Personalized Nutrition Through the Gut Microbiome in Metabolic Syndrome and Related Comorbidities

Population-based dietary guidelines are designed to improve human health at scale by targeting the “average” person within broad life-stage or sex categories [33]. This public-health logic is fundamentally different from personalized approaches, which start from the premise that interindividual variability in physiology, behaviors, and contexts, meaningfully shapes dietary response [34]. In this space, a widely used definition frames personalized nutrition “as the use of information on individual characteristics to deliver targeted advice, products, or services that facilitate sustained, health-relevant dietary change” [35].

Between generic guidelines and fully individualized prescriptions lies stratified nutrition, which tailors recommendations to subgroups (e.g., defined by phenotype, baseline risk, or other measurable characteristics) rather than “n = 1” designs, an increasingly relevant concept as dietary guidance evolves toward identifying population segments with distinct needs and response profiles. In parallel, precision nutrition is commonly used to emphasize the integration of multi-layer biological and behavioral data (multi-omics, clinical phenotypes, microbiome features, and digital measures) with analytic methods to generate recommendations that are more granular, dynamic, and potentially adaptative over time [35,36].

Operationally, “levels of personalization” can be organized as phenotype-based (clinical and biochemical traits), genotype-based (nutrigenetic/nutrigenomic information where evidence supports differential response), microbiome-based (composition and functional capacity), and digital/behavioral tailoring (preferences, barriers, context, and real-time behavioral/physiological signals) [37,38]. Importantly, the field’s credibility depends on demonstrating that these added layers improve prediction and, crucially, translate into durable behavior change and better outcomes; large pragmatic trials such as Food4Me [39] provide evidence that personalized advice can improve dietary behaviors compared with conventional guidance, while also underscoring the need for rigorous evaluation across populations and endpoints [39].

A precision nutrition architecture typically begins with host factors, spanning genetics/epigenetics and other omics, alongside clinical phenotypes that anchor recommendations in cardiometabolic risk, adiposity, and related traits [40]. Contemporary frameworks emphasize that these biomedical layers should not be treated in isolation [18]. They are most informative when combined with behavioral signatures and contextual determinants that influence both exposure (diet) and response (adherence and physiology) [18].

Microbiome adds an additional set of data layers that move from “who is there” to “what they can do” and “what they are doing.” In practice, 16S rRNA profiling provides taxonomic structure but limited functional resolution [41]. However, shotgun metagenomics improves taxonomic breadth and enables pathway-level functional inference; comparative work shows that 16S rRNA [42]. Metatranscriptomics extends this further by quantifying microbial gene expression (activity), and metabolomics captures downstream host-microbe co-metabolites that are often closest to mechanism and phenotype [43].

Finally, digital tools and artificial intelligence (AI) enable high-frequency, real-world measurement and iterative feedback [44]. They provide the substrate for machine-learning models that predict postprandial responses [45]. Evidence demonstrated that integrating clinical features, lifestyle data, and microbiome information can improve prediction of individualized glycemic (and broader metabolic) responses [45]. At the same time, systematic reviews highlight that model performance, generalizability, and clinical utility depend on transparent validation and careful feature selection, particularly when translating from controlled cohorts to diverse populations and settings.

Several observational and metagenomic studies indicates that obesity and MetS are accompanied by characteristic, albeit heterogeneous, alterations in gut microbiome composition [46,47,48]. A frequently reported feature is reduced microbial α-diversity, often interpreted as a loss of ecological resilience and functional redundancy, depending of the α-diversity metrics used [49].

With the intention to create a unique variable for disease, including obesity, the early work proposed an increased Bacillota/Bacteroidota ratio as a hallmark of obesity [50]. However, subsequent studies across different populations and sequencing platforms have yielded inconsistent results, with some reporting no differences or even opposite trends [50,51]. These discrepancies underscore that simple phylum-level metrics are unlikely to capture the complexity of obesity- and MetS-associated dysbiosis and that taxonomic shifts are context-dependent, influenced by diet, medication use, geography, and host genetics [52].

Regarding to species taxa, several recurrent patterns have been described [53,54,55,56], although not universally replicated. Results from several cohorts have shown that individuals living with obesity or MetS often show depletion of SCFA-producing species, such as Faecalibacterium prausnitzii [57] and certain Roseburia and Eubacterium species. In contrast, they also shown enrichment of genera like Collinsella, Blautia, or Prevotella [53,54,55,56]. These gut microbiome compositional shifts are related to adverse metabolic traits, including key components of MetS [58,59]. For example, microbiome profiles are enriched in lipopolysaccharide (LPS) biosynthesis pathways or branched-chain amino acid (BCAA) production. BA-modifying enzymes have also been associated with higher HOMA-IR, increased triglycerides, hepatic steatosis, and markers of vascular risk [58,59].

However, it is increasingly recognized that there is no single “obese” or “MetS” microbiome [60]. MetS microbial signature appears to be characterized by functionally convergent but taxonomically heterogeneous communities. Distinct microbial configurations may give rise to similar metabolic outputs (e.g., reduced butyrate production, increased endotoxin load or altered BA pools) [60,61]. Microbiome-based stratification may need to focus more on metabolic pathways and community functions than on the presence or absence of specific taxa.

Among the many determinants of gut microbiome structure, diet is arguably the most powerful and modifiable [104]. Long-term dietary patterns shape the overall community ecology. Moreover, short-term changes in energy intake or macronutrient distribution can induce rapid shifts in microbial composition and function [105]. Diets rich in plant-based foods and fermentable fibers generally increase microbial diversity and the abundance of SCFA-producing species. In contrast, diets high in saturated fat, refined carbohydrates, and low in fiber tend to relate to dysbiosis and pro-inflammatory profiles [106].

The Mediterranean diet has consistently been associated with increased microbial diversity, enrichment of butyrate-producing bacteria, reduced markers of gut inflammation and more favorable metabolic profiles in observational and interventional studies [107,108,109]. Conversely, Western-style dietary patterns have been linked to reduced SCFA production, increased LPS-producing bacteria, and BA profiles related to metabolic dysfunction [110].

Plant-based dietary patterns often promote species that participate in complex carbohydrate fermentation (e.g., Prevotella spp.), increase levels of SCFAs, and improve cardiometabolic markers [111]. Beyond macronutrient composition, fiber type and polyphenol content are important modulators of the microbiome. Different fibers (e.g., inulin-type fructans, resistant starch, β-glucans) select for distinct bacterial guilds with varying capacities to produce SCFAs and other metabolites. Polyphenol-rich foods (berries, cocoa, tea, coffee, extra-virgin olive oil) can exert prebiotic-like effects. Increasing these beneficial taxa and SCFA production while their microbial catabolites influence vascular and metabolic pathways [108,109,111].

More recently, attention has turned to ultra-processed foods (UPFs) as a potential disruptor of the diet–microbiome–metabolic axis. UPFs, typically energy-dense, fiber-poor, and rich in additives, are now major contributors to total energy intake in many countries and have been consistently associated with higher risks of obesity, T2D, and CVD [112,113]. Emerging evidence suggests that habitual UPF consumption is linked to reduced microbial diversity, depletion of beneficial commensals, increased gut permeability, and pro-inflammatory microbiota profiles. Providing a plausible mechanistic bridge between UPFs and cardiometabolic risk [113,114]. These effects may be mediated not only by nutrient composition but also by disruption of the food matrix and direct actions of additives on microbial communities and the intestinal barrier [113]. The main modulator effects of dietary patters on the microbiome are represented in Figure 2.

In the context of MetS, these data collectively support the view that diet is both a driver of dysbiosis and a primary lever for microbiome-targeted interventions. Understanding how specific dietary components and patterns reshape microbiome structure and function. Moreover, how these changes translate into metabolic outcomes, provides the foundation for developing microbiome-informed, personalized nutritional strategies in individuals with or at risk of MetS.

MetS management usually is based on lifestyle changes. Structured physical activity or physical exercise repeatedly showing clinically meaningful benefits across the main MetS domains [115,116,117]. Contemporary syntheses and clinical reviews consistently highlight improvements in insulin sensitivity and glycemic control, blood pressure, atherogenic dyslipidemia, and central and visceral adiposity [115,116,117]. Importantly, these improvements are observed across multiple exercise modalities (aerobic, resistance, and combined training), although the magnitude of benefit typically depends on baseline cardiometabolic risk, adherence, training volume, and whether concomitant dietary energy restriction is present [118]. Importantly, implementation in practice and even in many guideline-adjacent documents still tends to treat them as parallel “pillars” rather than as a single adaptive intervention [115,116,117]. From a mechanistic perspective, this separation is artificial: exercise modifies substrate flux, inflammation, gut motility, bile acid dynamics, and intestinal barrier physiology [115,116,117]. Each of which can plausibly influence microbial ecology and microbial metabolite production, creating a biologically coherent route linking physical activity to gut microbiome-mediated metabolic effects [119].

Intervention evidence in obesity/MetS-adjacent populations increasingly supports the idea that exercise, alone or combined with diet, can remodel gut ecology [143]. However, also makes clear that effects are often subtle, context-dependent, and require careful interpretation [143]. RCTs in adults with overweight/obesity demonstrate that structured exercise can shift beta-diversity and inferred functional potential, even when genus-level changes are limited [121,143,144]. This pattern suggests that exercise may primarily affect microbial “activity states” (functional capacity/expression) rather than producing large, consistent taxonomic turnover—an interpretation aligned with the observation that physiological improvements can occur in parallel with modest compositional changes [121,143,144]. Complementary controlled trials show that exercise-induced microbial changes can differ by obesity status and may revert when training stops. Moreover, they highlight the importance of adherence and long-term maintenance for durable microbiome modulation [122,144]. From a clinical perspective, this indicates that microbiome modulation should not be framed as an automatic consequence of prescribing exercise; it depends on sustained behavior change and may require complementary dietary design to support ecological stability [122,144].

Beyond exercise-only designs, combined lifestyle interventions provide a pragmatic template closer to real clinical care. In PREDIMED-Plus, a 1-year lifestyle intervention incorporating an energy-restricted Mediterranean diet and physical activity was associated with gut microbiota changes linked to SCFA-producing bacteria [140]. This is particularly relevant because it reflects a real-world intervention package where diet provides fermentable substrate and exercise may reinforce barrier and metabolic improvements, which are conditions expected to favor SCFA-related ecology [140]. A more recent RCT in the same framework has extended these observations to the gut metabolome and microbiota in relation to cardiometabolic risk factors [145]. The addition of metabolomic readouts is important because it enables testing whether microbiome changes translate into functional chemical outputs that plausibly mediate cardiometabolic improvements, rather than relying on taxonomy alone [145]. In metabolically compromised patients (NAFLD with prediabetes), a four-arm randomized controlled trial showed that the combined aerobic exercise + diet intervention was associated with diversified and stabilized keystone taxa and that baseline microbial network properties could help predict individual liver-fat response [141]. This is an important proof-of-concept for microbiome-informed stratification [141]. Critically, such results suggest that microbial network features (i.e., community connectivity/keystones) may provide more clinically useful “response biomarkers” than single taxa, because they capture ecological stability and resilience—properties likely relevant to long-term metabolic maintenance [141]. At the same time, network metrics can be sensitive to sequencing depth, compositionality, and analytic choices. Therefore, replication across cohorts and standardized network pipelines are essential before these approaches can be translated into clinical tools [141].

Taken together, these trials suggest three clinically relevant messages: (i) exercise can influence the gut microbiome in humans, (ii) the most translational signals may lie in functional/metabolite readouts and network properties rather than single taxa, and (iii) heterogeneity of response is not noise to be averaged away but a feature that precision lifestyle strategies should aim to explain and harness [122,140,141,145].

A consistent body of observational evidence indicates that dietary patterns linked to lower MetS risk [146]. In a large prospective analysis, Mediterranean-style diet adherence related to cardiometabolic outcomes varied according to baseline microbial composition [147]. This implying that a “one-size-fits-all” dietary recommendation may yield heterogeneous benefit partly due to differences in microbial functional potential (e.g., carbohydrate utilization, BA transformations, and other microbially mediated metabolic routes) [147].

Whole-diet interventions. Controlled dietary interventions provide stronger evidence that shifting dietary pattern can induce coordinated changes in gut microbiome structure and metabolic readouts relevant to MetS [148]. For example, switching to a Mediterranean diet has been shown to lower plasma cholesterol and reshape both the gut microbiome and metabolome [149]. Moreover, the diet-induced metabolic changes co-varied with specific microbial taxa and microbial metabolic outputs (including BA-related features) [149]. More “enhanced” Mediterranean variants (e.g., Green-MED) have further supported that microbiome features may partially mediate improvements in cardiometabolic risk markers. This reinforce the need to move from descriptive microbiome changes to mechanistically anchored mediators [150].

Specific components/supplements. A major strategy has been to increase fermentable substrates (prebiotics) and target SCFA production capacity. High-fiber dietary interventions can selectively promote SCFA-producing organisms and improve glycemic control in humans [151,152]. Resistant starch has also emerged as a promising substrate. In an 8-week supplementation trial in individuals with excess body weight reported improvements in insulin resistance alongside microbiome shifts, with Bifidobacterium adolescentis highlighted as a candidate taxon linked to benefit [153]. In a proof-of-concept randomized trial, pasteurized Akkermansia muciniphila supplementation in overweight/obese insulin-resistant adults was safe and showed directionally favorable metabolic signals versus placebo, bringing the field closer to organism-level, mechanism-driven interventions [154]. Evidence for conventional probiotics/synbiotics in MetS remains mixed but suggests modest improvements in selected cardiometabolic traits in meta-analytic summaries, tempered by strain specificity, short follow-up, and variability in endpoints and co-interventions [155,156]. Polyphenols, omega-3, and multi-component formulations are also being explored for microbiome modulation with cardiometabolic relevance. However, attribution to a single component is often limited by combined interventions and heterogeneous microbiome methods [157,158,159].

Advanced microbiome-based therapies. Fecal microbiota transplantation provides an informative “causal probe” in MetS. In a seminal randomized study, lean-donor intestinal microbiota infusion increased insulin sensitivity at 6 weeks in male recipients with MetS, with corresponding changes in microbial composition [160]. Subsequent work underscored the transient nature of benefit and the importance of baseline recipient microbiome configuration in predicting response, emphasizing that “donor–recipient matching” and ecological engraftment constraints are central barriers to reliable translation [161]. Newer trials testing adjunct strategies (e.g., fiber to support engraftment) reflect a pragmatic evolution toward combined, ecology-supportive protocols, but durability and scalability remain unresolved [162]. Table 1 summarizes the main effects of interventional studies regarding microbiome, exercise, and diet in patients with MetS.

The most mature “microbiome-informed personalization” paradigm has been the prediction of postprandial responses using integrated clinical, dietary, and microbiome features [28]. A landmark study demonstrated that machine-learning models incorporating microbiome data can predict individualized postprandial glycemic responses, and that algorithm-guided dietary advice can improve glycemic control compared with standardized guidance in controlled settings [163,164].

More recently, intervention studies have started to test “microbiome-aware” or multi-kingdom microbiome personalization approaches in dysglycemia/prediabetes—an adjacent phenotype tightly linked to MetS trajectories. For instance, microbiome features (gut and/or oral) have been integrated into dietary intervention frameworks, highlighting both predictive potential and the practical need for interpretable, clinic-friendly decision rules [165,166,167]. Overall, these trials position the microbiome not merely as a correlational marker but as a measurable layer that can (i) stratify responders, (ii) guide selection among dietary options (e.g., fiber types), and (iii) provide intermediate endpoints for monitoring adherence and biological effect—yet external validation across populations, labs, and diet cultures remains a key translational requirement.

Type 2 diabetes/prediabetes. Diet–microbiome interventions in dysglycemia provide some of the strongest proof-of-concept microbial functional targeting (especially SCFA-related ecology) [168]. However, generalization to broader MetS populations requires caution given differences in baseline phenotype and medication exposure [151]. Personalized nutrition algorithms leveraging the microbiome further support the feasibility of “response-guided” dietary prescriptions for glycemic control [169].

NAFLD/MAFLD. Microbial transformations of BA and signaling through FXR/TGR5 integrate with host lipid/glucose metabolism and inflammatory tone. This offers mechanistic targets for microbiome-informed dietary strategies [88,96,170]. Clinical lifestyle trials in NAFLD have reported microbiome rearrangements alongside improvements in hepatic steatosis-related measures, supporting the plausibility of microbiome-linked pathways in liver outcomes, although causal mediation remains incompletely established [171,172].

Cardiovascular disease. Microbiome-mediated metabolites provide a direct bridge from habitual diet to vascular risk biology [21]. The choline/carnitine/TMAO pathway, in particular, has been mechanistically tied to atherosclerosis-related processes and associated with cardiometabolic outcomes in prospective settings [173]. This makes it a prime example of a diet–microbiome–metabolite axis with potential utility for risk stratification and targeted dietary modification [174,175,176].

MetS is not a single biological entity but a syndrome-level label that aggregates distinct underlying pathophysiologies [177]. In practice, patients often cluster into partially overlapping tissue-dominant metabolic phenotypes [178]. Adipose dysfunction, hepatic insulin resistance/steatosis, or skeletal muscle insulin resistance may predominate [179], each with different biomarker profiles and potentially different dietary leverage points (e.g., macronutrient quality/quantity, energy restriction, or dietary fat composition) [180,181]. Evidence from long-term dietary interventions supports this concept: in the CORDIOPREV-DIAB randomized trial, baseline liver vs. muscle insulin-resistance phenotypes modified metabolic responses to different diet patterns over follow-up [182], illustrating why “one-size-fits-all” advice can yield heterogeneous results in MetS-like populations [183].

This heterogeneity is further amplified by the gut microbiome, where inter-individual differences in community structure and functional capacity can meaningfully shape metabolic responses to the same foods [184]. Large, deeply phenotyped studies of postprandial metabolism demonstrate that person-specific factors contribute to variability in glycemic and lipemic responses [17,35,184]. This reinforces the idea that microbiome-informed stratification could help explain non-response and guide more targeted dietary prescriptions.

Microbiome findings can vary substantially with choices across the analytic chain [185], sequencing platform and library preparation, reference databases, taxonomic/functional profiling tools, normalization, contaminant handling, and statistical models for differential abundance [186]. Comparative evaluations show that different differential abundance methods and pipelines can produce meaningfully different “discoveries” on the same underlying datasets [185,187,188]. These aspects directly impact biomarker credibility and downstream clinical claims. To address this, the field has increasingly emphasized standardized reporting and transparent methods [189]. The STORMS reporting guidelines were developed specifically to improve comparability and interpretability across human microbiome studies [189].

Many precision nutrition and microbiome intervention studies remain limited by small sample sizes, short follow-up, inconsistent outcome definitions, and limited replication and external validation [190]. Workshop-based and systematic syntheses highlight that robust translation will require better-powered studies, harmonized endpoints, and prospective validation in independent cohorts before clinical adoption can be justified [191,192].

Multi-omics profiling and continuous digital monitoring can be costly and logistically complex. In contrast, the clinical workforce is not uniformly trained to interpret omics-derived outputs or machine learning (ML)-based predictions [193,194]. Some reviews on the intersection of digital health and personalized nutrition repeatedly identify the need for user-friendly interfaces. Here, the important variables are related to clinical decision support, and clinician education so that precision recommendations are interpretable, actionable, and aligned with standard care pathways [30,193,195].

Ethically, the combination of omics data and high-frequency digital phenotypes (wearables and apps) raises non-trivial concerns around consent, data governance, secondary use, and privacy [196]. Recent reviews of AI-driven precision nutrition and digital-health ecosystems emphasize that privacy safeguards, transparency, and regulatory alignment must be treated as core design requirements rather than afterthoughts [196,197,198]. Particularly as commercial platforms increasingly mediate data capture and recommendation delivery.

Public microbiome resources and many precision nutrition datasets remain disproportionately drawn from Western, high-income settings [199]. This limits the portability of microbiome biomarkers and prediction models to populations with different ancestries, food environments, infectious exposures, and sociocultural contexts [200]. Quantitative audits of public microbiome data demonstrate strong geographic skew. Recent perspectives and large-scale efforts explicitly argue that underrepresentation constrains discovery and risks widening health disparities [201]. The field needs study designs that deliberately include diverse diets and contexts, invest in regional research capacity, and validate tools across settings—so that “precision nutrition” does not become “precision for the few”.

At present, clinically actionable use of microbiome science remains uneven across indications. The strongest evidence base and clearest care pathways are concentrated in selected gastrointestinal settings [19], particularly with recurrent Clostridioides difficile infection, where fecal microbiota-based therapies and microbiota restoration strategies have demonstrated clinical benefit and are increasingly reflected in clinical guidance and pivotal trials [202,203,204]. In contrast, for MetS, most outputs from 16S rRNA gene sequencing or metagenomics remain insufficiently validated for routine decision-making. This is emphasized by recent consensus efforts urging caution when translating microbiome test reports into clinical recommendations without rigorous validation and clear clinical action thresholds [205]. Consequently, when microbiome testing is obtained in MetS-like populations, results should generally be interpreted as hypothesis-generating and contextualized alongside diet quality, medication exposures (including antibiotics and acid-suppressing drugs), adiposity distribution, hepatic steatosis markers, and glycemic patterns, rather than used as stand-alone determinants of dietary prescriptions [205].

A feasible implementation pathway in cardiometabolic care requires moving beyond descriptive “dysbiosis” labels toward reproducible, function-centered outputs that can be audited clinically. First, microbiome measurement must become more reproducible through harmonized pre-analytics, sequencing, and bioinformatic workflows, and through transparent reporting standards; adoption of structured reporting frameworks such as STORMS is a necessary foundation to improve comparability and interpretability across human studies [189]. Second, methodological choices across the analytic chain can materially change results; comparative evaluations show that different differential-abundance methods and pipelines can yield meaningfully different “discoveries” on identical datasets, directly affecting biomarker credibility and downstream clinical claims [186]. Third, translation will depend on demonstrating incremental value over standard risk stratification using clinically meaningful endpoints (e.g., glycemic trajectories, blood pressure, lipids, hepatic fat, and weight maintenance), with external validation before adoption—an approach aligned with expert recommendations for clinical microbiome testing and interpretation [205]. Fourth, implementation should prioritize clinician-facing decision support that produces interpretable. Moreover, guideline-compatible recommendations rather than long lists of taxa reflect the broader consensus that clinical usefulness depends on actionable outputs with explicit uncertainty and validated thresholds [205].

Finally, feasibility, privacy, and equity must be treated as core design requirements. Public microbiome datasets are geographically skewed toward high-income settings, which constrains generalizability and risks widening disparities if biomarkers and models are deployed without validation in diverse ancestries, diets, and environments [201]. At the same time, microbiome-based precision approaches increasingly intersect with sensitive omics and digital phenotypes, raising privacy and governance challenges that require robust safeguards, particularly when data are handled through commercial or cross-institutional pipelines [206]. Together, these considerations reinforce that near-term progress in MetS will be driven less by additional associative findings and more by standardized measurement, rigorous validation, equity-conscious cohort building, and privacy-preserving translational infrastructure [186,189,201,205,206].

Not applicable.