Dynamic Prediction of Postprandial Glycemic Response and Personalized Dietary Interventions Based on Machine Learning

Shihan Wang, Shuoning Song, Junxiang Gao, Weiming Wu, Yong Fu, Tao Yuan, Weigang Zhao,
Dynamic Prediction of Postprandial Glycemic Response and Personalized Dietary Interventions Based on Machine Learning,
The Journal of Nutrition,
2025,
,
ISSN 0022-3166,
https://doi.org/10.1016/j.tjnut.2025.09.023.
(https://www.sciencedirect.com/science/article/pii/S002231662500567X)
Abstract: Effective interventions to manage postprandial glycemia are critical because postprandial glycemic response (PPGR) is strongly linked to cardiovascular and metabolic disease. Considering the interindividual variability in PPGR, the widespread application of dietary interventions has led to an increasing recognition that a universal, one-size-fits-all approach to dietary intervention is far from ideal. This highlights the need for personalized nutrition plans. In this context, we explored the potential benefits of leveraging machine learning to predict PPGR and guide personalized dietary interventions. We also critically examined the limitations of current approaches and outlined promising future directions for advancing this field.
Keywords: postprandial glycemic response; iAUC120; postprandial blood glucose; machine learning; personalized nutrition interventions; interindividual variability; gut microbiota