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Introduction <p>Reflux esophagitis (RE) is a common upper gastrointestinal disorder, and its diagnosis currently relies primarily on invasive endoscopic examination. The lack of reliable non-invasive biomarkers substantially limits early detection and large-scale screening. Saliva represents a promising biofluid for metabolomics research, as it can reflect metabolic alterations associated with upper gastrointestinal pathology.</p> Objectives <p>This study aimed to identify potential salivary lipid biomarkers associated with RE, and to develop a non-invasive diagnostic model using metabolomics and lipidomics.</p> Methods <p>Saliva samples from patients clinically diagnosed with RE and healthy controls were analyzed. The analysis included a discovery cohort (<i>n</i> = 144) and an independent validation cohort (<i>n</i> = 146). Differential metabolites were screened using the untargeted metabolomics approach of ultra-high performance liquid chromatography-mass spectrometry (UHPLC-MS), and then quantitative verification was conducted using targeted lipidomics. Multivariate statistical analysis, random forest algorithms, and receiver operating characteristic (ROC) analysis were applied.</p> Results <p>Untargeted metabolomics revealed significant metabolic differences between RE patients and healthy controls, with marked enrichment of sphingolipid and glycerophospholipid metabolism. Targeted lipidomics identified six consistently dysregulated salivary lipids: DAG (18:1_18:2), S-1-P, PE (P-16:0_18:1), DAG (16:0_18:2), DAG (18:1_18:1), and DAG (16:0_18:1). A multimetabolite model based on these lipids effectively distinguished RE patients from healthy controls, achieving an AUC of 99.45% in the discovery cohort and 97.17% in the validation cohort.</p> Conclusion <p>This study identified a salivary lipid signature associated with RE and supports the potential of this lipidomic approach as a non-invasive method to distinguish RE from healthy controls.</p>

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Identification of salivary lipid biomarkers for noninvasive diagnosis of reflux esophagitis via UHPLC-MS-based lipidomics

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月24日

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  • Ying Wang,
  • Ying Zhou,
  • Jian Gao,
  • Lin Zhang,
  • Jing Liu,
  • Lele Zhang,
  • Tongcan Cui,
  • Jingran Zhang,
  • Xiangqin Ou,
  • Xiumei Gao,
  • Lifeng Han,
  • Hai Li

摘要

Introduction

Reflux esophagitis (RE) is a common upper gastrointestinal disorder, and its diagnosis currently relies primarily on invasive endoscopic examination. The lack of reliable non-invasive biomarkers substantially limits early detection and large-scale screening. Saliva represents a promising biofluid for metabolomics research, as it can reflect metabolic alterations associated with upper gastrointestinal pathology.

Objectives

This study aimed to identify potential salivary lipid biomarkers associated with RE, and to develop a non-invasive diagnostic model using metabolomics and lipidomics.

Methods

Saliva samples from patients clinically diagnosed with RE and healthy controls were analyzed. The analysis included a discovery cohort (n = 144) and an independent validation cohort (n = 146). Differential metabolites were screened using the untargeted metabolomics approach of ultra-high performance liquid chromatography-mass spectrometry (UHPLC-MS), and then quantitative verification was conducted using targeted lipidomics. Multivariate statistical analysis, random forest algorithms, and receiver operating characteristic (ROC) analysis were applied.

Results

Untargeted metabolomics revealed significant metabolic differences between RE patients and healthy controls, with marked enrichment of sphingolipid and glycerophospholipid metabolism. Targeted lipidomics identified six consistently dysregulated salivary lipids: DAG (18:1_18:2), S-1-P, PE (P-16:0_18:1), DAG (16:0_18:2), DAG (18:1_18:1), and DAG (16:0_18:1). A multimetabolite model based on these lipids effectively distinguished RE patients from healthy controls, achieving an AUC of 99.45% in the discovery cohort and 97.17% in the validation cohort.

Conclusion

This study identified a salivary lipid signature associated with RE and supports the potential of this lipidomic approach as a non-invasive method to distinguish RE from healthy controls.