Skip to main content
Download PDF
Download ePub

The impact of dietary patterns on gut microbiota for the primary and secondary prevention of cardiovascular disease: a systematic review

Nutrition Journal volume 24, Article number: 17 (2025) Cite this article

Abstract

Background

Previous studies found that it is promising to achieve the protective effects of dietary patterns on cardiovascular health through the modulation of gut microbiota. However, conflicting findings have been reported on how dietary patterns impact gut microbiota in individuals either established or at risk of cardiovascular disease (CVD). Our systematic review aimed to explore the effect of dietary patterns on gut microbiota composition and on risk factors for CVD in these populations.

Methods

We systematically searched seven databases, including PubMed/MEDLINE, MEDLINE (Ovid), Embase (Ovid), CINHAL (EBSCO), Web of Science, CNKI (Chinese), and Wanfang (Chinese), covering literature from inception to October 2024. Studies were included if they focused on adults aged 18 years and older with CVD or at least two CVD risk factors, implemented dietary pattern interventions, and incorporated outcomes related to microbiome analysis. The risk of bias for included studies was assessed using the revised Cochrane risk of bias tool (RoB2) for randomized trials and the Risk Of Bias In Non-randomised Studies of Interventions (ROBINS-I) for non-randomized studies. Changes in the relative abundance of the gut microbiome were summarized at various taxonomic levels, including phylum, class, order, family, genus, and species. Random-effects meta-analysis was conducted to analyze the mean difference in cardiometabolic parameters pre- and post-intervention.

Results

Nineteen studies were identified, including 17 RCT and two self-controlled trails. Risk of bias across the studies was mixed but mainly identified as low and unclear. The most frequently reported increased taxa were Faecalibacterium (N = 8) with plant-rich diets, Bacteroides (N = 3) with restrictive diets, and Ruminococcaceae UCG 005 and Alistipes (N = 9) with the polyphenol-rich diets. The most frequently reported decreased taxa were Parabacteroides (N = 7) with plant-rich diets, Roseburia (N = 3) with restrictive diets, and Ruminococcus gauvreauii group (N = 6) with the polyphenol-rich diets. Plant-rich diets showed a significant decrease in total cholesterol (TC) with a mean difference of -6.77 (95% CI, -12.36 to -2.58; I2 = 84.7%), while restrictive diets showed a significant decrease in triglycerides (TG) of -22.12 (95% CI, -36.05 to -8.19; I2 = 98.4%).

Conclusions

Different dietary patterns showed distinct impacts on gut microbiota composition. Plant-rich diets promoted the proliferation of butyrate-producing bacteria, suggesting promising prospects for modulating gut microbiota and butyrate production through dietary interventions to enhance cardiovascular health. Further research is warranted to investigate the long-term effects of dietary patterns on clinical endpoints, such as CVD events or mortality.

Review registration

Registration number: CRD42024507660

Peer Review reports

Background

Cardiovascular disease (CVD) remains the leading cause of global mortality, contributing significantly to reduced quality of life and excess health system costs [1]. According to the Global Burden Disease Study, cases of total CVD, CVD-related deaths, and years lived with disability doubled from 1990 to 2019. Concurrently, disability-adjusted life years (DALYs) and years of life lost among CVD patients also increased significantly [1]. Amidst its multifaceted etiology, which includes environmental, metabolic, and behavioral risk factors, the crucial role of dietary patterns in the management of CVD has garnered increasing recognition [2]. Dietary patterns are defined as the amounts, proportions, variety, or combinations of different foods, beverages, and nutrients in the diet, and the frequency with which they are habitually consumed [3]. A healthy dietary pattern consists of nutrient-dense forms of foods and beverages across all food groups, in recommended amounts, and within calorie limits [3]. Unhealthy dietary patterns accounted for 6.58 million cardiovascular deaths (95% CI: 2.27–9.52 million) and contributed to an all-cause DALYs rate of 2,340 per 100,000 (95% CI: 836–3,380 per 100,000) in 2021 [4]. There is an urgent need for comprehensive strategies aimed at mitigating the burden of CVD through targeted dietary interventions [5, 6].

All individuals, regardless of their CVD risk status, can benefit from healthy diet, which focuses on increasing consumption of fruits, vegetables, whole grains, fat-free or low-fat dairy, lean proteins, and oils, and on decreasing consumption of foods high in sodium, saturated or trans fats, and added sugars [7]. Given the growing knowledge of the synergy between nutrients and their food sources, dietary guidelines have shifted from focusing on isolated nutrients to broader dietary patterns. Previous studies have highlighted the potential cardiovascular health benefits associated with dietary patterns such as the Mediterranean, Dietary Approaches to Stop Hypertension (DASH), vegetarian, Nordic, and low-fat diets, while also indicating that Western and high-fat diets may elevate the risk of CVD [8,9,10]. It is evident that multiple dietary patterns can be beneficial for cardiovascular health, although there is significant variability in the outcomes of dietary interventions.

The existence of the Gut-Heart Axis makes this issue more complicated. Gut microbiota play an important role in the digestive process and contribute to the absorption of nutrients and metabolites, potentially modulating host immune and metabolic functions, and influencing cardiovascular health [11]. An imbalance in the gut microbiota composition has been linked to a higher risk of major cardiovascular events, such as atherosclerosis, heart failure, and stroke [12, 13]. The gut microbiota can both influence and be influenced by virtually all known cardiovascular risk factors [12]. Diet is one of the most important modulators of gut microbiota composition and function [14]. Recent studies have implicated a potential link between dietary patterns, gut microbiota, and cardiovascular health outcomes, suggesting that the gut microbiome may mediate the beneficial effects of specific dietary patterns on CVD risk [15, 16]. It is promising to achieve the protective effects of dietary patterns on cardiovascular health through the modulation of gut microbiota.

However, current findings on the impact of dietary patterns on gut microbiota present conflicting results. Santos-Marcos et al. [17] observed a decrease in the levels of Roseburia with the Mediterranean diet, while other studies reported an increase [16, 18]. Additionally, Ghosh et al. [18] noted an increase in Bacteroides following the Mediterranean diet, whereas Di et al. [19] found results contradicting this observation. How diet patterns influenced the gut microbiota to improve cardiovascular health remains unclear.

Therefore, the aim of our systematic review and meta-analysis was to explore the effects of dietary patterns on both the relative abundance of gut microbiota and key microbiota-mediated CVD risk factors in individuals with established CVD or at risk of CVD. By synthesizing existing literature, we hope to identify dietary strategies that may mitigate CVD risk by modulating gut microbiota composition and function.

Methods

The Joanna Briggs Institute (JBI) Reviewer’s Manual [20] and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines [21] were employed to guide the methodology and reporting of this systematic review. The review was registered prior to its start in the PROSPERO international prospective register of systematic reviews (CRD42024507660).

Search strategy

A three-step comprehensive search strategy was conducted to identify both published and gray literatures in English and Chinese. The initial search was performed on PubMed/MEDLINE to identify synonyms and index terms, with the aim of formulating the final search strategy for all databases. After the final search strategy gained approved from all authors and a librarian, a comprehensive search was independently performed by two authors (JY & YW) across several databases, including PubMed/MEDLINE, MEDLINE (Ovid), Embase (Ovid), CINHAL (EBSCO), Web of Science, CNKI (Chinese), and Wanfang (Chinese). Grey literature was searched via Google Scholar and Baidu Scholar (Chinese). In PubMed/MEDLINE, we combined MeSH terms (“Diet” AND “Gastrointestinal Microbiome” AND “Cardiovascular Diseases”) with free words [(“Dietary pattern” OR “Dietary intervention” OR “Diet management”) AND (“gut microbio*” OR “gut intestinal microbio*” OR “gastrointestinal flora”) AND (“cardiovascular disease*” OR “atheroscleros*” OR “heart disease*”)]. The search time frame was set from inception to October 2024. The search strategies used for all databases are presented in Supplementary Table S1. Finally, two authors (JY & YW) conducted an extra search through manual inspection of the references in all included studies.

Inclusion and exclusion criteria

Studies were included if they: (1) focused on populations aged 18 years and older with cardiovascular diseases (CVD) or those at risk for CVD [7] (possessing at least two CVD risk factors). CVD encompassed conditions such as atherosclerosis, coronary heart disease, myocardial infarction, stroke, acute coronary syndrome, and carotid endarterectomy. The recommendation statement from the US Preventive Services Task Force suggests that individuals at increased risk of CVD should be defined as having two or more risk factors, including hypertension or elevated blood pressure, dyslipidemia, abnormal blood glucose levels, and overweight/obesity [7]. (2) implemented dietary pattern interventions in the intervention and/or control groups. (3) incorporated outcomes comprising microbiome analysis, such as 16S rRNA gene sequencing or shotgun metagenomic sequencing. (4) were randomized or nonrandomized clinical trials. (5) were published in English or Chinese. Studies involving interventions with prebiotics, probiotics, or synbiotics were excluded.

Study screening and selection

All records identified from the databases were imported into EndNote (Clarivate Analytics, PA, USA). After removing duplicates, the screening process consisted of two phases. In the primary screening, two reviewers (JY & YW) independently assessed the titles and abstracts of the records against the predefined inclusion and exclusion criteria. This initial review aimed to identify potentially relevant studies for further evaluation. In the secondary screening, the same reviewers conducted a detailed assessment of the full texts of the selected studies to confirm eligibility based on the inclusion criteria. Any discrepancies between the reviewers’ decisions during this stage were discussed until a consensus was reached. The reasons for exclusion at this stage were documented to ensure transparency in the selection process.

Risk of bias appraisal

The risk of bias of included studies was assessed using the revised Cochrane risk of bias tool (RoB2) for randomized trails and the Risk Of Bias In Non-randomised Studies—of Interventions (ROBINS-I) for non-randomized studies of interventions [22, 23]. Two reviewers (JY&YW) independently appraised the studies, and any disagreements were discussed to reach a consensus.

Data extraction

Data extraction was independently performed by two reviewers (JY&YW), including authors, year of publication, country, study design, study duration, participants characteristics, sample size, dietary intervention and control group diet, cardiometabolic parameter outcomes, methods of evaluating gut microbiota, and changes in gut microbiota composition.

Results were extracted when significant changes in the relative abundance of the gut microbiome were observed pre- and post-intervention. These results were then summarized at various taxonomic levels, including phylum, class, order, family, genus, and species. Due to the limited availability and absence of standard deviation for fold change values, a meta-analysis of gut microbiome composition was not feasible. To explore the effects of different dietary patterns, subgroup analyses were further conducted within the plant-rich diets (the plant-based, Mediterranean, and whole grain diets) and restrictive diets (low-fat and fasting diets).

Meta-analysis

Studies sharing the same dietary interventions and outcomes were included in the meta-analysis. To compare the effects of dietary interventions across studies, dietary patterns were categorized into three groups: plant-rich diets (including plant-based, vegetarian, 50% fruits and vegetables, Mediterranean, and whole grain diets), restrictive diets (including low-fat and fasting diets), and polyphenol-rich diets. Stata 17.0 (College Station, TX) was used to perform the analyses.

Mean difference (95% confidence interval [CI]) between baseline and post-intervention changes in cardiometabolic parameters were calculated for body mass index (BMI), weight, waist, systolic blood pressure (SBP), diastolic blood pressure (DBP), glucose, total cholesterol (TC), high-density lipoprotein-cholesterol (HDL-C), low-density lipoprotein-cholesterol (LDL-C), and triglycerides (TG). We employed the DerSimonian and Laird weighted random effects model to estimate the pooled results of cardiometabolic outcomes, which is particularly suitable for our analysis due to the heterogeneity among the included studies regarding dietary interventions and participant characteristics [24]. Statistical heterogeneity among the included studies was estimated using the I2 statistic. A two-sample t-test was used to evaluate the differences between the plant-rich diets group and the restrictive diets group for the above parameters. A p value of less than 0.05 indicates statistical significance.

We conducted several sensitivity analyses to check the robustness of findings. First, we performed a leave-one-out sensitivity analysis by sequentially removing each individual trial from the analysis to explore the potential influence of an outlier. Second, we used fixed-effects models to verify the robustness of the pooled results obtained from the random-effects models. A two-sample t-test was used to evaluate the differences between the results of the two models. Since there were fewer than ten trials available for the meta-analysis, Egger’s test was not conducted for assessing the risk of publication bias.

Certainty of evidence

The certainty of evidence was rated as high, moderate, low, or very low using the Grading of Recommendations, Assessment, Development, and Evaluations (GRADE) approach [25]. Randomized and non-randomized experimental trials were initially assessed as high-quality evidence. We then used the five GRADE criteria to potentially downgrade the initial rating: risk of bias, inconsistency, indirectness, imprecision, and publication bias.

Results

Literature search

The PRISMA flowchart is shown in Fig. 1. A total of 12,893 studies were identified in the database search. After removing duplicates and initially screening titles and abstracts, 69 studies were selected. After full text review, 19 studies were included in our systematic review.

Fig. 1
figure 1

Flow diagram of the selection process

Full size image

Characteristics of eligible studies

Table 1 shows the characteristics of the included studies. Among the 19 included studies, 9 were parallel RCTs [26,27,28,29,30,31,32,33,34], 8 were crossover RCTs [35,36,37,38,39,40,41,42], and two were self-controlled trails [43, 44]. Five studies were conducted in Spain [28,29,30, 32, 38], followed by the United States (n = 3) [41, 43, 44], Sweden (n = 2) [36, 37], Denmark (n = 2) [39, 42], Italy (n = 2) [34, 40], Australia (n = 1) [35], Mexico (n = 1) [26], China (n = 1) [27], Germany (n = 1) [31], and Netherlands (n = 1) [33]. The sample sizes ranged from 17 [44] to 362 [32]. The baseline age of participants ranged from a mean of 22.2 ± 3.4 years [44] to a median of 67 (63–70) years [36]. Three studies only contained male participants [29, 30, 37]. Four studies [28,29,30, 36] enrolled participants diagnosed with CVD diagnosed, four studies [31, 38, 39, 44] enrolled participants with metabolic syndrome, and seven studies [26, 27, 32, 34, 35, 41, 42] included participants with at least three CVD risk factors.

Table 1 Characteristics of the included studies
Full size table

Seven studies described the Mediterranean diets [28,29,30, 32, 35, 38, 40], followed by the low-fat diets (n = 5) [26, 28,29,30, 35], the plant-based diets (n = 4) [36, 40, 43, 44], the whole grain diets (n = 4) [33, 37, 39, 42], the fasting diets (n = 2) [27, 31], and the polyphenol-rich diets (n = 2) [34, 41]. Among the studies that conducted the Mediterranean diet intervention, four studies [28,29,30, 35] used the low-fat diet as the control group, and one [40] used the vegetarian diet as control. Among the studies conducted the whole grain diet intervention, three [33, 39, 42] of them used a refined grain diet as control, and one compared whole grain rye diet and whole grain wheat diet [37]. The intervention duration ranged from 6 days [43] to 2 years [28, 29], with 15 studies lasting no more than 3 months [26, 27, 31,32,33,34,35,36,37,38,39,40,41,42,43,44].

Eleven studies reported the outcome of cardiometabolic parameters, with the most reported measures being TC, TG, HDL-C, and LDL-C (n = 10) [26, 27, 30, 32, 36,37,38, 42,43,44] and weight (n = 9) [26, 27, 32, 36, 38, 39, 42,43,44]. All of the studies used 16S rRNA sequencing method for gut microbiome analysis, and 2 studies further used shotgun metagenomic sequencing [31, 42]. A variety of methods were used to calculate the α-diversity of gut microbiota, including Faith’s phylogenetic diversity (n = 8) [29, 30, 33, 35, 36, 38, 39, 41], Shannon index (n = 8) [26, 27, 31, 32, 38, 40, 42, 43], Chao 1 (n = 7) [26, 29,30,31,32, 39, 43], observed operational taxonomic units (n = 6) [26, 27, 29, 38, 39, 41], Simpson index (n = 5) [27, 31, 38, 40, 43], the Pielou’s evenness (n = 2) [35, 41], and the ACE estimator (n = 1) [43]. The β-diversity was calculated using the weighted UniFrac distance (n = 8) [26, 27, 30, 32, 33, 39,40,41], the unweighted UniFrac distance (n = 5) [26, 27, 29, 30, 32], and Bray–Curtis distance (n = 4) [31, 32, 38, 40].

Risk of bias assessment

Figure 2 shows the results of the study’s risk of bias assessment. There were four studies [28,29,30, 34] demonstrated potential biases related to randomization, while three studies [31, 38, 41] exhibited concerns due to missing outcome data. Both non-RCTs inadequately controlled for significant confounding variables [43, 44].

Fig. 2
figure 2

Risk of bias of included studies. A Results of parallel randomized controlled trials; B Results of cross over randomized controlled trials; C Results of non-randomized controlled trials

Full size image

Cardiometabolic parameters

Figure 3 shows the results of meta-analysis on clinical cardiometabolic outcomes. Detailed information can be found in Table S2, Table S3 and Figure S1. Ten studies, including 14 dietary intervention groups that evaluated clinical cardiometabolic outcomes, were included in our meta-analysis. Among these 14 groups, eight were plant-rich diets interventions (three plant-based diets [36, 43, 44], three Mediterranean diets [30, 32, 38], and two whole grain diets [39, 42]), while six were restrictive diet interventions (five low-fat diets [26, 30] and one fasting diets [27]). Results showed that plant-rich diets significantly reduce TC (mean difference, −6.77; 95% CI, −12.36 to −2.58; I2, 84.7%), LDL-C (−4.37, −7.59 to −1.16, 65.1%), and weight (−0.45, −0.77 to −0.13, 0.0%). Restrictive diets significantly reduce TG (−22.12, −36.05 to −8.19, 98.4%), TC (−8.74, −12.72 to −4.76, 94.1%), blood glucose (−7.13, −12.01 to −2.25, 99.4%), waist circumference (−4.25, −5.52 to −2.98, 95.7%), weight (−4.02, −5.26 to −2.77, 94.7%), and BMI (−1.59, −2.18 to −0.99, 97.7%). The restrictive diets group showed significantly greater reductions in BMI (t = 3.77, p = 0.0002), weight (t = 5.22, p < 0.0001), waist circumference (t = 4.55, p < 0.0001), and blood glucose (t = 2.25, p = 0.0248) compared to the plant-rich diets group.

Fig. 3
figure 3

Forest plots of effects of dietary patterns on clinical parameters. A Pooled effects of plant-rich diets on clinical parameters; B Pooled effects of restrictive diets on clinical parameters

Full size image

In the leave-one-out analysis, we found that no single trial significantly influenced the estimations (Figure S2). In addition, pooled results showed no significant differences between random- or fixed- effects models (Table S4).

Certainty of evidence

Table 2 shows the certainty of evidence regarding plant-rich diets and restrictive diets on cardiometabolic parameters. Reasons for the downgrade are listed in Table S5. Twelve pieces of evidence were downgraded by one level due to a high risk of bias. Additionally, ten pieces of evidence were downgraded by one level due to inconsistency, indicated by an I2 exceeding 90%. Furthermore, eight pieces of evidence were downgraded by one level due to imprecision, indicated by a sample size did not meet the optimal information size criterion or less than 400.

Table 2 Certainty of evidence table
Full size table

Microbiome results

Changes in the relative abundance of gut microbiota were extracted from 17 studies [27,28,29,30,31,32,33,34,35,36,37, 39,40,41,42,43,44]. Table S6 and Figure S3-10 show detailed information on significant changes in the gut microbiome following dietary interventions.

Increase in relative abundance following diet interventions

At the phylum level, changes in Firmicutes phylum were the most frequently reported across all three dietary patterns (N = 66 for plant-rich diets [28,29,30, 32, 35,36,37, 40, 42, 43], 18 for restrictive diets [27, 29,30,31], and 69 polyphenol-rich diets [34, 41]). Compared to the other two dietary patterns, the plant-rich diets increased the relative abundance of Euryarchaeota (N = 3) and Verrucomicrobia (N = 1), while the restrictive diets increased Lactococcus (N = 1) and Spirochaetes (N = 1), and the polyphenol-rich diets increased Tenericutes (N = 5).

At the order level, all three dietary patterns increased the relative abundance of Bacteroidales, Clostridiales, Coriobacteriales, Eubacteriales, Lactobacillales, and Oscillospirales. Compared to the other two dietary patterns, the plant-rich diets increased the relative abundance of Burkholderiales (N = 1), Caulobacterales (N = 1), Eggerthellales (N = 3), Methanobacteriales (N = 2), Monoglobales (N = 2), Veillonellales (N = 2) and Verrucomicrobiales (N = 1), while the restrictive diets increased Actinomycetales (N = 1), Erysipelotrichales (N = 1), Flavobacteriales (N = 1), Pasteurellales (N = 1) and Pseudomonadales (N = 3), and the polyphenol-rich diets increased Desulfovibrionales (N = 4), Mollicutes (N = 3), and Rhodospirillales (N = 1). The other two dietary patterns increased the abundance level of Bifidobacteriales (N = 3 and 1), except the polyphenol-rich diets.

At the genus level, the most frequently reported increased taxa were Faecalibacterium (N = 8) [28, 29, 42, 43] and Roseburia (N = 6) [29, 30, 32, 37, 43] following the plant-rich diets, Bacteroides (N = 3) [29, 31] and Prevotella (N = 3) [28,29,30] following the restrictive diets, and Ruminococcaceae UCG 005 (N = 9) [41], Alistipes (N = 9) [41], and Ruminococcus (N = 8) [41] following the polyphenol-rich diets.

Decrease in relative abundance following diet interventions

At the phylum level, all three dietary patterns resulted in a decreased relative abundance of taxa within Actinomycetota, Bacteroidota, Firmicutes, and Proteobacteria. Compared to the other two dietary patterns, the plant-rich diets reduced the relative abundance of Cyanobacteria (N = 1) and Lentisphaerota (N = 1), while the restrictive diets reduced Acidobacteria (N = 2) and Chloroflexi (N = 4).

At the order level, the most frequently reported decreased taxa were observed in Bacteroidales (N = 29) [30, 32, 36, 39, 40, 42, 43] following the plant-rich diets, and Clostridiales following restrictive diets (N = 13) [27, 29,30,31] and polyphenol-rich diets (N = 18) [34, 41]. Compared to the other two dietary patterns, the plant-rich diets reduced the relative abundance of Acidaminococcales (N = 3), Burkholderiales (N = 3), Desulfovibrionales (N = 3), Peptostreptococcales (N = 2) and Victivallales (N = 1), while the restrictive diets reduced Acidothermales (N = 2), Actinomycetales (N = 2), Hyphomicrobiales (N = 1), Ktedonobacterales (N = 2), Pasteurellales (N = 1), and Solibacterales (N = 1), and the polyphenol-rich diets reduced Selenomonadales (N = 2).

At the genus level, the most frequently reported decreased taxa were Parabacteroides (N = 7) [32, 36, 39, 40, 43], Ruminococcus (N = 4) [32, 33, 39, 42], Lachnospira (N = 4) [37, 39], and Bacteroides (N = 4) [39, 43] following the plant-rich diets, Roseburia (N = 3) [30, 31], Alistipes (N = 2) [27, 31], Clostridium (N = 2) [29, 31], and Eubacterium (N = 2) [31] following the restrictive diets, and Ruminococcus gauvreauii group (N = 6) [41], Ruminiclostridium (N = 5) [41], and Ruminococcaceae UCG 013 (N = 5) [41] following the polyphenol-rich diets.

Subgroup analysis

Within the plant-rich diets, the most frequently reported increased taxa were Blautia (N = 3) [43] and Anaerostipes (N = 3) [40, 43] following the plant-based diets, Roseburia (N = 3) [29, 30, 32], Parabacteroides (N = 3) [28,29,30], and Faecalibacterium (N = 3) [28, 29] following the Mediterranean diets, and Faecalibacterium (N = 3) [42] and Coprococcus (N = 2) [37, 42] following the whole grain diets. The most frequently reported decreased taxa were Parabacteroides (N = 4) [36, 43] and Bacteroides (N = 3) [43] following the plant-based diets, Parabacteroides (N = 2) [32, 40] for the Mediterranean diets, and Lachnospira (N = 4) [37, 39] and Ruminococcus (N = 3) [39, 42] following the whole grain diets.

Within the restrictive diets, the most frequently reported increased taxa were Prevotella (N = 3) [28,29,30] following the low-fat diets, and Roseburia (N = 2) [27, 31], Clostridium (N = 2) [31], and Bacteroides (N = 2) [31] following the fasting diets. The most frequently reported decreased taxa were Streptococcus (N = 1) [29], Roseburia (N = 1) [30], and Clostridium (N = 1) [29] following the low-fat diets, and Roseburia (N = 2) [31], Eubacterium (N = 2) [31], and Alistipes (N = 2) [27, 31] following the fasting diets.

Discussion

This is the first systematic review to provide a comprehensive understanding of the impact of dietary patterns on gut microbiota and cardiometabolic indicators in individuals with established CVD or at risk of CVD. Our findings highlighted the distinct effects of plant-rich, restrictive, and polyphenol-rich dietary patterns on the composition of gut microbiota. A plant-rich diet was shown to increase the relative abundance of butyrate-producing bacteria, including Faecalibacterium prausnitzii and Roseburia, which are known to play protective roles in various forms of CVD. Additionally, both restrictive diets and plant-rich diets significantly reduced cardiometabolic risk factors, with restrictive diets showing greater efficacy. These results underscore the potential of dietary interventions to modulate gut microbiota and improve both primary and secondary prevention of CVD.

Our results indicated that plant-rich diets may lead to an increase in the relative abundance of Faecalibacterium. This aligns with findings from previous studies [45, 46]. Furthermore, our subgroup analysis revealed that this increase mainly occurred in individuals following whole grain and Mediterranean diets. This may be because these two dietary patterns are rich sources of resistant starch (RS), which is found abundantly in whole grains, chickpeas, and tuberous vegetables. RS, a type of fiber resistant to digestion in the small intestine, has been previously found to be associated with the proliferation of Faecalibacterium [47]. It suggests that plant-rich diets could be considered as an intervention strategy for individuals who require modulation of Faecalibacterium abundance. However, as highlighted by a recent systematic review, the effects of the Mediterranean diet and other plant-rich diets on gut microbiota composition remain inconclusive due to substantial heterogeneity in study design and findings [48]. This suggests that while plant-rich diets hold promise as an intervention strategy for modulating Faecalibacterium abundance, the effects may vary depending on the specific context and individual variability.

Among restrictive diets, the fasting diet was shown to increase the relative abundance of Bacteroides among individuals with metabolic syndrome, aligning with findings observed in healthy adults with normal or obese BMI [49]. Additionally, we identified a study reporting an increase in Bacteroides abundance following a low-fat diet intervention [29]. However, contrasting this observation, prior evidence suggested that animal-based or typical Western diets, characterized by their high protein and fat content, were associated with elevated Bacteroides abundance [50, 51]. This discrepancy may be attributed to individual variations and racial/ethnic diversity. Moreresearch is warranted to elucidate the impact of low-fat diets on Bacteroides abundance and the underlying mechanisms.

A plant-rich diet was found to exert beneficial effects on the proliferation of butyrate-producing bacteria, notably Faecalibacterium prausnitzii and Roseburia. This is partially supported by the findings of a previous systematic review, which suggested that while the Mediterranean diet may influence bacterial abundance and fecal butyrate concentrations [48]. Faecalibacterium prausnitzii and Roseburia, which belong to the Firmicutes phylum, were the most frequently reported to increase significantly following plant-rich dietary interventions. These bacteria may degrade the cellulose and hemicellulose components of plant material, which are subsequently fermented into short-chain fatty acids (SCFAs), including butyrate [52]. Faecalibacterium prausnitzii, in particular, is recognized as one of the most abundant producers of butyrate, a crucial SCFA [53]. There is substantial evidence suggesting that SCFAs, particularly butyrate, play important roles in various CVD [54, 55]. Reduced production of butyrate and lower abundance of Faecalibacterium have been observed in hypertensive patients [56]. Furthermore, butyrate could inhibit the development of atherosclerosis by enhancing plaque stability and reducing the adhesion and migration of pro-inflammatory macrophages [57]. The Roseburia-fiber-butyrate axis could reduce atherosclerotic plaque sizes without impacting cholesterol and triglyceride levels. Additionally, it interacts with dietary plant polysaccharides to mitigate systemic inflammation and improve atherosclerosis [58]. Butyrate also plays a vital role in reducing circulating cholesterol levels by stimulating the secretion of lipoproteins containing apoA-IV, thereby facilitating reverse cholesterol transport [59]. Additionaly, Roseburia can activate fatty acid oxidation and de novo synthesis, while inhibiting lipolysis, leading to reduced circulating lipid plasma levels and body weight [60]. These findings highlight the potential cardioprotective effects of a plant-rich diet mediated by the modulation of gut microbiota and SCFA production. It is promising to manipulate SCFA production through dietary interventions to decrease the potential cardiovascular risk for both healthy individuals and patients with existing cardiovascular conditions. Further research is warranted to elucidate the underlying mechanisms and assess the clinical implications of plant-rich diets in the prevention and management of CVD.

Our findings revealed that both restrictive diets and plant-rich diets significantly reduced TC, LDL-C, and body weight. Notably, restrictive diets were more effective in lowering BMI, weight, waist circumference, and blood glucose levels among individuals with established CVD or at risk of CVD. This aligns with a previous umbrella review, which demonstrated the association of intermittent fasting with successful weight loss and metabolic benefits in adults with obesity [61]. Additionally, low-fat diets appear to be more effective for weight loss and reducing both systolic and diastolic blood pressure compared to standard diets, and they outperform low-carbohydrate diets in lowering LDL-C levels [62]. The benefits of low-fat diets may be due to the reduction of high-energy-dense foods, such as fats, which helps lower total energy intake, a key factor for weight loss [63]. Studies suggest that low-energy–density diets are effective in reducing weight, lowering inflammation, and improving cardiovascular risk factors [64]. However, simply restricting high-energy-dense foods without increasing low-energy-dense alternatives may result in greater hunger and cravings, making it harder to sustain the diet in the long term [63]. While low-fat diets have their advantages, they can also lower HDL-C, which is associated with increased coronary heart disease risk [65]. Additionally, the reduction in fat intake often leads to an increase in carbohydrate consumption, which can trigger carbohydrate-induced hypertriglyceridemia [66]. This potential downside of a simple restrictive diet diets should be considered when evaluating their effectiveness for long-term cardiovascular prevention. Therefore, we cannot unequivocally recommend restrictive diets as the optimal strategy for CVD prevention without considering these limitations.

Our analysis indicated that plant-rich diets have relatively lower efficacy in reducing cardiometabolic risk factors compared to low-fat diets. This may be attributed to the absence of energy intake restriction in the included studies. Previous studies have found that diets rich in fruits and vegetables, without a compensatory reduction in total energy intake, may not lead to significant weight loss [67]. Reductions in waist circumference and visceral fat were primarily observed when Mediterranean diets were energy-restricted, with even then, the effect sizes were only marginal [68]. Similarly, a long-term intervention with an unrestricted-calorie, high-vegetable-fat Mediterranean diet resulted in slight reductions in bodyweight and an increase in waist circumference [69]. However, it is important to emphasize that healthy plant foods, such as extra virgin olive oil and nuts, may still be associated with a substantially lower risk of CVD [70]. The high-fiber foods in plant-rich diets may decrease the level of inflammatory biomarkers [71], which are associated with improved endothelial function and a lower risk of atherosclerosis, ultimately benefiting cardiovascular health. The U.S. Dietary Guidelines Advisory Committee advocates the Mediterranean and vegetarian diets as optimal dietary patterns for preventing common chronic non-communicable diseases [72]. For individuals who do not seek weight loss, the Mediterranean diets may still offer cardiovascular health benefits. In contrast, restrictive diets, such as low-fat diets and fasting, may inherently promote greater reductions in cardiometabolic risk factors by reducing overall energy intake. While the studies included in this analysis did not explicitly report energy intake, it is plausible that low-fat diets, due to their reduction in the consumption of calorie-dense fats, and fasting diets, by limiting the frequency of food intake, lead to lower caloric consumption, thereby enhancing their effectiveness in improving metabolic health. Therefore, the findings from our study suggest that both plant-rich diets and restrictive diets hold promise as viable dietary patterns for losing weight and improving cardiometabolic health. Combining plant-rich diets with calorie restriction may offer synergistic benefits, potentially amplifying their effectiveness in promoting weight loss and improving metabolic parameters. Future research, particularly RCTs, should investigate the long-term effects of these dietary patterns on clinical endpoints such as CVD events or mortality.

Limitations

First, our meta-analysis was restricted to clinical parameters due to the limited availability of fold change values for gut microbiota and the absence of their standard deviations. Consequently, we could only describe gut microbiota alterations focusing on the occurrence of changes and the frequency of significant alterations, rather than quantitatively assessing the absolute extent of microbiota changes. Second, the heterogeneity among the included studies in terms of interventions and control groups makes it challenging to provide definitive recommendations on which dietary pattern-induced gut microbiota changes are most advantageous for cardiovascular health. Third, our inclusion criteria limited studies to those published in English and Chinese, thus caution is advised when generalizing the conclusions.

Conclusion

Our systematic review highlighted the different impacts of plant-rich diets, restrictive diets, and polyphenol-rich diets on gut microbiota in individuals with established CVD or at risk of CVD. The plant-rich diets were shown to promote the proliferation of butyrate-producing bacteria Faecalibacterium prausnitzii and Roseburia. Butyrate plays an important role in inhibiting the development of various CVD and in reducing circulating cholesterol levels. It is promising to increase the production of butyrate through dietary interventions to reduce potential cardiovascular risks. Policymakers should prioritize and promote plant-rich diets through public health campaigns and nutritional guidelines. Furthermore, the restrictive diets showed more effective in improving cardiometabolic factors compared to the plant-rich diets. However, when using low-fat diets for CVD prevention, it is important to consider their potential limitations and long-term effects. Further research is warranted to elucidate the underlying mechanisms and investigate the long-term effects of dietary patterns on clinical endpoints such as CVD events or mortality.

Data availability

No datasets were generated or analysed during the current study.

Abbreviations

BMI:

Body mass index

CI:

Confidence interval

CVD:

Cardiovascular disease

DALYs:

Disability-adjusted life years

DBP:

Diastolic blood pressure

GRADE:

The Grading of Recommendations, Assessment, Development, and Evaluations

HDL-C:

High-density lipoprotein-cholesterol

LDL-C:

Low-density lipoprotein-cholesterol

SBP:

Systolic blood pressure

TC:

Total cholesterol

TG:

Triglycerides

References

  1. Roth GA, Mensah GA, Johnson CO, Addolorato G, Ammirati E, Baddour LM, et al. Global Burden of Cardiovascular Diseases and Risk Factors, 1990–2019. J Am Coll Cardiol. 2020;76(25):2982–3021.

    Article  PubMed  PubMed Central  Google Scholar 

  2. Yusuf S, Joseph P, Rangarajan S, Islam S, Mente A, Hystad P, et al. Modifiable risk factors, cardiovascular disease, and mortality in 155 722 individuals from 21 high-income, middle-income, and low-income countries (PURE): a prospective cohort study. Lancet. 2020;395(10226):795–808.

    Article  PubMed  Google Scholar 

  3. U.S. Department of Agriculture and U.S. Department of Health and Human Services. Dietary Guidelines for Americans, 2020–2025. 9th Edition. December 2020. Available at https://www.dietaryguidelines.gov/.

  4. Vaduganathan M, Mensah GA, Turco JV, Fuster V, Roth GA. The Global Burden of Cardiovascular Diseases and Risk. J Am Coll Cardiol. 2022;80(25):2361–71.

    Article  PubMed  Google Scholar 

  5. Kaminsky LA, German C, Imboden M, Ozemek C, Peterman JE, Brubaker PH. The importance of healthy lifestyle behaviors in the prevention of cardiovascular disease. Prog Cardiovasc Dis. 2022;70:8–15.

    Article  PubMed  Google Scholar 

  6. Arnett DK, Blumenthal RS, Albert MA, Buroker AB, Goldberger ZD, Hahn EJ, et al. 2019 ACC/AHA Guideline on the Primary Prevention of Cardiovascular Disease: a Report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines. Circulation. 2019;140(11):e596–646.

    PubMed  PubMed Central  Google Scholar 

  7. Krist AH, Davidson KW, Mangione CM, Barry MJ, Cabana M, Caughey AB, et al. Behavioral Counseling Interventions to Promote a Healthy Diet and Physical Activity for Cardiovascular Disease Prevention in Adults with Cardiovascular Risk Factors: US Preventive Services Task Force Recommendation Statement. JAMA. 2020;324(20):2069–75.

    Article  PubMed  Google Scholar 

  8. Rosato V, Temple NJ, La Vecchia C, Castellan G, Tavani A, Guercio V. Mediterranean diet and cardiovascular disease: a systematic review and meta-analysis of observational studies. Eur J Nutr. 2019;58(1):173–91.

    Article  CAS  PubMed  Google Scholar 

  9. Ramezani-Jolfaie N, Mohammadi M, Salehi-Abargouei A. The effect of healthy Nordic diet on cardio-metabolic markers: a systematic review and meta-analysis of randomized controlled clinical trials. Eur J Nutr. 2019;58(6):2159–74.

    Article  CAS  PubMed  Google Scholar 

  10. Chiavaroli L, Viguiliouk E, Nishi S, Blanco Mejia S, Rahelić D, Kahleová H, et al. DASH Dietary Pattern and Cardiometabolic Outcomes: an Umbrella Review of Systematic Reviews and Meta-Analyses. Nutrients. 2019;11(2):338.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Belli M, Barone L, Longo S, Prandi FR, Lecis D, Mollace R, et al. Gut Microbiota Composition and Cardiovascular Disease: a Potential New Therapeutic Target? Int J Mol Sci. 2023;24(15):11971.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Nesci A, Carnuccio C, Ruggieri V, D’Alessandro A, Di Giorgio A, Santoro L, et al. Gut Microbiota and Cardiovascular Disease: Evidence on the Metabolic and Inflammatory Background of a Complex Relationship. Int J Mol Sci. 2023;24(10):9087.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Kazemian N, Mahmoudi M, Halperin F, Wu JC, Pakpour S. Gut microbiota and cardiovascular disease: opportunities and challenges. Microbiome, 2020;8(1):36.

  14. Perler BK, Friedman ES, Wu GD. The Role of the Gut Microbiota in the Relationship Between Diet and Human Health. Annu Rev Physiol. 2023;85(1):449–68.

    Article  CAS  PubMed  Google Scholar 

  15. Rinott E, Meir AY, Tsaban G, Zelicha H, Kaplan A, Knights D, et al. The effects of the Green-Mediterranean diet on cardiometabolic health are linked to gut microbiome modifications: a randomized controlled trial. Genome Med. 2022;14(1):29.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Meslier V, Laiola M, Roager HM, De Filippis F, Roume H, Quinquis B, et al. Mediterranean diet intervention in overweight and obese subjects lowers plasma cholesterol and causes changes in the gut microbiome and metabolome independently of energy intake. Gut. 2020;69(7):1258–68.

    Article  CAS  PubMed  Google Scholar 

  17. Santos-Marcos JA, Haro C, Vega-Rojas A, Alcala-Diaz JF, Molina-Abril H, Leon-Acuña A, et al. Sex Differences in the Gut Microbiota as Potential Determinants of Gender Predisposition to Disease. Mol Nutr Food Res. 2019;63(7):e1800870.

    Article  PubMed  Google Scholar 

  18. Ghosh TS, Rampelli S, Jeffery IB, Santoro A, Neto M, Capri M, et al. Mediterranean diet intervention alters the gut microbiome in older people reducing frailty and improving health status: the NU-AGE 1-year dietary intervention across five European countries. Gut. 2020;69(7):1218–28.

    Article  CAS  PubMed  Google Scholar 

  19. Di Iorio BR, Rocchetti MT, De Angelis M, Cosola C, Marzocco S, Di Micco L, et al. Nutritional Therapy Modulates Intestinal Microbiota and Reduces Serum Levels of Total and Free Indoxyl Sulfate and P-Cresyl Sulfate in Chronic Kidney Disease (Medika Study). J Clin Med. 2019;8(9).

  20. Aromataris E, Lockwood C, Porritt K, Pilla B, Jordan Z, editors. JBI Manual for Evidence Synthesis. JBI; 2024. Available from https://synthesismanual.jbi.global. https://doiorg.publicaciones.saludcastillayleon.es/10.46658/JBIMES-24-01.

  21. Page MJ, Mckenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. 2021;372:n71.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Sterne J, Savović J, Page MJ, Elbers RG, Blencowe NS, Boutron I, et al. RoB 2: a revised tool for assessing risk of bias in randomised trials. BMJ. 2019;366:l4898.

    Article  PubMed  Google Scholar 

  23. Sterne JA, Hernán MA, Reeves BC, Savović J, Berkman ND, Viswanathan M, et al. ROBINS-I: a tool for assessing risk of bias in non-randomised studies of interventions. BMJ. 2016;355:i4919.

    Article  PubMed  PubMed Central  Google Scholar 

  24. Dersimonian R, Kacker R. Random-effects model for meta-analysis of clinical trials: an update. Contemp Clin Trials. 2007;28(2):105–14.

    Article  PubMed  Google Scholar 

  25. Schünemann HJ, Brozek J, Guyatt G, Oxman AD. GRADE handbook: introduction to GRADE handbook. 2013. https://gdt.gradepro.org/app/handbook/handbook.html#h.w6r7mtvq3mjz.

  26. Guevara-Cruz M, Flores-López AG, Aguilar-López M, Sánchez-Tapia M, Medina-Vera I, Díaz D, et al. Improvement of Lipoprotein Profile and Metabolic Endotoxemia by a Lifestyle Intervention that Modifies the Gut Microbiota in Subjects with Metabolic Syndrome. J Am Heart Assoc. 2019;8(17):e012401.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Guo Y, Luo S, Ye Y, Yin S, Fan J, Xia M. Intermittent Fasting Improves Cardiometabolic Risk Factors and Alters Gut Microbiota in Metabolic Syndrome Patients. J Clin Endocrinol Metab. 2021;106(1):64–79.

    Article  PubMed  Google Scholar 

  28. Haro C, Garcia-Carpintero S, Alcala-Diaz JF, Gomez-Delgado F, Delgado-Lista J, Perez-Martinez P, et al. The gut microbial community in metabolic syndrome patients is modified by diet. J Nutr Biochem. 2016;27:27–31.

    Article  CAS  PubMed  Google Scholar 

  29. Haro C, García-Carpintero S, Rangel-Zúñiga OA, Alcalá-Díaz JF, Landa BB, Clemente JC, et al. Consumption of Two Healthy Dietary Patterns Restored Microbiota Dysbiosis in Obese Patients with Metabolic Dysfunction. Mol Nutr Food Res. 2017;61(12):1700300.

  30. Haro C, Montes-Borrego M, Rangel-Zúñiga OA, Alcalá-Díaz JF, Gómez-Delgado F, Pérez-Martínez P, et al. Two Healthy Diets Modulate Gut Microbial Community Improving Insulin Sensitivity in a Human Obese Population. J Clin Endocrinol Metab. 2016;101(1):233–42.

    Article  CAS  PubMed  Google Scholar 

  31. Maifeld A, Bartolomaeus H, Löber U, Avery EG, Steckhan N, Markó L, et al. Fasting alters the gut microbiome reducing blood pressure and body weight in metabolic syndrome patients. Nat Commun. 2021;12(1):1970.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Muralidharan J, Moreno-Indias I, Bulló M, Lopez JV, Corella D, Castañer O, et al. Effect on gut microbiota of a 1-y lifestyle intervention with Mediterranean diet compared with energy-reduced Mediterranean diet and physical activity promotion: PREDIMED-Plus Study. Am J Clin Nutr. 2021;114(3):1148–58.

    Article  PubMed  PubMed Central  Google Scholar 

  33. van Trijp MPH, Schutte S, Esser D, Wopereis S, Hoevenaars FPM, Hooiveld GJEJ, et al. Minor Changes in the Composition and Function of the Gut Microbiota During a 12-Week Whole Grain Wheat or Refined Wheat Intervention Correlate with Liver Fat in Overweight and Obese Adults. J Nutr. 2021;151(3):491–502.

    Article  PubMed  Google Scholar 

  34. Vetrani C, Maukonen J, Bozzetto L, Della PG, Vitale M, Costabile G, et al. Diets naturally rich in polyphenols and/or long-chain n-3 polyunsaturated fatty acids differently affect microbiota composition in high-cardiometabolic-risk individuals. Acta Diabetol. 2020;57(7):853–60.

    Article  CAS  PubMed  Google Scholar 

  35. Choo JM, Murphy KJ, Wade AT, Wang Y, Bracci EL, Davis CR, et al. Interactions between Mediterranean Diet Supplemented with Dairy Foods and the Gut Microbiota Influence Cardiovascular Health in an Australian Population. Nutrients. 2023;15(16):3645.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Djekic D, Shi L, Brolin H, Carlsson F, Särnqvist C, Savolainen O, et al. Effects of a Vegetarian Diet on Cardiometabolic Risk Factors, Gut Microbiota, and Plasma Metabolome in Subjects with Ischemic Heart Disease: a Randomized, Crossover Study. J Am Heart Assoc. 2020;9(18):e016518.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Eriksen AK, Brunius C, Mazidi M, Hellström PM, Risérus U, Iversen KN, et al. Effects of whole-grain wheat, rye, and lignan supplementation on cardiometabolic risk factors in men with metabolic syndrome: a randomized crossover trial. Am J Clin Nutr. 2020;111(4):864–76.

    Article  PubMed  Google Scholar 

  38. Galié S, García-Gavilán J, Camacho-Barcía L, Atzeni A, Muralidharan J, Papandreou C, et al. Effects of the Mediterranean Diet or Nut Consumption on Gut Microbiota Composition and Fecal Metabolites and their Relationship with Cardiometabolic Risk Factors. Mol Nutr Food Res. 2021;65(19):e2000982.

    Article  PubMed  Google Scholar 

  39. Hald S, Schioldan AG, Moore ME, Dige A, Laerke HN, Agnholt J, et al. Effects of Arabinoxylan and Resistant Starch on Intestinal Microbiota and Short-Chain Fatty Acids in Subjects with Metabolic Syndrome: a Randomised Crossover Study. PLoS ONE. 2016;11(7):e0159223.

    Article  PubMed  PubMed Central  Google Scholar 

  40. Pagliai G, Russo E, Niccolai E, Dinu M, Di Pilato V, Magrini A, et al. Influence of a 3-month low-calorie Mediterranean diet compared to the vegetarian diet on human gut microbiota and SCFA: the CARDIVEG Study. Eur J Nutr. 2020;59(5):2011–24.

    Article  CAS  PubMed  Google Scholar 

  41. Petersen KS, Anderson S, Chen See JR, Leister J, Kris-Etherton PM, Lamendella R. Herbs and Spices Modulate Gut Bacterial Composition in Adults at Risk for CVD: Results of a Prespecified Exploratory Analysis from a Randomized, Crossover, Controlled-Feeding Study. J Nutr. 2022;152(11):2461–70.

    Article  PubMed  PubMed Central  Google Scholar 

  42. Roager HM, Vogt JK, Kristensen M, Hansen L, Ibrügger S, Mærkedahl RB, et al. Whole grain-rich diet reduces body weight and systemic low-grade inflammation without inducing major changes of the gut microbiome: a randomised cross-over trial. Gut. 2019;68(1):83–93.

    Article  CAS  PubMed  Google Scholar 

  43. Ahrens AP, Culpepper T, Saldivar B, Anton S, Stoll S, Handberg EM, et al. A Six-Day, Lifestyle-Based Immersion Program Mitigates Cardiovascular Risk Factors and Induces Shifts in Gut Microbiota, Specifically Lachnospiraceae, Ruminococcaceae, Faecalibacterium prausnitzii: a Pilot Study. Nutrients. 2021;13(10):3459.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Clark RL, Famodu OA, Holásková I, Infante AM, Murray PJ, Olfert IM, et al. Educational intervention improves fruit and vegetable intake in young adults with metabolic syndrome components. Nutr Res. 2019;62:89–100.

    Article  CAS  PubMed  Google Scholar 

  45. Kahleova H, Rembert E, Alwarith J, Yonas WN, Tura A, Holubkov R, et al. Effects of a Low-Fat Vegan Diet on Gut Microbiota in Overweight Individuals and Relationships with Body Weight, Body Composition, and Insulin Sensitivity. A Randomized Clinical Trial. Nutrients. 2020;12(10):2917.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Barber TM, Kabisch S, Pfeiffer AFH, Weickert MO. The Effects of the Mediterranean Diet on Health and Gut Microbiota. Nutrients. 2023;15(9):2150.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Chen R, Zhang C, Xu F, Yu L, Tian F, Chen W, et al. Meta-analysis reveals gut microbiome and functional pathway alterations in response to resistant starch. Food Funct. 2023;14(11):5251–63.

    Article  CAS  PubMed  Google Scholar 

  48. Kimble R, Gouinguenet P, Ashor A, Stewart C, Deighton K, Matu J, et al. Effects of a mediterranean diet on the gut microbiota and microbial metabolites: a systematic review of randomized controlled trials and observational studies. Crit Rev Food Sci Nutr. 2023;63(27):8698–719.

    Article  PubMed  Google Scholar 

  49. Hu X, Xia K, Dai M, Han X, Yuan P, Liu J, et al. Intermittent fasting modulates the intestinal microbiota and improves obesity and host energy metabolism. NPJ Biofilms Microbiomes. 2023;9(1):19.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Gorvitovskaia A, Holmes SP, Huse SM. Interpreting Prevotella and Bacteroides as biomarkers of diet and lifestyle. Microbiome. 2016;4(1):15.

    Article  PubMed  PubMed Central  Google Scholar 

  51. David LA, Materna AC, Friedman J, Campos-Baptista MI, Blackburn MC, Perrotta A, et al. Host lifestyle affects human microbiota on daily timescales. Genome Biol. 2014;15(7):R89.

    Article  PubMed  PubMed Central  Google Scholar 

  52. Biddle A, Stewart L, Blanchard J, Leschine S. Untangling the Genetic Basis of Fibrolytic Specialization by Lachnospiraceae and Ruminococcaceae in Diverse Gut Communities. Diversity. 2013;5(3):627–40.

    Article  Google Scholar 

  53. Martin R, Bermudez-Humaran LG, Langella P. Searching for the Bacterial Effector: the Example of the Multi-Skilled Commensal Bacterium Faecalibacterium prausnitzii. Front Microbiol. 2018;9:346.

    Article  PubMed  PubMed Central  Google Scholar 

  54. Hu T, Wu Q, Yao Q, Jiang K, Yu J, Tang Q. Short-chain fatty acid metabolism and multiple effects on cardiovascular diseases. Ageing Res Rev. 2022;81:101706.

    Article  CAS  PubMed  Google Scholar 

  55. Fusco W, Lorenzo MB, Cintoni M, Porcari S, Rinninella E, Kaitsas F, et al. Short-Chain Fatty-Acid-Producing Bacteria: Key Components of the Human Gut Microbiota. Nutrients. 2023;15(9):2211.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Liu Y, Jiang Q, Liu Z, Shen S, Ai J, Zhu Y, et al. Alteration of Gut Microbiota Relates to Metabolic Disorders in Primary Aldosteronism Patients. Front Endocrinol (Lausanne). 2021;12:667951.

    Article  PubMed  Google Scholar 

  57. Yoo JY, Sniffen S, Mcgill Percy KC, Pallaval VB, Chidipi B. Gut Dysbiosis and Immune System in Atherosclerotic Cardiovascular Disease (ACVD). Microorganisms. 2022;10(1):108.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Bultman SJ. Bacterial butyrate prevents atherosclerosis. Nat Microbiol. 2018;3(12):1332–3.

    Article  CAS  PubMed  Google Scholar 

  59. Chen W, Zhang S, Wu J, Ye T, Wang S, Wang P, et al. Butyrate-producing bacteria and the gut-heart axis in atherosclerosis. Clin Chim Acta. 2020;507:236–41.

    Article  CAS  PubMed  Google Scholar 

  60. Vacca M, Celano G, Calabrese FM, Portincasa P, Gobbetti M, De Angelis M. The Controversial Role of Human Gut Lachnospiraceae. Microorganisms. 2020;8(4):573.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Patikorn C, Roubal K, Veettil SK, Chandran V, Pham T, Lee YY, et al. Intermittent Fasting and Obesity-Related Health Outcomes: an Umbrella Review of Meta-analyses of Randomized Clinical Trials. JAMA Netw Open. 2021;4(12):e2139558.

    Article  PubMed  PubMed Central  Google Scholar 

  62. Ge L, Sadeghirad B, Ball G, Da CB, Hitchcock CL, Svendrovski A, et al. Comparison of dietary macronutrient patterns of 14 popular named dietary programmes for weight and cardiovascular risk factor reduction in adults: systematic review and network meta-analysis of randomised trials. BMJ. 2020;369:m696.

    Article  PubMed  PubMed Central  Google Scholar 

  63. Vadiveloo M, Parker H, Raynor H. Increasing low-energy-dense foods and decreasing high-energy-dense foods differently influence weight loss trial outcomes. Int J Obes (Lond). 2018;42(3):479–86.

    Article  CAS  PubMed  Google Scholar 

  64. Izadi V, Haghighatdoost F, Moosavian P, Azadbakht L. Effect of Low-Energy-Dense Diet Rich in Multiple Functional Foods on Weight-Loss Maintenance, Inflammation, and Cardiovascular Risk Factors: a randomized controlled trial. J Am Coll Nutr. 2018;37(5):399–405.

    Article  CAS  PubMed  Google Scholar 

  65. Schwingshackl L, Hoffmann G. Comparison of effects of long-term low-fat vs high-fat diets on blood lipid levels in overweight or obese patients: a systematic review and meta-analysis. J Acad Nutr Diet. 2013;113(12):1640–61.

    Article  PubMed  Google Scholar 

  66. Chawla S, Tessarolo SF, Amaral MS, Mekary RA, Radenkovic D. The Effect of Low-Fat and Low-Carbohydrate Diets on Weight Loss and Lipid Levels: a Systematic Review and Meta-Analysis. Nutrients. 2020;12(12):3774.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Kaiser KA, Brown AW, Bohan Brown MM, Shikany JM, Mattes RD, Allison DB. Increased fruit and vegetable intake has no discernible effect on weight loss: a systematic review and meta-analysis. Am J Clin Nutr. 2014;100(2):567–76.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Bendall CL, Mayr HL, Opie RS, Bes-Rastrollo M, Itsiopoulos C, Thomas CJ. Central obesity and the Mediterranean diet: a systematic review of intervention trials. Crit Rev Food Sci Nutr. 2018;58(18):3070–84.

    Article  CAS  PubMed  Google Scholar 

  69. Estruch R, Martínez-González MA, Corella D, Salas-Salvadó J, Fitó M, Chiva-Blanch G, et al. Effect of a high-fat Mediterranean diet on bodyweight and waist circumference: a prespecified secondary outcomes analysis of the PREDIMED randomised controlled trial. Lancet Diabetes Endocrinol. 2019;7(5):e6–17.

    Article  PubMed  Google Scholar 

  70. Satija A, Bhupathiraju SN, Spiegelman D, Chiuve SE, Manson JE, Willett W, et al. Healthful and Unhealthful Plant-Based Diets and the Risk of Coronary Heart Disease in U.S. Adults. J Am Coll Cardiol. 2017;70(4):411–22.

    Article  PubMed  PubMed Central  Google Scholar 

  71. Arabzadegan N, Daneshzad E, Fatahi S, Moosavian SP, Surkan PJ, Azadbakht L. Effects of dietary whole grain, fruit, and vegetables on weight and inflammatory biomarkers in overweight and obese women. Eat Weight Disord. 2020;25(5):1243–51.

    Article  PubMed  Google Scholar 

  72. Estruch R, Ros E. The role of the Mediterranean diet on weight loss and obesity-related diseases. Rev Endocr Metab Disord. 2020;21(3):315–27.

    Article  PubMed  Google Scholar 

Download references

Funding

This work was supported by the National Natural Science Foundation of China [grant numbers 72104051], China Medical Board Open Competition Program [grant numbers #20–372], and the Shenzhen Third People’s Hospital project (No. 21250G1001; No. 22240G1005; No. XKJS-CRGRK-008).

Author information

Authors and Affiliations

  1. School of Nursing, Fudan University, 305 Fenglin Road, Shanghai, China

    Junwen Yu, Yue Wu & Zheng Zhu

  2. Fudan University Centre for Evidence-Based Nursing: A Joanna Briggs Institute Centre of Excellence, Shanghai, China

    Zheng Zhu

  3. NYU Rory Meyers College of Nursing, New York University, New York City, NY, USA

    Zheng Zhu

  4. Department of Infectious Diseases, National Clinical Research Center for Infectious Diseases, the Third People’s Hospital of Shenzhen, 29 Bulan Road, Shenzhen, Guangdong, 518000, China

    Hongzhou Lu

Authors
  1. Junwen Yu
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Yue Wu
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Zheng Zhu
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Hongzhou Lu
    View author publications

    You can also search for this author inPubMed Google Scholar

Contributions

JY, ZZ, and HL designed research; JY and YW conducted research and analyzed data; JY and YW wrote paper; ZZ and HL reviewed and edited the paper. All authors have read and approved the final manuscript.

Corresponding authors

Correspondence to Zheng Zhu or Hongzhou Lu.

Ethics declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yu, J., Wu, Y., Zhu, Z. et al. The impact of dietary patterns on gut microbiota for the primary and secondary prevention of cardiovascular disease: a systematic review. Nutr J 24, 17 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12937-024-01060-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12937-024-01060-x

Keywords

Nutrition Journal

ISSN: 1475-2891

Contact us