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

Kornwipa Rungrueang, Suraphong Yuma, Chanita Tantipoj, Siribang-on Piboonniyom Khovidhunkit, Pornpoj Fuangtharnthip, Thitima Thuramonwong, Muneedej Suwattipong and Sirirak Supa-amornkul
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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

IJERPH, 2021, vol. 18, issue 14, 1-15

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.

Keywords: prediabetes; oral microbiome; saliva; 16S rRNA; HbA1c; FPG (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2021
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