Patients and Information Collection
The research was conducted in the First Affiliated Hospital of Zhengzhou University. We selected 110 patients with severe infection, hospitalized in the general ICU between March and September 2019. This research was approved by the ethics committee of the First Affiliated Hospital of Zhengzhou University. Written informed consent was acquired from all patients or their agents. Patients that met the sepsis 3.0 diagnostic criteria by the Society of Critical Care Medicine (SCCM) and the European Society of Intensive Care Medicine (ESICM) during their first 48-hour stay in ICU were included in our research. The diagnostic criteria of sepsis 3.0 include the following factors: (1) Suspected or confirmed infection diagnosed by clinicians; (2) Evidence of acute organ dysfunction: patients without previous chronic organ dysfunction (assuming the baseline sequential organ failure assessment score (SOFA) is 0) — SOFA ≥ 2; patients with previous chronic organ dysfunction (SOFA should be based on the baseline situation)—SOFA increased ≥ 2. The exclusion standards were as follows: length of stay < 24 h, age < 18 years, terminal malignant tumor, pregnancy, data missing, and failure to obtain informed consent or authorization.
The following clinical and laboratory information were collected within 24 h of sepsis diagnosis: gender, age, smoke, alcohol, SOFA, Glasgow Coma score (GCS), Acute Physiology And Chronic Health Evaluation II score (APACHE II), temperature, respiratory rate, heart rate, blood pressure, blood routine test (red blood cell count, white blood cell count, hemoglobin, platelet count, neutrophil percent, lymphocyte percent, monocyte percent, eosinophil percent, neutrophil count, lymphocyte count, monocyte count, eosinophil count, basophil count, hematocrit, mean corpuscular volume, mean corpuscular hemoglobin, mean corpuscular hemoglobin concentration, red blood cell volume distribution width, mean platelet volume, platelet crit, and platelet volume distribution width), nucleated red blood cell count, nucleated red blood cell percent, glucose, lactic acid dehydrogenase, creatine kinase, creatine kinase isozyme, lactate, C-reactive protein, procalcitonin, α-amylase, lipase, serum lipid parameters (total cholesterol, triglyceride, high-density lipoprotein, low-density lipoprotein), liver function indexes (total protein, albumin, globulin, alanine (Ala) aminotransferase, aspartate aminotransferase, γ-glutamyl transferase, alkaline phosphatase (ALP), serum cholinesterase, total bilirubin, direct bilirubin, indirect bilirubin), renal function indicators (glomerular filtration rate, serum urea, serum creatinine, uric acid, 24h urine volume), coagulation function indexes (prothrombin time, prothrombin activity, international normalized ratio, activated partial thromboplastin time, fibrinogen, thrombin time, D-Dimer, fibrin or fibrinogen degradation products), the application of respiratory support, and pressor drugs.
Sample Collection and Grouping
Plasma samples were obtained from septic patients within 24 h of sepsis diagnosis. A small amount (5 mL) of venous blood from each participant was collected with ethylenediaminetetraacetic acid (EDTA) vacuum anticoagulant tube (purple head cover). After shaking gently, these anticoagulant tubes were stored at 4 ℃ in a refrigerator until sample preparation. A telephone follow-up was conducted in December 2019 to record the 28d-, hospital-, and 90d-prognosis, based on which the sample patients were divided into the 28d-survival group (28dS) and 28d-death group (28dD), hospital-survival group (HOS-survival) and hospital-death group (HOS-death), 90d-survival group (90dS) and 90d-death group (90dD). Additionally, we collected plasma samples of 66 healthy people as the healthy control group.
Instruments and Reagents
Ekspert nanoLC 400 series® liquid chromatography system (SCIEX, USA), a TripleTOF® 6600 mass spectrometer (SCIEX, USA), a high-speed refrigerated centrifuge 5810R (Eppendorf, Germany), and the − 80 ℃ refrigerator (Thermo, USA) were the instrument used for this study. The reagents used included mass spectrometry grade acetonitrile, methanol (Thermo Fisher Scientific, USA), mass spectrometry grade formic acid (Sigma-Aldrich, USA), and purified water (Mass spectrometric grade).
Plasma Samples Pretreatment
After thawing the plasma samples on ice at 4 ℃, 50 µL plasma of each sample was injected into the Eppendorf (EP) tube, followed by the addition of 150 µL methanol and 10 µL internal standard (0.5 µm/L CA-d4, 0.5 µm/L CDCA-d4). Subsequently, the mixtures were vortexed for 30 s and centrifuged at 14,000 rpm for 10 min. The supernatant (150 µL) was transferred into an injection bottle. Finally, 10 µL of each sample was mixed to obtain the quality control (QC) sample; a measured amount of the QC sample (200 µL) was used for metabolomics QC analysis.
Chromatography Conditions
The chromatographic column (ChromXP C18, 3 µm 120 Å, 0.3 mm × 150 mm, SCIEX, USA) was maintained at 30 ℃. The positive-ion mobile phase consisted of water with 0.1% formic acid (phase A) and acetonitrile (phase B); the negative-ion mobile phase comprised water (phase C) and acetonitrile (phase D). The gradient elution method was used for 18 min with a flow rate of 5 µL/min in the positive-ion mode. The linear gradient of elution started at 5% B, increased linearly to 25% in 1 min, ramped up linearly to 95% in the next 9 min, and maintained 95% for 2 min. Subsequently, phase B was recovered to 5% within 1 min and held for another 5 min. The negative-ion mode followed a 15-min gradient elution method at a flow rate of 5 µL/min. Similarly, the linear gradient elution began from 5% B, increased linearly to 30% in 1 min, further increased linearly to 95% in the next 8 min, and then remained constant for 3 min. Afterward, phase B was restored to 5% in 1 min and maintained for 2 min. The injection volume was 2.0 µL.
Mass Spectrometry
Data acquisition and processing were conducted using the Peakview 2.0 software (AB, Milford, MA). Electrospray ionization (ESI) was used as the ion source in this research. The ion source temperature and spray voltage were set at 350 ℃ and 5500 V, respectively, for the positive-ion scanning modes, while they were set at 350 ℃ and − 4500 V, respectively, in the negative-ion scanning mode. The declustering voltage, atomization gas 1, atomization gas 2, and the curtain gas were 80 V, 25 psi, 15 psi, and 30 psi, respectively. A full scan measured the samples with a range of mass spectral (m/z) 50–1000 Da in the positive and negative modes. The collision energy was at 35 ± 15 eV. Additionally, dynamic background subtraction-dependent data acquisition was used to gather the LC/MS data of low-level components.
Data Processing and Statistical Analysis
The preprocessing procedures, including data extraction, retention-time correction, peak recognition, peak extraction, peak integration, and peak alignment, were performed using the Software MarkerView TM (version 1.4.1, Waters Co., Milford, MA, USA). The data matrix comprising mass spectral (m/z), retention time (RT), and peak intensity was generated after total area normalization pretreatment. The orthogonal partial least squares discriminant analysis (OPLS-DA) was used for the multi-dimensional complex data (SIMCA 14.1 software). Further, variable importance in projection (VIP) of the OPLS-DA model was computed to identify differential metabolites that distinguish the survival group from the death group. Metabolites with VIP > 1.0 were selected for statistical analysis using the student’s t-test (SPSS software version 21.0, Chicago, IL). The metabolites with VIP > 1.0 and P < 0.05 were the expected differential markers. Finally, the identification results combined with Kyoto Encyclopedia of Genes and Genomes (KEGG) (http://www.genome.jp/kegg/), Human metabolite database (HMDB) (http://www.hmdb.ca/), MetaboAnalyst 4.0, other databases, and literature reports were used to identify the structural biomarkers. The pathway enrichment analyses were explained by secondary mass spectrometry fragment information, while the R software (R version 3.5.3, heatmap package) was used to generate the heat maps of differential metabolites and display the trend variations.
The Kolmogorov–Smirnova test was employed to analyze the conformity of continuous variables to normal distribution. Basic characteristics (gender, age, smoke, alcohol, and indicators within 24 h after diagnosis) of the survival- and death groups were compared using student’s t-test for normal distribution, the Mann Whitney U test for not conforming to the normal distribution, and the Chi-square test for the classified data. Furthermore, logistic regression prognostic models were constructed using statistically significant variables at 28d-, hospital-, and 90d-prognosis in the survival and death groups. The receiver operating characteristic curves (ROC) were drawn to evaluate the predictive value. All statistical analyses were two-sided tests, and P < 0.05 was considered statistically significant.