Evidence map›Paper›PMID 41888125›Full record

ArticleNPJ biofilms and microbiomes2026

A global metagenomic atlas of aging identifies a microbiota phase transition associated with disease risk.

JingXiang Fu, Jiahui Zhang, Ruowen He, Quanbin Dong, Hongyun Mao, Wei Shen, Wei Wu, Xiaojiao Chen, Wenjun Ma, Qixiao Zhai and 5 more

Abstract read
In one paragraph

Article in NPJ biofilms and microbiomes, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
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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

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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

15 authors.

JingXiang Fu *Microbiome Medicine Center, Department of Laboratory Medicine, Zhujiang Hospital, Southern Medical University, Guangzhou, China.
Jiahui Zhang *Microbiome Medicine Center, Department of Laboratory Medicine, Zhujiang Hospital, Southern Medical University, Guangzhou, China.
Ruowen He *Microbiome Medicine Center, Department of Laboratory Medicine, Zhujiang Hospital, Southern Medical University, Guangzhou, China.
Quanbin Dong *Department of Cardiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing Medical University, Nanjing, China.
Hongyun MaoMicrobiome Medicine Center, Department of Laboratory Medicine, Zhujiang Hospital, Southern Medical University, Guangzhou, China.
Wei ShenDepartment of Neonatology, Nanfang Hospital, Southern Medical University, Guangzhou, China.
Wei WuGuangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China.
Xiaojiao ChenMicrobiome Medicine Center, Department of Laboratory Medicine, Zhujiang Hospital, Southern Medical University, Guangzhou, China.
Wenjun MaDepartment of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangzhou, China.
Qixiao ZhaiSchool of Food Science and Technology, Jiangnan University, Wuxi, China.
Lianmin ChenDepartment of Cardiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing Medical University, Nanjing, China.
Hongwei ZhouMicrobiome Medicine Center, Department of Laboratory Medicine, Zhujiang Hospital, Southern Medical University, Guangzhou, China.
Shixian HuDepartment of Gastroenterology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China. hushx9@mail.sysu.edu.cn.
Yan HeMicrobiome Medicine Center, Department of Laboratory Medicine, Zhujiang Hospital, Southern Medical University, Guangzhou, China. yanhe@smu.edu.cn.
Cancan QiMicrobiome Medicine Center, Department of Laboratory Medicine, Zhujiang Hospital, Southern Medical University, Guangzhou, China. cc_qi@hotmail.com.

Funding

Basic and Applied Basic Research Foundation of Guangdong Province 2023A1515012538National Key Research and Development Program of China 2019YFA0802300National Natural Science Foundation of China NSFC82272391National Natural Science Foundation of China NSFC82300623National Natural Science Foundation of China NSFC82302610
6 · The paper itself

Abstract

Biological aging has been associated with altered risk of aging-related diseases, but the contribution of the gut microbiota to this process remains poorly understood. Here, we constructed an interpretable gut microbiota age clock using metagenomic data from 8115 fecal samples across five continents. We discovered a key microbial perturbation occurring at 56-60 years of chronological age, which was validated in an independent cohort of 2263 metagenomes. This perturbation was associated with a decline in ecological stability and substantial changes in the abundance of core species. Notably, the association between gut microbiota age and diseases was identified to be significantly altered before and after this inflection time. Moreover, within-species analyses uncovered phylogenetic divergence for seven age-related species, such as Escherichia coli, alongside functional alterations in older individuals, including enhanced cell motility, carbohydrate metabolism and horizontal gene transfer. Overall, our global gut microbiome atlas uncovers a critical age transition phase, highlighting opportunities for microbiota-based therapies and offering novel insights into evolutionary dynamics during aging.

Indexed as

AgingBacteriaGastrointestinal MicrobiomeMetagenomeMetagenomicsFecesHumansPhylogeny

Identifiers

PMID41888125
PMCPMC13187473

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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.