Evidence map›Paper›PMID 36376937›Full record

ArticleTranslational neurodegeneration2022

Gut microbiome, cognitive function and brain structure: a multi-omics integration analysis.

Xinxiu Liang, Yuanqing Fu, Wen-Ting Cao, Zhihong Wang, Ke Zhang, Zengliang Jiang, Xiaofang Jia, Chun-Ying Liu, Hong-Rou Lin, Haili Zhong and 8 more

Open access · goldAbstract read
In one paragraph

Article in Translational neurodegeneration, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 110 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
110citing papers in PubMed, 1 pooled it
16.3field-weighted citation impact, top 1% 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

110 citing papers in PubMed, 1 synthesis or guideline pooled it, 146 citations in OpenAlex.

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50 more citing papers are in PubMed but not listed here.

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

18 authors at 6 institutions in 1 country.

Xinxiu Liang *College of Life Sciences, Zhejiang University, Hangzhou, 310058, China.
Yuanqing Fu *School of Life Sciences, Westlake University, 18 Shilongshan Rd, Cloud Town, Hangzhou, 310024, China.
Wen-Ting Cao *Guangdong Provincial Key Laboratory of Food, Nutrition and Health, Department of Medical Statistics and Epidemiology, School of Public Health, Sun Yat-Sen University, Guangzhou, 510080, China.
Zhihong Wang *Chinese Center for Disease Control and Prevention, National Institute for Nutrition and Health, Beijing, 100050, China.
Ke ZhangSchool of Life Sciences, Westlake University, 18 Shilongshan Rd, Cloud Town, Hangzhou, 310024, China.
Zengliang JiangSchool of Life Sciences, Westlake University, 18 Shilongshan Rd, Cloud Town, Hangzhou, 310024, China.
Xiaofang JiaChinese Center for Disease Control and Prevention, National Institute for Nutrition and Health, Beijing, 100050, China.
Chun-Ying LiuGuangdong Provincial Key Laboratory of Food, Nutrition and Health, Department of Medical Statistics and Epidemiology, School of Public Health, Sun Yat-Sen University, Guangzhou, 510080, China.
Hong-Rou LinGuangdong Provincial Key Laboratory of Food, Nutrition and Health, Department of Medical Statistics and Epidemiology, School of Public Health, Sun Yat-Sen University, Guangzhou, 510080, China.
Haili ZhongGuangdong Provincial Key Laboratory of Food, Nutrition and Health, Department of Medical Statistics and Epidemiology, School of Public Health, Sun Yat-Sen University, Guangzhou, 510080, China.
Zelei MiaoSchool of Life Sciences, Westlake University, 18 Shilongshan Rd, Cloud Town, Hangzhou, 310024, China.
Wanglong GouSchool of Life Sciences, Westlake University, 18 Shilongshan Rd, Cloud Town, Hangzhou, 310024, China.
Menglei ShuaiSchool of Life Sciences, Westlake University, 18 Shilongshan Rd, Cloud Town, Hangzhou, 310024, China.
Yujing HuangSchool of Life Sciences, Westlake University, 18 Shilongshan Rd, Cloud Town, Hangzhou, 310024, China.
Shengdi ChenDepartment of Neurology and Institute of Neurology, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China. chensd@rjh.com.cn.
Bing ZhangChinese Center for Disease Control and Prevention, National Institute for Nutrition and Health, Beijing, 100050, China. zhangbing@chinacdc.cn.
Yu-Ming ChenGuangdong Provincial Key Laboratory of Food, Nutrition and Health, Department of Medical Statistics and Epidemiology, School of Public Health, Sun Yat-Sen University, Guangzhou, 510080, China. chenyum@mail.sysu.edu.cn.
Ju-Sheng ZhengSchool of Life Sciences, Westlake University, 18 Shilongshan Rd, Cloud Town, Hangzhou, 310024, China. zhengjusheng@westlake.edu.cn.
Westlake University · CNSun Yat-sen University · CNNational Health and Family Planning Commission · CNChinese Center For Disease Control and Prevention · CNRuijin Hospital · CNZhejiang University · CN

Funding

UNIV OF NORTH CAROLINA CLINICAL NUTRITION RESEARCH UNITP30DK056350 · NIDDK · UNIV OF NORTH CAROLINA CHAPEL HILL · PI Venkata Saroja Voruganti · 1999 to 2026
$31.6M
MONITORING SOCIAL CHANGE: HEALTH, REPRODUCTION, AGINGR01HD030880 · NICHD · UNIV OF NORTH CAROLINA CHAPEL HILL · PI ADAIR, LINDA S, POPKIN, BARRY M · 1994 to 2024
$27.0M
Carolina Population CenterR24HD050924 · NICHD · UNIV OF NORTH CAROLINA CHAPEL HILL · PI MORGAN, SAMUEL PHILIP · 2005 to 2014
$10.0M
Program Development (Pilot) CoreP30AG066615 · NIA · UNIV OF NORTH CAROLINA CHAPEL HILL · PI ELIZABETH A FRANKENBERG · 2020 to 2026
$4.1M
Monitoring Social Change: Dynamics Of Aging And Cognitive FunctionR01AG065357 · NIA · UNIV OF NORTH CAROLINA CHAPEL HILL · PI ADAIR, LINDA S, POPKIN, BARRY M · 2020 to 2024
$3.6M
Transition to a Western diet and cardiometabolic risk: biomarkers derived from the microbiomeR01DK104371 · NIDDK · UNIV OF NORTH CAROLINA CHAPEL HILL · PI GORDON-LARSEN, PENNY · 2015 to 2018
$2.5M
CHILD HEALTH EFFECTS OF RAPID SOCIO-DEMOGRAPHIC CHANGER01HD038700 · NICHD · UNIVERSITY OF NORTH CAROLINA CHAPEL HILL · PI POPKIN, BARRY M · 2000 to 2004
$1.5M
NIA NIH HHS P30 AG066615NIA NIH HHS R01 AG065357NICHD NIH HHS R01 HD030880NICHD NIH HHS R01 HD038700NICHD NIH HHS R24 HD050924NIDDK NIH HHS P30 DK056350NIDDK NIH HHS R01 DK104371NIDDK NIH HHS R01-DK104371NIH HHS DK056350NIH HHS R01-HD30880NIH HHS R01-HD38700
6 · The paper itself

Abstract

backgroundMicrobiome-gut-brain axis may be involved in the progression of age-related cognitive impairment and relevant brain structure changes, but evidence from large human cohorts is lacking. This study was aimed to investigate the associations of gut microbiome with cognitive impairment and brain structure based on multi-omics from three independent populations.

methodsWe included 1430 participants from the Guangzhou Nutrition and Health Study (GNHS) with both gut microbiome and cognitive assessment data available as a discovery cohort, of whom 272 individuals provided fecal samples twice before cognitive assessment. We selected 208 individuals with baseline microbiome data for brain magnetic resonance imaging during the follow-up visit. Fecal 16S rRNA and shotgun metagenomic sequencing, targeted serum metabolomics, and cytokine measurements were performed in the GNHS. The validation analyses were conducted in an Alzheimer's disease case-control study (replication study 1, n = 90) and another community-based cohort (replication study 2, n = 1300) with cross-sectional dataset.

resultsWe found protective associations of specific gut microbial genera (Odoribacter, Butyricimonas, and Bacteroides) with cognitive impairment in both the discovery cohort and the replication study 1. Result of Bacteroides was further validated in the replication study 2. Odoribacter was positively associated with hippocampal volume (β, 0.16; 95% CI 0.06-0.26, P = 0.002), which might be mediated by acetic acids. Increased intra-individual alterations in gut microbial composition were found in participants with cognitive impairment. We also identified several serum metabolites and inflammation-associated metagenomic species and pathways linked to impaired cognition.

conclusionsOur findings reveal that specific gut microbial features are closely associated with cognitive impairment and decreased hippocampal volume, which may play an important role in dementia development.

Indexed as

Gastrointestinal MicrobiomeBrainCase-Control StudiesCognitionCross-Sectional StudiesHumansRNA, Ribosomal, 16SRNA, Ribosomal, 16SBrain structureCognitive impairmentCohortGut microbiomeMetagenomicsMicrobiome–gut–brain axis

Identifiers

PMID36376937
PMCPMC9661756
OpenAlexW4308969921

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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.