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Oral Bacterial Microbiomes in Association with Potential Prediabetes Using Different Criteria of Diagnosis

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  • Kornwipa Rungrueang

    (Residency Training Program, Department of Advanced General Dentistry, Faculty of Dentistry, Mahidol University, Bangkok 10400, Thailand)

  • Suraphong Yuma

    (Department of Physics, Faculty of Science, Mahidol University, Bangkok 10400, Thailand)

  • Chanita Tantipoj

    (Department of Advanced General Dentistry, Faculty of Dentistry, Mahidol University, Bangkok 10400, Thailand)

  • Siribang-on Piboonniyom Khovidhunkit

    (Department of Advanced General Dentistry, Faculty of Dentistry, Mahidol University, Bangkok 10400, Thailand)

  • Pornpoj Fuangtharnthip

    (Department of Advanced General Dentistry, Faculty of Dentistry, Mahidol University, Bangkok 10400, Thailand)

  • Thitima Thuramonwong

    (Dental Hospital, Faculty of Dentistry, Mahidol University, Bangkok 10400, Thailand)

  • Muneedej Suwattipong

    (Dental Hospital, Faculty of Dentistry, Mahidol University, Bangkok 10400, Thailand)

  • Sirirak Supa-amornkul

    (Mahidol International Dental School, Faculty of Dentistry, Mahidol University, Bangkok 10400, Thailand
    Pornchai Matangkasombut Center for Microbial Genomic, Faculty of Science, Mahidol University, Bangkok 10400, Thailand)

Abstract

This study aimed to find a potential biomarker that can be used to diagnose prediabetic condition by comparing the salivary bacterial microbiomes between Thai dental patients with normoglycemia (NG) and those with potential prediabetes (PPG) conditions. Thirty-three subjects were randomly recruited. Demographic data were collected along with oral examination and unstimulated salivary collections. The salivary bacterial microbiomes were identified by high-throughput sequencing on the V3–V4 region of the bacterial 16S rRNA gene. Microbiomes in this study were composed of 12 phyla, 19 classes, 29 orders, 56 families, 81 genera, and 184 species. To check the validity of the selection criterion for prediabetes, we adopted two separate criteria to divide samples into PPG and NG groups using glycated hemoglobin A1c (HbA1c) or fasting plasma glucose (FPG) levels. Using the HbA1c level resulted in the significant reduction of Alloprevotella , Neisseria , Rothia, and Streptococcus abundances in PPG compared with those in NG ( p -value < 0.05). On the other hand, the abundance of Absconditabacteriales was significantly reduced whereas Leptotrichia, Stomatobaculum, and Ruminococcaceae increased in the PPG group when the samples were classified by the FPG level ( p -value < 0.05). It is implied that the group classifying criterion should be carefully concerned when investigating relative abundances between groups. However, regardless of the criteria, Rothia is significantly dominant in the NG groups, suggesting that Rothia might be a potential prediabetic biomarker. Due to the small sample size of this study, further investigation with a larger sample size is necessary to ensure that Rothia can be a potential biomarker for prediabetes in Thai people.

Suggested Citation

  • Kornwipa Rungrueang & Suraphong Yuma & Chanita Tantipoj & Siribang-on Piboonniyom Khovidhunkit & Pornpoj Fuangtharnthip & Thitima Thuramonwong & Muneedej Suwattipong & Sirirak Supa-amornkul, 2021. "Oral Bacterial Microbiomes in Association with Potential Prediabetes Using Different Criteria of Diagnosis," IJERPH, MDPI, vol. 18(14), pages 1-15, July.
  • Handle: RePEc:gam:jijerp:v:18:y:2021:i:14:p:7436-:d:592804
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    References listed on IDEAS

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    1. Paulo Mascarenhas & Bruno Fatela & Isabel Barahona, 2014. "Effect of Diabetes Mellitus Type 2 on Salivary Glucose – A Systematic Review and Meta-Analysis of Observational Studies," PLOS ONE, Public Library of Science, vol. 9(7), pages 1-15, July.
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    More about this item

    Keywords

    prediabetes; oral microbiome; saliva; 16S rRNA; HbA1c; FPG;
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