Use of Metabotyping to Identify Individuals With Different Triglyceride Response Curves After Intake of High‐Fat Meals

were included for analysis. A latent class mixed model was applied for clustering. This method identifies subgroups by modelling both the trajectory over time and the effect of different meals, while allowing for individual variability. Four different clusters were identified. Since one cluster contained only two subjects, we continued with three clusters (n = 45). Cluster 1 (n = 18) displayed low postprandial triglyceride response, Cluster 2 (n = 21) had the peak at 2 h and returned to baseline at 6 h, and Cluster 3 (n = 6) had a continuous high triglyceride level after 2 h. Significant differences (p < 0.05) between the clusters were found for sex, fat mass, fat mass percentage, and baseline GlycA level. Through an unsupervised clustering method, this work revealed subgroups in a population based on postprandial triglyceride changes. This approach holds a potential for stratifying individuals by dynamic metabolic responses and detecting metabolically dysfunctional phenotypes.