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Cambridge, United Kingdom

Aims/hypothesis: We examined the independent and combined associations of physical activity and obesity with incident type 2 diabetes in men and women. Methods: The InterAct case-cohort study consists of 12,403 incident type 2 diabetes cases and a randomly selected subcohort of 16,154 individuals, drawn from a total cohort of 340,234 participants with 3.99 million person-years of follow-up. Physical activity was assessed by a four-category index. Obesity was measured by BMI and waist circumference (WC). Associations between physical activity, obesity and case-ascertained incident type 2 diabetes were analysed by Cox regression after adjusting for educational level, smoking status, alcohol consumption and energy intake. In combined analyses, individuals were stratified according to physical activity level, BMI and WC. Results: A one-category difference in physical activity (equivalent to approximately 460 and 365 kJ/day in men and women, respectively) was independently associated with a 13% (HR 0.87, 95% CI 0.80, 0.94) and 7% (HR 0.93, 95% CI 0.89, 0.98) relative reduction in the risk of type 2 diabetes in men and women, respectively. Lower levels of physical activity were associated with an increased risk of diabetes across all strata of BMI. Comparing inactive with active individuals, the HRs were 1.44 (95% CI 1.11, 1.87) and 1.38 (95% CI 1.17, 1.62) in abdominally lean and obese inactive men, respectively, and 1.57 (95% CI 1.19, 2.07) and 1.19 (95% CI 1.01, 1.39) in abdominally lean and obese inactive women, respectively. Conclusions/interpretation: Physical activity is associated with a reduction in the risk of developing type 2 diabetes across BMI categories in men and women, as well as in abdominally lean and obese men and women. © The Author(s) 2012. Source


Bauman A.E.,University of Sydney | Reis R.S.,Pontifical Catholic University of Parana | Reis R.S.,Federal University of Parana | Sallis J.F.,University of California at San Diego | And 4 more authors.
The Lancet | Year: 2012

Physical inactivity is an important contributor to non-communicable diseases in countries of high income, and increasingly so in those of low and middle income. Understanding why people are physically active or inactive contributes to evidence-based planning of public health interventions, because effective programmes will target factors known to cause inactivity. Research into correlates (factors associated with activity) or determinants (those with a causal relationship) has burgeoned in the past two decades, but has mostly focused on individual-level factors in high-income countries. It has shown that age, sex, health status, self-efficacy, and motivation are associated with physical activity. Ecological models take a broad view of health behaviour causation, with the social and physical environment included as contributors to physical inactivity, particularly those outside the health sector, such as urban planning, transportation systems, and parks and trails. New areas of determinants research have identified genetic factors contributing to the propensity to be physically active, and evolutionary factors and obesity that might predispose to inactivity, and have explored the longitudinal tracking of physical activity throughout life. An understanding of correlates and determinants, especially in countries of low and middle income, could reduce the effect of future epidemics of inactivity and contribute to effective global prevention of non-communicable diseases. Source


Frayling T.M.,University of Exeter | Ong K.,Institute of Metabolic Science | Ong K.,MRC Unit for Lifelong Health and Ageing
Genome Biology | Year: 2011

Two recent studies of the FTO gene provide more information on how it affects body mass index. © 2011 BioMed Central Ltd. Source


Loos R.J.F.,Institute of Metabolic Science
Best Practice and Research: Clinical Endocrinology and Metabolism | Year: 2012

Genome-wide association studies (GWAS) have revolutionised the discovery of genes for common traits and diseases, including obesity-related traits. In less then four years time, 52 genetic loci were identified to be unequivocally associated with obesity-related traits. This vast success raised hope and expectations that genetic information would become soon an integral part of personalised medicine. However, these loci have only small effects on obesity-susceptibility and explain just a fraction of the total variance. As such, their accuracy to predict obesity is poor and not competitive with the predictive ability of traditional risk factors. Nevertheless, some of these loci are being used in commercially available personal genome tests to estimate individuals' lifetime risk of obesity. While proponents believe that personal genome profiling could have beneficial effects on behaviour, early reports do not support this hypothesis. To conclude, the most valuable contribution of GWAS-identified loci lies in their contribution to elucidating new physiological pathways that underlie obesity-susceptibility. © 2011 Elsevier Ltd. All rights reserved. Source


Erqou S.,York College | Lee C.C.,University of Toronto | Adler A.I.,Institute of Metabolic Science
Diabetologia | Year: 2014

Aims/hypotheses Current evidence indicates that statins increase the risk of incident diabetes; however, the relationship between statins and glycaemic control in people with established diabetes has not been well characterised. To address this question, we conducted a meta-analysis of randomised controlled trials (RCTs) of statins in patients with diabetes for whom there was available data on glycaemic control. Methods We identified studies published between January 1970 and November 2013 by searching electronic databases and reference lists. We included RCTs in which the intervention group received statins and the control group received placebo or standard treatment, with >0 participants enrolled, with the intervention lasting >12 weeks and with preand post-intervention HbA1c reported. We combined studyspecific estimates using random-effects model meta-analysis. Results In a pooled analysis of nine trials involving 9,696 participants (4,980 statin, 4,716 control) and an average follow-up of 3.6 years, the mean HbA1c of participants randomised to statins was higher than those randomised to the control group: mean difference (95% CI) was 0.12%(0.04, 0.20) or 1.3 mmol/mol (0.4, 2.2); p=0.003. There was moderate heterogeneity across the studies (I2=54%, p=0.014) not explained by available study-level characteristics. This review was limited by the small number of studies, available data on only three statins and sparse reporting on changes in use of glucose-lowering medications. Conclusions/interpretation Statin treatment is associated with a modest increase in HbA1c in patients with diabetes. © Springer-Verlag Berlin Heidelberg 2014. Source

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