Gut microbiome features and metabolites in non-alcoholic fatty liver disease among community-dwelling middle-aged and older adults - PubMed (original) (raw)

Gut microbiome features and metabolites in non-alcoholic fatty liver disease among community-dwelling middle-aged and older adults

Fangfang Zeng et al. BMC Med. 2024.

Abstract

Background: The specific microbiota and associated metabolites linked to non-alcoholic fatty liver disease (NAFLD) are still controversial. Thus, we aimed to understand how the core gut microbiota and metabolites impact NAFLD.

Methods: The data for the discovery cohort were collected from the Guangzhou Nutrition and Health Study (GNHS) follow-up conducted between 2014 and 2018. We collected 272 metadata points from 1546 individuals. The metadata were input into four interpretable machine learning models to identify important gut microbiota associated with NAFLD. These models were subsequently applied to two validation cohorts [the internal validation cohort (n = 377), and the prospective validation cohort (n = 749)] to assess generalizability. We constructed an individual microbiome risk score (MRS) based on the identified gut microbiota and conducted animal faecal microbiome transplantation experiment using faecal samples from individuals with different levels of MRS to determine the relationship between MRS and NAFLD. Additionally, we conducted targeted metabolomic sequencing of faecal samples to analyse potential metabolites.

Results: Among the four machine learning models used, the lightGBM algorithm achieved the best performance. A total of 12 taxa-related features of the microbiota were selected by the lightGBM algorithm and further used to calculate the MRS. Increased MRS was positively associated with the presence of NAFLD, with odds ratio (OR) of 1.86 (1.72, 2.02) per 1-unit increase in MRS. An elevated abundance of the faecal microbiota (f__veillonellaceae) was associated with increased NAFLD risk, whereas f__rikenellaceae, f__barnesiellaceae, and s__adolescentis were associated with a decreased presence of NAFLD. Higher levels of specific gut microbiota-derived metabolites of bile acids (taurocholic acid) might be positively associated with both a higher MRS and NAFLD risk. FMT in mice further confirmed a causal association between a higher MRS and the development of NAFLD.

Conclusions: We confirmed that an alteration in the composition of the core gut microbiota might be biologically relevant to NAFLD development. Our work demonstrated the role of the microbiota in the development of NAFLD.

Keywords: 16S rRNA gene sequence; Gut metabolites; Gut microbiota feature; Machine learning algorithms; Non-alcoholic fatty liver disease.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1

Flow chart. The flow chart in A shows the screening process used for the discovery cohort (Guangzhou Nutrition and Health Study), and B illustrates the study design

Fig. 2

Results based on machine learning model output. A Evaluation of the four machine learning algorithms based on the area under the curve AUC of the ROC curve. B Importance matrix plot of the top 20 features selected based on LightGBM machine learning algorithms and SHAP value, showing the relative contribution of each variable to NAFLD. C SHAP summary plot of the top 20 features selected based on LightGBM machine learning algorithms and SHAP values, in which one dot per individual per feature is coloured in accordance with an attribution value, with red denoting a greater value and blue denoting a lower value. A higher SHAP value indicates a greater risk of NAFLD

Fig. 3

Association of the microbiome risk score (MRS) with NAFLD risk in different cohorts Note: Logistic regression was used to estimate the odds ratio (OR) and 95% confidence interval (CI) of NAFLD per one unit change in the MRS, adjusting for age, sex, marital status, education, income, smoking status, drinking status, tea status, and total energy intake

Fig. 4

Association of the microbiomes selected through machine learning with NAFLD risk in different cohorts. Note: Logistic regression was used to estimate the odds ratio (OR) and 95% confidence interval (CI) of NAFLD incidence per SD change in the microbiota, adjusting for age, sex, marital status, education, income, smoking status, drinking status, tea status, and total energy intake

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Gut microbiome features and metabolites in non-alcoholic fatty liver disease among community-dwelling middle-aged and older adults - PubMed (original) (raw)