Lifestyle Genomics最新文献

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.

Background: Computing polygenic risk scores (PRS) to predict the degree of risk for obesity may contribute to weight management programs strategically.

Objectives: To investigate the combined effect of FTO rs9930501, rs9930506, and rs9932754 and ADRB2 rs1042713 and rs1042714 using PRS on (1) the odds of obesity and (2) post-intervention differences in dietary, anthropometric, and cardiometabolic parameters in response to high-protein calorie-restricted, high-vitamin E, high-fiber (Hipcref) diet intervention in Malaysian adults.

Methods: Both a cross-sectional study (n = 178) and a randomized controlled trial (RCT) (n = 128) were conducted to test the aforementioned objectives. PRS was computed as the weighted sum of the risk alleles possessed by each individual participant. Participants were stratified into first (PRS 0-0.64), second (PRS 0.65-3.59), and third (PRS 3.60-8.18) tertiles.

Results: The third tertile of PRS was associated with significantly higher odds of obesity: 2.29 (95% CI = 1.11-4.72, adjusted p = 0.025) compared to the first tertile. Indians (3.9 ± 0.3) had significantly higher PRS compared to Chinese (2.1 ± 0.4) (p = 0.010). In the RCT, a greater reduction in high-sensitivity C-reactive protein (hsCRP) levels was found in second and third tertiles after Hipcref diet intervention compared to the control diet (p interaction = 0.048).

Conclusion: Higher PRS was significantly associated with increased odds of obesity. Individuals with higher PRS had a significantly greater reduction in hsCRP levels after Hipcref diet compared to the control diet.

Background: The role of adiponectin (ADIPOQ) polymorphisms in weight loss and serum lipid changes following different dietary interventions remain unclear. The Mediterranean dietary pattern has been associated with improved cardiovascular risk factors in different studies.

Objective: Our aim was to analyze the effects of a hypocaloric diet with a Mediterranean dietary pattern on the metabolic response and adiposity parameters, taking into account the 712 G/A rs3774261 polymorphisms in ADIPOQ.

Design: A population of 135 obese patients was enrolled. Anthropometric and serum parameters (lipid profile, insulin, homeostasis model assessment for insulin resistance [HOMA-IR], glucose, C-reactive protein [CRP], adiponectin, resistin, and leptin levels) were measured before and after the dietary intervention (12 weeks). All of the patients were genotyped for the rs3774261 polymorphism.

Results: The genotype distribution of this population was 36 patients with AA (26.7%), 68 patients with AG (50.4%), and 31 patients with GG (22.9%). After the dietary intervention and in both genotypes, BMI, weight, fat mass, systolic blood pressure, waist circumference, glucose, insulin, HOMA-IR, and leptin levels all decreased. After the dietary intervention with secondary weight loss and in non-G-allele carriers (AA vs. AG+GG), total cholesterol (Δ = -15.7 ± 3.9 vs. -4.9 ± 2.9 mg/dL; p = 0.02), LDL cholesterol (Δ = -15.3 ± 3.8 vs. -1.7 ± 1.9 mg/dL; p = 0.01), triglyceride levels (Δ = -23.4 ± 5.6 vs. 2.3 ± 2.3 mg/dL; p = 0.01), and CRP (Δ = -1.1 ± 0.1 vs. -0.4 ± 0.2 mg/dL; p = 0.01) decreased. Adiponectin levels (Δ = 7.2 ± 2.1 vs. -0.4 ± 0.3 ng/dL; p = 0.02) increased. Notably, G-allele carriers did not show this improvement.

Conclusion: Non-G-allele carriers of the ADIPOQ variant (rs3774261) showed significant improvement in serum levels of adiponectin, lipid profiles, and CRP in response to a hypocaloric diet with a Mediterranean dietary pattern.

Background: Even though excessive adipose tissue is related to chronic metabolic disturbances, not all subjects with excess weight (EW) display metabolic alterations, and not all normal-weight (NW) subjects have a metabolically healthy (MH) phenotype, probably due to gene-environment interactions. The aim of this study was to investigate the interaction effects of ADIPOQ and PPARG genetic variants in NW and EW individuals with different metabolic phenotypes.

Methods: Data on 345 adults from western Mexico were analyzed. The individuals were classified into NW and EW groups according to body mass index, and were categorized as MH or metabolically unhealthy (MUH), considering homeostatic model assessment insulin resistance (HOMA-IR) and National Cholesterol Education Program Adult Treatment Panel III (NCEP-ATP III) cut-off points for glucose, triglycerides, high-density lipoprotein cholesterol, and blood pressure. Subjects with ≤1 altered parameter were classified as MH. The single nucleotide polymorphisms (SNPs) -11377C>G, -11391G>A, +45T>G, and +276G>T for ADIPOQ and Pro12Ala for PPARG were analyzed by allelic discrimination. High-molecular-weight adiponectin isoform levels were measured by ELISA.

Results: Lower serum adiponectin levels were associated with the MUH phenotype in EW subjects. NW subjects with the GG or TG genotype for the +45T>G SNP had reduced odds of the MUH phenotype. Individuals who carried two copies of the GG haplotype at the -11391G>A and -11377C>G SNPs for ADIPOQ had lower serum adiponectin levels than those with zero copies.

Conclusion: In this population, lower serum adiponectin levels were found in the EW-MUH phenotype, and no differences were observed between the NW-MH and the EW-MH phenotype. In addition, the +45T>G SNP was associated with reduced odds of the MUH phenotype.

Background: The past two decades have seen exponential growth in the number of genetic testing companies, but only a small percentage of these tests are being sold through health care professionals (HCPs). As each new genetic testing company appears, it is becoming more difficult for the practitioner and consumer to evaluate the credibility of the claims being made and the value of the tests being offered.

Summary: HCPs appear to have minimal nutrigenomics knowledge and little confidence in choosing and interpreting nutrigenetic tests. To remedy this, HCPs need access to credible education, professional support, networking, career development, mentorship, and a regulated testing environment. This will enable them to evaluate the credibility of genetic tests and testing companies, provide genetic results in context, and apply appropriate clinical translation. Key Message: In order to establish an expert group of nutrigenomic practitioners, collaboration is required between educational institutions, professional organizations, and genetic testing companies. This will provide the necessary support, skills, and knowledge to ensure that the best value is extracted from nutrigenetic tests in an ethical and responsible manner.

Background: At present, there is no clear understanding of the effect of long-duration spaceflight on the major enzymes that govern the metabolism of omega-6 and omega-3 fatty acids. To address this gap in knowledge, we used data from the NASA Twins Study, which includes a multiscale omics investigation of the changes that occurred during a year-long (340 days) human spaceflight. Embedded within the NASA Twins data are specific analytes associated with fatty acid metabolism.

Objectives: To examine the long-chain fatty acid desaturases and elongases in a single human during 1 year in space.

Method: One male twin was on board the International Space Station (ISS) for 1 year, while his monozygotic twin served as a genetically matched ground control. Longitudinal assessments included the genome, epige-nome, transcriptome, proteome, metabolome, microbiome, and immunome during the mission, as well as 6 months before and after. The gene-specific fatty acid desaturase and elongase transcriptome data (FADS1, FADS2, ELOVL2, and ELOVL5) were extracted from untargeted RNA-seq measurements derived from white blood cell fractions.

Results: Most data from the elongases and desaturases exhibited relatively similar expression profiles (R2 >0.6) over time for the CD8, CD19, and lymphocyte-depleted (LD) cell fractions, indicating overall conservation of function within and between the subjects. Both cell-type and temporal specificity was observed in some cases, and some differences were also apparent between the polyadenylated (polyA) fraction of processed RNAs versus the ribodepleted (ribo-) fraction. The flight subject showed a stronger enrichment of the fatty acid metabolic process pathway across almost all cell types (columns, CD4, CD8, CPT, and LD), most especially in the ribodepleted fraction of RNA, but also with the polyA+ fraction of RNA. Gene set enrichment analysis (GSEA) measures across three related fatty acid metabolism pathways showed a differential between the ground and the flight subject.

Conclusions: There appears to be no persistent alteration of desaturase and elongase gene expression associated with 1 year in space. However, these data provide evidence that cellular lipid metabolism can be responsive and dynamic to spaceflight, even though it appears cell-type and context specific, most notably in terms of the fraction of RNA measured and the collection protocols. These results also provide new evidence of mid-flight spikes in expression of selected genes, which may indicate transient responses to specific insults during spaceflight.

Background: Body composition is increasingly being recognized as an important prognostic factor for health outcomes across cancer, liver cirrhosis, and critically ill patients. Computed tomography (CT) scans, when taken as part of routine care, provide an excellent opportunity to precisely measure the quantity and quality of skeletal muscle and adipose tissue. However, manual analysis of CT scans is costly and time-intensive, limiting the widespread adoption of CT-based measurements of body composition.

Summary: Advances in deep learning have demonstrated excellent success in biomedical image analysis. Several recent publications have demonstrated excellent accuracy in comparison to human raters for the measurement of skeletal muscle, visceral adipose, and subcutaneous adipose tissue from the lumbar vertebrae region, indicating that analysis of body composition may be successfully automated using deep neural networks. Key Messages: The high accuracy and drastically improved speed of CT body composition analysis (<1 s/scan for neural networks vs. 15 min/scan for human analysis) suggest that neural networks may aid researchers and clinicians in better understanding the role of body composition in clinical populations by enabling cost-effective, large-scale research studies. As the role of body composition in clinical settings and the field of automated analysis advance, it will be critical to examine how clinicians interact with these systems and to evaluate whether these technologies are beneficial in improving treatment and health outcomes for patients.