Serum metabolites as diagnostic biomarkers for preterm labor: a metabolomics-based study | BMC Pregnancy and Childbirth

Study participants and research design

This retrospective case–control study included 46 pregnant women with signs of preterm labor 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 since 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 important 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.

Metabolite extraction and UHPLC-MS analysis

After slowly thawing the samples at 4 °C, an appropriate amount of sample was added to a pre-cooled mixture of methanol/acetonitrile/water (2:2:1, v/v), followed by vortex mixing, low-temperature sonication for 30 min, standing at −20 °C for 10 min, and centrifugation at 14000 g at 4 °C for 20 min. The supernatant was then vacuum-dried and reconstituted with 100 μL of acetonitrile–water solution (acetonitrile: water = 1:1, v/v) for mass spectrometry analysis. After vortexing and centrifugation at 14000 g at 4 °C for 15 min, the supernatant was used for injection analysis.

An Agilent 1290 Infinity LC ultra-high performance liquid chromatography system (UHPLC, Thermo Fisher Scientific, USA) with a HILIC column was used for metabolite separation. The column temperature was maintained at 25 °C, with a flow rate of 0.5 mL/min and an injection volume of 2 μL. The mobile phase composition included A: water + 25 mM ammonium acetate + 25 mM ammonium hydroxide and B: acetonitrile. The gradient elution program was as follows: 0–0.5 min, 95% B; 0.5–7 min, B linearly decreased from 95 to 65%; 7–8 min, B linearly decreased from 65 to 40%; 8–9 min, B was maintained at 40%; 9–9.1 min, B linearly increased from 40 to 95%; 9.1–12 min, B was maintained at 95%. Throughout the analysis, samples were kept at 4 °C in the automatic sampler to minimize instrument signal fluctuations. Samples were randomly analyzed in consecutive order to mitigate the impact of instrumental signal fluctuations. Quality control (QC) samples were inserted into the sample queue to monitor and assess system stability and experimental data reliability.

After separation, mass spectrometry was analyzed using a Triple TOF 6600 mass spectrometer (SCIEX, USA) in both positive and negative electrospray ionization (ESI) modes. The ESI source settings included the following parameters: Gas1: 60, Gas2: 60, Curtain Gas (CUR): 30 psi, Ion Source Temperature: 600 °C, Ion Spray Voltage (ISVF): ± 5500 V (for both positive and negative modes). The mass range for the first mass spectrometry scan was 60–1000 Da, and for the second, it was 25–1000 Da. The accumulation time for the first mass spectra scan was 0.20 s/spectrum; for the second, it was 0.05 s/spectrum. The second mass spectra scan was performed using data-dependent acquisition (IDA) mode with peak intensity value filtering, with declustering potential (DP) set at ± 60 V, collision energy at 35 ± 15 eV, and IDA settings included a dynamic exclusion range of 4 Da, acquiring 10 fragment spectra per scan.

Data processing and analysis

Raw data were converted to.mzXML format using ProteoWizard and processed with XCMS software for peak detection, retention time alignment, and peak area extraction. Statistical analysis was performed after metabolite identification, data preprocessing, and quality assessment.

To ensure the stability and reproducibility of LC–MS analysis, all serum samples were pooled in equal volumes to generate quality control (QC) samples. One QC sample was injected after every ten test samples during the LC–MS run. Blank samples were also used to assess background noise. Instrumental stability was monitored by evaluating the consistency of retention times and peak areas of representative ions in the QC samples. Metabolic features with a relative standard deviation (RSD) greater than 30% in QC samples were excluded. Signal drift was corrected using a QC-based robust LOESS signal correction algorithm (QC-RLSC) to improve data reliability.

Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA) was conducted using SIMCA software version 14.1 (Umetrics, Sweden), and 100 permutation tests validated model reliability. Variable Importance in Projection (VIP) scores were calculated to identify influential variables. Differences in metabolite intensities were assessed using the Mann–Whitney U test, and linear regression was applied to analyze associations between pairs of compounds, both conducted in IBM SPSS Statistics (version 21, IBM Corp., USA).

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 the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. Finally, ROC curve analysis was performed to assess the diagnostic value of these metabolites in distinguishing between the preterm birth and full-term groups.