Fig. 1: Quantifying postprandial response through CGM.

a, Overview of study design and data types. Left, at the baseline, the data on omics and clinical tests were collected. Mid-bottom, each individual ate seven different carbohydrate meals and three different mitigator foods with rice with CGM data and food log collected. Mid-top, an example CGM curve of a day is presented. Right, participants were then stratified into carb-response types based on which meal produced the highest spike and into metabolic traits results. b, Mean CGM curves of PPGR after different meals. c, Glucose AUC above baseline and time from baseline to peak for different meals. CGM curves are extracted into different features (and presented with mean and standard errors (error bar indicates 2 standard errors). Bars of standardized carbohydrate meals were ordered by mean value. The number of CGM curves (each dot) is as follows: rice 115, potatoes 92, bread 99, pasta 65, grapes 98, beans 46 and mixed berries 53. Extracted CGM features are compared between different carbohydrate meals. Each participant was instructed to eat each meal at least twice on two different days. d, Heatmap of extracted CGM PPGR features. Each element is the mean value of extracted CGM features. Each column (feature) is then centered and scaled. e, Association between different extracted features and nutrient contents of the standardized carbohydrate meal. For fiber, proteins and fat in study meals, it’s the Spearman correlation with statistics calculated from the R package ‘correlation’ under default settings with asymptotic t approximation with a two-sided test. The last column is treated as binary (starch versus non-starch food) with correlation (R) obtained from a simple linear model and P value obtained from a two-sided t-test of the coefficient. Asterisks indicate significance (pFDR < 0.05). P values were FDR-corrected for the whole matrix. Illustration in a created with BioRender.com.

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