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Regional variation in health is predominantly driven by lifestyle rather than genetics.

ABSTRACT: Regional differences in health-related phenotypes have been detected between and within countries. In Scotland, regions differ for a variety of health-related traits and display differences in mean lifespan of up to 7.5 years. Both genetics and lifestyle differences are potential causes of this variation. Using data on obesity-related traits of ~11,000 Scottish individuals with genome-wide genetic information and records of lifestyle and socioeconomic factors, we explored causes of regional variation by using models that incorporate genetic and environmental information jointly. We found that variation between individuals within regions showed substantial influence of both genetic variation and family environment. Regional variation for most obesity traits was associated with lifestyle and socioeconomic variables, such as smoking, diet and deprivation which are potentially modifiable. There was limited evidence that regional differences were of genetic origin. This has important implications for healthcare policies, suggesting that inequalities can be tackled with appropriate social and economic interventions.Health-related traits are known to vary geographically. Here, Amador and colleagues show that regional variation of obesity-related traits in a Scottish population is influenced more by lifestyle differences than it is by genetic differences.

SUBMITTER: Amador C

PROVIDER: S-EPMC5630587 | biostudies-literature | 2017 Oct

REPOSITORIES: biostudies-literature

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Regional variation in health is predominantly driven by lifestyle rather than genetics.

Amador Carmen C Xia Charley C Nagy Réka R Campbell Archie A Porteous David D Smith Blair H BH Hastie Nick N Vitart Veronique V Hayward Caroline C Navarro Pau P Haley Chris S CS

Nature communications 20171006 1

Regional differences in health-related phenotypes have been detected between and within countries. In Scotland, regions differ for a variety of health-related traits and display differences in mean lifespan of up to 7.5 years. Both genetics and lifestyle differences are potential causes of this variation. Using data on obesity-related traits of ~11,000 Scottish individuals with genome-wide genetic information and records of lifestyle and socioeconomic factors, we explored causes of regional vari ...[more]

PMID: 28986520

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Project description:Introduction: To determine the effects of a novel lifestyle intervention combining lifestyle behavioral education with the complementary-integrative health modality of guided imagery (GI) on dietary and physical activity behaviors in adolescents. The primary aim of this study was to determine the incremental effects of the lifestyle education, stress reduction GI (SRGI), and lifestyle behavior GI (LBGI) components of the intervention on the primary outcome of physical activity lifestyle behaviors (sedentary behavior, light, moderate, and vigorous physical activity), as well as dietary intake behaviors, at the completion of the 12-week intervention. The authors hypothesized that the intervention would improve obesity-related lifestyle behaviors. Materials and Methods: Two hundred and thirty-two adolescent participants (aged 14-17 years, sophomore or junior year of high school) were cluster randomized by school into one of four intervention arms: nonintervention Control (C), Lifestyle education (LS), SRGI, and LBGI. After-school intervention sessions were held two (LS) or three (SRGI, LBGI) times weekly for 12 weeks. Physical activity (accelerometry) and dietary intake (multiple diet recalls) outcomes were assessed pre- and postintervention. Primary analysis: intention-to-treat (ITT) mixed-effects modeling with diagonal covariance matrices; secondary analysis: ad hoc subgroup sensitivity analysis using only those participants adherent to protocol. Results: ITT analysis showed that the Healthy Eating Index (HEI) increased in the LS group compared with C (p = 0.02), but there was no additional effect of GI. Among adherent participants, sedentary behavior was decreased stepwise relative to C in SRGI (d = -0.73, p = 0.004) > LBGI (d = -0.59, p = 0.04) > LS (d = -0.41, p = 0.07), and moderate + vigorous physical activity was increased in SRGI (d = 0.58, p = 0.001). Among adherent participants, the HEI was increased in LS and SRGI, and glycemic index reduced in LBGI. Conclusions: While ITT analysis was negative, among adherent participants, the Imagine HEALTH lifestyle intervention improved eating habits, reduced sedentary activity, and increased physical activity, suggesting that GI may amplify the role of lifestyle education alone for some key outcomes. Clinical Trials.gov ID: NCT02088294.

Project description:BackgroundA better understanding of lifestyle behaviours of children < 7 years and the relation with childhood overweight is needed. The aim of our prospective study was to examine how lifestyle patterns in young children are associated with the development of childhood overweight. As ecological models suggest focusing on not only the child as an individual, but also their environment, we also considered the role of socio-economic status (SES) and spatial clustering of lifestyle and body mass index (BMI).MethodsIn 1792 children (aged 3-6 years) participating in the GECKO Drenthe cohort, diet, screen time, outdoor play and sleep were assessed by questionnaires and moderate-to-vigorous physical activity and sedentary time by accelerometry (Actigraph GT3X). At 10-11 years, height and weight were measured to calculate age- and sex-specific standardized BMI z-scores (zBMI). Lifestyle patterns were identified using principal component analysis. To assess spatial clustering for the lifestyle patterns and zBMI, we calculated the Global Moran's I statistic. Linear- and logistic regression models, taking into account SES, were performed to examine the association between the lifestyle patterns and the development of overweight. For the spatial analyses, we added spatial terms for the determinants, the outcome, and the error term.ResultsThree lifestyle patterns were identified: (1) 'high activity', (2) 'low screen time, high sleep and healthy diet', and (3) 'high outdoor play'. No associations were observed between the 'high activity' or 'high outdoor play' patterns at young age with the development of childhood overweight (all p > 0.05). In contrast, children who adhered to the 'low screen time, high sleep and healthy diet' pattern had lower odds to become overweight and a lower zBMI at 10-11 years (odds ratio [95% CI] = 0.766 [0.65; 0.90]). These findings remained similar after taking SES into account. Regarding the spatial analyses, we found spatial clustering of zBMI, but no spatial clustering of the lifestyle patterns.ConclusionsLow screen time, high sleep duration and a healthy diet cluster into a pattern that seems favourable in the prevention of childhood overweight, independent of individual SES. The spatial analyses suggest that there are likely other neighbourhood factors that contribute to the spatial clustering of childhood overweight.

Project description:Targeted genomic enrichment followed by next-generation sequencing dramatically increased the efficiency of mutation discovery in human genomes. Here we demonstrate that these techniques also revolutionize traditional genetic approaches in model systems. We developed a two-step protocol utilizing a traditional bulk-segregant analysis (BSA) approach for positional cloning mutants in phenotype-driven forward genetic screens. First, BSA pools are 'light' sequenced for rough mapping, followed by targeted enrichment and deep-sequencing of the mutant BSA pool for the linked genomic region to fine-map and discover candidate mutations. We applied this method successfully to three Arabidopsis mutants and show that it can be scaled by multiplexing. Similarly, we applied these techniques to a gene-driven reverse genetics method (chemical driven target-selected mutagenesis or TILLING) that is used for generating gene knockouts in a wide range of organisms, including plants, invertebrates and vertebrates. We developed an efficient multiplexed genomic enrichment protocol for pre-barcoded samples. As a proof-of-principle, 770 genes were screened for induced mutations in 30 rats, which identified all but one known variants (30) as well as a large series of novel knockout and missense alleles. Mutations were retrieved at the expected frequency with a the false-positive rate of less than 1 in 6 million basepairs, which is much lower as compared to traditional mutation discovery approaches. Both methods are largely independent of the genome size due to the targeted enrichment and can thus be applied to any genetic model system of interest. Targeted genomic enrichment followed by next-generation sequencing dramatically increased the efficiency of mutation discovery in human genomes. Here we demonstrate that these techniques also revolutionize traditional genetic approaches in model systems. We developed a two-step protocol utilizing a traditional bulk-segregant analysis (BSA) approach for positional cloning mutants in phenotype-driven forward genetic screens. First, BSA pools are 'light' sequenced for rough mapping, followed by targeted enrichment and deep-sequencing of the mutant BSA pool for the linked genomic region to fine-map and discover candidate mutations. We applied this method successfully to three Arabidopsis mutants and show that it can be scaled by multiplexing. Similarly, we applied these techniques to a gene-driven reverse genetics method (chemical driven target-selected mutagenesis or TILLING) that is used for generating gene knockouts in a wide range of organisms, including plants, invertebrates and vertebrates. We developed an efficient multiplexed genomic enrichment protocol for pre-barcoded samples. As a proof-of-principle, 770 genes were screened for induced mutations in 30 rats, which identified all but one known variants (30) as well as a large series of novel knockout and missense alleles. Mutations were retrieved at the expected frequency with a the false-positive rate of less than 1 in 6 million basepairs, which is much lower as compared to traditional mutation discovery approaches. Both methods are largely independent of the genome size due to the targeted enrichment and can thus be applied to any genetic model system of interest.

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Regional variation in health is predominantly driven by lifestyle rather than genetics.

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Regional variation in health is predominantly driven by lifestyle rather than genetics.

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