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World J Gastroenterol. Jun 28, 2026; 32(24): 116779
Published online Jun 28, 2026. doi: 10.3748/wjg.v32.i24.116779
Published online Jun 28, 2026. doi: 10.3748/wjg.v32.i24.116779
Letter to the Editor: Decoding pediatric-adult divergence in inflammatory bowel disease via multi-omics integration
Peng Zhang, Shang-Ming Liu, Medical Basic Experiment Teaching Center, School of Basic Medical Sciences, Shandong University, Jinan 250012, Shandong Province, China
Co-corresponding authors: Peng Zhang and Shang-Ming Liu.
Author contributions: Zhang P wrote the original draft; Liu SM contributed to conceptualization, writing, reviewing, and editing; Zhang P and Liu SM participated in drafting the manuscript; and all authors have read and approved the final version of the manuscript.
AI contribution statement: The authors used ChatGPT (OpenAI) during the preparation of the manuscript for language editing and for formatting. After using this tool, the authors reviewed and edited the content as needed and take full responsibility for the content of the published article.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
Corresponding author: Peng Zhang, PhD, Medical Basic Experiment Teaching Center, School of Basic Medical Sciences, Shandong University, No. 44 Wenhua Xi Road, Lixia District, Jinan 250012, Shandong Province, China. zhangpeng1990@sdu.edu.cn
Received: November 24, 2025
Revised: December 14, 2025
Accepted: January 13, 2026
Published online: June 28, 2026
Processing time: 197 Days and 8.5 Hours
Revised: December 14, 2025
Accepted: January 13, 2026
Published online: June 28, 2026
Processing time: 197 Days and 8.5 Hours
Core Tip
Core Tip: This multi-omics investigation delineates distinct age-specific microbial and metabolic signatures differentiating pediatric and adult inflammatory bowel disease, identifying biomarkers for early diagnosis and risk stratification. While demonstrating promising potential for refining inflammatory bowel disease diagnostics, these findings require further validation and mechanistic exploration to facilitate clinical translation. The integration of metabolomic and metagenomic methodologies provides a robust framework for elucidating pathophysiological divergence across age groups, providing a foundation for personalized management strategies.