Investigating the relationship between intestinal microbiota and Th1/Th2/Th17 imbalance in subclinical hypothyroidism during the first half of pregnancy: a multi-omics approach

Abstract

Background

Gestational subclinical hypothyroidism (SCH), marked by elevated Thyroid-stimulating hormone (TSH) with normal free thyroxine (FT4), links to adverse perinatal outcomes. During early pregnancy (< 20 weeks), maternal thyroid hormones are crucial for fetal neurodevelopment, with deficiencies risking irreversible deficits. SCH pregnancies show gut microbiota alterations and metabolic dysregulation. Emerging evidence suggests these changes may drive Th(helper T cells)1/Th2/Th17 immune imbalance, though mechanisms remain unclear. This study combines metagenomics and lipidomics to investigate gut microbiota-Th1/Th2/Th17 interactions in patients with SCH in the first 20 weeks during pregnancy.

Methods

This study included 20 pregnant women with SCH (SCH group) in the first half of pregnancy (≤ 20 gestational weeks) and 20 normal pregnant women (CON group) in the same period. Collect fecal and blood samples from both groups. Metagenomic sequencing was used to determine the differences in the composition of the intestinal microbiota between the two groups, and non-targeted lipidomics was used to compare the lipid differences between the two groups. Flow cytometry was used to assess Th1, Th2 and Th17 cells in peripheral blood, and a cell microbead array was used to determine cytokine levels.

Results

(1) Metagenomic sequencing showed an increased abundance of Faecalibacterium prausnitzii and a decreased abundance of Bacteroides uniformis in the SCH group. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis indicated significant enrichment in lipid and polysaccharide biosynthesis and mucopolysaccharide biodegradation pathways in the SCH group. (2) Lipidomics identified 692 different lipids, with Triglyceride (TG) being the most significant. KEGG pathway analysis revealed that TG was mainly concentrated in the Th1, Th2, and Th17 cell differentiation pathways. (3) Additionally, serological indicators of the two groups showed that TSH, Interleukin (IL)-2,IL-10, Tumor necrosis factor (TNF)-α, TG, Th1, and Th17 in the SCH group were higher than those in the CON group, while Th2 was significantly lower (P < 0.05).

Conclusion

In the first half of pregnancy, patients with SCH may experience intestinal microbiota disorder, characterized by increased levels of Faecalibacterium prausnitzii and decreased levels of Bacteroides uniformis, at the same time, it was accompanied by an increase in TG synthesis and a Th1/Th2/Th17 imbalance, these factors may be involved in the occurrence of SCH during pregnancy.

Introduction

Subclinical hypothyroidism is a mild metabolic disease defined by normal FT3 and FT4 levels and elevated TSH levels [1]. Recently, the incidence of SCH in pregnancy has been on the rise, ranging from 4.0–17.8% [2]. Hypothyroidism during pregnancy may cause many maternal and fetal complications, such as fetal growth restriction, placental abruption, gestational hypertension, and neurointellectual development disorders in offspring [3–5]. At 10–12 weeks of gestation, the fetus thyroid gland is formed, which has the ability to collect iodine and synthesize thyroid hormones. The high production of thyroid hormones does not begin until the second trimester, however it is not until 18–20 weeks of gestation that the fetal thyroid gland matures and begins to produce enough thyroid hormones. Therefore, in the first half of pregnancy, that is, the first 20 weeks of pregnancy, the thyroid hormone required for fetal development mainly comes from the mother, and if the mother has insufficient thyroid hormone synthesis and secretion during this period, it will cause irreversible damage to the development of the fetal nervous system. Therefore, hypothyroidism in the first half of pregnancy has a profound impact on fetal growth and development [5].

Elevated TSH levels are significantly associated with adverse lipid status and may be a risk factor for dyslipidemia during pregnancy [6]. The intestinal microbiota is considered a main factor regulating the maturation and activity of immune effector cells, as well as maintaining the intestinal barrier [7]. Our previous studies revealed significant differences in intestinal microbiota between pregnant women with hypothyroidism and healthy pregnant women [3]. The correlation between SCH and intestinal microbiota remains limited. Metagenomics enables the sequencing of all microorganisms in a sample to the species or subspecies level, providing insight into the functional properties, metabolic pathways, or interactions with the host of microorganisms. Although liver lipid metabolism dysfunction was found in mice with SCH, the specific mechanism requires further investigation [8]. Lipidomics can study the function of lipids and the dynamic changes and regulation of metabolism, and explore the relationship between abnormal changes in lipid metabolism regulation and the occurrence and development of diseases. Previously, we successfully screened 10 different lipids through non-targeted lipidomics analysis [9].

Hypothyroidism leads to immune system dysfunction [10, 11], and an imbalance of helper T (Th) cells, such as Th1/Th2, leads to immune dysfunction and autoimmune diseases [12]. The increased expression of Th17 cells may promote the infiltration of lymphocytes into thyroid tissue in autoimmune thyroid diseases. Meanwhile, the elevated release of pro-inflammatory factors such as IL-17 leads to immune imbalance, inducing autoimmune thyroiditis [13]. Additionally, intestinal microbiota disorders can lead to various diseases through the imbalance of T cell subsets, leading to autoimmune and inflammatory responses [14, 15]. However, few studies have examined the correlation between the intestinal microbiota and lipidomic characteristics of SCH in the first half of pregnancy and Th1/Th2/Th17.

This study aimed to investigate the relationship between intestinal microbiota characteristics and Th1/Th2/Th17 in SCH in the first half of pregnancy using metagenomic sequencing and lipidomics.

Materials and methods

Participants

Pregnant women who received routine perinatal care in the outpatient Department of the Third Affiliated Hospital of Zhengzhou University from July 2023 to December 2023 were randomly selected. The study included 20 pregnant women with SCH in the first half of pregnancy (SCH group) and 20 healthy pregnant women who met the inclusion criteria (CON group). The studies in this work abide by the Declaration of Helsinki principles.

Inclusion criteria included

(1) TSH exceeding the upper limit of the reference range (TSH > 4.0 mIU/L) based on the 2022 Guidelines for the Diagnosis and Treatment of Thyroid Diseases in Pregnancy and Postpartum (2nd edition) and the reference range standard for hypothyroidism in pregnancy formulated by the Clinical Laboratory of the Third Affiliated Hospital of Zhengzhou University, and (2) Gestational age: ≤ 20 weeks pregnant women.

Exclusion criteria

(1) age < 18 years or > 35 years; (2) presence of other pregnancy complications; (3) use of artificial conception or assisted reproductive technology; (4) severe anxiety and depression; (5) multiple pregnancy (twins or more); (6) history of circulatory or digestive system disease; (7) serious gastrointestinal diseases or have undergone gastrointestinal surgery; (8) long-term use of antibiotics or intestinal microbiota-regulating drugs; (9) use of antidiarrheal drugs, probiotics, and antibacterial drugs in the last 3 months; (10) daily consumption of yogurt or probiotic products; (11) The characteristics of the feces have changed abnormally compared to before and abnormal stool routine examination.

Data collection

Age, body mass index (BMI), gestational age, serum free T4 (FT4), TSH, serum total cholesterol (TC), TG, low-density lipoprotein (LDL), high-density lipoprotein (HDL), hypersensitive C-reactive protein (hsCRP), interleukin-2 (IL-2), interleukin-2 (IL-10),tumor necrosis factor α (TNF-α), fasting blood glucose (GLU), and hemoglobin (HGB) were collected.

Sample collection

Participants were asked to provide 50–100 mg of fresh stool in a 2.0 mL cryogenic tube using a sterile spoon on the day of diagnosis. Specimens were transported to the laboratory on dry ice within 2 h and stored at -80 °C. The samples were sent to BGI (formerly Beijing Genomics Institute; now BGI Group) for processing. Blood samples were collected simultaneously with stool, and all pregnant women fasted for 8–12 h before blood collection. Two 5 mL blood samples were collected from the cubital vein using a sterile syringe and needle and placed into a heparin anticoagulant tube. The blood samples were stored in a 4℃ refrigerator immediately after collection.

Cytokine detection

The levels of IL-2 and TNF-α were measured using a human cytokine kit (Jiangxi Segi Biotechnology Co., LTD.) based on flow fluorescence technology. The kit contains microspheres coated with IL-2 and TNF-α-specific antibodies. The capture microsphere mixture was mixed with 25 µL serum and incubated with 25 µL fluorescent-labeled detection antibodies in darkness at 20–25℃ for 2.5 h. The beads were washed and resuspended in PBS. The samples were analyzed using flow cytometry, and the data were recorded.

Flow cytometry

Th1 and Th2 cells and Th17 cells were quantitatively measured using intracellular cytokine staining. Peripheral blood of pregnant women was collected with heparin sodium anticoagulant tubes and cultured at 37℃ with 5% CO₂ for 4 h, after which cell surface labeled antibodies APC-CY7-CD3 (BD Inc. in the United States) and FITC-CD4 (BD Inc. in the United States) were added. After incubation in the dark for 20 min, red blood cell lysate was added and incubated for 25 min. The mixture was centrifuged at 500 g at 4℃, and the supernatant was discarded. Fixed breaking solution was added and incubated away from light for 30 min. Following surface staining, cells were fixed and permeabilized using for 20 min at 4 °C. Intracellular staining was then performed by incubating cells with the following antibodies for 30 min in the dark: PE-conjugated anti-IFN-γ, PE-Cy7-conjugated anti-IL-4, APC-conjugated anti-IL-17 (All from BD Biosciences, USA). After two washes with permeabilization buffer, cells were resuspended in PBS and analyzed on flow cytometer. Flowjo software (TreeStar, Ashland, OR, USA) was used for data analysis. All antibodies were derived from BDBiosciences (Franklin Lakes, New Jersey, USA).

Metagenomic sequencing and data analysis

We measured nucleic acid concentrations using the Qubit 4.0 fluorometer (ThermoFisher Scientific, Q33238) and Equalbit 1× dsDNA HS Assay Kit (Vazyme Biotech), followed by PCR amplification and agarose gel electrophoresis (Baygene) for product detection. We performed amplification on a PCR instrument based on nucleic acid concentration and target region, and detected the amplified PCR products using agarose gel electrophoresis (Baygene). A 1 µg of genomic DNA was taken, and the Size-selected ~ 300 bp fragments (290–310 bp) was obtained by ultrasound with a Covaris instrument. Sequencing was performed on the DNBSEQ platform (BGI, Shenzhen, China) using combined probe anchor polymerization technology (cPAS), and the raw data in fasta format was obtained. Fastp and MEGAHIT software were used for quality control and assembly. Metagenomic gene prediction was performed with Prodigal v2.6.3. Diamond software was used to annotate gene sets to the KEGG database. The non-redundant gene set was obtained by clustering with CD-HIT software and compared with the NCBI non-redundant (NR) database using BLAST version 2.2.28. Wilcoxon rank sum test and LDA analysis were used to analyze the difference.

Lipidomics sequencing and data analysis

Main Instruments of Lipidomics:

Sample processing utilized: 1. Centrifuge: Eppendorf Centrifuge 5430 (4 °C operation) 0.2. Vortex mixer: Qilin Beier QL-901 (China) 0.3. Ultrapure water: Milli-Q Integral system (Millipore, USA) 0.4. Vacuum concentration: Genevac Maxi Vacbeta. 5. Tissue homogenization: Jingxin JXFSTPRP grinder (Shanghai, China). Chromatographic-grade solvents included: Methanol (Thermo Fisher, #A454-4) ,Acetonitrile (Thermo Fisher, #A998-4) ,Ammonium formate (Honeywell Fluka, #17843250G), Formic acid (DIMKA, #50144-50 ml). Internal standards: 15:0–18:1(d7)PC, 18:1-d7 Lyso PE, 15:0–18:1(d7)PS.

Metabolite extraction: A 25 mg feces sample was weighed and placed in a 2 mL thickened centrifuge tube, and two small magnetic beads were added. Two small steel balls were added, 800 µL of precooled methylene chloride/methanol (3:1, V/V) precipitator was added, and 10 µL of configured internal standard 2 was added to each sample. The sample was ground using a TissueLyser for 5 min, followed by 10 min of ice bath ultrasonication, and stored at -20℃ in the refrigerator overnight. After centrifugation at 25,000 g at 4℃ for 15 min, 600 µL supernatant was collected and freeze-dried. Subsequently, the sample was redissolved in 600 µL lipid complex solution (isopropyl alcohol: acetonitrile: water = 2:1:1), shaken for 10 min, and subjected to ice bath ultrasound for 10 min. After another centrifugation at 25,000 g at 4℃ for 15 min, 20 µL of each sample was combined with QC, samples were transferred to the experimental personnel for immediate processing.

Uplc-ms analysis: Waters UPLC I-Class Plus (Waters, USA) in series Q Exactive high-resolution mass spectrometer (Thermo Fisher Scientific, USA) was used for the separation and detection of metabolites.

Chromatographic conditions: The column used was CSH C18 column (1.7 μm 2.1 × 100 mm, Waters, USA). Primary and secondary mass spectrum data were collected using a QExactive mass spectrometer (Thermo Fisher Scientific, USA).

Software and parameters: After importing the mass spectrum data into LipidSearch v.4.1 (Thermo Fisher Scientific, USA) for analysis, a data matrix containing lipid molecular identification results and quantitative results was obtained, which was further analyzed and processed.

Statistical analysis

All statistical analyses of metagenomic and lipidomic sequencing data were performed in R (version 3. 4. 1). SPSS 26.0 software (IBM Corp. Released 2019. IBM SPSS Statistics for Windows, Version 26.0. Armonk, NY: IBM Corp.) was used for general statistical analysis. Normally distributed data are presented as mean ± standard deviation, and t-test was used for inter-group comparison. Non-normally distributed data are expressed as median and quartile, and the Wilcoxon rank sum test was used for group comparisons.

Results

Comparison of basic data between the SCH group and CON group

This study recruited 20 participants in the SCH and CON groups during the same stage of pregnancy. Table 1 shows the general clinical data of both groups. No differences were observed in age, BMI at enrollment, gestational age at enrollment, GLU, and HGB between the two groups.

Table 1.

Clinical indicators of participants in the two groups

	SCH group	CON group	P-value
age	30.00 ± 4.155	31.85 ± 3.498	0.136
Body mass index(BMI)	24.07 ± 1.87	23.76 ± 341	0.716
gestational age	14.85 ± 6.02	13.10 ± 5.45	0.342
GLU(mmol/L)	4.71 ± 0.76	4.98 ± 1.06	0.392
HGB(g/L)	116.80 ± 8.48	118.50 ± 11.26	0.593

Note: P < 0.05 was statistically significant

Normal distribution measurement data are described using mean ± standard deviation, and group comparisons are conducted using the t-test. For non-normal distribution measurement data, the median and interquartile range are used for representation, and group comparisons are performed using the Wilcoxon rank-sum test

Differences in gut microbiota composition and functional enrichment analysis based on macro factors

Significant differences in gut microbiota α diversity were observed between the two groups (Fig. 1), and the Simpson index and Shannon index in the SCH group were higher than those in the CON group. The JSD distance showed no statistically significant difference in β diversity between the two groups (Fig. 1B) (P < 0.05). PCA and PCoA analyses (Fig. 1C) revealed statistically significant differences between the two groups, indicating an association between intestinal microbiota disturbance and SCH. NMDS analysis (Fig. 1D) showed a separation trend between the two groups, and the Anosim analysis test indicated that the similarity between the groups was smaller than the similarity within the groups, and the difference between the groups was significant (Fig. 1E). At the species level, as shown in the column of species composition (Fig. 1F, G), Faecalibacterial prausnitzii is the dominant strain in the SCH group, and Bacteroides uniformis is the dominant strain in the CON group. LefSe analysis illustrates the phylogenetic distribution of the microbiota, identifying 25 taxa differences between the SCH and CON groups. At the species level, the SCH group had relatively higher abundances of Faecalibacterium prausnitzii, Megamonas funiformis, Akkermansia muciniphila, Roseburia hominis, Dialister massiliensis, and 18 other species than the CON group. Conversely, the CON group had relatively higher abundances of Bacteroides fragilis, Bacteroides uniformis, Bacteroides, Bacteroidaceae, and Bacteroidales (Fig. 1I). The KEGG gene pathway classification annotation shows active metabolism, mainly in global overview maps, carbohydrate metabolism, amino acid metabolism, substance transport, and glycan biosynthesis and metabolism. (Fig. 2A). KEGG function analysis annotated genes into the KEGG database. Significant differences were found, with higher abundances of KO genes in the SCH group for lipopolysaccharide (LPS) biosynthesis and glycosaminoglycan degradation pathway enrichment (Fig. 2B).

Fig. 1.

Species diversity analysis and composition analysis of metagenomic sequencing. A. α diversity analysis: α diversity index box diagram. The difference between the two groups was determined by the Wilcoxon rank sum test. B. β box plot: The distance between any pair of samples in the same group is selected, the box plot is drawn, and the distance difference between different groups is statistically tested. The box plot reflects the dispersion degree of samples in the group. C. Peca diagram, where each sample is represented by a dot, with different colors representing different groups. For each sample, the first two principal coordinates, PC1 and PC2, are described. PC1 explained 20.18% of the variation, and PC2 explained 12.01%. D&E. NMDS diagram, Anoism test: Each sample is represented by a dot, with different colors representing different groups. The test results of NMDS were defined by stress coefficient (stress = 0.1737) and ANOSIM test (R = 0.4332, P = 0.001). F&G. Plot of species level and composition, where the horizontal coordinate is the group, and the vertical coordinate is the relative abundance of species annotated to the corresponding level. Species not annotated at this classification level and whose abundance was less than 0.5% in the sample were combined into others. H. In the left bar chart, the ordinate is the differential species, the abscissa is the average abundance (%) of the group, and the color of the column represents the group. In the scatter plot on the right, the colors of the points represent different significance test results, where the P-value is the statistical test result, and False Discovery Rate (FDR) is the error discovery rate, which is the corrected P-value. The points on the left of the dotted line (P < 0.05) indicate significant differences. I. LEfSe analysis of gut microbiota. LEFSE ring branching map. An evolutionary cladogram is a cyclic graph consisting of multiple rings, with the inner ring being at a high classification level and the outer ring being at a low classification level. Each dot represents a specific species classification, and the size of the dot represents the relative abundance

Fig. 2.

Functional analysis of metagenomic sequencing. A. Genes associated with the KEGG pathway. Each branch represents a level 2 KEGG channel, and a different color represents a different KEGG1 function. Species composition bar chart showing the relative abundance of the top 20 major species in all samples. B. Functional KEGG-pathway enrichment pathway map. The abscissa is the reporter score value, the ordinate is the path, and its color is the group where the enrichment is located. The figure shows only the function categories of reporter scores that exceed the threshold of LEfSe analysis of gut microbiota (after remapping) LEFSE ring branching map. An evolutionary cladogram is a cyclic graph consisting of multiple rings, with the inner ring being at a high classification level and the outer ring being at a low classification level. Each dot represents a specific species classification, and the size of the dot represents the relative abundance

Differences in lipid metabolites and KEGG pathway between the two groups

Based on lipidomics sequencing, 692 differential lipid metabolites were screened out. Compared to the CON group, 16 differential lipid metabolites were significantly up-regulated, and 3 were significantly down-regulated in the SCH group. PCA analysis (Fig. 3A) revealed statistically significant differences between the two groups. The KEGG database was used to analyze the pathway of differential lipid metabolites between the two groups, and the bubble diagram showed significant enrichment of Th1 cells and Th2 cell differentiation pathways (Fig. 3F).

Fig. 3.

Analysis of lipidomics sequencing. A: PCA diagram: PCA analysis model score diagram reflecting the two groups; B: Cumulative species bar chart: reflects the proportion of species between the two groups; C: Linear discriminant analysis (LDA) score map: assessing the impact of significantly different species; D: Z-score map: reflects the difference in sample influence strength between the two groups; E: Volcanic map: red dots are significantly up-regulated lipid metabolites, blue dots are significantly down-regulated, and gray dots indicate no significant change. F: KEGG Bubble map: each key statistic in the enrichment analysis results is shown

Comparison of serological indexes between SCH and CON groups

As shown in Table 2, TSH, apolipoprotein (APO)-A1, IL-2, IL-10, TNF-α, TG, Th1, Th1/Th2, and Th17 in the SCH group were significantly higher than those in the CON group, while FT4, TC, HDL-C, LDL-C, IL-6, and IL-4 did not differ significantly between the groups.

Table 2.

Serological indicators of participants in two groups

	SCH group	CON group	P-value
Thyroid-stimulating hormone(TSH)(mIU/L)	4.14 ± 1.90	2.18 ± 1.54	<0.001
free thyroxine (FT4)(mIU/L)	14.75 ± 2.33	15.29 ± 2.58	0.492
Hypersensitive C-reactive protein(hsCRP)	7.09 ± 7.99	2.87 ± 1.83	0.027
Total cholesterol(TC)(mmol/L)	4.63 ± 0.93	4.60 ± 1.27	0.945
Triglyceride(TG)(mmol/L)	2.64 ± 0.55	1.69 ± 0.70	0.024
High-density lipoprotein cholesterol (HDL-C)(mmol/L)	1.93 ± 0.72	2.10 ± 0.45	0.377
Low-density lipoprotein cholesterol (LDL-C)(mmol/L)	2.96 ± 0.70	2.70 ± 0.61	0.209
Interleukin-2(IL-2)	1.21 ± 1.49	0.45 ± 0.71	0.047
Interleukin-10(IL-10)	2.13 ± 2.18	0.91 ± 1.15	0.034
Interleukin-6(IL-6)	3.50 ± 3.98	1.61 ± 1.93	0.064
Interleukin-4(IL-4)	2.26 ± 3.10	1.10 ± 1.01	0.119
Tumor necrosis factor-alpha (TNF-a)	1.75 ± 1.65	0.44 ± 0.50	0.002
Helper T lymphocyte − 1(Th1)	33.85 ± 8.44	14.37 ± 12.03	<0.001
Helper T lymphocyte − 2(Th2)	0.85 ± 0.33	1.58 ± 1.36	0.03
Helper T lymphocyte − 17(Th17)	5.74 ± 2.31	3.03 ± 1.19	<0.001
Th1/Th2	45.27 ± 18.16	11.59 ± 7.62	<0.001
Th1/Th17	6.83 ± 3.21	5.73 ± 5.75	0.464

Note: P < 0.05 was statistically significant

Normal distribution measurement data are described using mean ± standard deviation, and group comparisons are conducted using the t-test. For non-normal distribution measurement data, the median and interquartile range are used for representation, and group comparisons are performed using the Wilcoxon rank-sum test

Comparison of serological indicators between the hypothyroidism and normal groups

Flow cytometry was used to detect Th1, Th2, and Th17 cells in peripheral blood from pregnant women in both groups, and the gating strategy was shown in Fig. 4. Lymphocytes expressing both CD3 and CD4 were gated as CD4 + T cells. We quantified Th1, Th2, and Th17 cells by measuring IFN-γ, IL-4, and IL-17 expression levels. The cytokine levels were determined using the cell microbead array method. As shown in Table 2, TSH, hsCRP, TNF-α, Th1, and Th17 in the SCH group were significantly higher than those in the normal group, while FT4 and Th2 were lower (P < 0.05). No other statistically significant difference was observed between the other two groups.

Fig. 4.

Flow cytometry detected Th1, Th2, and Th17 cells. (P < 0.05 was statistically significant) (A) Gating strategy of flow cytometry; (B) Percentage of Th1 cells; (C) Percentage of Th2 cells; (D) Percentage of Th17 cells

Correlation between different strains of SCH group and different lipid metabolites and serological indexes

Spearman correlation analysis showed that Faecalibacteria prausnitzii was positively correlated with TG, TSH, APO-A1, Th1/Th2, and Th17. Additionally, Bacteroides uniformis was negatively correlated with TG, TSH, TNFa, APO-A1, and Th1/Th2. TG showed a significantly positive correlation with TSH, TNF- α, APO-A1, and Th17.

Discussion

The incidence of SCH in pregnancy is increasing. During the first half of pregnancy (i.e., ≤ 20 weeks of gestation), thyroid hormones essential for fetal brain development are mainly derived from the mother [2, 4]. Recently, intestinal micromicrobiota has become a major research focus, with disturbance in intestinal micromicrobiota linked to the imbalance of thyroid homeostasis [16]. Dysregulation of lipid metabolism in pregnant women leads to vascular endothelial damage, impaired energy supply, and imbalanced immune and thyroid functions [17]. However, the pathogenesis of SCH in pregnancy is still unclear. Therefore, this study aimed to use metagenomics and lipidomics to explore the correlation between intestinal microbiota characteristics and Th1/Th2/Th17 balance in SCH in the first half of pregnancy.

This study found no difference in age, BMI, or other general conditions between the SCH and CON groups. Metagenomic sequencing results revealed a higher abundance of Faecalibacterium prausnitzii and a lower abundance of Bacteroides uniformis in the SCH group. Yang et al. used 16srRNA sequencing and found that the abundance of Faecalibacterium in the gut of pregnant women with SCH in the second trimester was upregulated, and Bacteroides was downregulated [18, 19], which is consistent with our results. The increase in the abundance of Faecalibacterium prausnitzii leads to an accelerated degradation of chondroitin sulfate, resulting in the production of more galactosamine [20]. Galactosamine enrichment can enhance the effect of 2,4,6-trinitrobenzene sulfonic acid (TNBS) activation on the pro-inflammatory cytokine IL-1β [21]. IL-1β can damage the intestinal barrier, leading to an increased absorption of LPS by the human body [22]. A decrease in the abundance of Bacteroides uniformis reduces the short-chain fatty acids produced by the metabolic pathway of linoleic acid, impairs the intestinal epithelial barrier [23], and promotes LPS entry into the blood circulation of the body [24]. LPS stimulates thyroid follicular cells to express the toll-like receptor 4 pathway, inhibits the immune response pathway of Th cells, and promotes autoimmune inflammation of the thyroid [25]. KEGG analysis revealed significant enrichment of lipid and polysaccharide biosynthesis and mucopolysaccharide biodegradation pathways in the SCH group. We hypothesized that an increase in Faecalibacterium prausnitzii abundance and a decrease in Bacteroides uniformis abundance may contribute to SCH as follows: an increase in Faecalibacterium prausnitzii can enhance the absorption of LPS by the human body, leading to elevated lipid A and pro-inflammatory cytokines. These changes weaken the intestinal barrier function [26], inducing thyroiditis and other opportunistic infections. LPS can induce autoimmune thyroid inflammation by increasing the expression of thyroglobulin and sodium/iodine isotransporter in response to TSH stimulation [27]. A decreased abundance of Bacteroides uniformis can reduce SAP-2 levels, weaken the breakdown of O-antigenic glycogen, and promote increased LPS synthesis [28]. Through the mitogen-activated protein kinase p38 signaling pathway, mononuclear macrophages can be stimulated to secrete TNF-α and play a pro-inflammatory role [29]. Therefore, we propose that both strains are involved in the occurrence of SCH.

In this study, non-targeted lipidomics analysis found that TG was significantly increased in the SCH group. Similarly, a study on patients with SCH found that TC, LDL-C, and TG were increased, while HDL-C was decreased [30]. These findings align with our results. Animal experiments have shown that TG can stimulate CD4 + T cell proliferation, IL-17 and IFN-γ expression, and Th1 and Th17 cell proliferation and differentiation, leading to immune imbalance [31]. KEGG analysis showed that Th1 and Th2 cell differentiation pathways and Th17 cell differentiation pathways were significantly enriched in the SCH group. We speculate that elevated TG levels may induce increased expression of T-cell transcription factor (T-BET), which regulates Th1 cell lineage setting [32], and make initial CD4 + T cells highly express and differentiate into Th1 cells [33], resulting in Th1/Th2 imbalance and rapid destruction of thyroid follicular cells, leading to hypothyroidism [34]. TG is involved in the inflammatory response of macrophages, promoting the production of IL-6 and IL-23 [35]. This process activates signal transducer and transcriptional activation protein 3 (STAT3), which induces an increase in retinoic acid receptor-associated orphan receptor γt, leading to elevated Th17 and the release of pro-inflammatory factors such as IL-17. These changes can cause immune imbalance [36], induce autoimmune thyroiditis [37], and contribute to the development of SCH during pregnancy.

Flow analysis in this study revealed higher levels of Th1 and Th17 cells and lower levels of Th2 cells in the SCH group than those in the CON group. The Th1/Th2 balance in the SCH group tilted toward Th1, which was consistent with our previous research [19]. When the levels of Th1 and Th17 cells were increased, they secreted more pro-inflammatory factors such as IL-2, resulting in the inflammatory environment of the body. Decreased Th2 cells reduce the body’s resistance to Th1-mediated inflammatory response [19]. An imbalance in Th1/Th2/Th17 affects autoimmune responses, amplifying its feedback [34], which promotes thyroid injury and leads to autoimmune thyroiditis [38].

Serological results showed that hsCRP, IL-2, IL-10and TNF-α were higher in the SCH group than in the CON group. Elevated hsCRP levels indicated an increased pro-inflammatory response, suggesting an inflammatory response in patients with SCH during the first half of pregnancy, consistent with our previous studies [9, 19]. When the level of Th1 cells increases, the secretion of pro-inflammatory cytokines such as IL6 and IL-2 increases, destroying thyroid follicular cells and lymphocytes [34]. Additionally, IL-2 can enhance the synthesis of perforin, granase, and proteoglycan by natural killer cells, promoting the apoptosis of thyroid cells [12]. When the level of Th2 cells is reduced, the secretion of anti-inflammatory cytokines such as IL-10 is reduced, and its effect against pro-inflammatory cytokines such as IL-2 and TNF-α is weakened, causing the body to be in a state of inflammation. In our study, the level of IL-10 in the SCH group was somewhat elevated, which may be attributed to bias resulting from the small sample size. Th17 cells secrete TNF-a, which induces the expression of inflammasome components in thyroid cells [10], triggering thyroid inflammation. TNF-α inhibits thyroid follicular cell proliferation by regulating the IL-6-JAK2/STAT3 pathway and miR155-5p signaling [11]. These pathways collectively contribute to the development of SCH during pregnancy.

Correlation analysis showed that Faecalibacteria prausnitzii was positively correlated with TG, TSH, Th1, and Th17, while Bacteroides uniformis was negatively correlated with TG, TSH, TNF-α, Th1, and Th17 (Fig. 5). Additionally, TG was positively correlated with TSH, TNF-α, Th1, and Th17. While Faecalibacterium prausnitzii is generally beneficial, our data suggest that in this specific context, its high abundance coinciding with low Bacteroides uniformis levels correlates with increased LPS absorption. Elevated levels of LPS can disrupt TG metabolism through a “leaky gut” metabolism [39] and stimulate cholesterol biosynthesis and lipid deposition in hepatocytes [36], resulting in increased TG levels. Elevated TG levels increase the level of Th1 cells [33]. Animal experiments have shown that high levels of TG can up-regulate the expression of serum/glucocorticoid-regulated kinase 1 (SGK1) [40], inactivate FoxO1, promote IL-23 receptor (IL-23R) expression, and promote the differentiation of pathogenic Th17 cells [41]. Additionally, elevated TG levels can stimulate toll-like receptors, triggering pro-inflammatory responses in endothelial cells [42], promoting the conversion of Tregs into Th17 cells [41], and influencing autoimmune response. Elevated Th1 and Th17 levels increase pro-inflammatory factors, creating an inflammatory environment. Additionally, reduced Th2 levels decrease the synthesis of IL-4, IL-5, and IL-13, resulting in the decrease of TSAb (thyroid cell stimulating antibody) released by B cells and a subsequent decline in thyroxine levels [43]. This reduction triggers the negative feedback, which modulates the increase in TSH level. These pathways collectively contribute to the development of SCH during pregnancy.

Fig. 5.

Heat map of correlation between different strains and clinical serological indexes and different lipids. Red represents a positive correlation, and blue represents a negative correlation.* is P < 0.05, ** is P < 0.01, and *** is P < 0.001

This study has several limitations. Firstly, the sample size is small; although we included pregnant women from the same region, there may be subtle differences in diet and lifestyle that could lead to biased results. Secondly, we only included pregnant women in the early stages of pregnancy, and the impact of microbial interferencae throughout the entire pregnancy is not yet clear. Additionally, the lack of validation of the results is another shortcoming on our part. Therefore, larger scale studies, complemented by animal experiments or other types of experiments, are needed to verify the occurrence of SCH in the early stages of pregnancy.

Conclusion

We analyzed intestinal micromicrobiota changes and lipidomics characteristics of patients with SCH in the first half of pregnancy using macrogenomics and lipidomics. Our findings indicate in the first half of pregnancy, patients with SCH may experience intestinal microbiota disorder, characterized by increased levels of Faecalibacterium prausnitzii and decreased levels of Bacteroides uniformis, at the same time, it was accompanied by an increase in TG synthesis and a Th1/Th2/Th17 imbalance. These factors may be involved in the occurrence of SCH during pregnancy. This study provides new insights into the pathogenesis of SCH by examining the effects of intestinal differential strains on lipids and immunity in the first half of pregnancy. Our findings suggest that monitoring and potentially modifying the gut microbiota and lipid levels could be beneficial in preventing or managing SCH during pregnancy, leading to improved maternal and fetal outcomes.

Author contributions

Pk. L and Yj. X wrote the main manuscript text. Zz. S and Jj. L helped review the manuscript. M. Z and Yj. B prepared Figs. 1, 2 and 3. Yx. W and Cc. Z prepared Table 1, and 2.

Funding

No funding was received for this work.

Data availability

Metagenomic data have been deposited in the NCBI (National Center for Biotechnology Information) with dataset identifier PRJNA1191807. Other data were deposited in Figshare database (https://figshare.com), dataset DOI is 10.6084/m9.figshare.27959601.v1.

Declarations

Ethics approval and consent to participate

This study was approved by the Medical Ethics Committee of the Third Affiliated Hospital of Zhengzhou University, and all participants signed informed consent. (2024-285-02).

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.