Dietary interventions and the gut microbiota: a systematic literature review of 80 controlled clinical trials

Abstract

Background

The gut microbiota are increasingly recognised as key mediators of diet-related health outcomes. This systematic review primarily aimed to comprehensively evaluate associations between dietary interventions and gut microbiota in healthy and clinical adult populations, utilising controlled trials. Secondary outcomes included biological markers measured in blood, faecal, and urine samples.

Methods

A systematic literature search was conducted following the PRISMA guidelines, using the electronic databases PubMed, EMBASE, The Cochrane Central Register of Controlled Trials (CENTRAL), CINAHL (via EBSCOhost), and Scopus to gather controlled trials of dietary interventions that reported within-group changes from baseline for gut microbiota and biomarkers. Study screening, data extraction, and risk-of-bias assessment were conducted by two independent reviewers.

Results

Eighty studies met the eligibility criteria, trialling 13 distinct dietary interventions: Mediterranean, Western, Korean, Japanese, Nordic, low fermentable oligosaccharides, disaccharides, monosaccharides and polyols (FODMAP), ketogenic, gluten-free, calorie-restricted, macronutrient altered, animal- and plant-based, as well as diets high in functional nutrients and bioactive compounds. Dietary interventions were associated with within-group changes in taxonomy and particular biological markers; however, they were not consistently associated with changes in the alpha or beta diversity of the gut microbiota. The Mediterranean, Japanese, Korean, calorie-restricted, high-polyphenol, high-fibre, plant-based, low-fat, and low-protein diets were all associated with increased abundances of either short chain fatty acids (SCFA)- or lactic acid-producing bacteria or reduced abundance of opportunistic pathogenic bacteria. Some of these diets were also associated with reduced levels of inflammatory markers. In contrast, Western, animal-based, low FODMAP, ketogenic, and gluten-free diets were associated with a reduced abundance of SCFA-producing bacteria. There was substantial heterogeneity across studies in methodology and reporting.

Conclusion

This systematic review identifies differential associations between a wide range of dietary interventions and gut microbiota and inflammatory outcomes, both beneficial and adverse.

Graphical Abstract

Supplementary Information

The online version contains supplementary material available at 10.1186/s12967-025-07428-9.

Introduction

There has been increasing interest in modulating gut microbiota for disease prevention and treatment. Gut microbiota are relevant to chronic diseases and conditions including metabolic diseases [1], chronic inflammation [2], immune-mediated disorders [3], and neurological [4] and psychiatric disorders [5]. Of the many factors that influence gut microbiota, diet is arguably the most influential and highly modifiable [6], making it a key focus of interest in this context. Dietary interventions therefore are increasingly used as a means of modulating gut microbiota to improve physiological outcomes for a variety of health conditions. Examples include using a Mediterranean-style diet (characterised by high intakes of vegetables, wholegrains, fish and lean meat, and olive oil, and low intakes of saturated fat and sugars) to improve age-related frailty [7], or increasing dietary fibre or fermented foods to improve immune function [8] via modulating the gut microbiota composition.

The existing narrative and systematic reviews in this field have been important in establishing a foundational understanding of how nutritional intakes relate to and/or influence gut microbiota [2, 9, 10]. For example, components of the Mediterranean diet, and other healthful diets including fibrous fruit, vegetables and wholegrains, are posited to beneficially influence gut microbiota composition and function [11]. However, as far as an evidence base for informing dietary recommendations targeting aspects of gut microbiota goes, the literature is less informative, as many existing reviews pool comparison arms/groups with heterogeneous dietary characteristics. By pooling different comparison diets, the potential for confounding, exaggerating, diluting, or masking the true intervention effect is increased. For example, Kimble et al.’s systematic review concluded that not all clinical trials of Mediterranean dietary interventions produced consistent effects on gut microbiota [10]. Critically, this review [10]—and others [9, 12, 13]—compare particular dietary interventions/patterns against a heterogeneous group of comparison conditions that includes either a habitual diet, low-fat diet, vegetarian diet [10], or an unspecified comparison condition [13]. This has implications for interpretation of results and complicates research translation. In these cases, the effects on gut microbiota may vary depending on how different the intervention and comparison group dietary patterns are, and whether the comparison diets are close to the habitual diet for both groups. Moreover, the gut microbiota at baseline [14, 15] predicts the microbial response to a dietary intervention, further complicating between-group comparisons.

Ideally, evidence syntheses should compare trials of dietary interventions against a ‘sham’ or control condition that has neutral or no effect on gut microbiota. This is difficult in practice because any variation in the comparison diet would be expected to influence gut microbiota in some way. To date, a large majority of dietary interventions compare two different dietary interventions in the same trial, through parallel arm or cross-over study designs, as opposed to comparing one dietary intervention to an operationalised control diet [16–18] making it difficult to clarify the effects of each particular dietary intervention. Therefore, we believe that foundational knowledge about the effects of distinct dietary interventions on gut microbiota is currently lacking and needs to be established by evaluating and synthesising within-group changes in gut microbiota composition from baseline for each dietary intervention being trialled.

Therefore, we aimed to undertake a comprehensive, up to date systematic review to evaluate the associations between various dietary interventions and within-group changes in gut microbiota composition from baseline (alpha and beta diversity and taxonomic) in both healthy and clinical adult populations. Given that gut microbiota composition is related to a range of health conditions and related risk markers, we also aimed to synthesise associations between dietary interventions and change from baseline in biomarkers measured in blood, faecal, and urine samples in conjunction with gut microbiota. Thus, the primary aim of this systematic review was to assess and synthesise the associations between different dietary interventions and compositional changes in gut microbiota. The secondary aim was to assess the associations between different dietary interventions and changes in biological markers.

Methods

This systematic review, including the full search terms related to dietary interventions evaluated in a controlled trial setting and the gut microbiota as an outcome, was pre-registered with PROSPERO (CRD42022302125). The electronic databases PubMed, EMBASE, The Cochrane Central Register of Controlled Trials (CENTRAL), CINAHL (via EBSCOhost), and Scopus were searched. The search was performed on December 10, 2024, with all relevant articles published up until this date retrieved. Hand-searching of relevant published articles was performed to identify additional studies (The full Boolean search strings are available in Supplement 1). The primary outcomes evaluated in this review include the alpha and beta diversity of the gut microbiota and differences in taxonomic composition. Secondary outcomes include all reported biological markers measured in blood, faecal, and urine samples.

Inclusion/Exclusion criteria

Only articles published in English were included. Articles from conference proceedings, theses, reports, unpublished grey literature, and reviews were excluded. Studies were included if they met the following criteria for study design, intervention and comparator, and outcomes.

Study design

For this review, we focused on interventional studies, specifically randomised controlled trials (RCTs), with either cross-over designs, parallel designs, or quasi-RCTs (RCTs using pseudo-randomisation), as well as single-arm trials. This approach was used to minimise potential biases, such as recall bias, and to reduce other methodological errors often associated with the collection of dietary data in observational studies [19]. To be eligible, studies had to include healthy or clinical adult populations (aged ≥18 years) and report on within-group changes in gut microbiota outcomes. Studies that met these criteria and also reported changes in biomarker data were also eligible for inclusion.

Intervention and comparator

Studies assessing dietary interventions including, but not limited to, Western, vegetarian/plant-based, vegan, paleolithic, high- or low-carbohydrate (CHO)/protein/fat/fibre, ketogenic, gluten free, Mediterranean, low fermentable oligosaccharides, disaccharides, monosaccharides, and polyols (FODMAP), dietary approaches to stop hypertension, low-glycaemic index (GI), pro-inflammatory, anti-inflammatory, or calorie-restricted diets were deemed eligible. Studies that only reported interventions trialling specific foods or food groups were excluded, such as fermented foods, supplementary nutrition (e.g., whey/casein, multivitamins, Zinc, psyllium husk), or food/meal replacements (e.g., shakes, soup, snack bars). However, when studies encompassed whole-of-diet modification to increase functional nutrients such as fibre and polyphenols, these dietary interventions were considered eligible. Studies that reported interventions that combined diet with another lifestyle intervention (e.g., exercise) were excluded, unless the study also included a diet-only intervention arm. Additionally, studies that focused on intermittent fasting (this approach focuses on food intake within a restricted time period rather modifying the components of diet) or general nutritional counselling/education (e.g., advocacy on balanced diet) were excluded. Our study design considered the wide variation in control diets that may differ in composition, cultural context, and methodological rigor, making between-group comparisons difficult to interpret and potentially misleading, Therefore the baseline diet was considered the comparator/reference diet.

Outcome

This study was designed to describe within-group microbiota changes associated with dietary patterns. The primary outcome was the within-group changes (from baseline to end of intervention) in gut microbiota alpha and beta diversity, as well as taxa composition, and the secondary outcome was within-group changes in biomarkers in response to a specific dietary intervention.

Screening and data extraction

Search results were managed using Endnote (V20) and the web application Covidence. Studies retrieved using the search strategy were screened against study inclusion/exclusion criteria. Full text of these potentially eligible studies was independently assessed for eligibility by two reviewers and disagreement over the eligibility of studies was resolved through discussion with a third reviewer. Data were extracted by two independent authors using pre-piloted data extraction forms.

The data extraction form included study details (e.g., first author’s name, journal), trial design (e.g., duration, sample size), participant characteristics (e.g., age, health status), intervention/(e.g., mode of delivery, personnel delivering the diet, resources provided, components of the diet, adherence), other trial related information (e.g., baseline diet, details of ‘run-in’ periods), gut microbiota (baseline and follow-up microbiota data or change), gut microbiota analysing technique (e.g., 16S rRNA, metagenomics, hypervariable region), other measures relevant to gut microbiota (e.g., stool collection method, time points measured, DNA extraction method), biomarkers assessed in the study (e.g., short chain fatty acids (SCFA)).

Dietary intervention categorisation and microbial and biomarkers data synthesis

When a trial assessed more than one eligible diet (e.g., Mediterranean diet versus vegan diet), each intervention arm was treated as a separate study and was reported and synthesised individually. Thirteen dietary interventions were identified: Mediterranean, Western, Korean, Japanese, Nordic, FODMAP, ketogenic, gluten-free, calorie-restricted, macronutrient-altered, animal-based (deriving a substantial proportion of energy and nutrients from animal-derived foods), and plant-based diets (these included diets such as vegan (only plants), vegetarian, and lacto-ovo vegetarian diets), as well as whole-diet modifications aimed at increasing functional nutrients and bioactive compounds. Studies on calorie-restricted diets examined varied macronutrient composition (i.e., high/low CHO, high/low protein, and low-fat content). (Supplement 1: Table 1). Whereas interventions that examined various macronutrient-compositions (without calorie restriction) were categorised as macronutrient-altered diets. (Supplement 1: Table 1).

Within-group changes in microbiota diversity, taxa, and biomarkers were reported and synthesised separately for each dietary intervention (Supplement 2: Table 2). However, a data-driven categorisation approach was used for certain dietary interventions (i.e., calorie-restricted and macronutrient-altered diets) and outcomes (i.e., microbial diversity and biomarkers) due to the variations in the diet intervention categories and high heterogeneity in results reported across the included studies. When synthesising within-group changes for microbial taxa in response to calorie-restricted and macronutrient-altered diets, we followed these procedures: Calorie-restricted diets, studies examining very low-CHO and low-CHO calorie-restricted diets, were grouped as low-CHO calorie-restricted diets, while moderately high-protein and high-protein calorie-restricted diets were grouped as high-protein calorie-restricted diets. Similarly, for macronutrient-altered diets, studies examining very-low-protein and low-protein diets were grouped as low-protein diets, while moderate-fat and high-fat diets were grouped as high-fat diets. When synthesising within-group changes for microbiota diversity and biomarkers, we grouped all sub-categories of calorie-restricted diets (high/low CHO, protein, and fat) together due to insufficient outcome data in each individual category.

All within-group bacterial changes were reported for each study (Supplement 2: Table 2). To synthesise common directional trends in within-group bacterial taxa (this applied at all taxonomic level including phyla, family, genus, and species) in response to a dietary intervention, we applied the following criteria: For a given diet, 1) If > 50% of studies reported within-group increase or decrease in a particular taxon or genus, we coded that diet as increasing or decreasing that taxon; 2) If an equal number of studies reported within-group increases and decreases in a specific taxon, we based the direction of change on study characteristics such as study design, sample size, and intervention duration. For instance, if two studies for a given diet reported conflicting results - one reporting an increase and the other a decrease—the conclusion regarding the direction of association was determined based on the characteristics of the studies as detailed above.

Adherence

Adherence data were extracted and reported in Supplement 1, Table 1. We coded adherence as “Optimal” when studies reported at least > 75% of participants followed the interventional diet, with adherence tracked using reliable monitoring methods such as food records and dietary recalls.

Risk of bias assessment

The Cochrane Collaboration Risk of Bias tool was used to assess the risk of bias for RCTs [20], across five domains: risk of bias from randomisation, deviations of intended interventions, missing outcome data, measurement of outcome, and bias in reporting results. A scoring algorithm was used to classify the risk of bias for each domain as “high-risk”, “low- risk”, or “some concerns”. A study was judged as “low-risk”, when all five domains had low-risk of bias, whereas a study was judged as “high-risk”, when at least one domain out of the five domains had high-risk of bias or multiple domains with some concerns. For single arm trials, we rated items on the Joanna Briggs Institute Critical Appraisal Checklist for quasi-experiments as “Yes,” “No,” “Unclear”, or “Not applicable.” We adopted a previously used algorithm for rating the risk profile of studies, where the percentage of “Yes” scores (excluding the items that scored “not applicable”) was used to determine the risk categorised as follows: < 49%: high-risk, 50–69%: moderate-risk, and > 70%: low-risk [21]. Conflicting judgment for studies were resolved collaboratively.

Grade assessment

The Grading of Recommendations Assessment, Development and Evaluation (GRADE) Framework was used to appraise confidence in the body of evidence for the primary gut microbial outcomes [22], and was applied to dietary interventions with three or more studies evaluating the given outcome. One author (PH) conducted the assessment and three reviewed and checked for agreement (SLD, HA, SM). The certainty of evidence for an outcome was reduced for studies with high risk of bias, publication bias, inconsistency in outcomes, indirectness of measures, and imprecision where there were a low number of participants across studies.

Results

The search yielded 19,276 deduplicated articles (Fig. 1), which were screened against the predefined eligibility criteria. This resulted in 80 eligible studies evaluating 13 dietary interventions: Mediterranean, Western, Korean, Japanese, Nordic, low FODMAP, ketogenic, gluten-free, calorie-restricted, macronutrient-altered, animal- and plant-based diets, and whole-diet modifications aimed at increasing functional nutrients and bioactive compounds (i.e., psychobiotics, high-polyphenol, and high-fibre) (Supplement 1: Table 1).

Fig. 1.

PRISMA diagram

Study characteristics

Mediterranean diet

A total of 23 trials assessed the impact of a Mediterranean diet on the gut microbiota in participants aged 18–79 years [7, 16–18, 23–41]. Nineteen were RCTs [7, 16–18, 23, 24, 26–29, 32–41] and three were single-arm trials [25, 30, 31]. Of the RCTs, seven had a cross-over study design [16–18, 23, 24, 35, 37] and eight had a parallel study design [7, 26–29, 32–34, 36, 38–41]. Intervention lengths ranged from four days to one year, with most being conducted for more than four weeks. Details on baseline diet was reported in 14 trials, with13 trials reporting baseline diet as habitual diet [16, 26–33, 35, 37, 39, 41] and one trial as a balanced diet [23]. Nine trials (eight RCTs [7, 16–18, 24, 34, 38, 40] and one single-arm trial [25]) did not report details on baseline diet. Adherence to the dietary intervention was monitored in all studies, but only 60% (n = 14/23) of the trials [17, 23, 24, 29–34, 36, 37, 39–41] reported adherence rate, and all of these studies had an adherence rate > 75%.

Western, Korean, Japanese, and Nordic diets

Four RCTs assessed the impact of a Western-style diet on the gut microbiota in participants aged 18–65 years [18, 23, 33, 42]. Three had a cross-over study design [18, 23, 42] and one had a parallel study design [33]. Intervention lengths ranged from four days to eight weeks, with only two trials [23, 33] reporting balanced or habitual diets as baseline diets and the rest [18, 42] not providing details on baseline diet. Adherence to the dietary intervention was monitored in three trials [18, 23, 33], two of which [23, 33] reported an adherence rate of > 75% and the other trial did not report on adherence rate [18]. Three RCTs assessed the impact of Korean [42], Japanese [43], and Norwegian [44] diets on the gut microbiome in participants aged18–65 years.^. Two had a parallel [43, 44] and the other had a cross-over study design [42]. Intervention lengths ranged from four to 24 weeks. None of the trials reported details on baseline diet. Adherence to the dietary intervention was monitored in one study [43], though adherence rate was not reported.

Low FODMAP diet

A total of 13 trials assessed the impact of a low FODMAP diet on the gut microbiota in participants aged 18–83 years [45–57]. Of these, ten were RCTs [45–50, 52, 55–57] and three were single-arm trials [51, 53, 54]. Of the RCTs, two had a cross-over study design [47, 49] and eight had a parallel study design [45, 46, 48, 50, 52, 55–57]. Interventions length varied across trials from three to 12 weeks. Four trials reported habitual diet [47, 52, 54, 56] as the baseline diet, while others did not provide details on baseline diet. Adherence to the dietary intervention was reported in most of the trial [45–47, 50–55, 57] (n = 10/13); however, only four trials reported adherence rates, which were > 75% [46, 47, 50, 57].

Ketogenic diet

Five trials assessed the impact of a ketogenic diet on the gut microbiota in participants aged 18–57 years [38, 58–61]. Three were parallel-arm RCTs [38, 59, 61] and two were single-arm trials [58, 60]. Whilst all RCTs ran for 12 weeks, the single-arm trials ran for four [58] and [12, 60] weeks. Both single arm trials [58, 60] had a run-in baseline phase and monitored adherence, with both achieving adherence rates > 75%. However, information on baseline diet was provided by only one RCT (as habitual diet) [61]. Of the RCTs, two monitored adherence [38, 61], and only one RCT [61] reported an adherence rate, which was > 75%.

Gluten-free diet

Two single-arm trials assessed the impact of a gluten-free diet on the gut microbiota in participants aged 18–61 years [62, 63]. Both studies ran for four weeks. One study reported a habitual baseline diet [62] and the other study did not provide details on baseline diet [63]. Adherence to the dietary intervention was monitored in both studies but not reported.

Calorie-restricted diet

A total of 14 RCTs assessed the impact of a calorie-restricted diet on the gut microbiota in participants aged 18–75 years [64–77]. Ten RCTs had parallel designs [64, 65, 70–77] and four were cross-over trials [66–69]. The macronutrient profiles of the calorie-restricted diets varied, with half of the trials focusing on calorie-restricted diets with high/low-protein [66–71] and the rest on calorie-restricted diets with high/low-CHO [64, 65, 75, 76] and low-fat [65, 71, 75]. Macronutrient modifications of the calorie-restricted diets were not described in four trials [72–74, 77]. Intervention length ranged from three weeks to one year, with most conducted over four weeks. Two RCTs [65, 69] reported habitual diet as baseline diets, while three RCTs [66–68] had a baseline run-in weight maintenance diet, and the rest did not report details on baseline diet. Adherence to the dietary intervention was monitored in half of the trials [64, 65, 67, 71–73, 75–77] (n = 9/14), with three trials [64, 76, 77] reporting on adherence rate, (two [64, 76] had an adherence rate ≥75%, and one [77] reported an adherence rate < 75%). Additionally, one study reported an adherence rate > 75% but did not provide details on monitoring adherence [74].

Macronutrients altered diet

A total of 15 trials [16, 34, 35, 37, 74, 78–86] assessed the impact of diets with different macronutrient profiles (these diets did not include calorie restriction), including altered CHO, protein, and fat, on the gut microbiota in participants aged 18–79 years.

Four parallel-arm RCTs assessed CHO altered diets: one RCT [78] assessed a high-CHO diet with varying levels of GI (i.e., high-CHO, high-GI and high-CHO, low-GI), where each arm was reported as separate diets. Three RCTs assessed low-CHO diets [74, 79, 85]. Intervention length ranged from 12 to 24 weeks, with only two trials [78, 85] reporting details on baseline diet (one reported a high-fat run-in baseline diet [78] whereas the other reported a habitual diet [85]). Three RCTs [78, 79, 85] monitored adherence to the intervention and two of those [78, 85] reported on adherence rates, which was > 75%. Of note, although one study [74] did not provide information on monitoring adherence, it reported an adherence rate > 75%.

Four RCTs [35, 80, 81, 86] assessed protein altered diets: one study assessed a high-protein diet (cross-over design) [80], while others assessed a low-protein diet (parallel design) [81, 86] and a very-low-protein diet (cross-over design) [35]. Intervention length ranged from two to 24 weeks, with two trials [35, 80] reporting details on baseline diet as habitual and one reporting a balanced baseline run-in diet [86]. Although all trials monitored adherence to the intervention, none provided adherence rates.

Seven trials [16, 34, 37, 82–84, 87] assessed fat altered diets: two trials assessed high-fat diets (one parallel design RCT [87] and one single-arm trial [82]), one trial assessed a moderate-fat diet [87] (parallel design RCT), and six trials assessed low-fat diets (three parallel design [34, 83, 87] and two cross-over design [16, 37, 84] RCTs). Intervention length ranged from four weeks to one year, with four trials [37, 82–84] reporting habitual diets for baseline, with the rest not reporting details on baseline diet. All trials monitored adherence to the intervention but only four [34, 37, 82, 84] reported on adherence, and this rate was > 75%.

Animal- or plant-based diet

Seven trials assessed the impact of animal- or plant-based diets on the gut microbiome in participants aged 18–77 years [17, 88–93]. Animal-based diets were assessed in two RCTs (one cross-over [88] and one parallel [89] study design). Intervention length ranged from five days to four weeks. One trial reported a habitual plant-based diet [88] as baseline diet, while the other trial [89] had a omnivore run in-diet for baseline [89]. Only one trial monitored adherence, achieving a rate > 80%. Plant-based diets were assessed in four RCTs (three parallel [89, 90, 93] and one cross-over [17]) and two single arm trials [91, 92]. Intervention length ranged between four to 16 weeks. Three trials reported a habitual diet as baseline diet [90, 92, 93], one trial had a run-in baseline diet [89], and the rest did not report details on baseline diet [17, 91]. Whilst all RCTs monitored intervention adherence, only three reported adherence rates (this rate was > 75%) [17, 89, 93]. Neither of the single-arm trials [91, 92] reported on monitoring adherence nor adherence rate.

Functional nutrient rich diet

Five RCTs assessed the impact of whole diet modifications aimed at increasing functional/bioactive nutrients such as psychobiotics [94], polyphenols [95], and fibre [8, 96, 97] on the gut microbiome in participants aged 18–71 years. Four RCTs had a parallel design [8, 94, 96, 97] and one had a cross-over study design [95]. Intervention length ranged from four weeks to 24 weeks. Except one [96], all RCTs reported habitual diet as baseline diet. RCTs assessing psychobiotic [94] and high-polyphenol [95] diets monitored adherence, but only the high-polyphenol diet RCT [95] reported on adherence rate, which was ≥80%. Of the RCTs on high-fibre diets [8, 96, 97], only one reported on monitoring adherence [97]. One RCT [8] reported an adherence rate > 75%, without providing details on monitoring adherence.

Gut microbiota assessment

Except one study that used colonic mucosal tissue [83], all other studies used stool samples for bacterial genomic DNA extraction. A majority of the studies used 16S rRNA gene sequencing for gut microbiota profiling (n = 58/80) and the rest used either bacterial enumeration (culture dependant techniques) [27, 64], metagenomics [32, 36, 40, 41, 61, 74, 92, 94], qubit fluorometric quantitation and fragment analyser [46], polymerase chain reaction (PCR) [47, 51, 63, 72, 75, 77, 96], or fluorescence in situ hybridization (FISH) [52, 67, 78] (Supplement 2: Table 1).

Dietary interventions and the gut microbiota

Alpha and beta diversity

More than half of the studies (n = 57/80) reported on within-group changes in measures of alpha diversity, including diversity indices (i.e., Shannon, Simpson, Inverse Simpson, Fisher’s), richness (i.e., Chao1, Gini Simpson, Observed OTUs), evenness (i.e., Pielou’s evenness), and phylogenetic diversity (i.e., Faith’s PD) (Supplement 1: Table 2). Mediterranean [33], Korean [42], calorie-restricted [70, 71], and low-fat [83, 84, 87] diets were associated with an increased alpha diversity (Fig. 2). Low FODMAP [46, 48, 50, 53, 55–57], ketogenic [38, 59–61], and low-CHO [74, 79, 85] diets demonstrated inconsistent associations with alpha diversity indices, with some studies reporting a positive association, while others reporting an inverse association (Fig. 2). However, studies assessing Western [18, 23, 33, 42], gluten-free [62], high-protein [80], low-protein [35], animal- [88, 89] and plant [17, 89–93]-based, psychobiotic [94], high-polyphenol [95], and high-fibre [8, 97] diets reported no within-group changes in alpha diversity (Fig. 2). Studies that assessed Japanese [43], Norwegian [44], high-CHO [78], and high-fat [82, 87] diets did not report on alpha diversity.

Fig. 2.

Dietary interventions and alpha diversity. This figure summarises the number of studies reporting a within-group change in alpha diversity with each dietary intervention. The numbers on each colour coded stacked bar represent the number of studies reporting either no change, an increase, or a decrease in alpha diversity post intervention. The bar width of each bar indicates the proportion of studies corresponding to each dietary intervention out of the total

Approximately half of the studies (n = 45/80) reported on beta diversity using either unweighted UniFrac, weighted UniFrac, Bray-Curtis, Euclidean, or Jensen-Shannon distances (Supplement 1: Table 2). Of these, 31% studies (n = 14/45) reported a within-group change in beta diversity associated with a dietary intervention: Western [42], Korean [42], low FODMAP [53, 57], calorie-restricted [65, 71], ketogenic [58], low-CHO [74, 85], low-fat [84], animal- [88] and plant [91, 92]-based, and high-fibre [97] diets. Studies of Mediterranean [16–18, 23–25, 28–31, 34–38, 41], gluten-free [62], high-protein [80], low-protein [35], psychobiotic [94], and high-polyphenol [94] diets reported no within-group change in beta diversity. Studies that assessed Japanese [43], Norwegian [44], high-CHO [78], and high-fat [82, 87] diets did not report on any beta diversity indices Fig. 3.

Fig. 3.

Dietary interventions and beta diversity. This figure summarises the number of studies reporting a within-group change in beta diversity with each dietary intervention. The numbers on each colour coded stacked bar represent the number of studies reporting either a change or no change in beta diversity. The bar width of each bar indicates the proportion of studies corresponding to each dietary intervention out of the total

Taxonomy

Phylum level: Within-group changes in the abundance of four major bacterial phyla—Bacteroidetes (now Bacteroidota), Firmicutes (now Bacillota), Actinobacteria (now Actinomycetota), Proteobacteria (now Pseudomonadota), and Verrucomicrobiota—were reported in 48% (n = 39/80) of studies across 12 diets. These diets included: Mediterranean [25, 30, 31, 35, 38], Korean [42], Japanese [43], low FODMAP [45, 49–51, 53, 55], Ketogenic [38, 58–60], calorie-restricted [74](including low-CHO [65, 75] and low-fat [65] calorie-restricted), low-CHO [74, 79], low-protein [35, 86], low-fat [83, 84], high-fat [87], animal- [88], and plant-based [91]diets (Fig. 4).

Fig. 4.

Within-group phylum-level changes associated with each dietary intervention. The green boxes represent an increase in the abundance of a phylum, while the orange boxes represent a decrease in the abundance of a phylum

An increased within-group abundance of Bacteroidetes was associated with low-fat, high-fat, low-CHO calorie-restricted, plant-based, low-FODMAP, and ketogenic diets, while the Mediterranean, Korean, and low-CHO diets were associated with reduced abundance. An increased within-group abundance of Firmicutes was associated with low-fat, high-fat, low-CHO calorie-restricted, and Korean diets, whereas the Mediterranean, plant-based, low-FODMAP, ketogenic, calorie-restricted, low-fat calorie-restricted, and animal-based diets were associated with reduced abundance. An increased within-group abundance of Actinobacteria was associated with Mediterranean, low-protein, low-CHO, and low-CHO calorie-restricted diets but was associated with a reduced abundance following low-FODMAP, low-fat, ketogenic, calorie-restricted, and low-fat calorie-restricted diets. An increased within-group abundance of Proteobacteria was associated with Mediterranean, Japanese, and low-CHO calorie-restricted diets, while a reduced abundance associated with ketogenic and low-protein diets. Notably, an increased within-group abundance of Verrucomicrobiota was exclusively associated with the Mediterranean diet.

Family, order, and genus level: Within-group changes in the abundance of 39 bacterial families and/or orders were reported in 34% (n = 27/80) of studies (Supplement 2: Table 1). Within-group family level changes were reported in studies that assessed Mediterranean [16, 24, 26, 30, 31, 35, 40], Japanese [43], low FODMAP [50, 51], ketogenic [38, 59, 60], gluten-free [62, 63], calorie-restricted [73], low-CHO [65] and low-fat [65, 71] calorie-restricted, low-CHO [85], low-protein [35, 81], plant-based [91] and high-polyphenol [95] diets. Within-group order level changes were reported by studies that assessed Mediterranean [29], low FODMAP [54], ketogenic [38, 60], calorie-restricted [73], and low-protein calorie restricted [71] diets.

Within-group changes in the abundance of 60 bacterial genera were reported in approximately 92% of the studies (n = 74/80) across 12 diets: Mediterranean [16–18, 23, 24, 26, 28–31, 34, 37, 39], Western [18, 42], Korean [42], Japanese [43], low FODMAP [45, 47, 49–55], ketogenic [38, 58–60], gluten-free [62, 63], calorie-restricted [73, 77], low-CHO [64, 65], high-protein [66–71] and low-fat [65, 71] calorie-restricted diets, high-CHO [78], low-CHO [79, 85], high-protein [80], low-protein [35], high-fat [87], low-fat [16, 34, 37, 83, 84, 87], animal- [88, 89] and plant-based [17, 89, 92, 93], high-polyphenol [95], and high-fibre [8, 96, 97] (Supplement 1: Fig. 1). The genus Bacteroides was the most within-group upregulated genus, showing positive associations with five diets: Mediterranean, calorie-restricted, high-CHO, high-fat, and animal-based diets (Supplement 1: Figure 1). The genus Bifidobacterium was the most within-group downregulated genus, showing inverse associations with five diets: Mediterranean, low FODMAP, gluten-free, high-protein, low-protein, and high-protein calorie-restricted diets (Supplement 1: Figure 2).

Species level: Within-group changes in the abundance of 64 bacterial species were reported in approximately 43% of the studies (n = 35/80) across 10 diets. These diets included: Mediterranean [7, 23, 26, 30–34, 39, 41], low FODMAP [45–47, 49, 54], gluten-free [62, 63], calorie-restricted [74], high-protein [66–69, 71] and low-fat [71] calorie-restricted, high-CHO [78], low-CHO [74, 85], low-protein [35], low-fat [34], animal- [88] and plant-based [17, 90–92], psychobiotic [94], high-polyphenol [95], and high-fibre [96] diets. The Mediterranean and calorie-restricted (including high-protein and low-fat calorie restricted) diets were associated with a greater number of bacterial species, notably being associated with the abundance of 14 species that are related to SCFA production (Fig. 5). A predominant portion of these were butyrate producers including, Agathobaculum butyriciproducens, Anaerostipes hadrus, Faecalibacterium prausnitzii, Intestinimonas butyriciproducens, Eubacterium rectale, Roseburia sp. Ruminococcus bromii (Table 1). Additionally, the Mediterranean diet was associated with a reduced abundance of opportunistic pathogens that produce hydrogen sulphide and possess proinflammatory properties, such as Bilophila wadsworthia and Clostridium bolteae (Table 1).

Fig. 5.

Within-group changes of short-chain fatty acid-producing taxa associated with dietary interventions. Dietary interventions that did not report on a specific short chain fatty acid do not have corresponding colour coded bar

Table 1.

Bacterial species and their relevance to physiological health in relation to dietary interventions

Dietary patterns															Bacterial species	Basic characteristics	Associated physiological functions/health condition
Mediterranean diet	Low FODMAP diet	Gluten free diet	Calorie restricted (high-protein) diet	Calorie restricted (low-fat) diet	Calorie restricted diet	High-CHO diet	Low-CHO diet	Low-protein diet	Low-fat diet	Animal-based diet	Plant-based diet	Psychobiotic diet	High-polyphenol diet	High-fibre diet
[23] ↑															Agathobaculum butyriciproducens	Gram +, anaerobic, non spore-forming, non-motile	Produces butyrate. Neuroprotective [98] and cognitive enhancing [99] properties demonstrated in animals.
[23] ↑															Anaerostipes hadrus	Gram +, anaerobic, non spore-forming, non-motile	Produces butyrate. Possess biotin synthesis genes that are critical for immune modulation [100].
[7, 26, 34, 40] ↑	[49]	[63]				[78] ↑		[35] ↑	[34] ↑		[90] ↑		[95] ↑		Faecalibacterium prausnitzii	Gram +, anaerobic, non spore-forming, non-motile	Produces butyrate. Anti-inflammatory properties demonstrated in preclinical studies [101, 102].
[30] ↑															Prevotella stercorea	Gram -, anaerobic, non spore-forming, non-motile	Associated with dysbiosis and pro-inflammation in humans [103].
[32, 33] ↑	[47]														Akkermansia muciniphila	Gram -, anaerobic, non spore-forming, non-motile, mucin degrading	Produces acetate and propionate. Associated with improved IBD, metabolic syndrome [104] and alleviation of inflammation in animals [105].
[33] ↑															Intestinimonas butyriciproducens	Gram +, anaerobic, spore-forming, non-motile	Produces butyrate. Associated with improved insulin resistance and inflammation in humans [106].
[34] ↑									[34] ↑						Parabacteroides distasonis	Gram -, anaerobic, non spore-forming, non-motile	Produces acetate, succinate and secondary bile acids. Associated with improved metabolic dysfunction and reduction in weight gain in animals [107].
[26] ↓															Bilophila wadsworthia	Gram -, anaerobic, non spore-forming, non-motile	Produces H2S. Associated with systemic inflammation [108] and metabolic dysfunction [109] in animals
[31] ↓															Clostridium bolteae	Gram +, anaerobic, spore-forming	Produces H2S. Associated with IBD [110], proinflammation [111] and pathogenic [112].
		[63] ↓													Clostridium lituseburense	Gram +, anaerobic, spore-forming, non-motile	Associated with UC [113] and considered pathogenic [114].
								[35] ↑							Clostridium cadaveris	Gram +, anaerobic, motile	Non- pathogenic, associated with bacteremia [115].
							[85] ↓				[17] ↓				Clostridium sensu stricto 1	Gram +, anaerobic, spore-forming	Associated with intestinal inflammation, decrease in SCFA production and an opportunistic pathogen [116].
											[91] ↑				Clostridium spp	Gram +, anaerobic, spore-forming	Commensal and produces butyrate [117].
[33] ↓															Streptococcus gallolyticus	Gram +, anaerobic, non spore-forming, non-motile	Associated with bacteremia and endocarditis [118].
											[92] ↑				Streptococcus thermophilus	Gram +, facultative anaerobe, non spore-forming, non-motile	Produces lactic acid and used in food fermentation. Posses health promoting properties such as production of SCFA, GABA and bioactive peptides [119].
													[95] ↓		Streptococcus agalactiae	Gram +, facultative anaerobe	Known as Group B streptococcus. Associated with a range of clinical manifestation including bacteremia [120].
[7, 33] ↓															Ruminococcus torques	Gram +, anaerobic, non spore-forming, non-motile	Mucin degrader. Associated with IBD [121, 122].
	[47] ↓														Ruminococcus gnavus	Gram +, anaerobic, non spore-forming, non-motile	Produces acetate and formate [123]. Associated with Crohn’s disease and produces an inflammatory polysaccharide [124].
		[62] ↓	[69] ↑							[88] ↓					Ruminococcus bromii	Gram +, anaerobic, non spore-forming, non-motile	Produces butyrate. Degrades resistant starch [125] and contain anti-inflammatory properties [126].
			[66] ↑												Ruminococcus albus	Gram +, anaerobic, non spore-forming, non-motile	Produces acetate and formate. Cellulolytic bacteria and demonstrated neuroprotective properties in preclinical studies [127].
			[66] ↑												Ruminococcus bicirculans	Gram +, anaerobic	A new species belonging to Firmicutes [128]. Associated with increased level of faecal tryptophan in patients with colorectal cancer [129].
											[92] ↑				Ruminococcus lactaris	Gram +, anaerobic	Associated with acetate and butyrate production [130], autism spectrum disorder in children [131] and rheumatoid arthritis [132].
[33] ↓												[94] ↓			Coprococcus comes	Gram +, anaerobic	Produces butyrate, which is a immune regulator [133].
								[35] ↑							Coprococcus eutactus	Gram +, obligate anaerobic, non spore-forming, non-motile	Produces butyrate. Negatively associated with IBD in animals [134].
[33] ↓															Flavonifractor plautii	Gram +, anaerobic	Catabolises catechin. Associated with attenuating antigen induced immune response [135], inflammation associated with acute colitis [136] and arterial stiffness [137] in animal studies.
	[45] ↑														Mycoplasma hominis	Do not gram stain (cell wall absent), facultative anaerobic	An opportunistic pathogen and associated with clinical manifestations such as urogenital infections [138].
	[46] ↓							[35] ↑							Bifidobacterium adolescentis	Gram +, anaerobic, non spore-forming, non-motile	A lactic acid producing probiotic and possess a range of benefits ranging from metabolic to mental health [139].
	[46] ↓				[74] ↑		[74] ↑					[94] ↓			Bifidobacterium longum	Gram +, anaerobic, non spore-forming, non-motile	Produces lactic and acetic acid. Associated with improved IBD symptoms and possess anti-inflammatory and antioxidant properties [140].
								[35] ↑							Bifidobacterium pseudolongum	Gram +, anaerobic, non spore-forming, non-motile	Produces lactic and acetic acid. Associated with reduced plasma triglycerides [141] and proinflammation linked to colitis in animals [142].
		[63] ↑												[96] ↓	Escherichia Coli	Gram -, facultative anaerobic, non-spore forming, motile	Ferment lactose and produces indole from tryptophan [143]. Some strains are pathogenic and associated with clinical manifestation [144].
		[62] ↓						[35] ↑							Roseburia faecis	Gram +, anaerobic, motile	Produces butyrate and associated with improved IBS and colonic microinflammation in animals [145].
[7] ↑				[71] ↑											Roseburia sp	Gram +, anaerobic, motile	Dominant butyrate producers in human colon. Associated with regulating inflammation and reduced abundance is noted in inflammatory diseases [146].
											[92] ↑				Roseburia inulinivorans	Gram +, anaerobic, motile	Produces butyrate. Reduce abundance is noted in Crohn’s disease patients [147].
			[66] ↑												Sutterella wadsworthensis	Gram-, anaerobic	Associated with mild pro-inflammatory conditions [148].
			[71] ↑												Phascolaretobacterium succinatutens	Gram-, anaerobic, non spore-forming, non-motile	Associated with acetate and propionate production [149]. Converts succinate to propionate [150]. Reduced abundance noted in patients with Crohn’s disease [151].
			[71] ↑												Ruthenibacterium lactatiformans_	Gram-, obligate anaerobic, non spore-forming, non-motile	Produces lactate [152]. Associated with multiple sclerosis [153].
[7] ↑			[67, 68] ↓							[88] ↓		[94] ↓			Eubacterium rectale	Gram +, anaerobic, motile	Produces butyrate. Associated with better intestinal health [154].
[7] ↓			[68] ↓												Collinsella aerofaciens	Gram +, obligatory anaerobic	Increased abundance associated with obese and non-alcoholic fatty liver disease [155] and rheumatoid arthritis [156] patients.
			[71] ↓												Dielma fastidiosa	Gram-, anaerobic	Commensal bacterium. Some data show association with bacteraemia [157].
			[71] ↓												Phascolarctobacterium succinatutens	Gram-, anaerobic, non spore-forming, non-motile	Associated with acetate and propionate production [149]. Converts succinate to propionate [150]. Reduced abundance noted in patients with Crohn’s disease [151].
				[71] ↑											Duncaniella dubosii	Gram -, anaerobic, non spore-forming, non-motile	Novel species identified in faecal pellets of mice [158].
				[71] ↑											Alistipes inops	Gram -, obligate anaerobic, non spore-forming, non-motile	Belongs to a novel genus of bacteria isolated primarily from clinical population [159].
				[71] ↓											Erysipelothrix inopinata	Gram +, facultative anaerobic, non spore-forming, non-motile	Novel species isolated from vegetable broth [160].
					[74] ↑		[74] ↑								Prevotella ruminicola	Gram -, anaerobic, non spore-forming, non-motile	A ruminal proteolytic bacterium [161].
	[54] ↓										[90] ↓				Bacteroides fragilis	Gram -, anaerobic, non spore-forming, non-motile	Commensal bacteria and modulate inflammation through SCFA production such as propionate and anti-inflammatory polysaccharide A [162]. If entered blood stream causes clinically significant infection [163].
[30] ↑													[95] ↓		Bacteroides uniformis	Gram -, anaerobic, non spore-forming, non-motile	Produces propionate, lactate, acetate and succinate. Associated with alleviated colitis in animals [164] and reduced abundance is noted in patients with ulcerative colitis [165].
				[71] ↓											Bacteroides clarus	Gram -, obligate anaerobic, non spore-forming, non-motile	Produces acetate and succinate. Inversely associated with p-cresol (a molecule linked to negative health outcomes) in patients with renal disease [166].
			[67] ↓												Bacteroides spp	Gram -, anaerobic	Commensal microbes and produces SCFA. Possess multiple roles including protection from pathogens and provision of nutrients to other gut bacteria [167].
								[35] ↑							Blautia coccoides	Gram +, anaerobic, spore-forming, non-motile	Higher abundance noted in faeces of leucine deprived mice and associated with improved insulin sensitivity in mice [168].
								[35] ↑							Blautia hydrogenotrophica	Gram +, anaerobic, non spore-forming, non-motile	Dominant intestinal acetogen-producing acetate from pyruvate [169].
								[35] ↑				[94] ↓			Blautia obeum	Gram +, anaerobic, non spore-forming, non-motile	Associated with aggravated colitis and proinflammation in mice [170].
								[35] ↑				[94] ↑			Blautia wexlerae	Gram +, anaerobic, non spore-forming, non-motile	Associated with improved type 2 diabetes and obesity in both humans and mice [171].
											[92] ↑				Lactobacillus plantarum	Gram +, facultative anaerobic, non spore-forming	A probiotic bacterium. Produces lactic acid, acetate and succinate. Possess a range of health properties such as antioxidant, anti-inflammatory and anti-obesity [172].
											[92] ↓				Proteus mirabilis	Gram +, facultative anaerobic, non spore-forming, motile	Pathogenic and associated with urinary tract infection [173].
												[94] ↓			Dorea longicatena	Gram +, obligate anaerobic, non-spore forming, non-motile	Associated with obesity [174].
												[94] ↓			Gemmiger formicilis	Gram +, anaerobic, non-spore forming, non-motile	Produces butyrate and formate, which contains potential health benefits [175].
													[95] ↑		Anaerobutyricum hallii	Gram +, anaerobic, non-spore forming, non-motile	Commensal bacteria. Produces butyrate and propionate. Associated with transforming carcinogen to non-harmful compounds [176].
[39] ↓															Veillonella dispar	Gram -, anaerobic, non-spore forming, non-motile	Associated with various infections and implicated as pathogens in infections [177].
[39] ↓															Veillonella obetsuensis	Gram -, anaerobic	Possess pathogenic characteristics [39].
[39] ↓															Prevotella copri	Gram -, anaerobic, non-spore forming, non-motile	Possess pro-inflammatory properties [178].
[39] ↓															Streptococcus australis	Gram+, facultative anaerobic, non-spore forming, non-motile	Associated with tumour progression [179].
[40] ↓															Eubacterium ventriosum	Gram+, anaerobic, non-spore forming, non-motile	Associated with production of SCFA production [180].
							[85] ↓								Ruminococcus gauvreauii	Gram+, anaerobic, spore forming, non-motile	Decreased abundance is associated with advanced coronary artery disease [181].
							[85] ↑								Bacteroides thetaiotaomicron	Gram-, anaerobic, non-spore forming, non-motile	Possess protective effects against IBD [182].

GABA: gamma-aminobutyric acid; IBD: inflammatory bowel disease; SCFA: short chain fatty acids; UC: ulcerative colitis. ↑ represents an increase in the abundance of taxa, whereas ↓ represent adecrease in the abundance of taxa

The low FODMAP and gluten-free diets were associated with reduced abundances of SCFA- and lactic acid-producing species (e.g., Bifidobacterium adolescentis). Low-protein and plant-based diets were associated with increased abundances of SCFA- and lactic acid-producing species. However, low-protein diets were also associated with increased abundances of species such as Blautia obeum and Blautia wexlerae, which have proinflammatory properties. When considering whole-diet modifications to increase functional nutrients and bioactive compounds, the psychobiotic diet was associated with a reduced abundance of SCFA- and lactic acid-producing species. In contrast, the high-polyphenol diet was associated with an increased abundance of SCFA- and lactic acid-producing species and a reduced abundance of Streptococcus agalactiae, a commensal that is linked to bacteraemia [120] and known as Group B streptococcus (Table 1).

Dietary interventions and biomarkers

Of the 80 studies included in this review, 50 reported within-group changes in a range of biological markers in response to a dietary intervention. We classified these biological markers into six groups: stool organic acids, immune markers, glucose regulation markers, urinary metabolites, lipid markers, and other metabolites in blood/stool (Table 2).

Table 2.

Dietary interventions and biological markers

Dietary pattern	Stool organic acids	Blood immune markers	Glucose regulation markers	Lipid markers	Urinary metabolites	Other metabolites (blood/stool)
Mediterranean diet	✓	✓	✓	✓	✓	✓
Calorie-restricted diet	✓	✓	✓	✓		✓
High-fat diet	✓	✓	✓	✓		✓
Low-fat diet	✓	✓	✓			✓
Low-CHO diet			✓	✓		✓
Plant-based diet	✓		✓	✓
High-fibre diet	✓	✓	✓
Low FODMAP diet	✓	✓				✓
Ketogenic diet			✓	✓
High-CHO diet			✓	✓
Western diet					✓	✓
Korean diet					✓	✓
Low-protein diet			✓			✓
Psychobioitc diet					✓	✓
Japanese diet				✓
Gluten-free diet		✓
Animal-based diet	✓
High-polyphenol diet						✓

The tick represents the type of biological markers that were assessed for each specific dietary intervention in the included studies

Stool organic acids

Studies that assessed Mediterranean [16, 24, 26], low FODMAP [46, 49, 55], calorie-restricted [64, 66, 67, 69], low-fat [16, 84, 87], high-fat [87], animal-based [88], plant-based [17, 91], and high-fibre [8] diets reported within-group changes on a wide range of stool organic acids such as SCFAs, branched chain fatty acids (BCFAs), and bile acids (Supplement 1: Table 3).

Butyrate was the most reported organic acid across studies [16, 46, 64, 66, 67, 87, 91]. The Mediterranean [16] diet was associated with increased levels of butyrate. In contrast, low FODMAP [46], calorie-restricted [64, 66, 67], high-fat [87], and plant-based [91] diets were associated with reduced levels of butyrate. The association between the low-fat diets [87] and levels of butyrate was inconsistent between studies; of the two studies one reported a direct association [87] and the other an inverse association [16].

Notably, low FODMAP, calorie-restricted, and plant-based diets were associated with reduced levels of acetate, butyrate, and propionate, resulting in a reduction in the total SCFAs.

A high-fibre diet [8] was associated with reduced levels of branched-chain SCFAs such as iso-valarate, iso-butyrate, and valeric acid. An animal-based diet [88] was associated with increased levels of bile acids, while a high-fat diet [87] was associated with increased levels of saturated fatty acids, such as palmitic and stearic acids, as well as the polyunsaturated omega-6 fatty acid arachidonic acid (Supplement 1: Table 3).

Immune markers (blood)

Studies that assessed Mediterranean [7, 29, 40], low FODMAP [49], gluten-free [63], high-fat [87], low-fat [84], calorie-restricted [77], and high-fibre [96] diets reported on immune markers such as C-reactive proteins (CRP), interleukins (e.g., IL-6 and IL-8), and immune activation cells (Supplement 1: Table 4). Except for one study that reported an association between a high-fat diet and increased CRP levels, all other diets were associated with reduced levels of inflammatory immune markers such as CRP, IL-1, IL-6, IL-8, IL-10, Il-17, IFN- producing T cells, IFN-, TNF-, and immune activation cells (e.g., CD4+). Additionally, the Mediterranean diet was associated with increased levels of sGP130 [7] (a soluble glycoprotein linked to reduced inflammation [183]) and Axin 1 and Sirtuin 2 [40] (markers linked to protective immune pathways) (Supplement 1: Table 4). Furthermore, a calorie-restricted diet [77] was associated with reduced levels of bacterial markers related to inflammation, such as lipopolysaccharide, a cell wall component of gram negative bacteria, and trimethylamine-N-oxide, a bacterial metabolite.

Glucose regulation markers (blood)

Studies that assessed Mediterranean [25, 28, 34], ketogenic [38], calorie-restricted [69, 71, 73–75, 77], high-CHO [78], low-CHO [74], low-protein [86], high-fat [82], low-fat [34], plant-based [90, 91], and high-fibre [96] diets reported on markers related to glucose regulation. Blood glucose [28, 71, 73, 78, 91, 96] and insulin [69, 71, 74, 82, 96] levels were the most reported markers. Mediterranean, calorie-restricted, ketogenic, high-CHO, low-CHO, low-protein, plant-based, and high-fibre diets were associated with reduced levels of insulin resistance index [71, 74, 96], glycated haemoglobin (HbA1c) [25, 28, 38, 91, 96], homeostatic model assessment for insulin resistance (HOMA-IR) [25, 77, 86], glucose [28, 71, 73, 78, 91, 96], glucose-dependent insulinotropic polypeptide [69], and/or insulin [69, 71, 74, 75, 77, 96]. Mediterranean, low-fat, and plant-based diets were associated with increased insulin sensitivity index [34]/insulin sensitivity [25, 90]. A high-fat diet [82] was associated with increased levels of insulin (Supplement 1: Table 5).

Lipid markers (blood)

Studies that assessed Mediterranean [7, 26, 28, 29, 32, 33], Japanese [43], ketogenic [58, 60], calorie-restricted [64, 71, 73, 75, 77], high-CHO [78], low-CHO [74], high-fat [82], and plant-based [91] diets reported on a range of markers related to lipid metabolism (Supplement 1: Table 6). Mediterranean [26], ketogenic [60], calorie-restricted [71, 73], high-CHO [78], and plant-based [91] diets were associated with reduced levels of total cholesterol, whereas a high-fat diet [82] was associated with increased levels of total cholesterol. Additionally, Mediterranean [28], Japanese [43], and ketogenic [60] diets were associated with reduced levels of high-density lipoprotein (HDL) cholesterol, whereas Japanese [43], calorie-restricted [71], and plant-based [91] diets were associated with reduced levels of low-density lipoprotein (LDL) cholesterol. The Mediterranean [28], Japanese [43], ketogenic [60], calorie-restricted [64, 73, 75, 77], low-CHO [74], and plant-based [91] diets were associated with reduced levels of triglycerides. Both Mediterranean [7] and high-fat [82] diets were associated with increased levels of adiponectin. A ketogenic diet [58] was associated with reduced levels of ketone bodies, including acetoacetate and β-hydroxybutyrate (Supplement 1: Table 6).

Urinary metabolites

Studies that assessed Mediterranean [23, 26], Western [23, 42], Korean [42], and psychobiotic [94] diets reported on a range of urinary metabolites (Supplement 1: Table 7). The Mediterranean diet was associated with increased levels of microbial metabolites such as Trimethylamine N-oxide [23] and urolithins [26]. The Western diet [42] was associated with increased levels of taurine, hypotaurine, glyoxylate, dicarboxylate, and tricaroboxylic acid cycle metabolites. The psychobiotic diet [94] was associated with reduced levels of L- tryptophan and L-phenylalanine (urinary amino acids), and quinolinic acid (metabolite of tryptophan). The Korean diet [42] was associated with reduced levels of urinary pyruvate (metabolite of glycolysis) (Supplement 1: Table 7).

Other blood and stool metabolites

Studies that assessed Mediterranean [18, 35, 39, 40], Western [18, 42], Korean [42], low FODMAP [50, 51], calorie-restricted [67, 69, 71, 74, 76], low-CHO [74], low-protein [35, 81], high-fat [82, 87], low-fat [84, 87], high-polyphenol [95], and psychobiotic [94] diets reported on a range of metabolites, including microbial metabolites, hormones, and markers of intestinal permeability (Supplement 1: Table 8). The Mediterranean diet [18] was associated with increased levels of microbial metabolites such as hippuric acid and betaine. The Western diet [42] was associated with increased levels of metabolites related to lipid and amino acid metabolism such as 3-hydroxy butyrate, 2-hydroxy butyrate, acetate, and 3-hydroxy isovalerate. The Korean diet was associated with reduced levels of branched chain amino acids (BCAAs) such as leucine, isoleucine, and valine [42]. The low FODMAP diet [50, 51] was associated with increased levels of anti-inflammatory compounds such as p-hydroxy benzoic [184] and azelaic [185] acids and reduced levels of inflammatory markers such as histamine [186] and faecal calprotectin [187]. The calorie-restricted diets [69, 71] were associated with increased levels of ghrelin and reduced levels of leptin. A low-CHO diet [74] was associated with increased levels of acylcarnitine. Low-protein diets [35, 81] were associated with increased levels of bicarbonate and reduced levels of D-lactate. High-fat diets [82, 87] were associated with increased levels of indole and c-peptide, whereas a low-fat diet [87]was associated with reduced levels of stool p-cresol and indole. High-polyphenol [95] and psychobiotic diets [94] were associated with reduced levels of zonulin and metabolites of the kynurenine pathway such as 3-hydroxy kynurenine, respectively (Supplement 1: Table 8).

Quality assessment

Of the eligible studies, 83% (n = 67/80) were RCTs, with varying levels of risk of bias. Of these RCTs, 69% were rated as either “some concerns” (n = 25/67) or “high-risk” (n = 21/67), while the remaining 31% were rated as “low-risk” (n = 21/67) (Supplement 2: Table 3). A majority of the single-arm trials were rated as “low-risk” (n = 9/13), with the rest being rated as either “moderate-risk” (n = 3/13) or high-risk” (n = 1/13) (Supplement 2: Table 4).

Grade summary

The GRADE certainty assessment reported very low to low certainty across the gut microbial outcomes (Supplement 3: Table 1). The certainty of evidence was consistently highest for the Mediterranean diet, calorie-restricted diet, and plant-based diets, although this evidence was still rated as low certainty overall. Gluten free, Japanese, Nordic, Korean, and animal-based diets had an insufficient body of evidence to evaluate the certainty of evidence.

Discussion

This systematic literature review provides foundational knowledge on 13 distinct dietary interventions and their associations, and likely impact on, gut microbiota and biomarkers of health. Encompassing 80 interventional studies, it is the most comprehensive review to date. While all dietary interventions had differential associations with changes in gut microbiota (composition and diversity) and biological markers, we identified commonalities across some diets.

Diets based on geographical regions

In our study, we identified five diets rooted in distinct geographical regions: the Mediterranean (most common), Western, Japanese, Korean, and Nordic diets. The Mediterranean, Japanese, and Korean diets emphasise a high intake of vegetables, fruits, and whole grains, with fish and seafood as primary protein sources [188, 189]. Their key difference lies with their fat quantities and sources. The Mediterranean diet is rich in monounsaturated fats from olive oil, the Japanese diet uses small amounts of vegetable oils, and the Korean diet incorporates moderate amounts of vegetable oils along with fats from nuts and seeds. Each diet features fermented foods, but their focus varies. For example, the Mediterranean diet emphasises dairy ferments, such as yoghurt, the Japanese soy ferments such as miso, and the Korean diet a variety of vegetable-based ferments such as kimchi.

Despite these differences, the Mediterranean, Japanese, and Korean diets were associated with somewhat similar changes in gut microbiota composition. The Mediterranean diet was associated with an increase in the abundance of SCFA-producing bacteria (e.g., Agathobaculum butyriciproducens, Anaerostipes hadrus, Faecalibacterium prausnitzii), along with a consistent increase in SCFA levels in stool, particularly butyrate. SCFAs modulate inflammation by improving transepithelial resistance, modifying various signalling pathways, and inhibiting pro-inflammatory cytokines while up-regulating anti-inflammatory cytokines [190]. Concomitantly, the Mediterranean diet was associated with reduced levels of blood inflammatory markers (e.g., CRP, IL-1, IL-6, IFN-, TNF-) and immune activation cells (e.g., CD4+) and increased levels of sGP130, a molecule linked to attenuating inflammation [183]. Additionally, the Mediterranean diet was associated with a reduced abundance of opportunistic pathogens, notably Bilophila wadsworthia, which flourishes on saturated fats and is linked to colitis [191]. Given the low content of saturated fats and a high content of unsaturated fats in the Mediterranean diet, it is plausible that this dietary pattern creates conditions that inhibit the proliferation of such bacterial species. Additionally, the abundant prebiotic fibres in this diet are fermented by gut bacteria to produce SCFAs, which lower gut pH and create an environment that inhibits opportunistic pathogen colonisation [192].

Similarly, both the Korean and Japanese diets were associated with increased abundances of SCFA-producing bacterial genera such as Blautia, Coprococcus, and Sutterella. At the same time, these diets were associated with a reduced abundance of opportunistic pathogenic genera like Pseudomonas, likely due to the low intake of sugar/refined CHO and high intake of fermented foods that inhibit such bacteria [193, 194]. Additionally, the Korean diet was associated with reduced levels of BCAAs, which in elevated levels are associated with poorer metabolic health [195]. The Nordic diet, characterised by high intakes of fruits, cruciferous vegetables, whole grains, and low intakes of sugar and saturated fats, was trialled in only one study [44]. The Nordic diet showed no significant associations with gut microbiota changes, and the lack of information on the baseline diet and intervention adherence limits further interpretation.

Conversely, the Western diet—which is high in saturated fats, added sugars, ultra-processed foods, and lower in fibrous foods [196]—was associated with a reduced abundance of SCFA-producing bacterial genera, such as Butyricicoccus and Lachnospiraceae, and an increased abundance of opportunistic pathogenic genera such as Bilophila, Gemella, and Citrobacter. Furthermore, the Western diet was also associated with increased levels of 3-hydroxybutyrate, a ketone body indicative of increased fat metabolism [197], increased levels of biological markers linked to cancer and type 2 diabetes, such as 2-hydroxybutyrate [198], as well as increased markers of impaired metabolism like 3-hydroxyisovalerate, which is related to mitochondrial dysfunction [199].

In summary, the Mediterranean, Japanese, and Korean diets were associated with an increased abundance of SCFAs-producing bacteria and a reduced abundance of opportunistic pathogenic bacteria. Notably, the Mediterranean diet may have potential in reducing inflammation, as evidenced by its inverse associations with inflammatory marker profiles in blood. Indeed, it is used as an intervention for health conditions, such as cardiometabolic and neuropsychiatric disorders, where inflammation is central to disease onset and progression [200]. Meanwhile, a traditional Korean diet may hold potential for managing metabolic conditions, particularly concerning BCAA levels. The Western diet may undermine gut health as it is associated with reduced SCFA-producing bacterial genera, and increased opportunistic bacterial genera, while also being positively associated with biological markers of serious health risk.

Diets used for therapeutic purposes

We examined four therapeutic diets—low FODMAP, ketogenic, gluten-free, and calorie-restricted diets—that are utilised to manage various health conditions.

The low FODMAP diet is used to manage irritable bowel syndrome (IBS) by limiting fermentable CHO, including oligosaccharides (e.g., fructans), disaccharides (e.g., lactose), monosaccharides (e.g., fructose), and polyols (e.g., sorbitol) [201]; however, definitions and practice may vary depending on the geographical regions [202]. The ketogenic diet, high in fats and protein with very low CHO, shifts the body’s primary energy source from glucose to ketones. The ketogenic diet is used to manage seizures in epilepsy, with potential benefits also reported in other neuropsychiatric conditions [203]. The gluten-free diet, which is essential for managing coeliac disease, eliminates gliadin and glutenin protein-containing foods [204]. Despite their distinct applications, reduction in non-digestible or fermentable CHO is a common feature, which may have contributed to the reductions in fibre-metabolising, SCFA- and lactic acid-producing bacterial genera such as Bifidobacteria observed with these dietary interventions. Additionally, both the low FODMAP and gluten-free diets were associated with reduced levels of inflammatory markers in blood, potentially mitigating microscopic inflammation associated with some IBS-sufferers and inflammation associated with coeliac disease [205, 206]. Calorie-restricted diets are widely used for obesity management and weight loss. Our findings revealed that various calorie restriction strategies—high-protein, low-fat, or general calorie-restricted diets—were consistently associated with increased abundances of SCFA- and lactic acid-producing bacteria. However, the calorie-restricted diets were associated with reduced levels of overall SCFAs in stool, including butyrate and acetate, which aligns with the findings of a systematic review that showed reduced/unchanged levels of SCFAs with calorie-restricted diets. These findings were likely due to lower CHO intake, decreased energy harvest from diet, or increased mucosal absorption [207]. Notably, a high-protein calorie-restricted diet was associated with a reduced abundance of Collinsella aerofaciens, which is linked to obesity and non-alcoholic fatty liver disease [155], and increased levels of BCFAs such as iso-valerate and iso-butyrate, reflecting increased protein fermentation in the colon [208]. Calorie-restricted diets were also associated with improved glucose and insulin regulation, and reduced total and LDL cholesterol levels. Further, these diets were associated with increased ghrelin levels and reduced leptin and alanine aminotransferase levels [209], suggesting potential benefits to metabolic health [210] and liver function.

In summary, the low FODMAP, ketogenic, and gluten-free diets were associated with reduced SCFA-producing bacteria and reduced inflammatory markers. In contrast, calorie-restricted diets were associated with enhanced health-associated bacteria, lowered obesity-related taxa, and improved metabolic health markers.

Diets with altered macronutrient composition

Modifying the macronutrient composition of the diet, specifically CHO, protein, or fat can serve multiple purposes, depending on the individual’s metabolic profile and habitual diet. In our study, we identified studies that assessed diets with varying CHO, protein, and fat profiles and their associations with the gut microbiota across different health conditions.

For instance, in individuals at elevated risk for metabolic syndrome who were consuming a high-fat diet at baseline, a shift to a high-CHO diet, regardless of GI levels (i.e., high/low GI) was associated with increased health-associated taxa such as Faecalibacterium prausnitzii and Bifidobacterium and improved total cholesterol and glucose levels [78]. In individuals with type 2 diabetes and obesity, a low-CHO dietary intervention was associated with increased abundance of Bifidobacterium longum and Prevotella ruminicola, as well as improved plasma triglycerides, reduced insulin resistance, and increased levels of acylcarnitines [211].

High-protein diets were associated with an increased abundance of genera such as Lactobacillus, Lactococcus, and Streptococcus, likely reflecting elevated protein intake from foods rich in these bacteria [212] such as yogurt and cheese; these foods can promote their growth in the gut and alter the gut pH, favouring the growth of lactic acid bacteria such as Lactobacillus and Lactococcus. Moreover, high-protein diets were associated with reduced abundance of genera that thrive on fibre such as Roseburia and Anerostipes.

A low-protein diet, which is often used in kidney disease management [213] was also associated with increased abundances of butyrate- and lactic acid-producing bacteria such as Faecalibacterium prausnitzii and Bifidobacterium pseudolongum. These diets were also associated with reduced levels of protein-derived uremic toxins such as total and free indoxyl sulfate and P-cresyl sulphate that, when reduced, have beneficial effects, particularly in individuals with kidney dysfunction, as high levels are associated with adverse health outcomes [214].

A High-fat diet was associated with an increased abundance of Alistipes and Bacteriodes, and reduced abundance of Faecalibacterium, correlating with elevated levels of inflammatory markers like CRP and total cholesterol. Conversely, a low-fat diet was associated with an increased abundance of SCFA-producing bacteria such as Faecalibacterium prausnitzii [215] and Parabacteroides distasonis [107], both of which are associated with improved metabolic function [107, 215]. This was further reflected in changes in biomarker profiles associated with a low-fat diet such as a decrease in inflammatory molecules, including amyloid A and CRP [84].

In summary, varying macronutrient profiles were differentially associated with changes in gut microbiota and health markers: high- and low-CHO diets with increased health-associated SCFA-producing bacteria, and low-CHO diets improve metabolic parameters. High-protein diets reduced fibre-dependent bacteria, whereas low-protein diets promoted health-associated taxa and decreased uremic toxins. High-fat diets elevated inflammation, while low-fat diets may support metabolic health by increasing health-associated SCFA producers and lowering inflammatory markers.

Animal- or plant-based diets

Consuming an animal-based diet was associated with a reduced abundance of butyrate producing bacteria, such as Ruminococcus bromii and Eubacterium rectale. Conversely, a plant-based diet was associated with increased abundance of butyrate and anti-inflammatory bacteria; however, lower levels of SCFAs were found in stool samples associated with plant-based diets. This may be due to several factors, including increased absorption of SCFAs, slower transit times, or enhanced metabolism of SCFAs by both the gut microbiota and the host cells [216, 217]. Notably, the studies involved obese participants [17, 91], with one study [91] documenting weight loss during the plant-based intervention. While there is a lack of consensus regarding the relationship between weight loss and faecal SCFA levels, some evidence suggests that weight loss is associated with reduced levels of SCFAs [207], and this may have contributed to the observed findings. Additionally, a plant-based diet was associated with reduced triglycerides, total cholesterol, LDL cholesterol, and both fasting and postprandial glucose levels, along with reduced HbA1c levels indicative of improved blood glucose control [218]. As plant-based diets are higher in dietary fibre [219], these benefits may be partly explained by the role of fibre in slowing gastric emptying and modulating the release of digestion-related gut hormones [220]. Consistent with this we also found that high fibre diets were also associated with improvements in metabolic markers such as HbAc1 and glucose levels.

In summary, while animal-based diets were associated with reduced abundances of butyrate-producing bacteria, plant-based diets were associated with increased abundances, highlighting their potential in managing cardiometabolic health by improving lipid and glucose profiles.

Whole-of-diet modifications for increasing functional nutrients and bioactives

In our review, five interventions modified whole diets to increase functional nutrients and bioactives such as psychobiotics, polyphenols, and fibre, aiming to assess their impact on gut microbiota.

A psychobiotic diet incorporates foods that target gut microbiota [94], such as whole grains, prebiotic fruits and vegetables [(i.e. those that contain substrates such as oligosaccharides (e.g. fructooligosaccharides, inulin etc), phenolics or phytochemicals, that are utilised by host microorganisms to confer a health benefit) [221]], fermented foods, and legumes, and was associated with minimal changes in bacterial composition, notably demonstrating an association with reduced SCFA- and butyrate-producing bacteria. This diet was also associated with reduced levels of quinolinic acid, which is an N-methyl-D-aspartate agonist that can exert neurotoxic effects, disrupting neurotransmission in high concentrations [222].

A high-polyphenol diet, examined in one study, was positively associated with an increased abundance of butyrate-producing bacteria such as Faecalibacterium prausnitzii and Anaerobutyricum hallii, whilst being associated with reduced abundance of opportunistic pathogens such as Streptococcus agalactiae. Importantly, this diet was associated with reduced serum zonulin levels, which can indicate improved intestinal barrier function [223].

Two studies reported on a high-fibre diet and demonstrated an increased abundance of genera such as Lachonspira [224], which has been positively associated with high dietary fibre intake, as well as Lactobacillus and Bifidobacterium. Meanwhile, this diet was associated with reduced levels of BCFAs, suggesting reduced fermentation of non-fibrous compounds in the colon. Additionally, the high-fibre diet was associated with improved glucose regulation (e.g., HbAc1) [96] and reduced inflammatory markers (e.g., CRP, IL-6) [96] potentially through microbiota-related pathways or independently [225].

In summary, with the exception of the one trial of a psychobiotic diet, diets rich in functional nutrients and bioactives were associated with potentially beneficial changes in the gut microbiota. For example, polyphenol- and fibre rich diets were associated with increased abundance of health-associated taxa, as well as improved intestinal barrier functions and levels of glucose and inflammatory markers.

Dietary interventions are not strongly associated with diversity changes

The significance of alpha and beta diversities in understanding the intricate dynamics of the gut microbiota and its connection to health outcomes is of intense interest [226]. Higher alpha diversity is generally associated with better health outcomes in adults [227], as it suggests enhanced ecosystem resilience and metabolic versatility [228, 229]. However, this construct is contested [230], as higher alpha diversity is also associated with diseases such as bacterial vaginosis [231] and HIV/AIDS [232], emphasising its context-dependent nature and challenging its reliability as an indicator of well-being.

Our review did not find strong evidence for associations between dietary interventions and changes in diversity metrics, despite changes observed at various taxonomic levels. This may be due to a range of factors, including:

1. Specificity of microbial changes: Dietary interventions might selectively enhance or reduce the abundance of specific bacterial taxa without affecting the overall diversity of the gut microbiota. For example, a Mediterranean diet might enhance SCFA- producing bacteria while leaving other taxa unchanged, leading to differences in abundance metrics but stable diversity metrics.

2. Baseline gut microbiota and diet: Individuals possess a highly individualised or stable core gut microbiota, shaped by their habitual diet, which may confer resistance to changes in response to a novel dietary intervention [233]. For example, individuals with higher alpha diversity (i.e., greater functional redundancy) or specific bacterial taxa may respond differently to dietary interventions compared to those with lower diversity or different microbial profiles [233], resulting in differential responses to dietary interventions. Moreover, the lack of evidence for global changes in diversity observed in our review may partly be attributed to baseline diet. When the baseline diet closely resembles the interventional diet, the degree of change in the gut microbiota would be expected to be minimal.

3. Intervention duration and adherence: Changes in dietary patterns may take time to reflect in diversity metrics [14] and the level of adherence to a dietary intervention may also influence gut microbiota outcomes including diversity.

Strengths

Our study has several strengths. Firstly, we employed a comprehensive search strategy and conducted article screening, data extraction, and quality appraisal using two independent reviewers. Secondly, in contrast to the approaches used in previous systematic reviews [9, 12, 13], we defined the baseline diet as the comparator/reference. Reviewing within-group changes from baseline allows for a more individualised interpretation of results, taking into account the highly personalised nature of gut microbiota responses to dietary interventions. These responses are influenced by baseline gut microbiota composition, including functional redundancy and presence of specific bacterial species [234]. By applying a systematic approach to account for variability in dietary interventions and change from baseline gut microbiota responses across studies, we provide a more nuanced interpretation of how specific diets are associated with changes in gut microbiota diversity and composition. This is critical for advancing the field of interventional gut microbiota research.

Limitations in results interpretation

While this study employed a robust design, several limitations must be considered when interpreting the findings. These include limitations inherent to our review methodology, such as the focus on within-group rather than between group changes in microbiota, which prevents assumptions of causality, as well as the limitations of the individual studies included in this review.

First, there was significant variation in how adherence was reported across the included trials, with some detailing adherence monitoring and others not, complicating the interpretation of the relationship between dietary interventions and gut microbiota outcomes. Notably, all studies that reported adherence rates, had an adherence of 75% or above, except for one study that reported a poor adherence [77]. This discrepancy may suggest reporting bias, as trials with lower adherence rates might not have reported these data. Adherence to dietary interventions, and reporting on such, is crucial in gut microbiota studies as it ensures consistent dietary changes and enables accurate assessment of their impact on gut microbiota composition. Without proper adherence monitoring, dietary variations can confound results, leading to unreliable conclusions [235, 236].

Second, most studies lacked an a priori power calculation for gut microbiota outcomes. Of the 80 studies included in this review, only seven trials performed a power calculation to detect differences in gut microbiota composition or diversity, when gut microbiota was the primary outcome. In contrast, 24 trials had the gut microbiota as the primary outcome, but did not conduct a power calculation. In the remaining studies the gut microbiota was not the primary outcome and thus lacked a power calculation. The absence of power calculations can significantly undermine the validity of microbiota studies, increasing the risk of false positives and negatives [237]. Additionally, as clinical trials are typically powered to detect between-group differences in change rather than within-group changes, some of the studies included in our synthesis may have been underpowered. Consequently, true effects may have gone undetected, potentially leading to underestimation or inconsistency in the findings.

Third, the duration of the trials of included studies varied significantly. This makes it challenging to determine the optimal duration for dietary interventions to induce meaningful changes in the gut microbiota. For example, studies that ran for longer periods (e.g., six months, one year) showed minimal/no changes in diversity or composition [7, 44, 65], while others with shorter durations (e.g., one month) reported alterations in global microbiota composition and diversity [33, 42].

Finally, due to the high degree of heterogeneity across studies such as study population and methodology, a meta-analysis was not feasible. As such, we did not conduct formal publication bias tests, report pooled effect sizes, or perform heterogeneity statistics or sensitivity analyses. This limits the ability to quantify the magnitude, precision, and robustness of observed associations.

Future directions and conclusion

Through a rigorous study design and robust analytical methods, our review showed that dietary interventions are associated with significant changes at the taxonomic level and biological markers measured in stool, blood, and urine; however, dietary interventions are not associated with substantial changes in the overall diversity of the gut microbiota. The findings highlight the potential of diets such as the Mediterranean and plant-based diets to benefit the gut microbiota and other health biomarkers. This review has synthesised evidence on the potential impact of different dietary interventions on gut microbiota. This foundational knowledge is a prerequisite for more complex multi-arm trials that assess the effects of multiple dietary interventions on gut microbiota. Although our study demonstrated within-group changes in gut microbiota in response to specific dietary interventions, several key considerations must be addressed in future research. The variability in study designs of microbiota-dietary interventions presents significant challenges in synthesising and interpreting results. To address this, standardised methodologies for trials examining the effects of diet on gut microbiota are needed to enhance the reliability and comparability of findings. Additionally, personalised factors such as baseline diet and gut microbiota composition must be more carefully considered, as these influence how the core gut microbiota responds to dietary interventions. Therefore, future studies should employ adaptive, personalised trial designs to optimise dietary interventions aimed at improving health outcomes through targeting gut microbiota.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Abbreviation

FODMAP

Fermentable oligosaccharides, disaccharides, monosaccharides and polyols

SCFA

Short chain fatty acid

RCT

Randomised controlled trial

CHO

Carbohydrate

GI

Glycaemic index

CRP

C-reactive protein

HbA1c

Glycated haemoglobin

HOMA-IR

Homeostatic model assessment for insulin resistance

HDL

High-density lipoprotein

LDL

Low-density lipoprotein

BCAAs

Branched chain amino acids

Author contributions

HA, FNJ, SD, and WM conceptualised and developed the study protocol. HA led the project, including development of search terms, PROSPERO registration, article screening, data extraction, quality assessment, figure/table creation, manuscript drafting, revising for important intellectual content, and submission. SD and FNJ provided senior oversight and contributed to interpretation of results and drafting. GT, SD, and AJM contributed to drafting sections of the manuscript. GT, TD, CA, TJ, EG, NT, SM, AR, TR, AJM, and LJ contributed to article screening, data extraction, and quality assessment. ET assisted with visualisation. PH conducted the GRADE assessment and SLD, HA and SM reviewed the GRADE assessment and checked for agreement. GT, TJ, EG, WM, MB, AON, AJM, SD, and FNJ reviewed the manuscript for important intellectual content. All authors read and approved the final manuscript.

Funding

HA is supported by a Deakin University Dean’s Postdoctoral Research Fellowship. MB is supported by a NHMRC Leadership 3 Investigator grant (GNT2017131). WM is currently funded by an NHMRC Investigator Grant (#2008971). FNJ is supported by an NHMRC Investigator Grant (#1194982). SD is supported by a Medical Research Future Fund Grant (#2025947) and has received research funding from the Waterloo Foundation UK and The Western Alliance Australia. AJM is funded through the NHMRC supported CREDIT CRE, the Centre for Research Excellence for the Development of Innovative Therapies.

Declarations

Competing interests

FNJ has received industry support for research from Meat and Livestock Australia, Woolworths Limited, the A2 Milk Company, Be Fit Foods, and Bega Cheese, and travel support and speakers’ honoraria from Sanofi-Synthelabo, Janssen Cilag, Servier, Pfizer, Network Nutrition, Angelini Farmaceutica, Eli Lilly, Metagenics, and The Beauty Chef. She is on the Scientific Advisory Board of the Dauten Family Centre for Bipolar Treatment Innovation and Zoe Limited (non-paid). FNJ has written two books for commercial publication. All other authors declare no competing interests.