Institue of Biomedicine, Physiology

Background: Lower levels of physical activity (PA) and sedentary behaviour (SB) have been associated with increased cardiometabolic risk among children. However, little is known about the independent and combined associations of PA and SB as well as different types of these behaviours with cardiometabolic risk in children. We therefore investigated these relationships among children. Methods: The subjects were a population sample of 468 children 6-8 years of age. PA and SB were assessed by a questionnaire administered by parents and validated by a monitor combining heart rate and accelerometry measurements. We assessed body fat percentage, waist circumference, blood glucose, serum insulin, plasma lipids and lipoproteins and blood pressure and calculated a cardiometabolic risk score using population-specific Z-scores and a formula waist circumference + insulin + glucose + triglycerides -HDL cholesterol + mean of systolic and diastolic blood pressure. We analysed data using multivariate linear regression models. Results: Total PA was inversely associated with the cardiometabolic risk score (β = −0.135, p = 0.004), body fat percentage (β = −0.155, p < 0.001), insulin (β = −0.099, p = 0.034), triglycerides (β = −0.166, p < 0.001), VLDL triglycerides (β = −0.230, p < 0.001), VLDL cholesterol (β = −0.168, p = 0.001), LDL cholesterol (β = −0.094, p = 0.046) and HDL triglycerides (β = −0.149, p = 0.004) and directly related to HDL cholesterol (β = 0.144, p = 0.002) adjusted for age and gender. Unstructured PA was inversely associated with the cardiometabolic risk score (β = −0.123, p = 0.010), body fat percentage (β = −0.099, p = 0.027), insulin (β = −0.108, p = 0.021), triglycerides (β = −0.144, p = 0.002), VLDL triglycerides (β = −0.233, p < 0.001) and VLDL cholesterol (β = −0.199, p < 0.001) and directly related to HDL cholesterol (β = 0.126, p = 0.008). Watching TV and videos was directly related to the cardiometabolic risk score (β = 0.135, p = 0.003), body fat percentage (β = 0.090, p = 0.039), waist circumference (β = 0.097, p = 0.033) and systolic blood pressure (β = 0.096, p = 0.039). Resting was directly associated with the cardiometabolic risk score (β = 0.092, p = 0.049), triglycerides (β = 0.131, p = 0.005), VLDL triglycerides (β = 0.134, p = 0.009), VLDL cholesterol (β = 0.147, p = 0.004) and LDL cholesterol (β = 0.105, p = 0.023). Other types of PA and SB had less consistent associations with cardiometabolic risk factors.

Habitual physical activity (PA) is decreasing and sedentary activity (SA), particularly media time (MT), is increasing among children. Few studies have provided comprehensive data on different types of PA and SA in population samples of children and the associations of socioeconomic status (SES) with PA and SA. We studied these issues in a large population sample of children at the baseline of The Physical Activity and Nutrition in Children (PANIC) Study in 2007-2009. We found that 66 % of the girls and 55 % of the boys spent less than the Finnish recommendation of at least 2 hours per day in PA. Moreover, 19 % of the boys and 10 % of the girls spent more than the recommended maximum of 2 hours of MT per day on weekdays. However, 57 % of the boys and 35 % of the girls exceeded the recommended MT on weekend. The most common types of PA in both genders were unorganized PA, commuting to school, PA during recess and physical education at school. The most common types of SA in the boys were watching TV, using computer and reading, and those in the girls were watching TV, drawing, reading and hobby crafts. A lower parental education was associated with lower levels of organized PA (p=0.002) and higher levels of unorganized PA (p=0.014) and a higher MT on weekdays (p=0.001). A lower family income was related to lower levels of organized PA (p<0.001) and a higher MT on weekdays (p<0.001). These associations were stronger in the boys than the girls.

Study Objectives: To examine independent and combined associations of sleep duration and sleep variability with body composition, obesity and type 2 diabetes (T2D) in elders at high cardiovascular risk. Methods: Cross-sectional analysis of 1986 community-dwelling elders with overweight/obesity and metabolic syndrome from PREDIMED-Plus trial. Associations of accelerometry-derived sleep duration and sleep variability with body mass index (BMI), waist circumference (WC) and body composition were assessed fitting multivariable-adjusted linear regression models. Prevalence ratios (PR) and 95% confidence intervals (CI) for obesity and T2D were obtained using multivariable-adjusted Cox regression with constant time. "Bad sleepers" (age-specific non-recommended sleep duration plus sleep variability above the median) and "good sleepers" (age-specific recommended sleep duration plus sleep variability below the median) were characterized by combining sleep duration and sleep variability, and their associations with these outcomes were examined. Results: One hour/night increment in sleep duration was inversely associated with BMI (β −0.38 kg/m 2 [95% CI −0.54, −0.23]), WC (β −0.86 cm [95% CI −1.25, −0.47]), obesity (PR 0.96 [95% CI 0.93, 0.98]), T2D (PR 0.93 [95% CI 0.88, 0.98]) and other DXA-derived adiposity-related measurements (android fat and trunk fat, all p < .05). Each 1-hour increment in sleep variability was positively associated with T2D (PR 1.14 [95% CI 1.01, 1.28]). Compared with "good sleepers," "bad sleepers" were positively associated with obesity (PR 1.12 [95% CI 1.01, 1.24]) and T2D (PR 1.62 [95% CI 1.28, 2.06]). Conclusions: This study revealed cross-sectional associations of sleep duration with adiposity parameters and obesity. Sleep duration and sleep variability were associated with T2D. Considering simultaneously sleep duration and sleep variability could have additional value, particularly for T2D, as they may act synergistically.

Little is known about the relationships of dietary factors, physical activity, and sedentary behavior to dehydroepiandrosterone sulfate (DHEAS) and insulin-like growth factor-1 (IGF-1) concentrations among prepubertal children. Therefore, we studied the associations of these lifestyle factors with serum DHEAS and IGF-1 in children. Cross-sectional analysis of a population sample of 431 prepubertal children aged 6-9 years. Assessment of dietary factors by food records and physical activity and sedentary behavior by a combined heart rate and movement monitor and a questionnaire. Measurement of serum DHEAS and IGF-1. Consumption of low-fiber grain products (standardized regression coefficient β=0.118, P=0.017) and intake of vegetable protein (β=0.100, P=0.045) were positively and consumption of sugar-sweetened beverages (β=-0.117, P=0.018) was inversely associated with DHEAS after adjustment for sex, age, and body fat percentage. Energy intake (β=0.160, P=0.001) was positively associat...