Variability In Postprandial Responses Research Articles

Personal metabolic responses to food predicted using multi-omics machine learning in 1,100 twins and singletons: The PREDICT I Study.

AbstractGlycemic, insulinemic and lipemic postprandial responses are multi-factorial and contribute to diabetes, obesity and CVD. The aim of the PREDICT I study is to assess the genetic, metabolic, metagenomic, and meal-context contribution to postprandial responses, integrating the metabolic burden and gut microbiome to predict individual responses to food using a machine learning algorithm.A multi-center postprandial study of 1,000 individuals from the UK (unrelated, identical and non-identical twins) and 100 unrelated individuals from the US, assessed postprandial (0–6h) metabolic responses to sequential mixed-nutrient dietary challenges (50 g fat and 85 g carbohydrate at 0 h; 22 g fat and 71 g carbohydrate at 4h) in a clinic setting. Glycemic responses to 5 duplicate isocaloric meals of different macronutrient content and self-selected meals (> 100,000), were tested at home using a continuous glucose monitor (CGM). Baseline factors included metabolomics, genomics, gut metagenomics and body composition. Genetic contributions to postprandial responses were determined by classical twin methods.Inter-individual variability in postprandial responses (glucose, insulin and triacylglycerol (TG)) was high in the clinic setting: iAUC IQR (median) was (n = 644); glucose (0–2h) 1.97 (1.89) mmol/L.h, insulin (0–2h) 45.6 (67.7) mIU/L.h and TG (0–6h) 2.37 (2.42) mmol/L.h. The unadjusted genetic contribution for glucose, insulin and TG responses were 54%, 29% and 27% respectively. Within-individual concordance (ICC) in glucose responses (iAUC 0–2h) for at home duplicate isocaloric meals was moderate-to-high, depending on the test meal: ICC (95%CI) was; high carbohydrate 0.62 (0.58,0.66), (carbohydrate = 95g/76% energy; n = 764), average lunch 0.57 (0.53, 0.62) (carbohydrate = 68g/54% energy; n = 763), OGTT 0.65 (0.61,0.70) (carbohydrate = 75 g; n = 754), high fat 0.35 (0.28, 0.41) (fat = 40g/71% energy; n = 576) and high protein 0.56, (0.48,0.62) (protein = 41g/32% energy; n = 364). An interim machine learning algorithm predicted 46% of the variation in glycemic responses based on meal content, meal context and participant's baseline characteristics, excluding genetic and microbiome features. Only 29% of variation could be explained by the macronutrient content of the meal.This is the most comprehensive postprandial study performed to date. The large and modifiable variation in metabolic responses to identical meals in healthy people explains why ‘one size fits all’ nutritional guidelines are problematic. The genetic component to these responses is moderate, leaving the majority of the variation potentially modifiable. By collecting information on glucose responses to > 100,000 meals, alongside environmental, genetic and microbiome variables, we will have excellent power to use machine learning to optimise and predict individual responses to foods.

69-LB: Postprandial 2-hr Rise in Insulin Is a Strong Predictor of Glycemic Responses to Different Meals in Healthy and Prediabetic Individuals: The PREDICT I Study

Objective: Postprandial glucose is a predictor of later diabetes and vascular complications. The study aims to identify determinants of inter-individual variability in postprandial responses in healthy and prediabetic individuals. Methods: A multi-centre study in the UK and the U.S. assessed the postprandial insulin, glucose and triglyceride responses to a dietary challenge (50g fat and 85g carbohydrate) in the clinic. The inter-individual variability in postprandial glycemic responses was then measured over 2 weeks for isocaloric meals of different macronutrient content using a continuous glucose monitor (CGM). Interim analyses of the 30- and 120-min insulin rises and meal content contributions to the incremental glucose area under the curve (iAUC) were performed in 573 individuals (age 47.5 (19-66) years, BMI 25.7 (17-53) kg/m2; F=76%; n=108 with HbA1c > 5.7% [prediabetic]). Results: A machine learning algorithm showed that 29% of variation in glucose iAUC of at home meals was explained by the meal’s macronutrient content. In clinic, the 30min insulin postprandial rise (strongly associated with same meal’s glucose iAUC r=0.39 p=1.25x10[-20]) was not correlated with at-home postprandial glucose iAUCs in healthy (r=-0.012 p=0.62) or prediabetic (r=-0.074 p=0.17) individuals after adjustment for age, sex and BMI. However, 2hr insulin rise was significantly associated in healthy (r=0.13 p=1.6 x 10[-7]) and prediabetic (r=0.22 p=3.2x10[-5]) individuals, explaining 15% of the variance in glucose iAUCs. This was the same for all meals regardless of macronutrient content. Conclusions: The 2hr postprandial insulin rise is a predictor of glycemic responses to different meal types. Ongoing exploration in PREDICT I using additional environmental, genetic and microbiome variables should advance our ability to predict an individual’s response to food. Disclosure P.W. Franks: Board Member; Self; Zoe Ltd. Research Support; Self; AstraZeneca, Boehringer Ingelheim International GmbH, Lilly Diabetes, Novo Nordisk A/S, Novo Nordisk Foundation, Sanofi, Servier. S. Berry: Consultant; Self; Zoe Ltd. A.T. Chan: Consultant; Self; Bayer AG, Janssen Pharmaceuticals, Inc., Pfizer Inc. R.J. Davies: Employee; Self; Zoe Global Limited. D.A. Drew: Consultant; Spouse/Partner; PathAI. T.D. Spector: Stock/Shareholder; Self; Zoe Global Ltd. A.M. Valdes: Consultant; Self; Zoe Ltd Global. Research Support; Self; Pfizer Inc. Funding UK National Institute for Health Research; UK Wellcome Trust; Zoe Global Limited

Nutrigenetics of the Postprandial Lipoprotein Metabolism: Evidences From Human Intervention Studies

Accumulating evidence suggests that elevated plasma triglycerides concentrations, in both the fasting and the postprandial states, may pose a significant independent risk for cardiovascular disease (CVD). Both fasting and postprandial lipoprotein concentrations vary substantially among individuals, and this inter individual variability is driven by a combination of non-genetic and genetic factors. Regarding the genetic component, the efforts to elucidate the variability in postprandial response have resulted in the identification of associations with multiple lipid candidate genes. However, most reported associations are based on very simple models including one single-nucleotide polymorphism (SNP) or haplotype at a time and small sample sizes. Progress in this promising area of research requires more comprehensive experimental models, including larger sample sizes that will allow investigating gene-gene interactions. Reviews of the literature in the area of ApoA5, GCKR, and PLIN genes and postprandial lipemia are used to demonstrate the complexities of genotype-phenotype associations. Knowledge of how these and other genes influence postprandial response should increase the understanding of personalised nutrition.

SNP analyses of postprandial responses in (an)orexigenic hormones and feelings of hunger reveal long-term physiological adaptations to facilitate homeostasis

The postprandial responses in (an)orexigenic hormones and feelings of hunger are characterized by large inter-individual differences. Food intake regulation was shown earlier to be partly under genetic control. This study aimed to determine whether the postprandial responses in (an)orexigenic hormones and parameters of food intake regulation are associated with single nucleotide polymorphisms (SNPs) in genes encoding for satiety hormones and their receptors. Peptide YY (PYY), glucagon-like peptide 1 and ghrelin levels, as well as feelings of hunger and satiety, were determined pre- and postprandially in 62 women and 41 men (age 31+/-14 years; body mass index 25.0+/-3.1 kg/m(2)). Dietary restraint, disinhibition and perceived hunger were determined using the three-factor eating questionnaire. SNPs were determined in the GHRL, GHSR, LEP, LEPR, PYY, NPY, NPY2R and CART genes. The postprandial response in plasma ghrelin levels was associated with SNPs in PYY (215G>C, P<0.01) and LEPR (326A>G and 688A>G, P<0.01), and in plasma PYY levels with SNPs in GHRL (-501A>C, P<0.05) and GHSR (477G>A, P<0.05). The postprandial response in feelings of hunger was characterized by an SNP-SNP interaction involving SNPs in LEPR and NPY2R (668A>G and 585T>C, P<0.05). Dietary restraint and disinhibition were associated with an SNP in GHSR (477G>A, P<0.05), and perceived hunger with SNPs in GHSR and NPY (477G>A and 204T>C, P<0.05). Part of the inter-individual variability in postprandial responses in (an)orexigenic hormones can be explained by genetic variation. These postprandial responses represent either long-term physiological adaptations to facilitate homeostasis or reinforce direct genetic effects.

Dietary, physiological, genetic and pathological influences on postprandial lipid metabolism

Most of diurnal time is spent in a postprandial state due to successive meal intakes during the day. As long as the meals contain enough fat, a transient increase in triacylglycerolaemia and a change in lipoprotein pattern occurs. The extent and kinetics of such postprandial changes are highly variable and are modulated by numerous factors. This review focuses on factors affecting postprandial lipoprotein metabolism and genes, their variability and their relationship with intermediate phenotypes and risk of CHD. Postprandial lipoprotein metabolism is modulated by background dietary pattern as well as meal composition (fat amount and type, carbohydrate, protein, fibre, alcohol) and several lifestyle conditions (physical activity, tobacco use), physiological factors (age, gender, menopausal status) and pathological conditions (obesity, insulin resistance, diabetes mellitus). The roles of many genes have been explored in order to establish the possible implications of their variability in lipid metabolism and CHD risk. The postprandial lipid response has been shown to be modified by polymorphisms within the genes for apo A-I, A-IV, A-V, E, B, C-I and C-III, lipoprotein lipase, hepatic lipase, fatty acid binding and transport proteins, microsomal triglyceride transfer protein and scavenger receptor class B type I. Overall, the variability in postprandial response is important and complex, and the interactions between nutrients or dietary or meal compositions and gene variants need further investigation. The extent of present knowledge and needs for future studies are discussed in light of ongoing developments in nutrigenetics.