Background and aims: Identifying specific patterns of the daily blood glucose profile (DGP) is crucial to implement personalized dietary strategies in individuals at risk for developing type 2 diabetes (T2D), in order to optimize their blood glucose levels. This study aimed to evaluate in real-life conditions if the dynamic parameters of the postprandial glucose response (PPGR) to a non-standardized breakfast could predict the features of the DGP in people at risk for developing T2D. Materials and methods: Continuous glucose monitoring (CGM) was performed for an average of 4 days at the baseline of the MEDGI-Carb trial in 159 adults at increased risk of T2D. According to a previously developed mechanistic model of glucose regulation after meals, 4 glucose response parameters were estimated on the PPGR to their non-standardized usual breakfast meal: baseline glucose; amplitude – the magnitude of the post-meal glucose concentrations; frequency – the rapidity of the post-meal glucose peak; damping – the rate of glucose decay after the meal. One-way ANOVA was used to assess differences between parameters characterizing different PPGR patterns identified by cluster analysis, and the Spearman correlation was used to evaluate relations between the PPGR parameters and the features of the DGP evaluated by CGM. Results: Two patterns (A & B) of PPGR were identified by cluster analysis. Pattern A compared to B had higher baseline, amplitude, frequency and damping. Individuals with pattern A showed a higher average daily glucose than those with pattern B (p=0.019), while the coefficient of variation (p=0.010) and the risk of low glucose levels (p<0.001) were lower. The average daily glucose concentration could be predicted by the baseline (rs=0.419, p<0.001) and the amplitude (rs=0.189, p=0.022) of the PPGR at breakfast, while the coefficient of variation of the DGP could be predicted by the amplitude (rs=0.218, p=0.008) – directly correlated – and by the frequency (rs=-0.179, p=0.031) and the damping (rs=-0.309, p<0.001) – inversely correlated – of the PPGR. Conclusions: Two different patterns of PPGR to a non-standardized meal could be identified in people at risk for T2D. The dynamic parameters of the PPGR were able to predict the features of the overall DGP, potentially facilitating the implementation of personalized dietary strategies to optimize glucose levels throughout the day, thus reducing the risk for developing T2D.