Predicting metabolite response to dietary intervention using deep learning

Tong Wang, Hannah D. Holscher, Sergei Maslov, Frank B. Hu, Scott T. Weiss and Yang-Yu Liu ()
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Tong Wang: Harvard Medical School
Hannah D. Holscher: University of Illinois at Urbana-Champaign
Sergei Maslov: University of Illinois at Urbana-Champaign
Frank B. Hu: Harvard Medical School
Scott T. Weiss: Harvard Medical School
Yang-Yu Liu: Harvard Medical School

Nature Communications, 2025, vol. 16, issue 1, 1-12

Abstract: 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.

Date: 2025
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DOI: 10.1038/s41467-025-56165-6

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