Abstract

BackgroundThe prevalence of hypertension in Qatar is 33 percent of the adult population. It is postulated that the salivary microbiome can regulate blood pressure (BP). However, limited investigations exist to prove this hypothesis. Therefore, we examined the difference in the salivary microbiome composition between hypertensive and normotensive Qatari subjects.MethodsA total of 1190 Qatar Genome Project (QGP) participants (Mean age = 43 years) were included in this study. BP for all participants was classified into Normal (n = 357), Stage1 (n = 336), and Stage2: (n = 161) according to the American Heart Association guidelines. 16S-rRNA libraries were sequenced and analyzed using QIIME-pipeline, and PICRUST was used to predict functional metabolic routes. Machine Learning (ML) strategies were applied to identify salivary microbiome-based predictors of hypertension.ResultsDifferential abundant analysis (DAA) revealed that Bacteroides and Atopobium were the significant members of the hypertensive groups. Alpha and beta diversity indices indicated dysbiosis between the normotensive and hypertensive groups. ML-based prediction models revealed that these markers could predict hypertension with an AUC (Area under the curve) of 0.89. Functional predictive analysis disclosed that Cysteine and Methionine metabolism and the sulphur metabolic pathways involving the renin-angiotensin system were significantly higher in the normotensive group. Therefore, members of Bacteroides and Atopobium can serve as predictors of hypertension. Likewise, Prevotella, Neisseria, and Haemophilus can be the protectors that regulate BP via nitric acid synthesis and regulation of the renin-angiotensin system.ConclusionIt is one of the first studies to assess salivary microbiome and hypertension as disease models in a large cohort of the Qatari population. Further research is needed to confirm these findings and validate the mechanisms involved.

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