Ecological change of the gut microbiota during pregnancy and progression to dyslipidemia

Dyslipidemia, characterized by abnormal lipid levels, is a prevalent metabolic disorder with significant cardiovascular implications. Its prevalence is high in North America and China, and its impact on cardiovascular events is substantial. Pregnancy induces physiological changes in lipid levels, but dyslipidemia during pregnancy increases the risk of complications like gestational diabetes mellitus (GDM), preterm birth, and future cardiovascular risk. The gut microbiota plays a crucial role in maintaining metabolic function and lipid metabolism balance. Studies have linked dyslipidemia to low gut microbiota richness and specific gut microbiota taxa with lipid levels. Microbiota transplantation has shown promise in treating dyslipidemia, highlighting the microbiome's therapeutic potential. However, the association between lipid levels during pregnancy and gut microbiota, and the predictive power of gut microbiota for dyslipidemia during pregnancy, remains unclear. This study aimed to investigate the dynamic changes in gut microbiota composition throughout pregnancy, the associations between gut microbiota profiles and maternal lipid profiles/dyslipidemia, and the potential of using gut microbiota to predict dyslipidemia risk during pregnancy, aiming to identify potential non-invasive diagnostic and therapeutic strategies.

Existing literature indicates a correlation between the gut microbiome and dyslipidemia, with studies showing associations between dyslipidemia and lower gut microbiota richness, as well as specific taxa and lipid levels. The effectiveness of microbiota transplantation in treating dyslipidemia suggests the microbiome's therapeutic potential. While studies have shown that the gut microbiota composition changes throughout pregnancy, and these changes are associated with conditions such as GDM and inflammatory bowel disease, the relationship between these changes and the development of dyslipidemia during pregnancy was not fully understood. The predictive ability of the gut microbiota in metabolic disorders has also been highlighted, further emphasizing the need to investigate its role in dyslipidemia during pregnancy.

This prospective cohort study used data from the Mother and Child Microbiome Cohort (MCMC) Study. Pregnant women (n=1527 initially, 513 after exclusions) were recruited from Nanjing Medical University's affiliated hospital between 2017 and 2018. Stool samples were collected at multiple time points during pregnancy (second and third trimesters). 16S rRNA amplicon sequencing and shotgun metagenomic sequencing were performed to determine the taxonomic composition and functional annotations of the gut microbiome. Clinical characteristics, including lipid profiles, were also recorded. Statistical analyses included Wilcoxon rank-sum test, PERMANOVA, linear mixed-effects models, MaAsLin analysis, mediation analysis, and random forest classification/regression. Alpha and beta diversity were calculated. Co-abundance groups (CAGs) were constructed based on Spearman correlation of genera. NetShift analysis identified taxonomic drivers. Associations between microbiota composition and dyslipidemia were evaluated using linear mixed effect models, adjusting for confounding factors. Metagenomic analysis identified species biomarkers and functional pathways affecting lipid levels. Machine learning models (random forest) were used to predict dyslipidemia risk based on mid-pregnancy gut microbiota and biochemical data. The study adhered to ethical guidelines, with informed consent obtained from all participants.

The study found significant dynamic changes in the gut microbiota during pregnancy, including a decrease in alpha diversity in dyslipidemic patients. Several genera, including *Alistipes*, *Bacteroides*, *Paraprevotella*, *Christensenellaceae R7 group*, *Clostridia UCG-014*, and *UCG-002*, were negatively associated with lipid profiles and dyslipidemia. Metagenomic analysis revealed key pathways in gastrointestinal inflammation linked to disease-specific microbes. A machine learning model, combining mid-pregnancy microbiome and blood biochemical data, effectively predicted the risk of dyslipidemia in late pregnancy (micro-averaged AUC of 0.824). *Alistipes* and *Bacteroides* were particularly associated with lipid profiles and dyslipidemia, potentially by modulating inflammatory pathways. The study also identified specific species biomarkers and KEGG orthologous (KOs) associated with lipid levels. Mediation analysis revealed links between certain genera (e.g., *Alistipes*, *Bacteroides*) and dyslipidemia risk, mediated by factors like uric acid and retinol-binding protein. Random forest models demonstrated the predictive power of combining gut microbiota and biochemical markers for dyslipidemia in late pregnancy, performing better than biochemical markers alone.

The findings demonstrate a clear association between the gut microbiome and dyslipidemia during pregnancy. Changes in the gut microbiota composition, particularly a reduction in alpha diversity and the abundance of specific genera, are linked to the development of dyslipidemia. The identified pathways involved in inflammation suggest potential mechanisms through which the gut microbiome influences lipid metabolism. The ability to predict dyslipidemia risk using mid-pregnancy data has significant implications for early intervention and preventative strategies. The study supports the exploration of gut microbiome-based therapies for managing dyslipidemia during pregnancy. The identified genera and species biomarkers, and the associated functional pathways, provide valuable targets for future research.

This study provides compelling evidence for the dynamic relationship between the gut microbiome and dyslipidemia during pregnancy. The significant association between specific gut microbiota taxa and dyslipidemia risk, along with the predictive power of a combined microbiome and biochemical marker model, highlights the potential for non-invasive diagnostic and therapeutic strategies. Future research should focus on validating these findings in larger, multi-center studies and exploring the mechanistic pathways involved in the gut microbiome-dyslipidemia relationship.

The study's limitations include its single-center design, limiting the generalizability of the findings. The relatively small number of samples selected for metagenomic analysis may also impact the robustness of the metagenomic findings. Future research should address these limitations by conducting multi-center studies and expanding the scope of metagenomic analysis. Further investigation into the complex interplay between clinical factors and the gut microbiome is needed to develop more sophisticated therapeutic models.