A New Approach to Personalized Nutrition: Postprandial Glycemic Response and its Relationship to Gut Microbiota

Mar 29, 2023Archives of medical research

Personalized Nutrition Based on Blood Sugar Response After Eating and Its Link to Gut Bacteria

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Circadian Biology on OpenScience ↗PubMed ↗DOI ↗

Abstract

Elevated postprandial glucose responses (PPGR) may contribute to the development of metabolic syndrome and type 2 diabetes.

Dietary interventions may help prevent alterations in PPGR, but past recommendations have not always succeeded.
PPGR is influenced by various factors, including genetics, body composition, and gut microbiota, in addition to dietary components.
Continuous glucose monitoring enables predictions of dietary effects on PPGR using machine learning that incorporates multiple biological variables.
Personalized nutrition can be enhanced by integrating findings from nutrigenomics, nutrigenetics, and metabolomics into predictive algorithms.
This review summarizes evidence for components of personalized nutrition aimed at preventing elevated PPGRs and discusses future directions for individualized dietary management.

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A prolonged and elevated postprandial glucose response (PPGR) is now considered a main factor contributing for the development of metabolic syndrome and type 2 diabetes, which could be prevented by dietary interventions. However, dietary recommendations to prevent alterations in PPGR have not always been successful. New evidence has supported that PPGR is not only dependent of dietary factors like the content of carbohydrates, or the glycemic index of the foods, but is also dependent on genetics, body composition, gut microbiota, among others. In recent years, continuous glucose monitoring has made it possible to establish predictions on the effect of different dietary foods on PPGRs through machine learning methods, which use algorithms that integrate genetic, biochemical, physiological and gut microbiota variables for identifying associations between them and clinical variables with aim of personalize dietary recommendations. This has allowed to improve the concept of personalized nutrition, since it is now possible to recommend through these predictions specific dietary foods to prevent elevated PPGRs that are highly variable among individuals. Additional components that can enrich the predictive algorithms are findings of nutrigenomics, nutrigenetics and metabolomics. Thus, this review aims to summarize the evidence of the components that integrate personalized nutrition focused on the prevention of PPGRs, and to show the future of personalized nutrition by laying the groundwork for the development of individualized dietary management and its impact on the improvement of metabolic diseases.

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A New Approach to Personalized Nutrition: Postprandial Glycemic Response and its Relationship to Gut Microbiota

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