Evidence map›Paper›PMID 41390484›Full record

Trial reportNature communications2025

Gut microbiome predicts personalized responses to dietary fiber in prediabetes: a randomized, open-label trial.

Delei Song, Guangli Feng, Yuhang Ma, Yu Shi, Chunfang Qian, Chaoxun Wang, Jixiong Xu, Yue Li, Xuejiao Wang, Nengguang Fan and 26 more

Abstract readRandomized Controlled Trial
In one paragraph

Trial report in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
–field-weighted citation impact
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

11 citing papers in PubMed.

  1. Review
  2. Gut microbiota in health and disease.Molecular biomedicine · 2026
    Review
  3. Review
  4. Remodelling of the gut virome after long-term fasting.NPJ biofilms and microbiomes · 2026
    Article
  5. Review
  6. Review
  7. Review
  8. Article
  9. Review
  10. Review
  11. Review
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

36 authors.

Delei Song *Department of Endocrinology and Metabolism of Shanghai General Hospital, State Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China.
Guangli Feng *Department of Endocrinology and Metabolism of Shanghai General Hospital, State Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China.
Yuhang Ma *Department of Endocrinology and Metabolism of Shanghai General Hospital, State Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China.
Yu Shi *Affiliated Qidong Hospital of Nantong University, Qidong People's Hospital, Qidong Liver Cancer Institute, Qidong, China.
Chunfang Qian *Community Health Service Center of Chedun Town in Songjiang District, Shanghai, China.
Chaoxun Wang *Department of Endocrinology, Shanghai Pudong Hospital, Fudan University Pudong Medical Center, Shanghai, China.
Jixiong Xu *Department of Endocrinology and Metabolism, The First Affiliated Hospital of Nanchang University, Nanchang, China.
Yue LiDepartment of Endocrinology and Metabolism of Shanghai General Hospital, State Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China.
Xuejiao WangDepartment of Endocrinology and Metabolism of Shanghai General Hospital, State Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China.
Nengguang FanDepartment of Endocrinology and Metabolism of Shanghai General Hospital, State Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China.
Weiping DongDepartment of Endocrinology and Metabolism of Shanghai General Hospital, State Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China.
Xiaofeng LiAffiliated Qidong Hospital of Nantong University, Qidong People's Hospital, Qidong Liver Cancer Institute, Qidong, China.
Jian FanAffiliated Qidong Hospital of Nantong University, Qidong People's Hospital, Qidong Liver Cancer Institute, Qidong, China.
Liping ChuCommunity Health Service Center of Chedun Town in Songjiang District, Shanghai, China.
Feng GaoCommunity Health Service Center of Chedun Town in Songjiang District, Shanghai, China.
Shasha HeDepartment of Endocrinology and Metabolism, The First Affiliated Hospital of Nanchang University, Nanchang, China.
Jiao WangDepartment of Endocrinology and Metabolism, The First Affiliated Hospital of Nanchang University, Nanchang, China.
Suijun WangDepartment of Endocrinology, Shidong Hospital, Yangpu District, Shidong Hospital Affiliated to University of Shanghai for Science and Technology, Shanghai, China.
Huimin ZhouDepartment of Endocrinology, Shidong Hospital, Yangpu District, Shidong Hospital Affiliated to University of Shanghai for Science and Technology, Shanghai, China.
Qing GuDepartment of Endocrinology, Shidong Hospital, Yangpu District, Shidong Hospital Affiliated to University of Shanghai for Science and Technology, Shanghai, China.
Hua WangDepartment of Endocrinology, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, China.
Bo FengDepartment of Endocrinology, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, China.
Hao ZhangDepartment of Endocrinology, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, China.
Xueli ZhangDepartment of Endocrinology, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, China.
Li LiDepartment of Endocrinology and Metabolism, The First Affiliated Hospital of Ningbo University, Ningbo, China.
Enfang FanShanghai Nicheng Community Health Service Center, Shanghai, China.
Yanping WangShanghai Pudong New Area Laogang Community Health Center, Shanghai, China.
Meihua WuShanghai Pudong New Area Laogang Community Health Center, Shanghai, China.
Yan ZhangShanghai Pudong New Area Xuanqiao Community Health Service Center, Shanghai, China.
Aimin HuangShanghai Pudong New Area Xuanqiao Community Health Service Center, Shanghai, China.
Jian TengAdfontes (Shanghai), Co.Ltd, Shanghai, China.
Yin ZhuAdfontes (Shanghai), Co.Ltd, Shanghai, China.
Rui ZhaiAdfontes (Shanghai), Co.Ltd, Shanghai, China.
Xiaoying DingDepartment of Endocrinology and Metabolism of Shanghai General Hospital, State Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China. dingxiaoying@sjtu.edu.cn.
Chenhong ZhangDepartment of Endocrinology and Metabolism of Shanghai General Hospital, State Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China. zhangchenhong@sjtu.edu.cn.ORCID http://orcid.org/0000-0002-4462-892X
Yongde PengDepartment of Endocrinology and Metabolism of Shanghai General Hospital, State Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China. pengyongde0908@126.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Gut microbiota contributes to prediabetes progression, however, whether microbiota features can guide targeted prevention and treatment for diabetes requires validation through large-scale clinical trials. Here, in a randomized, open-label trial, we randomly assigned 802 prediabetic subjects to a usual care control group (patient education and dietary recommendations, n = 393) or a dietary fiber intervention group (n = 409) for 6 months. The primary outcome was the percentage change in whole-blood HbA1c, and secondary outcomes were the changes in other glucose, insulin, lipid, liver and kidney function, and anthropometric parameters. There were no statistically significant differences in the primary and secondary outcomes between groups. In post-hoc analysis, we reclassified subjects into four clusters using a multivariate clustering model based on age, BMI, HbA1c, HOMA2-IR and HOMA2-B. These clusters differed in metabolic status, risks of diabetes and its complications, gut microbiome and serum metabolites. Notably, dietary fiber improved glycemic control in Clusters 3 and 4, but not in Clusters 1 and 2, consistent with observed gut microbiota alleviations. By using a LightGBM machine learning model, we calculated a microbiome-based clinical decision score to predict personalized fiber intervention responses and identified individuals who can get glycemic benefits. In conclusion, our study suggests that the gut microbiota response influences the effectiveness of dietary fiber intervention and provides a clinically applicable model to guide microbiome-targeted personalized medicine for prediabetes. Clinical Trial Registry: ChiCTR1900027663.

Indexed as

Dietary FiberGastrointestinal MicrobiomePrediabetic StateAdultAgedBlood GlucoseFemaleGlycated HemoglobinHumansInsulinMachine LearningMaleMiddle AgedBlood GlucoseDietary FiberGlycated Hemoglobinhemoglobin A1c protein, humanInsulin

Identifiers

PMID41390484
PMCPMC12749054

What OpenQuestion holds

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LicenceCC BY-NC-ND
Read underepoch 390

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.