The association between genetic variation and preprandial and postprandial digestive responses in healthy young men: A pilot study

doi:10.21203/rs.3.rs-2219674/v1

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The association between genetic variation and preprandial and postprandial digestive responses in healthy young men: A pilot study

https://doi.org/10.21203/rs.3.rs-2219674/v1

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Background

An elevated postprandial glycaemic and related physiological response is associated with diabetes and cardiovascular disease risk. Several factors, including genetics, may influence interpersonal differences in preprandial baseline markers and postprandial meal responses. This study examined the association between genetic variation and physiological outcomes during the preprandial and postprandial digestive responses in thirty healthy young men.

Methods

In this experimental study, thirty healthy men aged 20–34 consumed a standardised breakfast meal. Blood samples were collected before the meal and hourly for 4 hours after the meal to assess lipids and fatty acids (saturated and unsaturated fat, long-chain polyunsaturated fatty acids, cholesterol, low-density lipoprotein, high-density lipoprotein and triglycerides), nutrients (vitamin D, iron and zinc), glucose and insulin. Participants’ weight and height were collected to determine their body mass index (BMI). An online visual analogue 100-point scale was used to assess appetite changes upon arrival, immediately following meal consumption, 30 minutes after and hourly for 4 hours. Buccal swabs were collected and assessed for single nucleotide polymorphisms (SNPs). Data were analysed using multiple regression analysis.

Results

The insulin-receptor substrate 1 gene (IRS1) polymorphism rs2943641 significantly predicted elevated fasting insulin levels (R² = 0.639, F (3,26) = 15.34, β = 6.376, P = < 0.0001). The mitochondrial uncoupling protein 1 gene (UCP1) polymorphism rs1800592 and the peroxisome proliferator-activated receptor γ2 gene (PPARγ2) polymorphism rs1801282 significantly predicted participants’ BMI (R² = 0.261, F (2,27) = 4.759, β = -2.764, P = 0.007 and R² = 0.200, F (2,27) = 3.371, β = 3.291, P = 0.024 respectively). The remaining SNPs did not appear to associate with our cohort’s related physiological or nutrient outcome.

Conclusions

According to the finding of this exploratory study, three SNPs significantly predicted participants’ fasting insulin levels and BMI.

digestive response

gene–nutrient interaction gene variant

nutrigenomics

preprandial

postprandial

SNPs

A genetic and nutrient-based approach has been proposed for tailoring dietary requirements to maintain healthy conditions (Kohlmeier, 2015; Mead, 2007; Paoloni-Giacobino et al., 2003; Ramos-Lopez et al., 2017). Despite this, the dietary advice given to individuals to control their blood lipids and glucose levels is usually quite similar, even among those with various health histories. Research suggests that some people may not respond to these dietary recommendations to support optimal health (Fenech, 2005; Zeevi et al., 2015). Contrary to population-level guidance, there is considerable interpersonal variation in the glycaemic and lipid response to a meal and, as such, differences in how individuals respond to particular diets (Blaak et al., 2012; Carpenter et al., 2015; Cavalot et al., 2011; Dunstan et al., 2012; Evert et al., 2014; Gallwitz, 2009; Himsworth, 1934; Le Chatelier et al., 2013; Li et al., 2014; Mendes-Soares et al., 2019; Zeevi et al., 2015).

Postprandial hyperglycaemia is an independent risk factor for the development of type 2 diabetes (Evert et al., 2014), cardiovascular disease (Gallwitz, 2009), liver cirrhosis (Nishida et al., 2020) and is associated with obesity (Blaak et al., 2012), and enhanced all-cause mortality in both type 2 diabetes (Cavalot et al., 2011) and cancer (Lamkin et al., 2009). There is evidence that the postprandial digestive response may better indicate metabolic health and long-term health conditions (Azpiroz et al., 2014; Berry et al., 2020; Lopez-Miranda & Marin, 2010). The postprandial digestive response to a meal is therefore considered a relevant assessment for identifying the changes in digestion and metabolism since it allows evaluation during the entire digestive response to a meal (den Hoed et al., 2008; den Hoed et al., 2009; Ellis et al., 2021; Monrroy et al., 2019).

Interpersonal differences in the meal response may be related to genetics (Carpenter et al., 2015) and insulin sensitivity (Himsworth, 1934), both of which may be affected by body mass index (BMI) (Belkina & Denis, 2010). Gene–nutrient interactions are poorly understood, partly due to limited research and the complex interactions that cause disease in one individual and not another (Dib et al., 2019; Ellis et al., 2021). Well-designed gene–nutrient studies are needed to provide robust evidence that can translate into clinical applicable health benefits (Berry et al., 2020; Ordovas, 2008).

Single nucleotide polymorphisms (SNPs) are one aspect of genetic variability that can impact an individual’s digestive response to a meal, including lipid and glucose metabolism, insulin response and appetite (den Hoed et al., 2008; den Hoed et al., 2009; Desmarchelier et al., 2014; Mortensen et al., 2012). The process of the postprandial digestive responses depends on an individual’s metabolic efficiency and subtle differences in genetic variability (Desmarchelier et al., 2014). This variability is due to different allele forms caused by genes’ polymorphisms, and the most common form is site-specific variations or SNPs (Camp & Trujillo, 2014). Therefore, SNPs can be used as nutrient-related genetic markers to help study the associations between genetic variation and digestive responses influenced by dietary compositions (Lopez-Miranda & Marin, 2010; Vincent et al., 2002).

Over ten million SNPs exist in the human genome, and genome-wide associated studies (GWAS) have identified their interactions with nutrients that may increase disease susceptibility (Chial, 2008; Zeisel, 2007). Many disease risk variants have been identified that are influenced by gene–nutrient interactions. For example, the intake of folate, vitamin B6 and B12 are associated with the methylenetetrahydrofolate reductase (MTHFT) gene variant rs1801133. This gene variant regulates homocysteine metabolism and increases the risk of breast cancer if folate, B6 and B12 dietary intakes are low (Jiang-Hua et al., 2014). In many postprandial disease risk factors studies, polymorphisms associated with dietary nutrient-related genetic markers are examined within a specific population (Lairon et al., 2007; Lopez-Miranda & Marin, 2010). For instance, the association of the GLU2 gene variant rs5400 with glucose homeostasis and insulin release during the postprandial state. This gene variant increases the risk of type 2 diabetes, especially among people with the genotype TT or TC who have a greater desire for sugary foods (Eny et al., 2008; Pénicaud et al., 2002). Other research has focused on genetic susceptibility and eating behaviours contributing to obesity (Herle et al., 2020). However, the robustness of gene–nutrient interactions has yet to be determined (Dib et al., 2019; Ellis et al., 2021; Mullins et al., 2020).

To investigate associations between genetic variation and preprandial baseline markers, as well as postprandial digestive responses, we performed a pilot exploratory study. The research team prioritised SNPs associated with nutrition and health outcomes investigated following a meal challenge. The SNPs were assessed for their relationship to physiological and nutrient outcomes based on previously published GWAS results. This study assessed 26 SNPs and related physiological responses in 30 participants. We aimed to assess the impact of the nutrition- and lifestyle-related genes concerning changes in a related physiological outcome following a meal challenge. A key question is whether the previously published associations hold true for healthy young adults.

Study Design

Thirty healthy young men (20–34 years) participated in an experimental study and consumed a standardised meal. Blood samples and questionnaires were collected before and hourly for 4 hours after the meal. The trial was conducted between October and December 2020 at the University of Auckland Clinical Research Centre. The clinical trial, Ref: NCT04545398, was approved by the New Zealand Ministry of Health’s Health and Disability Ethics Committees (Ref: 19/STH/226) and conducted following the ethical standards in the 1964 Declaration of Helsinki. This study is a small subsection of a more extensive investigation (Pham et al., 2022).

Eligibility Criteria

All participants were omnivores willing to consume a standardised meal. Eligible participants completed an online screening which included a health survey. Males were recruited from the millennial generation (20–34 years), as males typically have a greater postprandial lipid response than females (Lopez-Miranda & Marin, 2010). The study excluded participants with chronic health conditions, hyperlipidaemia, BMI ≥ 30 kg/m², use of medications (except occasional use of nonsteroidal anti-inflammatory drugs and antihistamines), history of anosmia and ageusia (issues with taste and smell), current dieting or disordered eating pattern, smokers and recreational drug users.

Meal Preparation

The standardised meal consisted of two hot burrito wraps with meat, vegetables and sauce, served in the morning after an overnight fast. The meat was a 220 g raw serving of minced beef (approximately 160 g cooked). The meal was prepared according to standardised recipes by the research dietitians and served at the test kitchen site at the University of Auckland Clinical Research Centre. The recipe was analysed using the New Zealand food database for macronutrient status using Foodworks 10 Professional software (Xyris). The meat was grain-fed New Zealand Angus steer beef, specifically slaughtered, minced to ensure homogeneity and for more rapid digestion, packaged and stored at the research centre for this trial (Pennings et al., 2013). All other food items were purchased at a local supermarket.

Each participant had the same standardised meal prepared in the university kitchen. Salter scales were used to measure the exact quantities per person: 220 g of mince, 54 g of chopped brown onion, 72 g chopped red capsicum, 67 g of canned corn kernels, two jumbo tortilla wraps, 1/6 jar of salsa sauce and salt and pepper for seasoning. The onions were fried in a teaspoon of oil, using an electric wok, until tender, before adding the mince. Cooking of the mince continued until it reached a temperature of at least 70°C (checked with a PUREQ solo probe food thermometer). Next was the addition of the capsicum, corn, salsa and seasoning. The meal was left to simmer for 10 minutes before equally dividing the mixture into two portions onto two flat tortillas. The tortillas were folded and placed under a sandwich grill until toasted and then wrapped in aluminium foil and served. The nutritional value of the standardised meal is provided in Table 1, and the nutrient composition per 100 g of the cooked meal has been previously reported (Pham et al., 2022).

Table 1

Nutritional value of the standardised meal based on the standardised recipe
Nutrient	Nutrient value
Energy (kJ)	817
Protein (g)	61
Fat (g)	30
Saturated Fat (g)	12
Carbohydrates (g)	71
Fibre (g)	29
Sodium (mg)	983

Collection of Data

Participants were asked to maintain a normal lifestyle and physical activity schedule before the study. Reminder text messages were sent to participants the night before the visit to ask them to remain fasted from 10 pm (only water was allowed for the rest of the evening). The participants arrived at the clinical research facilities at 7.30 am, and their baseline data was collected before meal consumption. A wall-mounted stadimeter was used to measure the participant’s height with their shoes off and a digital scale was used to measure body weight. Participants were asked to rest for 15 minutes before their blood pressure and heart rate measurements were taken using an automatic blood pressure monitor (Omron HEM-7130, Japan). Buccal swabs were collected and sent for analysis.

A cannula was inserted into the antecubital vein of the forearm. Venous blood was collected into ethylenediaminetetraacetic acid (EDTA)-containing tubes immediately prior to the meal (premeal) and hourly for four postprandial hours. Participants were instructed to consume the provided meal within 15 minutes. Blood samples were centrifuged at 1,500 g for 15 minutes at 4°C. An aliquot of plasma was maintained at 4°C for the chylomicron-rich fraction (CMRF) isolation protocol. The remaining plasma samples were stored at -80°C for later analysis.

Postprandial appetite changes were assessed using an online visual analogue 100-point scale (Qualtrics software, SAP, Utah US), which included fat-taste perception, sugar preference, and hunger. This visual analogue scale has been previously validated for use in single-meal investigations (Flint et al., 2000). The participants completed the questionnaire upon arrival, immediately and 30 minutes after meal consumption and hourly for 4 hours. A post-meal survey was sent to all participants at the end of the test day to enquire whether participants experienced any adverse side effects.

Genetic Analysis

Oragene DNA ON-500 collection kits were used to collect samples for genetic analysis. The buccal swab stick was taken out of the blue top screw cap collection tube and the tip ends were rubbed six times on the inside of the participant’s cheek cavity. The swab was then placed back into the collection tube and sealed. Each collection tube was labelled with the participant’s research number and a unique identification number. Samples were sent to the Nutrigenomix CLIA-certified and CAP-accredited (College of American Pathologists) laboratory centre at the University of Sydney, Australia. The iPLEX Gold assay with mass spectrometry-based detection on the Sequenom MassARRAY platform (Agena Bioscience) was used for all genotyping (Nutrigenomix, 2020). The Illumina assay technique was used to find mutations in the DNA by identifying different insertions of the addition or deletion of a base to determine the risk variant (Sachidanandam et al., 2001).

Digestive and Biochemical Analysis

The CMRF were extracted from plasma samples, as previously described (Milan et al., 2016). Briefly, 3.5 mL of plasma was overlaid with 1.2 mL of saline solution (d = 1.006 g/mL) in an OptiSeal Polypropylene tube (Beckman Coulter Life Sciences, Indiana, US). After its cap was carefully sealed, the tubes were placed in a TLA-110 rotor (Beckman Coulter) and centrifuged at 117,000 g for 10 minutes in an Optima MAX-XP ultracentrifuge (Beckman Coulter). The visible top layer was aspirated into new microcentrifuge tubes and corrected to a final volume of 1.4 mL using saline solution. This step provided a standardised dilution factor of the collected CMRF volume relative to the initial plasma volume, and collected CMRF samples were stored at -80°C until further analysis. The CMRF samples were sent to AgResearch Ltd to analyse the fatty acid composition using the fatty acid methyl esters (FAME) assay previously described (Milan et al., 2016).

Plasma concentration of vitamin D was analysed using ultra-high-performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS) at the Liggins Institute, The University of Auckland, as previously described (Sharma et al., 2019).

The inductively coupled plasma mass spectrometry (ICP-MS) was used to analyse iron and zinc plasma concentrations at Analytica Laboratories, Hamilton, New Zealand. The plasma samples were digested in aqua regia on a hot block for 2 hours. After digestion, Type 1 water was added to dilute the sample 50-fold. Samples were analysed on a Perkin Elmer ICP-MS fitted with a CETAC autosampler. The internal standard and carrier solution were introduced into the instrument using an ESI peristaltic pump and were combined with the sample before injection into the instrument by the nebuliser. The plasma was formed using argon gas. Both standard and kinetic energy discrimination (KED) modes were used, with helium gas being introduced into the collision cell for the operation of KED mode to remove polyatomic interferences where required. The instrument was calibrated using a 1-point calibration, and this calibration was verified with a range of internal quality controls.

Plasma concentrations of insulin, glucose, total cholesterol, high-density lipoprotein (HDL), low-density lipoprotein (LDL), and triglyceride (TG) were analysed using Roche Cobas C311 and E411 autoanalyzer and commercially available Cobas Elecsys assays (Roche Diagnostics, Mannheim, Germany).

Data Associations and Justification

Each participant’s genotype report quantified their susceptibility to food-related traits based on their risk variants. Twenty-six prioritised SNPs associated with the meal response were filtered from the report. The participants’ SNP results were compared with their physiological measures to determine whether high-risk variants correlate with increased physiological responses. The research team was able to justify the association between SNPs and the related physiological measures using gene–nutrient interaction studies (Table 2).

Table 2

Investigated physiological measures with the related SNP justified alongside the published gene–nutrient interaction
Associated gene	Published gene–nutrient interaction^a	Gene abbreviation	SNP reference marker	Physiological measure
Cytochrome P450 family 2 subfamily R member 1	Involved in activation of vitamin D	CYP2R1	rs10741657	Vitamin D ng/mL premeal
Group-specific component vitamin D binding protein	Involved in vitamin D transport	GC	rs2282679	Vitamin D ng/mL premeal
Homeostatic iron regulator protein	Involved in iron transport	HFE	H63D-rs1799945, C282Y-rs1800562	Iron µmol/L premeal
Solute carrier family 17 member	Involved in iron transport	SLC17A1	rs17342717	Iron µmol/L premeal
Transmembrane protease serine 6	Involved in absorption of iron	TMPRSS6	rs4820268	Iron µmol/L premeal
Type-2 transferrin receptor	Involved in iron transport	TRF2	rs7385804	Iron µmol/L premeal
Transferrin coding	Involved in iron transport	TF	rs3811647	Iron µmol/L premeal
Solute carrier family 30 member 3	Involved in zinc transport	SLC30A3	rs1126936	Zinc mg/L premeal
Fatty acid desaturase 1	Involved in activation of omega-6 & -3	FADS1	rs174547 & rs174546	LCPUFA^bmmol/L changes (AUC^c) (18:2 n-6, 18:3 n-6, 18:3 n-3)
Mitochondrial uncoupling protein 1	Involved in stimulating oxidation of fatty acid and fat metabolism	UCP1	rs1800592	Body mass index (BMI) kg/m²
Transcription factor 7-like2	Involved in response to intake of total fat	TCF7L2	rs7903146	BMI kg/m²
Apolipoprotein A-II	Involved in response to intake of saturated fat	APOA2	rs5082	Low-density lipoprotein (LDL) mmol/L premeal
Fat-mass and obesity-related alpha-ketoglutarate-dependent dioxygenase	Involved in response to dietary intake of fats	FTO	rs9939609	BMI kg/m²
Fat-mass and obesity-related alpha-ketoglutarate-dependent dioxygenase	Involved in response to intake of protein	FTO	rs9939609	BMI kg/m²
Peroxisome proliferator-activated receptor γ2	Involved in fat cell formation in adipose tissue and lipid metabolism	PPARγ2	rs1801282	BMI kg/m²
Apolipoprotein A5	Involved in lipid metabolism	APOA5	rs662799	Total cholesterol premeal
ATP-binding cassette subfamily G member8	Involved in cholesterol uptake	ABCG8	rs6544713	LDL mmol/L premeal
ATP-binding cassette subfamily A member1	Involved in cholesterol metabolism	ABCA1	rs1883025	HDL mmol/L premeal
Angiopoietin-like 3	Involved in release of fatty acids and glycerol from adipose tissue	ANGPTL3	rs10889353	Triglycerides mmol/L premeal
Cluster determinant 36	Involved in lipid absorption and response to fat detection	CD36	rs1761667	Fat-taste perception changes (AUC)
Glucose transporter type 2	Involved in glucose transport and insulin release	GLUT2	rs5400	Sugar preference changes (AUC)
Neuromedin beta	Involved in eating behaviours	NMB	rs1051168	Hunger changes (AUC)
Adenylate cyclase 5	Involved in glucose levels	ADCY5	rs11708067	Glucose mmol/L premeal
Insulin-receptor substrate 1	Involved in insulin levels	IRS1	rs2943641	Insulin µU/mL premeal
^a Published gene–nutrient interactions listed in the table have been suggested by previous genome-wide association studies to be associated with disease risk in a population (Patron et al., 2019)
^b LCPUFA, long-chain polyunsaturated fatty acids
^c AUC, the area under the curve

Data Analysis and Statistics

Participants’ BMI was a physiological marker to assess body fat. The categories of BMI were Underweight < 18.5 kg/m², Healthy/Normal 18.5–24.9 kg/m² and Overweight 25–29.9 kg/m² (National Heart Foundation of New Zealand, 2022).

The least squares multiple regression method was used to test if genetic variation in each of the twenty-six SNPs significantly predicted participants’ physiological outcomes during the preprandial and postprandial state. GraphPad Prism (Version 9.4) was used for the multiple regression and encoded categorical variables as part of the performing analysis. Each test was conducted with age as a second predictor variable, apart from the insulin test, where age and BMI were used as the second and third predictor variables. The F significance P value and the coefficient (β) P value need to be < 0.05 to be considered statistically significant. GraphPad assessed the normality and homogeneity of data using the D'Agostino–Pearson omnibus K² test, Anderson–Darling test, Shapiro–Wilk test and Kolmogorov–Smirnov test.

The baseline (premeal) plasma measures were used to assess vitamin D, iron, zinc, total cholesterol, LDL, HDL, TG, glucose and insulin. For postprandial changes in long-chain polyunsaturated fatty acids (18:2 n-6, 18:3 n-6, 18:3 n-3) and postprandial changes in the online visual analogue 100-point scale (fat-taste perception, sugar preference and hunger), the area under the curve (AUC) values were calculated using all-times recorded data from each participant’s physiological measure. GraphPad was used to calculate the AUC based on the trapezoidal method for time-point differences during the postprandial digestive response and corrected for baseline values (Lairon et al., 2007).

Thirty participants completed the study and had their genetic profiles assessed. Participants' baseline data were recorded and analysed before meal consumption (Table 3).

Table 3

Participant characteristics
Baseline Anthropometrics	Mean (n = 30)	Standard deviation
Age (years)	27.7	3.6
Body weight (kg)	76.6	10.0
Body height (cm)	176.6	5.8
Body mass index (kg/m²)	24.5	2.7
Systolic pressure (mmHg)	117.3	11.7
Diastolic pressure (mmHg)	75.7	9.0
Resting heart rate (beats/min)	67.4	10.0
Baseline Blood Biomarkers
Vitamin D (ng/mL)	19.7	8.8
Iron (µmol/L)	17.2	6.1
Zinc (mg/L)	0.9	0.1
Total cholesterol (mmol/L)	4.6	0.7
Low-density lipoprotein (mmol/L)	2.9	0.7
High-density lipoprotein (mmol/L)	1.4	0.4
Triglycerides (mmol/L)	1.1	0.7
Essential fatty-acid C18:2n-6 (mmol/L)	119.1	65.7
Essential fatty-acid C18:3n-6 (mmol/L)	1.6	1.2
Essential fatty-acid C18:3n-3 (mmol/L)	6.2	7.0
Glucose (mmol/L)	5.0	0.4
Insulin (µU/mL)	7.7	4.1
Baseline Appetite Scores
Fat-taste perception (Yes 0, No 100)	52.1	23.3
Sugar preference (Yes 0, No 100)	48.0	28.1
Hunger (No 0, Yes 100)	72.7	25.3

The study found that the insulin-receptor substrate 1 gene (IRS1) polymorphism rs2943641 significantly predicted elevated fasting insulin levels (P = < 0.0001) and participants’ BMI (P = < 0.0001) significantly predicted elevated fasting insulin levels. Fifteen participants were carriers of the IRS1 gene variant rs2943641 genotype CT or CC. They had elevated fasting insulin levels of > 7.7 ± 4.1 µU/mL, compared to the other fifteen with TT genotype who had normal fasting insulin levels. The mitochondrial uncoupling protein 1 gene (UCP1) polymorphism rs1800592 and the peroxisome proliferator-activated receptor γ2 gene (PPARγ2) polymorphism rs1801282 significantly predicted participants’ BMI (P = 0.01 and P = 0.02, respectively). Ten participants had the high-risk UCP1 gene variant (rs1800592) G allele, four of whom had a BMI > 25 kg/m², classing them as overweight. Three participants had the PPARγ2 polymorphism Pro12Ala (rs1801282) risk variant minor G allele and a BMI > 25 kg/m². The remaining SNPs did not appear to associate with our cohort’s related physiological or nutrient outcomes (Table 4).

Table 4

Participants’ SNP results compared with their physiological measures to determine whether high-risk variants correlate with increased physiological responses (n = 30)
Gene	SNP	Genetic variant^a	Ref^b allele Alt^c allele or frequency	Physiological outcome measures (β)	R²	F Significance^d	P-value (β)
CYP2R1 Vitamin D activation	rs10741657	AA typical GG or GA elevated	Ref A = 0.38 Alt G = 0.62	Premeal vitamin D ng/mL	0.18	P = 0.15	0.03*
GC Vitamin D transport	rs2282679	TT or TG typical GG elevated	Ref T = 0.72 Alt G = 0.28	Premeal vitamin D ng/mL	0.18	P = 0.15	0.03*
SLC17A1 Iron transport	rs17342717	CC low CT typical TT elevated	Ref C = 0.92 Alt T = 0.84	Premeal iron µmol/L	0.15	P = 0.24	0.11
HFE (C282Y) Iron transport	rs1800562	GG low AG typical AA elevated	Ref G = 0.95 Alt A = 0.05		0.15	P = 0.24	0.11
HFE (H63D) Iron transport	rs1799945	CC Low GC typical GG elevated	Ref C = 0.86 Alt G = 0.14		0.15	P = 0.24	0.11
TMPRSS6 Iron absorption	rs4820268	GG or GA typical AA elevated	Ref G = 0.46 Alt A = 0.54	Premeal iron µmol/L	0.11	P = 0.39	0.25
TFR2 Iron transport	rs7385804	CA typical CC or AA elevated	Ref C = 0.36 Alt A = 0.64		0.11	P = 0.39	0.25
TF Iron transport	rs3811647	GA or GG typical AA elevated	Ref G = 0.67 Alt A = 0.33		0.11	P = 0.39	0.25
SLC30A3 Iron transport	rs11126936	AA or AC typical CC elevated	Alt A = 0.00 Alt C = 0.00	Premeal zinc mg/L	0.12	P = 0.34	0.84
FADS1 Activation of omega-6 & -3	rs174547 & rs174546	TT typical CC or CT elevated risk	Ref T = 0.67 Alt C = 0.33	Fatty-acid change mmol/L C18:2.n-6 C18:3.n-6 C18:3.n-3	0.40	P = 0.00	0.55
					0.36	P = 0.01	0.21
					0.58	P < 0.0001	0.19
UCP1 Fat metabolism	rs1800592	AA typical GG or GA elevated	Allele freq. A = 0.27 G = 0.47	BMI kg/m²	0.26	P = 0.02*	0.01*
FTO Response to intake fats	rs9939609	TT or AT typical AA elevated	Ref T = 0.60 Alt A = 0.40	BMI kg/m²	0.04	P = 0.56	0.58
TCF7L2 Response to intake fats	rs7903146	CC or CT typical TT elevated	Ref C = 0.71 Alt T = 0.29	BMI kg/m²	0.03	P = 0.64	0.81
APOA2 Response to intake of saturated fat	rs5082	TT or TC typical CC elevated	Allele Freq not known	Premeal LDL mmol/L	0.21	P = 0.10	0.24
FTO Response to intake protein	rs9939609	TT typical AA or AT elevated	Ref T = 0.60 Alt A = 0.40	BMI kg/m²	0.04	P = 0.56	0.58
PPARγ2 Lipid metabolism	rs1801282	CC typical GG or GC elevated	Ref C = 0.90 Alt G = 0.10	BMI kg/m²	0.20	P = 0.05*	0.02*
APOA5 Lipid metabolism	rs662799	TT typical CC or TC elevated	Allele freq. not known	Premeal total cholesterol mmol/L	0.13	P = 0.29	0.65
ABCG8 Cholesterol uptake	rs6544713	CC typical TT or TC elevated	Ref T = 0.30 Alt C = 0.70	Premeal LDL mmol/L	0.17	P = 0.19	0.90
ABCA1 Cholesterol metabolism	rs1883025	CC typical TT or TC elevated	Ref C = 0.74 Alt T = 0.26	Premeal HDL mmol/L	0.23	P = 0.07	0.85
ANGPTL3 Release of fatty acids and glycerol	rs10889353	CC typical AA or CA elevated	Ref A = 0.68 Alt C = 0.32	Premeal TG mmol/L	0.26	P = 0.05	0.76
CD36 Response to fat detection	rs1761667	AA typical GG or GA enhanced taste	Ref G = 0.48 Alt A = 0.52	Fat-taste perception changes	0.19	P = 0.14	0.49
GLUT2 Glucose transport	rs5400	CC typical CT or TT elevated	Allele Freq n/a	Sugar preference changes	0.04	P = 0.78	0.61
NMB Involved in eating behaviours	rs1051168	GG or GT typical TT elevated	Ref G = 0.73 Alt T = 0.27	Hunger changes	0.10	P = 0.43	0.88
ADCY5 Glucose levels	rs11708067	GG typical GA or AA elevated	Ref A = 0.79 Alt G = 0.21	Premeal glucose mmol/L	0.09	P = 0.47	0.88
IRS1 Insulin levels	rs2943641	TT typical CT or CC elevated	Ref T = 0.34 Alt C = 0.66	Premeal Insulin µU/mL & BMI kg/m²	0.64	P = < 0.0001*	< 0.0001* < 0.0001*
Each test was conducted using the least squares multiple regression, with age as a second predictor variable, apart from the insulin test, where age and BMI were used as the second and third predictor variables.
Reference allele, alternative allele and frequency allele retrieved from gnomAD (n.d.); and NCBI (2005), from population groups, European, African, African others, African American, Asian, East Asian, Other Asian, Latin American 1, Latin American 2, South Asian European.
^a The qualitative genetic risk results were low, typical, elevated or enhanced genetic risk. Low or typical risk indicates a low or normal response to a genetic variant and an elevated or enhanced genetic risk indicates an increased response to a genetic variant.
^b Reference (Ref) allele frequency is the base found in the reference genome and is not always the major allele.
^c Alternative (Alt) allele frequency is the base found at the locus, other than the reference allele.

^d The F significance P value and the coefficient (β) P value need to be < 0.05 to be considered statistically significant.

There were no adverse effects reported throughout the study.

This exploratory study investigated the association between genetic variants and physiological outcomes during the preprandial and postprandial digestive responses in thirty healthy young men. We report a strong association between the IRS1 gene variant rs2943641 and BMI with elevated fasting insulin levels, the PPARγ2 gene polymorphism rs1801282 and the UCP1 gene variant rs1800592 with participants’ BMI. Therefore, our finding confirmed three associations between genetic variants and physiological outcomes. This was remarkable, despite the relatively small sample size of men and the complex genetic assessment. Our finding regarding the lack of association between many of the SNPs tested and the physiological markers may be false negatives, given the small sample size.

Studies have shown that a person’s metabolic efficiency and subtle differences in genetic variability influence postprandial digestive response as an independent risk factor for health and disease (Berry et al., 2020; Lopez-Miranda & Marin, 2010; Vincent et al., 2002). Several factors, including genetics, may influence interpersonal differences in preprandial and postprandial meal responses and physiological outcomes (Berry et al., 2020). Thus, genetic variation and the digestive response are important areas of study (Ellis et al., 2021).

GWAS have identified regions of the genome where gene–nutrient interactions have been associated with disease-causing effects, but the strength of these associations may be weak (Dib et al., 2019). For instance, studies have associated LDL levels and lipoprotein metabolism with the ATP-binding cassette subfamily G member 8 (ABCG8) gene (Kathiresan et al., 2009). Excretion of the ABCG8 gene is via the intestines, and during this process, cholesterol absorption from the intestines reduces (Feingold & Grunfeld, 2015). The ABCG8 gene variant rs6544713, T allele, is associated with high LDL levels and can elevate cholesterol uptake and lower secretion from the intestines (Acalovschi et al., 2006; Schroor et al., 2021). Diet and lifestyle choices can increase the risk of diseases, such as high LDL intake, which can contribute to atherosclerosis and eventually, coronary artery disease or cardiovascular disease (Röhrl & Stangl, 2013). Another study combined 21 GWAS, using 46,186 nondiabetic European subjects, that included loci associated with fasting glucose near the adenylate cyclase 5 (ADCY5) gene (Dupuis et al., 2010). Meta-analysis after adjustments for BMI demonstrated that the ADCY5 gene was associated with elevated fasting glucose levels of 0.027 mmol/L in A allele carriers (P = 0.0001). Therefore, A allele carriers had an increased risk of type 2 diabetes compared to G allele carriers. It should be noted, however, that studies differ regarding allele frequency, effect size, and the population they studied, and associations contributed only to increasing odds of disease occurrence (Tam et al., 2017; Wray et al., 2007). Currently, the challenge is confirming gene–nutrient associations with disease risk and providing dietary advice to individuals with risk variants (Reddy et al., 2018). The use of genetic information to guide dietary decisions is in its infancy. Generally, advancement has been slow due to insufficient evidence and a low replication of studies (Corella et al., 2009).

Investigations on the IRS1 gene have reported its possible association with insulin levels and type 2 diabetes (Alharbi et al., 2014; Almgren et al., 2017; Kovacs et al., 2003). This is consistent with a finding from a population-based cohort study where they recruited 3,344 Swedish participants born between 1923 and 1950 and 4,905 Finnish participants (Almgren et al., 2017). The study searched for a link between nondiabetic participants and fasting insulin levels and found that a location near the IRS1 gene variant rs2943641 showed a significant association (P = 2.4 x 10^− 7). Almgren et al. (2017) concluded that participants who carried the CT or CC genotypes had greater fasting insulin levels than the TT genotype. This association has been reported in subjects with type 2 diabetes from a randomised control trial performed on 376 type 2 diabetes and 380 healthy participants from Saudi Arabia (Alharbi et al., 2014). Our study provides new evidence of a positive relationship between the IRS1 gene variant rs2943641 and elevated fasting insulin levels in thirty healthy young men. This evidence suggests that carriers of the C allele are more likely to have an increased risk of elevated fasting insulin levels, which could increase their risk of other noncommunicable diseases such as obesity, type 2 diabetes and cardiovascular disease. Besides diet and lifestyle, insulin levels can also be influenced by obesity, which is a causal factor affecting insulin resistance (Phillips et al., 2012). This agrees with our finding that BMI (P = < 0.0001) significantly predicted elevated fasting insulin levels.

In the current investigation, the PPARγ2 gene variant rs1801282 was correlated with BMI as the physiological measure. A previous study reported that the PPARγ2 gene polymorphism of the ALa allele in Pro12Ala is associated with fat cell formation in adipose tissue, linked to insulin resistance, obesity and BMI (Garaulet et al., 2011). They analysed the PPARγ2 polymorphism Pro12Ala (rs1801282) association with participants’ BMI and fat loss by studying 1,465 overweight and obese Spanish subjects (89% completed the study). The study found a positive gene–diet interaction between the PPARγ2 polymorphism Pro12Ala (rs1801282) and participants’ BMI. After the fat loss intervention, carriers of the minor G allele were significantly less obese with lower BMI (P = < 0.001) than homozygous major CC genotypes (P = 0.037) when their dietary intake of monounsaturated fats was high. Participants in this study with the high-risk variant could have a more significant weight loss if they consumed a diet high in monounsaturated fats compared to the ten participants with a BMI > 25 kg/m² and were genotype CC. Therefore, the finding in this study suggests that the positive association between BMI and the high-risk variant transcends diet and fatty acid consumption.

BMI has been identified as the physiological measure associated with the UCP1 gene (Cannon & Nedergaard, 2004). The UCP1 gene is located on chromosome 4 of the human genome and encodes the UCP1 protein (Dalgaard & Pedersen, 2001). This protein is found chiefly in brown adipose tissue (BAT) and facilitates proton transport across the mitochondrial inner membrane (Flouris et al., 2017). During UCP1 activation, there is an increase in fatty acid oxidation to compensate for the decrease in ATP synthesis, and this allows energy expenditure to be maintained (Brondani et al., 2012; Dalgaard & Pedersen, 2001). Research suggests that the UCP1 gene increases energy expenditure and decreases the mitochondrial membrane potential due to the UCP1 gene polymorphism of rs1800592 (-3826 GA, -1766GA, and − 112AC) in the intraperitoneal adipose tissue (Brondani et al., 2012; Nagai et al., 2011; Vimaleswaran & Loos, 2010). Several studies suggest that carriers of the UCP1 gene polymorphism rs1800592 are at greater risk of multifactorial diseases, including obesity and type 2 diabetes; however, findings are still ambiguous (Brondani et al., 2012; Vimaleswaran & Loos, 2010). A cohort study used 82 Japanese females aged 20–22 from the same university campus in Japan and genotyped for the UCP1 gene polymorphism rs1800592 (Nagai et al., 2011). The study explored the gene variant’s association with BMI and body weight. The conclusion was that G allele carriers had lower energy expenditure; therefore, their energy needs were lower than the AA genotypes. According to our findings, there was a significant association between the UCP1 gene polymorphism rs1800592 and participants’ BMI. Therefore, participants with the high-risk variant may have an increased risk of obesity, especially if they have a high BMI (Hill et al., 2012). These participants may need to reduce their intake of calories whilst increasing their energy expenditure because their energy needs are lower than AA genotypes (Brondani et al., 2012; Nagai et al., 2011; Pfannenberg et al., 2010).

Based on published articles, the best-matched genetic variant and physiological measure were chosen for this study. However, a significant limitation of the current investigation is that some physiological measures may be mismatched to the genetic variant as newly published research is being produced. For example, hunger was associated with the NMB gene; another association has been linked to the FTO gene variant rs9939609, which is associated with the postprandial sensation of hunger (Magno et al., 2018).

The association between physiological measures, genetic variation and health benefits can be complex since other factors may also play a role. SNPs, for instance, are linked with other genetic risk variants causing causal variants (linkage disequilibrium) and are affected by causal effects (lifestyle and environment) (Dandine-Roulland & Perdry, 2015).

According to PREDICT1, the largest nutritional study in the world, researchers found that a person’s gut microbiome has a greater influence on postprandial lipemia than meal composition. Genetic variants were found to have only a small impact on individual postprandial metabolism, indicating that modifiable environmental factors (e.g., lifestyle, exercise and sleep) are important factors to consider (Berry et al., 2020). Even so, more research and evidence will need to confirm any causal inference. Eventually, gene–nutrient studies could help clinicians, dietitians, and nutritionists tailor nutrition advice based on a genetic test (Braakhuis et al., 2021; Horne et al., 2021). However, all registered practitioners should consider the strength of the relationship between SNPs and the physiological parameter before incorporating nutrigenomics into their practice (Collins et al., 2013).

This pilot study examined an individual’s unique genetic variation and considered the association with related physiological outcomes during the preprandial and postprandial state. The results reinforced the findings of recent studies that the IRS1 gene polymorphism rs2943641 significantly predicted elevated fasting insulin levels. The UCP1 gene polymorphism rs1800592 and the PPARγ2 gene polymorphism rs1801282 were significant predictors of participants’ BMI. The remaining SNPs did not appear to associate with the related physiological or nutrient outcomes. Further research is needed to explore the complex relationship between genetic variation and the digestive response.

According to this study, an individual’s genetic variation could influence their physiological outcomes. A positive association between genetic variation and fasting insulin levels and BMI is demonstrated in this study, adding to our understanding of the implications of genetic variation. To conclude, genetic variation and physiological outcomes during preprandial and postprandial digestive responses are important research fields that could contribute to reducing disease risk.

ABCG8, ATP-binding cassette subfamily G member 8 gene; ADCY5, adenylate cyclase 5 gene; AUC, area under the curve; BMI, body mass index; CMRF, chylomicron-rich fraction; GWAS, genome-wide associated studies; HDL, high-density lipoprotein; IRS1, insulin-receptor substrate 1 gene; LDL, low-density lipoprotein; PPARγ2, peroxisome proliferator-activated receptor γ2 gene; SNPs, single nucleotide polymorphisms; TG, triglycerides; UCP1, Mitochondrial uncoupling protein 1 gene.

Availability of Data and Materials

All data generated or analysed during this study are available from the corresponding author.

Acknowledgement

We are thankful to all participants for their contribution to the study. We would like to acknowledge Sophie Broome, Eric Thorstensen and Christine Keven for their practical help with the blood collection procedures and plasma sample analysis.

Funding

The funding for the wider trial has been previously documented (Braakhuis et al. (2022) An acute, blinded, randomised cross-over design intervention to compare beef, lamb and a meat analogue on digestive, metabolic and nutritional outcomes, 31 May 2022, PREPRINT (Version 1) available at Research Square, https://doi.org/10.21203/rs.3.rs-1640468/v1). The current research was internally funded by the Faculty of Medical and Health Sciences, the University of Auckland. TP is supported by the National Heart Foundation of New Zealand Research Fellowship (1869) and Emerging Researcher First Grant from the Health Research Council of New Zealand (21/653).

Authors Contribution

The authors’ responsibilities were as follows: AB and JB: designed the research; TP, JB and AB: conducted the research; TP, JB and HB: assisted with the data collected, JB: conducted the statistical analysis; JB, TP and AB: wrote the manuscript; AB: had primary responsibility for the final content of the manuscript; and all authors: provided content and feedback to the manuscript and read and approved the final manuscript.

Ethics Declaration

This study is a small subsection of a more extensive investigation: An acute, blinded, randomised cross-over design intervention to compare beef, lamb and a meat analogue on digestive, metabolic and nutritional outcomes, 31 May 2022, PREPRINT (Version 1) available at Research Square (https://doi.org/10.21203/rs.3.rs-1640468/v1). The extensive investigation was registered as a universal trial number: U1111-1244-9426. The clinical trial prerecruitment (Ref: NCT04545398) was carried out under the auspices of the University of Auckland. The study was approved by the New Zealand Ministry of Health’s Health and Disability Ethics Committees (Ref: 19/STH/226) and conducted following the ethical standards in the 1964 Declaration of Helsinki. The informed consent of each participant in the study was obtained.

Consent for Publication: Not applicable

Competing Interests: The authors declare that they had no conflict of interest related to this study.

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The association between genetic variation and preprandial and postprandial digestive responses in healthy young men: A pilot study

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Digestive and Biochemical Analysis

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