Development and validation of a physical activity scale for measuring physical activity in 24 h of sports, work, and leisure time on an average weekday. For development of the physical activity scale, 2500 randomly selected Danish men and women between the age of 20 and 60 were contacted by mail and asked to select frequent and relevant physical activities from a number of activities of known MET intensity. The activities were included in the physical activity scale, organized in nine different MET levels ranging from sleep/rest (0.9 METs) to high-intensity physical activities (>6 METs). Face validity of the physical activity scale was explored through interviews with 10 volunteer men and women. Concurrent validity was assessed against 4 d of accelerometry (CSA 7164) and physical activity diary in 40 volunteer men and women between 20 and 60 yr of age. The correlation between the activity scale and the diary was high (r = 0.74, P = 0.000), whereas correlation between the activity scale and accelerometer measurements was poor (r = 0.20, NS). The physical activity scale MET-time was systematically higher than average MET-time estimated from the diary, and the difference increased with increasing total MET-time. The physical activity scale appears to be a simple and valid alternative to measuring physical activity by diary in adult sedentary to moderately active Danes. The scale encompasses work, leisure time, and sports activity in one measure; it is easy to administer, and it provides detailed information on different activity levels as well as a single measure of the total amount of physical activity on an average weekday.

Objective To investigate the effect of systematic screening for risk factors for ischaemic heart disease followed by repeated lifestyle counselling on the 10 year development of ischaemic heart disease at a population level. Design Randomised controlled community based trial. Setting Suburbs of Copenhagen, Denmark Participants 59 616 people aged 30-60 years randomised with different age and sex randomisation ratios to an intervention group (n=11 629) and a control group (n=47 987). Intervention The intervention group was invited for screening, risk assessment, and lifestyle counselling up to four times over a five year period. All participants with an unhealthy lifestyle had individually tailored lifestyle counselling at all visits (at baseline and after one and three years); those at high risk of ischaemic heart disease, according to predefined criteria, were furthermore offered six sessions of group based lifestyle counselling on smoking cessation, diet, and physical activity. After five years all were invited for a final counselling session. Participants were referred to their general practitioner for medical treatment, if relevant. The control group was not invited for screening. Main outcome measures The primary outcome measure was incidence of ischaemic heart disease in the intervention group compared with the control group. Secondary outcome measures were stroke, combined events (ischaemic heart disease, stroke, or both), and mortality. Results 6091 (52.4%) people in the intervention group participated at baseline. Among 5978 people eligible at five year follow-up (59 died and 54 emigrated), 4028 (67.4%) attended. A total of 3163 people died in the 10 year follow-up period. Among 58 308 without a history of ischaemic heart disease at baseline, 2782 developed ischaemic heart disease. Among 58 940 without a history of stroke at baseline, 1726 developed stroke. No significant difference was seen between the intervention and control groups in the primary end point (hazard ratio for ischaemic heart disease 1.03, 95% confidence interval 0.94 to 1.13) or in the secondary endpoints (stroke 0.98, 0.87 to 1.11; combined endpoint 1.01, 0.93 to 1.09; total mortality 1.00, 0.91 to 1.09). Conclusion A community based, individually tailored intervention programme with screening for risk of ischaemic heart disease and repeated lifestyle intervention over five years had no effect on ischaemic heart disease, stroke, or mortality at the population level after 10 years. Trial registration Clinical trials NCT00289237.

Hand grip strength is a widely used proxy of muscular fitness, a marker of frailty, and predictor of a range of morbidities and all-cause mortality. To investigate the genetic determinants of variation in grip strength, we perform a large-scale genetic discovery analysis in a combined sample of 195,180 individuals and identify 16 loci associated with grip strength (P<5 × 10⁻⁸) in combined analyses. A number of these loci contain genes implicated in structure and function of skeletal muscle fibres (ACTG1), neuronal maintenance and signal transduction (PEX14, TGFA, SYT1), or monogenic syndromes with involvement of psychomotor impairment (PEX14, LRPPRC and KANSL1). Mendelian randomization analyses are consistent with a causal effect of higher genetically predicted grip strength on lower fracture risk. In conclusion, our findings provide new biological insight into the mechanistic underpinnings of grip strength and the causal role of muscular strength in age-related morbidities and mortality.

Physical activity (PA) may modify the genetic effects that give rise to increased risk of obesity. To identify adiposity loci whose effects are modified by PA, we performed genome-wide interaction meta-analyses of BMI and BMI-adjusted waist circumference and waist-hip ratio from up to 200,452 adults of European (n = 180,423) or other ancestry (n = 20,029). We standardized PA by categorizing it into a dichotomous variable where, on average, 23% of participants were categorized as inactive and 77% as physically active. While we replicate the interaction with PA for the strongest known obesity-risk locus in the FTO gene, of which the effect is attenuated by ~30% in physically active individuals compared to inactive individuals, we do not identify additional loci that are sensitive to PA. In additional genome-wide meta-analyses adjusting for PA and interaction with PA, we identify 11 novel adiposity loci, suggesting that accounting for PA or other environmental factors that contribute to variation in adiposity may facilitate gene discovery. Decline in daily physical activity is thought to be a key contributor to the global obesity epidemic. However, the impact of sedentariness on adiposity may be in part determined by a person’s genetic constitution. The specific genetic variants that are sensitive to physical activity and regulate adiposity remain largely unknown. Here, we aimed to identify genetic variants whose effects on adiposity are modified by physical activity by examining ~2.5 million genetic variants in up to 200,452 individuals. We also tested whether adjusting for physical activity as a covariate could lead to the identification of novel adiposity variants. We find robust evidence of interaction with physical activity for the strongest known obesity risk-locus in the FTO gene, of which the body mass index-increasing effect is attenuated by ~30% in physically active individuals compared to inactive individuals. Our analyses indicate that other similar gene-physical activity interactions may exist, but better measurement of physical activity, larger sample sizes, and/or improved analytical methods will be required to identify them. Adjusting for physical activity, we identify 11 novel adiposity variants, suggesting that accounting for physical activity or other environmental factors that contribute to variation in adiposity may facilitate gene discovery.

Numerous obesity loci have been identified using genome-wide association studies. A UK study indicated that physical activity may attenuate the cumulative effect of 12 of these loci, but replication studies are lacking. Therefore, we tested whether the aggregate effect of these loci is diminished in adults of European ancestry reporting high levels of physical activity. Twelve obesity-susceptibility loci were genotyped or imputed in 111,421 participants. A genetic risk score (GRS) was calculated by summing the BMI-associated alleles of each genetic variant. Physical activity was assessed using self-administered questionnaires. Multiplicative interactions between the GRS and physical activity on BMI were tested in linear and logistic regression models in each cohort, with adjustment for age, age², sex, study center (for multicenter studies), and the marginal terms for physical activity and the GRS. These results were combined using meta-analysis weighted by cohort sample size. The meta-analysis yielded a statistically significant GRS × physical activity interaction effect estimate (P_interaction = 0.015). However, a statistically significant interaction effect was only apparent in North American cohorts (n = 39,810, P_interaction = 0.014 vs. n = 71,611, P_interaction = 0.275 for Europeans). In secondary analyses, both the FTO rs1121980 (P_interaction = 0.003) and the SEC16B rs10913469 (P_interaction = 0.025) variants showed evidence of SNP × physical activity interactions. This meta-analysis of 111,421 individuals provides further support for an interaction between physical activity and a GRS in obesity disposition, although these findings hinge on the inclusion of cohorts from North America, indicating that these results are either population-specific or non-causal. We undertook analyses in 111,421 adults of European descent to examine whether physical activity diminishes the genetic risk of obesity predisposed by 12 single nucleotide polymorphisms, as previously reported in a study of 20,000 UK adults (Li et al, PLoS Med. 2010). Although the study by Li et al is widely cited, the original report has not been replicated to our knowledge. Therefore, we sought to confirm or refute the original study's findings in a combined analysis of 111,421 adults. Our analyses yielded a statistically significant interaction effect (P_interaction = 0.015), confirming the original study's results; we also identified an interaction between the FTO locus and physical activity (P_interaction = 0.003), verifying previous analyses (Kilpelainen et al, PLoS Med., 2010), and we detected a novel interaction between the SEC16B locus and physical activity (P_interaction = 0.025). We also examined the power constraints of interaction analyses, thereby demonstrating that sources of within- and between-study heterogeneity and the manner in which data are treated can inhibit the detection of interaction effects in meta-analyses that combine many cohorts with varying characteristics. This suggests that combining many small studies that have measured environmental exposures differently may be relatively inefficient for the detection of gene × environment interactions.

Background Physical activity level and cardiorespiratory fitness are both inversely associated with the risk of cardiovascular diseases and with all-cause morta...