Introduction
Upper gastrointestinal bleeding (UGIB) remains one of the major emergencies. However, predicting the presence and degree of UGIB before endoscopy is still inaccurate. Therefore, the development of an accurate and non-invasive method is required. The purpose of this study was to discover potential metabolic biomarkers for UGIB using an in vitro digestion model, liquid chromatography-mass spectrometry (LC-MS) and gas chromatography-mass spectrometry (GC-MS).
Aims & Methods
Blood samples from 30 healthy volunteers were collected. To simulate UGIB , blood samples underwent either simulated gastric phase in vitro digestion (Blood + simulated gastric fluid (SGF)) or both simulated gastric and intestinal phase (Blood + SGF + simulated intestinal fluid (SIF)). Using untargeted metabolomics approaches with LC-MS and GC-MS metabolomic profiles and volatile organic compounds (VOC) profiles of digested blood were analyzed. Compounds that distinguish between undigested blood and digested blood were sorted out to screen potential biomarkers for UGIB.
Results
From the LC-MS analysis of the undigested blood (Control) and digested blood; blood that was digested with SGF (Blood + SGF) and blood that was digested with both SIF and SGF (Blood + SGF + SIF), a total of 47 metabolites were identified. The levels of 42 (7 from Blood + SGF and 37 from Blood + SGF + SIF) metabolites had significantly higher levels (p < 0.05, Log2(FC) > 2) in the digested blood as compared to the Control.
By analyzing extracted VOCs respectively in Control groups and metabolized samples(Blood+SGF and Blood + SGF + SIF) through GC-MS, 16 compounds were identified as statistically significant. Among them, three compounds appeared at higher levels in the Blood + SGF than the Control(p<0.05, Log2(FC) > 2). In addition, three compounds showed higher levels in the Blood + SGF + SIF than the Control.
Conclusion
With metabolomic approaches, we have detected 63 statistically significant compounds (47 compounds by LC-MS, 16 compounds by GC-MS). Specific features with the considerable possibilities to be biomarkers for UGIB were found. Through in-depth understanding of metabolic pathways and further investigation in clinical settings, discovered features could be utilized to diagnose UGIB in an accurate and non-invasive manner.
References
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Disclosure
The study was supporeted by the National Research Foundation of Korea(NRF) grant funded by Ministry of Science and ICT(MSIT) (No. RS-2022-00167020).
