Precision nutrition through diet-gut microbiome interactions: Emerging insights driven by artificial intelligence, microbiome health metrics, and mechanistic modeling

Abstract

Diet-gut microbiome interactions drive substantial inter-individual variability in metabolic responses to food, a fact that challenges the efficacy of uniform dietary recommendations. To address this complexity, advances in multi-omics profiling, dietary assessment technologies, and host clinical phenotyping now generate high-resolution multimodal datasets. However, managing these vast amounts of data necessitates the integration of artificial intelligence (AI) and machine learning (ML) approaches. In this review, we first delineate the multimodal data landscape and its associated computational workflows. These range from the initial preprocessing of heterogeneous inputs (filtering, normalization, dimensionality reduction) to ML modeling strategies designed to address high dimensionality, sparsity, and compositionality through feature engineering and regularization. We then summarize core ML applications, including the classification of habitual dietary patterns from microbiome signatures, prediction of postprandial metabolic responses, responder stratification, and in silico simulation of dietary perturbations. Furthermore, recent randomized controlled trials demonstrate the tangible clinical potential of AI-guided personalization. Next, we highlight composite microbiome health metrics and diet-specific indices, such as GMWI2 and DI-GM. These tools are essential because they condense high-dimensional taxonomic profiles into interpretable wellness scores for monitoring diet-induced shifts. We subsequently examine genome-scale metabolic models and microbiome “digital twins” that mechanistically link dietary substrates to community metabolism and host-relevant metabolites. We also discuss emerging hybrid AI-mechanistic frameworks that enhance interpretability, biological plausibility, and scalability. Finally, we outline translational priorities—including the development of diverse longitudinal cohorts, standardized benchmarking, and clinically trustworthy AI—that are required to realize equitable, microbiome-informed precision nutrition.

1. Introduction

The complex interplay among diet, the gut microbiome, and human physiology—three deeply interdependent systems—underlies the substantial inter-individual variation observed in metabolic and clinical responses to the same dietary interventions.^1,2 Growing evidence suggests that much of this variability is driven by differences in baseline gut microbial composition, establishing the microbiome as a key modulator of dietary efficacy.³

Although the adult gut microbiome exhibits relative stability,⁴ it remains highly responsive to external influences, particularly diet.⁵ As a result, dietary patterns exert selective pressures on gut microbial communities, reshaping their composition and metabolic potential.⁶ For example, plant-based diets rich in dietary fiber foster expansion of saccharolytic taxa, thereby enhancing production of short-chain fatty acids (SCFAs) and other bioactive metabolites. In contrast, animal-based diets are often associated with proteolytic microbial pathways and elevated levels of amino acid-derived metabolites.⁵ These diet-induced changes occur across temporal and geographical scales, ranging from seasonal fluctuations observed in hunter-gatherer communities^7,8 to marked shifts following migration to industrialized settings.⁹ Moreover, controlled studies in gnotobiotic mice^10,11 and human feeding trials^12,13 further demonstrate that acute dietary shifts can remodel gut microbial composition and function within as little as one day.

These rapid adaptations carry profound functional consequences, as gut microbes are active partners rather than passive residents. They contribute to host metabolic homeostasis and immune regulation,^14,15 maintain intestinal barrier integrity,¹⁶ facilitate nutrient absorption,¹⁷ and support the synthesis of essential vitamins and amino acids.^18,19 Consequently, disruption of microbiota composition or function (or “dysbiosis”) has been implicated in the pathogenesis and progression of numerous metabolic, inflammatory, and even neuropsychiatric disorders.^{10,15,20‐23}

Within this framework, personalized nutrition extends beyond population-based dietary guidelines by tailoring recommendations to an individual’s biological, metabolic, and lifestyle characteristics to improve health outcomes.²⁴ A major hurdle persists in resolving the complex, non-linear relationships between diet, microbiome, and host physiology. Traditional statistical techniques are frequently inadequate for handling the high dimensionality, heterogeneity, and multi-omic structure characteristic of modern datasets. Consequently, advanced computational approaches are increasingly used to identify hidden patterns, integrate diverse data types, and develop robust predictive models that extend beyond the limits of classical analyzes.²⁵ Among these, artificial intelligence (AI) offers a powerful framework for fusing high-dimensional data, from microbial and clinical markers to granular dietary information to inform personalized interventions and data-driven decision-making.^26‐28

This review synthesizes recent progress in applying AI and related computational tools to characterize diet-gut microbiome interactions and their implications for human health. We outline the current multi-omic and multimodal data landscape, examine key clinical and research applications, and address persistent challenges and limitations. Finally, we identify future priorities for embedding these approaches within precision nutrition frameworks. Collectively, the evidence highlights how AI and machine learning are reshaping our understanding of diet-gut microbiome dynamics and enabling evidence-based, personalized strategies to promote metabolic resilience and long-term health.

2. Multi-omics and multimodal data in diet-gut microbiome research

2.1. Multi-omics profiling of gut microbiota

In microbiome research, multi-omics datasets typically encompass four complementary layers: metagenomics, metatranscriptomics, metabolomics, and metaproteomics (Figure 1). Together, these approaches enable integrated profiling of microbial taxonomy, gene expression, metabolite production, and protein abundance. Although each omics layer targets a distinct molecular endpoint, the overall experimental workflow is conceptually similar. In all cases, biological samples are processed to selectively extract the molecules of interest—such as DNA, RNA, metabolites, or proteins—followed by measurement using high-throughput technologies. These procedures generate quantitative profiles describing the abundance and composition of genes, transcripts, metabolites, or proteins within each sample, which form the basis for downstream bioinformatic and statistical analyzes. Detailed reviews of multi-omics applications in microbiome research, including best practices for study design and analysis, are widely available.^29‐31

Figure 1.

Multimodal data landscape and computational workflow in AI-driven diet-gut microbiome research. The schematic illustrates an end-to-end framework integrating dietary, host, and gut microbiome data to enable precision nutrition. (a) Data acquisition: The gut microbiome, diet, and host health are linked through a complex interplay. Dietary components are absorbed directly by the host or fermented by gut microbes, generating metabolites that influence host physiology. In turn, the host supplies endogenous substrates to the microbiome. (top) food data (self-reported recalls/questionnaires, digital tools, and emerging stool-based food-DNA inference); (middle) host data (clinical biomarkers, medication/supplement history, circadian context, daily activity, and omics-derived markers); (bottom) and gut microbiome multi-omics (metagenomics, metatranscriptomics, metaproteomics, metabolomics via next-generation sequencing (NGS) and LC/GC-mass spectrometry). (b) Preprocessing: Raw multi-omic and metadata inputs undergo filtering, transformation, normalization/scaling, imputation, data augmentation, feature selection/extraction, and dimensionality reduction. (c) Model training and tuning: multi-omic and multi-modal data are integrated into ML and DL models, enabling identification, classification, annotation, and prediction, as well as digital twin simulations based on genome-scale metabolic models (GSMMs). (d) Outcomes and applications: Trained models support latent pattern discovery and microbiome community characterization, prediction of host metabolic and inflammatory outcomes and disease risk trajectories, population stratification and phenotyping (e.g., responder vs non-responder classification), and personalized interventions, including adaptive dietary recommendations, meal planning, and targeted pre-/probiotic strategies.

Genomic sequencing remains the cornerstone for characterizing the gut microbiome and is divided into two primary strategies: amplicon-based and shotgun metagenomic sequencing. Amplicon-based approaches target the bacterial/archaeal 16S rRNA gene or the eukaryotic 18S rRNA gene. These genes contain conserved regions (C1-C10) shared across microorganisms alternated with variable regions (V1-V9) that evolve over time. The conserved regions enable the design of broadly applicable primers, while the variable regions provide the taxonomic and phylogenetic signal used to distinguish microbial taxa. In contrast, Whole Genome Shotgun (WGS) metagenomics enables a more comprehensive assessment by untargeted sequencing of all DNA present in a sample. This approach achieves species- and often strain-level resolution,^32‐34 enables direct reconstruction of microbial gene catalogs,^35,36 and facilitates de novo assembly of draft genomes.^37,38

Both approaches have distinct advantages and limitations.^30,39 Amplicon sequencing is comparatively low-cost, scalable to large cohorts, and supported by well-stablished pipelines, making it suitable for profiling microbial community composition across many samples.⁴⁰ However, it is susceptible to primer bias and PCR amplification artifacts, exhibits uneven detection of taxa with variable gene copy numbers, and provides limited insight into functional potential.^40‐44 In contrast, WGS enables functional pathway inference, detection of viruses and fungi, and improved taxonomic resolution. Because all genetic material in a sample is sequenced, host DNA contamination represents a limitation not only at the analytical level but also at the sample level. High host-to-microbial DNA ratios—particularly in low microbial biomass samples—can substantially reduce microbial signal and, in some cases, render specimens unsuitable for sequencing. In addition, WGS metagenomics incurs higher sequencing costs, greater computational demands, dependence on reference databases for taxonomic and functional annotation, and analytical challenges related to uneven coverage, and complex assembly and binning steps.^45‐47 Importantly, both amplicon-based and WGS data are inherently compositional, such that observed relative abundances are constrained by total sequencing depth. To address this limitation, a range of compositional data analysis techniques, normalization strategies, and differential abundance frameworks have been developed specifically for this type of data.⁴⁸

While genomic sequencing captures the repertoire of genes present in a microbial community, it does not reflect transcriptional activity. Metatranscriptomics overcomes this limitation by profiling RNA to characterize time- and condition-dependent gene expression. Despite its promise, metatranscriptomics faces multiple obstacles, such as RNA instability, challenges associated with ribosomal RNA depletion, ongoing host RNA contamination, variable extraction efficiency, and difficulties in assigning transcripts to specific taxa due to gene homology.^49‐51 As a result, careful experimental and analytical protocols are essential for reliable data replication and interpretation.

Metaproteomics adds an additional layer of resolution by quantifying proteins, thereby linking transcript abundance to functional output shaped by post-transcriptional regulation, translational efficiency, and protein turnover. Metaproteomic methods based on mass spectrometry (MS) further facilitate detection of microbial enzymes, structural proteins, and host-microbial interaction factors, yielding insights into active metabolic pathways and functional states within the gut microbiome.^52‐56 However, the application of metaproteomics in gut microbiome research remains limited by the proteome’s broad dynamic range, incomplete reference databases, peptide-to-taxon ambiguity, and contamination from host proteins.⁵⁷

Metabolomics captures the diverse repertoire of small-molecule metabolites produced by the gut microbiota using MS-based platforms or nuclear magnetic resonance spectroscopy. These metabolites exert wide ranging effects on host physiology, including maintenance of intestinal barrier integrity, immune regulation, and control of lipid and glucose metabolism.^58‐61 A key strength of metabolomics is its ability to reveal functional outputs that are not strictly linked to microbial taxonomic composition, reflecting functional redundancy and metabolic plasticity within microbial communities.^62,63 Accordingly, stable microbial composition does not necessarily imply stable metabolic activity, and clinically relevant differences can occur without clear changes in community structure.^64,65 Despite these insights, metabolomic analyzes remain challenging to interpret, as metabolite profiles reflect integrated signals from microbial activity, host metabolism, diet, and environmental exposures,⁶⁶ and are further affected by technical variability and incomplete annotation in current platforms and databases.⁶⁷

Although each omics layer yields valuable insights, none provides a comprehensive perspective in isolation. Integrative analyzes are limited by differences in scale, data sparsity, temporal resolution, and noise. Moreover, incomplete and inconsistent metadata in public repositories can compromise reproducibility and introduces the risk of confounded cross-omics associations, underscoring the need for FAIR (Findable, Accessible, Interoperable, Reusable)-compliant metadata standards.⁶⁸

2.2. Dietary intake assessment methods

Accurate dietary assessment is a fundamental yet enduring challenge in diet-gut microbiome research. Conventional approaches rely primarily on self-reported instruments, including food frequency questionnaires (FFQs), 24-hour dietary recalls, and food diaries, which are scalable but susceptible to recall errors, reporting biases, portion misestimation, and high participant burden.^69,70 Their limitations are further affected by the vast diversity of global dietary patterns. Although major nutrient databases such as the U.S. Department of Agriculture’s FoodData Central⁷¹ and Canadian Nutrient File⁷² now catalog hundreds of thousands of food items, many bioactive food components remain poorly characterized or unquantified.⁷³ More objective methods, such as weighed records or image-assisted logging, can improve measurement precision but are labor-intensive and impractical for large cohorts.

AI is increasingly transforming dietary assessment. AI-based food image recognition systems—including NutriNet,⁷⁴ goFOOD,⁷⁵ the Mobile Food Record,⁷⁶ and related smartphone apps^77,78—automate meal segmentation, food identification, portion estimation, and nutrient mapping. These tools reduce participant burden and allow more frequent assessment of dietary intake. Additionally, food diary data can be mapped to nutrient composition tables to generate structured nutrient profiles,⁷⁹ although cross-database inconsistencies, regional food variability, and labeling discrepancies continue to limit comparability.

Biomarker-based validation provides an independent means of assessing dietary intake. Established markers include urinary nitrogen for estimating protein intake,⁸⁰ skin carotenoid levels for fruit/vegetable consumption,⁸¹ and stable carbon and nitrogen isotope ratios measured in hair or blood for animal protein⁸² or added sweeteners.⁸³ Despite its promise, metabolomics-based dietary assessment is restricted by the lack of specificity of many biomarkers.⁸⁴ For instance, metabolites such as carotenoids or vitamin C are shared across numerous fruits and vegetables, making it difficult to unambiguously attribute signals to individual food or dietary components.⁸⁵

An innovative approach to overcome the challenges inherent to self-reported dietary assessments involves using fecal metabolomics,⁸⁶ metaproteomics⁸⁷ or sequencing of food-derived DNA from stool to infer dietary intake directly. Computational tools like FoodSeq⁸⁸ (targeted metabarcoding of food-specific markers) and MEDI⁸⁹ (untargeted detection of food-derived reads in shotgun data) identify residual plant and animal DNA originating from consumed foods, providing an objective, sample-linked readout of dietary exposure. Complementing these molecular approaches, AI-based methods such as METRIC (Microbiome-based Nutrient Profile Corrector) use gut microbiome composition to statistically reduce random error in self-reported nutrient intake profiles.²⁷ Rather than replacing dietary records, METRIC denoises them, leveraging microbiome signals as a biological reference to improve the reliability of estimated nutrient intake. These approaches help detect foods or nutrient patterns that may be missed or underreported, providing dietary information that is often unavailable in retrospective analyzes.⁹⁰ Yet several important limitations remain, including variable persistence of food-derived DNA through digestion, incomplete reference databases, and inability to quantify portion size or habitual patterns. These emerging techniques are most effective when integrated with traditional self-reported data and established biomarkers.

2.3. Host physiological and clinical data types

Beyond microbiome composition and dietary intake, host physiological and clinical data provide essential context for understanding individualized responses to diet. These variables encompass a wide spectrum, from high-dimensional host omics to standard clinical and phenotypic measures—such as anthropometrics (e.g., BMI, waist-to-hip ratio), blood biomarkers, medication history, and lifestyle factors (e.g., physical activity, sleep). Integrating host features with microbiome and dietary information creates a more comprehensive view of the inter-individual variability that ultimately shapes dietary outcomes.⁹¹

Models that incorporate host variables alongside diet and microbiome profiles consistently outperform those based on diet or microbiome data alone, as demonstrated, for example, in the prediction of postprandial glycemic responses (PPGR), where inclusion of host variables improves the accuracy of predictive algorithms.⁹¹ Host factors directly reflect underlying metabolic status; individuals with insulin resistance or elevated fasting glucose, for instance, typically exhibit larger post-meal glucose excursions.⁹² Demographic attributes (age, sex) and lifestyle covariates (physical activity, medication use) further modulate these responses and are therefore routinely included as key predictors in algorithmic frameworks.

Despite their clear utility, host-derived measurements share many of the analytical challenges seen in microbiome and dietary data, including heterogeneity in scale and type, measurement error, mismatched temporal resolution, and substantial inter-individual variability. Addressing these substantial challenges is essential to support reliable multimodal integration and realizing truly personalized nutritional insights.

3. Machine learning and deep learning applications in diet-gut microbiome interactions

Diet-gut microbiome research increasingly relies on computational frameworks that draw from machine learning, deep learning, and mechanistic modeling. Given the interdisciplinary nature of this field, key terminology from artificial intelligence and predictive modeling is briefly summarized in Box 1 to provide conceptual clarity for readers from diverse backgrounds.

Diet-gut microbiome research yields high-dimensional and multimodal datasets characterized by non-linear and context-dependent interactions among diet, microbial communities, and host physiology. Although traditional statistical techniques are well suited for hypothesis testing, they often struggle to model complex biological systems, particularly when developing generalizable predictors across diverse individuals. Many classical models rely on linear assumptions, treat features independently, and require pre-specified hypotheses, which limits their ability to capture nonlinear relationships and complex interactions.⁹³ Moreover, these approaches are sensitive to high dimensionality, sparsity, and collinearity, and they often fail to account for substantial inter-individual variability in the microbiome.

As a result, the domain has pivoted toward AI solutions, specifically machine learning (ML) approaches. ML refers to a class of algorithms that learn patterns directly from data to make predictions or decisions without being explicitly programmed with fixed rules. These algorithms ranging from ensemble-based methods to deep learning (DL) architectures excel at fusing heterogeneous data modalities and maximizing performance on held-out samples.²⁹ Within this framework, DL represents a specialized subfield of ML that uses multi-layer neural networks to learn hierarchical feature representations directly from high-dimensional inputs.^94,95 The key difference lies in DL’s ability to discover intricate structures in complex datasets by using backpropagation to adjust internal parameters, thereby computing representations in each layer from the previous one.^94,96 This process enables end-to-end learning of hierarchical representation directly from raw data, without the need for manual feature engineering. Broader applications of ML in microbiome science have been extensively reviewed elsewhere.^25,97,98

In diet-gut microbiome research, ML has enabled several transformative applications (summarized in Table 1). First, supervised and unsupervised ML models have successfully classified habitual dietary patterns directly from taxonomic or functional gut microbiome profiles,^99‐102 revealing reproducible microbial signatures of long-term intake. For example, gut microbiomes differentiated vegan, vegetarian, and omnivore diets (mean AUC = 0.85), showing consistent taxonomic shifts: omnivores were enriched in bile-tolerant and protein-fermenting taxa, vegans in fiber-degrading and SCFA-producing bacteria, and vegetarians in dairy-associated microbes.⁹⁹ Second, integrative models combining dietary logs with baseline microbiome data reliably predict individual metabolic biomarkers. These include postprandial metabolic responses—most robustly glycemic excursions^{2,91,103‐108} but also lipidomic^2,107 and inflammatory markers in select contexts. These models consistently outperform diet-only or microbiome-only baselines by capturing person-specific interactions that drive variable responses to identical meals.^91,103

Table 1.

Representative applications of machine learning in diet-gut microbiome research.

Research theme	Representative application	Machine learning approach	Data type	Key findings	References
Habitual diet shapes gut microbiome composition	Identification of diet-associated microbial taxa and functional pathways across large population cohorts	RF classification and regression [87,99,107] Regularized regression [109] Unsupervised clustering [110,111,110,111]	Host data/dietary data/ Metagenomics [99,107,109,110] (or Metabolomics [111]) (or Metaproteomics [87])	Long-term dietary patterns strongly determine gut microbiome composition. Diets high in processed and animal-derived foods are associated with enrichment of bile-tolerant and pro-inflammatory taxa [99,107,109,110], alongside increased endotoxin synthesis pathways [109,110]. In contrast, plant-based and vegan patterns correlate with higher abundances of fiber-degrading, short-chain fatty acid-producing taxa and butyrate producers [99,107,109,110] which are linked to favorable cardiometabolic profiles.	[87,99,107,109‐111]
	Classification and prediction of dietary patterns based on gut microbiome composition	RF classification and regression [86,99‐102,112] Unsupervised clustering [113]	Host data/dietary data/Metatranscriptomics [100] (or Metagenomics [99,101,102] or Metabolomics [86,112,113])	Habitual dietary patterns can be robustly classified from gut microbiome composition, (e.g., distinguishing vegan, vegetarian, and omnivorous diets [99,100], diet quality.[113]) Diet quality classification also achieves high accuracy, with high-fiber diets enriched in butyrate-producing taxa (e.g., Lachnospiraceae) and Western/low-quality diets characterized by bile-tolerant and protein-fermenting bacteria (e.g., Bilophila, Blautia) [101]. In animal models, machine learning reliably discriminates high-fat diets and protein sources, identifying Lactococcus spp. as a key indicator and separating plant- from animal-protein diets, with plant protein linked to greater microbial diversity and fiber-degrading taxa, and animal protein to protein-fermenting Clostridia [102].	[86,99‐102,112‐114]
Prediction of metabolic biomarkers from diet and microbiome data	Prediction of postprandial glycemic responses (PPGRs)	GBDT [91,103‐106,115] RF classification and regression [2,107] Deep neural network [108]	Host data/dietary data/ Metagenomics [91,103‐108] (or Metatranscriptomics [115])	Across diverse populations and metabolic states, postprandial glycemic responses can be accurately predicted by integrating dietary intake with gut microbiome features. Models incorporating diet and microbiome outperform diet-only or carbohydrate-based approaches, capturing substantial inter-individual variability in responses to identical foods [2,91,103]. Microbiome features linked to plant-rich, fiber-dense diets are associated with more favorable PPGR profiles, whereas bile-tolerant or dysbiotic signatures predict higher glycemic excursions [107]. Incorporating microbiome data further improves PPGR prediction accuracy in pre-diabetic [104], type 2 diabetes mellitus [105,108] and pregnant women with or without gestational diabetes [106].	[91,103‐108,115]
	Prediction of lipid, inflammatory, cardiometabolic risk biomarkers or another host outcome	RF regression and classification [2,107]	Host data/dietary data/ Metagenomics [2,107]	Machine-learning models indicate that meal composition plays a relatively smaller role in metabolic responses, with fat-induced triglyceride excursions largely driven by inter-individual biological variation. These models achieve moderate accuracy in predicting postprandial triglyceride rises, [2] as well as total lipid, cholesterol, and triglyceride levels across multiple lipoprotein fractions in both fasting and postprandial states [107].	[2,107]
Personalized dietary interventions	Microbiome-guided dietary recommendations to minimize or maximize output	GBDT [91,104,105,116‐118] Proprietary ML model [119]	Host data/dietary data/Metatranscriptomics [119] (or Metagenomic [91,104,105,116‐118])	Machine learning-guided dietary personalization has been shown to reduce postprandial glycemic responses relative to control diets in multiple populations [91,104,105], as well as triglycerides [118], inflammatory markers [91], hepatic fat content [117], symptom score reduction [119], and HbA1c [118,120] levels. However, not all trials demonstrated superiority of personalized diet over standard dietary interventions [116,121].	[91,104,105,116‐119]
	Identification of microbiome-defined responder and non-responder to dietary interventions	RF regression and classification [122‐126] GBDT [127] Regularized regression [128] Unsupervised methods [123]	Host data/dietary data/Metagenomics [122‐127] (or Metabolomics) [128]	Baseline gut microbiome composition was used to stratify and cluster individuals prior to dietary intervention, enabling prediction of responders vs. non-responders for weight loss [122,124,125], hepatic fat content [129], and glycemic outcomes [122], including reductions in HbA1c [127], fasting plasma glucose [127], PPGRs and intestinal function [123,126]. These response-defined clusters were characterized by distinct dominant microbial taxa and microbiome structure.	[122,123,125‐128]
In silico modeling of microbiome-host response	Simulation of host metabolic and microbiome shifts to dietary perturbations	Deep multilayered neural networks [130,131]	Host data/dietary data/ Metagenomics [130,131] (or Metabolomics [130])	In silico models can predict host metabolic responses and microbiome shifts to dietary perturbations. ML-based models were able to learn latent representations of gut microbiome community structure and capture predictable shifts induced by dietary interventions [131]. By explicitly modeling diet-associated changes in microbial composition, these learned representations were then used to predict host metabolomic responses [130].	[130,131]

Abbreviations: RF, random forest; GBDT, gradient-boosted decision trees; PPGR, postprandial glycemic response; ML, machine learning.

Translationally, microbiome-informed ML has generated personalized dietary recommendations, optimizing macronutrient ratios or food choices for metabolic outcomes.^{91,104,105,116} Clinical trials based on this approach have demonstrated benefits in glycemic control and related endpoints, although superiority over standardized guidance is context-dependent and not universal across all populations or outcomes.¹⁰⁶ Additionally, ML stratification of baseline gut microbiomes can prospectively identify likely responders vs. non-responders to dietary interventions,^{122‐125,127} supporting precision enrollment and resource allocation.

More recently, representation-learning and multi-task DL frameworks have begun to model underlying microbial dynamics and their downstream functional consequences. A prominent example is the Metabolite-Response Predictor using Coupled Multilayer Perceptrons (McMLP),¹³⁰ which jointly predicts post-intervention microbiome reconfiguration and resulting metabolomic shifts. By chaining two neural networks—one forecasting community composition from baseline microbiome, metabolome, and diet, and the second mapping the predicted community to metabolite abundances—McMLP explicitly encodes microbe-microbe and microbe-metabolite dependencies. Across multiple dietary intervention cohorts, it outperformed uncoupled baselines, particularly for health-relevant microbial metabolites. Such architectures illustrate the capacity for DL to bridge structural and functional layers, moving beyond static prediction toward simulation of dietary perturbations.

While ML has shown great promise for predictive applications in diet-gut microbiome research, its practical use is often constrained by familiar challenges, notably interpretability, the need for large training datasets, and the requirement for rigorous model evaluation.^25,132 As most ML models capture statistical associations between inputs and responses without explicitly representing underlying biological mechanisms (associative signals rather than causal), their decision-making processes are often described as “black-box” predictions.¹³³ This lack of transparency becomes particularly problematic in clinical or translational settings, where mechanistic interpretability is essential for building trust, facilitating regulatory approval, and enabling actionable biological insights. Furthermore, ML models tend to require dense datasets, are vulnerable to overfitting, and prone to dataset sensitivity, making rigorous parameter regularization, careful (nested) cross-validation, and systematic, external benchmarking essential.

To enhance transparency, the community has increasingly adopted post-hoc interpretability methods. These include feature attribution techniques (e.g., permutation importance,¹³⁴ LIME¹³⁵) and SHAP values,^136,137 which are broadly applicable across ML models to quantify the contribution of individual features (e.g., specific taxa, pathways, or dietary components) to predictions; saliency mapping¹³⁸ for gradient-based sensitivity analysis; and attention weights,¹³⁹ which are particularly prominent in DL architectures to highlight relevant input elements within neural network processing. Comprehensive strategies for interpretable ML in biological contexts are discussed in depth elsewhere.¹⁴⁰

4. Advances in gut microbiome-informed precision nutrition

Precision nutrition marks a fundamental shift from conventional “one-size-fits-all” dietary guidelines toward tailored recommendations that leverage an individual’s unique biological profile, gut microbiome, and lifestyle factors.^24,141,142 By combining diverse datasets (e.g., multi-omics, dietary logs, and clinical phenotypes) via advanced computational pipelines, this approach enables mechanism-driven interventions grounded in host-microbe dynamics and physiological variability.²⁷ The overarching goal of precision nutrition is to fine-tune metabolic outputs, mitigate disease risk, and promote sustained health through data-centric personalization.

A prominent application of machine learning in precision nutrition is the prediction of individual clinical responses to dietary intake. Likewise, these trained models can be used to generate AI-guided dietary recommendations designed to achieve specific clinical objectives. Previous studies have demonstrated that identical dietary interventions often produce highly variable responses across individuals, a phenomenon partly explained by differences in gut microbiome composition.¹⁴³ In response to this variability, early efforts sought to integrate microbiome data into predictive frameworks, most notably in the pioneering study by Zeevi et al.⁹¹ In this work, dietary, clinical, behavioral, and gut microbiome features were integrated into a gradient-boosted ML model to predict individual post prandial glucose responses (PPGRs). By jointly modeling host and microbial factors, the framework far surpasses traditional carbohydrate-centric and population-level prediction approaches. A follow-up randomized controlled trial (RCT) validated the model's utility, showing that algorithm-derived “good” diets minimized glucose spikes.¹⁰⁵ Importantly, foods predicted to be optimal or suboptimal were highly individual-specific, such that the same food could elicit beneficial effects in one individual but adverse responses in another. Other studies have replicated and validated these findings.^{103,105,115,144}

Building on these foundations, large-scale cohorts have refined and expanded predictive scope. The PREDICT 1 study extended modeling beyond glycemic to lipidomic and insulinemic responses using a random forest–based approach.² In this study, gut microbiome composition explained a substantial portion of individual variability in PPGRs, exceeding the contribution of macronutrient content alone. Moreover, specific microbial profiles associated with plant-rich diets were linked to more favorable cardiometabolic outcomes, including improved fasting glucose, postprandial glycemic and lipid responses, and reduced inflammation.¹⁰⁷ Together, these findings highlight the central role of the gut microbiome in shaping individual dietary responses. More recently, DL architectures have been developed to integrate dietary intake, continuous glucose monitoring (CGM) data, clinical variables, and gut microbiome profiles to predict PPGRs in individuals with type 2 diabetes.¹⁰⁸ These models demonstrate robust predictive performance, particularly improving predictions in low-carbohydrate responders, and achieve accuracy comparable to conventional machine-learning approaches.

The field has progressed from in silico proofs-of-concept to rigorous RCTs assessing AI-guided diets’ clinical efficacy. In adults with prediabetes, a RCT compared a standard Mediterranean diet with a personalized postprandial-targeting (PPT) diet.¹⁴⁴ The personalized arm showed significantly greater reductions in CGM-derived hyperglycemia and HbA1c, with benefits sustained through 12-month follow-up. In a companion study, individuals with newly diagnosed type 2 diabetes who underwent the PPT diet intervention showed improved PPGRs control, reductions in HbA1c, fasting glucose, and triglycerides, and high rates of diabetes remission.¹⁰⁵

Similar findings have been reported in gastrointestinal disorders. In an RCT involving patients with irritable bowel syndrome (IBS), an AI-assisted, microbiome-based personalized diet was compared with a standard low-FODMAP diet.^121,145 Although both interventions produced meaningful improvements in symptoms and quality of life, the microbiome-targeted diet was associated with distinct shifts in gut microbial composition and diversity. In non-alcoholic fatty liver disease (NAFLD), AI-guided, microbiome-informed dietary interventions have likewise demonstrated clinical promise. In a randomized study, personalized diets designed using metagenomic profiling and ML, combined with targeted probiotic supplementation, resulted in greater reductions in liver fat and improvements in microbial and inflammatory markers compared with standard dietary advice.¹¹⁷

Despite promising outcomes, the efficacy of AI-guided personalized diets is not universal and appears context-dependent. For instance, in an RCT involving adults with obesity and abnormal glucose metabolism,¹¹⁶ participants were assigned to either a standardized low-fat diet based on the Zeevi et al.⁹¹ algorithm designed to predict postprandial glycemic responses. Both groups received equivalent behavioral counseling in the trial, in which evaluated weight loss as the primary outcome. At 6 months since initiation of the trial, the personalized approach did not yield additional weight-loss benefits compared to the standardized diet. These findings suggest that algorithms optimized for specific endpoints, such as glycemic control, may not translate effectively to other outcomes like weight loss, further suggesting the need for outcome-specific predictive models.

Extending beyond glycemia, dietary impacts on health often manifest through microbially generated metabolites. Evaluating these effects is a key aspect of nutritional research, as changes in the concentrations of specific health- or disease-associated metabolites provide a direct measure of an intervention’s impact. However, direct metabolomics, however, is resource-intensive, technically demanding, and susceptible to contextual artifacts. To circumvent this, researchers have developed computational frameworks that predict diet-induced metabolite dynamics from microbiome composition, thereby establishing a functional bridge between microbial communities and host metabolic outcomes. Beyond the McMLP discussed above, a variety of ML approaches have been proposed for inferring metabolite profiles from paired microbiome-metabolome datasets. These data-driven methods operate independently of predefined biochemical pathways or reference databases and encompass techniques such as Elastic Net regression,¹⁴⁶ sparsified neural encoder-decoder networks,¹⁴⁷ multilayer perceptrons,¹⁴⁸ word2vec embeddings,¹⁴⁹ neural ordinary differential equations,¹⁵⁰ and mixture-of-experts models with variational Gaussian processes.¹⁵¹

Despite these strides, AI applications in diet-gut microbiome-host modeling face persistent hurdles. Models often degrade under domain shifts (e.g., cohort, cuisine, or sequencing variations) and rarely incorporate temporal elements like circadian effects or meal sequencing.¹⁵² Although this may change as precision nutrition research increasingly emphasizes not only what people eat but also when they eat. Consistent with this direction, the NIH 2020–2030 Strategic Plan for Nutrition Research¹⁵³ identifies circadian rhythm as an addressable component of precision nutrition, specifically highlighting chrono-nutrition and meal-timing as emerging priorities. Most models also lack native uncertainty estimation or mechanistic transparency, undermining clinical trust and deployment. Emerging hybrids address this by blending deep representation learning with probabilistic inference (e.g., mixture-of-experts plus variational Gaussian processes^151,154) and attribution methods to dissect microbe-metabolite contributions^148,155—fostering interpretable insights into food-microbe-metabolite cascades. Looking ahead, practical translation hinges on closed-loop optimizers that iteratively design meals/timings for multi-objective metabolic gains, with safeguards for safety, practicality, and equity.

5. Mechanistic modeling of gut microbiome community metabolism

Complementing data-driven statistical and ML methods, genome-scale metabolic models (GSMMs) offer a mechanistic framework for mapping dietary inputs to microbial community metabolism. Well-established in systems biology,¹⁵⁶ GSMMs have become increasingly central in microbiome studies for simulating metabolic interactions in diverse gut consortia.¹⁵⁷ These models represent microbial species or strains as stoichiometrically balanced networks, incorporating thousands of reactions, transporters, and biomass objectives. Aggregated into multi-species community models,^93,158‐160 they capture ecological dynamics such as cross-feeding, competition for resources, and collective metabolite outputs—SCFAs, amino acid derivatives, and bile acid conjugates. In-depth reviews of GSMM tools and resources tailored to gut microbiome applications are available elsewhere.¹⁶¹

Personalization begins with aligning an individual’s metagenomic or amplicon profile to strain-level reconstructions from databases like AGORA,¹⁶² AGORA2,¹⁶³ or the Virtual Metabolic Human (VMH) collection,¹⁶⁴ and by constraining the models with subject-specific dietary intake data. Nutrient databases and luminal availability estimates convert food intakes into uptake bounds, thereby defining the substrate pools and flux capacities for the modeled community. Constraint-based optimization techniques—most commonly flux balance analysis (FBA), flux variability analysis (FVA), or dynamic FBA (dFBA)—then compute feasible metabolic flux distributions, including exchange fluxes that predict metabolite production or consumption.^165,166

GSMMs have significantly advanced mechanistic insights into dietary influences on gut microbial metabolism. Several dedicated computational frameworks—including COMETS,¹⁶⁷ BacArena,¹⁶⁸ dOptCom,¹⁶⁹ MatNet,¹⁷⁰ DyMMM,¹⁷¹ MCM,¹⁷² and the CASINO¹⁷³ toolbox—have been developed to investigate diet-gut microbiome interactions. These tools typically integrate multiple species- or strain-level GSMMs into microbial community-scale metabolic models (MCMMs), enabling simulation of community-wide metabolic adjustments under varying nutritional conditions.

Early translational efforts yielded encouraging results. CASINO, for example, generated personalized microbiota models for 45 overweight or obese individuals, and linked dietary composition to microbial metabolic capabilities along the diet-microbiota-host axis.¹⁷³ The models predicted marked shifts in amino acid fermentation and SCFA production in response to dietary changes, with simulations validated against paired fecal and plasma metabolomics.

More recent studies have shown that MCMMs derived from individual gut metagenomes can accurately predict personalized SCFA production across dietary, prebiotic, and probiotic interventions. In one large-scale example,¹⁷⁴ microbial community-scale models were constructed for hundreds of participants by integrating AGORA¹⁶² strain-level reconstructions with MICOM,¹⁷⁵ a scalable platform for simulating mixed communities under defined nutritional constraints. Each model incorporated subject-specific metagenomic abundances and diet-derived substrate availability, yielding individualized SCFA flux predictions across multiple dietary intervention scenarios. Predicted acetate, propionate, and butyrate production correlated strongly with measured fecal SCFA levels and host cardiometabolic/immunological biomarkers. Notably, these personalized MCMMs were further leveraged to design targeted interventions, identifying for each individual the fiber types or probiotic strains predicted to maximally boost SCFA output.

Collectively, these developments herald an emerging paradigm in which MCMMs serve as individualized “digital twins” of the gut ecosystem. Such digital twins can be perturbed in silico to evaluate alternative diets, prebiotics, probiotics, or combination therapies, and calibrated against metatranscriptomic or metabolomic data to refine parameter estimates. GSMMs integrate naturally with AI workflows, as derived features such as pathway fluxes and exchange rates provide biologically grounded priors for supervised ML, improving interpretability and reducing artifacts arising from compositional.¹⁷⁶ In contrast, neural-mechanistic hybrids models¹⁷⁷ replace FBA with differentiable approximations, enabling phenotype inference for cohort-scale analyzes. In all, GSMM-based digital twins may emerge as a hypothesis-generating counterpart to data-driven ML.

Despite these strengths, key limitations remain, including steady-state assumptions, uncertain objective functions, incomplete metabolic reconstructions, and limited incorporation of regulatory dynamics or host-microbe co-metabolism.^178,179 Objective functions, often defaulted to biomass maximization, may not reflect real-world microbial priorities like survival under nutrient limitation or cooperative behaviors in communities, introducing biases in flux estimates. Metabolic reconstructions remain incomplete, with gaps in pathway annotations—particularly for uncultured or understudied gut taxa—resulting in orphan reactions or erroneous inclusions from databases like KEGG or MetaCyc. Regulatory dynamics, including gene expression controls and post-transcriptional modifications, are poorly incorporated, decoupling models from omics realities where transcript-protein-flux relationships are non-linear. Host-microbe co-metabolism is similarly underrepresented, neglecting bidirectional exchanges like mucosal signaling or immune modulation that influence dietary outcomes. Furthermore, computational demands escalate for community-scale models, where solving large linear programs becomes intractable for high-resolution personalization or iterative sensitivity analyzes.

Looking ahead, the true power lies in hybrid AI-GSMM paradigms, where machine learning addresses core limitations to propel precision nutrition forward. Neural surrogates and tree-based ensembles can approximate FBA solvers, accelerating computations from hours to seconds for cohort-scale simulations and enabling real-time uncertainty quantification through ensemble variance or Bayesian priors.^177,180 Multi-task models fuse GSMM-derived fluxes as biologically informed features into predictive pipelines,¹⁸¹ enhancing interpretability (e.g., via SHAP attribution on pathway contributions¹⁸²) and mitigating compositional biases in microbiome data. Conversely, GSMM constraints can regularize ML objectives, imposing stoichiometric feasibility to curb overfitting and yield causally plausible predictions.¹⁷⁶ In diet-gut microbiome research, such synergies promise scalable, personalized tools—optimizing prebiotic/fiber blends for SCFA maximization or simulating host responses to novel diets¹⁸³—ultimately bridging empirical patterns to mechanistic precision for metabolic health.

6. Beyond α-diversity: Composite and diet-specific metrics of gut microbiome health

Conventional α-diversity metrics, which quantify microbial community richness and evenness, have long served as proxies for dysbiosis but show inconsistent associations with host health outcomes. These inconsistencies arise because α-diversity reflects overall community structure without capturing the direction, magnitude, or functional implications of finer-scale compositional changes.¹⁸⁴ Moreover, dysbiosis lacks a precise definition owing to the absence of a universal “healthy” reference microbiome and extensive inter-individual variation; a healthy gut microbiome is not represented by a single configuration but rather by multiple configurations linked to host well-being.¹⁸⁵ Conceptually, dysbiosis represents a deviation from configurations typically associated with well-being. Effective quantification therefore demands reference-based indices that compare individual profiles against empirically validated healthy or disease-linked benchmarks, rather than relying solely on diversity summaries.

To overcome these limitations, ML approaches have allowed the formulation of composite microbiome health metrics that condense thousands of microbial features into a single, interpretable score reflecting host microbiome health. In dietary research and precision nutrition, these reference-based metrics make microbiome changes more interpretable and actionable. However, they are indicative rather than diagnostic and should not be interpreted as clinical endpoints. Their utility in dietary optimization is therefore indirect, serving primarily to benchmark microbiome states and track diet-associated shifts rather than to define health outcomes per se. Leading examples include the Gut Microbiome Health Index (GMHI)¹⁸⁶ and its advanced successor, the Gut Microbiome Wellness Index 2 (GMWI2),¹⁸⁷ as well as the Health Index with Principal Component Analysis (hiPCA).¹⁸⁸ Representative applications of these metrics are summarized in Table 2.

Table 2.

Representative applications of composite microbiome health metrics.

Index	Application	Key findings
Gut Microbiome Health Index (GMHI) [186]	Characterize gut microbiome in breast cancer patients before and after chemotherapy [189]	GMHI detected significant deterioration in gut microbiome health following chemotherapy in breast cancer patients, with lower post-treatment GMHI scores indicating increased dysbiosis.
	Evaluate gut microbiome health across different microbiome states (defined as distinct community configurations) [190]	GMHI varied systematically across distinct gut microbiome community configurations, with Clostridiales-enriched states exhibiting higher GMHI values and Bacteroides-dominated low-diversity states showing lower GMHI.
	Quantify probiotic treatment in ulcerative colitis [191]	Probiotic supplementation significantly increased GMHI and reduced the microbial dysbiosis index in ulcerative colitis patients. Improvements in GMHI paralleled reductions in disease activity and inflammatory burden.
	Quantify whether puerarin treatment restored the gut microbiome from a high-fat-diet-induced dysbiotic state toward a healthier overall microbial profile [192]	Puerarin treatment restored GMHI in high-fat-diet-fed mice, reversing diet-induced dysbiosis despite minimal changes in alpha diversity.
	Assess microbiome health in relation to environmental factors (including diet) in a large Dutch population [193]	GMHI scores were significantly higher in healthy than in diseased participants and correlated with shared disease signatures. Diet emerged as a key factor, with plant-based patterns associated with health-enriched taxa (e.g., SCFA producers) and Western diets linked to inflammatory profiles and lower scores.
	Quantify how dietary adenine affects the gut microbiota [194]	Dietary adenine induced significant, dose-dependent reductions in GMHI across generations, indicating worsening gut microbiome health associated with elevated purine intake.
	Quantify the prebiotic effects of oligosaccharides on gut microbiome wellness using in vitro fecal fermentation [195]	Prebiotic treatment (FOS, GOS, XOS, IN, 2FL) resulted in positive GMHI values, indicating enhanced relative abundances of health-prevalent species (e.g., Bifidobacterium adolescentis, Bifidobacterium catenulatum) and reduced health-scarce species. Controls without substrate showed negative GMWI, reflecting a dysbiotic state. GMWI outperformed the traditional prebiotic index (PI) and α-diversity metrics in evaluating dietary intervention impacts on microbiome composition and health.
	Characterize the impact of kefir administration on the gut microbiome in critically ill adults [196]	GMHI improved significantly after kefir administration in ICU patients, indicating enhanced gut microbiome health despite no increase in α-diversity and ongoing antibiotic exposure. Kefir was safe and feasible, with variable engraftment of kefir-derived species.
Gut Microbiome Wellness Index 2 (GMWI2) [187]	Determine whether probiotic supplementation improves gut microbiome wellness in healthy individuals and its correlation with clinical markers [197]	Lower baseline GMWI2 identified individuals more responsive to probiotic intervention, while increases in GMWI2 were associated with improved gut microbiome function and lower blood glucose.
Health Index with Principal Component Analysis (hiPCA) [188]	Systematically evaluate and rank dietary factors based on their ability to improve gut microbiota health in overweight individuals [198]	hiPCA values were higher in overweight-associated gut microbiota states and decreased after dietary fermentation, with the largest reductions observed for galacto-oligosaccharides and pueraria polysaccharides.

The GMHI,¹⁸⁶ trained on 4,347 shotgun metagenomes from 34 multi-cohort studies, computes a log-ratio of health-enriched vs. health-depleted taxa to generate a disease-agnostic risk estimate, far outperforming α-diversity metrics in classification tasks. This framework was subsequently refined into GMWI2, which expanded the training dataset to 8,069 metagenomes across 54 studies, replacing the log-ratio with Lasso-penalized logistic regression across multiple taxonomic ranks.¹⁸⁷ GMWI2, anchored in the presence/absence of disease-associated microbes, provides a continuous wellness score reflecting the degree of association with health.

Beyond binary case-control classification, GMWI2 has demonstrated sensitivity in longitudinal and interventional contexts. In dietary trials, it detected a rapid decline in gut health within two days of a fiber-free exclusive enteral nutrition diet while remaining stable in vegan and omnivorous groups—changes undetectable by α-diversity. In antibiotic-treated cohorts, GMWI2 scores remained depressed for up to six months despite apparent α-diversity recovery; and in fecal microbiota transplantation (FMT) studies, only patients with symptom improvement exhibited increased scores.¹⁸⁷

Collectively, GMWI2’s responsiveness to microbiome dynamics makes it particularly suitable for large-scale dietary research (Figure 2). It facilitates cross-population comparisons of diet-associated wellness, where elevated scores often align with fiber-rich, plant-based diets and lower scores may signal risks from processed, low-fiber foods. Its ability to capture health-relevant shifts missed by α-diversity supports near-real-time monitoring of nutritional interventions. That said, GMWI2 remains indicative rather than diagnostic, and is susceptible to confounders such as geography¹⁹⁹ and recent antibiotic exposure,²⁰⁰ and—given its taxonomic focus and disease-oriented training—requires additional validation for purely nutritional contexts. A recent transformer-based autoencoder iteration has further improved accuracy and cross-cohort generalizability.²⁰¹

Figure 2.

The Gut Microbiome Wellness Index 2 (GMWI2): Construction, interpretation, and applications in diet-gut microbiome research. The upper panel illustrates the GMWI2 workflow: trained on 8,069 human stool shotgun metagenomes from 54 published studies across 26 countries and 6 continents, comprising 5,547 healthy samples and 2,522 samples from 11 disease conditions. Healthy/non-healthy phenotypic labels are combined with a presence/absence taxonomic abundance table, followed by Lasso-penalized logistic regression (with parameter tuning) to select discriminative microbial features. The resulting model outputs a continuous wellness score, where higher values indicate greater relative abundance of health-associated taxa (e.g., Faecalibacterium prausnitzii, Roseburia hominis, Roseburia intestinalis, Eubacterium rectale) and lower abundance of non-healthy-associated taxa (e.g., Clostridium bolteae, Clostridium symbiosum, Clostridium hathewayi, Ruthenibacterium gnavus). The lower panel highlights GMWI2’s utility for monitoring dynamic microbiome responses in nutritional contexts, including pre- and probiotic interventions, dietary trials, post-antibiotic recovery, functional foods/supplements, nutritional epidemiology, personalized dietary guidance, longitudinal diet tracking, and microbiome-based adherence assessment. Time-course examples contrast stable or improving GMWI2 trajectories in intervention arms against declines in controls, demonstrating applicability for detecting diet-induced shifts toward healthier community configurations.

The hiPCA was developed and trained on the same multi-cohort dataset as GMHI.¹⁸⁸ hiPCA uses a principal component-based statistical model to define a “healthy subspace” in metagenomic feature space and quantifies how far individual microbiome profiles deviate from this reference boundary. By evaluating each sample’s position relative to this healthy subspace, hiPCA not only identifies deviations indicative of dysbiosis but also pinpoints the specific microbes driving the divergence.

Despite their utility, indices such as GMHI/GMWI2 and hiPCA inherit key limitations from the underlying metagenomic data. Their reliance on species-level taxonomy derived from marker genes precludes strain-level resolution, even though strain variation strongly influences host associations. In addition, these indices capture genomic potential rather than microbial activity and do not reflect dynamic metabolic states or diet-induced functional changes. Consequently, they should be interpreted as associative markers of health-linked microbiome configurations rather than direct measures of microbial function, motivating integration with complementary functional modalities.

While these gut microbiome health metrics are primarily disease-agnostic or condition-specific, a growing focus in nutritional research has led to the development of explicitly diet-specific microbiome metrics. The Dietary Index for Gut Microbiota (DI-GM)²⁰² stands out, derived from systematic synthesis of over 100 longitudinal human diet-gut microbiome studies. It assigns scores based on intake of fourteen key foods and nutrients: positively weighting fermented dairy (e.g., yogurt), legumes (including chickpeas and soybeans), whole grains, dietary fiber, cranberries, avocados, broccoli, coffee, and green tea, while penalizing refined grains, red and processed meats, and high-fat diets (>40% energy from fat). Unlike general dietary quality scores such as the Healthy Eating Index (HEI) or Mediterranean Diet Score (MDS), the DI-GM was specifically designed to reflect dietary patterns that promote microbial diversity, short-chain fatty acid production, and enrichment of health-associated taxa. Large-cohort validation studies have demonstrated that higher DI-GM scores are associated with lower risks of metabolic syndrome, type 2 diabetes, cardiovascular disease, IBS, gastrointestinal cancers, and metabolic dysfunction-associated steatotic liver disease, along with reduced inflammation, improved BMI trajectories, and decreased all-cause and cardiovascular mortality. Though relatively new, the DI-GM is a valuable tool for precision nutrition, providing quantitative insights into how daily dietary choices influence gut microbiota composition and function. Its microbiome-specific design complements broader health metrics and holds strong potential for monitoring and optimizing diet-gut microbiome interactions in observational and interventional research.

In contrast to these globally trained, disease-agnostic indices, several population- or condition-specific frameworks have been developed using diverse statistical and machine learning methods. For example, the microbial risk score (MRS)²⁰³ adapts polygenic risk scoring principles to aggregate disease-associated microbial sub-communities into interpretable susceptibility scores, enabling cross-cohort stratification and seamless integration with other omics-derived risk metrics. Another example is the Microbiome Health Index for post-Antibiotic dysbiosis (MHI-A),²⁰⁴ a simple four-taxon ratio designed to monitor antibiotic-induced disruption and subsequent recovery. Similarly, pediatric microbiota maturity metrics, such as the Microbiota-for-Age Z-score (MAZ),²⁰⁵ quantify developmental progression of the infant and child gut microbiome relative to chronological age.

These targeted tools are typically trained on specific populations or narrowly defined clinical contexts, prioritizing context-specific precision at the expense of broader generalizability. Nevertheless, many of these indices—or adaptations thereof—hold considerable promise for nutritional research, where they could be used to assess microbiome resilience to dietary perturbations, monitor dynamic responses to nutritional interventions, or delineate diet-associated microbial signatures indicative of gut ecosystem health.

7. Translational priorities and future directions in AI-driven precision nutrition

In this review, we have synthesized the evolving landscape of precision nutrition, emphasizing the central role of diet-gut microbiome interactions in shaping individual metabolic responses. The marked inter-individual variability in dietary response and microbial composition highlights the limitations of traditional one-size-fits-all dietary recommendations and reinforces the need for personalized strategies. Advances in multi-omics profiling (e.g., metagenomics, metatranscriptomics, metaproteomics, and metabolomics) have provided unprecedented resolution into microbial structure, activity, and host-microbiome cross-talk. When integrated with dietary assessments (ranging from self-reported instruments and digital tools to emerging stool-based DNA inference) and host physiological markers (e.g., clinical biomarkers, circadian rhythms, and activity patterns), these modalities create a rich multimodal data ecosystem. Computational workflows, from preprocessing heterogeneous inputs to AI-driven integration, now support applications such as dietary pattern classification, prediction of metabolic phenotypes, responder stratification, and in silico simulation of dietary perturbations.

Key innovations include interpretable microbiome health metrics such as GMWI2 and DI-GM, which distill high-dimensional taxonomic data into actionable scores that outperform conventional diversity metrics. These indices enable monitoring of diet-induced shifts and have demonstrated responsiveness in randomized trials, including probiotic interventions associated with improved glycemic control. Complementing data-driven ML approaches, mechanistic GSMMs simulate community-level metabolism and nutrient flux, offering biologically grounded insight into microbe-host interactions. Emerging hybrid frameworks that merge AI-based prediction with mechanistic modeling enhance interpretability, scalability, and generalizability, addressing challenges such as compositionality, high dimensionality, and data heterogeneity.

Despite this progress, fully integrated multi-omics AI systems remain an evolving frontier. More importantly, translating these advances into clinically ready precision nutrition requires deliberate attention to scalability, robustness, and trustworthiness. Several priorities are urgently needed to bridge the gap between research innovation and real-world deployment: First, diverse and longitudinal cohorts remain essential. Many existing predictive models are trained predominantly on Western, adult populations and often rely on dominant modalities such as shotgun metagenomics. Broader inclusion of pediatric, geriatric, multi-ethnic, and chronically ill populations (e.g., diabetes, inflammatory bowel disease, metabolic syndrome) is critical to reduce bias and ensure equitable generalizability. Large-scale initiatives and biobanks integrating microbiome, dietary, and clinical data provide a blueprint, but future efforts must emphasize harmonized protocols, standardized metadata capture, and FAIR-compliant data sharing to enable robust cross-cohort validation. Second, standardized benchmarking and model evaluation frameworks are urgently required. Current studies frequently use heterogeneous preprocessing pipelines, performance metrics, and validation strategies, limiting reproducibility and cross-study comparability. Shared reference datasets, transparent reporting standards, nested cross-validation, and external validation across independent populations should become the norm. Benchmarking consortia (similar in spirit to CAMI initiatives^206,207) could be adapted for diet-gut microbiome AI applications, with evaluation criteria that explicitly account for sparsity, compositionality, domain shift, and uncertainty quantification. Third, clinically trustworthy AI systems must incorporate interpretability, uncertainty estimation, and regulatory readiness. Black-box models may achieve high predictive accuracy, but clinical adoption requires transparent reasoning, clear attribution of modifiable dietary drivers, and calibrated uncertainty outputs—particularly in edge cases such as antibiotic-induced dysbiosis or extreme dietary transitions. Hybrid AI-mechanistic models are especially promising in this regard, as they constrain predictions within biologically plausible bounds and provide mechanistic context. Regulatory pathways, including frameworks such as the United States Food and Drug Administration (FDA)’s Software as a Medical Device (SaMD) guidelines, offer potential routes for validation, but early engagement with regulatory and ethical standards is essential. Fourth, scalable clinical integration and closed-loop systems represent the next translational milestone. Real-time feedback architectures—linking wearable sensors (e.g., continuous glucose monitors), digital dietary logging, and periodic microbiome sampling—could enable adaptive dietary recommendations that evolve with an individual’s metabolic state.²⁰⁸ Pragmatic and hybrid clinical trials, including adaptive and N-of-1 designs,²⁰⁹ are well suited to test these systems under real-world conditions. Partnerships among academia, healthcare systems, and industry will be crucial to generate real-world evidence, integrate tools into electronic health records, and ensure cost-effectiveness beyond specialized research centers. Finally, broader implications must be considered. AI-enhanced precision nutrition holds substantial public health potential, including scalable digital platforms capable of reducing metabolic disease burden. However, over-reliance on algorithmic recommendations without behavioral support, equitable access, and bias mitigation could exacerbate disparities. Ethical safeguards, inclusive training datasets, and transparent governance must therefore be embedded from the outset.

Taken together, the coordinated integration of complementary omics technologies, advanced AI, mechanistic modeling, and rigorous translational frameworks is reshaping diet-gut microbiome science. By prioritizing diversity, standardization, interpretability, regulatory alignment, and real-world validation, the field can move beyond proof-of-concept toward clinically actionable precision nutrition. Ultimately, these integrative approaches offer a pathway to transform everyday dietary choices into personalized, evidence-based interventions that promote long-term metabolic health and resilience.

Appendix.

Box 1. Glossary of key artificial intelligence and machine learning terms in precision nutrition and diet-gut microbiome research. This glossary summarizes key artificial intelligence (AI), machine learning (ML), and modeling concepts used throughout this review, with emphasis on their application to integrating dietary, microbial, and host data in precision nutrition.

1. Learning Paradigms

   • Artificial Intelligence (AI): A broad field of computer science aimed at creating systems that perform tasks requiring human-like intelligence, such as pattern recognition, decision-making, and prediction. In diet-gut microbiome research, AI encompasses ML and other techniques to analyze complex interactions (e.g., predicting glycemic responses from gut microbial profiles).

     • ∘Example: AI-guided personalization in randomized controlled trials, where algorithms recommend diets based on multi-omics data, yielding metabolic benefits like improved blood glucose control.
     • ∘Nuances and Implications: AI is often conflated with ML (a subset), but includes rule-based systems. Ethical implications include bias in training data (e.g., underrepresentation of non-Western populations), potentially leading to inequitable nutrition advice.
     • ∘Considerations: Scalability for clinical use requires interpretable AI to build trust among practitioners.

   • Machine Learning (ML): A subset of AI in which algorithms learn patterns from data without explicit rule-based programming. ML enables prediction of metabolic responses, diet classification, and responder stratification from high-dimensional microbiome data.

     • ∘Example: Using ML to classify habitual diets from shotgun metagenomic signatures or stratify responders to probiotic interventions.
     • ∘Nuances and Implications: ML can be supervised (labeled data, e.g., healthy vs. diseased microbiomes) or unsupervised (no labels, e.g., clustering microbial communities). Overfitting (model memorizing noise) is a risk in sparse omics data; regularization techniques like LASSO mitigate this.
     • ∘Considerations: Generalizability across cohorts is key; diverse longitudinal datasets (e.g., from multiple continents) reduce biases.

   • Supervised learning: Models trained on labeled input-output pairs (e.g., microbiome features linked to glycemic response). Tasks include classification and regression. Common algorithms include regularized regression, random forests, gradient boosting, support vector machines, and neural networks.

   • Unsupervised learning: Models that identify structure in unlabeled data, used for clustering (e.g., microbiome states), dimensionality reduction, and latent pattern discovery.

   • Semi-supervised and weakly supervised learning: Approaches that leverage limited or noisy labels (e.g., self-reported diet) alongside abundant unlabeled omics data, improving sample efficiency.

   • Multi-task learning: A framework in which a single model simultaneously predicts multiple related outcomes (e.g., glycemic, lipid, and inflammatory responses), sharing internal representations to enhance generalization.

2. Model Architectures and Representation

   • Ensemble methods: Techniques that combine multiple base models to improve predictive accuracy and robustness. Examples include bagging (random forests), boosting (gradient-boosted decision trees), and stacking.

   • Deep Learning (DL): A subset of ML using multi-layer neural networks to learn hierarchical representations directly from high-dimensional inputs (e.g., metagenomes, metabolomes, continuous glucose traces). DL enables end-to-end multimodal integration but often requires large datasets.

     • ∘Example: DL models in Figure 1 for digital twin simulations, predicting metabolic outcomes from metagenomic and dietary inputs.
     • ∘Nuances and Implications: Requires large datasets and computational power; “black-box” nature reduces interpretability, but techniques like attention mechanisms help. Implications include improved accuracy (e.g., 20–30% better glycemic forecasting) but higher risk of overfitting in small cohorts.
     • ∘Considerations: Hybrid DL-mechanistic models balance data-driven power with biological plausibility.

   • Representation learning and embeddings: Methods that learn informative lower-dimensional feature spaces from complex inputs. Examples include autoencoders, word2vec-style embeddings for microbe-metabolite relationships, and transformer-based latent representations for microbiome health metrics.

3. Data Handling and Preprocessing

   • Multi-omics profiling: Integration of multiple molecular layers (metagenomics, metatranscriptomics, metaproteomics, metabolomics) to capture complementary aspects of microbial structure and function.

     • ∘Example: Combining shotgun metagenomics with host clinical biomarkers to predict individual responses to dietary perturbations.
     • ∘Nuances and Implications: Addresses data heterogeneity (e.g., varying scales across omics) but introduces high dimensionality (thousands of features). Enables finer resolution but risks information overload; preprocessing is crucial.
     • ∘Considerations: Standardization (e.g., via shared protocols) is needed for cross-study comparability.

   • High dimensionality and sparsity: Microbiome datasets typically contain thousands of features relative to sample size, with many low-abundance or zero-inflated taxa.

   • Compositional data: Relative abundance data constrained by constant-sum properties; requires specialized transformations (e.g., centered log-ratio) to avoid spurious correlations.

   • Feature engineering and feature selection: Processes that transform raw inputs into model-ready variables and identify informative subsets. Methods include domain-driven aggregation, LASSO-based selection, and tree-based importance metrics.

     • ∘Example: LASSO-penalized logistic regression in GMWI2 selects discriminative taxa for wellness scores.
     • ∘Nuances and Implications: Combats the “curse of dimensionality” in multi-omics, improving model performance. Biological interpretability (e.g., highlighting key microbes like Faecalibacterium prausnitzii).
     • ∘Considerations: Domain knowledge (e.g., microbial ecology) guides selection to avoid data-driven artifacts.

   • Dimensionality reduction: Techniques (e.g., PCA, t-SNE, UMAP, autoencoders) that compress high-dimensional data while retaining essential structure.

     • ∘Example: In hiPCA (Health Index with PCA), reducing taxonomic features to identify overweight-associated microbiome states, showing decreases post-dietary interventions like galacto-oligosaccharides.
     • ∘Nuances and Implications: Preserves variance but can lose rare signals (e.g., low-abundance taxa). Improves model efficiency but may introduce biases if assumptions (e.g., linearity in PCA) don't hold.
     • ∘Considerations: Use in conjunction with imputation for sparse microbiome datasets.

   • Data augmentation, imputation, normalization/scaling: Preprocessing steps that address missing data, heterogeneous measurement scales, and class imbalance in multimodal datasets.

     • ∘Example: In Figure 1 workflows, imputing missing metabolomic peaks or normalizing taxonomic abundances for ML integration.
     • ∘Nuances and Implications: Essential for heterogeneous inputs (e.g., self-reported diets vs. NGS data). Reduces bias but can introduce errors (e.g., mean imputation assuming normality).
     • ∘Considerations: Context-specific (e.g., compositionality-aware normalization for microbiome data).

4. Model Evaluation and Generalization

   • Overfitting: When a model captures dataset-specific noise rather than generalizable patterns, leading to poor external performance.

   • Regularization: Techniques (e.g., L1/LASSO, L2/Ridge, dropout, early stopping) that constrain model complexity to mitigate overfitting.

     • ∘Example: In GMWI2 construction, using LASSO to select health-associated taxa from 8,069 metagenomes, outputting wellness scores.
     • ∘Nuances and Implications: Balances bias-variance tradeoff; interpretable via selected features. Translates high-dimensional data into actionable metrics outperforming α-diversity.
     • ∘Considerations: Parameter tuning (e.g., via cross-validation) is critical for microbiome applications.

   • Cross-validation and nested cross-validation: Resampling frameworks used to assess generalization and tune hyperparameters without optimistic bias.

   • External validation: Evaluation on independent cohorts to assess robustness across populations, sequencing platforms, and dietary contexts.

   • Domain shift: Performance degradation when applying a model to data from different demographic, geographic, or technical contexts than those used in training.

   • Uncertainty estimation: Approaches (e.g., Bayesian inference, ensemble variance, conformal prediction) that quantify prediction confidence. Critical for clinical deployment.

5. Interpretability and Causality

   • Interpretability and explainability: Methods that clarify why a model produces a given prediction. Examples include permutation importance, SHAP values, LIME, saliency maps, and attention mechanisms.

   • Black-box models: High-capacity models (e.g., deep neural networks) whose internal decision processes are not directly transparent.

   • Causal inference vs. associative prediction: Most ML models identify statistical associations rather than causal effects. Integrating causal frameworks or mechanistic constraints helps distinguish predictive correlates from modifiable drivers.

6. Mechanistic and Hybrid Modeling

   • Genome-Scale Metabolic Models (GSMMs): Constraint-based mechanistic models that simulate metabolic fluxes within microbial communities or hosts, based on genome annotations.

     • ∘Example: In silico simulations of dietary perturbations, guiding targeted interventions like prebiotics.
     • ∘Nuances and Implications: Hypothesis-driven, contrasting data-driven ML; provides biological plausibility. Enhances interpretability but requires accurate annotations.
     • ∘Considerations: Integration with ML yields hybrid models for scalable predictions.

   • Flux Balance Analysis (FBA): Optimization-based method used in GSMMs to estimate feasible steady-state metabolic flux distributions.

   • Digital twins: Individualized in silico models of a person’s microbiome or host-microbe system used to simulate responses to dietary or probiotic interventions.

   • Hybrid AI-mechanistic models: Frameworks combining ML prediction with mechanistic constraints (e.g., GSMM-derived flux features or differentiable FBA surrogates) to enhance biological plausibility, interpretability, and generalizability.

     • ∘Example: Merging ML for pattern detection with GSMMs for metabolic insights, improving generalizability in precision nutrition.
     • ∘Nuances and Implications: Addresses ML’s lack of causality and mechanisms’ data limitations. Enhances scalability and interpretability (e.g., explaining why a diet boosts GMWI2).
     • ∘Considerations: Promising for translational challenges, like closed-loop optimization.

7. Translational Concepts

   • Responder stratification: ML-based classification of individuals into likely responders or non-responders to dietary interventions.

     • ∘Example: Using microbiome signatures to identify individuals benefiting from AI-guided diets, as in trials showing metabolic gains.
     • ∘Nuances and Implications: Enables personalized interventions; relies on robust features. Reduces trial failures but risks over-stratification.
     • ∘Considerations: Validates across diverse cohorts for equity.

   • In silico simulation: Computational modeling of dietary perturbations or probiotic effects without real-world experimentation.

     • ∘Example: Simulating probiotic effects on community metabolism to predict GMWI2 shifts. Digital Twins (virtual replicas for personalized simulations).
     • ∘Nuances and Implications: Cost-effective for hypothesis testing; limited by model assumptions. Guides trials but needs empirical validation.
     • ∘Considerations: Integrates with ML for adaptive recommendations.

   • Composite microbiome health metrics: Machine learning-derived indices (e.g., GMWI2, hiPCA, DI-GM) that summarize high-dimensional microbial data into interpretable wellness scores.

     • ∘Example: GMWI2 and DI-GM for monitoring diet-induced wellness, correlating with lower glucose in interventions.
     • ∘Nuances and Implications: Actionable for clinicians; context-dependent (e.g., diet-specific). Outperforms traditional metrics but requires large training data.
     • ∘Considerations: Longitudinal tracking enhances utility in epidemiology.

   • Closed-loop optimization systems: Adaptive frameworks that iteratively refine dietary recommendations based on real-time data streams (e.g., continuous glucose monitoring, periodic microbiome sampling).

   • Software as a Medical Device (SaMD): Regulatory classification for AI-driven clinical decision-support systems intended for medical use.

Disclosure of potential conflicts of interest

The authors report there are no competing interests to declare.

Funding

This study received no external funding.

Ethical approval

Not applicable.

Clinical trial registration number

Not applicable.

Data availability statement

Not applicable.

Generative AI statement

The author(s) declare that Generative AI was used in the creation of this manuscript. During the preparation of this work, the authors utilized ChatGPT (GPT-5.2, OpenAI) during the final editing stages to improve word choice and readability. After using this tool, the authors reviewed and edited the content as needed and take full responsibility for the content of the publication.