Zeng, F.* ; Su, X.* ; Liang, X.* ; Liao, M. ; Zhong, H.* ; Xu, J.* ; Gou, W.* ; Zhang, X.* ; Shen, L.* ; Zheng, J.S.* ; Chen, Y.M.*
     
 
    
        
Gut microbiome features and metabolites in non-alcoholic fatty liver disease among community-dwelling middle-aged and older adults.
    
    
        
    
    
        
        BMC Med. 22:104 (2024)
    
    
    
		
		
			
				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.
			
			
				
			
		 
		
			
				
					
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        Publikationstyp
        Artikel: Journalartikel
    
 
    
        Dokumenttyp
        Wissenschaftlicher Artikel
    
 
    
        Typ der Hochschulschrift
        
    
 
    
        Herausgeber
        
    
    
        Schlagwörter
        16s Rrna Gene Sequence ; Gut Metabolites ; Gut Microbiota Feature ; Machine Learning Algorithms ; Non-alcoholic Fatty Liver Disease; Bile-acid Metabolism; Modulation; Diet
    
 
    
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        Sprache
        englisch
    
 
    
        Veröffentlichungsjahr
        2024
    
 
    
        Prepublished im Jahr 
        0
    
 
    
        HGF-Berichtsjahr
        2024
    
 
    
    
        ISSN (print) / ISBN
        1741-7015
    
 
    
        e-ISSN
        1741-7015
    
 
    
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	    Band: 22,  
	    Heft: 1,  
	    Seiten: ,  
	    Artikelnummer: 104 
	    Supplement: ,  
	
    
 
  
        
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            Verlag
            BioMed Central
        
 
        
            Verlagsort
            Campus, 4 Crinan St, London N1 9xw, England
        
 
	
        
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        Begutachtungsstatus
        Peer reviewed
    
 
    
        Institut(e)
        Institute of Epidemiology (EPI)
    
 
    
        POF Topic(s)
        30202 - Environmental Health
    
 
    
        Forschungsfeld(er)
        Genetics and Epidemiology
    
 
    
        PSP-Element(e)
        G-504000-001
    
 
    
        Förderungen
        Guangdong Basic and Applied Basic Research Foundation
    
 
    
        Copyright
        
    
 	
    
    
    
    
    
        Erfassungsdatum
        2024-04-30