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Chronic atrophic gastritis (CAG) is one of the most important pre-cancerous states with a high prevalence. Deciphering its mechanical network is of significant importance for its diagnosis and treatment. The time-series factor associated with CAG progression specially needs to be considered together with its biological condition. In the present work,  1 H NMR-based dynamic metabonomics was firstly performed to analyze the urinary metabolic features of CAG coupled with ANOVA-simultaneous component analysis (ASCA). As results, 4 (alanine, lipids, creatine, and dimethylglycine), 2 (α-ketoglutarate and alanine) and 5 (succinate, α-ketoglutarate, alanine, hippurate, and allantoin) urine metabolites were finally selected as the candidate biomarkers related to phenotype, time, and their interaction, respectively. Mechanistically, the network pharmacology analysis further revealed these metabolites were involved into mitochondrial function, oxidation reduction, cofactor binding, generation of precursor metabolites and energy, nucleotide binging, coenzyme metabolic process, cofactor metabolic process, cellular respiration, and tricarboxylic acid cycle. Especially, mitochondria were the most important targeted organelle referred 30 targeted proteins. The present work provided a novel network pharmacology approach for elucidating the mechanisms underlying the pathogenesis of CAG based on urinary time dependent metabonomics.

Deciphering the Mechanical Network of Chronic Atrophic Gastritis: A Urinary Time-Dependent Metabonomics-Based Network Pharmacology Study