758 resultados para The healthy lifestyle


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Background: There is a current lack of consensus on defining metabolically healthy obesity (MHO). Limited data on dietary and lifestyle factors and MHO exist. The aim of this study is to compare the prevalence, dietary factors and lifestyle behaviours of metabolically healthy and unhealthy obese and non-obese subjects according to different metabolic health criteria. Method: Cross-sectional sample of 1,008 men and 1,039 women aged 45-74 years participated in the study. Participants were classified as obese (BMI ≥30kg/m2) and non-obese (BMI <30kg/m2). Metabolic health status was defined using five existing MH definitions based on a range of cardiometabolic abnormalities. Dietary composition and quality, food pyramid servings, physical activity, alcohol and smoking behaviours were examined. Results: The prevalence of MHO varied considerably between definitions (2.2% to 11.9%), was higher among females and generally increased with age. Agreement between MHO classifications was poor. Among the obese, prevalence of MH was 6.8% to 36.6%. Among the non-obese, prevalence of metabolically unhealthy subjects was 21.8% to 87%. Calorie intake, dietary macronutrient composition, physical activity, alcohol and smoking behaviours were similar between the metabolically healthy and unhealthy regardless of BMI. Greater compliance with food pyramid recommendations and higher dietary quality were positively associated with metabolic health in obese (OR 1.45-1.53 unadjusted model) and non-obese subjects (OR 1.37-1.39 unadjusted model), respectively. Physical activity was associated with MHO defined by insulin resistance (OR 1.87, 95% CI 1.19-2.92, p = 0.006).

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The aim of the 5-year European Union (EU)-Integrated Project GEnetics of Healthy Aging (GEHA), constituted by 25 partners (24 from Europe plus the Beijing Genomics Institute from China), is to identify genes involved in healthy aging and longevity, which allow individuals to survive to advanced old age in good cognitive and physical function and in the absence of major age-related diseases. To achieve this aim a coherent, tightly integrated program of research that unites demographers, geriatricians, geneticists, genetic epidemiologists, molecular biologists, bioinfomaticians, and statisticians has been set up. The working plan is to: (a) collect DNA and information on the health status from an unprecedented number of long-lived 90+ sibpairs (n = 2650) and of younger ethnically matched controls (n = 2650) from 11 European countries; (b) perform a genome-wide linkage scannning in all the sibpairs (a total of 5300 individuals); this investigation will be followed by linkage disequilibrium mapping (LD mapping) of the candidate chromosomal regions; (c) study in cases (i.e., the 2650 probands of the sibpairs) and controls (2650 younger people), genomic regions (chromosome 4, D4S1564, chromosome 11, 11.p15.5) which were identified in previous studies as possible candidates to harbor longevity genes; (d) genotype all recruited subjects for apoE polymorphisms; and (e) genotype all recruited subjects for inherited as well as epigenetic variability of the mitochondrial DNA (mtDNA). The genetic analysis will be performed by 9 high-throughput platforms, within the framework of centralized databases for phenotypic, genetic, and mtDNA data. Additional advanced approaches (bioinformatics, advanced statistics, mathematical modeling, functional genomics and proteomics, molecular biology, molecular genetics) are envisaged to identify the gene variant(s) of interest. The experimental design will also allow (a) to identify gender-specific genes involved in healthy aging and longevity in women and men stratified for ethnic and geographic origin and apoE genotype; (b) to perform a longitudinal survival study to assess the impact of the identified genetic loci on 90+ people mortality; and (c) to develop mathematical and statistical models capable of combining genetic data with demographic characteristics, health status, socioeconomic factors, lifestyle habits.

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In a scenario of increasing life expectancy worldwide, it is mandatory to identify the characteristics of a healthy aging phenotype, including survival predictors, and to disentangle those related to environment/lifestyle versus those related to familiarity/genetics. To this aim we comprehensively characterised a cohort of 1,160 Italian subjects of 90 years and over (90+, mean age 93 years; age range 90-106 years) followed for 6 years survival, belonging to 552 sib-ships (familiar longevity) recruited (2005-2008) within the EU-funded GEHA project in three Italian geographic areas (Northern, Central and Southern Italy) different for urban/rural and socio-economical characteristics. On the whole, the following factors emerged as significant predictors of survival after 90 years of age: absence of cognitive impairment and physical disability, high hand grip strength scores and body mass index (BMI) values, "excellent/good" self-reported health, high haemoglobin and total cholesterol levels and low creatinine levels. These parameters, excluding BMI values, were also significantly associated within sib-ships, suggesting a strong familial/genetic component. Geographical micro-heterogeneity of survival predictors emerged, such as functional and physical status being more important in Southern than in Central and Northern Italy. In conclusion, we identified modifiable survival predictors related to specific domains, whose role and importance vary according to the geographic area considered and which can help in interpreting the genetic results obtained by the GEHA project, whose major aim is the comprehensive evaluation of phenotypic and genetic data.

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Objectives We examined the characteristics and CHD risks of people who accessed the free Healthy Heart Assessment (HHA) service operated by a large UK pharmacy chain from August 2004 to April 2006. Methods Associations between participants’ gender, age, and socioeconomics were explored in relation to calculated 10-year CHD risks by cross-tabulation of the data. Specific associations were tested by forming contingency tables and using Pearson chi-square (χ2). Results Data from 8,287 records were analysable; 5,377 were at low and 2,910 at moderate-to-high CHD risk. The likelihood of moderate-to-high risk for a male versus female participant was significantly higher with a relative risk ratio (RRR) 1.72 (P < 0.001). A higher percentage of those in socioeconomic categories ‘constrained by circumstances’ (RRR 1.15; P < 0.05) and ‘blue collar communities’ (RRR 1.13; P < 0.05) were assessed with moderate-to-high risk compared to those in ‘prospering suburbs’. Conclusions People from ‘hard-to-reach’ sectors of the population, men and people from less advantaged communities, accessed the HHA service and were more likely to return moderate-to-high CHD risk. Pharmacists prioritised provision of lifestyle information above the sale of a product. Our study supports the notion that pharmacies can serve as suitable environments for the delivery of similar screening services.

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Originally domesticated in Ethiopia, tef is a wholegrain cereal that has become a lifestyle food alternative in the West. Its appeal is due to its gluten free qualities and its light and soft texture which can easily be combined with other cuisines. Tef products including flour, bread, cookies and the flattened bread injera can be found in organic/health food stores in Europe and the USA or can be purchased online. It is estimated that there are more than 90 restaurants in Europe providing Ethiopian cuisine, at the heart of which is injera.

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Background: Healthy diet and regular physical activity are powerful tools in reducing diabetes and cardiometabolic risk. Various international scientific and health organizations have advocated the use of new technologies to solve these problems. The PREDIRCAM project explores the contribution that a technological system could offer for the continuous monitoring of lifestyle habits and individualized treatment of obesity as well as cardiometabolic risk prevention. Methods: PREDIRCAM is a technological platform for patients and professionals designed to improve the effectiveness of lifestyle behavior modifications through the intensive use of the latest information and communication technologies. The platform consists of a web-based application providing communication interface with monitoring devices of physiological variables, application for monitoring dietary intake, ad hoc electronic medical records, different communication channels, and an intelligent notification system. A 2-week feasibility study was conducted in 15 volunteers to assess the viability of the platform. Results: The website received 244 visits (average time/session: 17 min 45 s). A total of 435 dietary intakes were recorded (average time for each intake registration, 4 min 42 s ± 2 min 30 s), 59 exercises were recorded in 20 heart rate monitor downloads, 43 topics were discussed through a forum, and 11 of the 15 volunteers expressed a favorable opinion toward the platform. Food intake recording was reported as the most laborious task. Ten of the volunteers considered long-term use of the platform to be feasible. Conclusions: The PREDIRCAM platform is technically ready for clinical evaluation. Training is required to use the platform and, in particular, for registration of dietary food intake.

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The proportion of elderly people in the population has increased rapidly in the last century and consequently "healthy aging" is expected to become a critical area of research in neuroscience. Evidence reveals how healthy aging depends on three main behavioral factors: social lifestyle, cognitive activity and physical activity. In this study, we focused on the role of cognitive activity, concentrating specifically on educational and occupational attainment factors, which were considered two of the main pillars of cognitive reserve. 21 subjects with similar rates of social lifestyle, physical and cognitive activity were selected from a sample of 55 healthy adults. These subjects were divided into two groups according to their level of cognitive reserve; one group comprised subjects with high cognitive reserve (9 members) and the other contained those with low cognitive reserve (12 members). To evaluate the cortical brain connectivity network, all participants were recorded by Magnetoencephalography (MEG) while they performed a memory task (modified version of the Sternberg¿s Task). We then applied two algorithms (Phase Locking Value & Phase-Lag Index) to study the dynamics of functional connectivity. In response to the same task, the subjects with lower cognitive reserve presented higher functional connectivity than those with higher cognitive reserve. These results may indicate that participants with low cognitive reserve needed a greater 'effort' than those with high cognitive reserve to achieve the same level of cognitive performance. Therefore, we conclude that cognitive reserve contributes to the modulation of the functional connectivity patterns of the aging brain.