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The relationship between obesity and grip strength, a key indicator of sarcopenia, has been inconsistently reported. We aimed to examine associations between grip strength and both body mass index (BMI), a clinical indicator of total adiposity, and waist circumference (WC), an indicator of central adiposity. Cross-sectional study. Data collected from 8,441 men and women, aged 48–92 years old, who attended the third health examination of the European Prospective Investigation into Cancer-Norfolk study was used. Maximum grip strength (Smedley dynamometer), BMI (weight/height2) and WC (measured at the natural waist) were ascertained at a research clinic. The associations between grip strength and adiposity measures were explored using linear regression with adjustment for age, height, social class, physical activity, prevalent disease, smoking status and alcohol intake. Men and women were examined separately and those in the upper quartile of BMI were 2.70kg (95%CI 2.07, 3.33) and 1.46kg (95%CI 1.05, 1.86) stronger respectively than those in the bottom quartile (P trends <0.001). Grip strength also increased weakly with increasing WC. However, including both BMI and WC in the same regression model revealed an inverse association between grip strength and WC, whilst the previously observed association with BMI strengthened. For every 10cm increase in WC, grip strength was 3.56kg (95%CI 3.04, 4.08) lower in men and 1.00kg (95%CI 0.74, 1.24) lower in women. Larger overall body mass, indicated by higher BMI, is associated with stronger grip strength but high WC, a clinical indicator of central obesity, is associated with lower grip strength. Abdominal fat is the most metabolically active adipose tissue and this provides a clue to potential mechanisms underlying relationships between fat and skeletal muscle. Additionally, it reinforces the recommendation to measure WC in clinical practice, especially when BMI is below obese ranges.

To examine the validity of the short, last 7-day, self-administered form of the International Physical Activity Questionnaire (IPAQ). All subjects wore an accelerometer for seven consecutive days and completed the IPAQ questionnaire on the eighth day. Criterion validity was assessed by linear regression analysis and by modified Bland-Altman analysis. Specificity and sensitivity were calculated for classifying respondents according to the physical activity guidelines of the American College of Sports Medicine/Centers for Disease Control and Prevention. Workplaces in Uppsala, Sweden. One hundred and eighty-five (87 males) participants, aged 20 to 69 years. Total self-reported physical activity (PA) (MET-min day(-1)) was significantly correlated with average intensity of activity (counts min(-1)) from accelerometry (r = 0.34, P < 0.001). Gender, age, education and body mass index did not affect this relationship. Further, subcomponents of self-reported PA (time spent sitting, time in PA, time in moderate and vigorous activity (MVPA)) were significantly correlated with objectively measured PA (P < 0.05). Self-reported time in PA was significantly different from time measured by accelerometry (mean difference: -25.9 min day(-1); 95% limits of agreement: -172 to 120 min day(-1); P < 0.001). IPAQ identified 77% (specificity) of those who met the current PA guidelines of accumulating more than 30 min day(-1) in MVPA as determined by accelerometry, whereas only 45% (sensitivity) of those not meeting the guidelines were classified correctly. Our results indicate that the short, last 7-days version of the IPAQ has acceptable criterion validity for use in Swedish adults. However, the IPAQ instrument significantly overestimated self-reported time spent in PA. The specificity to correctly classify people achieving current PA guidelines was acceptable, whereas the sensitivity was low.

Objective: The objective of this study is to examine the independent associations of time spent in moderate-to-vigorous physical activity (MVPA) and sedentary (SED-time), with total and abdominal body fat (BF), and the bidirectionality of these associations in adults at high risk of type 2 diabetes. Design and subjects: We measured MVPA (min per day) and SED-time (h per day) by accelerometry, and indices of total (body weight, fat mass (FM), BF% and FM index) and abdominal BF (waist circumference (WC)) using standard procedures in 231 adults (41.3±6.4 years) with parental history of type 2 diabetes (ProActive UK) at baseline, 1-year and 7-year follow-up. Mixed effects models were used to quantify the independent associations (expressed as standardised β-coefficients (95% confidence interval (CI))) of MVPA and SED-time with fat indices, using data from all three time points. All models were adjusted for age, sex, intervention arm, monitor wear time, follow-up time, smoking status, socioeconomic status and MVPA/SED-time. Results: MVPA was inversely and independently associated with all indices of total BF (for example, 1 s.d. higher MVPA was associated with a reduction in FM, β =−0.09 (95% CI: −0.14, −0.04) s.d.) and abdominal BF (for example, WC: β =−0.07 (−0.12, −0.02)). Similarly, higher fat indices were independently associated with a reduction in MVPA (for example, WC: β =−0.25 (−0.36, −0.15); FM: β =−0.27 (−0.36, −0.18)). SED-time was positively and independently associated with most fat indices (for example, WC: β =0.03 (−0.04, 0.09); FM: β =0.10 (0.03, 0.17)). Higher values of all fat indices independently predicted longer SED-time (for example, WC: β =0.10 (0.02, 0.18), FM: β =0.15 (0.07, 0.22)). Conclusions: The associations of MVPA and SED-time with total and abdominal BF are bidirectional and independent among individuals at high risk for type 2 diabetes. The association between BF and MVPA is stronger than the reciprocal association, highlighting the importance of considering BF as a determinant of decreasing activity and a potential consequence. Promoting more MVPA and less SED-time may reduce total and abdominal BF.

Ecological data on temporal trends suggest that the rising prevalence of obesity is, at least in part, attributable to declining population energy expenditure. However, population-level data on trends in physical activity are scarce. In longitudinal cohort studies individuals who report higher levels of leisure-time physical activity tend to be less likely to gain weight, but studies vary in their conclusions because of issues of confounding, reverse causality and measurement error. The majority of studies suggest that low levels of activity are only weakly associated with future weight gain. Questions about dose–response can only be properly addressed by studies including objective measures of activity with known measurement error. The observational studies leave uncertainties about the direction of causality, as individuals who are overweight are less likely to stay active. Adjustment for confounding can diminish the impact of known confounders, but only randomisation can deal with issues of unmeasured confounding. Although there are a large number of clinical trials on the treatment of individuals with obesity or the prevention of weight regain among weight losers, the updated review of trials to prevent weight gain de novo only reveals six trials published since 2000 in adults and eleven in children. Not only are these trials relatively few in number but, for various methodological reasons, they are uncertain in their conclusions about whether increasing activity will be effective in preventing obesity. Whilst efforts should continue to enhance the evidence base it is wise, in the meantime, to stick to the consensus public health advice of advocating 45–60 min moderate intensity activity daily to prevent obesity.

Objective: To investigate the relationship of body weight and its changes over time with physical activity (PA). Design: Population-based prospective cohort study (Norfolk cohort of the European Prospective Investigation into Cancer and Nutrition, EPIC-Norfolk, United Kingdom). Subjects: A total of 25 639 men and women aged 39–79 years at baseline. PA was self-reported. Weight and height were measured by standard clinical procedures at baseline and self-reported at 18-month and 10-year follow-ups (calibrated against clinical measures). Main outcome measure was PA at the 10-year follow-up. Results: Body weight and PA were inversely associated in cross-sectional analyses. In longitudinal analyses, an increase in weight was associated with higher risk of being inactive 10 years later, after adjusting for baseline activity, 18-month activity, sex, baseline age, prevalent diseases, socioeconomic status, education, smoking, total daily energy intake and alcohol intake. Compared with stable weight, a gain in weight of >2 kg per year in the short-, medium- and long-term was consistently and significantly associated with greater likelihood of physical inactivity after 10 years, with the most pronounced effect for long-term weight gain, OR=1.89 (95% CI: 1.30–2.70) in fully adjusted analysis. Weight gain of 0.5–2 kg per year over long-term was substantially associated with physical inactivity after full adjustment, OR=1.26 (95% CI: 1.11–1.41). Conclusion: Weight gain (during short-, medium- and long-term) is a significant determinant of future physical inactivity independent of baseline weight and activity. Compared with maintaining weight, moderate (0.5–2 kg per year) and large weight gain (>2 kg per year) significantly predict future inactivity; a potentially vicious cycle including further weight gain, obesity and complications associated with a sedentary lifestyle. On the basis of current predictions of obesity trends, we estimate that the prevalence of inactivity in England would exceed 60% in the year 2020.

Background/Objectives: Biomarkers for a mixed fruit and vegetable (FV) diet are needed to provide a better understanding of the association between FV intake and type 2 diabetes. We aimed to examine the prospective association between a composite score comprised of three biomarkers of FV intake in free-living populations and incident diabetes. Subjects/Methods: A total of 318 incident diabetes cases and 926 controls from the EPIC (European Prospective Investigation of Cancer)-Norfolk study aged 40–79 years at baseline (1993–1997), completed 7-day prospective food diary and had plasma vitamin C and carotenoid measures. A composite biomarker score (CB-score) comprising the sum of plasma vitamin C, beta-carotene and lutein was derived. Odds ratios (ORs) and 95% confidence intervals (CIs) for incident diabetes were estimated using multivariable logistic regression. Results: A strong inverse association was found between the CB-score and incident diabetes. The ORs (95% CI) of diabetes comparing quartiles Q2, Q3 and Q4 of the CB-score with Q1 (reference category) were 0.70 (0.49, 1.00), 0.34 (0.23, 0.52) and 0.19 (0.12, 0.32), respectively, and 0.49 (0.40, 0.58) per s.d. change in CB-score in a model adjusted for demographic and lifestyle factors. The association was marginally attenuated after additionally adjusting for body mass index and waist circumference (0.60 (0.49 and 0.74) per s.d. change in CB-score). Conclusions: A combination of biomarkers representing the intake of a mixed FV diet was strongly inversely associated with incident diabetes. These findings provide further support for measuring dietary biomarkers in studies of diet-disease associations and highlight the importance of consuming FV for the prevention of diabetes.

Dietary factors such as low energy density and low glycemic index were associated with a lower gain in abdominal adiposity. A better understanding of which food groups/items contribute to these associations is necessary. To ascertain the association of food groups/items consumption on prospective annual changes in \"waist circumference for a given BMI\" (WC(BMI)), a proxy for abdominal adiposity. We analyzed data from 48,631 men and women from 5 countries participating in the European Prospective Investigation into Cancer and Nutrition (EPIC) study. Anthropometric measurements were obtained at baseline and after a median follow-up time of 5.5 years. WC(BMI) was defined as the residuals of waist circumference regressed on BMI, and annual change in WC(BMI) (ΔWC(BMI), cm/y) was defined as the difference between residuals at follow-up and baseline, divided by follow-up time. The association between food groups/items and ΔWC(BMI) was modelled using centre-specific adjusted linear regression, and random-effects meta-analyses to obtain pooled estimates. Higher fruit and dairy products consumption was associated with a lower gain in WC(BMI) whereas the consumption of white bread, processed meat, margarine, and soft drinks was positively associated with ΔWC(BMI). When these six food groups/items were analyzed in combination using a summary score, those in the highest quartile of the score--indicating a more favourable dietary pattern--showed a ΔWC(BMI) of -0.11 (95% CI -0.09 to -0.14) cm/y compared to those in the lowest quartile. A dietary pattern high in fruit and dairy and low in white bread, processed meat, margarine, and soft drinks may help to prevent abdominal fat accumulation.

Background: As protein is considered to increase thermogenesis and satiety more than other macronutrients, it may have beneficial effects on prevention of weight gain and weight maintenance. Objective: The objective of this study is to assess the association between the amount and type of dietary protein, and subsequent changes in weight and waist circumference (WC). Methods: 89 432 men and women from five countries participating in European Prospective Investigation into Cancer and Nutrition (EPIC) were followed for a mean of 6.5 years. Associations between the intake of protein or subgroups of protein (from animal and plant sources) and changes in weight (g per year) or WC (cm per year) were investigated using gender and centre-specific multiple regression analyses. Adjustments were made for other baseline dietary factors, baseline anthropometrics, demographic and lifestyle factors and follow-up time. We used random effect meta-analyses to obtain pooled estimates across centres. Results: Higher intake of total protein, and protein from animal sources was associated with subsequent weight gain for both genders, strongest among women, and the association was mainly attributable to protein from red and processed meat and poultry rather than from fish and dairy sources. There was no overall association between intake of plant protein and subsequent changes in weight. No clear overall associations between intakes of total protein or any of the subgroups and changes in WC were present. The associations showed some heterogeneity between centres, but pooling of estimates was still considered justified. Conclusion: A high intake of protein was not found associated with lower weight or waist gain in this observational study. In contrast, protein from food items of animal origin, especially meat and poultry, seemed to be positively associated with long-term weight gain. There were no clear associations for waist changes.

ObjectiveThe value of a family history for coronary heart disease (CHD) in addition to established cardiovascular risk factors in predicting an individual's risk of CHD is unclear. In the European Prospective Investigation of Cancer (EPIC)-Norfolk cohort, the authors tested whether adding family history of premature CHD in first-degree relatives improves risk prediction compared with the Framingham risk score (FRS) alone.Methods and resultsThis study comprised 10 288 men and 12 553 women aged 40–79 years participating in the EPIC-Norfolk cohort who were followed for a mean of 10.9±2.1 years (mean±SD). The authors computed the FRS as well as a modified score taking into account family history of premature CHD. A family history of CHD was indeed associated with an increased risk of future CHD, independent of established risk factors (FRS-adjusted HR of 1.74 (95% CI 1.56 to 1.95) for family history of premature CHD). However, adding family history of CHD to the FRS resulted in a negative net reclassification of 2%. In the subgroup of individuals estimated to be at intermediate risk, family history of premature CHD resulted in an increase in net reclassification of 2%. The sensitivity increased with 0.4%, and the specificity decreased 0.8%.ConclusionAlthough family history of CHD was an independent risk factor of future CHD, its use did not improve classification of individuals into clinically relevant risk categories based on the FRS. Among study participants at intermediate risk of CHD, adding family history of premature CHD resulted in, at best, a modest improvement in reclassification of individuals into a more accurate risk category.

Background/Objective: Body mass index (BMI) is a surrogate measure of adiposity but does not distinguish fat from lean or bone mass. The genetic determinants of BMI are thought to predominantly influence adiposity but this has not been confirmed. Here we characterise the association between BMI-related genetic variants and body composition in adults. Subjects/Methods: Among 9667 adults aged 29–64 years from the Fenland study, a genetic risk score for BMI (BMI-GRS) was calculated for each individual as the weighted sum of BMI-increasing alleles across 96 reported BMI-related variants. Associations between the BMI-GRS and body composition, estimated by dual-energy X-ray absorptiometry (DXA) scans, were examined using age-adjusted linear regression models, separately by sex. Results: The BMI-GRS was positively associated with all fat, lean and bone variables. Across body regions, associations of the greatest magnitude were observed for adiposity variables, for example, for each s.d. increase in BMI-GRS predicted BMI, we observed a 0.90 s.d. (95% confidence interval (CI): 0.71, 1.09) increase in total fat mass for men ( P =3.75 × 10 −21 ) and a 0.96 s.d. (95% CI: 0.77, 1.16) increase for women ( P =6.12 × 10 −22 ). Associations of intermediate magnitude were observed with lean variables, for example, total lean mass: men: 0.68 s.d. (95% CI: 0.49, 0.86; P =1.91 × 10 −12 ); women: 0.85 s.d. (95% CI: 0.65, 1.04; P =2.66 × 10 −17 ) and of a lower magnitude with bone variables, for example, total bone mass: men: 0.39 s.d. (95% CI: 0.20, 0.58; P =5.69 × 10 −5 ); women: 0.45 s.d. (95% CI: 0.26, 0.65; P =3.96 × 10 6 ). Nominally significant associations with BMI were observed for 28 single-nucleotide polymorphisms. All 28 were positively associated with fat mass and 13 showed adipose-specific effects. Conclusions: In adults, genetic susceptibility to elevated BMI influences adiposity more than lean or bone mass. This mirrors the association between BMI and body composition. The BMI-GRS can be used to model the effects of measured BMI and adiposity on health and other outcomes.