and artificial intelligence to refine risk stratification and therapeutic planning. Methods We conducted a critical narrative review with translational scope. PubMed/MEDLINE, Scopus, Web of Science, and Embase were searched from January 2015 to June 2026. We prioritized adult studies across four axes: (i) biomarkers of collagen, extracellular matrix, and cellular mechanisms; (ii) genetics and susceptibility to AWH; (iii) immunonutritional markers; (iv) artificial intelligence (AI) and machine learning (ML) for predicting complexity and complications. Results The literature reveals convergent, albeit heterogeneous, signals indicating that host biology may contribute to variability in hernia phenotype and surgical outcomes. Alterations in collagen turnover and in the MMP-TIMP axis, fibroblast heterogeneity, inflammatory signaling, polygenic architecture related to connective tissue integrity, immunonutritional markers associated with perioperative vulnerability, and quantitative imaging metrics analyzed by AI/ML models represent biologically plausible, clinically relevant domains. Conclusion Available data support the plausibility that host biological heterogeneity contributes to the formation, healing, prosthetic integration, and recurrence of AWH, thereby broadening the traditional paradigm centered predominantly on defect anatomy. The main value of this work is the critical integration of molecular and cellular biomarkers, genetics, quantitative imaging, and AI/ML into a unified conceptual framework for future precision herniology research.