Evidence map›Paper›PMID 36440222›Full record

ArticleFrontiers in endocrinology2022

Study on the differences of gut microbiota composition between phlegm-dampness syndrome and qi-yin deficiency syndrome in patients with metabolic syndrome.

Haonan Shang, Lu Zhang, Tiegang Xiao, Li Zhang, Jun Ruan, Qiang Zhang, Kaili Liu, Zhonghai Yu, Yueqiong Ni, Bing Wang

Abstract read
In one paragraph

Article in Frontiers in endocrinology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed, 1 pooled it
–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

14 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Review
  4. Review
  5. Article
  6. Article
  7. Study on subtyping and Traditional Chinese Medicine treatment of depression based on machine learning and text mining.Journal of traditional Chinese medicine = Chung i tsa chih ying wen pan · 2025
    Article
  8. Article
  9. Article
  10. Review
  11. Article
  12. Significance of traditional herbal medicine for dyslipidemia.American journal of translational research · 2023
    Review
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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

10 authors.

Haonan ShangDepartment of Traditional Chinese Medicine, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Lu ZhangSystems Biology & Bioinformatics Unit, Leibniz Institute for Natural Product Research and Infection Biology - Hans Knöll Institute, Jena, Germany.
Tiegang XiaoDepartment of Traditional Chinese Medicine, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Li ZhangDepartment of Traditional Chinese Medicine, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Jun RuanShanghai municipal Hospital of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China.
Qiang ZhangDepartment of Traditional Chinese Medicine, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Kaili LiuDepartment of Traditional Chinese Medicine, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Zhonghai YuDepartment of Traditional Chinese Medicine, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Yueqiong NiSystems Biology & Bioinformatics Unit, Leibniz Institute for Natural Product Research and Infection Biology - Hans Knöll Institute, Jena, Germany.
Bing WangDepartment of Traditional Chinese Medicine, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Metabolic syndrome (MS) is a group of complex medical conditions that can lead to serious cardiovascular and cerebrovascular diseases. According to the theory of traditional Chinese medicine (TCM), MS can be divided into two main subtypes termed 'phlegm-dampness syndrome' (TSZE) and 'qi-yin deficiency syndrome' (QYLX). At present, the research into intestinal microbiota of different TCM syndromes of MS and its association with clinical manifestation is lacking. Materials and methods: Using 16S rRNA sequencing, we performed a cross-sectional analysis of human gut microbiota between two different TCM syndromes (QYLX and TSZE, n=60) of MS, and their differences with healthy participants (n=30). Results: We found that the QYLX and TSZE groups differ from the healthy control group in the overall gut microbiota composition, and some specific microbial taxa and functional pathways. Moreover, significantly differentially abundant taxa and distinct BMI-correlated taxa were observed between QYLX and TSZE groups, suggesting the potential contribution of gut microbiota to the distinction between the two TCM syndromes. The predicted functional profiles also showed considerable differences, especially pathways related to amino acid metabolism and lipopolysaccharide synthesis. Conclusion: Our study highlights the gut microbiota's contribution to the differentiation between two TCM syndromes of MS and may provide the rationale for adopting different microbiota-directed treatment strategies for different TCM syndromes of MS in the future.

Indexed as

Gastrointestinal MicrobiomeMetabolic SyndromeCross-Sectional StudiesHumansQiRNA, Ribosomal, 16SYin DeficiencyRNA, Ribosomal, 16Sgut microbiotametabolic syndromephlegm-dampness syndromeqi-yin deficiencytraditional Chinese medicine

Identifiers

PMID36440222
PMCPMC9682026

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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.