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

Study information

Reviewed Marked as Reviewed by Shaimaa Elsafoury on 2021/02/09

study design

case-control

Citation

PMID PubMed identifier for scientific articles.

30714640

DOI Digital object identifier for electronic documents.

10.1002/jcla.22842

URI

Authors

Qiu J, Zhou H, Jing Y, Dong C

Journal

Journal of clinical laboratory analysis

Year

2019

Keywords:

16S ribosomal RNA, blood microbiome, nested case-control study, type 2 diabetes mellitus

BACKGROUND: Although 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. METHODS: Fifty 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. RESULTS: Totally, 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). CONCLUSIONS: Blood 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.

Experiment 1

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Reviewed Marked as Reviewed by Fatima on 2021/07/16

Curated date: 2021/01/10

Curator: WikiWorks

Revision editor(s): Rimsha, WikiWorks, Atrayees

Subjects

Location of subjects: China

Host species Species from which microbiome was sampled. Contact us to have more species added.: Homo sapiens

Body site Anatomical site where microbial samples were extracted from according to the Uber Anatomy Ontology: Blood Portion of blood,Vertebrate blood,Whole blood,Blood,blood

Condition The experimental condition / phenotype studied according to the Experimental Factor Ontology: Type II diabetes mellitus adult onset diabetes,Adult-Onset Diabetes,adult-onset diabetes,Adult-Onset Diabetes Mellitus,diabetes mellitis type 2,diabetes mellitis type II,DIABETES MELLITUS TYPE 02,diabetes mellitus type 2,Diabetes Mellitus, Adult Onset,Diabetes Mellitus, Adult-Onset,Diabetes Mellitus, Ketosis Resistant,Diabetes Mellitus, Ketosis-Resistant,Diabetes Mellitus, Maturity Onset,Diabetes Mellitus, Maturity-Onset,Diabetes Mellitus, Non Insulin Dependent,Diabetes Mellitus, Non-Insulin-Dependent,Diabetes Mellitus, Noninsulin Dependent,diabetes mellitus, noninsulin-dependent,Diabetes Mellitus, Slow Onset,Diabetes Mellitus, Slow-Onset,Diabetes Mellitus, Stable,Diabetes Mellitus, Type 2,diabetes mellitus, type 2,diabetes mellitus, type 2, protection against,Diabetes Mellitus, Type II,Diabetes, Type 2,diabetes, type 2,insulin resistance, susceptibility to,Ketosis-Resistant Diabetes Mellitus,Maturity Onset Diabetes Mellitus,maturity-onset diabetes,Maturity-Onset Diabetes Mellitus,MODY,NIDDM,Non-Insulin Dependent Diabetes,non-insulin dependent diabetes,Non-Insulin Dependent Diabetes Mellitus,non-insulin dependent diabetes mellitus,non-insulin-dependent diabetes mellitus,noninsulin dependent diabetes,noninsulin-dependent diabetes mellitus,Slow-Onset Diabetes Mellitus,Stable Diabetes Mellitus,T2DM - Type 2 Diabetes mellitus,T2DM - type 2 diabetes mellitus,Type 2 Diabetes,type 2 diabetes,Type 2 Diabetes Mellitus,type 2 diabetes mellitus,Type 2 Diabetes Mellitus Non-Insulin Dependent,type 2 diabetes mellitus non-insulin dependent,Type II Diabetes,type II diabetes,type II diabetes mellitus,Type II diabetes mellitus

Group 0 name Corresponds to the control (unexposed) group for case-control studies: controls

Group 1 name Corresponds to the case (exposed) group for case-control studies: Type-2 DM cases

Group 0 sample size Number of subjects in the control (unexposed) group: 100

Group 1 sample size Number of subjects in the case (exposed) group: 50

Antibiotics exclusion Number of days without antibiotics usage (if applicable) and other antibiotics-related criteria used to exclude participants (if any): 2 months

Lab analysis

Sequencing type: 16S

16S variable region One or more hypervariable region(s) of the bacterial 16S gene: V5-V6

Sequencing platform Manufacturer and experimental platform used for quantifying microbial abundance: Illumina

Statistical Analysis

Statistical test: T-Test

Significance threshold p-value or FDR threshold used for differential abundance testing (if any): 0.05

MHT correction Have statistical tests be corrected for multiple hypothesis testing (MHT)?: No

Matched on Factors on which subjects have been matched on in a case-control study: age, sex

Confounders controlled for Confounding factors that have been accounted for by stratification or model adjustment: alcohol drinking, blood pressure, body mass index, smoking behavior, total cholesterol measurement, triglycerides

Alpha Diversity

Shannon Estimator of species richness and species evenness: more weight on species richness: unchanged

Chao1 Abundance-based estimator of species richness: unchanged

Simpson Estimator of species richness and species evenness: more weight on species evenness: unchanged

Signature 1

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Reviewed Marked as Reviewed by Shaimaa Elsafoury on 2021/02/09

Curated date: 2021/01/10

Curator: Yaseen Javaid

Revision editor(s): WikiWorks

Source: Table 5

Description: Association between blood microbiome and type 2 diabetes mellitus: A nested case-control study. (all the bacteria either have zero mean or not detectable in both groups except Aquabacterium)

Abundance in Group 1: increased abundance in Type-2 DM cases

NCBI	Quality Control	Links
Actinotalea
Alishewanella
Sediminibacterium
Pseudoclavibacter

Revision editor(s): WikiWorks

Signature 2

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Reviewed Marked as Reviewed by Shaimaa Elsafoury on 2021/02/09

Curated date: 2021/01/10

Curator: Yaseen Javaid

Revision editor(s): WikiWorks

Source: Table 5

Description: Association between blood microbiome and type 2 diabetes mellitus: A nested case-control study. (all the bacteria either have zero mean or not detectable in both groups except Aquabacterium)

Abundance in Group 1: decreased abundance in Type-2 DM cases

NCBI	Quality Control	Links
Aquabacterium
Xanthomonas
Pseudonocardia

Revision editor(s): WikiWorks

Experiment 2

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Your review change was submittedDuplicate this Experiment Add a new Signature

Reviewed Marked as Reviewed by Fatima on 2021/07/16

Curated date: 2021/01/10

Curator: WikiWorks

Revision editor(s): Rimsha, WikiWorks, Atrayees

Differences from previous experiment shown

Subjects

Group 0 name Corresponds to the control (unexposed) group for case-control studies: carier of pathogens controls

Group 1 name Corresponds to the case (exposed) group for case-control studies: Type-2 DM cariers of pathogens

Group 1 definition Diagnostic criteria applied to define the specific condition / phenotype represented in the case (exposed) group: The non-carrier meant that the individual did not have sequence read for the specific pathogen.

Group 0 sample size Number of subjects in the control (unexposed) group: Not specified

Group 1 sample size Number of subjects in the case (exposed) group: Not specified

Lab analysis

Statistical Analysis

Confounders controlled for Confounding factors that have been accounted for by stratification or model adjustment: blood pressure, body mass index, smoking behavior, total cholesterol measurement, alcohol drinking, triglycerides