Evidence map›Paper›PMID 36139478›Full record

SynthesisCells2022

Elucidation of Prebiotics, Probiotics, Postbiotics, and Target from Gut Microbiota to Alleviate Obesity via Network Pharmacology Study.

Ki-Kwang Oh, Haripriya Gupta, Byeong-Hyun Min, Raja Ganesan, Satya Priya Sharma, Sung-Min Won, Jin-Ju Jeong, Su-Been Lee, Min-Gi Cha, Goo-Hyun Kwon and 8 more

Open access · goldAbstract readMeta-Analysis
In one paragraph

Synthesis in Cells, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed
4.7field-weighted citation impact, top 4% 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

20 citing papers in PubMed, 30 citations in OpenAlex.

  1. Review
  2. Article
  3. Article
  4. Review
  5. Article
  6. Article
  7. Article
  8. Review
  9. Article
  10. Article
  11. Incorporating Postbiotics into Intervention for Managing Obesity.International journal of molecular sciences · 2025
    Review
  12. Review
  13. Anti-obesity peptides from food: Production, evaluation, sources, and commercialization.Comprehensive reviews in food science and food safety · 2025
    Review
  14. Article
  15. Article
  16. Article
  17. Potential of Gut Microbial Metabolites in Treating Osteoporosis and Obesity: A Network Pharmacology and Bioinformatics Approach.Medical science monitor : international medical journal of experimental and clinical research · 2024
    Article
  18. Article
  19. Hypolipidemic and Antithrombotic Effect of 6'-Molecules (Basel, Switzerland) · 2024
    Article
  20. Article
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 1 institution in 1 country.

Ki-Kwang OhInstitute for Liver and Digestive Diseases, College of Medicine, Hallym University, Chuncheon 24252, Korea.
Haripriya GuptaInstitute for Liver and Digestive Diseases, College of Medicine, Hallym University, Chuncheon 24252, Korea.ORCID 0000-0002-6008-1143
Byeong-Hyun MinInstitute for Liver and Digestive Diseases, College of Medicine, Hallym University, Chuncheon 24252, Korea.
Raja GanesanInstitute for Liver and Digestive Diseases, College of Medicine, Hallym University, Chuncheon 24252, Korea.ORCID 0000-0003-3060-6217
Satya Priya SharmaInstitute for Liver and Digestive Diseases, College of Medicine, Hallym University, Chuncheon 24252, Korea.
Sung-Min WonInstitute for Liver and Digestive Diseases, College of Medicine, Hallym University, Chuncheon 24252, Korea.
Jin-Ju JeongInstitute for Liver and Digestive Diseases, College of Medicine, Hallym University, Chuncheon 24252, Korea.ORCID 0000-0001-5728-9886
Su-Been LeeInstitute for Liver and Digestive Diseases, College of Medicine, Hallym University, Chuncheon 24252, Korea.
Min-Gi ChaInstitute for Liver and Digestive Diseases, College of Medicine, Hallym University, Chuncheon 24252, Korea.
Goo-Hyun KwonInstitute for Liver and Digestive Diseases, College of Medicine, Hallym University, Chuncheon 24252, Korea.
Min-Kyo JeongInstitute for Liver and Digestive Diseases, College of Medicine, Hallym University, Chuncheon 24252, Korea.
Ji-Ye HyunInstitute for Liver and Digestive Diseases, College of Medicine, Hallym University, Chuncheon 24252, Korea.
Jung-A EomInstitute for Liver and Digestive Diseases, College of Medicine, Hallym University, Chuncheon 24252, Korea.
Hee-Jin ParkInstitute for Liver and Digestive Diseases, College of Medicine, Hallym University, Chuncheon 24252, Korea.
Sang-Jun YoonInstitute for Liver and Digestive Diseases, College of Medicine, Hallym University, Chuncheon 24252, Korea.ORCID 0000-0003-4712-6359
Mi-Ran ChoiInstitute for Liver and Digestive Diseases, College of Medicine, Hallym University, Chuncheon 24252, Korea.
Dong Joon KimInstitute for Liver and Digestive Diseases, College of Medicine, Hallym University, Chuncheon 24252, Korea.ORCID 0000-0002-5792-1500
Ki-Tae SukInstitute for Liver and Digestive Diseases, College of Medicine, Hallym University, Chuncheon 24252, Korea.ORCID 0000-0002-9206-9245
Hallym University · KR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The metabolites produced by the gut microbiota have been reported as crucial agents against obesity; however, their key targets have not been revealed completely in complex microbiome systems. Hence, the aim of this study was to decipher promising prebiotics, probiotics, postbiotics, and more importantly, key target(s) via a network pharmacology approach. First, we retrieved the metabolites related to gut microbes from the gutMGene database. Then, we performed a meta-analysis to identify metabolite-related targets via the similarity ensemble approach (SEA) and SwissTargetPrediction (STP), and obesity-related targets were identified by DisGeNET and OMIM databases. After selecting the overlapping targets, we adopted topological analysis to identify core targets against obesity. Furthermore, we employed the integrated networks to microbiota-substrate-metabolite-target (MSMT) via R Package. Finally, we performed a molecular docking test (MDT) to verify the binding affinity between metabolite(s) and target(s) with the Autodock 1.5.6 tool. Based on holistic viewpoints, we performed a filtering step to discover the core targets through topological analysis. Then, we implemented protein-protein interaction (PPI) networks with 342 overlapping target, another subnetwork was constructed with the top 30% degree centrality (DC), and the final core networks were obtained after screening the top 30% betweenness centrality (BC). The final core targets were IL6, AKT1, and ALB. We showed that the three core targets interacted with three other components via the MSMT network in alleviating obesity, i.e., four microbiota, two substrates, and six metabolites. The MDT confirmed that equol (postbiotics) converted from isoflavone (prebiotics) via

Indexed as

Gastrointestinal MicrobiomeObesityPrebioticsProbioticsButyratesEquolHumansInterleukin-6Molecular Docking SimulationNetwork PharmacologyButyratesEquolInterleukin-6Prebioticsequolgut microbiotaIL6isoflavoneLactobacillus paracasei JS1obesity

Identifiers

PMID36139478
PMCPMC9496669
OpenAlexW4296312309

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.