Lifestyle Genomics最新文献

Introduction: Single nucleotide polymorphisms (SNP) in the fat mass and obesity-associated (FTO) gene have been associated with type 2 diabetes (T2D) and its complications. The aim of the present research was to investigate which and how (directly or indirectly) clinical and metabolic variables mediate the association between fat mass and the FTO gene and early chronic kidney disease (CKD) in individuals with T2D.

Methods: This cross-sectional study was conducted in a sample of 236 participants with T2D (53.4% women, mean age 60 ± 10 years). DNA samples were genotyped for the rs7204609 polymorphism (C/T) in the FTO gene. Clinical, anthropometric, and metabolic data were collected. Path analysis was used to evaluate the associations.

Results: Of the sample, 78 individuals with T2D had CKD (33%). Presence of the risk allele (C) was higher among participants with CKD (21.8 vs. 10.8%; p = 0.023). This polymorphism was positively associated with higher waist circumference, which in turn was associated with higher glycated hemoglobin and higher blood pressure. A higher blood-pressure level was associated with higher urinary albumin excretion (UAE) and as expected, higher UAE was associated with CKD. Path analysis showed an indirect relationship between the FTO gene and early CKD, mediated by waist circumference, blood-pressure levels, and UAE.

Conclusions: These findings suggest that the C allele may contribute to genetic susceptibility to CKD in individuals with T2D through the presence of central obesity, hypertension, and high albuminuria.

Background/aim: An alarming increase in vitamin D deficiency even in sunny regions highlights the need for a better understanding of the genetic background of the vitamin D endocrine system and the molecular mechanisms of gene polymorphisms of the vitamin D receptor (VDR). In this study, the serum levels of 25(OH)D3 were correlated with common VDR polymorphisms (ApaI, BsmI, FokI, and TaqI) in 98 subjects of a Greek homogeneous rural population.

Methods: 25(OH)D3 concentration was measured by ultra-HPLC, and the VDR gene polymorphisms were identified by quantitative real-time PCR followed by amplicon high-resolution melting analysis.

Results: Subjects carrying either the B BsmI (OR: 0.52, 95% CI: 0.27-0.99) or t TaqI (OR: 2.06, 95%: 1.06-3.99) allele presented twice the risk for developing vitamin D deficiency compared to the reference allele. Moreover, subjects carrying 1, 2, or all 3 of these genotypes (BB/Bb, Tt/tt, and FF) demonstrated 2-fold (OR: 2.04, 95% CI: 0.42-9.92), 3.6-fold (OR: 3.62, 95% CI: 1.07-12.2), and 7-fold (OR: 6.92, 95% CI: 1.68-28.5) increased risk for low 25(OH)D3 levels, respectively.

Conclusions: Our findings reveal a cumulative effect of specific VDR gene polymorphisms that may regulate vitamin D concentrations explaining, in part, the paradox of vitamin D deficiency in sunny regions, with important implications for precision medicine.

Introduction: Type 2 diabetes (T2D) is a leading cause of global mortality with diet and genetics being considered amongst the most significant risk factors. Recently, studies have identified a single polymorphism of the TCF7L2 gene (rs7903146) as the most important genetic contributor. However, no studies have explored this factor in a healthy population and using glycated haemoglobin (HbA1c), which is a reliable long-term indicator of glucose management. This study investigates the association of the genetic polymorphism rs7903146 and dietary intake with T2D risk in a population free of metabolic disease.

Methods: T2D risk was assessed using HbA1c plasma concentrations and dietary intake via a validated Food Frequency Questionnaire in 70 healthy participants.

Results: T allele carriers had higher HbA1c levels than the CC group (32.4 ± 7.2 mmol/mol vs. 30.3 ± 7.6 mmol/mol, p = 0.005). Multiple regression reported associations between diet, genotype and HbA1c levels accounting for 37.1% of the variance in HbA1c (adj. R2 = 0.371, p < 0.001). The following macronutrients, expressed as a median percentage of total energy intake (TEI) in the risk group, were positively associated with HbA1c concentration: carbohydrate (≥39% TEI, p < 0.005; 95% CI 0.030/0.130) protein (≥21% TEI, p < 0.005, 95% CI 0.034/0.141), monounsaturated (≥15% TEI p < 0.05, 95% CI 0.006/0.163) and saturated fatty acids (≥13% TEI; p < 0.05, 95% CI 0.036/0.188).

Conclusion: Carriers of the T allele showed significantly higher levels of HbA1c compared to non-carriers. Dietary intake affected T2D risk to a greater extent than genetic effects of TCF7L2rs7903146 genotype in a healthy population. The study focus on healthy individuals is beneficial due to the applicability of findings for T2D screening.

Introduction: Obesity results from an imbalance in the intake and expenditure of calories that leads to lifestyle-related diseases. Although genome-wide association studies (GWAS) have revealed many obesity-related genetic factors, the interactions of these factors and calorie intake remain unknown. This study aimed to investigate interactions between calorie intake and the polygenic risk score (PRS) of BMI.

Methods: Three cohorts, i.e., from the Korea Association REsource (KARE; n = 8,736), CArdioVAscular Disease Association Study (CAVAS; n = 9,334), and Health EXAminee (HEXA; n = 28,445), were used for this study. BMI-related genetic loci were selected from previous GWAS. Two scores, PRS, and association (a)PRS, were used; the former was determined from 193 single-nucleotide polymorphisms (SNPs) from 5 GWAS datasets, and the latter from 62 SNPs (potentially associated) from 3 Korean cohorts (meta-analysis, p < 0.01).

Results: PRS and aPRS were significantly associated with BMI in all 3 cohorts but did not exhibit a significant interaction with total calorie intake. Similar results were obtained for obesity. PRS and aPRS were significantly associated with obesity but did not show a significant interaction with total calorie intake. We further analyzed the interaction with protein, fat, and carbohydrate intake. The results were similar to those for total calorie intake, with PRS and aPRS found to not be associated with the interaction of any of the 3 nutrition components for either BMI or obesity.

Discussion: The interaction of BMI PRS with calorie intake was investigated in 3 independent Korean cohorts (total n = 35,094) and no interactions were found between PRS and calorie intake for obesity.

Background: The phenotypic expression of a high-density lipoprotein (HDL) genetic risk score has been shown to depend upon whether the phenotype (HDL-cholesterol) is high or low relative to its distribution in the population (quantile-dependent expressivity). This may be due to the effects of genetic mutations on HDL-metabolism being concentration dependent.

Method: The purpose of this article is to assess whether some previously reported HDL gene-lifestyle interactions could potentially be attributable to quantile-dependent expressivity.

Summary: Seventy-three published examples of HDL gene-lifestyle interactions were interpreted from the perspective of quantile-dependent expressivity. These included interactive effects of diet, alcohol, physical activity, adiposity, and smoking with genetic variants associated with the ABCA1, ADH3, ANGPTL4, APOA1, APOA4, APOA5, APOC3, APOE, CETP, CLASP1, CYP7A1, GALNT2, LDLR, LHX1, LIPC, LIPG, LPL, MVK-MMAB, PLTP, PON1, PPARα, SIRT1, SNTA1,and UCP1genes. The selected examples showed larger genetic effect sizes for lifestyle conditions associated with higher vis-à-vis lower average HDL-cholesterol concentrations. This suggests these reported interactions could be the result of selecting subjects for conditions that differentiate high from low HDL-cholesterol (e.g., lean vs. overweight, active vs. sedentary, high-fat vs. high-carbohydrate diets, alcohol drinkers vs. abstainers, nonsmokers vs. smokers) producing larger versus smaller genetic effect sizes. Key Message: Quantile-dependent expressivity provides a potential explanation for some reported gene-lifestyle interactions for HDL-cholesterol. Although overall genetic heritability appears to be quantile specific, this may vary by genetic variant and environmental exposure.

The ultimate goal of researching nutrigenetic interactions is to be able to provide individuals with genetically-tailored nutrition advice (when evidence is sufficient) in an effort to optimize health outcomes. Accordingly, original research often discusses the potential for the results to inform genetically-tailored nutrition advice. Despite this, many studies do not report their methods, results, and discussion in a manner that is conducive to knowledge translation. With several consumer nutritional genomics companies now offering genetic testing for personalized nutrition, proper reporting of nutritional genomics research for knowledge translation is of vital importance. Common reporting errors relate to SNP and genotype reporting, results lacking detail, consideration of linkage disequilibrium, mechanisms of action/functional SNPs, details of dietary intake, and sample reporting. Because of this, knowledge translation professionals may be unable or challenged in their attempt to use the findings from such research to inform clinical practice in nutritional genomics and personalized nutrition. The present article provides an overview of the issues at hand. It further pre-sents a checklist as well as table and figure templates for researchers to use when reporting the results of original research in nutritional genomics to inform knowledge translation.

Introduction: Carbohydrate intake and physical activity are related to glucose homeostasis, both being influenced by individual genetic makeup. However, the interactions between these 2 factors, as affected by genetics, on glycaemia have been scarcely reported.

Objective: We focused on analysing the interplay between carbohydrate intake and physical activity levels on blood glucose, taking into account a genetic risk score (GRS), based on SNPs related to glucose/energy metabolism.

Methods: A total of 1,271 individuals from the Food4Me cohort, who completed the nutritional intervention, were evaluated at baseline. We collected dietary information by using an online-validated food frequency questionnaire, a questionnaire on physical activity, blood biochemistry by analysis of dried blood spots, and by analysis of selected SNPs. Fifteen out of 31 SNPs, with recognized participation in carbohydrate/energy metabolism, were included in the component analyses. The GRS included risk alleles involved in the control of glycaemia or energy-yielding processes.

Results: Data concerning anthropometric, clinical, metabolic, dietary intake, physical activity, and genetics related to blood glucose levels showed expected trends in European individuals of comparable sex and age, being categorized by lifestyle, BMI, and energy/carbohydrate intakes, in this Food4Me population. Blood glucose was inversely associated with physical activity level (β = -0.041, p = 0.013) and positively correlated with the GRS values (β = 0.015, p = 0.047). Interestingly, an interaction affecting glycaemia, concerning physical activity level with carbohydrate intake, was found (β = -0.060, p = 0.033), which also significantly depended on the genetic background (GRS).

Conclusions: The relationships of carbohydrate intake and physical activity are important in understanding glucose homeostasis, where a role for the genetic background should be ascribed.

Background: Lifestyle genomics (LGx) is a science that explores interactions between genetic variation, lifestyle components such as physical activity (PA), and subsequent health- and performance-related outcomes. The objective of this study was to determine whether an LGx intervention could motivate enhanced engagement in PA to a greater extent than a population-based intervention.

Methods: In this pragmatic randomized controlled trial, participants received either the standard, population-based Group Lifestyle BalanceTM (GLB) program intervention or the GLB program in addition to the provision of LGx information and advice (GLB + LGx). Participants (n = 140) completed a 7-day PA recall at baseline, 3, 6, and 12 months. Data from the PA recalls were used to calculate metabolic equivalents (METs), a measure of energy expenditure. Statistical analyses included split plot analyses of covariance and binary logistic regression (generalized linear models). Differences in leisure time PA weekly METs, weekly minutes of moderate + high-intensity PA, and adherence to PA guidelines were compared between groups (GLB and GLB + LGx) across the 4 time points.

Results: Weekly METs were significantly higher in the GLB + LGx group (1,114.7 ± 141.9; 95% CI 831.5-1,397.8) compared to the standard GLB group (621.6 ± 141.9 MET/week; 95% CI 338.4-904.8) at the 6-month follow-up (p = 0.01). All other results were non-significant.

Conclusions: The provision of an LGx intervention resulted in a greater weekly leisure time PA energy expenditure after the 6-month follow-up. Future research should determine how this could be sustained over the long-term.

Clinical trial registration: NCT03015012.