Short sleep duration and obesity among Australian children

Extent: 6p.Background: There is limited information on sleep duration and obesity among Australian children. The objective of the study is to cross-sectionally examine the relationship between sleep duration and obesity in Australian children aged 5 to 15 years. Methods: Data were collected using the South Australian Monitoring and Surveillance System between January 2004 and December 2008. Each month a representative random sample of South Australians are selected from the Electronic White Pages with interviews conducted using Computer Assisted Telephone Interviewing (CATI). Within each household, the person who was last to have a birthday was selected for interview. Parents reported the number of hours their children slept each day. Obesity was defined according to the International Obesity Task Force (IOTF) definition based on BMI calculated from reported body weight and height. Results: Overall, parents of 3495 children aged 5-15 years (mean 10.7 years, 50.3% boys) were interviewed. The prevalence of obesity was 7.7% (8.9% in boys, 6.6% in girls). In multivariate analysis after adjusting for sociodemographic variables, intake of fruit and vegetables, physical activity and inactivity, the odds ratio (OR) for obesity comparing sleeping <9 hours with ≥10 hours was 2.23 (95% CI 1.04-4.76) among boys, 1.70(0.78-3.73) among girls, and 1.97(1.15-3.38) in both genders. The association between short sleep (<9 hours) and obesity was stronger in the younger age group. No significant association between short sleep and obesity was found among children aged 13-15. There was also an additive interaction between short sleep and low level of physical activity. Conclusion: Short sleep duration is associated with increased obesity in children especially among younger age groups and boys.Zumin Shi, Anne W Taylor, Tiffany K Gill, Jane Tuckerman, Robert Adams and James Marti

Estimating the survival benefits gained from providing national cancer genetic services to women with a family history of breast cancer

The aim of this paper is to compare a service offering genetic testing and presymptomatic surveillance to women at increased risk of developing breast cancer with its predecessor of no service at all in terms of survival and quality-adjusted survival (QALYs) by means of a Markov cohort chain simulation model. Genetic assessment and presymptomatic care provided between 0.07-1.61 mean additional life years and 0.05-1.67 mean QALYs over no services. Prophylactic surgery and surveillance extended mean life expectancy by 0.41-1.61 and 0.32-0.99 years, respectively over no services for high-risk women. Model outcomes were sensitive to all the parameters varied in the sensitivity analysis. Providing cancer genetic services increase survival and as long as services do not induce adverse psychological effects they also provide more QALYs. The greatest survival and QALY benefits were found for women with identified mutations. As more cancer genes are identified, the survival and cost-effectiveness of genetic services will improve. Although mastectomy provided most additional life years, when quality of life was accounted for oophorectomy was the optimal strategy. Delayed entry into coordinated genetic services was found to diminish the average survival and QALY gains for a woman utilising these services

Road Traffic Noise and Incident Myocardial Infarction: A Prospective Cohort Study

BACKGROUND Both road traffic noise and ambient air pollution have been associated with risk for ischemic heart disease, but only few inconsistent studies include both exposures. METHODS In a population-based cohort of 57 053 people aged 50 to 64 years at enrolment in 1993-1997, we identified 1600 cases of first-ever MI between enrolment and 2006. The mean follow-up time was 9.8 years. Exposure to road traffic noise and air pollution from 1988 to 2006 was estimated for all cohort members from residential address history. Associations between exposure to road traffic noise and incident MI were analysed in a Cox regression model with adjustment for air pollution (NO(x)) and other potential confounders: age, sex, education, lifestyle confounders, railway and airport noise. RESULTS We found that residential exposure to road traffic noise (L(den)) was significantly associated with MI, with an incidence rate ratio IRR of 1.12 per 10 dB for both of the two exposure windows: yearly exposure at the time of diagnosis (95% confidence interval (CI): 1.02-1.22) and 5-years time-weighted mean (95% CI: 1.02-1.23) preceding the diagnosis. Visualizing of the results using restricted cubic splines showed a linear dose-response relationship. CONCLUSIONS Exposure to long-term residential road traffic noise was associated with a higher risk for MI, in a dose-dependent manner

Proximal correlates of metabolic phenotypes during ‘at-risk' and ‘case' stages of the metabolic disease continuum

Extent: 11p.OBJECTIVE: To examine the social and behavioural correlates of metabolic phenotypes during ‘at-risk’ and ‘case’ stages of the metabolic disease continuum. DESIGN: Cross-sectional study of a random population sample. PARTICIPANTS: A total of 718 community-dwelling adults (57% female), aged 18--92 years from a regional South Australian city. MEASUREMENTS: Total body fat and lean mass and abdominal fat mass were assessed by dual energy x-ray absorptiometry. Fasting venous blood was collected in the morning for assessment of glycated haemoglobin, plasma glucose, serum triglycerides, cholesterol lipoproteins and insulin. Seated blood pressure (BP) was measured. Physical activity and smoking, alcohol and diet (96-item food frequency), sleep duration and frequency of sleep disordered breathing (SDB) symptoms, and family history of cardiometabolic disease, education, lifetime occupation and household income were assessed by questionnaire. Current medications were determined by clinical inventory. RESULTS: 36.5% were pharmacologically managed for a metabolic risk factor or had known diabetes (‘cases’), otherwise were classified as the ‘at-risk’ population. In both ‘at-risk’ and ‘cases’, four major metabolic phenotypes were identified using principal components analysis that explained over 77% of the metabolic variance between people: fat mass/insulinemia (FMI); BP; lipidaemia/lean mass (LLM) and glycaemia (GLY). The BP phenotype was uncorrelated with other phenotypes in ‘cases’, whereas all phenotypes were inter-correlated in the ‘at-risk’. Over and above other socioeconomic and behavioural factors, medications were the dominant correlates of all phenotypes in ‘cases’ and SDB symptom frequency was most strongly associated with FMI, LLM and GLY phenotypes in the ‘at-risk’. CONCLUSION: Previous research has shown FMI, LLM and GLY phenotypes to be most strongly predictive of diabetes development. Reducing SDB symptom frequency and optimising the duration of sleep may be important concomitant interventions to standard diabetes risk reduction interventions. Prospective studies are required to examine this hypothesis.MT Haren, G Misan, JF Grant, JD Buckley, PRC Howe, AW Taylor, J Newbury and RA McDermot

Proximal correlates of metabolic phenotypes during ‘at-risk' and ‘case' stages of the metabolic disease continuum

Extent: 11p.OBJECTIVE: To examine the social and behavioural correlates of metabolic phenotypes during ‘at-risk’ and ‘case’ stages of the metabolic disease continuum. DESIGN: Cross-sectional study of a random population sample. PARTICIPANTS: A total of 718 community-dwelling adults (57% female), aged 18--92 years from a regional South Australian city. MEASUREMENTS: Total body fat and lean mass and abdominal fat mass were assessed by dual energy x-ray absorptiometry. Fasting venous blood was collected in the morning for assessment of glycated haemoglobin, plasma glucose, serum triglycerides, cholesterol lipoproteins and insulin. Seated blood pressure (BP) was measured. Physical activity and smoking, alcohol and diet (96-item food frequency), sleep duration and frequency of sleep disordered breathing (SDB) symptoms, and family history of cardiometabolic disease, education, lifetime occupation and household income were assessed by questionnaire. Current medications were determined by clinical inventory. RESULTS: 36.5% were pharmacologically managed for a metabolic risk factor or had known diabetes (‘cases’), otherwise were classified as the ‘at-risk’ population. In both ‘at-risk’ and ‘cases’, four major metabolic phenotypes were identified using principal components analysis that explained over 77% of the metabolic variance between people: fat mass/insulinemia (FMI); BP; lipidaemia/lean mass (LLM) and glycaemia (GLY). The BP phenotype was uncorrelated with other phenotypes in ‘cases’, whereas all phenotypes were inter-correlated in the ‘at-risk’. Over and above other socioeconomic and behavioural factors, medications were the dominant correlates of all phenotypes in ‘cases’ and SDB symptom frequency was most strongly associated with FMI, LLM and GLY phenotypes in the ‘at-risk’. CONCLUSION: Previous research has shown FMI, LLM and GLY phenotypes to be most strongly predictive of diabetes development. Reducing SDB symptom frequency and optimising the duration of sleep may be important concomitant interventions to standard diabetes risk reduction interventions. Prospective studies are required to examine this hypothesis.MT Haren, G Misan, JF Grant, JD Buckley, PRC Howe, AW Taylor, J Newbury and RA McDermot

Chronotype Genetic Variant in PER2 is Associated with Intrinsic Circadian Period in Humans

This is the final version. Available on open access from Nature Research via the DOI in this recordData Availability: The data that support the findings of this study from the UK BioBank will be made available at https://sleepgenetics.org and the underlying genotype and phenotype data are available through application to the UK Biobank. Other phenotype data are available on request, due to privacy or other restrictions, through co-corresponding author Dr. Scheer ([email protected]).The PERIOD2 (PER2) gene is a core molecular component of the circadian clock and plays an important role in the generation and maintenance of daily rhythms. rs35333999, a missense variant of PER2 common in European populations, has been shown to associate with later chronotype. Chronotype relates to the timing of biological and behavioral activities, including when we sleep, eat, and exercise, and later chronotype is associated with longer intrinsic circadian period (cycle length), a fundamental property of the circadian system. Thus, we tested whether this PER2 variant was associated with circadian period and found significant associations with longer intrinsic circadian period as measured under forced desynchrony protocols, the ‘gold standard’ for intrinsic circadian period assessment. Minor allele (T) carriers exhibited significantly longer circadian periods when determinations were based on either core body temperature or plasma melatonin measurements, as compared to non-carriers (by 12 and 11 min, respectively; accounting for ~7% of inter-individual variance). These findings provide a possible underlying biological mechanism for inter-individual differences in chronotype, and support the central role of PER2 in the human circadian timing system.European CommissionWellcome TrustMedical Research Council (MRC

Clinical chronobiology: a timely consideration in critical care medicine

A fundamental aspect of human physiology is its cyclical nature over a 24-h period, a feature conserved across most life on Earth. Organisms compartmentalise processes with respect to time in order to promote survival, in a manner that mirrors the rotation of the planet and accompanying diurnal cycles of light and darkness. The influence of circadian rhythms can no longer be overlooked in clinical settings; this review provides intensivists with an up-to-date understanding of the burgeoning field of chronobiology, and suggests ways to incorporate these concepts into daily practice to improve patient outcomes. We outline the function of molecular clocks in remote tissues, which adjust cellular and global physiological function according to the time of day, and the potential clinical advantages to keeping in time with them. We highlight the consequences of "chronopathology", when this harmony is lost, and the risk factors for this condition in critically ill patients. We introduce the concept of "chronofitness" as a new target in the treatment of critical illness: preserving the internal synchronisation of clocks in different tissues, as well as external synchronisation with the environment. We describe methods for monitoring circadian rhythms in a clinical setting, and how this technology may be used for identifying optimal time windows for interventions, or to alert the physician to a critical deterioration of circadian rhythmicity. We suggest a chronobiological approach to critical illness, involving multicomponent strategies to promote chronofitness (chronobundles), and further investment in the development of personalised, time-based treatment for critically ill patients