Study participants and research design
This retrospective case-control study included 46 pregnant women with signs of preterm labour who underwent regular prenatal care at jiangxi Maternal and Child Health Hospital between January 1, 2021, and December 31, 2022. Participants were divided into a preterm birth group (less than 37 weeks of gestation) and a control group (37 weeks or more) based on gestational age at delivery. Inclusion criteria were singleton pregnancies between 28 and 37 weeks of gestation with either regular (at least four contractions in 20 min or eight in 60 min with cervical changes) or irregular uterine contractions, as irregular contractions may progress to preterm birth. Exclusion criteria included multiple pregnancies, gestational hypertension, chronic hypertension, gestational diabetes, fetal growth restriction, autoimmune diseases, and incomplete medical records.
Peripheral venous blood (5 mL) was collected before any clinical intervention, centrifuged at 3000 rpm for 10 min, and the upper serum layer was stored at − 80℃. The study was conducted by the Declaration of Helsinki and was approved by the Institutional Review Board of Jiangxi Maternal and Child Health Hospital (Ethics Approval No. EC-KT-202207). Written informed consent was obtained from all participants. Obstetric diagnoses were independently reviewed and confirmed by two senior obstetricians.
Clinical data collected included maternal age, body mass index, gravidity, parity, gestational age at sampling, interval as last delivery, and cervical length measured via transvaginal ultrasound. Laboratory data included complete blood count (white blood cells, red blood cells, platelets, neutrophil count and percentage, lymphocyte count and percentage, and red cell distribution width standard deviation), liver function tests (alanine aminotransferase, aspartate aminotransferase, total protein, albumin, alkaline phosphatase, and lactate dehydrogenase), and renal function indicators (creatinine and urea).
Reproductive tract health was evaluated through vaginal discharge tests and vaginal microbiota analysis.These assessments are crucial indicators of female reproductive health, reflecting the microbial balance of the vaginal environment, including bacterial species, quantity, pH, and cellular composition. Vaginal cleanliness is classified into four grades, which are directly related to the risk of gynecological diseases and are valuable for clinical diagnosis and treatment [20].
Blood samples were analyzed using the Sysmex-XN-2000 automated hematology analyzer (Sysmex Europe, Germany). liver and kidney function were assessed by radioimmunoassay on the AU5800 automated biochemical analyzer (Beckman Coulter,USA) to exclude the influence of systemic disease on metabolic outcomes. Vaginal tests were conducted using the LTS-V400 automated vaginal infection analyzer (Guokang, Shandong, China) with Swiss staining and combined morphological and dry chemical methods.
Neonatal outcomes, including birth weight, Apgar score, and gestational age, were recorded by two experienced neonatologists (Table 1). The study design and grouping are shown in Fig. 1.Table 1 Comparison of clinical characteristics between two groups of study subjects
Fig.1
Metabolomic Analysis of Serum Samples to Identify Biomarkers for Preterm Birth
This study details a metabolomic approach using liquid chromatography-mass spectrometry (LC-MS) to identify potential biomarkers for preterm birth.The analysis focused on serum samples, employing rigorous quality control and statistical methods to ensure data reliability and identify notable metabolic differences between preterm and full-term birth groups.
Methods
Sample Readiness: Serum samples were prepared for analysis following a standardized protocol,including protein precipitation to remove interfering substances. A pooled quality control (QC) sample, created by combining equal volumes of all serum samples, was included throughout the LC-MS run to monitor instrument stability and data quality. Blank samples were also utilized to assess background noise.
LC-MS Analysis: Samples were analyzed using a Triple TOF 6600 mass spectrometer (SCIEX, USA) coupled with liquid chromatography. Both positive and negative electrospray ionization (ESI) modes were employed to maximize metabolite detection. The ESI source parameters were optimized as follows: Gas1: 60, Gas2: 60, Curtain Gas (CUR): 30 psi, Ion Source Temperature: 600 °C, Ion Spray Voltage (ISVF): ±5500 V. The mass spectrometer scanned across a range of 60-1000 Da for the first scan and 25-1000 Da for the second. Accumulation times were 0.20 s/spectrum for the first scan and 0.05 s/spectrum for the second. Data-dependent acquisition (IDA) was used for the second scan, with declustering potential (DP) set at ±60 V, collision energy at 35 ±15 eV, a dynamic exclusion range of 4 Da, and acquisition of 10 fragment spectra per scan. The instrument’s performance was continuously monitored via the QC sample queue to assess system stability and experimental data reliability.
Data Processing and statistical Analysis: raw data files were converted to .mzXML format using ProteoWizard, an open-source software tool for mass spectrometry data conversion. Peak detection, retention time alignment, and peak area extraction were performed using XCMS, a widely used software package for LC-MS metabolomics data analysis.
To ensure data quality, several steps were implemented. Instrumental stability was assessed by monitoring the consistency of retention times and peak areas of representative ions in the QC samples. Metabolic features exhibiting a relative standard deviation (RSD) greater than 30% in the QC samples were excluded from further analysis. Signal drift was corrected using a quality control-based robust LOESS signal correction algorithm (QC-RLSC) to enhance data reliability.
Statistical analysis was performed using SIMCA software version 14.1 (Umetrics, Sweden). Orthogonal Partial Least Squares discriminant Analysis (OPLS-DA) was used to identify metabolic differences between the preterm and full-term groups,and model reliability was validated using 100 permutation tests. Variable Importance in Projection (VIP) scores were calculated to identify the most influential metabolites. Differences in metabolite intensities were assessed using the Mann-Whitney U test, and linear regression was applied to analyze associations between compounds, both conducted in IBM SPSS Statistics (version 21).
Metabolite Identification and Pathway Analysis: Differential metabolites were selected based on VIP > 1.0 and p < 0.05.Correlation and clustering analysis were used to evaluate metabolite relationships and grouping. Identified differential metabolites were subjected to Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis to understand their biological importance.