ArticleNature communications2025
Predicting metabolite response to dietary intervention using deep learning.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
26 citing papers in PubMed.
- Precise probiotic therapy: Advances, bottlenecks, and the road to microbiome-informed nutrition.Gut microbes · 2026Review
- Gut microbiome and metabolic health: mechanisms and precision interventions.Gut microbes · 2026Review
- Human-Centered Innovation: Precision Nutrition and the Future of Food.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Applying Artificial Intelligence and machine learning in precision nutrition.Nature communications · 2026Review
- The nutrigenomic-epigenetic axis in cancer: from dietary bioactives to precision oncology.Nutrition & metabolism · 2026Review
- AI-empowered human microbiome research.Gut · 2026Review
- Machine Learning and Artificial Intelligence in Nutrition Research: Analytical Methods, Applications, and Key Considerations.The Journal of nutrition · 2026Review
- Probiotics, Psychobiotics, Paraprobiotics, and Postbiotics in Gut-Brain Axis Modulation: Multi-Omics and AI-Driven Precision Nutrition for Cognitive Health.Probiotics and antimicrobial proteins · 2026Review
- A Dual-Target Microbial Therapeutic Strategy for Treating Metabolic Diseases: Complementary Mechanisms and Clinical Prospects ofMetabolites · 2026Review
- Mathematical Modelling and Intuition in Microbiology: A Perspective.Environmental microbiology · 2026Review
- Review
- Advancing Precision Nutrition Through Multimodal Data and Artificial Intelligence.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Artificial intelligence in functional food innovation: Bioactive enhancement and formulation optimization: A quasi-systematic review.Food chemistry: X · 2026Review
- Gut Microbial Composition, Oxidative Stress, and Immunity in Metabolic Disease: Toward Personalized Interventions.Antioxidants (Basel, Switzerland) · 2026Review
- Beyond scalar metrics: functional data analysis of postprandial continuous glucose monitoring in the AEGIS study.BMC medical research methodology · 2026Article
- Precision nutrition through diet-gut microbiome interactions: Emerging insights driven by artificial intelligence, microbiome health metrics, and mechanistic modeling.Gut microbes reports · 2026Review
- Individual polyphenol metabotypes and longevity: microbial biotransformation and personalized nutrition interventions.Frontiers in nutrition · 2026Article
- Nutrigenomics meets multi-omics: integrating genetic, metabolic, and microbiome data for personalized nutrition strategies.Genes & nutrition · 2025Review
- Harnessing Metabolomics to Advance Nutrition-Based Therapeutics for Inflammation: A Systematic Review of Randomized Clinical Trials.Metabolites · 2025Review
- Type 2 Diabetes and the Multifaceted Gut-X Axes.Nutrients · 2025Review
Corrections and comments
- Update of
Authors and funding
6 authors.
Funding
Abstract
Due to highly personalized biological and lifestyle characteristics, different individuals may have different metabolite responses to specific foods and nutrients. In particular, the gut microbiota, a collection of trillions of microorganisms living in the gastrointestinal tract, is highly personalized and plays a key role in the metabolite responses to foods and nutrients. Accurately predicting metabolite responses to dietary interventions based on individuals' gut microbial compositions holds great promise for precision nutrition. Existing prediction methods are typically limited to traditional machine learning models. Deep learning methods dedicated to such tasks are still lacking. Here we develop a method McMLP (Metabolite response predictor using coupled Multilayer Perceptrons) to fill in this gap. We provide clear evidence that McMLP outperforms existing methods on both synthetic data generated by the microbial consumer-resource model and real data obtained from six dietary intervention studies. Furthermore, we perform sensitivity analysis of McMLP to infer the tripartite food-microbe-metabolite interactions, which are then validated using the ground-truth (or literature evidence) for synthetic (or real) data, respectively. The presented tool has the potential to inform the design of microbiota-based personalized dietary strategies to achieve precision nutrition.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.