MetaboLights

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the same time, the metabolites were identified by searching database, and the main databases were the HMDB (http://www.hmdb.ca/), Metlin ( https://metlin.scripps.edu/)  and Database . MetaboLightsPublicLiquid Chromatography MS - alternating - reverse-phaseSample separation and detection were accomplished via the Ultra High Performance Liquid Chromatography Orbitrap Exploris 120 mass spectrometry (UHPLC-Q-orbitrap-MS) platform (Thermo Fisher Scientific, San Jose, CA, USA). Separation was achieved via a Waters ACQUITY UPLC HSS T3 column (2.1 × 100 mm, 1.8 μm) provided by Waters Corporation (Ireland) at a temperature of 35 °C, with a constant flow rate of 0.3 mL/min. The mobile phase consisted of a 0.1% aqueous formic acid solution (A) and acetonitrile (B). The following gradient was applied: 5% B (0-1 min), increased linearly to 100% B over 10 min (1-11 min), held at 100% B for 0.5 min (11-11.5 min), returned to 5% B over 0.1 min (11.5-11.6 min), and equilibrated at 5% B for 1.4 min (11.6-13 min). The total run time was 13 min. The injection volume was 2 μL.Identification of Salivary Lipid Biomarkers for Noninvasive Diagnosis of Reflux Esophagitis via UHPLC-MS-based Lipidomics.ying wangTianjin University of Traditional Chinese Medicinesalivamass spectrometry assayThe optimized sample pretreatment process is as follows: Take the saliva sample from the -80℃ refrigerator and place it in a 4℃ environment for thawing. Transfer 200 μL of the thawed saliva sample to a 1.5 mL polypropylene centrifuge tube, add 400 μL of acetonitrile, and perform 5 minutes of vortex mixing. The sample was then centrifuged at 20,817 × g for 20 min at 4 °C to precipitate the proteins. Following centrifugation, the supernatant was carefully transferred to a new vial and dried under a gentle stream of nitrogen gas. The dried residue was reconstituted in 100 μL of 50% (v/v) aqueous methanol by vortexing. Finally, a 50 μL aliquot of the resulting supernatant was injected for UHPLC-MS analysis.Homo sapienshttps://www.ebi.ac.uk/metabolights/MTBLS14198ying wang. Tianjin University of Traditional Chinese Medicine. wangying64190296@163.com.The pretreatment of LC/MS raw data was performed by Progenesis QI (Waters Corporation, Milford, USA) software, and a three-dimensional data matrix in CSV format was exported. The information in this three-dimensional matrix included: sample information, metabolite name and mass spectral response intensity. Internal standard peaks, as well as any known false positive peaks (including noise, column bleed, and derivatized reagent peaks), were removed from the data matrix, deredundant and peak pooled. Treatmentwangying64190296@163.comAll participants collected saliva samples using the 'Saliva Collection System' (Sarstedt, Nümbrecht, Germany). All samples were collected in the morning between 9:00 and 11:00 under fasting conditions. Prior to collection, participants were instructed to refrain from smoking, eating, and drinking for one hour, rinse their mouth once with purified water, and avoid intense physical activity and emotional stress. During collection, participants remained seated in an upright position. The unstimulated 'Saliva Collection' swab was placed under the tongue or inside the oral cavity for 5 min without chewing to obtain at least 1 mL of saliva. The swab was then transferred to the provided collection tube and centrifuged at 3,461 × g for 5 min. During sample collection and processing, samples with visible blood contamination were discarded based on visual inspection. The resulting saliva supernatant was aliquoted into cryovials and stored at −80 °C until analysis.MetabolomicssalivaMetabolomicsThermo Scientific LTQ OrbitrapEsophagitis, Peptictargeted analysisBiomarkersHomo sapienstargeted metabolite profilingAgilent 1290 Infinity UHPLCsalivaMetabolomicsThermo Scientific LTQ OrbitrapEsophagitis, Peptictargeted analysisBiomarkersHomo sapienstargeted metabolite profilingAgilent 1290 Infinity UHPLCThe heated electrospray ionization (HESI) parameters were as follows. For the formal untargeted metabolomics analysis, data from all study samples were acquired in full MS/dd-MS2 mode with positive and negative ion switching, and data from both ionization modes were included in the downstream analysis. The spray voltage was set to 3.5 kV (positive) and -3.0 kV (negative). The full scan range was 100–1000 m/z at a resolution of 60,000. The capillary temperature was 320 °C, and the auxiliary temperature was 350 °C. The normalized collision energy (NCE) was -35 V. Sheath gas (N2) was 35 arbitrary units (Arb), and auxiliary gas (N2) was 10 Arb. falseIdentification of Salivary Lipid Biomarkers for Noninvasive Diagnosis of Reflux Esophagitis via UHPLC-MS-based LipidomicsIntroduction 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.2026-03-312026-03-31MTBLS14198