Association of oral frailty and gut microbiota with hypertension: cross-sectional results in the Shika study

Abstract

Although recent studies have reported the association between toxins produced by certain gut microbiota and elevated blood pressure, the relationship between oral frailty (OF) and gut microbiota has rarely been investigated. The purpose of this study was to epidemiologically investigate the relationship between the combination of OF and specific gut microbiota on hypertension in the residents of Shika Town, Ishikawa Prefecture, Japan. A total of 322 residents aged ≥ 50 years in Shika Town agreed to participate and met the criteria. The OF was evaluated difficulty in chewing and swallowing, oral dryness, number of remaining teeth, and frequency of tooth brushing. Blood pressure was measured using an automatic digital blood pressure meter. Next-generation sequencing was used to analyze the gut microbiota. A two-way analysis of covariance revealed a significant interaction between the two OF groups and the two hypertension groups on Megamonas. The binomial logistic regression analysis stratified by OF revealed a positive correlation between Megamonas and hypertension (OR 1.317; P = 0.023). This cross-sectional epidemiological study of the local residents revealed that the abundance of Megamonas in the OF group was significantly higher in the hypertension group than in the normotension group; however, no such relationship was observed in the non-OF group.

Keywords

gut microbiota / hypertension / Megamonas / oral health / regression analysis

1 Introduction

The oral frailty (OF) is characterized by overlapping tooth loss and a minor decline in various oral functions, such as eating and speaking, and is associated with an increased risk of oral dysfunction; however, proper intervention and treatment can effectively improve this condition [1]. It is known to roughly double the risk of physical frailty and sarcopenia [2]. Both physical frailty and OF have been reported to increase the risk of mild cognitive impairment 10 years later [3]. Our previous OF studies found that the combination of OF and decreased mineral-containing food intake, bone mineral density, and low animal protein intake is associated with decreased bone mineral density [4], renal function [5], and weight-adjusted lower calf circumference, respectively [6]. Conversely, only a few reports investigated the relationship between OF and hypertension. In the relationship between oral health and hypertension, C-reactive protein, interleukin-6, and tumor necrosis factor-α, which are released as a result of periodontitis, have affected vascular endothelial function and may be associated with increased blood pressure [7]. Although OF is related to hypertension through a similar mechanism because OF is initiated by tooth loss and impaired masticatory function due to dental caries and periodontal disease, a direct relationship between OF and hypertension has not yet been reported; thus, it is worth investigating.

One of the factors involved in hypertension has recently attracted focus on changes in the gut microbiota. A cross-sectional study of rural Chinese residents reported that the increased number of Megasphaera and Megamonas was positively correlated with systolic blood pressure (SBP) [8]. Li et al. [9] found the overgrowth of Prevotella and Klebsiella in patients with hypertension. Because the gut microbiota associated with hypertension varies across different countries and diets, no satisfactory conclusions can be drawn at present.

OF is presumably associated with periodontal disease, which is responsible for the direct increase in blood pressure due to vascular endothelial cell damage [7] and the indirect increase in blood pressure due to periodontopathogenic bacteria, altering the composition of the gut microbiota via the gastrointestinal tract [10]. Because decline in swallowing function due to OF progression is associated with decreased immunity [11,12], the involvement of gut microbiota may enhance the mechanism that increases blood pressure. This study epidemiologically evaluated the relationship between the combination of OF and specific gut microbiota on hypertension in residents of Shika, Ishikawa Prefecture, Japan.

2 Methods

2.1 Study design and participants

This cross-sectional study was conducted among residents of Shika, Ishikawa Prefecture, Japan. Participants were recruited between October 2017 and December 2019. The population of Shika Town was 20 055 (9525 males and 10 530 females), and the number of individuals aged 65 years and older was 8491 (aging rate, 42.3%) [13]. Kanazawa University and Shika signed an agreement on health promotion in 2011 and have been conducting annual super preventive medical checkups since 2013. Over the years, several articles have been reported on the Shika study [14]. Written informed consent was obtained from all 560 participants who agreed to participate in the study. The target population in this study was individuals aged 50 years and older, based on our previous research [6]. Among them, 462 participants were aged 50 years and older. Furthermore, 132 participants with missing blood pressure or blood biochemistry data and 8 participants with unassessed OF and gut microbiota were excluded. Finally, 322 individuals (142 males and 180 females) were analyzed (Fig. 1). Additionally, individuals taking oral antimicrobials and those with infections were not included in this study.

2.2 Blood pressure assessment

Well-trained nurses or clinical technologists measured blood pressure during annual super preventive medical checkups using an automatic digital blood pressure meter UM-15P (Parama-tech Co., Ltd., Fukuoka, Japan) or HEM-907 (OMRON Co., Ltd., Kyoto, Japan), with the average of two measurements performed on the right upper arm. Hypertension was defined as SBP of ≥ 140 mmHg and/or diastolic blood pressure (DBP) ≥ 90 mmHg [15]. Participants receiving antihypertensive treatment were included in this study.

2.3 Blood biochemistry data assessment

Blood samples for biochemistry examinations were collected during annual super medical checkups after fasting for a minimum of 12 h. The study utilized low-density lipoprotein cholesterol (LDL), high-density lipoprotein cholesterol (HDL), triglyceride (TG), total cholesterol (TC), hemoglobin A1c (HbA1c), and estimated glomerular filtration rate (eGFR). eGFR was calculated using the serum creatinine levels.

2.4 OF assessment

OF was evaluated using the same method as in the Shika study [5,6]. This was assessed based on 5 factors: difficulty chewing, difficulty swallowing, oral dryness, number of remaining teeth, and frequency of tooth brushing. Difficulty chewing and swallowing were assessed by rating the difficulty of consuming hard foods compared to 6 months ago (yes: 2 points; no: 0 points) and rating whether they sometimes choked on tea or soup (yes: 2 points; no: 0 points). Oral dryness was assessed by asking if the individual was bothered by it (yes: 1 point; no: 0 points). The number of remaining teeth was evaluated based on the number of their teeth excluding implants, bridge pontics, and removable dentures (19 or fewer: 1 point; 20 or more: 0 points). The frequency of tooth brushing was evaluated by the number of brushing times per day (less than once per day: 1 point; more than twice per day: 0 points). A total of ≥ 3 points were considered to indicate OF.

2.5 Gut microbiota assessment

The gut microbiota assessment followed the procedures used in previous Shika studies [16,17]. Participants collected fecal samples at home using clean paper and a spatula with a plastic tube (AS ONE Corporation, Osaka, Japan). They then froze the samples overnight. All collected samples were stored frozen at −80 °C. DNA was extracted using the NucleoSpin DNA Stool Kit (Machery-Nagel, Düren, Germany).

The DNA extracted from the gut microbiota was processed for 16S rRNA gene sequencing using next-generation sequencing methods. The V3–4 regions of the 16S rRNA gene were amplified with Ex Taq® Hot Start Version polymerase using a TaKaRa polymerase chain reaction (PCR) Thermal Cycler Dice® Gradient (TaKaRa Bio Inc., Shiga, Japan) [18]. The PCR products were purified using Agencourt AMPure XP magnetic beads (Beckman Coulter, Inc., CA, USA) and then indexed using the Nextera XT Index Kit version 2 (Illumina Inc., San Diego, CA, USA). After purifying the indexed products with magnetic beads, the prepared library was sequenced using the MiSeq System (Illumina) with the MiSeq Reagent Kit version 3 and PhiX Control v3 (Illumina).

The QIIME2 program was used to analyze the microbiota [19]. The paired-end sequence data were cleaned up using the DADA2 plugin [20]. Chimeric sequences were removed using USEARCH (version 10.0.240_i86linux32) [21] and SILVA 16S rRNA database (release 132) [22]. From the non-chimeric sequences, the “pick_de_novo_otus.py” command in QIIME (version 1.9.1) and the SILVA 16S rRNA gene database (release 132) were used to generate operational taxonomic units (OTUs) (97% similarity threshold) [23]. Finally, global singletons were removed using the “filter_otus_from_otu_table.py” command in QIIME. Samples with fewer than 5000 sequences were excluded from the analysis.

2.6 Questionnaire on demographics and body size

Participants completed self-reported questionnaires covering age (years), sex (1: female; 2: male), drinking status (1: non-drinking or drinking fewer than once a month; 2: drinking at least once a month), and smoking status (1: non-smoker or past smoker; 2: current smoker). The body mass index (BMI) was calculated by dividing a person’s weight (in kilograms) by the square of their height (in meters). Diabetes, dyslipidemia, and chronic kidney disease (CKD) were based on the results of medical checkups.

2.7 Statistical analysis

Participants were categorized into non-OF (NOF) and OF groups, and further divided into normotensive (NT) and hypertensive (HT) groups. IBM SPSS Statistics version 29 for Windows (IBM, Armonk, NY, USA) was used for statistical analyses. The normality of the data distribution was assessed using the Kolmogorov–Smirnov test. Normally distributed data are expressed as mean ± standard deviation (SD), whereas non-normally distributed data are presented as median (25th–75th percentile). Student’s t-test was used to compare normally distributed data between the two groups, and the Mann–Whitney U test for non-normally distributed data. The Mann–Whitney U test effect size (r) was obtained from the standardized test statistic and the number of samples. The data for categorical variables are presented as n (%) and analyzed using chi-square tests. A two-way analysis of covariance (ANCOVA) adjusted for age, sex, and BMI was performed to investigate the main effects and interactions between the two OF and two HT groups on gut microbiota. Binomial logistic regression analysis was used to assess the relationship between OF and HT in the gut microbiota to verify the results of the two-way ANCOVA. The analytical method involved stratifying the data by OF. NT and HT were dependent variables evaluated in several models using different variable selections as forced input methods. Alpha diversity was assessed using the Shannon index at a sampling depth of 5000 [24]. The significance level for all tests was set at P < 0.05.

2.8 Sample size and statistical power

G*power software (free version) was used to calculate the sample size and statistical power. For F-tests of ANCOVA, the effect size, alpha error probability, power, number of groups, and number of covariates were set to 0.25, 0.05, 0.95, 4, and 3, respectively. The total sample size and actual power were 210 and 0.950, respectively. For the Z-tests for logistic regression, the tails, odds ratio, null hypothesis, alpha error probability, power, X distribution, and X parm π were set to 2, 2, 0.25, 0.05, 0.8, binomial, and 0.5, respectively. The total sample size and actual power were found to be 308 and 0.801, respectively. Therefore, the sample size of this study was verified to be sufficient.

3 Results

3.1 Participant characteristics

Table 1 shows the participant characteristics. Among the 322 participants, the mean age of 65.7 years (SD = 7.0) in 142 males was not significantly different from that of 64.3 years (SD = 7.8) in 180 females. The percentages of males who were drinkers (P < 0.001), current smokers (P < 0.001), diagnosed with hypertension (P = 0.027), diabetes (P < 0.001), and CKD (P = 0.005) were significantly higher than those of females. BMI (P < 0.001), SBP (P = 0.030), TG (P = 0.042), and HbA1c (P = 0.005) were significantly higher in males than in females. Conversely, LDL (P < 0.001), HDL (P < 0.001), TC (P < 0.001), and eGFR (P = 0.016) were significantly higher in females than in males. The percentage of participants with OF did not differ significantly by sex.

3.2 Characteristics of the two OF groups

Table 2 shows the comparison between the two OF groups. The mean age was significantly older in the 68.0 years (SD = 7.1) of the OF group than in the 63.5 years (SD = 7.2) of the NOF group (P < 0.001). HDL (P = 0.020), TC (P = 0.044), and the number of teeth (P < 0.001) were significantly higher in the NOF group than in the OF group.

3.3 Characteristics of the two HT groups

Table 3 shows the comparison of the two HT groups. The mean age was significantly older in the 66.6 years (SD = 7.7) of the HT group than in the 63.5 years (SD = 7.0) of the NT group (P < 0.001). BMI (P = 0.002), SBP (P < 0.001), DBP (P < 0.001), and TG (P < 0.036) were significantly higher in the HT group than in the NT group. The proportion of females was significantly greater in the NT group than in the HT group (P = 0.027).

3.4 Characteristics of the gut microbiota based on the OF and the HT groups

Table 4 shows the characteristics of the gut microbiota based on the OF and the HT groups. The abundance ratios of Blautia (P = 0.049) and Bifidobacterium (P = 0.015) were significantly higher in the NOF group than in the OF group. Namely, Blautia was shown to be dominant in the NOF and NT groups, respectively.

3.5 Main effects and interactions between the OF and HT groups on the gut microbiota

The NOF group was subdivided into 123 and 96 participants in the NT and HT groups, respectively. The OF group was subdivided into 49 and 54 participants in the NT and HT groups, respectively (Table 5). A two-way ANCOVA was used to assess the main effects and interactions between OF and HT on the gut microbiota after adjusting for age, sex, and BMI. Among the gut microbiota we analyzed, only Megamonas showed a significant interaction in the two-way OF and HT groups (P = 0.002). Since Blautia was significantly more abundant in both NOF and NT groups than in OF or HT groups in the univariate analysis, the Megamonas/Blautia ratio was calculated, which showed a significant interaction in the two-way OF and HT groups (P = 0.004).

Fig. 2A shows the composition of the top 30 genera of intestinal bacteria according to the two-way OF and HT groups. A Shannon index classified in the same way is shown in Fig. 2B. In multiple comparisons using the Bonferroni method in two-way ANCOVA, the HT in the OF group had a significantly higher Shannon index than the NT group (P = 0.012); however, no such relationship was found in the NOF group. In similar multiple comparisons, Megamonas showed a significantly higher abundance ratio in the HT group than in the NT group in the OF group (P = 0.013) but not in the NOF group (Fig. 2C). The Megamonas/Blautia ratio was also significantly higher in the HT group than in the NT group in the OF group (P = 0.005) but not in the NOF group (Fig. 2D). Therefore, Megamonas was found to be higher only in the OF group with HT.

3.6 Relationship between the gut microbiota and HT stratified by OF

Table 6 shows the results of the binomial logistic regression analysis stratified by the NOF and OF groups, with the dependent variable of HT. In model 1, covariates were adjusted for age, sex, and BMI, with Megamonas as the independent variable. Significant independent variables for HT were age (OR 1.066, 95% CI 1.002–1.134; P = 0.044) and Megamonas (OR 1.274, 95% CI 1.025–1.585; P = 0.029) in the OF group. Megamonas was not a significant independent value for HT in the NOF group. Model 2 was the same analysis as model 1, adding drinking and smoking status as covariates. Significant independent variables in the OF group were age (OR 1.081, 95% CI 1.011–1.155; P = 0.022), drinking status (OR 3.099, 95% CI 1.041–9.232; P = 0.042), and Megamonas (OR 1.317, 95% CI 1.038–1.672; P = 0.023). In the NOF group, Megamonas was not a significant independent valuable for HT. Similar results were confirmed for Models 1 and 2 to analyze Megamonas as Megamonas/Blautia ratio (Table 7). Therefore, Megamonas was found to be a gut microbe associated with HT in the OF group in various models.

4 Discussion

Our results revealed that in the OF group, the abundance ratio of Megamonas in the HT group was significantly higher than that in the NT group; however, this relationship was not observed in the NOF group. Megamonas is a Gram-negative bacterium producing lipopolysaccharide (LPS). Colon-derived LPS enhances systemic inflammation associated with various metabolic diseases, including obesity [25], diabetes, and nonalcoholic liver disease, by increasing plasma LPS levels as it enters the circulatory system [26]. A cross-sectional study by Li et al. [8] reported that the presence of Megamonas was positively correlated with SBP, demonstrating the relationship between elevated LPS-derived inflammatory substances and increased blood pressure. Although no study has directly evaluated the relationship between OF and Megamonas, a study evaluating the correlation between oral and gut bacteria reported a high abundance of Streptococcus, Lactobacillus, and Klebsiella and a low abundance of Faecalibacterium, Blautia, Megamonas, and Parabacteroides, indicating aging and plaque accumulation as factors associated with the relationship [27]. As Megamonas is unlikely to have a direct effect on the gut–oral axis and was not directly associated with OF in the univariate analysis of this study, OF indirectly affects Megamonas. One possible mechanism is that in periodontal diseases related to OF [28], LPS produced by periodontopathogenic bacteria affects the gut microbiota via the gastrointestinal tract or hematogenous route, and dietary preference and nutritional status changes associated with OF can alter the gut microbiota. The interaction between these changes in the gut microbiota and OF has an inverse relationship: a low count of Megamonas is not associated with blood pressure, whereas a high count of Megamonas is positively associated with blood pressure. However, further research is warranted to validate this hypothesis as this study did not evaluate periodontal disease, oral microbiome, LPS, or nutrient intake.

Regarding hypertension and gut microbiota, studies have shown increased LPS-producing Gram-negative bacteria, including Megamonas, and decreased short-chain fatty acid (SCFA)-producing bacteria, including Blautia, in patients with hypertension [29] and the primary aldosteronism that causes it [30]. Our univariate analysis also revealed a decrease in the proportion of SCFA-producing bacteria, Blautia, in the HT group. In a study analyzing the relationship between the renin-angiotensin system and gut microbiota in the same Shika residents, Blautia was negatively correlated with SBP [17], revealing that SCFA-producing bacteria may have a protective effect against elevated blood pressure. Blautia was significantly reduced in OF than in NOF in our results, indicating that it is related to oral health. In addition, the finding that the Megamonas/Blautia ratio—i.e., the LPS-producing bacteria/SCFA-producing bacteria ratio—was related to the combination of OF and HT in the two-way ANCOVA and binomial logistic regression analysis is a novelty of this study.

Our Shannon index evaluation showed a high diversity in the OF and HT groups. Regarding the relationship between gut microbiota diversity and HT, studies have reported that various taxa of the gut microbiota are associated with HT and may influence each other in a complex metabolic network, not due to a single factor or a limited number of species alone [8] and that reduced bacterial richness or diversity may have an impact on HT [9], without a unanimous view. A systematic review by Palmu et al. [31] indicated that key gut microbial characteristics such as diversity index, microbial abundance, and variation rate vary from study to study due to technical factors or biological variability, making the comparison and replication of results difficult across studies. Therefore, although the relationship between gut microbiota diversity and HT has not yet been concluded in previous studies, the fact that the Shannon index was higher in the OF and HT groups in our results may be hypothesized to be related to hypertension if OF-mediated changes in the composition and diversity of the gut microbiota may have been favorable for an increased expression of Megamonas.

It is well known that obesity is a risk factor for hypertension [32]. In our logistic regression analysis stratified by OF, BMI was a significant independent variable only in the NOF group. In contrast, a possible reason why BMI was not a significant variable in the OF group may be that the LPS production by Megamonas raises blood pressure independently of obesity. Although excessive drinking is a risk factor for hypertension [33], our results showed that drinking status was a significant independent variable only in the OF group. A study targeting alcohol use disorder reported an abundance of Megamonas [34]. However, it is unclear whether the combination of OF and heavy drinking increases Megamonas; hence, further investigation is necessary.

Several methods are used to evaluate the OF. The number of evaluation criteria ranges from 5 to 8, with varied points assigned to each criterion [6,35–39]. Consequently, previous studies evaluating the relationship between OF and systemic factors reported different conclusions may occur if one article’s evaluation methods are used to other articles that use different evaluation methods. The Japan Geriatrics Society, the Japanese Society of Gerodontology, and the Japanese Association on Sarcopenia and Frailty jointly issued a consensus statement on OF in 2024 [1]. The assessment method adopted was the oral frailty five-item checklist (OF-5) [40]. When comparing the evaluation methods of the OF-5 and Shika study, 4 of 5 items are commonly used. The difference is that the OF-5 assesses low articulatory oral motor skills, whereas the Shika study evaluates whether tooth brushing is less than twice a day. Our evaluation of OF will yield results comparable to the standard method for OF evaluations. However, future systematic reviews on using the OF-5 for OF studies would be preferable to evaluate OF.

One strength of this study is the assessment of the direct relationship between OF and gut microbiota on hypertension, with no similar studies previously reported. Conversely, the limitations of this study are that causal relationships cannot be elucidated in a cross-sectional study, the methods for evaluating OF are not unified, analyses excluding the effects of antihypertensive drugs were not performed, the oral microbiota was not investigated, and periodontal disease indicators were not evaluated. Furthermore, the use of probiotics was not confirmed, suggesting that their influence should be investigated in the future.

5 Conclusions

This cross-sectional epidemiological study of the local residents revealed that only the OF group had a significantly higher abundance of Megamonas in the HT group than in the NT group, but not in the NOF group. In addition to the high abundance of Megamonas, the low ratio of Blautia was found to be a modifier to the HT with OF. Further studies should elucidate the causal relationship through longitudinal studies and the effects of food intake associated with OF on the gut microbiota.

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