Frontiers in Cellular and Infection Microbiology · 2022 · 21 citations · 33 references
Life SciencesBiostatisticsMicrobiologyEpidemiologyMedical MicrobiologyMicrobiome ResearchDiet-disease AssociationsGut MicrobiomeGut MicrobiotaMicrobiome Data AnalysisMicrobiome DysbiosisGut Microbiota DysbiosisGut Microbiota EffectsLinear Non-gaussian Acyclic ModelsLipid Profile AnalysisLipid Profile ChangesLogistic RegressionMicrobial Ecology Of The GutPrevotella Species
Dyslipidemia (DL) is one of the most common lifestyle-related diseases. There are few reports showing the causal relationship between gut microbiota (GM) and DL. In the present study, we used a linear non-Gaussian acyclic model (LiNGAM) to evaluate the causal relationship between GM and DL. A total of 79 men and 82 women aged 40 years or older living in Shika-machi, Ishikawa Prefecture, Japan were included in the analysis, and their clinical information was investigated. DNA extracted from the GM was processed to sequence the 16S rRNA gene using next-generation sequencing. Participants were divided into four groups based on sex and lipid profile information. The results of one-way analysis of covariance, linear discriminant analysis effect size, and least absolute value reduction and selection operator logistic regression model indicated that several bacteria between men and women may be associated with DL. The LiNGAM showed a presumed causal relationship between different bacteria and lipid profiles in men and women. In men, Prevotella 9 and Bacteroides were shown to be potentially associated with changes in low- and high-density lipoprotein cholesterol levels. In women, the LiNGAM results showed two bacteria, Akkermansia and Escherichia/Shigella , had a presumptive causal relationship with lipid profiles. These results may provide a new sex-based strategy to reduce the risk of developing DL and to treat DL through the regulation of the intestinal environment using specific GM.
33
Metagenomic biomarker discovery and explanation
Nicola Segata, Jacques Izard, Levi Waldron et al. · Genome biology · 2011 · 16.7K citations · Full text
Structure, function and diversity of the healthy human microbiome
Curtis Huttenhower, J. Fah Sathirapongsasuti, Nicola Segata et al. · Nature · 2012 · 11.9K citations · Full text
Life Sciences, Biology, Microbiology +17
Scikit-learn: Machine Learning in Python
PedregosaFabian, VaroquauxGaël, GramfortAlexandre et al. · Journal of Machine Learning Research · 2011 · 8.2K citations