Association Between Gut Microbiome Alterations and Hypertension-Related Cardiovascular Outcomes: A Systematic Review and Meta-Analysis

Abstract

Hypertension (HTN) remains a major modifiable risk factor for cardiovascular disease (CVD), yet the mechanisms linking environmental and metabolic factors to vascular injury are incompletely understood. Recent evidence implicates gut microbiome dysbiosis and microbial metabolites, particularly short-chain fatty acids (SCFAs) and trimethylamine N-oxide (TMAO), in the pathogenesis of hypertension and its cardiovascular complications. We systematically searched PubMed, Embase, Cochrane, Web of Science, and Scopus from inception to 1 October 2025 for observational studies evaluating gut microbiome composition or circulating TMAO levels in adults with hypertension or related cardiovascular outcomes. Random-effects meta-analyses were conducted using standardized mean differences (SMD) for alpha diversity indices and hazard ratios (HR) for TMAO-associated major adverse cardiovascular events (MACE). Heterogeneity (I²), publication bias (Egger’s test), and certainty of evidence (GRADE) were assessed according to PRISMA 2020 guidelines (PROSPERO CRD420251162222). A total of 22 studies (n = 24,512 participants) were included, of which 15 were eligible for quantitative synthesis (11 for alpha diversity, 4 for TMAO). Pooled analysis showed significantly lower microbial diversity among hypertensive versus normotensive individuals (SMD = −0.15, 95% CI −0.25 to −0.05; p = 0.004; I² = 35%). Circulating TMAO was associated with increased risk of major adverse cardiovascular events (HR = 1.25, 95% CI 1.10 to 1.42; p < 0.001). Funnel plots were symmetric, and Egger’s test indicated no significant bias (p > 0.3). The certainty of evidence was graded as moderate for microbial diversity and high for TMAO-related outcomes. This meta-analysis provides robust evidence that gut microbiome dysbiosis and elevated TMAO levels are associated with hypertension and heightened cardiovascular risk, supporting the concept of a “gut–vascular axis.” Microbiota-targeted interventions such as high-fiber diets, prebiotics, or TMAO-lowering strategies warrant further investigation as adjunctive tools in precision hypertension management.

1. Introduction

Hypertension (HTN), defined as sustained systolic blood pressure (SBP) ≥ 130 mmHg or diastolic blood pressure (DBP) ≥ 80 mmHg according to the 2017 American College of Cardiology/American Heart Association guidelines, represents a major modifiable risk factor for cardiovascular disease (CVD) [1]. Globally, it affects over 1.28 billion adults, with projections estimating a rise to 1.56 billion by 2025, disproportionately burdening low- and middle-income countries where awareness, treatment, and control rates remain suboptimal [2]. The pathophysiology of HTN involves a complex interplay of genetic predisposition, endothelial dysfunction, vascular remodeling, and neurohumoral activation, leading to increased peripheral resistance and cardiac workload [3]. It typically emerges insidiously over decades, driven by modifiable factors such as high-sodium diet, physical inactivity, obesity, and stress, alongside non-modifiable elements like age and ethnicity [2]. Uncontrolled HTN precipitates adverse cardiovascular outcomes, including myocardial infarction, stroke, heart failure, and chronic kidney disease, accounting for approximately 10.8 million deaths annually—more than any other preventable cause [4]. These sequelae arise from chronic shear stress on arterial walls, promoting atherosclerosis, left ventricular hypertrophy, and arrhythmogenesis, underscoring the urgent need for novel therapeutic targets beyond traditional pharmacotherapy [3].

Emerging evidence implicates the gut microbiome as a pivotal modulator in HTN pathogenesis, bridging environmental exposures to systemic vascular effects [5]—a concept often referred to as the “gut–vascular axis.” This axis links intestinal dysbiosis and microbial metabolites to vascular inflammation, endothelial dysfunction, and neurohumoral activation, providing a mechanistic rationale for microbiome-targeted interventions in hypertension [5]. The human gut harbors over 100 trillion microorganisms, influencing host metabolism, immunity, and hemodynamics through bioactive metabolites like short-chain fatty acids (SCFAs; e.g., acetate, propionate, butyrate) and trimethylamine N-oxide (TMAO) [6]. Dysbiosis—characterized by reduced alpha diversity (e.g., Shannon index) and shifts in Firmicutes/Bacteroidetes ratio—disrupts this homeostasis, often triggered by Western diets low in fiber, antibiotic overuse, or aging [5]. Such alterations impair SCFA production by butyrate-generating taxa (e.g., Faecalibacterium, Roseburia), which normally exert vasodilatory and anti-inflammatory effects via G-protein-coupled receptors (GPR41/43) and histone deacetylase inhibition [7]. Conversely, dysbiosis elevates TMAO via microbial choline/carnitine metabolism, fostering platelet hyperreactivity, foam cell formation, and endothelial insulin resistance—key accelerators of HTN-related atherothrombosis [8]. Cross-sectional studies consistently report lower microbial richness in HTN cohorts, with opportunistic pathogens (e.g., Streptococcus, Escherichia-Shigella) enriched and predictive classifiers achieving AUCs up to 0.93 for SBP elevation [9]. Prospective data further link these signatures to incident HTN and severity, including co-abundance networks of non-differential genera correlating with DBP variability and left ventricular hypertrophy [10]. In CVD contexts, TMAO independently predicts major adverse cardiac events (MACE), with hazard ratios (HRs) ranging from 1.23 to 3.3, mediated by proinflammatory monocytes and post-myocardial infarction remodeling [11]. These associations highlight a gut-vascular axis, where dysbiosis exacerbates HTN’s cardiovascular burden through endotoxemia, renin-angiotensin system dysregulation, and autonomic imbalance [5].

Despite these insights, the literature reveals inconsistencies: while some cohorts show inverse diversity-BP associations attenuated by BMI or sodium intake, others report null or paradoxical increases in richness among severe HTN grades, potentially due to methodological heterogeneity in sequencing (16S rRNA vs. shotgun metagenomics) and adjustment for confounders like antidiabetic medications [9]. Systematic reviews affirm dysbiosis in HTN but lack quantitative synthesis of cardiovascular endpoints, limiting causal inference and clinical translation [12]. No comprehensive meta-analysis has pooled effect sizes across prospective designs to delineate microbiome-HTN-CVD links, hindering integration into guidelines like those from the European Society of Hypertension [5].

This systematic review and meta-analysis aims to synthesize evidence on gut microbiome alterations (focusing on alpha diversity indices and TMAO levels) and their association with HTN-related cardiovascular outcomes, including incident HTN, blood pressure variability, and MACE. Secondary objectives include exploring dose–response relationships, subgroup effects (e.g., by sex, geography, or comorbidity), and publication bias. By employing PRISMA guidelines and GRADE assessment, we seek to provide robust estimates informing microbiome-targeted interventions, such as prebiotic supplementation or fecal microbiota transplantation, to mitigate CVD risk in hypertensive populations.

2. Materials and Methods

2.1. Protocol and Registration

This systematic review and meta-analysis was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines [13]. The protocol was prospectively registered with the International Prospective Register of Systematic Reviews (PROSPERO) under registration number CRD420251162222. Any deviations from the protocol will be documented and justified in the final report. A detailed search strategy for each database is provided in Supplementary Table S1. The PubMed search example was as follows: (“hypertension” [MeSH] OR “blood pressure”) AND (“gut microbiota” OR “microbiome” OR “trimethylamine N-oxide” OR “TMAO”) AND (“cardiovascular” OR “myocardial infarction” OR “stroke”). Filters: Humans, English, 2010–2025.

2.2. Eligibility Criteria

Studies were eligible if they met the following PICO criteria: Population (P): Adults (≥18 years) diagnosed with hypertension (HTN), or with measured systolic/diastolic blood pressure, compared with normotensive or healthy control groups. Intervention/Exposure (I): Assessment of gut microbiota composition, microbial diversity (α- or β-diversity indices), or gut-derived metabolites such as trimethylamine-N-oxide (TMAO). Comparison (C): Normotensive individuals, healthy controls, or participants with lower blood pressure values within the same cohort. Outcome (O): Quantitative measures of microbial composition or metabolites, reported as mean ± SD, odds ratios (OR), hazard ratios (HR), or other effect sizes relevant to hypertension or cardiovascular outcomes.

Only original observational studies (cross-sectional, case–control, or cohort) published in peer-reviewed journals were included in the quantitative synthesis. Systematic reviews and meta-analyses were used only to provide contextual or background information and were not included in the pooled quantitative analyses.

Exclusion criteria were: (1) studies enrolling pediatric populations or animals; (2) articles lacking a comparator group; (3) reviews, editorials, case reports, or conference abstracts without extractable data; and (4) studies with overlapping cohorts, where only the most complete or recent dataset was retained. To avoid duplicate inclusion of overlapping cohorts, studies were cross-checked by author lists, recruitment period, and registry identifiers. When two publications reported on the same population, the study with the largest sample size or the most complete outcome data was retained.

2.3. Information Sources

We searched electronic databases including PubMed/MEDLINE, Embase, Cochrane Library, Web of Science, and Scopus from inception to 1 October 2025. Grey literature was screened via Google Scholar and ClinicalTrials.gov. Reference lists of included studies and relevant reviews were hand-searched for additional records. Only English-language full-text studies were included.

2.4. Search Strategy

The search strategy combined MeSH terms and free-text keywords related to gut microbiome (e.g., “gut microbiota,” “dysbiosis,” “microbiome diversity,” “TMAO”), HTN (e.g., “hypertension,” “high blood pressure,” “blood pressure”), and CV outcomes (e.g., “cardiovascular events,” “MACE,” “mortality”). Boolean operators (AND/OR) were used. An example PubMed search string was: (“gut microbiome” OR “intestinal microbiota” OR dysbiosis OR “alpha diversity” OR TMAO) AND (hypertension OR “high blood pressure” OR “blood pressure”) AND (“cardiovascular disease” OR MACE OR mortality OR “left ventricular hypertrophy”). The full search strategies for all databases are available in Supplementary Table S1.

2.5. Study Selection

Two independent reviewers screened titles and abstracts using Rayyan.ai (Version 1.0) followed by full-text assessment for eligibility. Disagreements were resolved by consensus or consultation with a third reviewer. The selection process was summarized in a PRISMA 2020 flow diagram [13], detailing records identified, screened, excluded (with reasons), and included.

2.6. Data Collection Process

Data extraction was performed independently by two reviewers using a standardized Excel form, and discrepancies were resolved by discussion. Extracted items included study characteristics (author, year, design, location, sample size), participant details (age, sex, hypertension status, and comorbidities), exposure metrics (diversity indices, TMAO levels, sequencing method), outcomes (effect sizes with 95% confidence intervals), and covariate adjustments (e.g., age, BMI, smoking, diet, medication use).

Authors were contacted via email to clarify missing or ambiguous information. In the absence of response after two attempts, analyses were conducted using the available data from published reports. Missing or incomplete data were handled using available-case analysis; when summary statistics (e.g., mean ± SD) were not reported, they were derived from medians and interquartile ranges using standard statistical conversion methods. No multiple imputations were applied.

2.7. Data Items

Key data items aligned with PICO: population demographics, exposure quantification (e.g., standardized mean difference [SMD] for diversity; log-transformed TMAO), comparators (e.g., tertiles), and outcomes (e.g., HR for incident HTN; β coefficients for BP). Subgroup data (e.g., by sex, geography) were extracted where available.

2.8. Risk of Bias in Individual Studies

Risk of bias was assessed using the Newcastle-Ottawa Scale (NOS) for cohort and case–control studies (scoring selection, comparability, and outcome, with ≥7/9 stars indicating low risk) and the Agency for Healthcare Research and Quality (AHRQ) tool for cross-sectional studies [14]. Funnel plots and Egger’s test assessed publication bias for outcomes with ≥10 studies. Quality was graded using GRADEpro GDT software (Version 2023.2) (high, moderate, low, very low) considering inconsistency, imprecision, indirectness, and publication bias [15].

2.9. Summary Measures

Pooled effect sizes were calculated as odds ratios (OR) or hazard ratios (HR) for dichotomous outcomes (e.g., HTN prevalence), standardized mean differences (SMD) for continuous diversity indices, and weighted mean differences (WMD) for BP changes, with 95% confidence intervals (CIs). For TMAO, relative risks (RR) per 1-SD increase were prioritized.

2.10. Synthesis of Results

Quantitative synthesis was performed using random-effects models (DerSimonian-Laird) to account for heterogeneity, via Review Manager (RevMan) version 5.4 (Cochrane Collaboration, Oxford, UK) and R software (version 4.3.1; meta package). Heterogeneity was quantified using I² statistic (<25% low, 25–50% moderate, >50% high) and Cochran’s Q test (p < 0.10 significant). If I² > 50%, subgroup analyses (e.g., by study design, sequencing method, adjustment for BMI) or sensitivity analyses (excluding high-risk studies) were conducted. Dose–response meta-regression assessed non-linear associations using restricted cubic splines. Meta-regression analyses were conducted using the nlme package in R (version 4.3.1). Forest plots visualized results; narrative synthesis addressed non-poolable data.

2.11. Reporting Bias Assessment

Publication bias was evaluated using both visual and quantitative methods. Funnel plots were generated to assess asymmetry, and formal statistical tests were conducted using Begg’s rank correlation and Egger’s linear regression approaches (significance threshold, p < 0.10). Egger’s regression test was specifically selected as it provides a quantitative measure of small-study effects by regressing the standard normal deviate of the effect size on its precision, thereby detecting potential publication bias beyond visual inspection. This method is recommended for meta-analyses including ≥10 studies according to the Cochrane Handbook and PRISMA 2020 guidelines. When asymmetry was detected, the trim-and-fill method (Duval and Tweedie) was applied to estimate adjusted pooled effects.

2.12. Additional Analyses

Subgroup analyses explored effect modification by age (>65 years), sex, geography (Western vs. Asian cohorts), HTN treatment status, and microbiome metric (diversity vs. TMAO). Sensitivity analyses tested robustness by excluding outliers or low-quality studies. Certainty of evidence was rated using GRADE.

3. Results

3.1. Study Selection

The literature search yielded 1247 records across databases (PubMed: 562; Embase: 389; Cochrane: 112; Web of Science: 98; Scopus: 86), with 324 duplicates removed. After title and abstract screening, 156 full-text articles were assessed for eligibility. Of these, 134 were excluded (e.g., 62 for irrelevant outcomes, 28 for non-human studies, 22 for insufficient data, 12 for review articles, 10 for pediatric populations). Ultimately, 22 studies met inclusion criteria and were included in the qualitative synthesis, with 15 eligible for quantitative meta-analysis (11 for alpha diversity outcomes, 4 for TMAO), as shown in

Table 1. Characteristics of included studies assessing gut microbiome or TMAO in relation to hypertension and cardiovascular outcomes.

ID	Author (Year) [Ref]	Design	n (HTN Cases)	Population/ Setting	Exposure Measure	Outcome(s)	Effect Size (95% CI)	Key Findings/ Adjustments
1	Yan et al. (2017) [16]	Cross-sectional	120 (60)	Chinese adults, mean age 57 yrs	Whole-metagenome shotgun; Shannon index	HTN prevalence, BP	AUC 0.78 (0.73–0.82)	↓ alpha diversity; ↑ pathogenic taxa; ↓ SCFA producers; TMAO–CV link. Adj: age, gender, BW.
2	Sun et al. (2019) [17]	Cross-sectional	529 (183)	US (CARDIA); 46% male, age 55.3 ± 3.4 yrs	16S rRNA V3–V4; Shannon, richness	HTN, SBP	OR 0.75 (0.60–0.94); β −1.52 (−2.92–−0.12)	Inverse diversity–BP; BMI attenuates; adj: age, sex, race, education, activity, smoking, diet, meds, BMI.
3	Palmu et al. (2020) [18]	Cross-sectional	6953 (3291)	Finnish cohort, age 49.2 ± 12.9 yrs, 45% male, BMI 27 ± 4.7	Shotgun metagenomics; Shannon, Bray–Curtis	HTN, SBP/DBP	OR 0.91 (0.86–0.96); β −0.54 (−0.96–−0.12)	45 genera ↔ BP; Lactobacillus ↓ BP; adj: age, sex, BMI, smoking, exercise, DM, meds.
4	Louca et al. (2021) [19]	Cross-sectional	1319 (454)	UK females, mean age 56 ± 11.3 yrs	16S rRNA; ASVs/UniFrac	HTN, BP levels	β −0.05 (−0.095–−0.004)	↓ Ruminiclostridium 6; ↑ Erysipelotrichaceae; linked to thiamine/tryptophan pathways. Adj: age, BMI, NSP intake.
5	Lin et al. (2025) [20]	Cross-sectional	3695 (NS)	Scandinavian adults, age 57.3 ± 4.4 yrs	Shotgun metagenomics; Shannon	24 h ABPM (SBP/DBP var)	β −0.32 (−0.59–−0.06)	Lower diversity ↔ DBP variability; ↑ Streptococcus, ↓ Intestinimonas. Adj: age, sex, birth country, smoking, fiber/Na/energy, meds, BMI.
6	Lv et al. (2023) [21]	Cross-sectional	132 (87)	China (NW), untreated HTN, mean age ~54 yrs	16S rRNA + metagenomic; Shannon	BP, HTN status	AUC 0.80 (0.62–0.92)	↑ Diversity in females; sex-dimorphic pathways; ↑ signal transduction. Adj: age, BMI, waist, glucose, lipids.
7	Joishy et al. (2022) [22]	Cross-sectional	71 (34)	Rural India, Assamese, age 35.9 ± 11.4 yrs	16S rRNA; Shannon/richness	SBP, high BP ≥ 120 mmHg	p = 0.02 (richness)	↑ Prevotella/Megasphaera in high BP; classifier AUC 0.93. Adj: age, sex, location, milk, gastric, meds, DBP, BMI, labs.
8	Guo et al. (2021) [23]	Systematic review	9085 (~4279)	Multi-country	16S rRNA (various)	HTN, inflammation	—	↓ Diversity/inflammation ↑ in HTN; inconsistent alpha results. Adj: varies (age, BMI, etc.).
9	Han et al. (2024) [24]	Meta-analysis (cohort)	15,498 (NS)	CVD patients, age 59–80 yrs	Circulating TMAO (μmol/L)	HTN risk in CVD	RR 1.14 (1.08–1.20)	↑ TMAO → HTN risk (+1%/μmol L); endothelial injury. Adj: country, disease, n, age, sex, BMI, smoking, DM, lipids, meds.
10	Qi et al. (2018) [25]	Meta-analysis (11 cohorts)	10,245 (NS)	CAD patients, mean age 63 ± 11 yrs	Plasma TMAO (LC–MS)	CV events, mortality	HR 1.23 (1.07–1.42); 1.55 (1.19–2.02)	↑ TMAO ↔↑ CV events/mortality; platelet activation pathway. Adj: age, gender, eGFR, NT-proBNP, CVD risks.
11	Haghikia et al. (2018) [26]	Prospective cohort	671 (NS)	Post-stroke patients	Plasma TMAO	CV events	HR 3.3 (1.2–10.9)	↑ TMAO predicts CV events; corr. proinflammatory monocytes (r = 0.70). Adj: HTN, DM, LDL, eGFR, stroke severity/etiology.
12	Zhou et al. (2020) [27]	Prospective cohort	1208 (NS)	CHF post-MI, median age 73 yrs	Plasma TMAO (HPLC–MS)	MACE, mortality	HR 2.31 (1.42–3.59); 2.15 (1.37–3.24)	Independent predictor MACE/mortality; improves risk prediction. Adj: age, gender, BMI, HTN, DM, lipids, NT-proBNP, eGFR, hsCRP.
13	Yang et al. (2015) [28]	Cross-sectional	17 (7)	Adults, SBP 144 ± 9 vs. 119 ± 2 mmHg	16S rRNA; Chao/Shannon	BP, HTN	p < 0.05	↓ Richness & evenness; ↑ F/B ratio; ↓ butyrate producers. Adj: NS.
14	Qu et al. (2022) [29]	Cohort	97 (63)	Chinese HTN patients, mean age 59.9 yrs	16S rRNA; Shannon/Simpson	Cognitive impairment	AUC 0.94 (0.89–1.00)	↑ Escherichia–Shigella; ↓ Prevotella; LPS–neuroinflammation link. Adj: age, gender, education, BMI.
15	Liu et al. (2025) [30]	Prospective cohort	6999 (2355)	Guangdong Gut Project	Co-abundances (188 genera)	HTN prevalence, severity	61% ↑ co-abundances (FDR < 0.05)	Microbial networks ↔ HTN severity; tryptophan/androgen pathways. Adj: covariates (Kruskal–Wallis, linear reg.).

Note: NS = Not Stated in the original article; NA = Not Applicable for this study design; ↑ indicates an increase; ↓ indicates a decrease; ↔ indicates no significant change.

. The PRISMA 2020 flow diagram is presented in Figure 1 [13].

3.2. Characteristics of Included Studies

The included studies spanned 2015–2025, encompassing 8 cross-sectional designs [16,17,18,19,20,21,22,28], 4 prospective cohorts [26,27,29,30], 2 meta-analyses with extractable cohort-level data [24,25], and 1 systematic review with descriptive synthesis [23]. Sample sizes ranged from 17 to 6999 participants (total N = 24,512; n = 11,456 with hypertension). Populations were predominantly middle-aged adults (mean age 49–62 years; 45–72% male), with broad geographic representation (Asia: 9 studies [16,21,22,25,26,27,29,30]; Europe/North America: 5 [17,18,19,20]; multi-regional: 1 [23]).

This diversity in population characteristics and study designs may contribute to between-study heterogeneity and affect the generalizability of pooled estimates, as further acknowledged in the study limitations.

Hypertension (HTN) was diagnosed according to ACC/AHA or ESH guidelines [1,3], with comorbid cardiovascular disease (CVD) reported in five studies [24,25,26,27,30] and diabetes mellitus (DM) in three [17,18,23].

Exposures primarily assessed gut microbial α-diversity (Shannon or Chao1 indices in 11 studies) using 16S rRNA or shotgun metagenomic sequencing [16,17,18,19,20,21,22,28,29,30], and trimethylamine N-oxide (TMAO) levels in four studies employing LC–MS assays [24,25,26,27]. Outcomes included hypertension prevalence or incidence (n = 12) [16,17,18,19,20,21,22,23,28,29,30], blood pressure levels or variability (n = 8) [16,17,18,19,20,28,29,30], and cardiovascular events or mortality (n = 4) [24,25,26,27].

Covariate adjustments most frequently included age, sex, BMI, and smoking status, along with diet, physical activity, and medication use [16,17,18,19,20,21,22,24,25,26,27,29,30]. The included studies spanned 2015–2025, encompassing 11 cross-sectional designs [16,17,18,19,20,21,22,28], 7 prospective cohorts [26,27,29,30], 3 case–control or cohort extensions [21,25,29], and 1 systematic review with extractable primary data [23].

3.3. Risk of Bias in Included Studies

Risk of bias was generally low to moderate across studies, as shown in

Table 2. Risk of bias assessment of included studies according to the Newcastle–Ottawa Scale (NOS) and AHRQ tool.

ID	Author (Year) [Ref]	Study Design	Tool Applied	Selection	Comparability	Outcome/ Exposure	Total Score/Quality	Risk of Bias	Main Limitations
1	Yan et al. (2017) [16]	Cross-sectional	AHRQ	✔✔	✔	✔✔	9/11 (Good)	Low	Limited temporality, moderate confounder adjustment.
2	Sun et al. (2019) [17]	Cross-sectional	AHRQ	✔✔✔	✔✔	✔✔	10/11 (Good)	Low	Dietary recall bias.
3	Palmu et al. (2020) [18]	Cross-sectional	AHRQ	✔✔✔	✔✔✔	✔✔	10/11 (Good)	Low	Self-reported BP medication use.
4	Louca et al. (2021) [19]	Cross-sectional	AHRQ	✔✔✔	✔✔	✔✔	9/11 (Good)	Low–Moderate	Female-only cohort limits generalizability.
5	Lin et al. (2025) [20]	Cross-sectional	AHRQ	✔✔✔	✔✔	✔✔	9/11 (Good)	Low	Potential unmeasured confounding (diet/smoking).
6	Lv et al. (2023) [21]	Cross-sectional	AHRQ	✔✔	✔	✔✔	8/11 (Moderate)	Moderate	Small sample, regional selection bias.
7	Joishy et al. (2022) [22]	Cross-sectional	AHRQ	✔✔	✔	✔✔	8/11 (Moderate)	Moderate	Limited adjustments, rural sample.
8	Guo et al. (2021) [23]	Systematic review	AMSTAR-2	✔✔	✔✔	✔✔	10/11 (High)	Low	Heterogeneous data, secondary extraction.
9	Han et al. (2024) [24]	Meta-analysis (cohort)	NOS/AMSTAR	✔✔✔	✔✔✔	✔✔✔	8/9 (High)	Low	Publication bias possible; dose–response variation.
10	Qi et al. (2018) [25]	Meta-analysis (cohort)	NOS/AMSTAR	✔✔✔	✔✔✔	✔✔✔	8/9 (High)	Low	Residual confounding (renal function).
11	Haghikia et al. (2018) [26]	Prospective cohort	NOS	✔✔✔	✔✔	✔✔✔	8/9 (Good)	Low	Small sample size; no dietary data.
12	Zhou et al. (2020) [27]	Prospective cohort	NOS	✔✔✔	✔✔✔	✔✔✔	9/9 (High)	Low	Well-adjusted, minimal bias.
13	Yang et al. (2015) [28]	Cross-sectional	AHRQ	✔✔	✔	✔	7/11 (Moderate)	Moderate	Small n; limited sequencing depth.
14	Qu et al. (2022) [29]	Cohort	NOS	✔✔✔	✔✔	✔✔✔	8/9 (Good)	Low	Modest sample size, cognitive bias possible.
15	Liu et al. (2025) [30]	Prospective cohort	NOS	✔✔✔	✔✔✔	✔✔✔	8/9 (High)	Low	Variable adjustment across regions.

Note: AHRQ = Agency for Healthcare Research and Quality; NOS = Newcastle–Ottawa Scale; AMSTAR-2 = A Measurement Tool to Assess Systematic Reviews. Quality assessment was performed using three validated tools according to study design: AHRQ (maximum 11 points) for cross-sectional studies, NOS (maximum 9 points) for cohort or Case–Control studies, and AMSTAR-2 (maximum 11 equivalent points) for systematic reviews and meta-analyses. Scores ≥ 9 (AHRQ/AMSTAR-2) or ≥7 (NOS) indicate Good quality/Low risk of bias. Symbol definitions: ✔ = criterion fulfilled; ✔✔ = two criteria fulfilled; ✔✔✔ = three criteria fulfilled within that domain of the quality assessment tool.

. For cohort and case–control studies assessed using the Newcastle–Ottawa Scale (NOS; n = 6), median quality score was 8/9 (range 7–9), with strengths in comparability (adjusted analyses) and follow-up completeness, but occasional weaknesses in exposure ascertainment (self-reported dietary data in three studies).

Cross-sectional studies evaluated with the AHRQ tool (n = 8) scored moderately to well (median 8/11 items fulfilled), primarily limited by the lack of temporality. Publication bias was low (Egger’s test p = 0.42 for α-diversity and p = 0.31 for TMAO; funnel plots symmetric, Figure 2).

Overall, GRADE certainty was rated as moderate for the association between microbial α-diversity and hypertension (downgraded for inconsistency) and high for TMAO-related cardiovascular outcomes (precise and consistent estimates).

3.4. Results of Individual Studies

A total of 15 studies [16,17,18,19,20,21,22,23,24,25,26,27,28,29,30] were included in the qualitative and quantitative synthesis [16,17,18,19,20,21,22,23,24,25,26,27,28,29,30]. Of these, 11 provided extractable data suitable for meta-analysis, while the remainder contributed complementary mechanistic or descriptive insights. The included cohorts spanned Asia, Europe, and North America, with sample sizes ranging from 17 to 6999 participants and mean ages between 45 and 69 years [16,17,18,19,20,21,22,24,25,26,27,28,29,30]. Overall, most studies demonstrated consistent associations between gut microbial composition and blood pressure parameters [16,17,18,19,20,21,22,28,29,30].

For instance, Joishy et al. [22] conducted a population-based study in Assam, India, showing that reduced microbial α-diversity was significantly correlated with higher systolic blood pressure (SBP), and a random-forest classifier based on microbial taxa achieved an AUC of 0.93 for distinguishing hypertensive from normotensive individuals. Similarly, Guo et al. [23] reported enrichment of pro-inflammatory genera (Prevotella, Enterobacter) and depletion of Faecalibacterium and Roseburia among hypertensive adults—findings reproduced in other Asian and European cohorts [16,17,18,19,20,21,24,25,26,27]. Large-scale community-based studies from China and Finland further confirmed that lower α-diversity indices (Shannon and Chao1) correlated inversely with both systolic and diastolic blood pressure values [17,18,21,25].

Metabolomic analyses revealed that elevated serum trimethylamine-N-oxide (TMAO) levels were associated with increased risk of hypertension and major adverse cardiovascular events (MACE) [24,25,26,27]. These studies consistently demonstrated that TMAO is an independent predictor of cardiovascular outcomes, even after multivariable adjustment for traditional risk factors.

Collectively, these findings suggest that microbial diversity loss and functional shifts toward pro-atherogenic and pro-inflammatory taxa are reproducible microbial signatures of hypertension across geographically distinct populations [16,17,18,19,20,21,22,23,24,25,26,27,28,29,30].

3.5. Synthesis of Results

Pooled quantitative synthesis of studies assessing gut microbial α-diversity (Shannon index) demonstrated a consistent reduction among hypertensive compared with normotensive individuals (SMD = −0.15; 95% CI −0.25 to −0.05; p = 0.004; I² = 35%; τ² = 0.02), as shown in Figure 3.

Subgroup analyses explored potential sources of heterogeneity (I² = 35%), including sequencing method (16S rRNA vs. shotgun metagenomics), geographic region (Asian vs. Western cohorts), and adjustment for major confounders (e.g., BMI, medication use). These factors contributed modestly to the observed heterogeneity but did not materially alter the pooled estimates, indicating robustness of the overall findings.

Sensitivity and leave-one-out analyses further confirmed the stability of the summary effect, while Egger’s test (p > 0.3) and funnel plot symmetry (Figure 2) indicated no significant publication bias.

Overall, this meta-analysis supports a modest yet reproducible reduction in microbial diversity in hypertension, independent of sequencing platform, geographic region, or confounder adjustment.

Across individual studies, depletion of short-chain fatty acid (SCFA)–producing genera (Faecalibacterium, Roseburia, Blautia), alongside enrichment of pro-inflammatory taxa (Prevotella, Enterococcus, Desulfovibrio), was consistently reported [16,17,18,19,20,21,22,28,29,30]. These compositional shifts were paralleled by functional alterations in butyrate biosynthesis, bile acid metabolism, and microbial pathways involved in trimethylamine (TMA) generation.

For circulating trimethylamine-N-oxide (TMAO), pooled data from four primary studies (total n = 11,122; excluding overlapping data from Han et al., 2024) demonstrated a significant association with an increased risk of major adverse cardiovascular events (MACE) (HR = 1.25; 95% CI 1.10–1.42; p < 0.001) [24,25,26,27]. Only four studies were eligible for quantitative synthesis, as several others reported non-extractable effect sizes or used non-standardized metabolomic platforms. Differences in TMAO quantification (LC–MS vs. HPLC–MS) and population characteristics (post-stroke vs. heart failure cohorts) may partly explain this limited number but did not compromise the robustness of the pooled estimate. Individual studies also reported consistent dose–response relationships, supporting a linear association between elevated TMAO levels and cardiovascular risk.

Collectively, these findings reveal a multidimensional dysbiotic pattern involving both microbial composition and metabolic function, leading to reduced SCFA production, enhanced vascular inflammation, and endothelial dysfunction—mechanistic hallmarks of the gut–vascular axis in hypertensive disease. Subgroup analyses and dose–response meta-regression results are summarized in Supplementary Table S3.

4. Discussion

4.1. Principal Findings

This systematic review and meta-analysis, encompassing 15 eligible studies with a combined sample of 24,512 participants, provides robust evidence linking gut microbiome dysbiosis to hypertension (HTN) and its associated cardiovascular (CV) outcomes. The pooled standardized mean difference (SMD = −0.15; 95% CI −0.25 to −0.05) demonstrates a consistent reduction in microbial α-diversity (Shannon/Chao1 indices) among hypertensive individuals.

In parallel, the pooled hazard ratio (HR = 1.25; 95% CI 1.10–1.42) for elevated trimethylamine-N-oxide (TMAO) indicates a significant increase in the risk of major adverse cardiovascular events (MACE).

Together, these findings reinforce the concept of a “gut–vascular axis” that mechanistically connects intestinal dysbiosis, microbial metabolite imbalance, and vascular dysfunction in hypertension.

4.2. Comparison with Previous Meta-Analyses

Our findings are consistent with the recent meta-analysis by Cai et al. (2023) [5], which first quantified a reduction in microbial α-diversity and an altered Firmicutes/Bacteroidetes ratio among hypertensive patients, although it did not explore cardiovascular outcomes.

Additional studies have highlighted the downstream effects of gut dysbiosis on systemic inflammation, vascular tone, and neurohumoral regulation in hypertension [29].

The present meta-analysis extends this evidence base by integrating microbiome (diversity) and metabolomic (TMAO) data within a unified analytical framework and linking them to clinically relevant outcomes.

Similarly, Guo et al. (2021) [23] and O’Donnell et al. (2023) [12] summarized qualitative associations between gut dysbiosis and hypertension but did not provide quantitative synthesis or pooled estimates.

By incorporating recent large-scale cohorts, such as Lin et al. (2025) [20] and Liu et al. (2025) [30], the present analysis provides improved statistical precision and broader cross-regional representation.

Wang et al. (2019) [31] further demonstrated parallel shifts in plasma metabolites and microbial species related to blood pressure variability, but without calculating pooled effect sizes. These findings collectively strengthen the biological plausibility of trimethylamine-N-oxide (TMAO) as a pro-atherogenic metabolite contributing to vascular inflammation and endothelial injury [32].

The pooled diversity effect size (SMD = −0.15) aligns with smaller individual studies such as Sun et al. (2019) [17] and Louca et al. (2021) [19], suggesting a universal yet modest reduction in microbial richness across populations. On the metabolomic side, our pooled hazard ratio for TMAO (HR = 1.25) is consistent with estimates reported by Qi et al. (2018) [25] (HR = 1.23) and Han et al. (2024) [24] (RR = 1.14), further supporting the reproducibility of this association across independent cohorts.

4.3. Biological and Mechanistic Insights

Multiple interrelated mechanisms explain how gut dysbiosis may contribute to hypertension and cardiovascular (CV) injury, as shown in Figure 4. Reduced abundance of butyrate-producing genera (e.g., Faecalibacterium prausnitzii, Roseburia) leads to decreased short-chain fatty acids (SCFAs), impairing vasodilation via G-protein-coupled receptor (GPR43) signaling and histone deacetylase inhibition [33]. This SCFA deficiency disrupts endothelial homeostasis by reducing anti-inflammatory effects and promoting vascular stiffness, as SCFAs normally activate GPR41/GPR43 receptors on vascular smooth muscle cells to induce vasodilation and inhibit pro-hypertensive pathways like nuclear factor-κB (NF-κB)-mediated inflammation [33].

Concurrently, enrichment of pathobionts such as Escherichia–Shigella, Streptococcus, and Prevotella promotes lipopolysaccharide-mediated inflammation, activation of the renin–angiotensin–aldosterone system (RAAS), and sympathetic overactivity [34]. These pathobionts trigger endotoxemia, where leaked bacterial lipopolysaccharides (LPS) activate Toll-like receptor 4 (TLR4) on immune and vascular cells, leading to cytokine release (e.g., IL-6, TNF-α), heightened sympathetic nervous system activity via central neural pathways, and RAAS upregulation, all of which elevate blood pressure and contribute to vascular remodeling [34].

At the metabolic level, increased TMA (trimethylamine) production—derived from gut bacterial metabolism of dietary precursors like choline and carnitine—occurs in dysbiotic states; TMA is then oxidized in the liver by flavin-containing monooxygenase 3 (FMO3) to form TMAO, which drives vascular remodeling through endothelial dysfunction, oxidative stress, and platelet activation [35]. TMAO exacerbates these effects by promoting foam cell formation in macrophages, enhancing monocyte activation, and inducing reactive oxygen species (ROS) via mitochondrial pathways, thereby accelerating atherosclerosis and thrombotic risk in hypertensive individuals [35]. Cao et al. (2024) [36] linked these microbial and metabolic signatures to hypertensive left-ventricular hypertrophy, while Fan et al. (2023) [37] highlighted gut-lipidome alterations paralleling vascular inflammation in arteritis models.

Overall, these findings support the concept that microbiota-derived metabolites act as endocrine regulators of vascular tone and structure.

4.4. Clinical and Translational Implications

Clinically, the consistent yet modest decrease in microbial diversity suggests potential for microbiota-targeted interventions as adjuncts to standard antihypertensive therapy. High-fiber diets and acetate supplementation have been shown to enhance SCFA-producing taxa and lower blood pressure in preclinical models [38].

Measurement of circulating trimethylamine-N-oxide (TMAO) could serve as a biomarker for cardiovascular risk stratification in hypertensive or post-myocardial infarction patients [39].

Personalized nutrition or probiotic approaches aiming to restore eubiosis may modulate systemic inflammation and improve vascular outcomes [40].

However, references [38,39,40] refer primarily to preclinical or animal models, which, although mechanistically informative, may not directly translate to human hypertension.

Sex-specific analyses are particularly relevant, as hormonal and metabolic differences influence both microbial composition and vascular reactivity. Prior studies, including Louca et al. [19] and Lv et al. [21], highlight sex-dimorphic microbial pathways that may underpin differential hypertension phenotypes.

Integration of microbiome profiling into cardiovascular risk-prediction algorithms may therefore advance precision hypertension management. Nevertheless, these translational suggestions should be interpreted as hypothesis-generating rather than clinically actionable, given the predominance of observational evidence.

4.5. Strengths and Limitations

Key strengths include a comprehensive multi-database search, dual independent review, and a PRISMA-guided methodology with quantitative pooling using random-effects models and formal certainty grading (GRADE). Inclusion of both microbiome and metabolome data offers a multidimensional understanding of the gut–vascular relationship, strengthening the mechanistic interpretation of results.

Nevertheless, several limitations persist. Most included studies were cross-sectional, limiting causal inference. In gut microbiome research, establishing causality is particularly challenging because microbial composition is dynamically influenced by host lifestyle, diet, and medication use. Reverse causality cannot be excluded, as hypertension itself may alter gut microbial ecology, potentially confounding directionality in cross-sectional analyses. Sequencing heterogeneity (16S rRNA vs. shotgun metagenomics) and variable α-diversity metrics contributed to residual heterogeneity. Adjustments for confounders such as diet, BMI, and medication use was inconsistent across studies.

Geographic clustering toward Asia and Europe restricts global generalizability. Underrepresented regions include Africa, South America, and Eastern Europe, where dietary patterns, microbial exposures, and antihypertensive practices differ substantially. This imbalance may limit the extrapolation of findings to diverse global populations. Finally, reporting bias cannot be entirely excluded despite symmetric funnel plots.

4.6. Future Perspectives

Future research should prioritize large, prospective, multi-omic studies integrating microbiome, metabolome, and host transcriptome data to clarify causality. Beyond microbiome and metabolome profiling, integration with host proteomics and transcriptomics will help delineate mechanistic pathways linking microbial metabolites to vascular remodeling, inflammation, and immune signaling.

Randomized controlled trials (RCTs) evaluating microbiota modulation—through prebiotics, probiotics, or TMAO-lowering agents—are essential to establish therapeutic efficacy.

Additionally, harmonized sequencing and bioinformatic pipelines, sex-specific analyses, and standardized cardiovascular endpoints will enhance reproducibility, cross-study comparability, and translational potential.

5. Conclusions

This systematic review and meta-analysis consolidates evidence that gut microbiome dysbiosis—characterized by reduced microbial diversity and altered metabolic activity—plays a significant role in the development and progression of hypertension and its cardiovascular complications. Elevated circulating levels of trimethylamine N-oxide (TMAO) further predict adverse cardiovascular outcomes, reinforcing the concept of a gut–vascular axis linking intestinal homeostasis with vascular health.

While causality remains to be fully established, these findings highlight the potential of microbiota-derived biomarkers such as TMAO for cardiovascular risk stratification and the promise of microbiome-targeted interventions—dietary fiber enrichment, prebiotics, probiotics, and TMAO-lowering agents—as adjunctive strategies in hypertension management.

Future large-scale, longitudinal, and interventional studies integrating metagenomics, metabolomics, and host transcriptomics are warranted to clarify causal pathways and identify modifiable microbial signatures for personalized prevention of cardiovascular disease.