(Figure 2c), and less than 1% of variation for postprandial triglyceride and postprandial C-peptide (Figure 2b and 2d). Gut microbiome (16S rRNA). We estimated the contribution of gut microbiome composition using relative bacterial taxonomic abundances and measures of community diversity and richness, derived from 16S rRNA high-throughput sequencing of baseline stool specimens (Supplemental Table 4). We found that without adjusting for any other individual characteristics the gut microbiome composition explained 7.5% of postprandial triglyceride6h-rise, 6.4% of postprandial glucoseiAUC0-2h and 5.8% of postprandial C-peptide1h-rise. Meal composition, habitual diet and meal context. To determine the impact of the macronutrient composition of meals, we measured triglyceride6h-rise and C-peptide1h-rise for two standardized home phase meals of contrasting macronutrient compositions (for triglyceride, comparison of meals 1 and 7: 85 vs 28g of carbohydrate and 50 vs 40 g of fat at breakfast, both followed by a lunch of 71g carbohydrate and 22g fat; for C-peptide, comparison of meal 2 and 3: 71 vs 41 g of carbohydrate and 22 vs 35 g of fat; Supplement Table 2) in subsets of participants (n=712 and n=186, respectively). GlucoseiAUC0-2h was measured for seven standardized meals (comparison of meals 1, 2, 4, 5, 6, 7 and 8: 28 -95 g carbohydrate; 0 -53 g fat) totalling 9,102 meals in 920 individuals. The proportions of variance explained by meal composition, habitual diet, and by meal context are shown for triglyceride6h-risein Figure 2b, for glucoseiAUC0-2hin Figure 2c, and for C-peptide1h-risein Figure 2d. A multivariate regression model (meals 1, 2, 4, 5, 6, 7 and 8) revealed that the Glucosei AUC0-2h (mmol/L*s) was significantly (P<0.001) reduced by 79, 142 and 185 for every 1g fat, fiber and protein respectively, after adjustment for carbohydrate consumption. Machine learning model. To estimate the unbiased predictive utility of the factors analysed, we used a machine learning approach robust to overfitting 22 . Random Forest regression models 23 were fitted using all the informative features (meal composition, habitual diet, meal context, anthropometry, genetics, microbiome, clinical and biochemical parameters) to predict triglyceride6h10 described in the Methods, we considered not only the effect of the meal macronutrient and energy content in the response (meal composition), but also considered how each individual responded on average to all their set meals relative to the population (individual glucose scaling), as well as the effect of the individual's meal-specific response, the error attributable to the glucose measurement and other sources of variation (including modifiable sources of variation such as sleep, circadian rhythm and exercise). We found that, consistent with the linear models described earlier, the ANOVA models show that there are three meal-related factors explaining individual glycemic responses. Meal macronutrient composition alters iAUC by 16.73% (95%CI 15.37 -18.92%), but the individual glucose...
