Association between the gut microbiotic composition and dietary patterns in hypertensive elderly patients: a cross-sectional study

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This cross-sectional study aimed to examine the gut microbiota profiles of hypertensive elderly individuals in relation to their dietary patterns and nutrient intake. Methods Twenty hypertensive patients and 21 age-matched healthy controls (aged 65–80 years) were recruited from Cathay General Hospital (Taipei, Taiwan). Data collected included anthropometric measurements, blood pressure, blood biochemical analyses, and dietary intake (24-h recall and food frequency questionnaires) and fecal microbiotic composition (via16S rRNA sequencing). Results Hypertensive patients had significantly higher BMI, waist circumference, and systolic blood pressure. They also showed lower levels of Bacteroides caccae and Barnesiella , and higher levels of Enterobacteriaceae , Enterobacter , Acidaminococcus , and Bacteroides plebeius . Bacteroides caccae abundance was negatively correlated with the intake of saturated fats, sodium, staple foods (e.g., bread, steamed buns, noodles), nut oils, and seasonings. Conclusions Hypertensive patients showed distinct gut microbiota profiles, with lower levels of Bacteroides caccae and Barnesiella , and higher levels of Enterobacteriaceae -related taxa. The abundance of Bacteroides caccae was negatively associated with the intake of saturated fats, sodium, and staple foods, suggesting a link between diet, gut microbiota, and hypertension. hypertension dietary pattern nutrient intake microbiotic composition older adult Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Hypertension is one of the most common chronic diseases worldwide and a major risk factor for cardiovascular, brain, and kidney diseases. According to the latest data from the World Health Organization (WHO), approximately 1.3 billion people aged 30–79 years worldwide had hypertension in 2023. In Taiwan, based on the results of the 2017–2020 National Nutrition and Health Survey, the prevalence of hypertension among individuals aged 18 years and older had reached 26.8%, and the prevalence increases with age [ 2 ]. Risk factors for hypertension include an unhealthy diet (such as excessive salt consumption, high intake of saturated fats and trans fats, and low consumption of fruits and vegetables), physical inactivity, tobacco and alcohol consumption, being overweight or obese, being over 65 years of age, and having coexisting conditions such as diabetes or kidney disease [ 3 ]. On the other hand, the elderly in Asia are increasingly vulnerable due to the "triple burden" of an aging population, hypertension, and mental health issues [ 4 ]. Therefore, the causes of hypertension in the elderly and its impacts on health are crucial health issues for growing aged populations in Asia. Although antihypertensive treatments are widely implemented, a 2021 community-based survey in Taiwan revealed that over 40% of patients with hypertension still failed to achieve optimal blood pressure control, particularly among older adults [ 5 ]. This highlights the urgent need for novel and complementary strategies to regulate blood pressure beyond conventional pharmacological approaches. Among these, the modulation of gut microbiota has emerged as a promising and innovative direction. Animal models have demonstrated gut microbial effects on blood pressure (BP) [ 6 – 8 ]. Hypertension was induced in normotensive rats through transplantation of cecal contents from hypertensive rats [ 6 , 9 ]. The gut microbiota generates various metabolites, including trimethylamine-N-oxide (TMAO), short-chain fatty acids (SCFAs), corticosterone, hydrogen sulfide (H 2 S), choline, bile acids (BAs), indole sulfate, and lipopolysaccharides (LPSs). Among these, SCFAs, TMAO, BAs, H 2 S, and LPSs are closely linked to the development of hypertension [ 10 – 13 ]. The CARDIA and HELIUS studies, two prospective cohort studies conducted in the United States and Europe with 529 and 4672 participants, respectively, both found that hypertensive patients exhibited significantly lower α and β diversities in their gut microbiota compared to individuals with normal BP [ 14 , 15 ]. Additionally, there were significant differences in the abundances of certain bacterial species between the two groups, although most of the bacterial species identified in the two studies did not overlap. A study conducted in various regions of China found that gut microbiotic compositions varied across different regions and ethnic groups, indicating that lifestyle and ethnicity might influence the gut microbiotic composition [ 16 ]. While changes in the gut microbiotic composition have been linked to hypertension, most such studies focused on Western populations, which might not be applicable to Asian countries including Taiwan, a region with distinct ethnicities and dietary habits. Therefore, in this study, we investigated relationships among dietary patterns, the gut microbiota, and hypertension in elderly individuals, using data from Taiwan to explore how regional dietary habits and microbiotic composition influence BP regulation. Materials and Methods Study design and participant recruitment This study was approved by the Institutional Review Board (IRB) of Cathay General Hospital (Taipei, Taiwan), Cathay Medical Foundation (CGH-P110063, approved on December 24, 2022). The international registration number is NCT05057039, and it was listed on ClinicalTrials.gov PRS and reviewed on April 14, 2023. Participants were recruited from elderly individuals undergoing health check-ups at Cathay General Hospital. The inclusion criteria for the hypertension group were individuals aged 65–80 years who had a systolic BP exceeding 140 mmHg or a diastolic BP exceeding 90 mmHg during a health check-up, or those who had taken antihypertensive medication within the past month. For the normotensive group, participants were age- and sex-matched individuals who had a systolic BP below 139 mmHg, a diastolic BP below 89 mmHg, and had not taken antihypertensive medication within the past month. Exclusion criteria included individuals over 80 years old, and those who had used antibiotics in the past month, consumed probiotics in the past week, had a history of inflammatory bowel disease or gastrointestinal surgery, suffered from chronic gastrointestinal diseases requiring long-term medication, had experienced acute gastrointestinal symptoms such as vomiting or diarrhea in the past week, had been diagnosed with secondary hypertension, or were unable to complete the dietary assessment questionnaire. Participants who were willing to participate and met the eligibility criteria provided informed consent before data collection. The collected data included height, weight, body-mass index (BMI), BP, pulse, medical history, and blood and biochemical test results. Additionally, a registered dietitian conducted dietary records, food frequency questionnaires, and nutritional analyses. Participants were also provided with a fecal sample collection kit to obtain stool samples for a gut microbiotic analysis. Materials and Methods Basic information, anthropometric data, and BP For anthropometric measurements, researchers used a fully automated height and weight scale (HW-210, Universal Weight Electronics, New Taipei City, Taiwan) at the Cathay General Hospital Health Examination Center to measure participants' height and weight and calculate their BMI. BP was measured using office BP monitoring with a fully automated medical-grade electronic sphygmomanometer (HBP-9030, Omron, Kyoto, Japan). Before the measurement, participants were required to rest for at least 5 min. A nurse assisted in the measurement process, recording three BP and pulse readings, with the average value used for the statistical analysis. Blood biochemical analysis Blood biochemical data were analyzed using a fully automated hematology analyzer (XN-10/XN-20, Sysmex, Kobe, Japan) to measure hemoglobin, the white blood cell (WBC) count, and platelet count. Additionally, biochemical analyses were conducted using a fully automated analyzer (AU5800, Beckman Coulter, Brea, CA, USA). The analyzed parameters included liver function markers (aspartate aminotransferase, AST; and alanine aminotransferase, ALT), kidney function marker (creatinine), and metabolism-related indicators such as fasting blood glucose, total cholesterol (TC), triglycerides (TGs), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), uric acid, albumin, and globulin levels. Dietary intake On the day of enrollment, participants underwent an interview with a registered dietitian to complete a 24-h dietary recall and a food frequency questionnaire (FFQ). The 2016 Edition of the Simplified Nutrition Calculation Table (Microsoft Excel) was used to assess participants’ total energy, macronutrient, dietary fiber, vitamin, and mineral intake levels. Food intake was categorized based on six major food groups, with further classification of staple foods into whole grains, legumes, tubers, and others (e.g., bread, steamed buns, noodles). Additional analyses were conducted on seasoning usage, cooking methods, processed meats, and pickled foods to evaluate their potential impacts on BP. The calculation method for dietary frequency was based on a previous study, by converting weekly and monthly intake frequencies into daily equivalents [ 17 ]. For instance, food consumed one or two times per week was averaged to 1.5 times per week, corresponding to 0.21 times per day, while once per month was converted to 0.03 times per day. Fecal microbiotic analysis This study used a 16S ribosomal (r)RNA variable region analysis to classify the fecal microbiota. Fecal samples were stored at -20°C until being analyzed. DNA was extracted from fecal samples using a QIAamp Fast DNA Stool Mini Kit (Qiagen, Hilden, Germany). The V3 + V4 region was amplified by a polymerase chain reaction (PCR) for the microbiotic analysis. The first round of PCR amplification was carried out using Kapa HiFi HotStart ReadyMix (KapaBiosystems, Wilmington, MA, USA) with 0.2 µM forward and reverse primers targeting the V3 + V4 region, and DNA was purified using Agencourt AMPure XP Reagent beads (Beckman Coulter, Brea, CA, USA). In the second round of the PCR, Nextera XT Index 1 and 2 (Illumina, San Diego, CA, USA) were added, and a quantitative (q)PCR was performed. Samples were then uniformly mixed and sequenced using the Illumina MiSeq NGS system. After sequencing, more than 80,000 paired-end 300-bp sequences were generated. These sequences were classified and analyzed using the DADA2 workflow and SILVA v132 taxonomic database. Statistical analysis SPSS statistical software (SPSS vers. 19, IBM, Chicago, IL, USA) was used for data analysis. For categorical variables, data are presented as counts ( n ) and percentages (%); for continuous variables, the Kolmogorov-Smirnov test was first applied to assess the normality of the distribution. If variables followed a normal distribution, the data were expressed as means and standard deviations (SDs); if variables did not follow a normal distribution, data are presented as medians and interquartile ranges (IQRs). To compare differences between groups, the Chi-squared test was used for categorical variables. For continuous variables, an independent t -test was used for normally distributed variables, and the Mann-Whitney U-test was used for non-normally distributed variables. Furthermore, to explore correlations between the gut microbiotic composition and clinical factors or dietary frequency, Spearman’s rank correlation coefficient analysis was used. A p value of < 0.05 was set as the threshold for statistical significance. In the fecal microbiotic analysis, this study used the MicrobiomeAnalyst platform to perform richness and diversity analyses, as well as differential abundance analyses, comparing the hypertensive group and healthy control group [ 18 , 19 ]. The α diversity of the gut microbiota was assessed using five indices: Observed, Chao1, ACE (abundance-based coverage estimator), Shannon index, and Simpson index. β diversity was visualized using a principal coordinates analysis (PCoA), with diversity differences between groups calculated using three methods: Bray-Curtis distance, weighted UniFrac, and unweighted UniFrac. To assess the statistical significance of differences between groups, a permutational multivariate analysis of variance (PERMANOVA) was applied. Additionally, for the differential abundance analysis of the fecal microbiota, this study used the edgeR statistical method for single-factor statistical comparisons, and applied the Benjamini-Hochberg method to adjust original p values for the false discovery rate (FDR), yielding corresponding q values. Finally, a q value of < 0.05 was set as the threshold for statistical significance to determine whether there were significant differences in gut microbiotic compositions between the hypertensive group and normotensive group. Results Basic information and characteristics Initially 56 participants were enrolled in this study, among whom 15 were excluded due to failing to meet the inclusion criteria. Ultimately, 20 hypertensive patients and 21 normotensive individuals were included (Fig. 1 ). When comparing the characteristics of the two groups, there were no significant differences in terms of gender, age, diabetes, or hyperlipidemia. However, compared to the normotensive group, the hypertensive group showed a significantly higher BMI, waist circumference, and systolic BP (Table 1 ). Table 1 Characteristics of hypertensive patients and healthy controls Variable Hypertensive patients ( N = 20) Normotensive controls ( N = 21) p value Male (%) 10 (50) 10 (48) 0.883 Age (years) 72 ± 5.3 70 ± 3.6 0.086 Body-mass index (kg/m 2 ) 24.8 ± 3.4 21.2 ± 2.5 < 0.001 Waist circumference (cm) 83.4 ± 9.0 76.5 ± 8.9 0.018 Systolic blood pressure (mmHg) 143 ± 14 122 ± 12 < 0.001 Diastolic blood pressure (mmHg) 75 ± 12 70 ± 7 0.166 Diabetes (%) 4 (20) 3 (14) 0.637 Hyperlipidemia (%) 3 (15) 5 (24) 0.489 Values are expressed as the number (%) of patients or the mean ± standard deviation. Blood biochemical parameters As shown in Table 2 , the TC level was significantly lower in the hypertensive group compared to the normotensive group. However, there were no significant differences between the two groups in other blood-related indicators, including hemoglobin, WBCs, platelets, AST, ALT, creatinine, fasting blood glucose, TGs, HDL-C, LDL-C, uric acid, albumin, and globulin (Table 2 ). Table 2 Blood analysis of hypertensive patients and healthy controls Variable Hypertensive patients ( N = 26) Normotensive controls ( N = 27) p value Hemoglobin (g/dL) 13.9 ± 1.5 14.5 ± 1.2 0.166 WBCs (1000/µL) 5.4 ± 1.8 5.2 ± 1.2 0.692 Platelets (1000/µL) 224 ± 43 230 ± 64 0.703 Hematocrit (%) 41.4 ± 4.2 42.4 ± 3.2 0.368 RBCs (10 6 /µL) 4.6 ± 0.4 4.7 ± 0.5 0.793 RDW (%) 13.1 ± 1.4 12.7 ± 0.5 0.211 MCV (fL) 89.2 ± 4.2 91.1 ± 4.2 0.156 AST (U/L) 20 ± 5 24 ± 12 0.223 ALT (U/L) 15 ± 8 18 ± 18 0.440 Creatinine (mg/dL) 0.88 ± 0.18 0.84 ± 0.17 0.388 Fasting glucose (mg/dL) 104 ± 15 106 ± 43 0.821 Total cholesterol (mg/dL) 171 ± 40 206 ± 32 0.004 Triglycerides (mg/dL) 95 ± 36 121 ± 48 0.061 HDL-C (mg/dL) 65 ± 13 68 ± 16 0.514 LDL-C (mg/dL) 95 ± 32 120 ± 28 0.015 Uric acid (mg/dL) 5.9 ± 1.2 5.7 ± 0.9 0.584 Albumin (g/dL) 4.4 ± 0.3 4.4 ± 0.2 0.632 Globulin (g/dL) 3.1 ± 0.2 3.1 ± 0.3 0.543 Values are expressed as the number (%) of patients or the mean ± standard deviation. WBCs, white blood cells; RBCs, red blood cells; RDW, red blood cell distribution width; MCV, mean corpuscular volume; AST, aspartate transaminase; ALT, alanine transaminase; HDL-C, high-density-lipoprotein cholesterol; LDL-C, low-density-lipoprotein cholesterol. Dietary intake Based on the 24-h dietary recall, there were no significant differences between the hypertensive group and normotensive group in daily intake of calories, macronutrients, vitamins, and minerals (Table 3 ). Additionally, comparisons of the intake frequencies for the six major food groups, condiments, and cooking methods based on the dietary frequency questionnaire also showed no significant differences between the two groups (Table 4 ). Table 3 Nutrient intake levels in hypertensive patients and healthy controls Nutrient Hypertensive patients ( N = 20) Normotensive controls ( N = 21) p value Energy (kcal) 1451.6 ± 372.8 1545.3 ± 282.3 0.368 Carbohydrates (% of total kcal) 46.6 ± 4.7 49.6 ± 10.3 0.253 Protein (% of total kcal) 17.1 ± 3.2 17.1 ± 3.5 0.975 Fat (% of total kcal) 36.3 ± 5.2 33.4 ± 9.4 0.231 Saturated fat (g) 16.9 ± 5.1 17.1 ± 6.9 0.942 Trans fat (mg) 165.7 ± 118.2 155.3 ± 115.5 0.779 Cholesterol (mg) 298.7 ± 181.5 253.5 ± 141.9 0.379 Dietary fiber (g) 16.4 ± 5.6 17.6 ± 7.1 0.548 Vitamin A (IU) 16933.7 ± 8821.3 17055.7 ± 6455.1 0.960 Vitamin D (µg) 0.9 ± 0.9 0.8 ± 1 0.772 Vitamin E (mg) 20.6 ± 8.6 22.3 ± 17.7 0.689 Vitamin K 1 (µg) 1.2 ± 3.5 7 ± 21.4 0.247 Vitamin B 1 (mg) 0.9 ± 0.4 1 ± 0.3 0.382 Vitamin B 2 (mg) 1.1 ± 0.5 1 ± 0.4 0.783 Niacin (mg) 12.5 ± 6.2 15.3 ± 5.9 0.140 Vitamin B 6 (mg) 1.4 ± 0.6 1.4 ± 0.5 0.900 Vitamin B 12 (µg) 3.5 ± 3.8 3.3 ± 2.6 0.867 Folate (µg) 293.9 ± 83.7 301.9 ± 103.6 0.788 Vitamin C (mg) 168.4 ± 82.4 164.2 ± 67.5 0.861 Sodium (mg) 916.2 ± 411.2 785.1 ± 361.7 0.284 Potassium (mg) 2148 ± 584.3 2228.6 ± 613.4 0.669 Calcium (mg) 726.5 ± 261.5 765.6 ± 369.4 0.699 Magnesium (mg) 258.6 ± 67.7 296.2 ± 125.9 0.244 Iron (mg) 9.8 ± 2.5 10.4 ± 4 0.548 Zinc (mg) 7.8 ± 2.3 8.4 ± 2.6 0.392 Phosphorus (mg) 905.5 ± 296.4 981.8 ± 318.1 0.432 Copper (mg) 0.2 ± 0.2 0.2 ± 0.1 0.496 Manganese (mg) 0.1 ± 0.3 0.1 ± 0.2 0.765 Values are expressed as the mean ± standard deviation. Fecal microbiotic composition The α diversity analysis showed no significant differences between hypertensive patients and healthy controls in the Observed, Chao1, ACE, Shannon, and Simpson indices, indicating that there were no notable differences in the richness and diversity of the gut microbiota between the two groups (Fig. 2 ). Similarly, in the β diversity analysis, calculations using Bray-Curtis distance, unweighted UniFrac distance, and weighted UniFrac distance, followed by a PERMANOVA, also revealed no significant differences between the two groups (Fig. 3 ). For gut microbiotic abundances, the study used the edgeR method for statistical analysis and applied the Benjamini-Hochberg method for FDR correction. In total, 153 amplicon sequence variants (ASVs) were compared, and six taxonomic features showed significant differences in relative abundances (Table 5 ). Among these, Bacteroides caccae had a significantly lower relative abundance in hypertensive patients compared to the normotensive group, approximately 2 2.95 times lower, with a log2 fold change (log2FC) of -2.95. The total sequencing read count of Bacteroides caccae accounted for approximately 2 8.41 counts per million (CPM), with a logCPM of 8.41 ( p < 0.001, FDR q = 0.013) indicating a significant difference in relative abundances between the two groups. The Barnesiella genus also had a significantly lower relative abundance in hypertensive patients compared to the healthy group (FDR q = 0.043). The remaining four taxonomic features showed significantly higher relative abundances in the hypertensive group, including the family Enterobacteriaceae (FDR q = 0.008), Bacteroides plebeius (FDR q = 0.043), the Enterobacter genus (FDR q = 0.043), and the Acidaminococcus genus (FDR q = 0.043) (Fig. 4 ). A post hoc power analysis was conducted for Bacteroides caccae . Assuming a dispersion range of 0.2 and based on the current sample size (hypertension group, n = 20; normotensive group, n = 21) as well as the observed effect size (log₂FC = -2.95), the calculated statistical power was 0.95. Table 5 Differential abundance analysis of specific taxonomic features between hypertensive patients and healthy controls Taxonomic feature log2FC logCPM p value FDR q value Bacteroides caccae -2.95 8.41 < 0.001 0.013 Barnesiella genus -3.17 12.18 0.003 0.043 Enterobacteriaceae family 4.71 12.53 < 0.001 0.008 Bacteroides plebeius 2.73 10.41 0.002 0.043 Enterobacter genus 3.48 13.44 0.003 0.043 Acidaminococcus genus 3.61 13.74 0.003 0.043 The differential abundance analysis was performed using the edgeR statistical method. The Benjamini-Hochberg method was applied to adjust raw p values for the false discovery rate (FDR), resulting in q values. A q value of < 0.05 indicates a significant difference between the two groups. FC, fold change; CPM, counts per million. This study further employed a Spearman’s rank correlation analysis to investigate the relationship between the six gut microbiota species significantly associated with hypertension and various clinical factors and dietary components. Figure 6 presents the correlation analysis results between these six gut bacteria and clinical indicators. In hypertensive or prehypertensive individuals, Bacteroides caccae and the Barnesiella genus showed negative correlations, whereas the Enterobacteriaceae family, Bacteroides plebeius , Enterobacter genus, and Acidaminococcus genus showed positive correlations. Among these, Bacteroides caccae , Enterobacter , and Acidaminococcus demonstrated statistically significant correlations with hypertension. Figure 5 presents the correlation analysis between gut microbiota and clinical factors. Bacteroides caccae was negatively correlated with the BMI and positively correlated with blood HDL-C levels. The Barnesiella genus showed positive correlations with age, hemoglobin, creatinine, and TC levels. The Enterobacteriaceae family was negatively correlated with WBCs and albumin concentrations. Additionally, Bacteroides plebeius showed positive correlations with age and waist circumference, but a negative correlation with HDL-C levels. The Enterobacter genus was positively correlated with the BMI but negatively correlated with WBC count, while Acidaminococcus was positively correlated with the BMI. In the correlation analysis between gut microbiota and dietary intake (Fig. 6 ), the abundance of Bacteroides caccae was negatively correlated with the daily intake of saturated fat and sodium. The Barnesiella genus showed a negative correlation with the daily total calorie intake and protein percentage. The Enterobacteriaceae family exhibited a positive correlation with the percentage of daily protein intake, while Bacteroides plebeius was positively correlated with the daily total calorie intake. The Enterobacter genus showed a positive correlation with the daily protein intake percentage, while Acidaminococcus showed no significant correlation in the dietary analysis. Furthermore, in the analysis of correlations between the gut microbiota and food consumption frequencies (Fig. 6 ), the abundance of Bacteroides caccae was negatively correlated with the consumption frequency of staple foods (such as bread, steamed buns, and noodles) and with the use frequency of nut oils and seasonings. The abundance of the Enterobacteriaceae was positively correlated with egg consumption frequency, but negatively correlated with the frequency of consuming braised dishes. The Acidaminococcus genus showed a significant negative correlation with the frequency of legume consumption. Discussion In this study, we investigated the gut microbiotic characteristics of elderly patients with hypertension. Study participants were individuals aged 65–80 years who were undergoing geriatric health check-ups, with a final selection of 20 participants in the hypertension group and 21 in the normotensive group. Results showed no significant differences in gender or age between the two groups. However, hypertensive patients had a significantly higher BMI, waist circumference, and systolic BP compared to the normotensive group, which aligns with the clinical characteristics of hypertension. Previous studies also confirmed the correlation between hypertension and obesity [ 20 ]. In the blood analysis, hypertensive patients had significantly lower TC and LDL-C levels, while other blood parameters showed no significant differences. This phenomenon may be related to the fact that 75% of the hypertensive participants were undergoing antihypertensive medication treatment. These participants may have also received lifestyle modification recommendations during outpatient visits, including weight loss, dietary adjustments, and exercise, which could have contributed to cholesterol reduction [ 21 ]. However, LDL-C levels in both groups remained within the normal range (below 130 mg/dL). Results of nutritional intake showed no significant differences between hypertensive patients and the normotensive group in intake of daily total calories, macronutrients, dietary fiber, vitamins, or minerals. In this study, we found no significant differences in gut microbiotic α-diversity or β-diversity between hypertensive patients and the normotensive group. Previous studies reported inconsistent results for the fecal microbiotic composition in hypertensive individuals. Sun et al. showed that hypertension and systolic BP were inversely associated with measures of α-diversity, including richness and the Shannon diversity index [ 14 ]. Palmu et al. also indicated that the α and β diversities of the taxonomic composition were strongly related to BP indexes in age- and sex-adjusted models [ 22 ]. Average ages of the study populations in those two experiments were 55.3 ± 3.4 and 49.2 ± 12.9 years, respectively. On the other hand, a cross-sectional study in Spain analyzed 16S rRNA gene sequencing data from 29 untreated hypertensive patients and 32 healthy individuals, revealing no significant differences in Chao1 richness estimates, Shannon diversity index, or β-diversity between hypertensive (53.7 ± 9.6 years old) and normotensive groups (41.1 ± 9.1 years old) [ 23 ]. Similarly, a study in Japanese subjects with an average age of 68 (range: 16–88) years involving 97 hypertensive patients, 162 diabetic patients, and 96 hyperlipidemic patients found no significant differences in gut microbiotic richness, the Shannon diversity index, or β-diversity among these populations [ 24 ]. The inconsistencies in these findings suggest that the overall gut microbiotic diversity is not a key factor influencing hypertension. Instead, changes in relative abundances or the presence of specific bacterial taxa may play more significant roles in BP regulation. We found significant differences in the relative abundances of six bacterial taxa between hypertensive patients and healthy controls. The relative abundances of Bacteroides caccae and the Barnesiella genus were lower in hypertensive patients, whereas abundances of the Enterobacteriaceae family, Bacteroides plebeius , and the Enterobacter and Acidaminococcus genera were significantly higher. The gut microbiota is influenced by various factors, including ethnicity, age, dietary patterns, medical history, and medication use [ 25 ]. In this study, we specifically recruited elderly participants aged 65–80 years and assessed their dietary intake using 24-h dietary recall and a food frequency questionnaire. Additionally, we excluded individuals with a history of related diseases or prior probiotic use to minimize the potential confounding effects of those factors. Taxonomically, both Bacteroides caccae and Bacteroides plebeius belong to the genus Bacteroides , but their relative abundances exhibited opposite trends, consistent with previous studies [ 23 ]. Since Bacteroides plebeius primarily produces acetate, whereas Bacteroides caccae mainly generates butyrate [ 26 ], these differences in SCFAs may influence BP through distinct G-protein-coupled receptors (GPCRs), potentially contributing to the observed variations in bacterial abundances. SCFAs, such as acetate, propionate, and butyrate, can activate GPR41 and GPR43 on the surface of intestinal epithelial cells, promoting the production of chemokines and cytokines involved in protective immune responses and tissue inflammation in mice [ 27 ]. Moreover, when propionate binds to the GPR41 receptor in the sympathetic nervous system, it enhances sympathetic activity, thereby increasing BP [ 28 ]. GPR41 knockout mice exhibit elevated systolic BP, increased pulse pressure, higher end-diastolic pressure, and aggravated perivascular fibrosis [ 29 , 30 ]. While acetate and propionate can stimulate renin secretion via olfactory receptor 78 (Olfr78) in the kidneys, further contributing to BP elevation, a two- to three-fold increase in colonic butyrate concentration has been shown to exert a significant hypotensive effect via the colon–vagus nerve signaling pathway [ 31 ]. Taken together, SCFAs may exert opposing effects on BP by activating different receptors, stimulating renin release via Olfr78 in the kidney to raise BP, and inducing vasodilation via GPR41 to lower BP. These contrasting actions highlight their complex role in blood pressure regulation. Additionally, relative abundances of the Enterobacteriaceae family and the Enterobacter genus were significantly higher in hypertensive patients. Previous studies showed that Enterobacteriaceae-derived TMAO can activate the NLR family pyrin domain containing 3 (NLRP3) inflammasome in carotid endothelial cells, leading to endothelial dysfunction and its association with hypertension [ 32 ]. This bacterial group is also linked to oxidative stress and inflammatory responses, which may accelerate telomere attrition [ 33 ]. Notably, its higher prevalence in elderly populations aligns with the age characteristics of participants in this study. Finally, this study found that the relative abundance of the Acidaminococcus genus was significantly higher in hypertensive patients. Previous research indicated that this bacterial group is associated with propionate production [ 34 ], and lower propionate levels were correlated with reduced BP [ 35 ]. These findings suggest that Acidaminococcus may influence BP regulation through SCFAs. Regarding correlations between the fecal gut microbiota and clinical factors in this study, the BMI was negatively correlated with Bacteroides caccae but positively correlated with the Enterobacter and Acidaminococcus genera. Previous studies also reported that Bacteroides caccae is associated with lower metabolic syndrome scores, fasting blood glucose, and insulin resistance [ 36 ]. In contrast, Enterobacter was found to be more abundant in obese patients [ 37 ], and its endotoxin production may contribute to obesity development. Similarly, Acidaminococcus was significantly associated with obesity in Hispanic populations [ 38 ], further supporting the correlations observed in this study. According to results of this study, hemoglobin levels were positively correlated with the Barnesiella genus, while WBC counts were negatively correlated with the Enterobacteriaceae family and the Enterobacter genus. Previous studies showed that gut dysbiosis was associated with various hematological disorders, including iron-deficiency anemia, thrombosis, thrombocytosis, thrombocytopenia, and hematologic malignancies [ 37 ]. However, no studies have yet explored the relationship between the Barnesiella genus or the Enterobacteriaceae family and hematological diseases, highlighting a potential direction for future research. The current findings may contribute to a better understanding of hypertension-related gut microbiotic variations in aging populations and their potential implications for dietary interventions in different regions. However, this study was limited by the small sample size and all participants being recruited from the health examination cohort of a single hospital. Additionally, 75% of hypertensive patients were already receiving antihypertensive medication, which may have influenced the gut microbiotic composition. Furthermore, as a cross-sectional study, this research could not establish a causal relationship between the gut microbiota and hypertension. In conclusion, the study indicates that older adults with hypertension had gut microbiota imbalances linked to higher BMI and blood pressure. Certain beneficial bacteria, such as Bacteroides caccae , were less abundant and showed negative associations with unhealthy dietary habits, including high intake of saturated fats, sodium and staple foods. This suggests that dietary pattern and nutrient intake may play a key role in shaping the gut microbiota and could be a target for improving blood pressure in older adults. Declarations Acknowledgements: The authors express their appreciation to all subjects who participated in this study for helping with study conduction and data collection. Author contributions All authors read and approved the final manuscript. H.-C.H. and S.-C.Y. designed the study. H.-C.H., Y.-Y.L ., and W.-J.T analyzed and interpreted data. H.-C.H., Y.-Y.C and S.-C.Y. conducted the analysis and drafted the manuscript. H.-C.H. and S.-C.Y. revised the manuscript. H.-C.H., Y.-Y.C and S.-C.Y. finalized the manuscript. The corresponding authors attests that all listed authors meet the authorship criteria and that no others meeting the criteria have been omitted. Funding This research was funded by Cathay General Hospital, Taipei, Taiwan (CGH-MR-A11017). Data Availability The data that support the findings of this study are available from the corresponding author upon reasonable request. Ethics approval and consent to participate All participants volunteered, and informed consent was obtained from both the subjects and their guardians. The study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Board (IRB) of Cathay General Hospital (Taipei, Taiwan), Cathay Medical Foundation (CGH-P110063, approved on December 24, 2022). The international registration number is NCT05057039, and it was listed on ClinicalTrials.gov PRS and reviewed on April 14, 2023.. Consent for publication Not applicable. Competing interests The authors declare no competing interests. References World Health Organization. Hypertension 2023 https://www.who.int/health-topics/hypertension#tab=tab_1. Accessed 20 Feb 2025 Health Promotion Administration, Ministry of Health and Welfare, Taiwan. (2020). The report of the 2017-2020 National Nutrition and Health Survey. https://www.hpa.gov.tw/. Accessed 20 Feb 2025 Guzik TJ, Nosalski R, Maffia P, Drummond GR. Immune and inflammatory mechanisms in hypertension. Nat Rev Cardiol. 2024;21(6):396-416. doi: 10.1038/s41569-023-00964-1.Turana Y, Tengkawan J, Chia YC, Shin J, Chen CH, Park S, Tsoi K, Buranakitjaroen P, Soenarta AA, Siddique S, Cheng HM, Tay JC, Teo BW, Wang TD, Kario K. Mental health problems and hypertension in the elderly: Review from the HOPE Asia Network. J Clin Hypertens (Greenwich). 2021;23(3):504-512. doi: 10.1111/jch.14121. Lin HJ, Pan HY, Beaney T, Partington G, Poulter NR, Chen WJ, Wang TD. May Measurement Month 2019: an analysis of blood pressure screening results from Taiwan. Eur Heart J Suppl. 2021;23(Suppl B):B141-B143. doi: 10.1093/eurheartj/suab046. Adnan S, Nelson JW, Ajami NJ, Venna VR, Petrosino JF, Bryan RM, Durgan DJ. Alterations in the gut microbiota can elicit hypertension in rats. Physiol Genomics. 2017;49(2):96-104. doi: 10.1152/physiolgenomics.00081.2016 Yang T, Santisteban MM, Rodriguez V, Li E, Ahmari N, Carvajal JM, Zadeh M, Gong M, Qi Y, Zubcevic J, Sahay B, Pepine CJ, Raizada MK, Mohamadzadeh M. 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Renal and cardiovascular sensory receptors and blood pressure regulation. Am J Physiol Renal Physiol . 2013;305(4):F439–F444. doi: 10.1152/ajprenal.00252.2013 Gomez-Arango LF, Barrett HL, McIntyre HD, Callaway LK, Morrison M, Dekker Nitert M; SPRING Trial Group. Increased systolic and diastolic blood pressure is associated with altered gut microbiota composition and butyrate production in early pregnancy. Hypertension. 2016;68(4):974–981. doi: 10.1161/HYPERTENSIONAHA.116.07910 Sun S, Lulla A, Sioda M, Winglee K, Wu MC, Jacobs DR Jr, Shikany JM, Lloyd-Jones DM, Launer LJ, Fodor AA, Meyer KA. Gut Microbiota Composition and Blood Pressure. Hypertension. 2019;73(5):998-1006. doi: 10.1161/HYPERTENSIONAHA.118.12109. Verhaar BJH, Collard D, Prodan A, Levels JHM, Zwinderman AH, Bäckhed F, Vogt L, Peters MJL, Muller M, Nieuwdorp M, van den Born BH. Associations between gut microbiota, faecal short-chain fatty acids, and blood pressure across ethnic groups: the HELIUS study. Eur Heart J. 2020;41(44):4259-4267. doi: 10.1093/eurheartj/ehaa704. Lu J, Zhang L, Zhai Q, Zhao J, Zhang H, Lee YK, Lu W, Li M, Chen W. Chinese gut microbiota and its associations with staple food type, ethnicity, and urbanization. NPJ Biofilms Microbiomes. 2021;7(1):71. doi: 10.1038/s41522-021-00245-0. Yáñez F, Soler Z, Oliero M, Xie Z, Oyarzun I, Serrano-Gómez G, Manichanh C. Integrating Dietary Data into Microbiome Studies: A Step Forward for Nutri-Metaomics. Nutrients. 2021;13(9):2978. doi: 10.3390/nu13092978. Chong J, Liu P, Zhou G, Xia J. Using MicrobiomeAnalyst for comprehensive statistical, functional, and meta-analysis of microbiome data. Nat Protoc. 2020;15(3):799-821. doi: 10.1038/s41596-019-0264-1. Dhariwal A, Chong J, Habib S, King IL, Agellon LB, Xia J. MicrobiomeAnalyst: a web-based tool for comprehensive statistical, visual and meta-analysis of microbiome data. Nucleic Acids Res. 2017;45(W1):W180-W188. doi: 10.1093/nar/gkx295. Leggio M, Lombardi M, Caldarone E, Severi P, D'Emidio S, Armeni M, Bravi V, Bendini MG, Mazza A. The relationship between obesity and hypertension: an updated comprehensive overview on vicious twins. Hypertens Res. 2017;40(12):947-963. doi: 10.1038/hr.2017.75. Wang TD, Chiang CE, Chao TH, Cheng HM, Wu YW, Wu YJ, Lin YH, Chen MY, Ueng KC, Chang WT, Lee YH, Wang YC, Chu PH, Chao TF, Kao HL, Hou CJ, Lin TH. 2022 Guidelines of the Taiwan Society of Cardiology and the Taiwan Hypertension Society for the Management of Hypertension. Acta Cardiol Sin. 2022;38(3):225-325. doi: 10.6515/ACS.202205_38(3).20220321A. Palmu J, Salosensaari A, Havulinna AS, Cheng S, Inouye M, Jain M, Salido RA, Sanders K, Brennan C, Humphrey GC, Sanders JG, Vartiainen E, Laatikainen T, Jousilahti P, Salomaa V, Knight R, Lahti L, Niiranen TJ. Association Between the Gut Microbiota and Blood Pressure in a Population Cohort of 6953 Individuals. J Am Heart Assoc. 2020;9(15):e016641. doi: 10.1161/JAHA.120.016641. Calderón-Pérez L, Gosalbes MJ, Yuste S, Valls RM, Pedret A, Llauradó E, Jimenez-Hernandez N, Artacho A, Pla-Pagà L, Companys J, Ludwig I, Romero MP, Rubió L, Solà R. Gut metagenomic and short chain fatty acids signature in hypertension: a cross-sectional study. Sci Rep. 2020;10(1):6436. doi: 10.1038/s41598-020-63475-w. Takagi T, Naito Y, Kashiwagi S, Uchiyama K, Mizushima K, Kamada K, Ishikawa T, Inoue R, Okuda K, Tsujimoto Y, Ohnogi H, Itoh Y. Changes in the gut microbiota are associated with hypertension, hyperlipidemia, and type 2 diabetes mellitus in Japanese subjects. Nutrients. 2020;12(10):2996. doi: 10.3390/nu12102996. Guo Y, Li X, Wang Z, Yu B. Gut microbiota dysbiosis in human hypertension: a systematic review of observational studies. Front Cardiovasc Med. 2021;8:650227. doi: 10.3389/fcvm.2021.650227. Shagaleeva OY, Kashatnikova DA, Kardonsky DA, Konanov DN, Efimov BA, Bagrov DV, Evtushenko EG, Chaplin AV, Silantiev AS, Filatova JV, Kolesnikova IV, Vanyushkina AA, Stimpson J, Zakharzhevskaya NB. Investigating volatile compounds in the Bacteroides secretome. Front Microbiol. 2023;14:1164877. doi: 10.3389/fmicb.2023.1164877. Kim MH, Kang SG, Park JH, Yanagisawa M, Kim CH. Short-chain fatty acids activate GPR41 and GPR43 on intestinal epithelial cells to promote inflammatory responses in mice. Gastroenterology. 2013;145(2):396-406.e1-10. doi: 10.1053/j.gastro.2013.04.056. Kimura I, Inoue D, Maeda T, Hara T, Ichimura A, Miyauchi S, Kobayashi M, Hirasawa A, Tsujimoto G. Short-chain fatty acids and ketones directly regulate sympathetic nervous system via G protein-coupled receptor 41 (GPR41). Proc Natl Acad Sci U S A. 2011;108(19):8030-5. doi: 10.1073/pnas.1016088108 Natarajan N, Hori D, Flavahan S, Steppan J, Flavahan NA, Berkowitz DE, Pluznick JL. Microbial short chain fatty acid metabolites lower blood pressure via endothelial G protein-coupled receptor 41. Physiol Genomics. 2016;48(11):826-834. doi: 10.1152/physiolgenomics.00089.2016 Kaye DM, Shihata WA, Jama HA, Tsyganov K, Ziemann M, Kiriazis H, Horlock D, Vijay A, Giam B, Vinh A , Johnson C, Fiedler A, Donner D, Snelson M, Coughlan MT, Phillips S, Du XJ, El-Osta A, Drummond G, Lambert GW, Spector TD, Valdes AM, Mackay CR, Marques FZ. Deficiency of prebiotic fiber and insufficient signaling through gut metabolite-sensing receptors leads to cardiovascular disease. Circulation. 2020;141(17):1393-1403. doi: 10.1161/CIRCULATIONAHA.119.043081 Onyszkiewicz M, Gawrys-Kopczynska M, Konopelski P, Aleksandrowicz M, Sawicka A, Koźniewska E, Samborowska E, Ufnal M. Butyric acid, a gut bacteria metabolite, lowers arterial blood pressure via colon-vagus nerve signaling and GPR41/43 receptors. Pflugers Arch. 2019;471(11-12):1441-1453. doi: 10.1007/s00424-019-02322-y. Boini KM, Hussain T, Li PL, Koka S. Trimethylamine-N-Oxide Instigates NLRP3 Inflammasome Activation and Endothelial Dysfunction. Cell Physiol Biochem. 2017;44(1):152-162. doi: 10.1159/000484623. Ilmonen P, Kotrschal A, Penn DJ. Telomere attrition due to infection. PLoS One. 2008;3(5):e2143. doi: 10.1371/journal.pone.0002143. Wu Y, Xu H, Tu X, Gao Z. The Role of Short-Chain Fatty Acids of Gut Microbiota Origin in Hypertension. Front Microbiol. 2021;12:730809. doi: 10.3389/fmicb.2021.730809. Pluznick JL. Microbial Short-Chain Fatty Acids and Blood Pressure Regulation. Curr Hypertens Rep. 2017;19(4):25. doi: 10.1007/s11906-017-0722-5. Del Chierico F, Manco M, Gardini S, Guarrasi V, Russo A, Bianchi M, Tortosa V, Quagliariello A, Shashaj B, Fintini D, Putignani L. Fecal microbiota signatures of insulin resistance, inflammation, and metabolic syndrome in youth with obesity: a pilot study. Acta Diabetol. 2021;58(8):1009-1022. doi: 10.1007/s00592-020-01669-4. Kaplan RC, Wang Z, Usyk M, Sotres-Alvarez D, Daviglus ML, Schneiderman N, Talavera GA, Gellman MD, Thyagarajan B, Moon JY, Vázquez-Baeza Y, McDonald D, Williams-Nguyen JS, Wu MC, North KE, Shaffer J, Sollecito CC, Qi Q, Isasi CR, Wang T, Knight R, Burk RD. Gut microbiome composition in the Hispanic Community Health Study/Study of Latinos is shaped by geographic relocation, environmental factors, and obesity. Genome Biol. 2019 Nov 1;20(1):219. doi: 10.1186/s13059-019-1831-z. Erratum in: Genome Biol. 2020;21(1):50. doi: 10.1186/s13059-020-01970-z. D'Angelo G. Microbiota and hematological diseases. Int J Hematol Oncol Stem Cell Res. 2022 Jul 1;16(3):164-173. doi: 10.18502/ijhoscr.v16i3.10139. Table 4 Table 4 is available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files Table4.docx Cite Share Download PDF Status: Published Journal Publication published 07 Jul, 2025 Read the published version in Nutrition & Metabolism → Version 1 posted Editorial decision: Revision requested 05 Jun, 2025 Reviews received at journal 04 Jun, 2025 Reviewers agreed at journal 04 Jun, 2025 Reviewers invited by journal 04 Jun, 2025 Editor assigned by journal 02 Jun, 2025 Submission checks completed at journal 02 Jun, 2025 First submitted to journal 24 May, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6739379","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":466995325,"identity":"a5609907-7eb1-4686-ad12-cc66e7f25967","order_by":0,"name":"Hsi-Cheng Hung","email":"","orcid":"","institution":"Taipei Medical University","correspondingAuthor":false,"prefix":"","firstName":"Hsi-Cheng","middleName":"","lastName":"Hung","suffix":""},{"id":466995326,"identity":"3df14b80-1249-4075-b74f-8828a7c429d8","order_by":1,"name":"Yuan-Yuan Lin","email":"","orcid":"","institution":"Cathay General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yuan-Yuan","middleName":"","lastName":"Lin","suffix":""},{"id":466995327,"identity":"75dbf6a5-03e0-4049-b45d-8c480897d8a1","order_by":2,"name":"Wan-Jung Tien","email":"","orcid":"","institution":"Cathay General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Wan-Jung","middleName":"","lastName":"Tien","suffix":""},{"id":466995328,"identity":"c3c83c55-ec43-4048-ab89-e9edb57d177d","order_by":3,"name":"Yu-Yoh Chen","email":"","orcid":"","institution":"Taipei Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yu-Yoh","middleName":"","lastName":"Chen","suffix":""},{"id":466995329,"identity":"657844a6-b2ce-4ce7-8b3f-d3f415eb991e","order_by":4,"name":"Suh-Ching Yang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAsklEQVRIiWNgGAWjYFACNgaGhAoIU4IELWcMYFoMiNTC2EaKFvn2tsQHD+f9iTY4wHzwNg/Dn8QGQloMzhw7bJC4zSB3wwG2ZGseBgMitEikt0lAtPCYSQO15BLUIj8jvf1H4hyQFv5vxGlhuJF2jCGxAWwLG3FagH5Jlkg4Zpw78zCbseUcA+N6wg5rbzP8+KNGLrfvePPDG28q5IwJugsBmMGWkqBhFIyCUTAKRgFuAADzWzq/hPJwJwAAAABJRU5ErkJggg==","orcid":"","institution":"Taipei Medical University","correspondingAuthor":true,"prefix":"","firstName":"Suh-Ching","middleName":"","lastName":"Yang","suffix":""}],"badges":[],"createdAt":"2025-05-24 13:38:46","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6739379/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6739379/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12986-025-00963-8","type":"published","date":"2025-07-07T15:57:01+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":84216854,"identity":"307ded4c-b7ba-41a6-804b-b662f52a9230","added_by":"auto","created_at":"2025-06-09 10:52:51","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":520846,"visible":true,"origin":"","legend":"\u003cp\u003eParticipant enrollment and flowchart.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6739379/v1/c9ce78df62cc772d4d0b5bab.png"},{"id":84216500,"identity":"ceb8649a-6c6f-4fa3-9e9c-c4090fa701e5","added_by":"auto","created_at":"2025-06-09 10:44:51","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":428654,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis of gut microbiota α-diversity in hypertensive patients and normotensive individuals. ACE, abundance-based coverage estimator.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6739379/v1/dc2f30350edc62dc7a6231e6.png"},{"id":84217690,"identity":"7162b1ce-5882-4caa-a685-065ea0e9f105","added_by":"auto","created_at":"2025-06-09 11:00:51","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":657882,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis of gut microbiota β-diversity in hypertensive patients and healthy controls in a principal coordinate analysis (PCoA). (a) Bray-Curtis distance, (b) unweighted UniFrac distances, (c) weighted UniFrac distances.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6739379/v1/5249e7a7ef5b3dc0ba93019a.png"},{"id":84216503,"identity":"5142703a-a031-4664-ae95-178442a8e7a3","added_by":"auto","created_at":"2025-06-09 10:44:52","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":559903,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of the gut microbiota between hypertensive patients and normotensive controls.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6739379/v1/04c1102539014f0c020b5b2c.png"},{"id":84216508,"identity":"04f9c5d5-d285-433a-a11b-475f509d0747","added_by":"auto","created_at":"2025-06-09 10:44:52","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":295897,"visible":true,"origin":"","legend":"\u003cp\u003eColored heatmap of correlations between the fecal microbiota and clinical factors.\u003c/p\u003e\n\u003cp\u003eColored heatmap of Spearman's rank correlation coefficients for fecal microbiota and clinical factors. The colors refer to the correlation coefficient direction and magnitude, ranging from -1 (blue) to 1 (red). * \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05 (2-tailed). **\u003cem\u003e p\u003c/em\u003e \u0026lt; 0.01 (2-tailed).\u003c/p\u003e\n\u003cp\u003eHTN, hypertensive group or control group; SBP, systolic blood pressure; DBP, diastolic blood pressure; BMI, body-mass index; WC, waist circumference; Hb, hemoglobin; WBCs, white blood cells; PLTs, platelets; AST, aspartate transaminase; ALT, alanine transaminase; Cre, Creatinine; FG, fasting glucose; TC, total cholesterol; TGs, triglycerides; HDL-C, high-density-lipoprotein cholesterol; LDL-C, low-density-lipoprotein cholesterol; UA, uric acid; Alb, albumin; Glo, globulin.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6739379/v1/9911084eccd61087c977e370.png"},{"id":84216523,"identity":"71f6332f-bad6-46e2-b3bc-e8fa3a1bb1ea","added_by":"auto","created_at":"2025-06-09 10:44:52","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":672132,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-6739379/v1/37fac93a418d1f8d9327a944.png"},{"id":86699258,"identity":"eda58d8e-fc03-4e90-abb2-5232d93e5b43","added_by":"auto","created_at":"2025-07-14 16:06:08","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4746017,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6739379/v1/5264fd8c-2ab5-45f5-8388-d406961d1935.pdf"},{"id":84216497,"identity":"aca12a07-20b7-4bd5-8c7a-046d5161e1b0","added_by":"auto","created_at":"2025-06-09 10:44:51","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":18629,"visible":true,"origin":"","legend":"","description":"","filename":"Table4.docx","url":"https://assets-eu.researchsquare.com/files/rs-6739379/v1/597110e5a215ab0cf52051fd.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association between the gut microbiotic composition and dietary patterns in hypertensive elderly patients: a cross-sectional study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eHypertension is one of the most common chronic diseases worldwide and a major risk factor for cardiovascular, brain, and kidney diseases. According to the latest data from the World Health Organization (WHO), approximately 1.3\u0026nbsp;billion people aged 30\u0026ndash;79 years worldwide had hypertension in 2023. In Taiwan, based on the results of the 2017\u0026ndash;2020 National Nutrition and Health Survey, the prevalence of hypertension among individuals aged 18 years and older had reached 26.8%, and the prevalence increases with age [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Risk factors for hypertension include an unhealthy diet (such as excessive salt consumption, high intake of saturated fats and trans fats, and low consumption of fruits and vegetables), physical inactivity, tobacco and alcohol consumption, being overweight or obese, being over 65 years of age, and having coexisting conditions such as diabetes or kidney disease [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. On the other hand, the elderly in Asia are increasingly vulnerable due to the \"triple burden\" of an aging population, hypertension, and mental health issues [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Therefore, the causes of hypertension in the elderly and its impacts on health are crucial health issues for growing aged populations in Asia. Although antihypertensive treatments are widely implemented, a 2021 community-based survey in Taiwan revealed that over 40% of patients with hypertension still failed to achieve optimal blood pressure control, particularly among older adults [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. This highlights the urgent need for novel and complementary strategies to regulate blood pressure beyond conventional pharmacological approaches. Among these, the modulation of gut microbiota has emerged as a promising and innovative direction.\u003c/p\u003e \u003cp\u003eAnimal models have demonstrated gut microbial effects on blood pressure (BP) [\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Hypertension was induced in normotensive rats through transplantation of cecal contents from hypertensive rats [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. The gut microbiota generates various metabolites, including trimethylamine-N-oxide (TMAO), short-chain fatty acids (SCFAs), corticosterone, hydrogen sulfide (H\u003csub\u003e2\u003c/sub\u003eS), choline, bile acids (BAs), indole sulfate, and lipopolysaccharides (LPSs). Among these, SCFAs, TMAO, BAs, H\u003csub\u003e2\u003c/sub\u003eS, and LPSs are closely linked to the development of hypertension [\u003cspan additionalcitationids=\"CR11 CR12\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The CARDIA and HELIUS studies, two prospective cohort studies conducted in the United States and Europe with 529 and 4672 participants, respectively, both found that hypertensive patients exhibited significantly lower α and β diversities in their gut microbiota compared to individuals with normal BP [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Additionally, there were significant differences in the abundances of certain bacterial species between the two groups, although most of the bacterial species identified in the two studies did not overlap. A study conducted in various regions of China found that gut microbiotic compositions varied across different regions and ethnic groups, indicating that lifestyle and ethnicity might influence the gut microbiotic composition [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWhile changes in the gut microbiotic composition have been linked to hypertension, most such studies focused on Western populations, which might not be applicable to Asian countries including Taiwan, a region with distinct ethnicities and dietary habits. Therefore, in this study, we investigated relationships among dietary patterns, the gut microbiota, and hypertension in elderly individuals, using data from Taiwan to explore how regional dietary habits and microbiotic composition influence BP regulation.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and participant recruitment\u003c/h2\u003e \u003cp\u003e This study was approved by the Institutional Review Board (IRB) of Cathay General Hospital (Taipei, Taiwan), Cathay Medical Foundation (CGH-P110063, approved on December 24, 2022). The international registration number is NCT05057039, and it was listed on ClinicalTrials.gov PRS and reviewed on April 14, 2023. Participants were recruited from elderly individuals undergoing health check-ups at Cathay General Hospital. The inclusion criteria for the hypertension group were individuals aged 65\u0026ndash;80 years who had a systolic BP exceeding 140 mmHg or a diastolic BP exceeding 90 mmHg during a health check-up, or those who had taken antihypertensive medication within the past month. For the normotensive group, participants were age- and sex-matched individuals who had a systolic BP below 139 mmHg, a diastolic BP below 89 mmHg, and had not taken antihypertensive medication within the past month. Exclusion criteria included individuals over 80 years old, and those who had used antibiotics in the past month, consumed probiotics in the past week, had a history of inflammatory bowel disease or gastrointestinal surgery, suffered from chronic gastrointestinal diseases requiring long-term medication, had experienced acute gastrointestinal symptoms such as vomiting or diarrhea in the past week, had been diagnosed with secondary hypertension, or were unable to complete the dietary assessment questionnaire. Participants who were willing to participate and met the eligibility criteria provided informed consent before data collection. The collected data included height, weight, body-mass index (BMI), BP, pulse, medical history, and blood and biochemical test results. Additionally, a registered dietitian conducted dietary records, food frequency questionnaires, and nutritional analyses. Participants were also provided with a fecal sample collection kit to obtain stool samples for a gut microbiotic analysis.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMaterials and Methods\u003c/h3\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eBasic information, anthropometric data, and BP\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eFor anthropometric measurements, researchers used a fully automated height and weight scale (HW-210, Universal Weight Electronics, New Taipei City, Taiwan) at the Cathay General Hospital Health Examination Center to measure participants' height and weight and calculate their BMI.\u003c/p\u003e \u003cp\u003eBP was measured using office BP monitoring with a fully automated medical-grade electronic sphygmomanometer (HBP-9030, Omron, Kyoto, Japan). Before the measurement, participants were required to rest for at least 5 min. A nurse assisted in the measurement process, recording three BP and pulse readings, with the average value used for the statistical analysis.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eBlood biochemical analysis\u003c/h3\u003e\n\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eBlood biochemical data were analyzed using a fully automated hematology analyzer (XN-10/XN-20, Sysmex, Kobe, Japan) to measure hemoglobin, the white blood cell (WBC) count, and platelet count. Additionally, biochemical analyses were conducted using a fully automated analyzer (AU5800, Beckman Coulter, Brea, CA, USA). The analyzed parameters included liver function markers (aspartate aminotransferase, AST; and alanine aminotransferase, ALT), kidney function marker (creatinine), and metabolism-related indicators such as fasting blood glucose, total cholesterol (TC), triglycerides (TGs), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), uric acid, albumin, and globulin levels.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eDietary intake\u003c/h3\u003e\n\u003cp\u003eOn the day of enrollment, participants underwent an interview with a registered dietitian to complete a 24-h dietary recall and a food frequency questionnaire (FFQ). The 2016 Edition of the Simplified Nutrition Calculation Table (Microsoft Excel) was used to assess participants\u0026rsquo; total energy, macronutrient, dietary fiber, vitamin, and mineral intake levels. Food intake was categorized based on six major food groups, with further classification of staple foods into whole grains, legumes, tubers, and others (e.g., bread, steamed buns, noodles). Additional analyses were conducted on seasoning usage, cooking methods, processed meats, and pickled foods to evaluate their potential impacts on BP.\u003c/p\u003e \u003cp\u003eThe calculation method for dietary frequency was based on a previous study, by converting weekly and monthly intake frequencies into daily equivalents [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. For instance, food consumed one or two times per week was averaged to 1.5 times per week, corresponding to 0.21 times per day, while once per month was converted to 0.03 times per day.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eFecal microbiotic analysis\u003c/h2\u003e \u003cp\u003eThis study used a 16S ribosomal (r)RNA variable region analysis to classify the fecal microbiota. Fecal samples were stored at -20\u0026deg;C until being analyzed. DNA was extracted from fecal samples using a QIAamp Fast DNA Stool Mini Kit (Qiagen, Hilden, Germany). The V3\u0026thinsp;+\u0026thinsp;V4 region was amplified by a polymerase chain reaction (PCR) for the microbiotic analysis. The first round of PCR amplification was carried out using Kapa HiFi HotStart ReadyMix (KapaBiosystems, Wilmington, MA, USA) with 0.2 \u0026micro;M forward and reverse primers targeting the V3\u0026thinsp;+\u0026thinsp;V4 region, and DNA was purified using Agencourt AMPure XP Reagent beads (Beckman Coulter, Brea, CA, USA). In the second round of the PCR, Nextera XT Index 1 and 2 (Illumina, San Diego, CA, USA) were added, and a quantitative (q)PCR was performed. Samples were then uniformly mixed and sequenced using the Illumina MiSeq NGS system. After sequencing, more than 80,000 paired-end 300-bp sequences were generated. These sequences were classified and analyzed using the DADA2 workflow and SILVA v132 taxonomic database.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eSPSS statistical software (SPSS vers. 19, IBM, Chicago, IL, USA) was used for data analysis. For categorical variables, data are presented as counts (\u003cem\u003en\u003c/em\u003e) and percentages (%); for continuous variables, the Kolmogorov-Smirnov test was first applied to assess the normality of the distribution. If variables followed a normal distribution, the data were expressed as means and standard deviations (SDs); if variables did not follow a normal distribution, data are presented as medians and interquartile ranges (IQRs). To compare differences between groups, the Chi-squared test was used for categorical variables. For continuous variables, an independent \u003cem\u003et\u003c/em\u003e-test was used for normally distributed variables, and the Mann-Whitney U-test was used for non-normally distributed variables. Furthermore, to explore correlations between the gut microbiotic composition and clinical factors or dietary frequency, Spearman\u0026rsquo;s rank correlation coefficient analysis was used. A \u003cem\u003ep\u003c/em\u003e value of \u0026lt;\u0026thinsp;0.05 was set as the threshold for statistical significance.\u003c/p\u003e \u003cp\u003eIn the fecal microbiotic analysis, this study used the MicrobiomeAnalyst platform to perform richness and diversity analyses, as well as differential abundance analyses, comparing the hypertensive group and healthy control group [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. The α diversity of the gut microbiota was assessed using five indices: Observed, Chao1, ACE (abundance-based coverage estimator), Shannon index, and Simpson index. β diversity was visualized using a principal coordinates analysis (PCoA), with diversity differences between groups calculated using three methods: Bray-Curtis distance, weighted UniFrac, and unweighted UniFrac. To assess the statistical significance of differences between groups, a permutational multivariate analysis of variance (PERMANOVA) was applied. Additionally, for the differential abundance analysis of the fecal microbiota, this study used the edgeR statistical method for single-factor statistical comparisons, and applied the Benjamini-Hochberg method to adjust original \u003cem\u003ep\u003c/em\u003e values for the false discovery rate (FDR), yielding corresponding q values. Finally, a q value of \u0026lt;\u0026thinsp;0.05 was set as the threshold for statistical significance to determine whether there were significant differences in gut microbiotic compositions between the hypertensive group and normotensive group.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eBasic information and characteristics\u003c/h2\u003e \u003cp\u003eInitially 56 participants were enrolled in this study, among whom 15 were excluded due to failing to meet the inclusion criteria. Ultimately, 20 hypertensive patients and 21 normotensive individuals were included (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). When comparing the characteristics of the two groups, there were no significant differences in terms of gender, age, diabetes, or hyperlipidemia. However, compared to the normotensive group, the hypertensive group showed a significantly higher BMI, waist circumference, and systolic BP (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristics of hypertensive patients and healthy controls\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHypertensive patients \u003c/p\u003e \u003cp\u003e(\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;20)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNormotensive controls\u003c/p\u003e \u003cp\u003e(\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;21)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 (50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.883\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e72\u0026thinsp;\u0026plusmn;\u0026thinsp;5.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e70\u0026thinsp;\u0026plusmn;\u0026thinsp;3.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.086\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBody-mass index (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24.8\u0026thinsp;\u0026plusmn;\u0026thinsp;3.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.2\u0026thinsp;\u0026plusmn;\u0026thinsp;2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWaist circumference (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e83.4\u0026thinsp;\u0026plusmn;\u0026thinsp;9.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e76.5\u0026thinsp;\u0026plusmn;\u0026thinsp;8.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSystolic blood pressure (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e143\u0026thinsp;\u0026plusmn;\u0026thinsp;14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e122\u0026thinsp;\u0026plusmn;\u0026thinsp;12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiastolic blood pressure (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e75\u0026thinsp;\u0026plusmn;\u0026thinsp;12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e70\u0026thinsp;\u0026plusmn;\u0026thinsp;7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.166\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.637\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHyperlipidemia (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.489\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eValues are expressed as the number (%) of patients or the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eBlood biochemical parameters\u003c/h2\u003e \u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, the TC level was significantly lower in the hypertensive group compared to the normotensive group. However, there were no significant differences between the two groups in other blood-related indicators, including hemoglobin, WBCs, platelets, AST, ALT, creatinine, fasting blood glucose, TGs, HDL-C, LDL-C, uric acid, albumin, and globulin (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBlood analysis of hypertensive patients and healthy controls\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHypertensive patients (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;26)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNormotensive controls (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;27)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHemoglobin (g/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e13.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e14.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.166\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWBCs (1000/\u0026micro;L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e5.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e5.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.692\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatelets (1000/\u0026micro;L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e224\u0026thinsp;\u0026plusmn;\u0026thinsp;43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e230\u0026thinsp;\u0026plusmn;\u0026thinsp;64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.703\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHematocrit (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e41.4\u0026thinsp;\u0026plusmn;\u0026thinsp;4.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e42.4\u0026thinsp;\u0026plusmn;\u0026thinsp;3.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.368\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRBCs (10\u003csup\u003e6\u003c/sup\u003e/\u0026micro;L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e4.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e4.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.793\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRDW (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e13.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e12.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.211\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMCV (fL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e89.2\u0026thinsp;\u0026plusmn;\u0026thinsp;4.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e91.1\u0026thinsp;\u0026plusmn;\u0026thinsp;4.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.156\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAST (U/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e20\u0026thinsp;\u0026plusmn;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e24\u0026thinsp;\u0026plusmn;\u0026thinsp;12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.223\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALT (U/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e15\u0026thinsp;\u0026plusmn;\u0026thinsp;8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e18\u0026thinsp;\u0026plusmn;\u0026thinsp;18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.440\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreatinine (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.88\u0026thinsp;\u0026plusmn;\u0026thinsp;0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.84\u0026thinsp;\u0026plusmn;\u0026thinsp;0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.388\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFasting glucose (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e104\u0026thinsp;\u0026plusmn;\u0026thinsp;15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e106\u0026thinsp;\u0026plusmn;\u0026thinsp;43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.821\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal cholesterol (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e171\u0026thinsp;\u0026plusmn;\u0026thinsp;40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e206\u0026thinsp;\u0026plusmn;\u0026thinsp;32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriglycerides (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e95\u0026thinsp;\u0026plusmn;\u0026thinsp;36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e121\u0026thinsp;\u0026plusmn;\u0026thinsp;48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.061\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL-C (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e65\u0026thinsp;\u0026plusmn;\u0026thinsp;13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e68\u0026thinsp;\u0026plusmn;\u0026thinsp;16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.514\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL-C (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e95\u0026thinsp;\u0026plusmn;\u0026thinsp;32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e120\u0026thinsp;\u0026plusmn;\u0026thinsp;28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUric acid (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e5.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e5.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.584\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlbumin (g/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e4.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e4.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.632\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlobulin (g/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e3.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e3.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.543\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eValues are expressed as the number (%) of patients or the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eWBCs, white blood cells; RBCs, red blood cells; RDW, red blood cell distribution width; MCV, mean corpuscular volume; AST, aspartate transaminase; ALT, alanine transaminase; HDL-C, high-density-lipoprotein cholesterol; LDL-C, low-density-lipoprotein cholesterol.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eDietary intake\u003c/h2\u003e \u003cp\u003eBased on the 24-h dietary recall, there were no significant differences between the hypertensive group and normotensive group in daily intake of calories, macronutrients, vitamins, and minerals (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Additionally, comparisons of the intake frequencies for the six major food groups, condiments, and cooking methods based on the dietary frequency questionnaire also showed no significant differences between the two groups (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eNutrient intake levels in hypertensive patients and healthy controls\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNutrient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHypertensive patients (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;20)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNormotensive controls (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;21)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnergy (kcal)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e1451.6\u0026thinsp;\u0026plusmn;\u0026thinsp;372.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1545.3\u0026thinsp;\u0026plusmn;\u0026thinsp;282.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.368\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCarbohydrates (% of total kcal)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e46.6\u0026thinsp;\u0026plusmn;\u0026thinsp;4.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e49.6\u0026thinsp;\u0026plusmn;\u0026thinsp;10.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.253\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProtein (% of total kcal)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e17.1\u0026thinsp;\u0026plusmn;\u0026thinsp;3.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e17.1\u0026thinsp;\u0026plusmn;\u0026thinsp;3.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.975\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFat (% of total kcal)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e36.3\u0026thinsp;\u0026plusmn;\u0026thinsp;5.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e33.4\u0026thinsp;\u0026plusmn;\u0026thinsp;9.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.231\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSaturated fat (g)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e16.9\u0026thinsp;\u0026plusmn;\u0026thinsp;5.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e17.1\u0026thinsp;\u0026plusmn;\u0026thinsp;6.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.942\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrans fat (mg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e165.7\u0026thinsp;\u0026plusmn;\u0026thinsp;118.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e155.3\u0026thinsp;\u0026plusmn;\u0026thinsp;115.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.779\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCholesterol (mg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e298.7\u0026thinsp;\u0026plusmn;\u0026thinsp;181.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e253.5\u0026thinsp;\u0026plusmn;\u0026thinsp;141.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.379\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDietary fiber (g)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e16.4\u0026thinsp;\u0026plusmn;\u0026thinsp;5.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e17.6\u0026thinsp;\u0026plusmn;\u0026thinsp;7.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.548\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVitamin A (IU)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e16933.7\u0026thinsp;\u0026plusmn;\u0026thinsp;8821.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e17055.7\u0026thinsp;\u0026plusmn;\u0026thinsp;6455.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.960\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVitamin D (\u0026micro;g)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.772\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVitamin E (mg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e20.6\u0026thinsp;\u0026plusmn;\u0026thinsp;8.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e22.3\u0026thinsp;\u0026plusmn;\u0026thinsp;17.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.689\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVitamin K\u003csub\u003e1\u003c/sub\u003e (\u0026micro;g)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e1.2\u0026thinsp;\u0026plusmn;\u0026thinsp;3.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e7\u0026thinsp;\u0026plusmn;\u0026thinsp;21.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.247\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVitamin B\u003csub\u003e1\u003c/sub\u003e (mg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.382\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVitamin B\u003csub\u003e2\u003c/sub\u003e (mg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e1.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.783\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNiacin (mg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e12.5\u0026thinsp;\u0026plusmn;\u0026thinsp;6.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e15.3\u0026thinsp;\u0026plusmn;\u0026thinsp;5.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.140\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVitamin B\u003csub\u003e6\u003c/sub\u003e (mg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e1.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.900\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVitamin B\u003csub\u003e12\u003c/sub\u003e (\u0026micro;g)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e3.5\u0026thinsp;\u0026plusmn;\u0026thinsp;3.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e3.3\u0026thinsp;\u0026plusmn;\u0026thinsp;2.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.867\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFolate (\u0026micro;g)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e293.9\u0026thinsp;\u0026plusmn;\u0026thinsp;83.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e301.9\u0026thinsp;\u0026plusmn;\u0026thinsp;103.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.788\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVitamin C (mg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e168.4\u0026thinsp;\u0026plusmn;\u0026thinsp;82.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e164.2\u0026thinsp;\u0026plusmn;\u0026thinsp;67.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.861\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSodium (mg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e916.2\u0026thinsp;\u0026plusmn;\u0026thinsp;411.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e785.1\u0026thinsp;\u0026plusmn;\u0026thinsp;361.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.284\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePotassium (mg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e2148\u0026thinsp;\u0026plusmn;\u0026thinsp;584.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e2228.6\u0026thinsp;\u0026plusmn;\u0026thinsp;613.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.669\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCalcium (mg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e726.5\u0026thinsp;\u0026plusmn;\u0026thinsp;261.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e765.6\u0026thinsp;\u0026plusmn;\u0026thinsp;369.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.699\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMagnesium (mg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e258.6\u0026thinsp;\u0026plusmn;\u0026thinsp;67.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e296.2\u0026thinsp;\u0026plusmn;\u0026thinsp;125.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.244\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIron (mg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e9.8\u0026thinsp;\u0026plusmn;\u0026thinsp;2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e10.4\u0026thinsp;\u0026plusmn;\u0026thinsp;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.548\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZinc (mg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e7.8\u0026thinsp;\u0026plusmn;\u0026thinsp;2.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e8.4\u0026thinsp;\u0026plusmn;\u0026thinsp;2.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.392\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhosphorus (mg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e905.5\u0026thinsp;\u0026plusmn;\u0026thinsp;296.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e981.8\u0026thinsp;\u0026plusmn;\u0026thinsp;318.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.432\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCopper (mg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.496\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eManganese (mg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.765\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eValues are expressed as the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e\u003c/p\u003e\n\u003ch2\u003eFecal microbiotic composition\u003c/h2\u003e\n\u003cp\u003eThe \u0026alpha; diversity analysis showed no significant differences between hypertensive patients and healthy controls in the Observed, Chao1, ACE, Shannon, and Simpson indices, indicating that there were no notable differences in the richness and diversity of the gut microbiota between the two groups (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). Similarly, in the \u0026beta; diversity analysis, calculations using Bray-Curtis distance, unweighted UniFrac distance, and weighted UniFrac distance, followed by a PERMANOVA, also revealed no significant differences between the two groups (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eFor gut microbiotic abundances, the study used the edgeR method for statistical analysis and applied the Benjamini-Hochberg method for FDR correction. In total, 153 amplicon sequence variants (ASVs) were compared, and six taxonomic features showed significant differences in relative abundances (Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e). Among these, \u003cem\u003eBacteroides caccae\u003c/em\u003e had a significantly lower relative abundance in hypertensive patients compared to the normotensive group, approximately 2\u003csup\u003e2.95\u003c/sup\u003e times lower, with a log2 fold change (log2FC) of -2.95. The total sequencing read count of \u003cem\u003eBacteroides caccae\u003c/em\u003e accounted for approximately 2\u003csup\u003e8.41\u003c/sup\u003e counts per million (CPM), with a logCPM of 8.41 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, FDR q\u0026thinsp;=\u0026thinsp;0.013) indicating a significant difference in relative abundances between the two groups. The \u003cem\u003eBarnesiella\u003c/em\u003e genus also had a significantly lower relative abundance in hypertensive patients compared to the healthy group (FDR q\u0026thinsp;=\u0026thinsp;0.043). The remaining four taxonomic features showed significantly higher relative abundances in the hypertensive group, including the family Enterobacteriaceae (FDR q\u0026thinsp;=\u0026thinsp;0.008), \u003cem\u003eBacteroides plebeius\u003c/em\u003e (FDR q\u0026thinsp;=\u0026thinsp;0.043), the \u003cem\u003eEnterobacter\u003c/em\u003e genus (FDR q\u0026thinsp;=\u0026thinsp;0.043), and the \u003cem\u003eAcidaminococcus\u003c/em\u003e genus (FDR q\u0026thinsp;=\u0026thinsp;0.043) (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). A post hoc power analysis was conducted for \u003cem\u003eBacteroides caccae\u003c/em\u003e. Assuming a dispersion range of 0.2 and based on the current sample size (hypertension group, \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;20; normotensive group, \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;21) as well as the observed effect size (log₂FC = -2.95), the calculated statistical power was 0.95. \u0026nbsp;\u003c/p\u003e\n\u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDifferential abundance analysis of specific taxonomic features between hypertensive patients and healthy controls\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTaxonomic feature\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003elog2FC\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003elogCPM\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFDR\u003c/p\u003e\n \u003cp\u003eq 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\u003e\u003cem\u003eBacteroides caccae\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eBarnesiella\u003c/em\u003e genus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEnterobacteriaceae family\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eBacteroides plebeius\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eEnterobacter\u003c/em\u003e genus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eAcidaminococcus\u003c/em\u003e genus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003eThe differential abundance analysis was performed using the edgeR statistical method. The Benjamini-Hochberg method was applied to adjust raw \u003cem\u003ep\u003c/em\u003e values for the false discovery rate (FDR), resulting in \u003cem\u003eq\u003c/em\u003e values. A \u003cem\u003eq\u003c/em\u003e value of \u0026lt;\u0026thinsp;0.05 indicates a significant difference between the two groups. FC, fold change; CPM, counts per million.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003eThis study further employed a Spearman\u0026rsquo;s rank correlation analysis to investigate the relationship between the six gut microbiota species significantly associated with hypertension and various clinical factors and dietary components. Figure \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e presents the correlation analysis results between these six gut bacteria and clinical indicators. In hypertensive or prehypertensive individuals, \u003cem\u003eBacteroides caccae\u003c/em\u003e and the \u003cem\u003eBarnesiella\u003c/em\u003e genus showed negative correlations, whereas the Enterobacteriaceae family, \u003cem\u003eBacteroides plebeius\u003c/em\u003e, \u003cem\u003eEnterobacter\u003c/em\u003e genus, and \u003cem\u003eAcidaminococcus\u003c/em\u003e genus showed positive correlations. Among these, \u003cem\u003eBacteroides caccae\u003c/em\u003e, \u003cem\u003eEnterobacter\u003c/em\u003e, and \u003cem\u003eAcidaminococcus\u003c/em\u003e demonstrated statistically significant correlations with hypertension.\u003c/p\u003e\n\u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e presents the correlation analysis between gut microbiota and clinical factors. \u003cem\u003eBacteroides caccae\u003c/em\u003e was negatively correlated with the BMI and positively correlated with blood HDL-C levels. The \u003cem\u003eBarnesiella\u003c/em\u003e genus showed positive correlations with age, hemoglobin, creatinine, and TC levels. The Enterobacteriaceae family was negatively correlated with WBCs and albumin concentrations. Additionally, \u003cem\u003eBacteroides plebeius\u003c/em\u003e showed positive correlations with age and waist circumference, but a negative correlation with HDL-C levels. The \u003cem\u003eEnterobacter\u003c/em\u003e genus was positively correlated with the BMI but negatively correlated with WBC count, while \u003cem\u003eAcidaminococcus\u003c/em\u003e was positively correlated with the BMI.\u003c/p\u003e\n\u003cp\u003eIn the correlation analysis between gut microbiota and dietary intake (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e), the abundance of \u003cem\u003eBacteroides caccae\u003c/em\u003e was negatively correlated with the daily intake of saturated fat and sodium. The \u003cem\u003eBarnesiella\u003c/em\u003e genus showed a negative correlation with the daily total calorie intake and protein percentage. The Enterobacteriaceae family exhibited a positive correlation with the percentage of daily protein intake, while \u003cem\u003eBacteroides plebeius\u003c/em\u003e was positively correlated with the daily total calorie intake. The \u003cem\u003eEnterobacter\u003c/em\u003e genus showed a positive correlation with the daily protein intake percentage, while \u003cem\u003eAcidaminococcus\u003c/em\u003e showed no significant correlation in the dietary analysis.\u003c/p\u003e\n\u003cp\u003eFurthermore, in the analysis of correlations between the gut microbiota and food consumption frequencies (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e), the abundance of \u003cem\u003eBacteroides caccae\u003c/em\u003e was negatively correlated with the consumption frequency of staple foods (such as bread, steamed buns, and noodles) and with the use frequency of nut oils and seasonings. The abundance of the Enterobacteriaceae was positively correlated with egg consumption frequency, but negatively correlated with the frequency of consuming braised dishes. The \u003cem\u003eAcidaminococcus\u003c/em\u003e genus showed a significant negative correlation with the frequency of legume consumption.\u003c/p\u003e\n"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we investigated the gut microbiotic characteristics of elderly patients with hypertension. Study participants were individuals aged 65\u0026ndash;80 years who were undergoing geriatric health check-ups, with a final selection of 20 participants in the hypertension group and 21 in the normotensive group. Results showed no significant differences in gender or age between the two groups. However, hypertensive patients had a significantly higher BMI, waist circumference, and systolic BP compared to the normotensive group, which aligns with the clinical characteristics of hypertension. Previous studies also confirmed the correlation between hypertension and obesity [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. In the blood analysis, hypertensive patients had significantly lower TC and LDL-C levels, while other blood parameters showed no significant differences. This phenomenon may be related to the fact that 75% of the hypertensive participants were undergoing antihypertensive medication treatment. These participants may have also received lifestyle modification recommendations during outpatient visits, including weight loss, dietary adjustments, and exercise, which could have contributed to cholesterol reduction [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. However, LDL-C levels in both groups remained within the normal range (below 130 mg/dL). Results of nutritional intake showed no significant differences between hypertensive patients and the normotensive group in intake of daily total calories, macronutrients, dietary fiber, vitamins, or minerals.\u003c/p\u003e \u003cp\u003eIn this study, we found no significant differences in gut microbiotic α-diversity or β-diversity between hypertensive patients and the normotensive group. Previous studies reported inconsistent results for the fecal microbiotic composition in hypertensive individuals. Sun et al. showed that hypertension and systolic BP were inversely associated with measures of α-diversity, including richness and the Shannon diversity index [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Palmu et al. also indicated that the α and β diversities of the taxonomic composition were strongly related to BP indexes in age- and sex-adjusted models [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Average ages of the study populations in those two experiments were 55.3\u0026thinsp;\u0026plusmn;\u0026thinsp;3.4 and 49.2\u0026thinsp;\u0026plusmn;\u0026thinsp;12.9 years, respectively. On the other hand, a cross-sectional study in Spain analyzed 16S rRNA gene sequencing data from 29 untreated hypertensive patients and 32 healthy individuals, revealing no significant differences in Chao1 richness estimates, Shannon diversity index, or β-diversity between hypertensive (53.7\u0026thinsp;\u0026plusmn;\u0026thinsp;9.6 years old) and normotensive groups (41.1\u0026thinsp;\u0026plusmn;\u0026thinsp;9.1 years old) [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Similarly, a study in Japanese subjects with an average age of 68 (range: 16\u0026ndash;88) years involving 97 hypertensive patients, 162 diabetic patients, and 96 hyperlipidemic patients found no significant differences in gut microbiotic richness, the Shannon diversity index, or β-diversity among these populations [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. The inconsistencies in these findings suggest that the overall gut microbiotic diversity is not a key factor influencing hypertension. Instead, changes in relative abundances or the presence of specific bacterial taxa may play more significant roles in BP regulation.\u003c/p\u003e \u003cp\u003eWe found significant differences in the relative abundances of six bacterial taxa between hypertensive patients and healthy controls. The relative abundances of \u003cem\u003eBacteroides caccae\u003c/em\u003e and the \u003cem\u003eBarnesiella\u003c/em\u003e genus were lower in hypertensive patients, whereas abundances of the Enterobacteriaceae family, \u003cem\u003eBacteroides plebeius\u003c/em\u003e, and the \u003cem\u003eEnterobacter\u003c/em\u003e and \u003cem\u003eAcidaminococcus\u003c/em\u003e genera were significantly higher. The gut microbiota is influenced by various factors, including ethnicity, age, dietary patterns, medical history, and medication use [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. In this study, we specifically recruited elderly participants aged 65\u0026ndash;80 years and assessed their dietary intake using 24-h dietary recall and a food frequency questionnaire. Additionally, we excluded individuals with a history of related diseases or prior probiotic use to minimize the potential confounding effects of those factors.\u003c/p\u003e \u003cp\u003eTaxonomically, both \u003cem\u003eBacteroides caccae\u003c/em\u003e and \u003cem\u003eBacteroides plebeius\u003c/em\u003e belong to the genus \u003cem\u003eBacteroides\u003c/em\u003e, but their relative abundances exhibited opposite trends, consistent with previous studies [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Since \u003cem\u003eBacteroides plebeius\u003c/em\u003e primarily produces acetate, whereas \u003cem\u003eBacteroides caccae\u003c/em\u003e mainly generates butyrate [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], these differences in SCFAs may influence BP through distinct G-protein-coupled receptors (GPCRs), potentially contributing to the observed variations in bacterial abundances.\u003c/p\u003e \u003cp\u003eSCFAs, such as acetate, propionate, and butyrate, can activate GPR41 and GPR43 on the surface of intestinal epithelial cells, promoting the production of chemokines and cytokines involved in protective immune responses and tissue inflammation in mice [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Moreover, when propionate binds to the GPR41 receptor in the sympathetic nervous system, it enhances sympathetic activity, thereby increasing BP [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. GPR41 knockout mice exhibit elevated systolic BP, increased pulse pressure, higher end-diastolic pressure, and aggravated perivascular fibrosis [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. While acetate and propionate can stimulate renin secretion via olfactory receptor 78 (Olfr78) in the kidneys, further contributing to BP elevation, a two- to three-fold increase in colonic butyrate concentration has been shown to exert a significant hypotensive effect via the colon\u0026ndash;vagus nerve signaling pathway [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Taken together, SCFAs may exert opposing effects on BP by activating different receptors, stimulating renin release via Olfr78 in the kidney to raise BP, and inducing vasodilation via GPR41 to lower BP. These contrasting actions highlight their complex role in blood pressure regulation.\u003c/p\u003e \u003cp\u003eAdditionally, relative abundances of the Enterobacteriaceae family and the \u003cem\u003eEnterobacter\u003c/em\u003e genus were significantly higher in hypertensive patients. Previous studies showed that Enterobacteriaceae-derived TMAO can activate the NLR family pyrin domain containing 3 (NLRP3) inflammasome in carotid endothelial cells, leading to endothelial dysfunction and its association with hypertension [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. This bacterial group is also linked to oxidative stress and inflammatory responses, which may accelerate telomere attrition [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Notably, its higher prevalence in elderly populations aligns with the age characteristics of participants in this study. Finally, this study found that the relative abundance of the \u003cem\u003eAcidaminococcus\u003c/em\u003e genus was significantly higher in hypertensive patients. Previous research indicated that this bacterial group is associated with propionate production [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], and lower propionate levels were correlated with reduced BP [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. These findings suggest that \u003cem\u003eAcidaminococcus\u003c/em\u003e may influence BP regulation through SCFAs.\u003c/p\u003e \u003cp\u003eRegarding correlations between the fecal gut microbiota and clinical factors in this study, the BMI was negatively correlated with \u003cem\u003eBacteroides caccae\u003c/em\u003e but positively correlated with the \u003cem\u003eEnterobacter\u003c/em\u003e and \u003cem\u003eAcidaminococcus\u003c/em\u003e genera. Previous studies also reported that \u003cem\u003eBacteroides caccae\u003c/em\u003e is associated with lower metabolic syndrome scores, fasting blood glucose, and insulin resistance [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. In contrast, \u003cem\u003eEnterobacter\u003c/em\u003e was found to be more abundant in obese patients [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e], and its endotoxin production may contribute to obesity development. Similarly, \u003cem\u003eAcidaminococcus\u003c/em\u003e was significantly associated with obesity in Hispanic populations [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e], further supporting the correlations observed in this study.\u003c/p\u003e \u003cp\u003eAccording to results of this study, hemoglobin levels were positively correlated with the \u003cem\u003eBarnesiella\u003c/em\u003e genus, while WBC counts were negatively correlated with the Enterobacteriaceae family and the \u003cem\u003eEnterobacter\u003c/em\u003e genus. Previous studies showed that gut dysbiosis was associated with various hematological disorders, including iron-deficiency anemia, thrombosis, thrombocytosis, thrombocytopenia, and hematologic malignancies [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. However, no studies have yet explored the relationship between the \u003cem\u003eBarnesiella\u003c/em\u003e genus or the Enterobacteriaceae family and hematological diseases, highlighting a potential direction for future research.\u003c/p\u003e \u003cp\u003eThe current findings may contribute to a better understanding of hypertension-related gut microbiotic variations in aging populations and their potential implications for dietary interventions in different regions. However, this study was limited by the small sample size and all participants being recruited from the health examination cohort of a single hospital. Additionally, 75% of hypertensive patients were already receiving antihypertensive medication, which may have influenced the gut microbiotic composition. Furthermore, as a cross-sectional study, this research could not establish a causal relationship between the gut microbiota and hypertension.\u003c/p\u003e \u003cp\u003eIn conclusion, the study indicates that older adults with hypertension had gut microbiota imbalances linked to higher BMI and blood pressure. Certain beneficial bacteria, such as \u003cem\u003eBacteroides caccae\u003c/em\u003e, were less abundant and showed negative associations with unhealthy dietary habits, including high intake of saturated fats, sodium and staple foods. This suggests that dietary pattern and nutrient intake may play a key role in shaping the gut microbiota and could be a target for improving blood pressure in older adults.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors express their appreciation to all subjects who participated in this study for helping with study conduction and data collection.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors read and approved the final manuscript. H.-C.H. and S.-C.Y. designed the study. H.-C.H., Y.-Y.L ., and W.-J.T analyzed and interpreted data. H.-C.H., Y.-Y.C and \u0026nbsp;S.-C.Y. conducted the analysis and drafted the manuscript. H.-C.H. and \u0026nbsp;S.-C.Y. \u0026nbsp; revised the manuscript. H.-C.H., Y.-Y.C and S.-C.Y. finalized the manuscript. The corresponding authors attests that all listed authors meet the authorship criteria and that no others meeting the criteria have been omitted.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis research was funded by Cathay General Hospital, Taipei, Taiwan (CGH-MR-A11017).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll participants volunteered, and informed consent was obtained from both the subjects and their guardians. The study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Board (IRB) of Cathay General Hospital (Taipei, Taiwan), Cathay Medical Foundation (CGH-P110063, approved on December 24, 2022). The international registration number is NCT05057039, and it was listed on ClinicalTrials.gov PRS and reviewed on April 14, 2023..\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWorld Health Organization. Hypertension 2023 https://www.who.int/health-topics/hypertension#tab=tab_1. Accessed 20 Feb 2025\u003c/li\u003e\n\u003cli\u003eHealth Promotion Administration, Ministry of Health and Welfare, Taiwan. (2020). 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J Am Heart Assoc. 2020;9(15):e016641. doi: 10.1161/JAHA.120.016641.\u003c/li\u003e\n\u003cli\u003eCalder\u0026oacute;n-P\u0026eacute;rez L, Gosalbes MJ, Yuste S, Valls RM, Pedret A, Llaurad\u0026oacute; E, Jimenez-Hernandez N, Artacho A, Pla-Pag\u0026agrave; L, Companys J, Ludwig I, Romero MP, Rubi\u0026oacute; L, Sol\u0026agrave; R. Gut metagenomic and short chain fatty acids signature in hypertension: a cross-sectional study. Sci Rep. 2020;10(1):6436. doi: 10.1038/s41598-020-63475-w. \u003c/li\u003e\n\u003cli\u003eTakagi T, Naito Y, Kashiwagi S, Uchiyama K, Mizushima K, Kamada K, Ishikawa T, Inoue R, Okuda K, Tsujimoto Y, Ohnogi H, Itoh Y. Changes in the gut microbiota are associated with hypertension, hyperlipidemia, and type 2 diabetes mellitus in Japanese subjects. Nutrients. 2020;12(10):2996. doi: 10.3390/nu12102996.\u003c/li\u003e\n\u003cli\u003eGuo Y, Li X, Wang Z, Yu B. Gut microbiota dysbiosis in human hypertension: a systematic review of observational studies. Front Cardiovasc Med. 2021;8:650227. doi: 10.3389/fcvm.2021.650227.\u003c/li\u003e\n\u003cli\u003eShagaleeva OY, Kashatnikova DA, Kardonsky DA, Konanov DN, Efimov BA, Bagrov DV, Evtushenko EG, Chaplin AV, Silantiev AS, Filatova JV, Kolesnikova IV, Vanyushkina AA, Stimpson J, Zakharzhevskaya NB. Investigating volatile compounds in the Bacteroides secretome. Front Microbiol. 2023;14:1164877. doi: 10.3389/fmicb.2023.1164877.\u003c/li\u003e\n\u003cli\u003eKim MH, Kang SG, Park JH, Yanagisawa M, Kim CH. Short-chain fatty acids activate GPR41 and GPR43 on intestinal epithelial cells to promote inflammatory responses in mice. Gastroenterology. 2013;145(2):396-406.e1-10. doi: 10.1053/j.gastro.2013.04.056.\u003c/li\u003e\n\u003cli\u003eKimura I, Inoue D, Maeda T, Hara T, Ichimura A, Miyauchi S, Kobayashi M, Hirasawa A, Tsujimoto G. Short-chain fatty acids and ketones directly regulate sympathetic nervous system via G protein-coupled receptor 41 (GPR41). Proc Natl Acad Sci U S A. 2011;108(19):8030-5. doi: 10.1073/pnas.1016088108\u003c/li\u003e\n\u003cli\u003eNatarajan N, Hori D, Flavahan S, Steppan J, Flavahan NA, Berkowitz DE, Pluznick JL. Microbial short chain fatty acid metabolites lower blood pressure via endothelial G protein-coupled receptor 41. Physiol Genomics. 2016;48(11):826-834. doi: 10.1152/physiolgenomics.00089.2016\u003c/li\u003e\n\u003cli\u003eKaye DM, Shihata WA, Jama HA, Tsyganov K, Ziemann M, Kiriazis H, Horlock D, Vijay A, Giam B, Vinh A , Johnson C, Fiedler A, Donner D, Snelson M, Coughlan MT, Phillips S, Du XJ, El-Osta A, Drummond G, Lambert GW, Spector TD, Valdes AM, Mackay CR, Marques FZ. Deficiency of prebiotic fiber and insufficient signaling through gut metabolite-sensing receptors leads to cardiovascular disease. 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Front Microbiol. 2021;12:730809. doi: 10.3389/fmicb.2021.730809.\u003c/li\u003e\n\u003cli\u003ePluznick JL. Microbial Short-Chain Fatty Acids and Blood Pressure Regulation. Curr Hypertens Rep. 2017;19(4):25. doi: 10.1007/s11906-017-0722-5.\u003c/li\u003e\n\u003cli\u003eDel Chierico F, Manco M, Gardini S, Guarrasi V, Russo A, Bianchi M, Tortosa V, Quagliariello A, Shashaj B, Fintini D, Putignani L. Fecal microbiota signatures of insulin resistance, inflammation, and metabolic syndrome in youth with obesity: a pilot study. Acta Diabetol. 2021;58(8):1009-1022. doi: 10.1007/s00592-020-01669-4.\u003c/li\u003e\n\u003cli\u003eKaplan RC, Wang Z, Usyk M, Sotres-Alvarez D, Daviglus ML, Schneiderman N, Talavera GA, Gellman MD, Thyagarajan B, Moon JY, V\u0026aacute;zquez-Baeza Y, McDonald D, Williams-Nguyen JS, Wu MC, North KE, Shaffer J, Sollecito CC, Qi Q, Isasi CR, Wang T, Knight R, Burk RD. Gut microbiome composition in the Hispanic Community Health Study/Study of Latinos is shaped by geographic relocation, environmental factors, and obesity. Genome Biol. 2019 Nov 1;20(1):219. doi: 10.1186/s13059-019-1831-z. Erratum in: Genome Biol. 2020;21(1):50. doi: 10.1186/s13059-020-01970-z.\u003c/li\u003e\n\u003cli\u003eD\u0026apos;Angelo G. Microbiota and hematological diseases. Int J Hematol Oncol Stem Cell Res. 2022 Jul 1;16(3):164-173. doi: 10.18502/ijhoscr.v16i3.10139.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table 4","content":"\u003cp\u003eTable 4 is available in the Supplementary Files section.\u003c/p\u003e\n"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nutrition-and-metabolism","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nuam","sideBox":"Learn more about [Nutrition \u0026 Metabolism](http://nutritionandmetabolism.biomedcentral.com/)","snPcode":"12986","submissionUrl":"https://submission.nature.com/new-submission/12986/3","title":"Nutrition \u0026 Metabolism","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"hypertension, dietary pattern, nutrient intake, microbiotic composition, older adult","lastPublishedDoi":"10.21203/rs.3.rs-6739379/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6739379/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eMany studies on gut microbiota and hypertension have not focused on detailed dietary intake and eating habits, especially in older adults. This cross-sectional study aimed to examine the gut microbiota profiles of hypertensive elderly individuals in relation to their dietary patterns and nutrient intake.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eTwenty hypertensive patients and 21 age-matched healthy controls (aged 65\u0026ndash;80 years) were recruited from Cathay General Hospital (Taipei, Taiwan). Data collected included anthropometric measurements, blood pressure, blood biochemical analyses, and dietary intake (24-h recall and food frequency questionnaires) and fecal microbiotic composition (via16S rRNA sequencing).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eHypertensive patients had significantly higher BMI, waist circumference, and systolic blood pressure. They also showed lower levels of \u003cem\u003eBacteroides caccae\u003c/em\u003e and \u003cem\u003eBarnesiella\u003c/em\u003e, and higher levels of \u003cem\u003eEnterobacteriaceae\u003c/em\u003e, \u003cem\u003eEnterobacter\u003c/em\u003e, \u003cem\u003eAcidaminococcus\u003c/em\u003e, and \u003cem\u003eBacteroides plebeius\u003c/em\u003e. \u003cem\u003eBacteroides caccae\u003c/em\u003e abundance was negatively correlated with the intake of saturated fats, sodium, staple foods (e.g., bread, steamed buns, noodles), nut oils, and seasonings.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eHypertensive patients showed distinct gut microbiota profiles, with lower levels of \u003cem\u003eBacteroides caccae\u003c/em\u003e and \u003cem\u003eBarnesiella\u003c/em\u003e, and higher levels of \u003cem\u003eEnterobacteriaceae\u003c/em\u003e-related taxa. The abundance of \u003cem\u003eBacteroides caccae\u003c/em\u003e was negatively associated with the intake of saturated fats, sodium, and staple foods, suggesting a link between diet, gut microbiota, and hypertension.\u003c/p\u003e","manuscriptTitle":"Association between the gut microbiotic composition and dietary patterns in hypertensive elderly patients: a cross-sectional study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-09 10:44:47","doi":"10.21203/rs.3.rs-6739379/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-06-05T11:44:28+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-04T19:09:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"248110591523423669201522971244863648933","date":"2025-06-04T18:54:48+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-06-04T12:32:27+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-06-02T23:08:39+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-06-02T23:07:52+00:00","index":"","fulltext":""},{"type":"submitted","content":"Nutrition \u0026 Metabolism","date":"2025-05-24T13:33:46+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nutrition-and-metabolism","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nuam","sideBox":"Learn more about [Nutrition \u0026 Metabolism](http://nutritionandmetabolism.biomedcentral.com/)","snPcode":"12986","submissionUrl":"https://submission.nature.com/new-submission/12986/3","title":"Nutrition \u0026 Metabolism","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a2d7eda5-5502-4fa8-a917-187eb2d4e95f","owner":[],"postedDate":"June 9th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-07-14T15:59:07+00:00","versionOfRecord":{"articleIdentity":"rs-6739379","link":"https://doi.org/10.1186/s12986-025-00963-8","journal":{"identity":"nutrition-and-metabolism","isVorOnly":false,"title":"Nutrition \u0026 Metabolism"},"publishedOn":"2025-07-07 15:57:01","publishedOnDateReadable":"July 7th, 2025"},"versionCreatedAt":"2025-06-09 10:44:47","video":"","vorDoi":"10.1186/s12986-025-00963-8","vorDoiUrl":"https://doi.org/10.1186/s12986-025-00963-8","workflowStages":[]},"version":"v1","identity":"rs-6739379","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6739379","identity":"rs-6739379","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}