Intestinal Microbiota Composition in Iranian Diabetic, Pre-diabetic and Healthy Individuals

Ghaemi, Farahnaz; Fateh, Abolfazl; Sepahy, Abbas Akhavan; Zangeneh, Mehrangiz; Ghanei, Mostafa; Siadat, Seyed Davar

doi:10.1007/s40200-020-00625-x

Cited by 18 publications

(26 citation statements)

References 33 publications

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“…The 18 observational studies had been conducted between 2013 and 2021. They included twelve case-control ( Zhang et al., 2013 ; Lambeth et al., 2015 ; Bhute et al., 2017 ; Allin et al., 2018 ; Chen et al., 2019 ; Nuli et al., 2019 ; Zhao et al., 2019 ; Zhong et al., 2019 ; Gaike et al., 2020 ; Ghaemi et al., 2020 ; Li et al., 2020 ; Wang et al., 2021 ), four cross-sectional studies ( Diener et al., 2021 ; Egshatyan et al., 2016 ; Chávez-Carbajal et al., 2020 ; Wu et al., 2020 ) and two cohort studies ( Karlsson et al., 2013 ; Ericson et al., 2020 ). Six studies were conducted in Asia [India ( Bhute et al., 2017 ; Gaike et al., 2020 ), Iran ( Ghaemi et al., 2020 ), Taiwan ( Chen et al., 2019 ) and China ( Zhang et al., 2013 ; Nuli et al., 2019 ; Zhao et al., 2019 ; Zhong et al., 2019 ; Li et al., 2020 ; Wang et al., 2021 )], five in Europe [Denmark ( Allin et al., 2018 ), Russia ( Egshatyan et al., 2016 ) and Sweden ( Karlsson et al., 2013 ; Wu et al., 2020 ; Ericson et al., 2020 )] and three in North America [USA ( Lambeth et al., 2015 ) and Mexico ( Diener et al., 2021 ; Chávez-Carbajal et al., 2020 )].…”

Section: Resultsmentioning

confidence: 99%

“…When the 16S rDNA was targeted for sequencing, the hypervariable regions targeted included V4 (Diener et al, 2021;Lambeth et al, 2015;Allin et al, 2018;Gaike et al, 2020), V3-V4 (Egshatyan et al, 2016;Nuli et al, 2019;Zhao et al, 2019), V3 (Bhute et al, 2017;Chavez-Carbajal et al, 2020), V1-V3 (Ericson et al, 2020) or V3-V5 (Zhang et al, 2013) regions, while five studies did not specify the target regions (Karlsson et al, 2013;Zhong et al, 2019;Wu et al, 2020;Wang et al, 2021). The two studies using qPCR to assess the microbiota had used ten and six pairs of specific bacterial 16S rRNA primers, respectively, to target Atopobium cluster, Bacteroides fragilis, Bifidobacterium, Clostridium coccoides, Clostridium leptum, Clostridium perfringens, Enterobacteriaceae, Enterococcus, Lactobacillus and Prevotella (Chen et al, 2019) and Akkermansia, Bacteroides, Bifidobacterium, E.coli, Faecalibacterium and Lactobacillus (Ghaemi et al, 2020). Following sequencing, the microorganisms were classified and reported as operational taxonomic units (OTUs) (Zhang et al, 2013;Lambeth et al, 2015;Egshatyan et al, 2016;Bhute et al, 2017;Allin et al, 2018;Nuli et al, 2019;Zhao et al, 2019;Gaike et al, 2020;Chavez-Carbajal et al, 2020;Ericson et al, 2020), metagenomic clusters (MGCs) (Karlsson et al, 2013;Wu et al, 2020), microbiota (log10 cell/g) (Chen et al, 2019;Ghaemi et al, 2020), metagenome (Wang et al, 2021), metagenomic linkage group (MLGs) (Zhong et al, 2019) or amplicon sequence variants (ASVs)…”

Section: Microbiome Analysis Methodsmentioning

confidence: 99%

“…The two studies using qPCR to assess the microbiota had used ten and six pairs of specific bacterial 16S rRNA primers, respectively, to target Atopobium cluster, Bacteroides fragilis, Bifidobacterium, Clostridium coccoides, Clostridium leptum, Clostridium perfringens, Enterobacteriaceae, Enterococcus, Lactobacillus and Prevotella (Chen et al, 2019) and Akkermansia, Bacteroides, Bifidobacterium, E.coli, Faecalibacterium and Lactobacillus (Ghaemi et al, 2020). Following sequencing, the microorganisms were classified and reported as operational taxonomic units (OTUs) (Zhang et al, 2013;Lambeth et al, 2015;Egshatyan et al, 2016;Bhute et al, 2017;Allin et al, 2018;Nuli et al, 2019;Zhao et al, 2019;Gaike et al, 2020;Chavez-Carbajal et al, 2020;Ericson et al, 2020), metagenomic clusters (MGCs) (Karlsson et al, 2013;Wu et al, 2020), microbiota (log10 cell/g) (Chen et al, 2019;Ghaemi et al, 2020), metagenome (Wang et al, 2021), metagenomic linkage group (MLGs) (Zhong et al, 2019) or amplicon sequence variants (ASVs) (Diener et al, 2021).…”

Section: Microbiome Analysis Methodsmentioning

confidence: 99%

“…The 18 observational studies had been conducted between 2013 and 2021. They included twelve case-control (Zhang et al, 2013;Lambeth et al, 2015;Bhute et al, 2017;Allin et al, 2018;Chen et al, 2019;Nuli et al, 2019;Zhao et al, 2019;Zhong et al, 2019;Gaike et al, 2020;Ghaemi et al, 2020;Wang et al, 2021), four cross-sectional studies (Diener et al, 2021;Egshatyan et al, 2016;Chavez-Carbajal et al, 2020;Wu et al, 2020) and two cohort studies (Karlsson…”

Section: Study Characteristicsmentioning

confidence: 99%

“…Search ((((((((("treatment naive") OR "newly diagnosed") OR "new diagnosis")) OR ((((((prediabetes) OR prediabetics) OR prediabetic) OR pre-diabetes) OR pre-diabetics) OR pre-diabetic)))) OR (("impaired glucose tolerance") OR "impaired fasting glucose"))) AND (((((microbiome) OR microbiota) OR microflora) OR "gut bacteria")) 9 Remove duplicates from 8 Ericson et al, 2020). Six studies were conducted in Asia [India (Bhute et al, 2017;Gaike et al, 2020), Iran (Ghaemi et al, 2020), Taiwan (Chen et al, 2019) and China (Zhang et al, 2013;Nuli et al, 2019;Zhao et al, 2019;Zhong et al, 2019;Wang et al, 2021)], five in Europe [Denmark (Allin et al, 2018), Russia (Egshatyan et al, 2016) and Sweden (Karlsson et al, 2013;Wu et al, 2020;Ericson et al, 2020)] and three in North America [USA (Lambeth et al, 2015) and Mexico (Diener et al, 2021;Chavez-Carbajal et al, 2020)]. These studies had compared the gut microbial profiles of either preDM alone (Allin et al, 2018;Ericson et al, 2020), newDM alone (Chen et al, 2019;, preDM and newDM (Zhang et al, 2013;Egshatyan et al, 2016;Nuli et al, 2019;Zhong et al, 2019;Wu et al, 2020), preDM and knownDM (Karlsson et al, 2013;Lambeth et al, 2015;Ghaemi et al, 2020;Chavez-Carbajal et al, 2020;Wang et al, 2021), newDM and knownDM (Bhute et al, 2017;Zhao et al, 2019) or all three preDM, newDM and knownDM (Diener et al, 2021;…”

Section: Search Terms and Search Strategymentioning

confidence: 99%

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Gut Microbiota Composition in Prediabetes and Newly Diagnosed Type 2 Diabetes: A Systematic Review of Observational Studies

Letchumanan

Abdullah

Marlini

et al. 2022

Front. Cell. Infect. Microbiol.

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Evidence of gut microbiota involvement in regulating glucose metabolism and type 2 diabetes mellitus (T2DM) progression is accumulating. The understanding of microbial dysbiosis and specific alterations of gut microbiota composition that occur during the early stages of glucose intolerance, unperturbed by anti-diabetic medications, is especially essential. Hence, this systematic review was conducted to summarise the existing evidence related to microbiota composition and diversity in individuals with prediabetes (preDM) and individuals newly diagnosed with T2DM (newDM) in comparison to individuals with normal glucose tolerance (nonDM). A systematic search of the PubMed, MEDLINE and CINAHL databases were conducted from inception to February 2021 supplemented with manual searches of the list of references. The primary keywords of “type 2 diabetes”, “prediabetes”, “newly-diagnosed” and “gut microbiota” were used. Observational studies that conducted analysis of the gut microbiota of respondents with preDM and newDM were included. The quality of the studies was assessed using the modified Newcastle-Ottawa scale by independent reviewers. A total of 18 studies (5,489 participants) were included. Low gut microbial diversity was generally observed in preDM and newDM when compared to nonDM. Differences in gut microbiota composition between the disease groups and nonDM were inconsistent across the included studies. Four out of the 18 studies found increased abundance of phylum Firmicutes along with decreased abundance of Bacteroidetes in newDM. At the genus/species levels, decreased abundance of Faecalibacterium prausnitzii, Roseburia, Dialister, Flavonifractor, Alistipes, Haemophilus and Akkermansia muciniphila and increased abundance of Lactobacillus, Streptococcus, Escherichia, Veillonella and Collinsella were observed in the disease groups in at least two studies. Lactobacillus was also found to positively correlate with fasting plasma glucose (FPG), HbA1c and/or homeostatic assessment of insulin resistance (HOMA-IR) in four studies. This renders a need for further investigations on the species/strain-specific role of endogenously present Lactobacillus in glucose regulation mechanism and T2DM disease progression. Differences in dietary intake caused significant variation in specific bacterial abundances. More studies are needed to establish more consistent associations, between clinical biomarkers or dietary intake and specific gut bacterial composition in prediabetes and early T2DM.

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Section: Resultsmentioning

confidence: 99%

Section: Microbiome Analysis Methodsmentioning

confidence: 99%

Section: Microbiome Analysis Methodsmentioning

confidence: 99%

Section: Study Characteristicsmentioning

confidence: 99%

Section: Search Terms and Search Strategymentioning

confidence: 99%

See 3 more Smart Citations

Gut Microbiota Composition in Prediabetes and Newly Diagnosed Type 2 Diabetes: A Systematic Review of Observational Studies

Letchumanan

Abdullah

Marlini

et al. 2022

Front. Cell. Infect. Microbiol.

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Bacteroides Fragilis Exacerbates T2D Vascular Calcification by Secreting Extracellular Vesicles to Induce M2 Macrophages

Chen,

Liang,

et al. 2024

Advanced Science

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Vascular calcification (VC) in type 2 diabetes (T2D) poses a serious threat to the life and health of patients. However, its pathogenesis remains unclear, resulting in a lack of effective treatment for the root cause. It is found that both intestinal Bacteroides fragilis (BF) and peripheral M2 monocytes/macrophages are significantly elevated in patients with T2D VC. M2 macrophages are identified as a significant risk factor for T2D VC. Both BF and their extracellular vesicles (EV) promote T2D VC and facilitate macrophage M2 polarization. Macrophages clearance significantly antagonized BF EV‐induced T2D VC in mice. Mechanistically, EV‐rich double‐stranded DNA (dsDNA) activates stimulator of interferon response cGAMP interactor 1 (Sting), promotes myocyte enhancer factor 2D (Mef2d) phosphorylation, upregulates tribbles pseudokinase 1 (Trib1) expression, and induces macrophage M2 polarization. Concurrently, Mef2d activated by the EV targets and upregulates the expression of pro‐calcification factor Serpine1, thereby exacerbating T2D VC. Clinical studies have shown that Serpine1 is significantly elevated in the peripheral blood of patients with T2D VC and is closely associated with T2D VC. In summary, this study reveals that intestinal BF promotes Trib1 expression through the EV‐Sting‐Mef2d pathway to induce macrophage M2 polarization and upregulates serpin family E member 1 (Serpine1) expression, thereby aggravating T2D VC. The findings provide a new theoretical and experimental bases for optimizing the strategies for prevention and treatment of T2D VC.

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Gut microbiota composition and function in pregnancy as determinants of prediabetes at two-year postpartum

Houttu¹,

Benchraka²,

Lotankar³

et al. 2023

Acta Diabetol

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Aims Deep metagenomics offers an advanced tool for examining the relationship between gut microbiota composition and function and the onset of disease; in this case, does the composition and function of gut microbiota during pregnancy differ in women who develop prediabetes and those who do not at two-year postpartum, and whether the gut microbiota composition associates with glycemic traits. Methods In total, 439 women were recruited in early pregnancy. Gut microbiota was assessed by metagenomics analysis in early (13.9 ± 2.0 gestational weeks) and late pregnancy (35.1 ± 1.0 gestational weeks). Prediabetes was determined using American Diabetes Association criteria as fasting plasma glucose 5.6–6.9 mmol/l analyzed by an enzymatic hexokinase method. Of the women, 39 (22.1%) developed prediabetes by two-year postpartum. Results The relative abundances of Escherichia unclassified (FDR < 0.05), Clostridiales bacterium 1_7_ 47FAA (FDR < 0.25) and Parabacteroides (FDR < 0.25) were higher, and those of Ruminococcaceae bacterium D16 (FDR < 0.25), Anaerotruncus unclassified (FDR < 0.25) and Ruminococcaceae noname (FDR < 0.25) were lower in early pregnancy in those women who later developed prediabetes. In late pregnancy, Porphyromonas was higher and Ruminococcus sp 5_1_39BFAA was lower in prediabetes (FDR < 0.25). Furthermore, fasting glucose concentrations associated inversely with Anaerotruncus unclassified in early pregnancy and directly with Ruminococcus sp 5_1_39BFAA in late pregnancy (FDR < 0.25). α-Diversity or β-diversity did not differ significantly between the groups. Predictions of community function during pregnancy were not associated with prediabetes. Conclusions Our study shows that some bacterial species during pregnancy contributed to the onset of prediabetes within two-year postpartum. These were attributable primarily to a lower abundance of short-chain fatty acids-producing bacteria.

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Intestinal Microbiota Composition in Iranian Diabetic, Pre-diabetic and Healthy Individuals

Cited by 18 publications

References 33 publications

Gut Microbiota Composition in Prediabetes and Newly Diagnosed Type 2 Diabetes: A Systematic Review of Observational Studies

Gut Microbiota Composition in Prediabetes and Newly Diagnosed Type 2 Diabetes: A Systematic Review of Observational Studies

Bacteroides Fragilis Exacerbates T2D Vascular Calcification by Secreting Extracellular Vesicles to Induce M2 Macrophages

Gut microbiota composition and function in pregnancy as determinants of prediabetes at two-year postpartum

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