[A nested case-control study on dyslipidemia in Chinese adults of 10 provinces based on gut microbiota and targeted lipid metabolomics]

To investigate the mechanisms by which the interaction between gut microbiota and host lipid metabolites affects dyslipidemia.

Utilizing data from the China Health and Nutrition Survey(2018) and the "China Development and Nutrition Health Impact Cohort Study"(2022-2023), a nested case-control study design was employed. A total of 229 newly diagnosed dyslipidemia patients(case group) and 229 normolipidemic individuals(control group) were enrolled through 1∶1 matching by age and gender based on demographic information. Fasting blood samples collected were analyzed for serum lipid metabolic profiles using ultra-performance lipid chromatography-tandem mass spectrometry(UPLC-MS/MS), and fecal samples were subjected to 16 S rRNA high-throughput sequencing to analyze the structure of gut microbiota. Univariate analyses were performed to identify differentially expressed metabolites and microbial taxa between groups, followed by Spearman correlation analysis and conditional logistic regression modeling to explore the impact of microbiota-metabolite interactions on dyslipidemia.

A total of 18 differential lipid metabolites(9 upregulated, 9 downregulated) and 5 significant bacterial taxa were identified: Clostridium sensu stricto 1, Muribaculaceae, Holdemanella, Veillonella, and Faecalitalea. Correlation analysis revealed significant associations between 2 bacterial taxa and 5 metabolites. Conditional logistic regression indicated that increased abundance of Clostridium sensu stricto 1 was associated with a reduced risk of dyslipidemia(OR=0.69, 95%CI 0.41-1.50, P=0.04), while elevated levels of triglyceride TG(15:0＿18:2＿18:2) significantly increased the risk of developing the condition(OR=1.94, 95%CI 1.08-3.49, P=0.03).

Clostridium sensu stricto 1 may reduce the risk of dyslipidemia by lowering the level of the lipid metabolite triglyceride(15:0＿18:2＿18:2).