Evidence mapPaperPMID 35768541Full record

ArticleDiabetologia2022

Gut microbiota mediate melatonin signalling in association with type 2 diabetes.

Xueran Huang, Yang Qiu, Yongfen Gao, Rong Zhou, Qiantu Hu, Zouyan He, Yingnan Lv, Xi Wang, Wanrong Chen, Yuqing Deng and 4 more

Open access · bronzeAbstract read
PubMed Publisher
In one paragraph

Article in Diabetologia, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.

0numbers the graph read from it
0cells of the map it votes in
23citing papers in PubMed
3.1field-weighted citation impact, top 7% 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

23 citing papers in PubMed, 36 citations in OpenAlex.

  1. Review
  2. Article
  3. Article
  4. Article
  5. Hypoglycemic effect ofGut microbes · 2025
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  7. Article
  8. Review
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  11. A species ofFrontiers in endocrinology · 2025
    Article
  12. Article
  13. Review
  14. Review
  15. Review
  16. Article
  17. Article
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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

14 authors at 1 institution in 2 countries.

Xueran HuangCenter for Genomic and Personalized Medicine, Guangxi Key Laboratory for Genomic and Personalized Medicine, Guangxi Collaborative Innovation Center for Genomic and Personalized Medicine, Guangxi Medical University, Nanning, China.
Yang QiuCenter for Genomic and Personalized Medicine, Guangxi Key Laboratory for Genomic and Personalized Medicine, Guangxi Collaborative Innovation Center for Genomic and Personalized Medicine, Guangxi Medical University, Nanning, China.
Yongfen GaoCenter for Genomic and Personalized Medicine, Guangxi Key Laboratory for Genomic and Personalized Medicine, Guangxi Collaborative Innovation Center for Genomic and Personalized Medicine, Guangxi Medical University, Nanning, China.
Rong ZhouCenter for Genomic and Personalized Medicine, Guangxi Key Laboratory for Genomic and Personalized Medicine, Guangxi Collaborative Innovation Center for Genomic and Personalized Medicine, Guangxi Medical University, Nanning, China.
Qiantu HuCenter for Genomic and Personalized Medicine, Guangxi Key Laboratory for Genomic and Personalized Medicine, Guangxi Collaborative Innovation Center for Genomic and Personalized Medicine, Guangxi Medical University, Nanning, China.
Zouyan HeDepartment of Nutrition and Food Hygiene, School of Public Health, Guangxi Medical University, Nanning, China.ORCID 0000-0001-6545-2719
Yingnan LvCenter for Genomic and Personalized Medicine, Guangxi Key Laboratory for Genomic and Personalized Medicine, Guangxi Collaborative Innovation Center for Genomic and Personalized Medicine, Guangxi Medical University, Nanning, China.ORCID 0000-0002-5129-5887
Xi WangCenter for Genomic and Personalized Medicine, Guangxi Key Laboratory for Genomic and Personalized Medicine, Guangxi Collaborative Innovation Center for Genomic and Personalized Medicine, Guangxi Medical University, Nanning, China.
Wanrong ChenCenter for Genomic and Personalized Medicine, Guangxi Key Laboratory for Genomic and Personalized Medicine, Guangxi Collaborative Innovation Center for Genomic and Personalized Medicine, Guangxi Medical University, Nanning, China.
Yuqing DengCenter for Genomic and Personalized Medicine, Guangxi Key Laboratory for Genomic and Personalized Medicine, Guangxi Collaborative Innovation Center for Genomic and Personalized Medicine, Guangxi Medical University, Nanning, China.
Zhuangzhuang AnCenter for Genomic and Personalized Medicine, Guangxi Key Laboratory for Genomic and Personalized Medicine, Guangxi Collaborative Innovation Center for Genomic and Personalized Medicine, Guangxi Medical University, Nanning, China.
Haiying ZhangCenter for Genomic and Personalized Medicine, Guangxi Key Laboratory for Genomic and Personalized Medicine, Guangxi Collaborative Innovation Center for Genomic and Personalized Medicine, Guangxi Medical University, Nanning, China. zhanghaiying@gxmu.edu.cn.ORCID 0000-0002-3823-7498
Zengnan MoCenter for Genomic and Personalized Medicine, Guangxi Key Laboratory for Genomic and Personalized Medicine, Guangxi Collaborative Innovation Center for Genomic and Personalized Medicine, Guangxi Medical University, Nanning, China. mozengnan@gxmu.edu.cn.ORCID 0000-0002-3047-3138
Rui LinCenter for Genomic and Personalized Medicine, Guangxi Key Laboratory for Genomic and Personalized Medicine, Guangxi Collaborative Innovation Center for Genomic and Personalized Medicine, Guangxi Medical University, Nanning, China. linrui@gxmu.edu.cn.ORCID 0000-0002-3846-9301
Guangxi Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

aims/hypothesisIt has been shown that melatonin plays a general beneficial role in type 2 diabetes in rodents but its role in humans is controversial. In the present study, we investigated the association between serum melatonin and type 2 diabetes risk in a southern Chinese population in a case-control study. We also examined the role of gut microbiota in this relationship.

methodsIndividuals with type 2 diabetes (cases) and healthy individuals (controls) (n=2034) were recruited from a cross-sectional study and were matched for age and sex in a case-control study. The levels of serum melatonin were measured and the association between serum melatonin and type 2 diabetes risk was examined using a multivariable logistic regression model. We further conducted a rigorously matched case-control study (n=120) in which gut microbial 16S rRNA was sequenced and metabolites were profiled using an untargeted LC-MS/MS approach.

resultsHigher levels of serum melatonin were significantly associated with a lower risk of type 2 diabetes (OR 0.82 [95% CI 0.74, 0.92]) and with lower levels of fasting glucose after adjustment for covariates (β -0.25 [95% CI -0.38, -0.12]). Gut microbiota exhibited alteration in the individuals with type 2 diabetes, in whom lower levels of serum melatonin, lower α- and β-diversity of gut microbiota (p<0.05), greater abundance of Bifidobacterium and lower abundance of Coprococcus (linear discriminant analysis [LDA] >2.0) were found. Seven genera were correlated with melatonin and type 2 diabetes-related traits; among them Bifidobacterium was positively correlated with serum lipopolysaccharide (LPS) and IL-10, whereas Coprococcus was negatively correlated with serum IL-1β, IL-6, IL-10, IL-17, TNF-α and LPS (Benjamini-Hochberg-adjusted p value [false discovery rate (FDR)] <0.05). Moreover, altered metabolites were detected in the participants with type 2 diabetes and there was a significant correlation between tryptophan (Trp) metabolites and the melatonin-correlated genera including Bifidobacterium and Coprococcus (FDR<0.05). Similarly, a significant correlation was found between Trp metabolites and inflammation factors, such as IL-1β, IL-6, IL-10, IL-17, TNF-α and LPS (FDR<0.05). Further, we showed that Trp metabolites may serve as a biomarker to predict type 2 diabetes status (AUC=0.804). CONCLUSIONS/

interpretationA higher level of serum melatonin was associated with a lower risk of type 2 diabetes. Gut microbiota-mediated melatonin signalling was involved in this association; especially, Bifidobacterium- and Coprococcus-mediated Trp metabolites may be involved in the process. These findings uncover the importance of melatonin and melatonin-related bacteria and metabolites as potential therapeutic targets for type 2 diabetes.

Indexed as

Diabetes Mellitus, Type 2Gastrointestinal MicrobiomeMelatoninBiomarkersCase-Control StudiesChromatography, LiquidCross-Sectional StudiesGlucoseHumansInterleukin-10Interleukin-17Interleukin-6LipopolysaccharidesRNA, Ribosomal, 16STandem Mass SpectrometryTryptophanBiomarkersGlucoseInterleukin-10Interleukin-17Interleukin-6LipopolysaccharidesMelatoninRNA, Ribosomal, 16STryptophanTumor Necrosis Factor-alphaCase–control studyGut microbiotaMelatoninMetaboliteTryptophan metabolic pathwayType 2 diabetes

Identifiers

PMID35768541
OpenAlexW4283716284

What Socratic holds

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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 Socratic graph.