Evidence map›Paper›PMID 30714640›Full record

ArticleJournal of clinical laboratory analysis2019

Association between blood microbiome and type 2 diabetes mellitus: A nested case-control study.

Jing Qiu, Hui Zhou, Yang Jing, Chen Dong

Open access · goldAbstract read
In one paragraph

Article in Journal of clinical laboratory analysis, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 77 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
77citing papers in PubMed, 1 pooled it
7.6field-weighted citation impact, top 2% 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

77 citing papers in PubMed, 1 synthesis or guideline pooled it, 137 citations in OpenAlex.

  1. Pooled it
  2. Review
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  12. The Microbiome and Metabolic Dysfunction-Associated Steatotic Liver Disease.International journal of molecular sciences · 2025
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  14. Differences in cell-associated and cell-free microbial DNA in blood.bioRxiv : the preprint server for biology · 2025
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  15. Effects ofFrontiers in physiology · 2025
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  16. Review
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17 more citing papers are in PubMed but not listed here.

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

4 authors at 2 institutions in 2 countries.

Jing QiuDepartment of Epidemiology and Statistics, School of Public Health, Jiangsu Key Laboratory and Translational Medicine for Geriatric Disease, Medical College of Soochow University, Suzhou, Jiangsu, China.
Hui ZhouSuzhou Industrial Park Centers for Disease Control and Prevention, Suzhou, China.
Yang JingDepartment of Epidemiology and Statistics, School of Public Health, Jiangsu Key Laboratory and Translational Medicine for Geriatric Disease, Medical College of Soochow University, Suzhou, Jiangsu, China.
Chen DongDepartment of Epidemiology and Statistics, School of Public Health, Jiangsu Key Laboratory and Translational Medicine for Geriatric Disease, Medical College of Soochow University, Suzhou, Jiangsu, China.ORCID https://orcid.org/0000-0001-5175-1662
Soochow University · CNCenters for Disease Control and Prevention · US

Funding

Foundation for the key technology study on the major disease and communicable disease prevention and Control Gwzx201605National Natural Science Foundation of China 81502869National Natural Science Foundation of China 81773507
6 · The paper itself

Abstract

backgroundAlthough recent studies have indicated that gut microbiome dysbiosis was significantly associated with the onset of type 2 diabetes mellitus (T2DM), information on the role of blood microbiome in T2DM development is scarce.

methodsFifty incident T2DM cases and 100 matched non-T2DM controls were selected from a prospective cohort study of "135." The composition of the blood microbiome was characterized using bacterial 16S ribosomal RNA (16S rRNA) gene sequencing from pre-diagnostic blood sample. The amplicons were normalized, pooled, and sequenced on the Illumina MiSeq instrument using a MiSeq Reagent Kit PE300 v3 kit.

resultsTotally, 3 000 391 and 6 244 227 high-quality sequences were obtained from T2DM patients and non-T2DM controls, respectively. The mean diversity of the blood microbiome (Simpson, Chao1 and Shannon indices) was not different between two groups at baseline. At genus level, the Aquabacterium, Xanthomonas, and Pseudonocardia were presented with lower abundance, while Actinotalea, Alishewanella, Sediminibacterium, and Pseudoclavibacter were presented with higher abundance among T2DM cases compared to those in non-T2DM controls. As the results shown, participants carried the genus Bacteroides in blood were significantly associated with a decreased risk for T2DM development, with 74% vs 88% (adjusted OR: 0.367, 95% CI: 0.151-0.894). However, participants carried the genus Sediminibacterium have an increased risk for T2DM, with adjusted OR (95% CI) being 14.098 (1.358, 146.330).

conclusionsBlood microbiome may play an etiology role in the development of T2DM. These findings would be useful to develop microbiota-based strategies for T2DM prevention and control.

Indexed as

BacteroidesBloodCase-Control StudiesCohort StudiesDiabetes Mellitus, Type 2FemaleHigh-Throughput Nucleotide SequencingHumansMaleMicrobiotaMiddle AgedProspective StudiesRNA, Ribosomal, 16SRNA, Ribosomal, 16S16S ribosomal RNAblood microbiomenested case-control studytype 2 diabetes mellitus

Identifiers

PMID30714640
PMCPMC6528574
OpenAlexW2914091352

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.