Guizar-Heredia Rocio, Noriega Lilia G, Rivera Ana Leonor, Resendis-Antonio Osbaldo, Guevara-Cruz Martha, Torres Nimbe, Tovar Armando R
Departamento de Fisiología de la Nutrición, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Ciudad de México, México.
Centro de Ciencias de la Complejidad, Universidad Nacional Autónoma de México, Ciudad de México, México.
Arch Med Res. 2023 Apr;54(3):176-188. doi: 10.1016/j.arcmed.2023.02.007. Epub 2023 Mar 27.
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.
餐后血糖反应延长和升高（PPGR）现在被认为是导致代谢综合征和2型糖尿病发生的主要因素，而饮食干预可以预防这种情况。然而，预防PPGR改变的饮食建议并不总是成功的。新的证据表明，PPGR不仅取决于饮食因素，如碳水化合物含量或食物的血糖指数，还取决于遗传、身体组成、肠道微生物群等。近年来，持续葡萄糖监测使得通过机器学习方法对不同饮食食物对PPGR的影响进行预测成为可能，这些方法使用整合遗传、生化、生理和肠道微生物群变量的算法来识别它们与临床变量之间的关联，目的是个性化饮食建议。这使得个性化营养的概念得到了改进，因为现在可以通过这些预测推荐特定的饮食食物，以预防个体间差异很大的PPGR升高。营养基因组学、营养遗传学和代谢组学的研究结果是可以丰富预测算法的其他组成部分。因此，本综述旨在总结整合以预防PPGR为重点的个性化营养的组成部分的证据，并通过为个性化饮食管理的发展奠定基础及其对改善代谢疾病的影响来展示个性化营养的未来。
