Dietary Index for Gut Microbiota is negatively associated with Hypertension

Dietary Index for Gut Microbiota is negatively associated with Hypertension | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Dietary Index for Gut Microbiota is negatively associated with Hypertension Yu Fu, Mengling Peng, He Cai, Yaoting Zhang, Yang Zheng, Bing Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6437987/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background The relationship between the gut microbiota and hypertension has gained increasing attention. However, the associations between Dietary Index for Gut Microbiota (DI-GM), an indicator of microbiota diversity, and hypertension remain insufficiently explored. We hypothesized that a higher DI-GM score would be associated with a lower prevalence of hypertension. Methods We analyzed data from 20,283 participants in the National Health and Nutrition Examination Survey (NHANES) 2007–2016 cycle via weighted generalized linear models and smooth curve fitting to examine the association between DI-GM and hypertension. Mediation analysis was conducted to evaluate the role of the eGFR and waist circumference. Results After adjusting for confounding factors, a higher DI-GM score was significantly associated with a lower prevalence of hypertension (OR = 0.94, 95% CI = 0.92–0.97). Compared with individuals with a DI-GM score of 0–3, those with a score of ≥ 6 had a significantly lower prevalence of hypertension (OR = 0.77, 95% CI = 0.69–0.86). The mediation analysis indicated that eGFR and waist circumference accounted for 5.91% and 3.91%, respectively, of the association between DI-GM and hypertension. Conclusion A higher DI-GM score is associated with a lower prevalence of hypertension, which is partially mediated by the eGFR and waist circumference. dietary index for gut microbiota hypertension estimated glomerular filtration rate mediation analysis waist circumference Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction Hypertension poses a significant global health challenge, impacting more than one billion people, with the prevalence reaching two-thirds in those aged 60 and above. 1 , 2 . Hypertension is closely linked to serious complications, such as coronary artery disease, heart failure, stroke, chronic kidney disease, and cognitive decline. 3 . While intensive blood pressure control has been shown to reduce cardiovascular events 4 , existing treatments fail to fully mitigate hypertension-related health risks. Thus, the discovery of novel molecular targets for the prevention of hypertension and its complications remains clinically crucial. Hypertension arises from a complex interaction of genetic and environmental factors. However, genome-wide association studies indicate that genetic factors contribute to fewer than 5% of cases 5 . A large-scale cohort study comprising 277,005 UK Biobank participants demonstrated a robust inverse correlation between adherence to nutritional guidelines and reduced blood pressure levels, persisting across all strata of polygenic risk scores for hypertension 6 . These results highlight the pivotal contribution of nutritional modifications to both primary prevention and therapeutic control of elevated blood pressure. The composition and metabolic activities of intestinal microbial communities are significantly influenced by nutritional intake, while dietary patterns concurrently exert direct effects on human physiological well-being 7 . The bidirectional relationship between dietary intake and intestinal microbial communities significantly modulates key metabolic and physiological pathways, including lipid homeostasis, adiposity control, endothelial integrity, immune regulation, and cardiometabolic function 8 – 11 . Accumulating evidence indicates the presence of a microbial imbalance in the intestinal flora of individuals with hypertension and experimental hypertensive animals 12 . Experimental evidence shows that spontaneously hypertensive rats display markedly diminished microbial richness and diversity in the gut 13 , whereas interventions such as antibiotic administration or high-fat dietary intake exacerbate hypertension in this model 14 , 15 . Collectively, these observations imply that the interplay between diet and the gut microbiota could play a pivotal role in modulating blood pressure, offering innovative perspectives on nutritional approaches for hypertension prevention and control. For objective measurement of diet‒microbiome interactions, the Dietary Index for Gut Microbiota (DI-GM) was created by research teams. The DI-GM was developed through comprehensive analysis of 106 published studies, employing a 14-item scoring system to assess dietary effects on microbial community structure and biodiversity, where elevated scores correlate with improved gut microbial health 16 . In contrast to conventional approaches involving invasive specimen collection or targeted metabolic profiling, DI-GM synthesizes nutritional data to accurately capture diet‒microbiota associations. This noninvasive characteristic renders it especially valuable for population-based research initiatives such as the NHANES. Emerging evidence has demonstrated an inverse association between DI-GM and stroke and diabetes 17 , 18 , but its relationship with hypertension has not been fully explored. Moreover, kidney function and obesity are crucial factors influencing blood pressure 19 , 20 , with kidney function typically assessed by the eGFR 21 and waist circumference serving as a simple method for evaluating obesity 22 . The gut microbiota can impair kidney function by producing uremic toxins such as trimethylamine-N-oxide (TMAO) or regulating lipid metabolism through butyrate 23 , 24 . Building upon this evidence, we postulate that elevated DI-GM scores correlate with a lower prevalence of hypertension. To investigate this relationship, we performed a population-based cross-sectional analysis utilizing NHANES datasets. This pioneering study represents the inaugural application of NHANES data to evaluate DI-GM-hypertension associations, offering novel perspectives for tailored nutritional approaches in hypertension prevention. 2. Methods and Materials 2.1 Study population Our analysis incorporated data from six consecutive NHANES survey cycles (2007--2016). As a nationally representative surveillance system, the NHANES employs a complex, stratified probability sampling design to obtain comprehensive health, nutritional, and sociodemographic information from the civilian, noninstitutionalized U.S. population. The study exclusively used deidentified, publicly accessible datasets that received ethical approval from the NCHS Research Ethics Review Board. Written informed consent was obtained from all survey participants prior to data collection. The reporting methodology strictly adheres to the STROBE statement for observational epidemiological research. 2.2 Study Design and Population The participants were adults aged 20 years and older who participated in the NHANES from 2007--2016. During the screening process, individuals lacking DI-GM components, covariates, and hypertension diagnosis data were excluded. A total of 20,283 eligible participants were included in the analysis, of whom 7,979 were diagnosed with hypertension ( Figure 1 ). 2.3 DI-GM The DI-GM consists of 14 foods or nutrients, with beneficial components, including fermented dairy, chickpeas, soybean, whole grains, fiber, cranberries, avocados, broccoli, coffee, and green tea, and unfavorable components, including red meat, processed meat, refined grains, and a high-fat diet (≥40% of energy from fat) 16 . The DI-GM was computed using 24-hour dietary recall data obtained from NHANES. Scoring methodology differed for beneficial versus unfavorable components: (1) Beneficial components received 1 point for intake at or above sex-stratified median consumption levels (0 points otherwise), with cumulative scores generating the Beneficial Gut Microbiota Score (BGMS; range 0-10); (2) Unfavorable components were assigned 0 points for consumption meeting or exceeding sex-specific medians (or >40% energy from fat for high-fat diets), otherwise 1 point, yielding the Unfavorable Gut Microbiota Score (UGMS; range 0-4). The composite DI-GM score (potential range 0-14) represented the sum of BGMS and UGMS, subsequently categorized into four groups: 0–3, 4, 5, and ≥6 17 . The components and scoring criteria of DI-GM are shown in Supplementary Table S1 . 2.4 Hypertension This study included participants surveyed between 2007 and 2016 with complete hypertension data. Hypertension was defined as currently taking prescription medication for hypertension, having been told by a doctor at least twice that they had hypertension, or having an average systolic blood pressure of ≥140 mmHg and an average diastolic blood pressure of ≥90 mmHg on three consecutive measurements 25,26 . 2.5 Covariates The selection of covariates in this study was based on expert judgment and previous research 18,27 . The included covariates included age, gender, race, education level, marital status, poverty income ratio (PIR), body mass index (BMI), waist circumference, physical activity, smoking status, alcohol intake, eGFR, hyperlipidemia, physical activity, diabetes, CVD (cardiovascular disease), and stroke. These covariates were obtained from the demographic, dietary, examination, laboratory, questionnaire sections of NHANES database. Specific covariate information can be found in the Supplementary Materials. 2.6 Statistical analysis All the statistical analyses were performed via R statistical software (v4.4.1; R Foundation) and EmpowerStats (v4.2; X&Y Solutions). To account for NHANES's complex sampling design (stratified multistage probability sampling), analyses incorporated sampling weights via the R 'survey' package. Baseline characteristics were compared by hypertension status, with continuous variables presented as weighted means ± standard deviations (calculated via svyglm) and categorical variables as weighted proportions. DI-GM and hypertension were assessed via weighted multivariable generalized linear models, with the results expressed as OR and 95% confidence intervals. The models included Model 1: Crude model without adjustment for any covariates. Model 2: adjustments for age, gender, and race; Model 3: adjustments for age, gender, race, education level, marital status, PIR, smoking status, alcohol intake, BMI, waist circumference, eGFR, hyperlipidemia, physical activity, diabetes, CVD, and stroke. Smooth curve fitting was subsequently performed to further assess the relationship between the two variables. Sensitivity analyses included (1) subgroup analysis and (2) multiple imputation: missing data were handled via the multiple imputation by chained equations (MICE) method, which generated five imputed datasets. Detailed information on multiple imputation can be found in the supplementary methods. The R mediation package was subsequently used with the bootstrap method to perform 1,000 simulations to calculate the 95% confidence interval for the mediating effect 28 . The mediating role of the eGFR and waist circumference in the relationship between DI-GM and hypertension was examined. Receiver operating characteristic (ROC) curves were used to evaluate the predictive efficacy of the nomogram incorporating DI-GM and other covariates for hypertension. The predictive performance was assessed via the area under the ROC curve (AUC). 3. Results 3.1. Participant characteristics As shown in Table 1 , the study included 20,283 participants from NHANES 2007–2016, of whom 7,979 were diagnosed with hypertension and 12,304 were not. The mean age was 48.89 years (SD = 17.51). Compared with nonhypertensive individuals, hypertensive participants were older (59.96 years vs. 41.87 years, p < 0.001). Significant differences were also observed in terms of race, PIR, education level, smoking status, alcohol intake, physical activity, BMI, waist circumference, eGFR, and comorbidities such as cardiovascular disease, stroke, diabetes, and hyperlipidemia (p < 0.05). Table 1 Characteristics of the NHANES 2007–2016 participants. Non-Hypertension (N = 12,304) Hypertension (N = 7,979) P value Age, Mean ± SD 41.87 ± 15.61 59.96 ± 14.33 < 0.001 Gender, % 0.741 Female 50.87 50.60 Male 49.13 49.40 Race, % < 0.001 Mexican American 9.46 5.47 Other Hispanic 6.02 3.73 Non-Hispanic White 68.58 72.60 Non-Hispanic Black 8.75 12.58 Other Race 7.20 5.61 Education level, % < 0.001 high school or below 34.52 42.47 Some college or AA degree 31.86 32.62 College graduate or above 33.62 24.91 Marital status, % 3.5 44.38 42.15 Smoking status, % < 0.001 No 58.11 49.30 Yes 41.89 50.70 Alcohol intake, % < 0.001 No 19.85 25.19 Yes 80.15 74.81 BMI (kg/m2), Mean ± SD 28.03 ± 6.34 30.90 ± 7.06 < 0.001 Waist circumference (cm), Mean ± SD 95.91 ± 15.52 105.25 ± 15.96 < 0.001 Hyperlipidemia, % < 0.001 No 34.44 16.37 Yes 65.56 83.63 Physical activity, % < 0.001 No 21.42 34.19 Yes 78.58 65.81 Diabetes, % < 0.001 No 93.80 75.07 Yes 6.20 24.93 CVD, % < 0.001 No 97.52 86.60 Yes 2.48 13.40 Stroke, % < 0.001 No 99.04 94.46 Yes 0.96 5.54 DI-GM score, Mean ± SD 4.66 ± 1.61 4.62 ± 1.65 0.009 DI-GM group, % 0.018 0–3 21.28 23.72 4 23.72 22.55 5 23.62 23.34 ≥ 6 31.37 30.39 Beneficial to gut microbiota 2.05 ± 1.34 1.97 ± 1.36 0.035 Unfavorable to gut microbiota 2.61 ± 1.04 2.65 ± 1.09 0.217 eGFR, ml/min/1.73 m 2 102.34 ± 25.48 85.75 ± 26.23 < 0.001 Note: Mean ± standard deviation for continuous variables: P value was calculated by survey-weighted linear regression (svyglm). Survey-weighted percentages for categorical variables: survey-weighted linear regression (svyglm). Abbreviations: BMI: body mass index; CVD: cardiovascular disease; DI-GM: Dietary Index for Gut Microbiota; eGFR: estimated glomerular filtration rate; PIR: poverty income ratio. 3.2. Relationship between DI-GM and hypertension As shown in Table 2 , weighted generalized linear model analysis indicated that higher DI-GM scores are associated with a lower prevalence of hypertension. In Model 1, for each unit increase in DI-GM, the odds of hypertension decreased by 3% (OR = 0.97, 95% CI = 0.95–0.99, p = 0.009); in Model 3, this association was more pronounced (OR = 0.94, 95% CI = 0.92–0.97, p < 0.001). Compared with the group with DI-GM scores of 0–3, patients with scores ≥ 6 (OR = 0.77, 95% CI = 0.69–0.86, p < 0.001) had a significantly lower prevalence of hypertension. Further analysis revealed that higher scores for unfavorable dietary components (lower intake of unhealthy foods) were significantly associated with a decreased prevalence of hypertension (OR = 0.94, 95% CI = 0.91–0.98, p = 0.003), whereas higher scores for beneficial components (greater intake of beneficial foods) were also significantly associated with a decreased prevalence of hypertension (OR = 0.95, 95% CI = 0.92–0.98, p = 0.002). Table 2 Relationships between DI-GM and hypertension according to the NHANES Model1 Model2 Model3 DI-GM 0.97 (0.95, 0.99) 0.009 0.90 (0.87, 0.92) < 0.001 0.94 (0.92,0.97) < 0.001 DI-GM group 0–3 Reference Reference Reference 4 0.85 (0.77,0.95) 0.005 0.85 (0.75,0.95) 0.007 0.90 (0.79,1.11) 0.076 5 0.89 (0.79,0.99) 0.033 0.77 (0.68,0.88) < 0.001 0.85 (0.74,0.98) 0.028 ≥ 6 0.87 (0.79,0.96) 0.007 0.63 (0.56,0.70) < 0.001 0.77 (0.69,0.86) < 0.001 P for trend 0.020 < 0.001 < 0.001 Beneficial to gut microbiota 0.97 (0.94,0.99) 0.035 0.91 (0.89,0.94) < 0.001 0.95 (0.92,0.98) 0.002 Unfavorable to gut microbiota 0.98 (0.95,1.01) 0.218 0.89 (0.86,0.92) < 0.001 0.94 (0.91,0.98) 0.003 Model 1: Crude model without adjustment for any covariates. Model 2: adjustments for age, gender, and race; Model 3: adjustments for age, gender, race, education level, marital status, PIR, smoking status, alcohol intake, BMI, waist circumference, eGFR, hyperlipidemia, physical activity, diabetes, CVD, stroke. Abbreviations: BMI: body mass index; CVD: cardiovascular disease; DI-GM: Dietary Index for Gut Microbiota; eGFR: estimated glomerular filtration rate; PIR: poverty income ratio. The smooth curve fitting plot further revealed a negative correlation between DI-GM score and hypertension (Fig. 2 ). 3.3 Sensitivity analysis Subgroup analysis revealed that the negative correlation between DI-GM and hypertension was significant in most subgroups, indicating the applicability and robustness of DI-GM across different populations (Fig. 3 ). Moreover, the results from multiple imputation and unweighted association analyses confirmed that the association between DI-GM and hypertension remained significant and robust ( Supplementary Tables S2–S3 ). 3.4 Mediation analysis Additionally, mediation analysis was performed to explore the mediating role of the eGFR. The results shown in Fig. 4 indicate that eGFR and waist circumference play a significant mediating role in the relationship between DI-GM and hypertension. The total effect coefficient of DI-GM on hypertension mediated by eGFR was − 0.0122 (p = 0.002), with a mediating effect of -0.0007 (p < 0.001) and a mediating percentage of 5.91% (p = 0.002) (Fig. 4 A). The total effect coefficient of DI-GM on hypertension mediated by waist circumference was − 0.0152 (p = 0.008), with a mediating effect of -0.0006 (p = 0.004) and a mediating percentage of 3.91% (p = 0.012) (Fig. 4 B). 3.5 Establishment of the predictive nomogram Model 3 (fully adjusted) was used to create a predictive nomogram. Each predictor was assigned a specific score on a rating scale, and the total points for each variable were aggregated. The total points corresponded to the probability of hypertension according to the nomogram. Elevated scores indicated greater hypertension risk (Fig. 5 A). The nomogram showed good predictive accuracy (AUC = 0.838, 95% CI 0.832–0.843) according to the ROC analysis (Fig. 5 B). 4. Discussion To our knowledge, this is the largest and first cross-sectional study to investigate the association between the DI-GM and hypertension. Our findings support the hypothesis that a higher DI-GM score is associated with a lower prevalence of hypertension. Generalized linear model analysis revealed a significant negative correlation between DI-GM and hypertension. Our findings demonstrate a significant inverse relationship between DI-GM scores and hypertension risk, with the high DI-GM score group (≥ 6) showing a 23% lower hypertension prevalence compared to the low score group (0–3) (OR = 0.77, 95% CI = 0.69–0.86, p < 0.001). Smooth curve fitting further revealed the trend of this negative association, and sensitivity analysis underscored the reliability and robustness of these results. Notably, the mediation analysis demonstrated that eGFR and waist circumference serve as crucial mediators in the association between DI-GM and hypertension. Hypertension, a complex multifactorial disease, involves interactions between genetic susceptibility and environmental factors in its pathogenesis. Existing evidence suggests that a healthy dietary pattern is significantly associated with lower blood pressure levels, and this association is independent of an individual’s genetic risk 6 . The gut microbiota can respond rapidly to dietary changes and influence human health through various mechanisms 29 . Therefore, the consumption of foods beneficial to the gut microbiota has become an important strategy for maintaining health. Studies have shown that a diet high in fruits and vegetables and low in sodium can effectively lower blood pressure 30 , 31 . Additionally, probiotics and their metabolites can reshape the gut microbiota composition 32 . They can positively influence blood pressure through mechanisms such as the production of short-chain fatty acids, the regulation of lipid metabolism, the improvement of insulin resistance, and the modulation of the renin‒angiotensin system 33 , 34 . Research by Jieping Yang revealed that avocado significantly increases gut microbiota diversity 35 . Gut microbiota diversity has been shown to be closely associated with blood pressure levels 36 . Furthermore, research by Hamdi A. Jama and colleagues suggested that the Mediterranean diet promotes the growth of beneficial gut microbiota and lowers blood pressure by reducing inflammation 37 . Although the Bezawit E. Kase team identified 14 foods/nutrients associated with the gut microbiota and developed the DI-GM index through a systematic review 16 , the exact relationship between DI-GM and hypertension remains unclear. This study revealed a significant association between higher DI-GM scores and lower hypertension prevalence, which aligns with findings from animal studies. Compared with wild-type animals, hypertensive model animals presented significant differences in gut microbiota composition, and their blood pressure levels could be altered through fecal microbiota transplantation and antibiotic intervention 13 . Additionally, population studies have confirmed significant differences in gut microbiota characteristics between hypertensive patients and individuals with normal blood pressure 38 – 42 . These studies, along with our findings, provide strong support for the crucial role of the gut microbiota in blood pressure regulation. This study is the first to reveal that eGFR and waist circumference significantly mediate the relationship between DI-GM and hypertension, suggesting that higher DI-GM scores may help lower hypertension prevalence by enhancing kidney function and reducing obesity. This finding has important pathophysiological implications. First, renal insufficiency and obesity have been confirmed as independent risk factors for the onset and progression of hypertension 20 , 43 , 44 ; second, there is a close relationship between the gut microbiota and kidney function as well as obesity 12 , 45 , 46 . Therefore, the eGFR and waist circumference may play key roles in the "diet-gut microbiota-hypertension" pathway. A diet with a high DI-GM score can increase gut microbiota diversity, which may contribute to lowering blood pressure 13 , 36 , 47 , 48 . A high DI-GM score may regulate the gut microbiota balance and reduce the production of toxins 12 , 49 , thereby improving kidney function and reducing obesity, as reflected by the eGFR and waist circumference. The strengths of this study include the use of a large, nationally representative NHANES dataset, which enhances the robustness of the conclusions. We adjusted for multiple potential confounders to minimize bias and validated the robustness of the results through sensitivity analyses. However, several limitations remain. First, the cross-sectional design precludes establishing causal relationships. Longitudinal studies and randomized controlled trials are needed to confirm causal associations. Second, dietary intake data were self-reported, which may introduce recall bias. Future studies could employ more objective dietary assessment methods. Third, unmeasured confounders, such as genetic factors and direct gut microbiota sequencing data, may have influenced the results. This study has important translational implications. In clinical practice, the DI-GM score could be utilized in primary care for hypertension risk stratification, guiding personalized dietary interventions such as prebiotic supplementation for individuals with low DI-GM scores. In summary, this study is the first to reveal a significant negative association between DI-GM scores and hypertension prevalence, and further demonstrates the important mediating role of the eGFR and waist circumference in this relationship. These findings not only provide new evidence for the "diet‒gut microbiota‒blood pressure regulation" pathway but also suggest that optimizing dietary patterns, improving the gut microbiota, enhancing kidney function and reducing obesity may help lower the prevalence of hypertension in the future. However, as this study is cross-sectional, causal relationships need further validation. Future longitudinal studies could explore the potential value of DI-GM in hypertension prevention and intervention. Abbreviations AUC: area under the ROC curve BGMS: beneficial to gut microbiota score BMI: body mass index; CKD-EPI: CKD Epidemiology Collaboration CIs: confidence intervals CVD: cardiovascular disease DI-GM: Dietary Index for Gut Microbiota eGFR: estimated glomerular filtration rate NCHS: National Center for Health Statistics NHANES: National Health and Nutrition Examination Survey PIR: poverty income ratio. ROC: Receiver operating characteristic STROBE: Strengthening the Reporting of Observational Studies in Epidemiology TMAO: trimethylamine-N-oxide UGMS: unfavorable to gut microbiota score Declarations Ethics approval and consent to participate The NHANES Institutional Review Board approved the study protocol, which adhered to the Declaration of Helsinki. All participants provided written informed consent. Consent for publication Not applicable. Availability of data and materials The datasets analyzed in this study are publicly available from the NHANES at: https://www.cdc.gov/nchs/nhanes/index.htm. Competing interests The authors affirm that this research was conducted without any commercial or financial relationships that could present a potential conflict of interest. Funding Not applicable. Author Contributions Yu Fu acquired the data and performed the statistical analysis. Yu Fu drafted the article. Yang Zheng checked the statistical analysis and substantially revised the draft. Mengling Peng and He Cai read the entire manuscript and revised it. Yaoting Zhang and Bing Li provided some necessary statistical suggestions and improved the English writing. All authors gave final approval of the version to be submitted. Acknowledgment The authors have no acknowledgments to declare. Clinical trial number Not applicable. 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Lacticaseibacillus rhamnosus HF01 fermented yogurt alleviated high-fat diet-induced obesity and hepatic steatosis via the gut microbiota-butyric acid-hepatic lipid metabolism axis. Food Funct . Apr 22 2024;15(8):4475-4489. doi:10.1039/d3fo04985j Chen Z, Liang W, Liang J, et al. Probiotics: functional food ingredients with the potential to reduce hypertension. Front Cell Infect Microbiol . 2023;13:1220877. doi:10.3389/fcimb.2023.1220877 Khalesi S, Sun J, Buys N, Jayasinghe R. Effect of probiotics on blood pressure: a systematic review and meta-analysis of randomized, controlled trials. Hypertension . Oct 2014;64(4):897-903. doi:10.1161/hypertensionaha.114.03469 Yang J, Lei OK, Bhute S, et al. Impact of daily avocado consumption on gut microbiota in adults with abdominal obesity: an ancillary study of HAT, a randomized controlled trial. Food Funct . Jan 2 2025;16(1):168-180. doi:10.1039/d4fo03806a Verhaar BJH, Prodan A, Nieuwdorp M, Muller M. Gut Microbiota in Hypertension and Atherosclerosis: A Review. Nutrients . Sep 29 2020;12(10)doi:10.3390/nu12102982 Jama HA, Beale A, Shihata WA, Marques FZ. The effect of diet on hypertensive pathology: is there a link via gut microbiota-driven immunometabolism? Cardiovasc Res . Jul 1 2019;115(9):1435-1447. doi:10.1093/cvr/cvz091 Dan X, Mushi Z, Baili W, et al. Differential Analysis of Hypertension-Associated Intestinal Microbiota. Int J Med Sci . 2019;16(6):872-881. doi:10.7150/ijms.29322 de la Cuesta-Zuluaga J, Mueller NT, Álvarez-Quintero R, et al. Higher Fecal Short-Chain Fatty Acid Levels Are Associated with Gut Microbiome Dysbiosis, Obesity, Hypertension and Cardiometabolic Disease Risk Factors. Nutrients . Dec 27 2018;11(1)doi:10.3390/nu11010051 Huart J, Leenders J, Taminiau B, et al. Gut Microbiota and Fecal Levels of Short-Chain Fatty Acids Differ Upon 24-Hour Blood Pressure Levels in Men. Hypertension . Oct 2019;74(4):1005-1013. doi:10.1161/hypertensionaha.118.12588 Kim S, Goel R, Kumar A, et al. Imbalance of gut microbiome and intestinal epithelial barrier dysfunction in patients with high blood pressure. Clin Sci (Lond) . Mar 30 2018;132(6):701-718. doi:10.1042/cs20180087 Sun S, Lulla A, Sioda M, et al. Gut Microbiota Composition and Blood Pressure. Hypertension . May 2019;73(5):998-1006. doi:10.1161/hypertensionaha.118.12109 Nishimoto M, Griffin KA, Wynne BM, Fujita T. Salt-Sensitive Hypertension and the Kidney. Hypertension . Jun 2024;81(6):1206-1217. doi:10.1161/hypertensionaha.123.21369 Takase H, Dohi Y, Toriyama T, et al. Evaluation of risk for incident hypertension using glomerular filtration rate in the normotensive general population. J Hypertens . Mar 2012;30(3):505-12. doi:10.1097/HJH.0b013e32834f6a1d Mahmoodpoor F, Rahbar Saadat Y, Barzegari A, Ardalan M, Zununi Vahed S. The impact of gut microbiota on kidney function and pathogenesis. Biomed Pharmacother . Sep 2017;93:412-419. doi:10.1016/j.biopha.2017.06.066 Virtue AT, McCright SJ, Wright JM, et al. The gut microbiota regulates white adipose tissue inflammation and obesity via a family of microRNAs. Sci Transl Med . Jun 12 2019;11(496)doi:10.1126/scitranslmed.aav1892 Qi Y, Kim S, Richards EM, Raizada MK, Pepine CJ. Gut Microbiota: Potential for a Unifying Hypothesis for Prevention and Treatment of Hypertension. Circ Res . May 26 2017;120(11):1724-1726. doi:10.1161/circresaha.117.310734 Tsiavos A, Antza C, Trakatelli C, Kotsis V. The Microbial Perspective: A Systematic Literature Review on Hypertension and Gut Microbiota. Nutrients . Oct 30 2024;16(21)doi:10.3390/nu16213698 Perler BK, Friedman ES, Wu GD. The Role of the Gut Microbiota in the Relationship Between Diet and Human Health. Annu Rev Physiol . Feb 10 2023;85:449-468. doi:10.1146/annurev-physiol-031522-092054 Additional Declarations No competing interests reported. Supplementary Files Supplementary.docx Cite Share Download PDF Status: Posted Version 1 posted 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-6437987","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":454938931,"identity":"65466f3e-9ca2-4932-8130-223c181a4475","order_by":0,"name":"Yu Fu","email":"","orcid":"","institution":"Department of Cardiovascular Diseases, The First Hospital of Jilin University, Changchun, 130021","correspondingAuthor":false,"prefix":"","firstName":"Yu","middleName":"","lastName":"Fu","suffix":""},{"id":454938932,"identity":"d5a88d15-3fec-40a2-8dd3-28934bd4f629","order_by":1,"name":"Mengling Peng","email":"","orcid":"","institution":"Department of Cardiovascular Diseases, The First Hospital of Jilin University, Changchun, 130021","correspondingAuthor":false,"prefix":"","firstName":"Mengling","middleName":"","lastName":"Peng","suffix":""},{"id":454938933,"identity":"3dc142ba-9ec5-4b26-a453-ac62c4c06437","order_by":2,"name":"He Cai","email":"","orcid":"","institution":"Department of Cardiovascular Diseases, The First Hospital of Jilin University, Changchun, 130021","correspondingAuthor":false,"prefix":"","firstName":"He","middleName":"","lastName":"Cai","suffix":""},{"id":454938934,"identity":"d0b196a0-dfb2-4279-b979-5fd85d594810","order_by":3,"name":"Yaoting Zhang","email":"","orcid":"","institution":"Department of Cardiovascular Diseases, The First Hospital of Jilin University, Changchun, 130021","correspondingAuthor":false,"prefix":"","firstName":"Yaoting","middleName":"","lastName":"Zhang","suffix":""},{"id":454938935,"identity":"704df417-0f1e-4a78-b212-2b30cc7f1343","order_by":4,"name":"Yang Zheng","email":"","orcid":"","institution":"Department of Cardiovascular Diseases, The First Hospital of Jilin University, Changchun, 130021","correspondingAuthor":false,"prefix":"","firstName":"Yang","middleName":"","lastName":"Zheng","suffix":""},{"id":454938936,"identity":"4fa33de4-9e51-4fd8-b141-6e9fd77d4ed3","order_by":5,"name":"Bing Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAvklEQVRIiWNgGAWjYBACPmYehgMMDDbMfCAeDzFa2CBa0pjZiNcCUXaYgQQt7LwHDxf8Os/OJpHA+OBtG4O8OWGH8SUcntl3mxmohdlwbhuD4c4Gglp4DA7z9oC1sEnztjEkGBwgTss5kBb238Rr4flxAGwLM5FagH7hbUhmZuN52Cw555yE4QZCWvj5zx7+zPPHLpmfPfnghzdlNvIEbQEDxjaGZCDZAGRKEKMeBP4w2BGrdBSMglEwCkYgAADWrjO+ULvMPgAAAABJRU5ErkJggg==","orcid":"","institution":"Department of Cardiovascular Diseases, The First Hospital of Jilin University, Changchun, 130021","correspondingAuthor":true,"prefix":"","firstName":"Bing","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2025-04-13 08:08:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6437987/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6437987/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":82714991,"identity":"cf9f470c-9346-443c-83c5-27df244338ab","added_by":"auto","created_at":"2025-05-14 12:08:00","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1977985,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlow chart of the study participants.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAbbreviations: DI-GM: Dietary Index for Gut Microbiota; NHANES: National Health and Nutrition Examination Survey.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-6437987/v1/a49fa58189777db12f5c33d0.png"},{"id":82714996,"identity":"a9955419-0a62-4f08-a571-558f3433f107","added_by":"auto","created_at":"2025-05-14 12:08:01","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1113338,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe association of DI-GM and hypertension.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe solid red line represents the smooth curve fit between variables, and the blue bands represent the 95% confidence interval from the fit, adjusted for age, gender, race, education level, marital status, PIR, smoking status, alcohol intake, BMI, waist circumference, eGFR, hyperlipidemia, physical activity, diabetes, CVD, and stroke. Abbreviations: BMI, body mass index; CVD, cardiovascular disease; DI-GM, dietary index for gut microbiota; eGFR, estimated glomerular filtration rate; PIR, poverty income ratio.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-6437987/v1/f7e6d88a5d83aa0a3005bf44.png"},{"id":82716420,"identity":"0b0c77ae-6fb5-478e-b95b-df70cbf48f2a","added_by":"auto","created_at":"2025-05-14 12:16:01","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":14830917,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSubgroup analysis of the relationship between DI-GM and hypertension.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe OR (95% CI) is represented by solid red dots and blue dashed lines, respectively. In all subgroups, the association between DI-GM and hypertension was inversely correlated, and the interaction tests showed non-significant p-values. Subgroup analyses were conducted based on age, gender, BMI, eGFR, smoking status, alcohol intake, physical activity, hyperlipidemia, diabetes, cardiovascular disease (CVD), and stroke, and were adjusted for age, gender, race, education level, marital status, poverty income ratio (PIR), smoking status, alcohol intake, BMI, waist circumference, eGFR, hyperlipidemia, physical activity, diabetes, CVD, and stroke. Abbreviations: BMI: body mass index; CVD: cardiovascular disease; DI-GM: dietary index for gut microbiota; eGFR: estimated glomerular filtration rate; PIR: poverty income ratio.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-6437987/v1/227737be575fe54c37d9d28c.png"},{"id":82714993,"identity":"c5cd773f-5778-430f-9a58-bd86ba8e7ea9","added_by":"auto","created_at":"2025-05-14 12:08:01","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":2112941,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMediation analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMediation analysis was performed to explore the potential mediating roles of eGFR and waist circumference in the association between DI-GM and hypertension. eGFR and waist circumference mediated 5.91% and 3.91% of the association between DI-GM and hypertension, respectively. Adjustments were made for age, gender, race, education level, marital status, PIR, smoking status, alcohol intake, BMI, waist circumference, eGFR, hyperlipidemia, physical activity, diabetes, CVD, and stroke. Abbreviations: BMI: body mass index; CVD: cardiovascular disease; DI-GM: dietary index for gut microbiota; eGFR: estimated glomerular filtration rate; PIR: poverty income ratio.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-6437987/v1/856b79699dd459bcc4c8ec20.png"},{"id":82715009,"identity":"c80efa00-bfa8-4923-a83e-e5085d2992ef","added_by":"auto","created_at":"2025-05-14 12:08:01","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":2701171,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eNomogram for hypertension risk prediction and its performance evaluation using ROC curve.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) The nomogram model was constructed based on the fully adjusted model (Model 3). Each predictor was calculated as a specific score on a rating scale, the total points of each variable were summed, and a vertical line was drawn downward at the total points to correspond to the probability of hypertension. A higher score indicated a higher probability of hypertension. The numbers in the figure represent the following values: Race: 1 = Mexican American, 2 = Other Hispanic, 3 = Non-Hispanic White, 4 = Non-Hispanic Black, 5 = Other Race - Including Multi-Racial. Education level: 1 = high school or below, 2 = Some college or AA degree, 3 = College graduate or above. marital status: 1= Married/Living with Partner, 2= Widowed/Divorced/Separated, 3= Never married. (B) The ROC curve derived from Model 3 was used to evaluate the predictive performance of the nomogram model for hypertension. The predictive accuracy of this nomogram was assessed using the receiver operating characteristic (ROC) curve, yielding an area under the curve (AUC) of 0.838 (95% CI: 0.832–0.843). Model3: Adjustments for age, gender, race, education level, marital status, PIR, smoking status, alcohol intake, BMI, waist circumference, eGFR, hyperlipidemia, physical activity, diabetes, CVD, stroke. Abbreviations: BMI: body mass index; CVD: cardiovascular disease; DI-GM: dietary index for gut microbiota; eGFR: estimated glomerular filtration rate; PIR: poverty income ratio.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-6437987/v1/3a329e0650e8e038cc0a126c.png"},{"id":90241010,"identity":"986aec8b-3e83-4d72-bc50-1c783075d611","added_by":"auto","created_at":"2025-08-30 21:31:49","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":20416105,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6437987/v1/9642d947-f9df-4e27-b228-7cbf830483d0.pdf"},{"id":82714992,"identity":"04e9608c-2842-4c02-94df-b29c730c9b54","added_by":"auto","created_at":"2025-05-14 12:08:00","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":54473,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementary.docx","url":"https://assets-eu.researchsquare.com/files/rs-6437987/v1/8d38edc1552850dae6a9f06f.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Dietary Index for Gut Microbiota is negatively associated with Hypertension","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eHypertension poses a significant global health challenge, impacting more than one billion people, with the prevalence reaching two-thirds in those aged 60 and above. \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Hypertension is closely linked to serious complications, such as coronary artery disease, heart failure, stroke, chronic kidney disease, and cognitive decline. \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. While intensive blood pressure control has been shown to reduce cardiovascular events\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e, existing treatments fail to fully mitigate hypertension-related health risks. Thus, the discovery of novel molecular targets for the prevention of hypertension and its complications remains clinically crucial.\u003c/p\u003e \u003cp\u003eHypertension arises from a complex interaction of genetic and environmental factors. However, genome-wide association studies indicate that genetic factors contribute to fewer than 5% of cases\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. A large-scale cohort study comprising 277,005 UK Biobank participants demonstrated a robust inverse correlation between adherence to nutritional guidelines and reduced blood pressure levels, persisting across all strata of polygenic risk scores for hypertension\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. These results highlight the pivotal contribution of nutritional modifications to both primary prevention and therapeutic control of elevated blood pressure.\u003c/p\u003e \u003cp\u003eThe composition and metabolic activities of intestinal microbial communities are significantly influenced by nutritional intake, while dietary patterns concurrently exert direct effects on human physiological well-being\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. The bidirectional relationship between dietary intake and intestinal microbial communities significantly modulates key metabolic and physiological pathways, including lipid homeostasis, adiposity control, endothelial integrity, immune regulation, and cardiometabolic function\u003csup\u003e\u003cspan additionalcitationids=\"CR9 CR10\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Accumulating evidence indicates the presence of a microbial imbalance in the intestinal flora of individuals with hypertension and experimental hypertensive animals\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Experimental evidence shows that spontaneously hypertensive rats display markedly diminished microbial richness and diversity in the gut \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e, whereas interventions such as antibiotic administration or high-fat dietary intake exacerbate hypertension in this model \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Collectively, these observations imply that the interplay between diet and the gut microbiota could play a pivotal role in modulating blood pressure, offering innovative perspectives on nutritional approaches for hypertension prevention and control.\u003c/p\u003e \u003cp\u003eFor objective measurement of diet‒microbiome interactions, the Dietary Index for Gut Microbiota (DI-GM) was created by research teams. The DI-GM was developed through comprehensive analysis of 106 published studies, employing a 14-item scoring system to assess dietary effects on microbial community structure and biodiversity, where elevated scores correlate with improved gut microbial health\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. In contrast to conventional approaches involving invasive specimen collection or targeted metabolic profiling, DI-GM synthesizes nutritional data to accurately capture diet‒microbiota associations. This noninvasive characteristic renders it especially valuable for population-based research initiatives such as the NHANES.\u003c/p\u003e \u003cp\u003eEmerging evidence has demonstrated an inverse association between DI-GM and stroke and diabetes\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e, but its relationship with hypertension has not been fully explored. Moreover, kidney function and obesity are crucial factors influencing blood pressure\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e, with kidney function typically assessed by the eGFR\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e and waist circumference serving as a simple method for evaluating obesity\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. The gut microbiota can impair kidney function by producing uremic toxins such as trimethylamine-N-oxide (TMAO) or regulating lipid metabolism through butyrate\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Building upon this evidence, we postulate that elevated DI-GM scores correlate with a lower prevalence of hypertension. To investigate this relationship, we performed a population-based cross-sectional analysis utilizing NHANES datasets. This pioneering study represents the inaugural application of NHANES data to evaluate DI-GM-hypertension associations, offering novel perspectives for tailored nutritional approaches in hypertension prevention.\u003c/p\u003e"},{"header":"2. Methods and Materials","content":"\u003cp\u003e2.1 Study population\u003c/p\u003e\n\u003cp\u003eOur analysis incorporated data from six consecutive NHANES survey cycles (2007--2016). As a nationally representative surveillance system, the NHANES employs a complex, stratified probability sampling design to obtain comprehensive health, nutritional, and sociodemographic information from the civilian, noninstitutionalized U.S. population. The study exclusively used deidentified, publicly accessible datasets that received ethical approval from the NCHS Research Ethics Review Board. Written informed consent was obtained from all survey participants prior to data collection. The reporting methodology strictly adheres to the STROBE statement for observational epidemiological research.\u003c/p\u003e\n\u003cp\u003e2.2 Study Design and Population\u003c/p\u003e\n\u003cp\u003eThe participants were adults aged 20 years and older who participated in the NHANES from 2007--2016. During the screening process, individuals lacking DI-GM components, covariates, and hypertension diagnosis data were excluded. A total of 20,283 eligible participants were included in the analysis, of whom 7,979 were diagnosed with hypertension (\u003cstrong\u003eFigure 1\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e2.3 DI-GM\u003c/p\u003e\n\u003cp\u003eThe DI-GM consists of 14 foods or nutrients, with beneficial components, including fermented dairy, chickpeas, soybean, whole grains, fiber, cranberries, avocados, broccoli, coffee, and green tea, and unfavorable components, including red meat, processed meat, refined grains, and a high-fat diet (\u0026ge;40% of energy from fat)\u003csup\u003e16\u003c/sup\u003e. The DI-GM was computed using 24-hour dietary recall data obtained from NHANES. Scoring methodology differed for beneficial versus unfavorable components: (1) Beneficial components received 1 point for intake at or above sex-stratified median consumption levels (0 points otherwise), with cumulative scores generating the Beneficial Gut Microbiota Score (BGMS; range 0-10); (2) Unfavorable components were assigned 0 points for consumption meeting or exceeding sex-specific medians (or \u0026gt;40% energy from fat for high-fat diets), otherwise 1 point, yielding the Unfavorable Gut Microbiota Score (UGMS; range 0-4). The composite DI-GM score (potential range 0-14) represented the sum of BGMS and UGMS, subsequently categorized into four groups: 0\u0026ndash;3, 4, 5, and \u0026ge;6\u003csup\u003e17\u003c/sup\u003e. The components and scoring criteria of DI-GM are shown in \u003cstrong\u003eSupplementary Table S1\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e2.4\u0026nbsp; Hypertension\u003c/p\u003e\n\u003cp\u003eThis study included participants surveyed between 2007 and 2016 with complete hypertension data. Hypertension was defined as currently taking prescription medication for hypertension, having been told by a doctor at least twice that they had hypertension, or having an average systolic blood pressure of \u0026ge;140 mmHg and an average diastolic blood pressure of \u0026ge;90 mmHg on three consecutive measurements\u003csup\u003e25,26\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e2.5\u0026nbsp; \u0026nbsp;Covariates\u003c/p\u003e\n\u003cp\u003eThe selection of covariates in this study was based on expert judgment and previous research\u003csup\u003e18,27\u003c/sup\u003e. The included covariates included age, gender, race, education level, marital status, poverty income ratio (PIR), body mass index (BMI), waist circumference, physical activity, smoking status, alcohol intake, eGFR, hyperlipidemia, physical activity, diabetes, CVD (cardiovascular disease), and stroke. These covariates were obtained from the demographic, dietary, examination, laboratory, questionnaire sections of NHANES database. Specific covariate information can be found in the \u003cstrong\u003eSupplementary Materials.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e2.6 Statistical analysis\u003c/p\u003e\n\u003cp\u003eAll the statistical analyses were performed via R statistical software (v4.4.1; R Foundation) and EmpowerStats (v4.2; X\u0026amp;Y Solutions). To account for NHANES\u0026apos;s complex sampling design (stratified multistage probability sampling), analyses incorporated sampling weights via the R \u0026apos;survey\u0026apos; package. Baseline characteristics were compared by hypertension status, with continuous variables presented as weighted means \u0026plusmn; standard deviations (calculated via svyglm) and categorical variables as weighted proportions. DI-GM and hypertension were assessed via weighted multivariable generalized linear models, with the results expressed as OR and 95% confidence intervals. The models included Model 1: Crude model without adjustment for any covariates. Model 2: adjustments for age, gender, and race; Model 3: adjustments for age, gender, race, education level, marital status, PIR, smoking status, alcohol intake, BMI, waist circumference, eGFR, hyperlipidemia, physical activity, diabetes, CVD, and stroke. Smooth curve fitting was subsequently performed to further assess the relationship between the two variables.\u003c/p\u003e\n\u003cp\u003eSensitivity analyses included (1) subgroup analysis and (2) multiple imputation: missing data were handled via the multiple imputation by chained equations (MICE) method, which generated five imputed datasets. Detailed information on multiple imputation can be found in the supplementary methods.\u003c/p\u003e\n\u003cp\u003eThe R mediation package was subsequently used with the bootstrap method to perform 1,000 simulations to calculate the 95% confidence interval for the mediating effect\u003csup\u003e28\u003c/sup\u003e. The mediating role of the eGFR and waist circumference in the relationship between DI-GM and hypertension was examined. Receiver operating characteristic (ROC) curves were used to evaluate the predictive efficacy of the nomogram incorporating DI-GM and other covariates for hypertension. The predictive performance was assessed via the area under the ROC curve (AUC).\u003c/p\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Participant characteristics\u003c/h2\u003e \u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, the study included 20,283 participants from NHANES 2007\u0026ndash;2016, of whom 7,979 were diagnosed with hypertension and 12,304 were not. The mean age was 48.89 years (SD\u0026thinsp;=\u0026thinsp;17.51). Compared with nonhypertensive individuals, hypertensive participants were older (59.96 years vs. 41.87 years, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Significant differences were also observed in terms of race, PIR, education level, smoking status, alcohol intake, physical activity, BMI, waist circumference, eGFR, and comorbidities such as cardiovascular disease, stroke, diabetes, and hyperlipidemia (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\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 the NHANES 2007\u0026ndash;2016 participants.\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\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-Hypertension (N\u0026thinsp;=\u0026thinsp;12,304)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHypertension (N\u0026thinsp;=\u0026thinsp;7,979)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41.87\u0026thinsp;\u0026plusmn;\u0026thinsp;15.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59.96\u0026thinsp;\u0026plusmn;\u0026thinsp;14.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.741\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e49.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMexican American\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther Hispanic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic White\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e68.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e72.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic Black\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther Race\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation level, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehigh school or below\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSome college or AA degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCollege graduate or above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital status, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried/Living with Partner\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e64.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e64.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWidowed/Divorced/Separated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever married\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePIR, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.034\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1.3\u0026ndash;3.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;3.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking status, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e58.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlcohol intake, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e80.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e74.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg/m2), Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28.03\u0026thinsp;\u0026plusmn;\u0026thinsp;6.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30.90\u0026thinsp;\u0026plusmn;\u0026thinsp;7.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWaist circumference (cm), Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e95.91\u0026thinsp;\u0026plusmn;\u0026thinsp;15.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e105.25\u0026thinsp;\u0026plusmn;\u0026thinsp;15.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e65.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e83.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhysical activity, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e78.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e65.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e93.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e75.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCVD, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e97.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e86.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStroke, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e99.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e94.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDI-GM score, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.66\u0026thinsp;\u0026plusmn;\u0026thinsp;1.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.62\u0026thinsp;\u0026plusmn;\u0026thinsp;1.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.009\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDI-GM group, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.018\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u0026ndash;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBeneficial to gut microbiota\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.05\u0026thinsp;\u0026plusmn;\u0026thinsp;1.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.97\u0026thinsp;\u0026plusmn;\u0026thinsp;1.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.035\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnfavorable to gut microbiota\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.61\u0026thinsp;\u0026plusmn;\u0026thinsp;1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.65\u0026thinsp;\u0026plusmn;\u0026thinsp;1.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.217\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eeGFR, ml/min/1.73 m\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e102.34\u0026thinsp;\u0026plusmn;\u0026thinsp;25.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e85.75\u0026thinsp;\u0026plusmn;\u0026thinsp;26.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cem\u003eNote: Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation for continuous variables: P value was calculated by survey-weighted linear regression (svyglm). Survey-weighted percentages for categorical variables: survey-weighted linear regression (svyglm). Abbreviations: BMI: body mass index; CVD: cardiovascular disease; DI-GM: Dietary Index for Gut Microbiota; eGFR: estimated glomerular filtration rate; PIR: poverty income ratio.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Relationship between DI-GM and hypertension\u003c/h2\u003e \u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, weighted generalized linear model analysis indicated that higher DI-GM scores are associated with a lower prevalence of hypertension. In Model 1, for each unit increase in DI-GM, the odds of hypertension decreased by 3% (OR\u0026thinsp;=\u0026thinsp;0.97, 95% CI\u0026thinsp;=\u0026thinsp;0.95\u0026ndash;0.99, p\u0026thinsp;=\u0026thinsp;0.009); in Model 3, this association was more pronounced (OR\u0026thinsp;=\u0026thinsp;0.94, 95% CI\u0026thinsp;=\u0026thinsp;0.92\u0026ndash;0.97, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Compared with the group with DI-GM scores of 0\u0026ndash;3, patients with scores\u0026thinsp;\u0026ge;\u0026thinsp;6 (OR\u0026thinsp;=\u0026thinsp;0.77, 95% CI\u0026thinsp;=\u0026thinsp;0.69\u0026ndash;0.86, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) had a significantly lower prevalence of hypertension. Further analysis revealed that higher scores for unfavorable dietary components (lower intake of unhealthy foods) were significantly associated with a decreased prevalence of hypertension (OR\u0026thinsp;=\u0026thinsp;0.94, 95% CI\u0026thinsp;=\u0026thinsp;0.91\u0026ndash;0.98, p\u0026thinsp;=\u0026thinsp;0.003), whereas higher scores for beneficial components (greater intake of beneficial foods) were also significantly associated with a decreased prevalence of hypertension (OR\u0026thinsp;=\u0026thinsp;0.95, 95% CI\u0026thinsp;=\u0026thinsp;0.92\u0026ndash;0.98, p\u0026thinsp;=\u0026thinsp;0.002).\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\u003eRelationships between DI-GM and hypertension according to the NHANES\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=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModel2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModel3\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDI-GM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.97 (0.95, 0.99) 0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.90 (0.87, 0.92)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.94 (0.92,0.97)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDI-GM group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u0026ndash;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.85 (0.77,0.95) 0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.85 (0.75,0.95) 0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.90 (0.79,1.11) 0.076\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.89 (0.79,0.99) 0.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.77 (0.68,0.88)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.85 (0.74,0.98) 0.028\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.87 (0.79,0.96) 0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.63 (0.56,0.70)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.77 (0.69,0.86)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP for trend\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" 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\u003eBeneficial to gut microbiota\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.97 (0.94,0.99) 0.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.91 (0.89,0.94)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.95 (0.92,0.98) 0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnfavorable to gut microbiota\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.98 (0.95,1.01) 0.218\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.89 (0.86,0.92)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.94 (0.91,0.98) 0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cem\u003eModel 1: Crude model without adjustment for any covariates. Model 2: adjustments for age, gender, and race; Model 3: adjustments for age, gender, race, education level, marital status, PIR, smoking status, alcohol intake, BMI, waist circumference, eGFR, hyperlipidemia, physical activity, diabetes, CVD, stroke.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cem\u003eAbbreviations: BMI: body mass index; CVD: cardiovascular disease; DI-GM: Dietary Index for Gut Microbiota; eGFR: estimated glomerular filtration rate; PIR: poverty income ratio.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe smooth curve fitting plot further revealed a negative correlation between DI-GM score and hypertension (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Sensitivity analysis\u003c/h2\u003e \u003cp\u003eSubgroup analysis revealed that the negative correlation between DI-GM and hypertension was significant in most subgroups, indicating the applicability and robustness of DI-GM across different populations (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Moreover, the results from multiple imputation and unweighted association analyses confirmed that the association between DI-GM and hypertension remained significant and robust (\u003cb\u003eSupplementary Tables S2\u0026ndash;S3\u003c/b\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Mediation analysis\u003c/h2\u003e \u003cp\u003eAdditionally, mediation analysis was performed to explore the mediating role of the eGFR. The results shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e indicate that eGFR and waist circumference play a significant mediating role in the relationship between DI-GM and hypertension. The total effect coefficient of DI-GM on hypertension mediated by eGFR was \u0026minus;\u0026thinsp;0.0122 (p\u0026thinsp;=\u0026thinsp;0.002), with a mediating effect of -0.0007 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and a mediating percentage of 5.91% (p\u0026thinsp;=\u0026thinsp;0.002) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). The total effect coefficient of DI-GM on hypertension mediated by waist circumference was \u0026minus;\u0026thinsp;0.0152 (p\u0026thinsp;=\u0026thinsp;0.008), with a mediating effect of -0.0006 (p\u0026thinsp;=\u0026thinsp;0.004) and a mediating percentage of 3.91% (p\u0026thinsp;=\u0026thinsp;0.012) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Establishment of the predictive nomogram\u003c/h2\u003e \u003cp\u003eModel 3 (fully adjusted) was used to create a predictive nomogram. Each predictor was assigned a specific score on a rating scale, and the total points for each variable were aggregated. The total points corresponded to the probability of hypertension according to the nomogram. Elevated scores indicated greater hypertension risk (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). The nomogram showed good predictive accuracy (AUC\u0026thinsp;=\u0026thinsp;0.838, 95% CI 0.832\u0026ndash;0.843) according to the ROC analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eTo our knowledge, this is the largest and first cross-sectional study to investigate the association between the DI-GM and hypertension. Our findings support the hypothesis that a higher DI-GM score is associated with a lower prevalence of hypertension. Generalized linear model analysis revealed a significant negative correlation between DI-GM and hypertension. Our findings demonstrate a significant inverse relationship between DI-GM scores and hypertension risk, with the high DI-GM score group (\u0026ge;\u0026thinsp;6) showing a 23% lower hypertension prevalence compared to the low score group (0\u0026ndash;3) (OR\u0026thinsp;=\u0026thinsp;0.77, 95% CI\u0026thinsp;=\u0026thinsp;0.69\u0026ndash;0.86, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Smooth curve fitting further revealed the trend of this negative association, and sensitivity analysis underscored the reliability and robustness of these results. Notably, the mediation analysis demonstrated that eGFR and waist circumference serve as crucial mediators in the association between DI-GM and hypertension.\u003c/p\u003e \u003cp\u003eHypertension, a complex multifactorial disease, involves interactions between genetic susceptibility and environmental factors in its pathogenesis. Existing evidence suggests that a healthy dietary pattern is significantly associated with lower blood pressure levels, and this association is independent of an individual\u0026rsquo;s genetic risk\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. The gut microbiota can respond rapidly to dietary changes and influence human health through various mechanisms\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. Therefore, the consumption of foods beneficial to the gut microbiota has become an important strategy for maintaining health. Studies have shown that a diet high in fruits and vegetables and low in sodium can effectively lower blood pressure\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e,\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. Additionally, probiotics and their metabolites can reshape the gut microbiota composition\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. They can positively influence blood pressure through mechanisms such as the production of short-chain fatty acids, the regulation of lipid metabolism, the improvement of insulin resistance, and the modulation of the renin‒angiotensin system\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. Research by Jieping Yang revealed that avocado significantly increases gut microbiota diversity\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. Gut microbiota diversity has been shown to be closely associated with blood pressure levels\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. Furthermore, research by Hamdi A. Jama and colleagues suggested that the Mediterranean diet promotes the growth of beneficial gut microbiota and lowers blood pressure by reducing inflammation\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAlthough the Bezawit E. Kase team identified 14 foods/nutrients associated with the gut microbiota and developed the DI-GM index through a systematic review\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e, the exact relationship between DI-GM and hypertension remains unclear. This study revealed a significant association between higher DI-GM scores and lower hypertension prevalence, which aligns with findings from animal studies. Compared with wild-type animals, hypertensive model animals presented significant differences in gut microbiota composition, and their blood pressure levels could be altered through fecal microbiota transplantation and antibiotic intervention\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Additionally, population studies have confirmed significant differences in gut microbiota characteristics between hypertensive patients and individuals with normal blood pressure\u003csup\u003e\u003cspan additionalcitationids=\"CR39 CR40 CR41\" citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. These studies, along with our findings, provide strong support for the crucial role of the gut microbiota in blood pressure regulation.\u003c/p\u003e \u003cp\u003eThis study is the first to reveal that eGFR and waist circumference significantly mediate the relationship between DI-GM and hypertension, suggesting that higher DI-GM scores may help lower hypertension prevalence by enhancing kidney function and reducing obesity. This finding has important pathophysiological implications. First, renal insufficiency and obesity have been confirmed as independent risk factors for the onset and progression of hypertension\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e,\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e,\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e; second, there is a close relationship between the gut microbiota and kidney function as well as obesity\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e,\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. Therefore, the eGFR and waist circumference may play key roles in the \"diet-gut microbiota-hypertension\" pathway. A diet with a high DI-GM score can increase gut microbiota diversity, which may contribute to lowering blood pressure\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e,\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e,\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. A high DI-GM score may regulate the gut microbiota balance and reduce the production of toxins\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e, thereby improving kidney function and reducing obesity, as reflected by the eGFR and waist circumference.\u003c/p\u003e \u003cp\u003eThe strengths of this study include the use of a large, nationally representative NHANES dataset, which enhances the robustness of the conclusions. We adjusted for multiple potential confounders to minimize bias and validated the robustness of the results through sensitivity analyses. However, several limitations remain. First, the cross-sectional design precludes establishing causal relationships. Longitudinal studies and randomized controlled trials are needed to confirm causal associations. Second, dietary intake data were self-reported, which may introduce recall bias. Future studies could employ more objective dietary assessment methods. Third, unmeasured confounders, such as genetic factors and direct gut microbiota sequencing data, may have influenced the results.\u003c/p\u003e \u003cp\u003eThis study has important translational implications. In clinical practice, the DI-GM score could be utilized in primary care for hypertension risk stratification, guiding personalized dietary interventions such as prebiotic supplementation for individuals with low DI-GM scores.\u003c/p\u003e \u003cp\u003eIn summary, this study is the first to reveal a significant negative association between DI-GM scores and hypertension prevalence, and further demonstrates the important mediating role of the eGFR and waist circumference in this relationship. These findings not only provide new evidence for the \"diet‒gut microbiota‒blood pressure regulation\" pathway but also suggest that optimizing dietary patterns, improving the gut microbiota, enhancing kidney function and reducing obesity may help lower the prevalence of hypertension in the future. However, as this study is cross-sectional, causal relationships need further validation. Future longitudinal studies could explore the potential value of DI-GM in hypertension prevention and intervention.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAUC: area under the ROC curve\u003c/p\u003e\n\u003cp\u003eBGMS: beneficial to gut microbiota score\u003c/p\u003e\n\u003cp\u003eBMI: body mass index;\u003c/p\u003e\n\u003cp\u003eCKD-EPI: CKD Epidemiology Collaboration\u003c/p\u003e\n\u003cp\u003eCIs: confidence intervals\u003c/p\u003e\n\u003cp\u003eCVD: cardiovascular disease\u003c/p\u003e\n\u003cp\u003eDI-GM: Dietary Index for Gut Microbiota\u003c/p\u003e\n\u003cp\u003eeGFR: estimated glomerular filtration rate\u003c/p\u003e\n\u003cp\u003eNCHS: National Center for Health Statistics\u003c/p\u003e\n\u003cp\u003eNHANES: National Health and Nutrition Examination Survey\u003c/p\u003e\n\u003cp\u003ePIR: poverty income ratio.\u003c/p\u003e\n\u003cp\u003eROC: Receiver operating characteristic\u003c/p\u003e\n\u003cp\u003eSTROBE: Strengthening the Reporting of Observational Studies in Epidemiology\u003c/p\u003e\n\u003cp\u003eTMAO: trimethylamine-N-oxide\u003c/p\u003e\n\u003cp\u003eUGMS: unfavorable to gut microbiota score\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe NHANES Institutional Review Board approved the study protocol, which adhered to the Declaration of Helsinki. All participants provided written informed consent.\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\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets analyzed in this study are publicly available from the NHANES at: https://www.cdc.gov/nchs/nhanes/index.htm.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors affirm that this research was conducted without any commercial or financial relationships that could present a potential conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYu Fu acquired the data and performed the statistical analysis. Yu Fu drafted the article. Yang Zheng checked the statistical analysis and substantially revised the draft. Mengling Peng and He Cai read the entire manuscript and revised it. Yaoting Zhang and Bing Li provided some necessary statistical suggestions and improved the English writing. All authors gave final approval of the version to be submitted.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no acknowledgments to declare.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHuman Ethics and Consent to Participate declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study protocol was approved by the NHANES Institutional Review Board, and was performed in accordance with the Declaration of Helsinki, with all NHANES participants providing signed informed consent.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eKearney PM, Whelton M, Reynolds K, Muntner P, Whelton PK, He J. Global burden of hypertension: analysis of worldwide data. \u003cem\u003eLancet\u003c/em\u003e. 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Gut Microbiota: Potential for a Unifying Hypothesis for Prevention and Treatment of Hypertension. \u003cem\u003eCirc Res\u003c/em\u003e. May 26 2017;120(11):1724-1726. doi:10.1161/circresaha.117.310734\u003c/li\u003e\n\u003cli\u003eTsiavos A, Antza C, Trakatelli C, Kotsis V. The Microbial Perspective: A Systematic Literature Review on Hypertension and Gut Microbiota. \u003cem\u003eNutrients\u003c/em\u003e. Oct 30 2024;16(21)doi:10.3390/nu16213698\u003c/li\u003e\n\u003cli\u003ePerler BK, Friedman ES, Wu GD. The Role of the Gut Microbiota in the Relationship Between Diet and Human Health. \u003cem\u003eAnnu Rev Physiol\u003c/em\u003e. Feb 10 2023;85:449-468. doi:10.1146/annurev-physiol-031522-092054\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"dietary index for gut microbiota, hypertension, estimated glomerular filtration rate, mediation analysis, waist circumference","lastPublishedDoi":"10.21203/rs.3.rs-6437987/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6437987/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe relationship between the gut microbiota and hypertension has gained increasing attention. However, the associations between Dietary Index for Gut Microbiota (DI-GM), an indicator of microbiota diversity, and hypertension remain insufficiently explored. We hypothesized that a higher DI-GM score would be associated with a lower prevalence of hypertension.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe analyzed data from 20,283 participants in the National Health and Nutrition Examination Survey (NHANES) 2007\u0026ndash;2016 cycle via weighted generalized linear models and smooth curve fitting to examine the association between DI-GM and hypertension. Mediation analysis was conducted to evaluate the role of the eGFR and waist circumference.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eAfter adjusting for confounding factors, a higher DI-GM score was significantly associated with a lower prevalence of hypertension (OR\u0026thinsp;=\u0026thinsp;0.94, 95% CI\u0026thinsp;=\u0026thinsp;0.92\u0026ndash;0.97). Compared with individuals with a DI-GM score of 0\u0026ndash;3, those with a score of \u0026ge;\u0026thinsp;6 had a significantly lower prevalence of hypertension (OR\u0026thinsp;=\u0026thinsp;0.77, 95% CI\u0026thinsp;=\u0026thinsp;0.69\u0026ndash;0.86). The mediation analysis indicated that eGFR and waist circumference accounted for 5.91% and 3.91%, respectively, of the association between DI-GM and hypertension.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eA higher DI-GM score is associated with a lower prevalence of hypertension, which is partially mediated by the eGFR and waist circumference.\u003c/p\u003e","manuscriptTitle":"Dietary Index for Gut Microbiota is negatively associated with Hypertension","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-14 12:07:56","doi":"10.21203/rs.3.rs-6437987/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7f800d7a-0d0c-4dac-b400-fabe4a34bf95","owner":[],"postedDate":"May 14th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-08-30T21:23:18+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-14 12:07:56","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6437987","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6437987","identity":"rs-6437987","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}