Gut Microbiota Composition is Associated with Responses to Peanut Intervention in Multiple Parameters Among Adults with Metabolic Syndrome Risk

Jul 30, 2021Molecular nutrition & food research

Gut Bacteria Are Linked to How Adults at Risk for Metabolic Syndrome Respond to Peanut Treatment in Different Ways

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Abstract

Improvements in metabolic syndrome risk factors can be predicted with over 70% accuracy based on gut microbiota data.

Peanut consumption may influence gut microbiota composition in adults at risk of metabolic syndrome.
High inter-individual variability is observed in clinical and anthropometrical responses to peanut consumption.
Gut microbiota composition shows minor changes both across individuals and over time.
A machine-learning algorithm predicts responses to peanut intervention based on microbiome data.
Individual gut microbiota configurations could modulate metabolic responses to dietary peanut intake.

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INTRODUCTION: Peanuts are widely consumed as a meal ingredient and a snack, and are commonly considered as a healthy food based on their nutrient profile. Peanut consumption has been associated with a lower risk of metabolic syndrome (MetS) in epidemiological studies. This study aims to investigate whether consuming peanuts affects the gut microbiota in adults with risk of MetS and whether the intervention effect of peanuts is associated with gut microbiota composition.

METHODS AND RESULTS: This study analyzes the gut microbiota of subjects from a 12-week randomized clinical trial comparing consumption of either peanuts or isocaloric carbohydrate bars. It is observed that there is high inter-individual variability on multiple clinical and anthropometrical parameters in response to peanut consumption. Meanwhile, the gut microbiota composition is also highly person-specific and have minor changes when compared laterally or longitudinally. This study employs a machine-learning algorithm and establishes prediction models using the microbiome data and the responsiveness data of different parameters in subjects with peanut intervention. As a result, it is found that the improvement of MetS risk and numerous parameters, including diastolic blood pressure, body weight, waist circumference, and fasting blood glucose level can be predicted for responsiveness with high accuracy that has a value of area under curve over 0.70 by receiver operating characteristic analysis.

CONCLUSION: Together, the findings of this study suggest that individual gut microbiota configuration may modulate host metabolism and alter an individual's response to peanut intervention, thus highlighting the importance of personalized nutrition.

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Gut Microbiota Composition is Associated with Responses to Peanut Intervention in Multiple Parameters Among Adults with Metabolic Syndrome Risk

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