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Updated: Jun 18, 2026

An In Vitro Batch-culture Model to Estimate the Effects of Interventional Regimens on Human Fecal Microbiota
Published on: July 31, 2019
Causal interplay between lactose intolerance and gut microbiota: a combined bidirectional Mendelian randomization and
Mengxiong Lu1, Zheng Wang1, ChunFeng Mei2
1Institute of Digestive Diseases, Peking University Traditional Chinese Medicine Clinical Medical School (Xiyuan), Beijing, China.
Objectives:
The causal relationship between gut microbiota and lactose intolerance (LI) remains elusive due to confounding factors in observational studies. This study aims to decipher the bidirectional causal link between specific gut bacterial taxa and LI by integrating genetic inference with experimental validation.
Methods:
We employed a two-sample bidirectional Mendelian Randomization (MR) analysis using summary statistics from the MiBioGen Consortium (gut microbiota) and the FinnGen study (LI). Robustness was assessed via inverse-variance weighted (IVW), MR-Egger, and sensitivity analyses. To validate the genomic findings, we established a lactose-intolerant rat model induced by a high-lactose diet and analyzed cecal microbiota composition using 16S rRNA high-throughput sequencing.
Results:
MR analysis identified significant causal associations: the class Deltaproteobacteria, genus Bilophila were identified as potential risk factors for LI, whereas the genus Paraprevotella and Blautia exhibited protective effects. Notably, reverse MR analysis suggested that LI genetically influences host gut microbiota, particularly suppressing carbohydrate metabolism and blooming Bifidobacterium. Experimental sequencing in rats corroborated these findings, showing a distinctive alteration in microbial structure, specifically an increased abundance of Bifidobacterium and a depletion of Blautia in the high-lactose group, consistent with the genetic inference.
Conclusion:
This study provides robust evidence for a causal interplay between gut microbiota and LI. The convergence of genetic and experimental data highlights specific taxa, particularly Bifidobacterium and Blautia, as potential biomarkers or therapeutic targets. These findings offer new insights into the microbial etiology of metabolic disorders and suggest microbiota-targeted strategies for LI management.
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