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Updated: Sep 14, 2025

Microbiota Analysis Using Two-step PCR and Next-generation 16S rRNA Gene Sequencing
Published on: October 15, 2019
Association between gut microbiota and disorders of lipoprotein metabolism: a two sample Mendelian randomization
Qingyao Du1, Yiqing Chen2, Changhan Kou3
1Guangzhou Key Laboratory for Clinical Rapid Diagnosis and Early Warning of Infectious Diseases, KingMed School of Laboratory Medicine, Guangzhou Medical University, Guangzhou, 510180, China; Microbiome Medicine Center, Department of Laboratory Medicine, Zhujiang Hospital, Southern Medical University, Guangzhou, 510282, China.
Background And Aim:
Hyperlipidemia is a major risk factor for cardiovascular diseases, but its relationship with gut microbiota remains unclear. This study aimed to systematically assess genetic associations between gut microbiota and lipoprotein metabolism disorders using Mendelian randomization (MR) and explore their links with cardio-metabolic risk factors such as obesity, diabetes and BMI.
Methods And Results:
We performed a two-sample MR analysis integrating gut microbiota GWAS data from the MiBioGen consortium (n = 18,340) and lipoprotein metabolism disorder data from FinnGen (14,101 cases/197,259 controls). Inverse variance weighted (IVW) analysis identified five protective taxa, including Lachnoclostridium (OR = 0.748) and an unknown genus (id.826; OR = 0.789), and four risk taxa such as Actinobacteria (OR = 1.226) and Ruminiclostridium5 (OR = 1.227). Cross-phenotype analyses revealed consistent protective effects: Lachnoclostridium abundance inversely correlated with BMI (OR = 0.942), while the unknown genus (id.826) was protective against diabetes (OR = 0.874). Sensitivity analyses confirmed robustness (no heterogeneity or horizontal pleiotropy).
Conclusions:
This study reveals the complex role of gut microbiota in metabolic diseases through MR, and its effects may depend on the host's metabolic status. The results provide a theoretical basis for precise intervention strategies targeting microbial communities, and in the future, it is necessary to combine multi omics and experimental models to validate key microbial communities and their mechanisms.
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