Lymph-interpositional flap transfer promotes lymphatic flow restoration: A rabbit hindlimb model supports the lymph axiality concept

초록

Background: Lymph-interpositional flap transfer (LIFT) is a novel lymphatic reconstruction technique that maintains alignment between donor and recipient lymphatic vessels-termed lymph axiality-without requiring lymph node harvest. Despite its conceptual rationale and growing clinical application, experimental validation remains limited. We aimed to evaluate the mechanistic impact of lymphatic interposition on lymphatic regeneration using a rabbit hindlimb model. Methods: A rabbit hindlimb model was developed. Limbs were assigned to either a LIFT (+) group (lymph axiality maintained via lymphatic interposition) or a LIFT (-) group (lymph axiality disrupted via rotational misalignment). After 6 weeks, outcome measures included limb volume assessment, indocyanine green (ICG) lymphography, histological analysis, and reverse transcription polymerase chain reaction (RT-PCR) analysis of lymphangiogenic and fibrotic markers. Results: The LIFT (+) group exhibited significantly reduced hindlimb volume at postoperative week 3 compared to the LIFT (-) group (p = 0.02), although no significant difference was observed at week 6. ICG lymphography revealed a significantly higher rate of lymphatic reconnection in the LIFT (+) group (p = 0.021). Histology confirmed the presence of lymphatics within the wound area and immunohistochemical staining to validate their lymphatic identity. RT-PCR demonstrated significantly elevated VEGF-C, CD31 and podoplanin expression (p < 0.05), along with reduced TGF-beta levels in adjacent popliteal lymph nodes (p < 0.05). Conclusions: The LIFT procedure was associated with enhanced indicators of lymphatic restoration. Although preliminary, this first experimental evaluation of LIFT offers proof-of-concept evidence supporting lymphatic interposition as a key element of the lymph axiality concept. (c) 2026 British Association of Plastic, Reconstructive and Aesthetic Surgeons. Published by Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

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