(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...

The larval stages of malaria vector mosquitoes develop in water pools, feeding mostly on microorganisms and environmental detritus. Richness in the nutrient supply to larvae influences the development and metabolism of larvae and adults. Here, we investigated the effects of larval diet on the development, microbiota content and permissiveness to Plasmodium of Anopheles coluzzii. We tested three fish diets often used to rear mosquitoes in the laboratory, including two pelleted diets, Dr. Clarke’s Pool Pellets and Nishikoi Fish Pellets, and one flaked diet, Tetramin Fish-Flakes. Larvae grow and develop faster and produce bigger adults when feeding on both types of pellets compared with flakes. This correlates with a higher microbiota load in pellet-fed larvae, in agreement with the known positive effect of the microbiota on mosquito development. Larval diet also significantly influences the prevalence and intensity of Plasmodium berghei infection in adults, whereby Nishikoi Fish Pellets-fed larvae develop into adults that are highly permissive to parasites and survive longer after infection. This correlates with a lower amount of Enterobacteriaceae in the midgut microbiota. Together, our results shed light on the influence of larval feeding on mosquito development, microbiota and vector competence; they also provide useful data for mosquito rearing.

Summary Understanding how to modulate appetite in humans is key to developing successful weight loss interventions. Here, we show that postprandial glucose dips 2-3h after a meal are a better predictor of postprandial self-reported hunger and subsequent energy intake than peak glucose 0-2h and glucose iAUC 0-2h. We explore the link between postprandial glucose, appetite, and subsequent energy intake in 1070 participants from a UK discovery and US validation cohort, consuming 8,624 standardised meals followed by 71,715 ad libitum meals, using continuous glucose monitors to record postprandial glycemia. For participants eating each of the standardised meals, the average postprandial glucose dip 2-3h relative to baseline level predicts an increase in hunger 2-3h (r=0.16 P=<0.001), shorter time until next meal (r=-0.14 P=<0.001), greater energy intake 3-4h (r=0.19 P=<0.001) and greater energy intake 24h (r=0.27 P<=0.001). Results aredirectionally consistent in the US validation cohort. These data provide a quantitative assessment of the relevance of postprandial glycemia in appetite and energy intake modulation. Funding Zoe Global Ltd, Wellcome Trust, NIHR.

The Personalised REsponses to DIetary Composition Trial (PREDICT) was a single-arm, single-blind intervention study that utilized both standardized “test” meals and captured “free-living” non-standardized food consumption, to predict an individual’s metabolic response to foods based on the person’s characteristics (including their metabolomic and gut microbiome profiles), meal composition (macronutrients and energy content) and meal context (including time of day, sleep and exercise). The initial study commenced in June 2018 and targeted 1,000 generally healthy participants between the ages of 18 and 65 years for enrollment in the United Kingdom, and recruited from the existing and ongoing TwinsUK research cohort and the general population. In December 2018, an independent trial mirroring the UK protocol was launched in parallel at Massachusetts General Hospital to serve as a US-based validation cohort including 100 healthy individuals in the general population. The overall objective of these trials was to predict glucose, insulin, lipid and other postprandial responses to foods based on the individual’s characteristics, including molecular biomarkers and lifestyle factors as well as the nutritional composition of the food. The studies, collectively termed PREDICT 1, were completed in May 2019. The study has delivered highly-dimensional multi-omic data from which the predictors of individual postprandial responses have been determined. These findings will be used to design personalized dietary interventions focused on key health outcomes. This protocol is registered with the US National Institutes of Health trial registry, ClinicalTrials.gov: NCT03479866.

No abstract