Association between oral microbiome diversity and hypertension in the US population

Association between oral microbiome diversity and hypertension in the US population | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Association between oral microbiome diversity and hypertension in the US population Jiajun Liu, Kundou Jiang, Tianzuo Lan, Zexu Jin, Shuheng Liao, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7481563/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Population-based research on the relationship between oral microbiome diversity and hypertension risk remains limited. This study aims to investigate the relationship between oral microbiome diversity and hypertension. Methods We conducted a cross-sectional analysis of National Health and Nutrition Examination Survey (NHANES) data from 2009–2012. The association between oral microbiome α-diversity and hypertension risk was assessed using multivariable logistic regression models. Restricted cubic splines were employed to examine dose-response relationships. β-diversity differences between hypertensive and normotensive groups were evaluated using Principal Coordinate Analysis (PCoA) and Permutational Multivariate Analysis of Variance (PERMANOVA). Results Among 7,737 participants analyzed, observed amplicon sequence variants (ASVs) and Faith's phylogenetic diversity (Faith's PD) demonstrated significant inverse associations with hypertension risk. Compared to the lowest quartile, multivariable-adjusted odds ratios (ORs) for quartiles 2–4 were 0.821 (95% CI: 0.679–0.993), 0.780 (0.639–0.951), and 0.790 (0.640–0.974) for ASVs ( P = 0.042, 0.014, and 0.027, respectively), and 0.833 (0.688–1.008), 0.755 (0.619–0.922), and 0.744 (0.604–0.916) for Faith's PD ( P = 0.060, 0.006, and 0.005, respectively). β-diversity analysis revealed significant differences between hypertensive and normotensive groups across all distance metrics (Bray-Curtis, unweighted and weighted UniFrac; all P < 0.01). Conclusion Significant disparities in oral microbiome α-diversity and β-diversity were identified between individuals with hypertension and those without. Notably, higher α-diversity, particularly observed ASVs and Faith's PD, exhibited a negative correlation with hypertension risk. Oral microbiome Hypertension Diversity NHANES Figures Figure 1 Figure 2 Figure 3 1 Introduction Hypertension, defined as elevated blood pressure (140/90 mmHg or higher), is the leading preventable cause of premature death worldwide and the second leading contributor to the global disease burden [ 1 ][ 2 ] . It affects more than a quarter of men and a fifth of women-over a billion people in total [ 3 ] . Driven by population aging and the increase in genetic and lifestyle risk factors (including high-salt diets, excessive alcohol consumption, and lack of physical activity), the prevalence of hypertension is rising globally, with an average annual increase of 0.20%, particularly in countries with high and high-middle Socio-demographic Index (SDI) [ 4 – 6 , 7 ] . Among 195 countries and regions, hypertension is one of the leading risk factors for years of life lost (YLLs) and the third largest avoidable risk factor globally, following high BMI and tobacco use [ 8 ] . Despite the high prevalence of hypertension, interventions remain inadequate. The oral microbiome is a complex ecosystem that comprises bacteria, eukaryotic microorganisms (such as fungi), archaea, and viruses, exhibiting a high degree of diversity [ 9 ] . The interplay between its diversity and the host has significant implications for both oral and systemic health [ 10 ] . Dysbiosis of the oral microbiome has been implicated in various oral diseases (such as dental cariesand periodontal disease) and systemic diseases (such as cardiovascular disease, diabetes, and Alzheimer's disease) [ 11 – 15 ] . Additional research have demonstrated a significant association between periodontal disease and hypertension, with periodontal disease patients often presenting with higher blood pressure levels, and periodontal treatment significantly reducing blood pressure [ 16 , 17 ] . The relationship between periodontal disease and blood pressure suggests that the microbiome may influence the pathogenesis of systemic diseases such as hypertension through mechanisms involving inflammatory responses, microbial dissemination, and immune reactions. While the association between the gut microbiome and hypertension has been extensively studied [ 18 , 19 ] , the association between the oral microbiome and hypertension remains less well characterized, especially in large-scale representative populations. Therefore, understanding the risk factors for hypertension and enhancing hypertension screening and management (such as improving dietary patterns) are of great significance for formulating effective prevention and treatment strategies and alleviating the public health burden. This study aimed to examine the relationship between oral microbiome diversity (including α- diversity and β-diversity) and the risk of hypertension through a cross-sectional analysis of a large and nationally representative dataset, in order to enhance the understanding of the potential role of the oral microbiome in the development of hypertension. 2 Materials and Methods 1. Study population The National Health and Nutrition Examination Survey (NHANES) is a continuous, cross-sectional initiative administered by the National Center for Health Statistics (NCHS) that employs a sophisticated, multistage probability sampling framework to generate nationally representative estimates of the health and nutritional status of U.S. adults and children. Data are collected through in-home interviews, standardized physical examinations, and comprehensive laboratory tests performed in mobile examination centers. All NHANES protocols have been reviewed and approved by the NCHS Institutional Review Board, and every participant provided written informed consent prior to data collection. We pooled data from the 2009–2010 and 2011–2012 NHANES cycles and restricted the analytic cohort to adults aged ≥ 20 years who had both oral microbiome profiles and documented blood-pressure status. Relevant sociodemographic characteristics, health behaviors, and co-existing medical conditions were extracted concurrently. A detailed participant-selection flow diagram is provided in Fig. 1, and ultimately, 7,737 eligible individuals were included in the final analysis. 2. Exposure and outcomes Participants were classified as hypertensive if they fulfilled any of the following: (1) a mean systolic blood pressure ≥ 140 mmHg, (2) a mean diastolic blood pressure ≥ 90 mmHg, (3) self-reported physician-diagnosed hypertension, or (4) current use of antihypertensive medications [ 20 ] . Oral microbiota were profiled from NHANES 2009–2012 mouth-rinse samples. After DNA extraction, amplicon sequencing and standard bioinformatic filtering, ASVs were delineated. α-diversity (observed amplicon sequence variants (ASVs), Faith’s phylogenetic diversity (Faith’s PD), Shannon and Simpson indices) was computed from 10 rarefactions (2k-10k reads, averaged) [ 21 – 23 ] . β-diversity was derived as pairwise distances (unweighted/weighted UniFrac, Bray–Curtis) and stored in distance matrices. 3. Covariates The following covariates were included, such as: age, gender, race, education level, marital status, and PIR (poverty income ratio, 3.5), and BMI (body mass index, 30 kg/m2). Household interviews were conducted to collect data on smoking and drinking behaviors. Diagnoses of hyperlipidemia, diabetes mellitus, and cardiovascular disease were based on self-reported information obtained through questionnaires. The diagnosis of periodontitis was based on the presence of probing depth (PD) ≥ 5 mm or attachment loss (AL) ≥ 3 mm at two or more non-adjacent sites [ 24 ] . 4. Statistical analysis For continuous variables, data were expressed as means ± standard deviations (SD) and P-values (from Student's t-tests) were reported. For categorical variables, percentages (95% CI) and P-values (from the Chi-square test) were reported. Multivariable logistic regression models were used to examine the relationship between oral microbiome α-diversity indicators and hypertension risk. In these models, α- diversity indices were incorporated both as continuous variables (scaled for each standard deviation increase to calculate the odds ratio [OR]) and as categorical variables (divided into quartiles). When quartiles were used, the lowest quartile (Q1) was set as the reference category, and a trend P value was computed by treating the median value of each quartile as a continuous variable. Three sequential models were developed: Model 1 (Non-adjusted); Model 2 (Adjust I), adjusted for demographic factors including age, sex, race/ethnicity, education level, marital status, and poverty-to-income ratio; and Model 3 (Adjust II), the fully adjusted model, which additionally controlled for lifestyle factors (smoking status and alcohol consumption), clinical parameters (body mass index), and comorbidities (hyperlipidemia, diabetes mellitus, cardiovascular disease, and periodontitis). To assess potential non-linear dose-response relationships, restricted cubic splines (RCS) with four knots positioned at the 5th and 95th percentiles were fitted. β-diversity differences between hypertensive and normotensive groups were evaluated using Principal Coordinate Analysis (PCoA) based on Bray-Curtis dissimilarity, unweighted UniFrac, and weighted UniFrac distance matrices. Statistical significance of β-diversity differences was determined using Permutational Multivariate Analysis of Variance (PERMANOVA) Subgroup analyses were performed to evaluate effect modification across demographic and clinical strata, with formal interaction tests conducted by including multiplicative interaction terms in the regression models. To address missing covariate data, multiple imputation using chained equations was implemented with five imputed datasets, and results were pooled using Rubin's rules. Sensitivity analyses were conducted using complete-case analysis, restricting the sample to participants with complete data for all covariates in the fully adjusted model. All statistical analyses were performed using R version 4.3.3 (R Foundation for Statistical Computing, Vienna, Austria). Statistical significance was set at P < 0.05 for all analyses. 3 Results 3.1 Basic characteristics of the study population A total of 7737 participants were analyzed, including 5,097 normotensive and 2,640 hy pertensive participants. Compared to the normotensive group, participants in the hypertension group were older and had a higher proportion of males (52.16% vs 48.15%), a lower proportion of participants with college-level education (48.20% vs 57.11%), and a higher rate of being married or living with a partner (59.29% vs 58.72%). Never smoking (51.27% vs 59.48%) and alcohol intake (42.74% vs 43.06%) were less prevalent among hypertensive individuals. They also exhibited higher BMI (53.71% vs 29.60%) and greater comorbidity rates of hyperlipidemia (78.70% vs 61.80%), diabetes (29.86% vs 7.82%), cardiovascular disease (15.08% vs 2.45%), and periodontitis (60.71% vs 60.71%) (all P < 0.05). Table 1 provided more details on the characteristics of the participants. Regarding α-diversity metrics, the hypertension group exhibited significantly lower values in observed ASVs (126.49 ± 45.59 vs. 135.47 ± 44.12), Faith’s PD (14.45 ± 3.97 vs. 15.17 ± 3.65), and the Shannon-Weiner index (4.58 ± 0.71 vs. 4.65 ± 0.70) (all P < 0.001), excluding the Simpson Index, compared to the normotensive group, as detailed in Table 1 . Table 1 Characteristics of study participants Variables All Hypertension P-value No Yes N 7737 5097 2640 Age(y) 43.55 ± 14.28 39.10 ± 13.25 52.15 ± 12.10 < 0.001 Observed ASVs 132.41 ± 44.83 135.47 ± 44.12 126.49 ± 45.59 < 0.001 Faith’s phylogenetic diversity 14.93 ± 3.78 15.17 ± 3.65 14.45 ± 3.97 < 0.001 Shannon-Weiner index 4.62 ± 0.70 4.65 ± 0.70 4.58 ± 0.71 < 0.001 Inverse Simpson index 0.90 ± 0.06 0.90 ± 0.06 0.90 ± 0.06 0.538 Sex < 0.001 Male 3831 (49.52%) 2454 (48.15%) 1377 (52.16%) Female 3906 (50.48%) 2643 (51.85%) 1263 (47.84%) Race < 0.001 Mexican American 1272 (16.44%) 891 (17.48%) 381 (14.43%) Other Hispanic 805 (10.40%) 552 (10.83%) 253 (9.58%) Non-Hispanic White 2930 (37.87%) 2016 (39.55%) 914 (34.62%) Non-Hispanic Black 1811 (23.41%) 951 (18.66%) 860 (32.58%) Other Race 919 (11.88%) 687 (13.48%) 232 (8.79%) Education level < 0.001 Less than high school 1867 (24.16%) 1130 (22.19%) 737 (27.95%) High school or equivalent 1683 (21.78%) 1054 (20.70%) 629 (23.85%) College or above 4179 (54.07%) 2908 (57.11%) 1271 (48.20%) Marital status < 0.001 Married and a partner 4555 (58.91%) 2991 (58.72%) 1564 (59.29%) Never married 1767 (22.85%) 1394 (27.37%) 373 (14.14%) Widowed, divorced or separated 1410 (18.24%) 709 (13.92%) 701 (26.57%) Poverty to income ratio 0.637 3.5 2153 (30.32%) 1426 (30.57%) 727 (29.82%) Smoking status < 0.001 Never 4384 (56.68%) 3031 (59.48%) 1353 (51.27%) Current 1833 (23.70%) 1213 (23.80%) 620 (23.49%) Former 1518 (19.63%) 852 (16.72%) 666 (25.24%) Alcohol intake < 0.001 Never 3031 (42.95%) 1974 (43.06%) 1057 (42.74%) Current 2913 (41.28%) 2023 (44.13%) 890 (35.99%) Former 1113 (15.77%) 587 (12.81%) 526 (21.27%) Body mass index < 0.001 30 2906 (37.80%) 1502 (29.60%) 1404 (53.71%) Hyperlipidemia < 0.001 No 2509 (32.44%) 1947 (38.20%) 562 (21.30%) Yes 5226 (67.56%) 3150 (61.80%) 2076 (78.70%) Diabetes mellitus < 0.001 No 6463 (84.61%) 4621 (92.18%) 1842 (70.14%) Yes 1176 (15.39%) 392 (7.82%) 784 (29.86%) Cardiovascular disease < 0.001 No 7213 (93.24%) 4972 (97.55%) 2241 (84.92%) Yes 523 (6.76%) 125 (2.45%) 398 (15.08%) Periodontitis < 0.001 No 2551 (47.93%) 1744 (53.37%) 807 (39.29%) Yes 2771 (52.07%) 1524 (46.63%) 1247 (60.71%) Missing data: Education level 8, Marital status 5, Poverty to income ratio 635, Smoking status 2, Alcohol intake 680, Body mass index 49, Hyperlipidemia 2, Diabetes mellitus 98, Cardiovascular disease 1, Periodontitis 2415 3.2 Relationship between α-diversity and hypertension Table 2 presented the association between four α-diversity metrics and the risk of hypertension. The results showed that observed ASVs (OR = 0.908; 95% CI: 0.841–0.982) and Faith’s PD (OR = 0.898; 95% CI: 0.830–0.972) were significantly and negatively associated with the risk of hypertension (all P < 0.05). Taking observed ASVs as an example, our results showed that for each unit increase in observed ASVs, the risk of hypertension decreased to 90.8% of the original risk. In the fully adjusted model, participants in the highest quartile (Q4) for both observed ASVs and Faith’s PD had a significantly lower risk of hypertension compared with those in the lowest quartile (Q1). The Shannon-Weiner index and Simpson index did not show a statistically significant association with hypertension through multiple logistic regression models (all P > 0.05). Table 2 Association between α-diversity metrics and the risk of hypertension Exposure Non-adjusted AdjustI AdjustII OR(95%CI) P -value OR(95%CI) P -value OR(95%CI) P -value Observed ASVs 0.814 (0.776, 0.855) < 0.00001 0.894 (0.845, 0.946) 0.00011 0.908 (0.841, 0.982) 0.01516 Observed ASVs quartile Q1 1 1 1 Q2 0.660 (0.579, 0.752) < 0.00001 0.773 (0.664, 0.899) 0.00083 0.821 (0.679, 0.993) 0.04204 Q3 0.628 (0.550, 0.716) < 0.00001 0.784 (0.672, 0.913) 0.00181 0.780 (0.639, 0.951) 0.01423 Q4 0.580 (0.508, 0.662) < 0.00001 0.730 (0.623, 0.856) 0.00011 0.790 (0.640, 0.974) 0.02733 P for trend < 0.00001 0.00023 0.0236 Faith’s phylogenetic diversity 0.814 (0.775, 0.856) < 0.00001 0.898 (0.849, 0.951) 0.00024 0.898 (0.830, 0.972) 0.00765 Faith’s phylogenetic diversity quartile Q1 1 1 1 Q2 0.698 (0.613, 0.796) < 0.00001 0.824 (0.709, 0.958) 0.01174 0.833 (0.688, 1.008) 0.05990 Q3 0.603 (0.528, 0.688) < 0.00001 0.737 (0.632, 0.860) 0.00010 0.755 (0.619, 0.922) 0.00573 Q4 0.586 (0.514, 0.670) < 0.00001 0.721 (0.616, 0.845) 0.00005 0.744 (0.604, 0.916) 0.00529 P for trend < 0.00001 0.00002 0.00336 Shannon-Weiner index 0.900 (0.859, 0.943) 0.00001 0.960 (0.909, 1.014) 0.14770 0.959 (0.891, 1.033) 0.27140 Shannon-Weiner index quartile Q1 1 1 1 Q2 0.855 (0.749, 0.975) 0.01906 0.951 (0.818, 1.107) 0.51992 1.007 (0.830, 1.222) 0.94125 Q3 0.789 (0.692, 0.901) 0.00046 0.909 (0.780, 1.060) 0.22260 0.983 (0.808, 1.197) 0.86748 Q4 0.778 (0.681, 0.888) 0.00020 0.918 (0.786, 1.072) 0.27774 0.914 (0.747, 1.119) 0.38465 P for trend 0.00009 0.22234 0.36612 Inverse Simpson index 1.015 (0.968, 1.064) 0.53793 1.014 (0.960, 1.070) 0.62992 1.004 (0.935, 1.079) 0.90450 Inverse Simpson index quartile Q1 1 1 1 Q2 1.095 (0.960, 1.250) 0.17758 1.056 (0.907, 1.230) 0.48174 1.093 (0.898, 1.329) 0.37528 Q3 0.941 (0.823, 1.076) 0.37508 0.914 (0.784, 1.067) 0.25569 0.918 (0.755, 1.117) 0.39131 Q4 0.995 (0.871, 1.136) 0.93669 1.050 (0.900, 1.224) 0.53757 1.045 (0.858, 1.271) 0.66366 P for trend 0.43362 0.99763 0.87856 Non-adjusted: Crude model. Adjust I: Adjusted for age, sex, race, education level, marital status, and poverty-to-income ratio. Adjust II: Further adjusted for smoking status, alcohol intake, body mass index, hyperlipidemia, diabetes mellitus, cardiovascular disease, and periodontitis, in addition to covariates from Adjust I 3.3 Subgroup analysis and interaction test To assess the robustness of this association across different subgroups, we conducted subgroup analyses and interaction tests. As shown in Table S1, significant associations with hypertension risk were observed for observed ASVs and Faith’s PD in several subgroups, including individuals aged < 50 years, never-smokers, never-drinkers, those with a BMI of 25–30, participants with hyperlipidemia, participants with diabetes mellitus, and individuals without cardiovascular disease. Moreover, Faith’s PD was also negatively associated with hypertension risk among former smokers and in individuals with periodontitis. For the Shannon-Wiener index, a significant association with hypertension risk was observed only in the BMI 25–30 group, whereas the Simpson index showed no association with COPD risk in any of the examined subgroups. In the sensitivity analysis, excluding participants with missing values yielded consistent results (Table S2-4). We explored the relationship between different α-diversity indices and the risk of hypertension by plotting the RCS curves. As shown in Fig. 2, a linear association between α-diversity indicators and the risk of hypertension was found (observed ASVs: P overall < 0.001; Faith's PD: P overall < 0.001), and higher α-diversity indices were associated with a lower risk of hypertension. In contrast, no significant association was observed between Shannon-Weiner index or the Simpson index and hypertension risk (all P >0.05). Notably, we observed no significant interaction between α-diversity and any of the subgroups ( P for interaction > 0.05). 3.4 β-diversity comparison As shown in Fig. 3, we compared the differences in β-diversity metrics between the hypertension and normotensive populations using PCoA analysis. To quantify the extent of these differences in oral microbial β-diversity between groups, PERMANOVA analysis was conducted. The differences in β-diversity between the two groups were statistically significant (Bray-Curtis dissimilarity: R2 = 0.003; unweighted UniFrac distance: R2 = 0.004; weighted UniFrac distance: R2 = 0.003; all P = 0.005). 4 Discussion This cross-sectional study based on NHANES 2009–2012 data investigated the relationship between the oral microbiome and hypertension. The α-diversity indicators and β-diversity metrics differed markedly between hypertensive and normotensive individuals. Further analysis demonstrated a negative correlation between α-diversity and hypertension risk, indicating that higher microbial diversity corresponds to lower hypertension risk. Specifically, each unit increase in observed ASVs and Faith's PD was associated with 9.2% and 10.2% lower hypertension risk, respectively. These findings suggest that individuals with hypertension may harbor a distinctive pattern of oral dysbiosis. The oral microbiome has been associated with periodontal disease, cardiovascular diseases, diabetes, and a variety of other conditions [ 25 – 27 ] . These findings lay the groundwork for exploring the relationship between the oral microbiome and hypertension, suggesting that oral microbes may influence the development and progression of hypertension through mechanisms such as inflammation, production of specific metabolites, or modulation of immune responses [ 33 ] . These insights underscore the significance of the oral microbiome in overall health and provide a crucial direction for future research on the link between the oral microbiome and hypertension. Previous studies have observed a trend of decreased diversity in the oral and gut microbiota of individuals with hypertension, but these findings were not statistically significant due to small sample sizes [ 5 ] . Our study, with an expanded sample size, further confirms the significant differences in the oral microbiota between hypertensive and normotensive individuals, filling a critical gap in this research area. Additionally, while prior research has established associations between gut microbiota diversity and composition and hypertension, large-scale human studies specifically examining the relationship between the oral microbiome and hypertension have been lacking [ 6 ] . Our study reveals significant alterations in the oral microbiota in hypertensive patients, providing an essential foundation for future investigations. Our findings indicate that both observed ASVs and Faith’s PD are negatively correlated with the risk of hypertension. This suggests that maintaining a more diverse oral microbiota may have a protective effect on blood pressure. Although the two indicators focus on slightly different aspects, they jointly reveal the potential value of community richness and diversity in reducing the risk of hypertension. Studies have shown that dysbiosis decreases beneficial metabolites, such as short-chain fatty acids, while increasing detrimental metabolites, exemplified by trimethylamine N-oxide, thereby influencing blood pressure [ 30 , 31 ] . In the Angiotensin II (Ang II)-induced hypertension mouse model, supplementation with short-chain fatty acids (such as butyrate) altered the composition of the microbiota, increased the abundance of beneficial bacteria (e.g., Akkermansia muciniphila), and significantly reduced mean arterial pressure [ 28 ] . Animal and in vitro studies have further demonstrated that lipopolysaccharides (LPS) derived from oral pathobionts—such as Porphyromonas and Fusobacterium—can enter the systemic circulation, activate the Toll-like receptor 4/nuclear factor-κB (TLR4/NF-κB) signaling axis, and thereby stimulate the release of pro-inflammatory mediators including interleukin-6 and tumor necrosis factor-α, ultimately precipitating vascular endothelial dysfunction and arterial hypertension [ 18 , 32 – 34 ] . Concurrently, studies have shown that microbiota-tailored dietary interventions improved blood pressure indices and significantly elevated α-diversity [ 35 ] .These findings highlight the negative correlation between oral microbiota α-diversity and the susceptibility to hypertension, emphasizing the necessity of maintaining a diverse and stable oral microbial ecosystem in clinical practice to optimize blood pressure control. The oral cavity harbors one of the most diverse and abundant microbial communities within the human body, second only to the community that resides in the gastrointestinal tract. The association between the gut microbiota and hypertension has been extensively studied in the past [ 18 , 19 ] . Recent studies have revealed that the direct "oral-gut axis" can also impact gut microbiota and metabolism [ 36 ] . Studies have shown that the decrease in α-diversity in the oropharynx and gut is accompanied by a reduction in beneficial short-chain fatty acid (SCFA)-producing bacteria and an increase in potential pathogenic bacteria. This imbalance is associated with systemic inflammatory markers such as serum IL-6 and TNF-α and may affect the systemic inflammatory status through the oropharyngeal-gut migration pathway or direct micro-aspiration [ 37 – 41 ] . Reduced α-diversity may suggest a depletion of beneficial symbionts and a relative enrichment of potential pathogens. In a randomized, blinded, placebo-controlled clinical trial, patients with hypertension who received fecal microbiota transplantation showed a peak in microbial α-diversity (species richness) on day 14 after the intervention, along with a decrease in systolic blood pressure [ 42 ] . Another study found that mice receiving saliva from human hypertension participants had significantly higher systolic (SBP) and diastolic blood pressure (DBP) compared to mice receiving saliva from normotensive participants or water. Moreover, saliva-derived Veillonella colonized the mouse gut, suggesting that the oral microbiota (especially Veillonella) can influence hypertension via the oral-gut axis [ 43 ] . These findings further highlight the complex relationship between the oral microbiome and hypertension, particularly through the interaction via the oral-gut axis. Reduced microbial diversity and increased inflammatory responses may be key factors in the development and progression of hypertension. Therefore, modulating the microbiota (especially the oral and gut microbiota) could become a novel intervention strategy for hypertension.. Results from PcoA and PERMANOVA showed that there were significant differences in the structure of oral microbiota between the hypertension group and the normotensive group. Although the differences were subtle overall, the results were robust enough to indicate significant differences between the two groups (R² ≈ 0.004, P < 0.01). Previous studies on the oral microbiota of hypertension and normotensive groups have also shown clear differences [ 44 , 45 ] . For example, the relative abundance of Prevotella, Neisseria, and Haemophilus was significantly higher in the normotensive group, while Bacteroides, Lactobacillus, and Atopobium were more abundant in the hypertension group [ 44 ] . The negative impact of reduced salivary microbiota diversity on blood pressure elevation has been found and confirmed in subjects using chlorhexidine mouthwash [ 46 – 48 ] . Further research has shown that the microbial β-diversity of hypertensive female patients is significantly altered, and differences in specific genera (such as increased Faecalibacillus and decreased Ruminiclostridium 6) may be involved in disease mechanisms through metabolic or inflammatory pathways [ 29 ] . Animal experiments also support the above views, showing that moderate-intensity exercise can reshape the host's microbiota (by increasing beneficial metabolic bacteria and restoring community balance), thereby continuously lowering blood pressure [ 49 ] . In addition, a study that transplanted the microbiota of hypertensive human donors into germ-free mice found that hypertension can be transferred through the microbiota, proving the direct impact of the microbiota on host blood pressure [ 18 ] . These findings suggest that a more diverse oral microbiome might contribute more effectively to the maintenance of blood pressure homeostasis . Despite the limited effect size of this study, the results still suggest that the hypertensive state may be accompanied by overall community restructuring, rather than simple increases or decreases in individual bacterial species. The decline in α-diversity provides risk signals at the individual level, while differences in β-diversity reveal microbial differences at the population level. Together, they form the "quantity-quality" dual evidence of microbial imbalance. This study has revealed a significant association between oral microbiota diversity and hypertension risk, but several limitations should be noted. First, the cross-sectional design of this study precludes the determination of the causality and temporal sequence between oral microbiota diversity and hypertension. Therefore, future longitudinal studies are needed to elucidate the temporal relationship and potential bidirectional relationship between hypertension and oral microbiota diversity. Moreover, these future longitudinal studies should incorporate additional variables related to the hypertension process, such as long-term blood pressure trends, the frequency of cardiovascular events, and the use of antihypertensive medications, to better capture their clinical trajectories. Second, although the α-diversity indices observed in this study were significantly associated with hypertension risk, these indices have not yet been directly validated with the microbial composition or functional characteristics in the sequencing data. The existing correlations only provide indirect evidence for the construction of these indices. Further validation based on microbiome data is needed to enhance their credibility as hypertension risk indicators. Third, the study data were derived from a US population, which may limit the generalizability of the results. Although the findings are significant for this specific population, there are significant differences among populations in terms of genetic background, lifestyle, dietary habits, and environmental factors, all of which may influence the relationship between oral microbiota and hypertension. Therefore, future studies should consider validating these findings in populations with different geographic regions, ethnicities, and cultural backgrounds to assess their external validity. In summary, although this study has revealed a significant association between oral microbiota diversity and hypertension risk, further research is needed to address the above limitations and validate these findings in more diverse populations and environments. Conclusions This study reveals that there are significant differences in the oral microbiota between the hypertension and normotensive groups in terms of both α-diversity and β-diversity, and that a decrease in α-diversity is associated with an increased risk of hypertension. These findings lay the foundation for further investigation into the potential role of the oral microbiota in hypertension. Declarations Author Contribution Jiajun Liu and Kundou Jiang were responsible for article writing,Tianzuo Lan and Zexu Jin were responsible for data collection, literature review，Shuheng Liao and Zuqiao Zhao are responsible for drawing the graph，Xin Cai responsible for method development, article review. References GBD 2021 Risk Factors Collaborators. Global burden and strength of evidence for 88 risk factors in 204 countries and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021. 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Child saliva microbiota and caries: a randomized controlled maternal education trial in rural Uganda. Sci Rep. 2022;12(1):7857. Published 2022 May 12. 10.1038/s41598-022-11979-y Kapila YL. Oral health's inextricable connection to systemic health: Special populations bring to bear multimodal relationships and factors connecting periodontal disease to systemic diseases and conditions. Periodontol 2000. 2021;87(1):11–6. 10.1111/prd.12398 . Tonelli A, Lumngwena EN, Ntusi NAB. The oral microbiome in the pathophysiology of cardiovascular disease. Nat Rev Cardiol. 2023;20(6):386–403. 10.1038/s41569-022-00825-3 . Matsha TE, Prince Y, Davids S, Chikte U, Erasmus RT, Kengne AP, et al. Oral Microbiome Signatures in Diabetes Mellitus and Periodontal Disease. J Dent Res. 2020;99(6):658–65. 10.1177/0022034520913818 . Lu J, Zhang S, Huang Y, Qian J, Tan B, Qian X, et al. Periodontitis-related salivary microbiota aggravates Alzheimer's disease via gut-brain axis crosstalk. 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Chobanian AV, Bakris GL, Black HR, et al. The Seventh Report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure: the JNC 7 report. JAMA. 2003;289(19):2560–72. 10.1001/jama.289.19.2560 . Oral. Microbiome Data Documentation. Feng X, Patel EU, White JL, Li S, Zhu X, Zhao N, et al. Association of Oral Microbiome With Oral Human Papillomavirus Infection: A Population Study of the National Health and Nutrition Examination Survey, 2009–2012. J Infect Dis. 2024;230(3):726–35. 10.1093/infdis/jiae004 . Hu Y, Luo J, Xie J, Wang Y, Bao D, Hua L. Exploring the relationship between the oral microbiota and fasting blood glucose level based on the analysis of NHANES data. Panminerva Med. 2024;66(3):334–6. 10.23736/S0031-0808.23.04946-7 . Eke PI, Page RC, Wei L, Thornton-Evans G, Genco RJ. Update of the case definitions for population-based surveillance of periodontitis. J Periodontol. 2012;83(12):1449–54. 10.1902/jop.2012.110664 . Kleine Bardenhorst S, Hagenfeld D, Matern J, Prior K, Harks I, Eickholz P et al. The role of the oral microbiota in the causal effect of adjunctive antibiotics on clinical outcomes in stage III-IV periodontitis patients. Microbiome. 2024;12(1):220. Published 2024 Oct 26. 10.1186/s40168-024-01945-3 Tonelli A, Lumngwena EN, Ntusi NAB. The oral microbiome in the pathophysiology of cardiovascular disease. Nat Rev Cardiol. 2023;20(6):386–403. 10.1038/s41569-022-00825-3 . Xiao E, Mattos M, Vieira GHA, Chen S, Corrêa JD, Wu Y, et al. Diabetes Enhances IL-17 Expression and Alters the Oral Microbiome to Increase Its Pathogenicity. Cell Host Microbe. 2017;22(1):120–e1284. 10.1016/j.chom.2017.06.014 . Chen BY, Lin WZ, Li YL, Bi C, Du LJ, Liu Y, et al. Roles of oral microbiota and oral-gut microbial transmission in hypertension. J Adv Res. 2023;43:147–61. 10.1016/j.jare.2022.03.007 . Louca P, Nogal A, Wells PM, Asnicar F, Wolf J, Steves CJ, et al. Gut microbiome diversity and composition is associated with hypertension in women. J Hypertens. 2021;39(9):1810–6. 10.1097/HJH.0000000000002878 . Marques FZ, Nelson E, Chu PY, Horlock D, Fiedler A, Ziemann M, et al. High-Fiber Diet and Acetate Supplementation Change the Gut Microbiota and Prevent the Development of Hypertension and Heart Failure in Hypertensive Mice. Circulation. 2017;135(10):964–77. 10.1161/CIRCULATIONAHA.116.024545 . Jiang S, Shui Y, Cui Y, Tang C, Wang X, Qiu X, et al. Gut microbiota dependent trimethylamine N-oxide aggravates angiotensin II-induced hypertension. Redox Biol. 2021;46:102115. 10.1016/j.redox.2021.102115 . Chen X, Li P, Liu M, Zheng H, He Y, Chen MX, et al. Gut dysbiosis induces the development of pre-eclampsia through bacterial translocation. Gut. 2020;69(3):513–22. 10.1136/gutjnl-2019-319101 . Lezutekong JN, Nikhanj A, Oudit GY. Imbalance of gut microbiome and intestinal epithelial barrier dysfunction in cardiovascular disease. Clin Sci (Lond). 2018;132(8):901–4. 10.1042/CS20180172 . Published 2018 Apr 30. Santisteban MM, Qi Y, Zubcevic J, Kim S, Yang T, Shenoy V, et al. Hypertension-Linked Pathophysiological Alterations in the Gut. Circ Res. 2017;120(2):312–23. 10.1161/CIRCRESAHA.116.309006 . Kallapura G, Prakash AS, Sankaran K, Manjappa P, Chaudhary P, Ambhore S, et al. Microbiota based personalized nutrition improves hyperglycaemia and hypertension parameters and reduces inflammation: a prospective, open label, controlled, randomized, comparative, proof of concept study. PeerJ. 2024;12:e17583. 10.7717/peerj.17583 . Published 2024 Jun 26. Yamazaki K, Kamada N. Exploring the oral-gut linkage: Interrelationship between oral and systemic diseases. Mucosal Immunol. 2024;17(1):147–53. 10.1016/j.mucimm.2023.11.006 . He Q, Li G, Zhao J, Zhu H, Mo H, Xiong Z et al. The impact of dysbiosis in oropharyngeal and gut microbiota on systemic inflammatory response and short-term prognosis in acute ischemic stroke with preceding infection. Front Microbiol. 2024;15:1432958. Published 2024 Aug 22. 10.3389/fmicb.2024.1432958 Eladham MW, Selvakumar B, Saheb Sharif-Askari N, Saheb Sharif-Askari F, Ibrahim SM, Halwani R. Unraveling the gut-Lung axis: Exploring complex mechanisms in disease interplay. Heliyon. 2024;10(1):e24032. 10.1016/j.heliyon.2024.e24032 . Published 2024 Jan 3. Chen BY, Lin WZ, Li YL, Bi C, Du LJ, Liu Y, et al. Roles of oral microbiota and oral-gut microbial transmission in hypertension. J Adv Res. 2023;43:147–61. 10.1016/j.jare.2022.03.007 . Kunath BJ, De Rudder C, Laczny CC, Letellier E, Wilmes P. The oral-gut microbiome axis in health and disease. Nat Rev Microbiol. 2024;22(12):791–805. 10.1038/s41579-024-01075-5 . Natalini JG, Singh S, Segal LN. The dynamic lung microbiome in health and disease. Nat Rev Microbiol. 2023;21(4):222–35. 10.1038/s41579-022-00821-x . Fan L, Chen J, Zhang Q, Ren J, Chen Y, Yang J et al. Fecal microbiota transplantation for hypertension: an exploratory, multicenter, randomized, blinded, placebo-controlled trial. Microbiome. 2025;13(1):133. Published 2025 May 23. 10.1186/s40168-025-02118-6 Chen BY, Lin WZ, Li YL, Bi C, Du LJ, Liu Y, et al. Roles of oral microbiota and oral-gut microbial transmission in hypertension. J Adv Res. 2023;43:147–61. 10.1016/j.jare.2022.03.007 . Murugesan S, Al Khodor S. Salivary microbiome and hypertension in the Qatari population. J Transl Med. 2023;21(1):454. 10.1186/s12967-023-04247-8 . Published 2023 Jul 8. Barbadoro P, Ponzio E, Coccia E, Prospero E, Santarelli A, Rappelli GGL, et al. Association between hypertension, oral microbiome and salivary nitric oxide: A case-control study. Nitric Oxide. 2021;106:66–71. 10.1016/j.niox.2020.11.002 . Brookes ZLS, Belfield LA, Ashworth A, Casas-Agustench P, Raja M, Pollard AJ, et al. Effects of chlorhexidine mouthwash on the oral microbiome. J Dent. 2021;113:103768. 10.1016/j.jdent.2021.103768 . Bondonno CP, Liu AH, Croft KD, Considine MJ, Puddey IB, Woodman RJ, et al. Antibacterial mouthwash blunts oral nitrate reduction and increases blood pressure in treated hypertensive men and women. Am J Hypertens. 2015;28(5):572–5. 10.1093/ajh/hpu192 . Kapil V, Haydar SM, Pearl V, Lundberg JO, Weitzberg E, Ahluwalia A. Physiological role for nitrate-reducing oral bacteria in blood pressure control. Free Radic Biol Med. 2013;55:93–100. 10.1016/j.freeradbiomed.2012.11.013 . Xia WJ, Xu ML, Yu XJ, Du MM, Li XH, Yang T, et al. Antihypertensive effects of exercise involve reshaping of gut microbiota and improvement of gut-brain axis in spontaneously hypertensive rat. Gut Microbes. 2021;13(1):1–24. 10.1080/19490976.2020.1854642 . Additional Declarations No competing interests reported. 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10:43:40","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":303368,"visible":true,"origin":"","legend":"\u003cp\u003eLegend not included with this version.\u003c/p\u003e","description":"","filename":"Fig2.RCS.png","url":"https://assets-eu.researchsquare.com/files/rs-7481563/v1/2e3e9ec860592572a5854abf.png"},{"id":90320282,"identity":"7fa64ce0-e0e9-447b-804c-0448cd932a24","added_by":"auto","created_at":"2025-09-01 10:43:40","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":612680,"visible":true,"origin":"","legend":"\u003cp\u003eLegend not included with this version.\u003c/p\u003e","description":"","filename":"Fig3.diversity.png","url":"https://assets-eu.researchsquare.com/files/rs-7481563/v1/df24c6d827f1f3dbd78728aa.png"},{"id":91200199,"identity":"b750c2fd-7b7d-450c-a358-d2ad2da9686a","added_by":"auto","created_at":"2025-09-12 15:23:41","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2325505,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7481563/v1/76f0ae35-f59a-4035-bd86-fc395c6556d0.pdf"},{"id":90320281,"identity":"aac43662-b46b-4fe9-8ad0-8e15e6ef2b12","added_by":"auto","created_at":"2025-09-01 10:43:40","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":50763,"visible":true,"origin":"","legend":"","description":"","filename":"tablesupplement.docx","url":"https://assets-eu.researchsquare.com/files/rs-7481563/v1/025213fd2e24b6f520cbdf38.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eAssociation between oral microbiome diversity and hypertension in the US population\u003c/p\u003e","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eHypertension, defined as elevated blood pressure (140/90 mmHg or higher), is the leading preventable cause of premature death worldwide and the second leading contributor to the global disease burden\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e][\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. It affects more than a quarter of men and a fifth of women-over a billion people in total\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. Driven by population aging and the increase in genetic and lifestyle risk factors (including high-salt diets, excessive alcohol consumption, and lack of physical activity), the prevalence of hypertension is rising globally, with an average annual increase of 0.20%, particularly in countries with high and high-middle Socio-demographic Index (SDI)\u003csup\u003e[\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. Among 195 countries and regions, hypertension is one of the leading risk factors for years of life lost (YLLs) and the third largest avoidable risk factor globally, following high BMI and tobacco use\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. Despite the high prevalence of hypertension, interventions remain inadequate.\u003c/p\u003e\u003cp\u003eThe oral microbiome is a complex ecosystem that comprises bacteria, eukaryotic microorganisms (such as fungi), archaea, and viruses, exhibiting a high degree of diversity\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. The interplay between its diversity and the host has significant implications for both oral and systemic health\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. Dysbiosis of the oral microbiome has been implicated in various oral diseases (such as dental cariesand periodontal disease) and systemic diseases (such as cardiovascular disease, diabetes, and Alzheimer's disease)\u003csup\u003e[\u003cspan additionalcitationids=\"CR12 CR13 CR14\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. Additional research have demonstrated a significant association between periodontal disease and hypertension, with periodontal disease patients often presenting with higher blood pressure levels, and periodontal treatment significantly reducing blood pressure\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. The relationship between periodontal disease and blood pressure suggests that the microbiome may influence the pathogenesis of systemic diseases such as hypertension through mechanisms involving inflammatory responses, microbial dissemination, and immune reactions.\u003c/p\u003e\u003cp\u003eWhile the association between the gut microbiome and hypertension has been extensively studied\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e, the association between the oral microbiome and hypertension remains less well characterized, especially in large-scale representative populations. Therefore, understanding the risk factors for hypertension and enhancing hypertension screening and management (such as improving dietary patterns) are of great significance for formulating effective prevention and treatment strategies and alleviating the public health burden. This study aimed to examine the relationship between oral microbiome diversity (including α- diversity and β-diversity) and the risk of hypertension through a cross-sectional analysis of a large and nationally representative dataset, in order to enhance the understanding of the potential role of the oral microbiome in the development of hypertension.\u003c/p\u003e"},{"header":"2 Materials and Methods","content":"\u003ch3\u003e1. Study population\u003c/h3\u003e\u003cp\u003eThe National Health and Nutrition Examination Survey (NHANES) is a continuous, cross-sectional initiative administered by the National Center for Health Statistics (NCHS) that employs a sophisticated, multistage probability sampling framework to generate nationally representative estimates of the health and nutritional status of U.S. adults and children. Data are collected through in-home interviews, standardized physical examinations, and comprehensive laboratory tests performed in mobile examination centers. All NHANES protocols have been reviewed and approved by the NCHS Institutional Review Board, and every participant provided written informed consent prior to data collection.\u003c/p\u003e\u003cp\u003eWe pooled data from the 2009\u0026ndash;2010 and 2011\u0026ndash;2012 NHANES cycles and restricted the analytic cohort to adults aged\u0026thinsp;\u0026ge;\u0026thinsp;20 years who had both oral microbiome profiles and documented blood-pressure status. Relevant sociodemographic characteristics, health behaviors, and co-existing medical conditions were extracted concurrently. A detailed participant-selection flow diagram is provided in Fig.\u0026nbsp;1, and ultimately, 7,737 eligible individuals were included in the final analysis.\u003c/p\u003e\n\u003ch3\u003e2. Exposure and outcomes\u003c/h3\u003e\n\u003cp\u003eParticipants were classified as hypertensive if they fulfilled any of the following: (1) a mean systolic blood pressure\u0026thinsp;\u0026ge;\u0026thinsp;140 mmHg, (2) a mean diastolic blood pressure\u0026thinsp;\u0026ge;\u0026thinsp;90 mmHg, (3) self-reported physician-diagnosed hypertension, or (4) current use of antihypertensive medications\u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eOral microbiota were profiled from NHANES 2009\u0026ndash;2012 mouth-rinse samples. After DNA extraction, amplicon sequencing and standard bioinformatic filtering, ASVs were delineated. α-diversity (observed amplicon sequence variants (ASVs), Faith\u0026rsquo;s phylogenetic diversity (Faith\u0026rsquo;s PD), Shannon and Simpson indices) was computed from 10 rarefactions (2k-10k reads, averaged)\u003csup\u003e[\u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e. β-diversity was derived as pairwise distances (unweighted/weighted UniFrac, Bray\u0026ndash;Curtis) and stored in distance matrices.\u003c/p\u003e\n\u003ch3\u003e3. Covariates\u003c/h3\u003e\n\u003cp\u003eThe following covariates were included, such as: age, gender, race, education level, marital status, and PIR (poverty income ratio, \u0026lt;\u0026thinsp;1.3; 1.3\u0026ndash;3.5; \u0026gt;3.5), and BMI (body mass index, \u0026lt;\u0026thinsp;25 kg/m2, 25\u0026ndash;30 kg/m2, \u0026gt;\u0026thinsp;30 kg/m2). Household interviews were conducted to collect data on smoking and drinking behaviors. Diagnoses of hyperlipidemia, diabetes mellitus, and cardiovascular disease were based on self-reported information obtained through questionnaires. The diagnosis of periodontitis was based on the presence of probing depth (PD)\u0026thinsp;\u0026ge;\u0026thinsp;5 mm or attachment loss (AL)\u0026thinsp;\u0026ge;\u0026thinsp;3 mm at two or more non-adjacent sites\u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\n\u003ch3\u003e4. Statistical analysis\u003c/h3\u003e\n\u003cp\u003eFor continuous variables, data were expressed as means\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviations (SD) and P-values (from Student's t-tests) were reported. For categorical variables, percentages (95% CI) and P-values (from the Chi-square test) were reported. Multivariable logistic regression models were used to examine the relationship between oral microbiome α-diversity indicators and hypertension risk. In these models, α- diversity indices were incorporated both as continuous variables (scaled for each standard deviation increase to calculate the odds ratio [OR]) and as categorical variables (divided into quartiles). When quartiles were used, the lowest quartile (Q1) was set as the reference category, and a trend P value was computed by treating the median value of each quartile as a continuous variable.\u003c/p\u003e\u003cp\u003eThree sequential models were developed: Model 1 (Non-adjusted); Model 2 (Adjust I), adjusted for demographic factors including age, sex, race/ethnicity, education level, marital status, and poverty-to-income ratio; and Model 3 (Adjust II), the fully adjusted model, which additionally controlled for lifestyle factors (smoking status and alcohol consumption), clinical parameters (body mass index), and comorbidities (hyperlipidemia, diabetes mellitus, cardiovascular disease, and periodontitis). To assess potential non-linear dose-response relationships, restricted cubic splines (RCS) with four knots positioned at the 5th and 95th percentiles were fitted. β-diversity differences between hypertensive and normotensive groups were evaluated using Principal Coordinate Analysis (PCoA) based on Bray-Curtis dissimilarity, unweighted UniFrac, and weighted UniFrac distance matrices. Statistical significance of β-diversity differences was determined using Permutational Multivariate Analysis of Variance (PERMANOVA) Subgroup analyses were performed to evaluate effect modification across demographic and clinical strata, with formal interaction tests conducted by including multiplicative interaction terms in the regression models. To address missing covariate data, multiple imputation using chained equations was implemented with five imputed datasets, and results were pooled using Rubin's rules. Sensitivity analyses were conducted using complete-case analysis, restricting the sample to participants with complete data for all covariates in the fully adjusted model.\u003c/p\u003e\u003cp\u003eAll statistical analyses were performed using R version 4.3.3 (R Foundation for Statistical Computing, Vienna, Austria). Statistical significance was set at \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 for all analyses.\u003c/p\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1 Basic characteristics of the study population\u003c/h2\u003e\n \u003cp\u003eA total of 7737 participants were analyzed, including 5,097 normotensive and 2,640 hy pertensive participants. Compared to the normotensive group, participants in the hypertension group were older and had a higher proportion of males (52.16% vs 48.15%), a lower proportion of participants with college-level education (48.20% vs 57.11%), and a higher rate of being married or living with a partner (59.29% vs 58.72%). Never smoking (51.27% vs 59.48%) and alcohol intake (42.74% vs 43.06%) were less prevalent among hypertensive individuals. They also exhibited higher BMI (53.71% vs 29.60%) and greater comorbidity rates of hyperlipidemia (78.70% vs 61.80%), diabetes (29.86% vs 7.82%), cardiovascular disease (15.08% vs 2.45%), and periodontitis (60.71% vs 60.71%) (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e provided more details on the characteristics of the participants. Regarding \u0026alpha;-diversity metrics, the hypertension group exhibited significantly lower values in observed ASVs (126.49\u0026thinsp;\u0026plusmn;\u0026thinsp;45.59 vs. 135.47\u0026thinsp;\u0026plusmn;\u0026thinsp;44.12), Faith\u0026rsquo;s PD (14.45\u0026thinsp;\u0026plusmn;\u0026thinsp;3.97 vs. 15.17\u0026thinsp;\u0026plusmn;\u0026thinsp;3.65), and the Shannon-Weiner index (4.58\u0026thinsp;\u0026plusmn;\u0026thinsp;0.71 vs. 4.65\u0026thinsp;\u0026plusmn;\u0026thinsp;0.70) (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), excluding the Simpson Index, compared to the normotensive group, as detailed in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eCharacteristics of study participants\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eAll\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003eP-value\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7737\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5097\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2640\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge(y)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43.55\u0026thinsp;\u0026plusmn;\u0026thinsp;14.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39.10\u0026thinsp;\u0026plusmn;\u0026thinsp;13.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52.15\u0026thinsp;\u0026plusmn;\u0026thinsp;12.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eObserved ASVs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e132.41\u0026thinsp;\u0026plusmn;\u0026thinsp;44.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e135.47\u0026thinsp;\u0026plusmn;\u0026thinsp;44.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e126.49\u0026thinsp;\u0026plusmn;\u0026thinsp;45.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFaith\u0026rsquo;s phylogenetic diversity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.93\u0026thinsp;\u0026plusmn;\u0026thinsp;3.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.17\u0026thinsp;\u0026plusmn;\u0026thinsp;3.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.45\u0026thinsp;\u0026plusmn;\u0026thinsp;3.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eShannon-Weiner index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.62\u0026thinsp;\u0026plusmn;\u0026thinsp;0.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.65\u0026thinsp;\u0026plusmn;\u0026thinsp;0.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.58\u0026thinsp;\u0026plusmn;\u0026thinsp;0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInverse Simpson index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.90\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.90\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.90\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.538\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3831 (49.52%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2454 (48.15%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1377 (52.16%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3906 (50.48%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2643 (51.85%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1263 (47.84%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRace\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMexican American\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1272 (16.44%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e891 (17.48%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e381 (14.43%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOther Hispanic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e805 (10.40%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e552 (10.83%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e253 (9.58%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon-Hispanic White\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2930 (37.87%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2016 (39.55%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e914 (34.62%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon-Hispanic Black\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1811 (23.41%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e951 (18.66%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e860 (32.58%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOther Race\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e919 (11.88%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e687 (13.48%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e232 (8.79%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEducation level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLess than high school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1867 (24.16%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1130 (22.19%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e737 (27.95%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh school or equivalent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1683 (21.78%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1054 (20.70%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e629 (23.85%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCollege or above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4179 (54.07%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2908 (57.11%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1271 (48.20%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMarital status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMarried and a partner\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4555 (58.91%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2991 (58.72%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1564 (59.29%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNever married\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1767 (22.85%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1394 (27.37%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e373 (14.14%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWidowed, divorced or separated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1410 (18.24%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e709 (13.92%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e701 (26.57%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePoverty to income ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.637\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;1.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2547 (35.86%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1655 (35.48%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e892 (36.59%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.3\u0026ndash;3.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2402 (33.82%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1583 (33.94%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e819 (33.59%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;3.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2153 (30.32%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1426 (30.57%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e727 (29.82%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSmoking status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4384 (56.68%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3031 (59.48%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1353 (51.27%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCurrent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1833 (23.70%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1213 (23.80%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e620 (23.49%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFormer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1518 (19.63%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e852 (16.72%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e666 (25.24%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAlcohol intake\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3031 (42.95%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1974 (43.06%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1057 (42.74%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCurrent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2913 (41.28%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2023 (44.13%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e890 (35.99%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFormer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1113 (15.77%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e587 (12.81%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e526 (21.27%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBody mass index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2284 (29.71%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1872 (36.89%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e412 (15.76%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25\u0026ndash;30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2498 (32.49%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1700 (33.50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e798 (30.53%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2906 (37.80%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1502 (29.60%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1404 (53.71%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHyperlipidemia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2509 (32.44%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1947 (38.20%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e562 (21.30%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5226 (67.56%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3150 (61.80%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2076 (78.70%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiabetes mellitus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6463 (84.61%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4621 (92.18%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1842 (70.14%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1176 (15.39%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e392 (7.82%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e784 (29.86%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCardiovascular disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7213 (93.24%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4972 (97.55%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2241 (84.92%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e523 (6.76%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e125 (2.45%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e398 (15.08%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePeriodontitis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2551 (47.93%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1744 (53.37%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e807 (39.29%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2771 (52.07%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1524 (46.63%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1247 (60.71%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003eMissing data: Education level 8, Marital status 5, Poverty to income ratio 635, Smoking status 2, Alcohol intake 680, Body mass index 49, Hyperlipidemia 2, Diabetes mellitus 98, Cardiovascular disease 1, Periodontitis 2415\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2 Relationship between \u0026alpha;-diversity and hypertension\u003c/h2\u003e\n \u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e presented the association between four \u0026alpha;-diversity metrics and the risk of hypertension. The results showed that observed ASVs (OR\u0026thinsp;=\u0026thinsp;0.908; 95% CI: 0.841\u0026ndash;0.982) and Faith\u0026rsquo;s PD (OR\u0026thinsp;=\u0026thinsp;0.898; 95% CI: 0.830\u0026ndash;0.972) were significantly and negatively associated with the risk of hypertension (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Taking observed ASVs as an example, our results showed that for each unit increase in observed ASVs, the risk of hypertension decreased to 90.8% of the original risk. In the fully adjusted model, participants in the highest quartile (Q4) for both observed ASVs and Faith\u0026rsquo;s PD had a significantly lower risk of hypertension compared with those in the lowest quartile (Q1). The Shannon-Weiner index and Simpson index did not show a statistically significant association with hypertension through multiple logistic regression models (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eAssociation between \u0026alpha;-diversity metrics and the risk of hypertension\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eExposure\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNon-adjusted\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAdjustI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAdjustII\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOR(95%CI)\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOR(95%CI)\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOR(95%CI)\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eObserved ASVs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.814 (0.776, 0.855)\u0026thinsp;\u0026lt;\u0026thinsp;0.00001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.894 (0.845, 0.946) 0.00011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.908 (0.841, 0.982) 0.01516\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eObserved ASVs quartile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.660 (0.579, 0.752)\u0026thinsp;\u0026lt;\u0026thinsp;0.00001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.773 (0.664, 0.899) 0.00083\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.821 (0.679, 0.993) 0.04204\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.628 (0.550, 0.716)\u0026thinsp;\u0026lt;\u0026thinsp;0.00001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.784 (0.672, 0.913) 0.00181\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.780 (0.639, 0.951) 0.01423\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.580 (0.508, 0.662)\u0026thinsp;\u0026lt;\u0026thinsp;0.00001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.730 (0.623, 0.856) 0.00011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.790 (0.640, 0.974) 0.02733\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e for trend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.00001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0236\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFaith\u0026rsquo;s phylogenetic diversity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.814 (0.775, 0.856)\u0026thinsp;\u0026lt;\u0026thinsp;0.00001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.898 (0.849, 0.951) 0.00024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.898 (0.830, 0.972) 0.00765\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFaith\u0026rsquo;s phylogenetic diversity quartile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.698 (0.613, 0.796)\u0026thinsp;\u0026lt;\u0026thinsp;0.00001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.824 (0.709, 0.958) 0.01174\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.833 (0.688, 1.008) 0.05990\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.603 (0.528, 0.688)\u0026thinsp;\u0026lt;\u0026thinsp;0.00001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.737 (0.632, 0.860) 0.00010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.755 (0.619, 0.922) 0.00573\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.586 (0.514, 0.670)\u0026thinsp;\u0026lt;\u0026thinsp;0.00001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.721 (0.616, 0.845) 0.00005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.744 (0.604, 0.916) 0.00529\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e for trend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.00001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00336\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eShannon-Weiner index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.900 (0.859, 0.943) 0.00001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.960 (0.909, 1.014) 0.14770\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.959 (0.891, 1.033) 0.27140\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eShannon-Weiner index quartile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.855 (0.749, 0.975) 0.01906\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.951 (0.818, 1.107) 0.51992\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.007 (0.830, 1.222) 0.94125\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.789 (0.692, 0.901) 0.00046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.909 (0.780, 1.060) 0.22260\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.983 (0.808, 1.197) 0.86748\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.778 (0.681, 0.888) 0.00020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.918 (0.786, 1.072) 0.27774\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.914 (0.747, 1.119) 0.38465\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e for trend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.22234\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.36612\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInverse Simpson index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.015 (0.968, 1.064) 0.53793\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.014 (0.960, 1.070) 0.62992\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.004 (0.935, 1.079) 0.90450\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInverse Simpson index quartile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.095 (0.960, 1.250) 0.17758\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.056 (0.907, 1.230) 0.48174\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.093 (0.898, 1.329) 0.37528\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.941 (0.823, 1.076) 0.37508\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.914 (0.784, 1.067) 0.25569\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.918 (0.755, 1.117) 0.39131\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.995 (0.871, 1.136) 0.93669\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.050 (0.900, 1.224) 0.53757\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.045 (0.858, 1.271) 0.66366\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e for trend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.43362\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.99763\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.87856\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003eNon-adjusted: Crude model.\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003eAdjust I: Adjusted for age, sex, race, education level, marital status, and poverty-to-income ratio.\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003eAdjust II: Further adjusted for smoking status, alcohol intake, body mass index, hyperlipidemia, diabetes mellitus, cardiovascular disease, and periodontitis, in addition to covariates from Adjust I\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"char\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3 Subgroup analysis and interaction test\u003c/h2\u003e\n \u003cp\u003eTo assess the robustness of this association across different subgroups, we conducted subgroup analyses and interaction tests. As shown in Table S1, significant associations with hypertension risk were observed for observed ASVs and Faith\u0026rsquo;s PD in several subgroups, including individuals aged\u0026thinsp;\u0026lt;\u0026thinsp;50 years, never-smokers, never-drinkers, those with a BMI of 25\u0026ndash;30, participants with hyperlipidemia, participants with diabetes mellitus, and individuals without cardiovascular disease. Moreover, Faith\u0026rsquo;s PD was also negatively associated with hypertension risk among former smokers and in individuals with periodontitis. For the Shannon-Wiener index, a significant association with hypertension risk was observed only in the BMI 25\u0026ndash;30 group, whereas the Simpson index showed no association with COPD risk in any of the examined subgroups. In the sensitivity analysis, excluding participants with missing values yielded consistent results (Table S2-4).\u003c/p\u003e\n \u003cp\u003eWe explored the relationship between different \u0026alpha;-diversity indices and the risk of hypertension by plotting the RCS curves. As shown in Fig.\u0026nbsp;2, a linear association between \u0026alpha;-diversity indicators and the risk of hypertension was found (observed ASVs: \u003cem\u003eP\u003c/em\u003e overall\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Faith\u0026apos;s PD: \u003cem\u003eP\u003c/em\u003e overall\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and higher \u0026alpha;-diversity indices were associated with a lower risk of hypertension. In contrast, no significant association was observed between Shannon-Weiner index or the Simpson index and hypertension risk (all \u003cem\u003eP\u003c/em\u003e \u0026gt;0.05). Notably, we observed no significant interaction between \u0026alpha;-diversity and any of the subgroups (\u003cem\u003eP\u003c/em\u003e for interaction\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003e3.4 \u0026beta;-diversity comparison\u003c/h2\u003e\n \u003cp\u003eAs shown in Fig.\u0026nbsp;3, we compared the differences in \u0026beta;-diversity metrics between the hypertension and normotensive populations using PCoA analysis. To quantify the extent of these differences in oral microbial \u0026beta;-diversity between groups, PERMANOVA analysis was conducted. The differences in \u0026beta;-diversity between the two groups were statistically significant (Bray-Curtis dissimilarity: R2\u0026thinsp;=\u0026thinsp;0.003; unweighted UniFrac distance: R2\u0026thinsp;=\u0026thinsp;0.004; weighted UniFrac distance: R2\u0026thinsp;=\u0026thinsp;0.003; all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.005).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eThis cross-sectional study based on NHANES 2009\u0026ndash;2012 data investigated the relationship between the oral microbiome and hypertension. The α-diversity indicators and β-diversity metrics differed markedly between hypertensive and normotensive individuals. Further analysis demonstrated a negative correlation between α-diversity and hypertension risk, indicating that higher microbial diversity corresponds to lower hypertension risk. Specifically, each unit increase in observed ASVs and Faith's PD was associated with 9.2% and 10.2% lower hypertension risk, respectively. These findings suggest that individuals with hypertension may harbor a distinctive pattern of oral dysbiosis.\u003c/p\u003e\u003cp\u003eThe oral microbiome has been associated with periodontal disease, cardiovascular diseases, diabetes, and a variety of other conditions\u003csup\u003e[\u003cspan additionalcitationids=\"CR26\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e. These findings lay the groundwork for exploring the relationship between the oral microbiome and hypertension, suggesting that oral microbes may influence the development and progression of hypertension through mechanisms such as inflammation, production of specific metabolites, or modulation of immune responses\u003csup\u003e[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003e. These insights underscore the significance of the oral microbiome in overall health and provide a crucial direction for future research on the link between the oral microbiome and hypertension.\u003c/p\u003e\u003cp\u003ePrevious studies have observed a trend of decreased diversity in the oral and gut microbiota of individuals with hypertension, but these findings were not statistically significant due to small sample sizes\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. Our study, with an expanded sample size, further confirms the significant differences in the oral microbiota between hypertensive and normotensive individuals, filling a critical gap in this research area. Additionally, while prior research has established associations between gut microbiota diversity and composition and hypertension, large-scale human studies specifically examining the relationship between the oral microbiome and hypertension have been lacking\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. Our study reveals significant alterations in the oral microbiota in hypertensive patients, providing an essential foundation for future investigations.\u003c/p\u003e\u003cp\u003eOur findings indicate that both observed ASVs and Faith\u0026rsquo;s PD are negatively correlated with the risk of hypertension. This suggests that maintaining a more diverse oral microbiota may have a protective effect on blood pressure. Although the two indicators focus on slightly different aspects, they jointly reveal the potential value of community richness and diversity in reducing the risk of hypertension. Studies have shown that dysbiosis decreases beneficial metabolites, such as short-chain fatty acids, while increasing detrimental metabolites, exemplified by trimethylamine N-oxide, thereby influencing blood pressure\u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e. In the Angiotensin II (Ang II)-induced hypertension mouse model, supplementation with short-chain fatty acids (such as butyrate) altered the composition of the microbiota, increased the abundance of beneficial bacteria (e.g., Akkermansia muciniphila), and significantly reduced mean arterial pressure\u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e. Animal and in vitro studies have further demonstrated that lipopolysaccharides (LPS) derived from oral pathobionts\u0026mdash;such as Porphyromonas and Fusobacterium\u0026mdash;can enter the systemic circulation, activate the Toll-like receptor 4/nuclear factor-κB (TLR4/NF-κB) signaling axis, and thereby stimulate the release of pro-inflammatory mediators including interleukin-6 and tumor necrosis factor-α, ultimately precipitating vascular endothelial dysfunction and arterial hypertension\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan additionalcitationids=\"CR33\" citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/sup\u003e. Concurrently, studies have shown that microbiota-tailored dietary interventions improved blood pressure indices and significantly elevated α-diversity\u003csup\u003e[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/sup\u003e.These findings highlight the negative correlation between oral microbiota α-diversity and the susceptibility to hypertension, emphasizing the necessity of maintaining a diverse and stable oral microbial ecosystem in clinical practice to optimize blood pressure control.\u003c/p\u003e\u003cp\u003e The oral cavity harbors one of the most diverse and abundant microbial communities within the human body, second only to the community that resides in the gastrointestinal tract. The association between the gut microbiota and hypertension has been extensively studied in the past\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. Recent studies have revealed that the direct \"oral-gut axis\" can also impact gut microbiota and metabolism\u003csup\u003e[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/sup\u003e. Studies have shown that the decrease in α-diversity in the oropharynx and gut is accompanied by a reduction in beneficial short-chain fatty acid (SCFA)-producing bacteria and an increase in potential pathogenic bacteria. This imbalance is associated with systemic inflammatory markers such as serum IL-6 and TNF-α and may affect the systemic inflammatory status through the oropharyngeal-gut migration pathway or direct micro-aspiration\u003csup\u003e[\u003cspan additionalcitationids=\"CR38 CR39 CR40\" citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]\u003c/sup\u003e. Reduced α-diversity may suggest a depletion of beneficial symbionts and a relative enrichment of potential pathogens. In a randomized, blinded, placebo-controlled clinical trial, patients with hypertension who received fecal microbiota transplantation showed a peak in microbial α-diversity (species richness) on day 14 after the intervention, along with a decrease in systolic blood pressure\u003csup\u003e[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]\u003c/sup\u003e. Another study found that mice receiving saliva from human hypertension participants had significantly higher systolic (SBP) and diastolic blood pressure (DBP) compared to mice receiving saliva from normotensive participants or water. Moreover, saliva-derived Veillonella colonized the mouse gut, suggesting that the oral microbiota (especially Veillonella) can influence hypertension via the oral-gut axis\u003csup\u003e[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]\u003c/sup\u003e. These findings further highlight the complex relationship between the oral microbiome and hypertension, particularly through the interaction via the oral-gut axis. Reduced microbial diversity and increased inflammatory responses may be key factors in the development and progression of hypertension. Therefore, modulating the microbiota (especially the oral and gut microbiota) could become a novel intervention strategy for hypertension..\u003c/p\u003e\u003cp\u003eResults from PcoA and PERMANOVA showed that there were significant differences in the structure of oral microbiota between the hypertension group and the normotensive group. Although the differences were subtle overall, the results were robust enough to indicate significant differences between the two groups (R\u0026sup2; \u0026asymp; 0.004, P\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Previous studies on the oral microbiota of hypertension and normotensive groups have also shown clear differences\u003csup\u003e[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]\u003c/sup\u003e. For example, the relative abundance of Prevotella, Neisseria, and Haemophilus was significantly higher in the normotensive group, while Bacteroides, Lactobacillus, and Atopobium were more abundant in the hypertension group\u003csup\u003e[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]\u003c/sup\u003e. The negative impact of reduced salivary microbiota diversity on blood pressure elevation has been found and confirmed in subjects using chlorhexidine mouthwash\u003csup\u003e[\u003cspan additionalcitationids=\"CR47\" citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]\u003c/sup\u003e. Further research has shown that the microbial β-diversity of hypertensive female patients is significantly altered, and differences in specific genera (such as increased Faecalibacillus and decreased Ruminiclostridium 6) may be involved in disease mechanisms through metabolic or inflammatory pathways\u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e. Animal experiments also support the above views, showing that moderate-intensity exercise can reshape the host's microbiota (by increasing beneficial metabolic bacteria and restoring community balance), thereby continuously lowering blood pressure \u003csup\u003e[\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]\u003c/sup\u003e. In addition, a study that transplanted the microbiota of hypertensive human donors into germ-free mice found that hypertension can be transferred through the microbiota, proving the direct impact of the microbiota on host blood pressure\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. These findings suggest that a more diverse oral microbiome might contribute more effectively to the maintenance of blood pressure homeostasis .\u003c/p\u003e\u003cp\u003eDespite the limited effect size of this study, the results still suggest that the hypertensive state may be accompanied by overall community restructuring, rather than simple increases or decreases in individual bacterial species. The decline in α-diversity provides risk signals at the individual level, while differences in β-diversity reveal microbial differences at the population level. Together, they form the \"quantity-quality\" dual evidence of microbial imbalance.\u003c/p\u003e\u003cp\u003eThis study has revealed a significant association between oral microbiota diversity and hypertension risk, but several limitations should be noted. First, the cross-sectional design of this study precludes the determination of the causality and temporal sequence between oral microbiota diversity and hypertension. Therefore, future longitudinal studies are needed to elucidate the temporal relationship and potential bidirectional relationship between hypertension and oral microbiota diversity. Moreover, these future longitudinal studies should incorporate additional variables related to the hypertension process, such as long-term blood pressure trends, the frequency of cardiovascular events, and the use of antihypertensive medications, to better capture their clinical trajectories. Second, although the α-diversity indices observed in this study were significantly associated with hypertension risk, these indices have not yet been directly validated with the microbial composition or functional characteristics in the sequencing data. The existing correlations only provide indirect evidence for the construction of these indices. Further validation based on microbiome data is needed to enhance their credibility as hypertension risk indicators. Third, the study data were derived from a US population, which may limit the generalizability of the results. Although the findings are significant for this specific population, there are significant differences among populations in terms of genetic background, lifestyle, dietary habits, and environmental factors, all of which may influence the relationship between oral microbiota and hypertension. Therefore, future studies should consider validating these findings in populations with different geographic regions, ethnicities, and cultural backgrounds to assess their external validity. In summary, although this study has revealed a significant association between oral microbiota diversity and hypertension risk, further research is needed to address the above limitations and validate these findings in more diverse populations and environments.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study reveals that there are significant differences in the oral microbiota between the hypertension and normotensive groups in terms of both α-diversity and β-diversity, and that a decrease in α-diversity is associated with an increased risk of hypertension. These findings lay the foundation for further investigation into the potential role of the oral microbiota in hypertension.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eJiajun Liu and Kundou Jiang were responsible for article writing,Tianzuo Lan and Zexu Jin were responsible for data collection, literature review，Shuheng Liao and Zuqiao Zhao are responsible for drawing the graph，Xin Cai responsible for method development, article review.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eGBD 2021 Risk Factors Collaborators. Global burden and strength of evidence for 88 risk factors in 204 countries and 811 subnational locations, 1990\u0026ndash;2021: a systematic analysis for the Global Burden of Disease Study 2021. 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Gut Microbes. 2021;13(1):1\u0026ndash;24. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1080/19490976.2020.1854642\u003c/span\u003e\u003cspan address=\"10.1080/19490976.2020.1854642\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Oral microbiome, Hypertension, Diversity, NHANES","lastPublishedDoi":"10.21203/rs.3.rs-7481563/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7481563/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003ePopulation-based research on the relationship between oral microbiome diversity and hypertension risk remains limited. This study aims to investigate the relationship between oral microbiome diversity and hypertension.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eWe conducted a cross-sectional analysis of National Health and Nutrition Examination Survey (NHANES) data from 2009\u0026ndash;2012. The association between oral microbiome α-diversity and hypertension risk was assessed using multivariable logistic regression models. Restricted cubic splines were employed to examine dose-response relationships. β-diversity differences between hypertensive and normotensive groups were evaluated using Principal Coordinate Analysis (PCoA) and Permutational Multivariate Analysis of Variance (PERMANOVA).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eAmong 7,737 participants analyzed, observed amplicon sequence variants (ASVs) and Faith's phylogenetic diversity (Faith's PD) demonstrated significant inverse associations with hypertension risk. Compared to the lowest quartile, multivariable-adjusted odds ratios (ORs) for quartiles 2\u0026ndash;4 were 0.821 (95% CI: 0.679\u0026ndash;0.993), 0.780 (0.639\u0026ndash;0.951), and 0.790 (0.640\u0026ndash;0.974) for ASVs (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.042, 0.014, and 0.027, respectively), and 0.833 (0.688\u0026ndash;1.008), 0.755 (0.619\u0026ndash;0.922), and 0.744 (0.604\u0026ndash;0.916) for Faith's PD (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.060, 0.006, and 0.005, respectively). β-diversity analysis revealed significant differences between hypertensive and normotensive groups across all distance metrics (Bray-Curtis, unweighted and weighted UniFrac; all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eSignificant disparities in oral microbiome α-diversity and β-diversity were identified between individuals with hypertension and those without. Notably, higher α-diversity, particularly observed ASVs and Faith's PD, exhibited a negative correlation with hypertension risk.\u003c/p\u003e","manuscriptTitle":"Association between oral microbiome diversity and hypertension in the US population","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-01 10:43:35","doi":"10.21203/rs.3.rs-7481563/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4de5ebcf-f712-4427-bf02-1fa40d2b9122","owner":[],"postedDate":"September 1st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-09-12T15:23:22+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-01 10:43:35","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7481563","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7481563","identity":"rs-7481563","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}