Differential lipids in pregnant women with subclinical hypothyroidism and their correlation to the pregnancy outcomes

Abstract

Subclinical hypothyroidism (SCH) has become a prevalent complication in pregnancy. Recent research links SCH to disturbed thyroid lipid profile; however, it is unclear how lipid metabolism disorders contribute to the pathogenesis of SCH during pregnancy. Thus, we used nontargeted lipidomics to identify and compare the lipids and metabolites expressed by pregnant women with SCH and healthy pregnant women. Multivariate analysis revealed 143 lipid molecules differentially expressed between the SCH group and the control group. Based on fold change, 30 differentially expressed lipid metabolites are potential biomarkers. KEGG pathway enrichment analysis showed that the differentially expressed metabolites participate in several pathways, including response to pathogenic Escherichia coli infection, regulation of lipolysis in adipocytes, metabolic pathways, glycerophospholipid metabolism, and fat digestion and absorption pathways. Correlation analyses revealed sphingomyelin (SM) and phosphatidylcholine (PC) positively correlate to tumor necrosis factor-α (TNF-α), C-reactive protein (CRP), and interleukin-6 (IL-6), while phosphatidylglycerol (PG), and phosphatidylinositol (PI) negatively correlate with them. In addition, PG positively correlates to birth weight. Thus, the lipid profile of pregnant women with SCH is significantly different from that of healthy pregnant women. Lipid molecules associated with the differential lipid metabolism, such as SM, phosphatidylethanolamine (PE), and PI, should be further investigated for their roles in the pathogenesis of SCH in pregnancy, as they might be targets for reducing the incidence of adverse pregnancy outcomes.

Figure 1

Difference analysis for the SCH group and the control group. (a) The PLS-DA model score plot; (b) response sequencing test plot of the PLS-DA analysis model. (c) The cluster analysis diagram shows the aggregation trend in lipid profile between the SCH and control groups. (Note: One point in (a) corresponds to one sample, green represents the control group, and red represents the SCH group. Each column in (c) represents a differential ion, and each row represents a sample. The different shades of color indicate the intensities, of which the blue indicates low-intensity and red high-intensity).

Figure 2

Screening of differential metabolites. (a) Volcano plot shows the number of dysregulated lipids in the SCH group compared to the control group. (b) Potential biomarkers. Note: (rep) indicates we repeated the identification results of this lipid.

Figure 3

Pathways analyses and correlation between potential biomarkers and clinical data. (a) Scatter plot of KEGG enrichment. (b) Correlation between potential biomarkers and clinical data. (c) Pathogenic Escherichia coli infection response pathway. (Note: BMI BMI at enrollment; BW birth weight; Apgar-1: Apgar scores at 1 min; Apgar-5: Apgar scores at 5 min. *, p < 0.05; **, p < 0.01).