Human postprandial responses to food and potential for precision nutrition - PubMed (original) (raw)

. 2020 Jun;26(6):964-973.

doi: 10.1038/s41591-020-0934-0. Epub 2020 Jun 11.

Ana M Valdes # 2 3, David A Drew 4, Francesco Asnicar 5, Mohsen Mazidi 6, Jonathan Wolf 7, Joan Capdevila 7, George Hadjigeorgiou 7, Richard Davies 7, Haya Al Khatib 1 7, Christopher Bonnett 7, Sajaysurya Ganesh 7, Elco Bakker 7, Deborah Hart 6, Massimo Mangino 6, Jordi Merino 8 9 10 11, Inbar Linenberg 7, Patrick Wyatt 7, Jose M Ordovas 12 13, Christopher D Gardner 14, Linda M Delahanty 15, Andrew T Chan 4, Nicola Segata # 5, Paul W Franks # 6 16 17, Tim D Spector # 18

Affiliations

Human postprandial responses to food and potential for precision nutrition

Sarah E Berry et al. Nat Med. 2020 Jun.

Erratum in

Abstract

Metabolic responses to food influence risk of cardiometabolic disease, but large-scale high-resolution studies are lacking. We recruited n = 1,002 twins and unrelated healthy adults in the United Kingdom to the PREDICT 1 study and assessed postprandial metabolic responses in a clinical setting and at home. We observed large inter-individual variability (as measured by the population coefficient of variation (s.d./mean, %)) in postprandial responses of blood triglyceride (103%), glucose (68%) and insulin (59%) following identical meals. Person-specific factors, such as gut microbiome, had a greater influence (7.1% of variance) than did meal macronutrients (3.6%) for postprandial lipemia, but not for postprandial glycemia (6.0% and 15.4%, respectively); genetic variants had a modest impact on predictions (9.5% for glucose, 0.8% for triglyceride, 0.2% for C-peptide). Findings were independently validated in a US cohort (n = 100 people). We developed a machine-learning model that predicted both triglyceride (r = 0.47) and glycemic (r = 0.77) responses to food intake. These findings may be informative for developing personalized diet strategies. The ClinicalTrials.gov registration identifier is NCT03479866.

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Conflict of interest statement

Conflict of interest statement: TD Spector, SE Berry, AM Valdes, F Asnicar, PW Franks, LM Delahanty, N Segata, are consultants to Zoe Global Ltd (“Zoe”). J Wolf, G Hadjigeorgiou, R Davies, H Al Khatib, J Capdevila, C Bonnett, S Ganesh, E Bakker, P Wyatt and I Linenberg are or have been employees of Zoe. Other authors have no conflict of interest to declare.

Figures

Extended Data Figure 1.

Extended Data Figure 1.

Consort Diagrams for (a) UK and (b) US populations in the PREDICT 1 study.

Extended Data Figure 2.

Extended Data Figure 2.

Repeatability in the PREDICT 1 study

Extended Data Figure 3.

Extended Data Figure 3.

Frequency distribution of in-person ranking for 6 of meals shown in Figure 6a (High fat 40g = meal 7, High protein = meal 8, UK average = meal 2, High carb = meal 4, OGTT = meal 5, Uk avg at lunch = meal 2). n = 1102 participants

Extended Data Figure 4.

Extended Data Figure 4.

Machine Learning comparisons, cross validation and repeatability

Figure 1.

Figure 1.. Experimental design.

The PREDICT 1 study comprised a primary UK-based cohort (nmax = 1,002) and an independent US-based validation cohort (nmax = 100).

Figure 2.

Figure 2.. Variation in postprandial responses.

a. Inter-individual variation in triglyceride, glucose and insulin postprandial responses to the breakfast and lunch meal challenges in the clinic (n = 1002). b. Determinants of triglyceride6h-rise measured from DBS (comparison of meals 1 and 7). c. Determinants of glucoseiAUC0-2h measured by CGM (comparison of 7 test meals; 1, 2, 4, 5, 6, 7 and 8). d. Determinants of C-peptide1h-rise measured from DBS as a proxy for insulin (comparison of meals 2 and 3). Trait variations explained for each input variable are derived from separate (non-hierarchical) regression models. Values represent adjusted-R2 and error bars reflect 95% confidence intervals. Meal composition and Meal context adjusted-R2 values were derived from meal sample sizes as follows; triglyceride6h-rise, n = 712; glucoseiAUC0-2h, n = 9102; C-peptide1h-rise, n = 186. All other determinant values were derived from meal sample sizes as follows; triglyceride6h-rise, n = 920; glucoseiAUC0-2h, n = 958; C-peptide1h-rise, n = 960. TG = triglyceride, DBS = dried blood spots, CGM = continuous glucose monitor. * p<0.05, ** p<0.01, *** p<0.001 using multivariable linear regression.

Figure 3.

Figure 3.. Relationship of baseline values, genetic and microbiome factors to postprandial responses.

a. Pearson correlations between baseline values and postprandial prediction measures of 980 participants from the UK cohort. b. Heritability of postprandial responses (the ACE model was fitted on log-scaled postprandial responses for triglyceride, glucose, insulin and C-peptide) in 183 MZ and 47 DZ twin pairs. A; additive genetic component, C; shared environmental component, E; individual environmental component. c. SNP associations with postprandial measures focusing on SNPs identified in published postprandial trait GWAS– (n = 241; * p<0.05, *** p<0.001, using two-sided chi-squared test).

Figure 4 -

Figure 4 -. Machine learning models fitted in to postprandial measures.

a. Machine learning model for TG6h-rise in the UK cohort. b. Machine learning model for glucoseiAUC0-2h in the UK cohort. c. Machine learning model for C-peptide1h-rise postprandial responses in the UK cohort. The machine learning models in the US validation cohort are shown in Figures 4 d-f. The relationship between variables is expressed as Pearson’s correlation coefficient (r) and denoted with a regression line; n represents participant number; the features used to predict each value are the same as those listed in the linear models in Figure 2b–d.

Figure 5.

Figure 5.. Associations between fasting and postprandial values for TG, C-peptide and glucose concentrations with clinical measures in the UK cohort.

Receiver operator characteristics curves illustrating the predictive utility of fasting and postprandial TG, glucose and C-peptide measures to discriminate the bottom 70% from the top 30% of the cohort (cut-off ASCVD 10 year risk of 0.0183) for a. atherosclerotic cardiovascular disease (ASCVD) 10-year risk n = 951 independent samples from the UK and b. impaired glucose tolerance (IGT) n = 826 independent samples from the UK. The same analyses were performed in the US cohort (n = 92 independent samples) resulting in ROC AUC (95%CI) values for ASCVD 10 year risk of: C-peptide fasting AUC = 0.68 (0.56–0.80), postprandial AUC = 0.66 (0.54–0.77), both AUC = 0.69 (0.58–0.81); TG fasting AUC = 0.73(0.63–0.84), postprandial AUC = 0.75 (0.65–0.85), both AUC = 0.77 (0.67–0.88); and glucose fasting AUC = 0.74-(0.63–0.85), postprandial AUC = 0.64 (0.52–0.76), both AUC = 0.76 (0.64–0.85). For impaired glucose tolerance values were: C-peptide fasting AUC = 0.66 (0.53–0.80), postprandial AUC = 0.59 (0.46–0.72), both AUC = 0.67 (0.54–0.80); and Triglyceride fasting AUC = 0.66 (0.53–0.80), postprandial AUC = 0.59 (0.46–0.72), both AUC = 0.61 (0.54–0.80).

Figure 6.

Figure 6.. Person-specific diversity in postprandial response.

a. Proportion of times in the PREDICT 1 study that the ranking of the glycemic response (glucoseiAUC0-2h) to pairs of set meals was altered (n = 828, UK cohort). b. Effect size for factors explaining glycemic response. The different sources of variation were estimated using ANOVA, as described in Supplemental Table 3. The x-axis can be approximately interpreted as percent increase (or decrease) in iAUC attributable to the model parameters (n = 483 individuals) c. Time of day effects. (n = 920, UK cohort). Boxes show quartiles (25th, 50th, 75th percentiles); whiskers show the 95% interval.

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