A Comprehensive Dataset on Microbiome Dynamics in Rheumatoid Arthritis from a Large-Scale Cohort Study.

IF 5.8 2区综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES Scientific Data Pub Date : 2025-02-07 DOI:10.1038/s41597-025-04422-0

Jing Li, Jun Xu, Jiayang Jin, Congmin Xu, Yuzhou Gan, Yifan Wang, Ruiling Feng, Wenqiang Fan, Yingni Li, Xiaozhen Zhao, Yucui Li, Shushi Gong, Linchong Su, Yueming Cai, Lianjie Shi, Xiaolin Sun, Yang Xiang, Qingwen Wang, Ru Li, Jinxia Zhao, Yulan Liu, Junjie Qin, Zhanguo Li, Jing He

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引用次数: 0

Abstract

Alterations in intestinal microbiota have been identified as a key risk factor in rheumatoid arthritis (RA). This study presents a multidimensional gut microbiota profile from a large cohort of RA patients, stratified by disease stage and treatment regimens, and compared to healthy controls. Our dataset comprises gut microbiota profiles from 2,238 individuals, including 1,034 RA patients (Ascia Pacific RA cohort, APRAC) and 1,204 healthy controls. This dataset is enriched with detailed clinical metadata, including patient profiles, treatment histories, and environmental factors, providing a comprehensive "disease exposome" for RA. By integrating 16S rRNA gene sequencing with demographic, clinical, and environmental data, we offer a valuable resource to explore the complex relationships between gut microbiota and RA progression. This large-scale dataset is expected to be a foundation for collaborative research, advancing our understanding of the microbiome's systemic effects in RA and other autoimmune diseases and potentially guiding new therapeutic approaches.

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来源期刊

Scientific Data Social Sciences-Education

CiteScore

11.20

自引率

4.10%

发文量

689

审稿时长

16 weeks

期刊介绍： Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data. The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.

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