2026-10-13T22:23:34Z https://www.scidb.cn/oai

10.57760/sciencedb.37534 2026-05-25T18:04:47Z

2026-05-25 A dataset of tear metabolomics for predicting delirium during anesthesia emergence and postoperative period in elderly patients undergoing abdominal surgery (2022-2023) doi:10.57760/sciencedb.37534 en This dataset originates from a clinical observational study investigating the relationship between tear metabolomics and perioperative delirium (including emergence delirium and ward delirium) in elderly patients undergoing abdominal surgery. The study was approved by the Ethics Committee of the Second Affiliated Hospital of Chongqing Medical University (Approval No. 2022-122) and was registered on ClinicalTrials.gov (Registration No. NCT05548153) prior to initiation. Study participants were recruited from elderly patients (age ≥65 years, ASA physical status I-III) undergoing elective abdominal surgery at the Second Affiliated Hospital of Chongqing Medical University between August 2022 and January 2023. Exclusion criteria included severe vision, hearing, or language impairment, or any condition impairing communication; a history of preoperative mental illness or cognitive impairment; a preoperative Mini-Mental State Examination (MMSE) score < 24; long-term use of sedative medication; a history of drug abuse; or a history of alcoholism. Tear samples were collected using Schirmer strips at two time points: after anesthesia induction (coded as sample type A, representing post-induction sample) and before completion of surgical suturing (coded as sample type B, representing pre-emergence sample). Collected tear strips were immediately placed into Eppendorf (EP) tubes and stored at -80°C. Sample preparation involved adding 1000 μL of a methanol : acetonitrile : water (2:2:1, v/v) mixture, followed by vortex homogenization for 30 seconds, ice-cooled ultrasonication for 20 minutes, and stabilization at -40°C for 1 hour. Phase separation was achieved by centrifugation at 12,000 rpm (13,800 ×g, 4°C, 15 minutes), and 800 μL of the supernatant was collected for vacuum drying. The residues were reconstituted in 100 μL of the same solvent system supplemented with isotopic internal standards, followed by vortexing and ice-bath sonication for 10 minutes. After a final centrifugation under the same conditions, 80 μL aliquots were prepared for LC-MS injection. Metabolomics analysis was performed using a Vanquish ultra-high performance liquid chromatography system coupled to an Orbitrap Exploris 120 mass spectrometer (Thermo Fisher Scientific). Data were acquired in both positive ion mode (POS) and negative ion mode (NEG). Instrument control and data acquisition were managed using Xcalibur software (v4.4, Thermo). Raw data were converted to mzXML format using ProteoWizard software (Version 3.0.21229) and organized according to ion mode and processing batch. Data preprocessing was performed using the R packages XCMS (Version 3.2) and CAMERA (Version 3.16), including spectral deconvolution, peak detection, peak alignment, peak matching, quantification, feature annotation, and metabolite cluster merging. Quality control steps included: removal of peaks with a relative standard deviation (RSD) > 30% in quality control (QC) samples; removal of peaks with missing values (intensity = 0) in > 50% of samples; missing value imputation using half of the minimum intensity value; and internal standard normalization. Data processing and statistical analysis were performed using SIMCA 16.0.2 software for principal component analysis (PCA) and orthogonal partial least squares-discriminant analysis (OPLS-DA). In the associated research article, differential metabolites were selected using thresholds of VIP > 1 and a t-test p-value < 0.05. The Results tables provided in this dataset contain raw metabolite detection results that have not been subjected to these statistical filtering thresholds. This dataset includes the following files: NEG folder:mzXML spectral data acquired in negative ion mode. Contains three subfolders: NEG_A (96 files, post-induction samples), NEG_B (97 files, pre-emergence samples), and NEG_QC (21 files, quality control samples). POS folder: mzXML spectral data acquired in positive ion mode. Contains three subfolders: POS_A (96 files, post-induction samples), POS_B (97 files, pre-emergence samples), and POS_QC (21 files, quality control samples).Sample Information Registration Form.xlsx: Sample information registration form. Contains five worksheets detailing the grouping information for all samples (ED group, non-ED group, WD group, non-WD group) and the comparison strategies used in the study. Results _ (ED vs Non-ED).xlsx:Raw metabolite detection results comparing the emergence delirium (ED) group and the non-ED group. Contains three worksheets listing all metabolite information obtained from mass spectrometry analysis for both groups. Fields include MS2 name, MS2 score, confidence level, MS1 name, MS1 ppm, retention time, m/z, acquisition mode, mean relative quantitative values, VIP, P-value, Q-value, and fold change. The metabolites in this table represent raw results and have not been subjected to statistical filtering thresholds (e.g., VIP > 1 and p < 0.05); therefore, they are not considered final differential metabolites.Results _ (WD vs Non-WD).xlsx: Raw metabolite detection results comparing the ward delirium (WD) group and the non-WD group. Contains three worksheets with the same fields as described above. The metabolites in this table similarly represent raw detection results that have not undergone statistical filtering. All mzXML files are in an open format and can be viewed using open-source software such as MSConvert (part of the ProteoWizard suite), OpenMS, or MZmine, all of which are freely available from their official websites. The Excel files can be opened with standard office software (e.g., Microsoft Excel or WPS). The dataset contains no systematic errors; QC samples were used throughout to monitor instrument stability. Data preprocessing and missing value imputation methods are clearly described above. The complete content of this dataset is available for independent metabolite screening, validation, or secondary analysis by other researchers. Delirium following surgical procedure; tear; untargeted metabolite profiling; Biomarkers; Ultra High-performance Liquid Chromatography/Tandem Mass Spectrometry Mingju Xiang He Huang RESTRICTED dataset Science Data Bank