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Objective: Gene-diet interaction plays a key role in the inter-individual differences in lipid abnormalities as a major risk factor for cardiovascular diseases (CVDs). Thus, we explored the interaction between CETP TaqB1 polymorphism with dietary acid load (DAL) on lipid profile among type 2 diabetes mellitus (T2DM). Method: This cross-sectional study conducted on 220 Iranian patients with T2DM. Dietary acid load (PRAL and NEAP) was calculated via a validated food-frequency questionnaire (FFQ). The polymerase chain reaction (PCR) used for genotyping Taq1B polymorphism. Biochemical markers were measured by standard protocol. The interaction between CETP Taq1B polymorphism and DAL (PRAL and NEAP) on lipid profile was performed by a generalized linear regression model (GLM). Results: The overall prevalence of rs708272 genotypes was 8.6%, 72.7% and 18.6% for B1B1, B1B2 and B2B2 genotype respectively. This study showed that people with the B1B1 genotype had greater LDL, TC, LDL/HDL, and TG when they consumed diets that scored higher on the NEAP and PRAL indexes than those with the B1B2 and B2B2 genotypes. Besides, carriers of the B1B1 allele who were in the highest tertile of NEAP, had lower HDL (P Interaction < 0.05). Conclusions: In summary, the lipid profile might be improved in B1B1 homozygotes by less adherence to DAL indexes, however, the findings should be validated in high-quality interventional studies. [ABSTRACT FROM AUTHOR]

The progression of cardiometabolic diseases is determined by both genetic and environmental factors. Gene–diet interactions may therefore be important in modulating the risks of developing metabolic diseases. The objectives were to investigate the effect of the interaction between brain-derived neurotrophic factor (BDNF) Val66Met polymorphisms and dietary insulin index (DII) and dietary insulin load (DIL) on cardiometabolic markers among diabetic patients. In this cross-sectional study, blood samples were collected from 667 patients. DIL and DII were defined using a validated FFQ. Genotyping the BDNF Val66Met polymorphism was conducted by the PCR-Restriction fragment length polymorphism (RFLP) method. Interactions between dietary indices and gene variants were evaluated using a generalised linear model. PGF2a concentrations were significantly higher among Val homozygotes than Met-allele carrier. This study revealed that, compared with individuals with the Val/Val genotype, those with the Met/Val or Met/Met genotype had lower BMI (Pinteraction = 0·04), TAG (Pinteraction = 0·04), leptin (Pinteraction = 0·01), LDL (Pinteraction = 0·04) and total cholesterol (Pinteraction = 0·01) when they consumed diets higher on the DIL index. Moreover, the highest quartile of the DIL, compared with the lowest, showed increase in waist circumference (Pinteraction = 0·02) and LDL/HDL (Pinteraction = 0·04) for Val/Val homozygotes compared with Met-allele carriers. BDNF Val66Met variants may interact with DIL and DII, thus be involved in the development of cardiometabolic risk factors. If diabetic patients with Met alleles regulate dietary intakes, they have a protective opportunity to regulate their cardiometabolic markers. [ABSTRACT FROM AUTHOR]

This investigation with aimed the effect of APOA2–265 T > C polymorphism and dietary acid load (DAL) as either potential renal acid load (PRAL) and net endogenous acid production (NEAP) intake interaction on metabolic markers in type 2 diabetes mellitus (T2DM). In present cross-sectional study, 737 patients with T2DM (290 men and 447 women) were enlisted from diabetes centers in Tehran. The dietary intakes of all participants during the last year was acquired by a validated semi-quantitative food frequency (FFQ) questionnaire. Polymerase chain reaction (PCR) was used for genotyping the APOA2–265 T > C. Biochemical indises containing leptin, ghrelin, total cholesterol (Bailey et al., J Clin Invest 97:1147–1453, 1996), low-density lipoprotein cholestrol (LDL-C), high-density lipoprotein cholestrol (HDL-C), triglyceride (TG), superoxide dismutase (SOD), high sensitivy C-reactive protein (hs-CRP), total antioxidant capacity (TAC), pentraxin-3 (PTX3), prostaglandin F2α (PGF2α) and interleukin 18 (IL18) were measured by standard method. Atherogenic indices (AIP, AC, CR-I, CR-II) were calculated. The gene-diet interactions were evaluated using an GLM. The frequency overall prevalence of rs5082 genotypes was 63.82 and 36.17% for T-allele and C-allele respectively. TG, Ghrelin, and hs-CRP concentrations were significantly higher among carriers with C allele than TT homozygotes. However, TC/CC genotypes have lower PTX3 than TT homozygotes (P < 0.05). C-allele carriers had highest mean of BMI (PNEAP=0.04, PPRAL = 0.006), WC (PNEAP=0.04, PPRAL = 0.04), TC (PNEAP=0.03, PPRAL = 0.01), ghrelin (PNEAP=0.01, PPRAL = 0.04), and leptin (PNEAP=0.04, PPRAL = 0.03) when placed in top tertiles of NEAP and PRAL.BMI, WC, TC, ghrelin, and leptin levels may be modified in C carriers by decreasing DAL, though, further investigations are required to confirm these findings. [ABSTRACT FROM AUTHOR]

Purpose: Differences in metformin effect on glycemic control in type 2 Diabetes (T2D) have been associated with diet, obesity, years since T2D diagnosis and genetic factors, such as the Met408Val (rs628031) SLC22A1/OCT1 gene polymorphism. This study aimed to analyze the effect of metformin and diet on glycemic control and its association with the Met408Val polymorphism in patients with T2D from western Mexico. Patients and Methods: A total of 240 T2D adult patients were enrolled in this cross-sectional study. Anti-hyperglycemic therapy, dietary intake, body composition and glycemic profile were recorded and the determination of genotypes of SLC22A1/OCT1 gene (rs628031) was performed using an allelic discrimination assay. Results: The type of metformin therapy was 47% monotherapy, 45% dual therapy (metformin+glibenclamide or metformin+insulin) and 8% triple therapy (metformin+glibenclamide+insulin). Individuals with metformin monotherapy had a higher glycemic control frequency (%HbA1c < 7.0) compared with the dual and triple treatment schemes (77% vs 35% and 15%, respectively; p< 0.001). Interestingly, a high potassium intake was documented in the three anti-hyperglycemic therapies and a lower intake of micronutrients, including calcium, magnesium, and zinc. An interaction was found between calcium intake and carriers of the risk allele A (408Val) with %HbA1c (P interaction=0.028), and potassium intake with the TyG index (P interaction=0.027). In addition, there was a positive correlation between calcium intake and %HbA1c (r=0.682; p=0.010), and potassium intake vs TyG index (r=0.593; p=0.033) in risk allele A (408Val) carriers with metformin monotherapy. Genotype frequencies were GG homozygotes (76.6%), GA heterozygotes (21.5%) and AA homozygotes (1.9%). The allele frequency was 87.4% for the ancestral allele G and 12.6% for the risk allele A. Conclusion: These findings suggest a differing effect of metformin on glycemic control regarding calcium and potassium intake and the Met408Val SLC22A1/OCT1 gene polymorphism in T2D patients. [ABSTRACT FROM AUTHOR]

Purpose: The interactions between lifestyle and genetic factors play an important role in obesity development. Mutations in melanocortin-4-receptor (MC4R) gene are one of the most common cause of monogenic obesity, however, the functional effects of polymorphic variants near MC4R gene in general populations remain uncertain. The aim of our study was to analyze whether the common single nucleotide polymorphisms (SNPs) of MC4R gene influence the food preferences, physical activity, body fat content and distribution, as well as fasting and postprandial energy expenditure and substrates utilization. Methods: We genotyped previously identified MC4R SNPs: rs17782313, rs633265, rs1350341, rs12970134 in 927 subjects, who underwent anthropometric, total body fat content, visceral (VAT) and subcutaneous adipose tissue (SAT) measurements, and daily physical activity and dietary intake analysis. In randomly selected 47 subjects the energy expenditure, carbohydrate and lipid utilizations were evaluated in fasting state and after high-carbohydrate and control meals intake. Results: We found the significant associations between studied SNPs of MC4R gene and VAT and VAT/SAT ratio. Moreover, the GG genotype carriers of rs1350341, who had the lowest VAT accumulation (p = 0.012), presented higher relative increase in postprandial carbohydrate utilization (p = 0.013, p = 0.024). Conclusions: We have observed that common SNPs of the MC4R gene influence the body fat content and distribution, as well as relative increase in postprandial carbohydrate utilization. We believe that our study may help to understand better the impact of MC4R gene on obesity development, and to help to provide personalized prevention/treatment strategies to fight against obesity and its metabolic consequences. [ABSTRACT FROM AUTHOR]

It is known that our physiology changes throughout the day and that several physiological hormones display circadian rhythmicity. The alteration of this normal pattern is called chronodisruption (CD). In recent years, it has been demonstrated that CD is related to obesity. Although several factors may be causing CD, one important aspect to consider is the failure in our internal clock. Indeed, studies performed in mutant animals have demonstrated that mutations in clock genes are related to obesity. In human subjects, mutations are rare (<1 % of the population). Nevertheless, it is rather common to have genetic variations in one SNP, which underlie differences in our vulnerability to disease. Several SNP in clock genes are related to obesity and weight loss. Taking into account that genetics is behind CD, as has already been demonstrated in twins’ models, the question is: Are we predestinated? We will see along these lines that nutrigenetics and epigenetics answer: ‘No, we are not predestinated’. Through nutrigenetics we know that our behaviours may interact with our genes and may decrease the deleterious effect of one specific risk variant. From epigenetics the message is even more positive: it is demonstrated that by changing our behaviours we can change our genome. Herein, we propose modifying ‘what, how, and when we eat’ as an effective tool to decrease our genetic risk, and as a consequence to diminish CD and decrease obesity. This is a novel and very promising area in obesity prevention and treatment. [ABSTRACT FROM PUBLISHER]

The intake of tomatoes and tomato products, which constitute the main dietary source of the red pigment lycopene (LYC), has been associated with a reduced risk of prostate cancer and cardiovascular disease, suggesting a protective role of this carotenoid. However, LYC bioavailability displays high interindividual variability. This variability may lead to varying biological effects following LYC consumption. Based on recent results obtained with two other carotenoids, we assumed that this variability was due, at least in part, to several single nucleotide polymorphisms (SNPs) in genes involved in LYC and lipid metabolism. Thus, we aimed at identifying a combination of SNPs significantly associated with the variability in LYC bioavailability. In a postprandial study, 33 healthy male volunteers consumed a test meal containing 100 g tomato puree, which provided 9.7 mg all- trans LYC. LYC concentrations were measured in plasma chylomicrons (CM) isolated at regular time intervals over 8 h postprandially. For the study 1885 SNPs in 49 candidate genes, i.e., genes assumed to play a role in LYC bioavailability, were selected. Multivariate statistical analysis (partial least squares regression) was used to identify and validate the combination of SNPs most closely associated with postprandial CM LYC response. The postprandial CM LYC response to the meal was notably variable with a CV of 70%. A significant ( P =0.037) and validated partial least squares regression model, which included 28 SNPs in 16 genes, explained 72% of the variance in the postprandial CM LYC response. The postprandial CM LYC response was also positively correlated to fasting plasma LYC concentrations ( r =0.37, P <0.05). The ability to respond to LYC is explained, at least partly, by a combination of 28 SNPs in 16 genes. Interindividual variability in bioavailability apparently affects the long-term blood LYC status, which could ultimately modulate the biological response following LYC supplementation. [ABSTRACT FROM AUTHOR]