Evidence map›Paper›PMID 38454425›Full record

ArticleBMC medicine2024

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

Fangfang Zeng, Xin Su, Xinxiu Liang, Minqi Liao, Haili Zhong, Jinjian Xu, Wanglong Gou, Xiangzhou Zhang, Luqi Shen, Ju-Sheng Zheng and 1 more

Open access · goldAbstract read
In one paragraph

Article in BMC medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
28citing papers in PubMed, 2 pooled it
7.7field-weighted citation impact, top 2% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

28 citing papers in PubMed, 2 syntheses or guidelines pooled it, 33 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors at 4 institutions in 2 countries.

Fangfang Zeng *Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, No.601 Huangpu Road West, Guangzhou, 510632, China. zengffjnu@126.com.
Xin Su *Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, No.601 Huangpu Road West, Guangzhou, 510632, China.
Xinxiu Liang *Zhejiang Key Laboratory of Multi-Omics in Infection and Immunity, School of Medicine and School of Life Sciences, Westlake University, Hangzhou, 310030, China.
Minqi LiaoInstitute of Epidemiology, Helmholtz Zentrum München-German Research Center for Environmental Health, Ingolstädter Landstr. 1, 85764, Neuherberg, Germany.
Haili ZhongDepartment of Epidemiology, Guangdong Provincial Key Laboratory of Food, Nutrition and Health, School of Public Health, Sun Yat-Sen University, Guangzhou, 510275, China.
Jinjian XuDepartment of Epidemiology, Guangdong Provincial Key Laboratory of Food, Nutrition and Health, School of Public Health, Sun Yat-Sen University, Guangzhou, 510275, China.
Wanglong GouZhejiang Key Laboratory of Multi-Omics in Infection and Immunity, School of Medicine and School of Life Sciences, Westlake University, Hangzhou, 310030, China.
Xiangzhou ZhangBig Data Decision Institute, Jinan University, No.601 Huangpu Road West, Guangzhou, 510632, China.
Luqi ShenZhejiang Key Laboratory of Multi-Omics in Infection and Immunity, School of Medicine and School of Life Sciences, Westlake University, Hangzhou, 310030, China.
Ju-Sheng ZhengZhejiang Key Laboratory of Multi-Omics in Infection and Immunity, School of Medicine and School of Life Sciences, Westlake University, Hangzhou, 310030, China. zhengjusheng@westlake.edu.cn.
Yu-Ming ChenDepartment of Epidemiology, Guangdong Provincial Key Laboratory of Food, Nutrition and Health, School of Public Health, Sun Yat-Sen University, Guangzhou, 510275, China. chenyum@mail.sysu.edu.cn.
Westlake University · CNJinan University · CNSun Yat-sen University · CNHelmholtz Zentrum München · DE

Funding

Guangdong Basic and Applied Basic Research Foundation 2023A1515030155Innovative Research Group Project of the National Natural Science Foundation of China 81602853Innovative Research Group Project of the National Natural Science Foundation of China 82073529Innovative Research Group Project of the National Natural Science Foundation of China 82073546the 5010 Program for Clinical Researches 2007032
6 · The paper itself

Abstract

backgroundThe 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.

methodsThe 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.

resultsAmong 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.

conclusionsWe 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.

Indexed as

Gastrointestinal MicrobiomeMicrobiotaNon-alcoholic Fatty Liver DiseaseAgedAnimalsHumansIndependent LivingLiverMiceMiddle Aged16S rRNA gene sequenceGut metabolitesGut microbiota featureMachine learning algorithmsNon-alcoholic fatty liver disease

Identifiers

PMID38454425
PMCPMC10921631
OpenAlexW4392562344

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.