To Investigate Associations Between Postprandial Meal Response and Genetic Variation in Young Healthy Male Adults: A Pilot Study

Abstract

The science of nutrigenomics assesses gene–nutrient interactions using nutrient-related genetic markers or site-specific genetic variations in genes known as single nucleotide polymorphisms (SNPs). SNPs are one aspect of genetic variability that can impact an individual’s response to food, including lipid and glucose metabolism, insulin response and appetite. In general, genetic differences can influence absorption, metabolism, uptake, utilisation and excretion of nutrients, ultimately affecting several physiological and nutritional outcomes. The effect of genetic differences can be assessed by changes in physiological outcomes during the postprandial state. The literature reports known associations between a particular SNP and a change in a physiological outcome; the robustness of many of these associations is uncertain. This study aimed to investigate the association between a range of physiological measures and the related 26 SNPs located in specific genes to consider the strength of their relationship during the postprandial digestive response to a standardised breakfast meal. This included: plasma concentrations of vitamin D and the cytochrome P450 family 2 subfamily R member 1 gene (CYP2R1) and the group-specific component vitamin D binding protein gene (GC); iron and the homeostatic iron regulator protein gene (HFE) and the solute carrier family 17 member 1 (SLC17A1) and the transmembrane protease serine 6 gene (TMPRSS6) and the type-2 transferrin receptor gene (TRF2) and the transferrin coding gene (TF); zinc and the solute carrier family 30 member 3 gene (SLC30A3); saturated fat and the apolipoprotein A-II gene (APOA2); total cholesterol and the apolipoprotein A5 gene (APOA5); low-density lipoprotein and the ATP-binding cassette subfamily G member 8 gene (ABCG8); high-density lipoprotein and the ATP-binding cassette subfamily A member 1 gene (ABCA1); triglycerides and the angiopoietin-like 3 gene (ANGPTL3); glucose and the adenylate cyclase 5 gene (ADCY5); insulin and the insulin-receptor substrate 1 gene (IRS1); dietary intake of omega-6 & -3 and the fatty acid desaturase 1 gene (FADS1); nutrients to assess energy balance and the mitochondrial uncoupling protein 1 gene (UCP1); total fat and the transcription factor 7-like 2 gene (TCF7L2); saturated and unsaturated fat and the fat-mass and obesity-related alpha-ketoglutarate dependent dioxygenase gene (FTO); monounsaturated fatty acids and the peroxisome proliferator-activated receptor γ2 gene (PPARγ2); protein and FTO gene; appetite scores for fat-taste perception and the cluster determinant 36 gene (CD36); sugar preference and the glucose transporter type 2 gene (GLUT2); hunger and the neuromedin beta gene (NMB). Thirty young, healthy males (20–34 years) participated in an experimental study and consumed a standardised breakfast meal. Blood samples were collected before and hourly for 4 hours after a meal. Plasma samples were used to assess nutrient concentrations or physiological biomarker status. Buccal swabs were collected and analysed using the Illumina assay technique to assess SNPs. An online visual analogue 100-point scale was used to assess appetite scores upon arrival, immediately following ingestion, 30 minutes after ingestion and hourly for 4 hours after ingestion. There was a positive association between the insulin-signalling IRS1 gene variant rs2943641, the typical risk (TT) compared to the increased risk (CT or CC), in relation to postprandial insulin levels, χ² = 1, N = 30, P = 0.0025, 95% confidence interval (CI) [1.61, 4.93]. The UCP1 gene variant -3826 rs1800592, the typical risk (AA) compared to the increased risk (GG or GA), was positively associated with participants’ body-mass index (BMI), χ² = 1, N = 30, P = 0.011, 95% CI [0.081, 0.757]. The “sugar preference” GLUT2 gene variant rs5400 was insignificant between the typical risk (CC) compared to the increased risk (CT or TT) in relation to an elevated preference for sugar intake, χ² = 1, N = 30, P = 0.07, 95% CI [0.94, 19.81]. However, a larger sample size may have revealed differences as significant. The remaining measures (vitamin D, iron, zinc, omega-6 and -3, protein, total fat, unsaturated and monounsaturated fat, cholesterol, low density lipoprotein, high density lipoprotein, triglycerides, fat taste, hunger and glucose) did not appear to associate with the genetic variants. The findings of this study suggest a significant relationship between the associated SNP and digestive responses for the IRS1 gene variant rs2943641 and the UCP1 gene variant -3826 rs1800592. Despite scientific literature indicating statistically significant associations between other genetic variants and physiological outcomes, this study did not confirm the associations. However, as a pilot experimental study, we acknowledge that the power to determine other associations may be too small due to the limited sample size and the complexity of the genetic assessment. This study has emphasised many known associations between a particular SNP and a change in a physiological outcome, whilst providing information on how a genetic variant could increase health risks. Future research to establish the robustness and statistically significant associations between genetic variation and related psychological outcomes is needed. Whether the science of nutrigenomics is the key to producing the “perfect diet,” the efficacy and utility of nutrient-related genetic markers are still under investigation.