A 2 year physical activity and dietary intervention attenuates the increase in insulin resistance in a general population of children: the PANIC study

AIMS/HYPOTHESIS

We studied for the first time the long-term effects of a combined physical activity and dietary intervention on insulin resistance and fasting plasma glucose in a general population of predominantly normal-weight children.

METHODS

We carried out a 2 year non-randomised controlled trial in a population sample of 504 children aged 6-9 years at baseline. The children were allocated to a combined physical activity and dietary intervention group (306 children at baseline, 261 children at 2-year follow-up) or a control group (198 children, 177 children) without blinding. We measured fasting insulin and fasting glucose, calculated HOMA-IR, assessed physical activity and sedentary time by combined heart rate and body movement monitoring, assessed dietary factors by a 4 day food record, used the Finnish Children Healthy Eating Index (FCHEI) as a measure of overall diet quality, and measured body fat percentage (BF%) and lean body mass by dual-energy x-ray absorptiometry. The intervention effects on insulin, glucose and HOMA-IR were analysed using the intention-to-treat principle and linear mixed-effects models after adjustment for sex, age at baseline, and pubertal status at baseline and 2 year follow-up. The measures of physical activity, sedentary time, diet and body composition at baseline and 2 year follow-up were entered one-by-one as covariates into the models to study whether changes in these variables might partly explain the observed intervention effects.

RESULTS

Compared with the control group, fasting insulin increased 4.65 pmol/l less (absolute change +8.96 vs +13.61 pmol/l) and HOMA-IR increased 0.18 units less (+0.31 vs +0.49 units) over 2 years in the combined physical activity and dietary intervention group. The intervention effects on fasting insulin (regression coefficient β for intervention effect -0.33 [95% CI -0.62, -0.04], p = 0.026) and HOMA-IR (β for intervention effect -0.084 [95% CI -0.156, -0.012], p = 0.023) were statistically significant after adjustment for sex, age at baseline, and pubertal status at baseline and 2 year follow-up. The intervention had no effect on fasting glucose, BF% or lean body mass. Changes in total physical activity energy expenditure, light physical activity, moderate-to-vigorous physical activity, total sedentary time, the reported consumption of high-fat (≥60%) vegetable oil-based spreads, and FCHEI, but not a change in BF% or lean body mass, partly explained the intervention effects on fasting insulin and HOMA-IR.

CONCLUSIONS/INTERPRETATION

The combined physical activity and dietary intervention attenuated the increase in insulin resistance over 2 years in a general population of predominantly normal-weight children. This beneficial effect was partly mediated by changes in physical activity, sedentary time and diet but not changes in body composition.

TRIAL REGISTRATION

ClinicalTrials.gov NCT01803776 Graphical abstract.

The effects of a 2-year physical activity and dietary intervention on plasma lipid concentrations in children: the PANIC Study

PURPOSE

We studied the effects of a physical activity and dietary intervention on plasma lipids in a general population of children. We also investigated how lifestyle changes contributed to the intervention effects.

METHODS

We carried out a 2-year controlled, non-randomized lifestyle intervention study among 504 mainly prepubertal children aged 6-9 years at baseline. We assigned 306 children to the intervention group and 198 children to the control group. We assessed plasma concentrations of total, LDL, HDL, and VLDL cholesterol, triglycerides, HDL triglycerides, and VLDL triglycerides. We evaluated the consumption of foods using 4-day food records and physical activity using a movement and heart rate sensor. We analyzed data using linear mixed-effect models adjusted for age at baseline, sex, and pubertal stage at both time points. Furthermore, specific lifestyle variables were entered in these models.

RESULTS

Plasma LDL cholesterol decreased in the intervention group but did not change in the control group ( - 0.05 vs. 0.00 mmol/L, regression coefficient (β) =  - 0.0385, p = 0.040 for group*time interaction). This effect was mainly explained by the changes in the consumption of high-fat vegetable oil-based spreads (β =  - 0.0203, + 47% change in β) and butter-based spreads (β =  - 0.0294, + 30% change in β), moderate-to-vigorous physical activity (β =  - 0.0268, + 30% change in β), light physical activity (β =  - 0.0274, + 29% change in β) and sedentary time (β =  - 0.0270, + 30% change in β). The intervention had no effect on other plasma lipids.

CONCLUSION

Lifestyle intervention resulted a small decrease in plasma LDL cholesterol concentration in children. The effect was explained by changes in quality and quantity of dietary fat and physical activity.

CLINICAL TRIAL REGISTRY NUMBER

NCT01803776, ClinicalTrials.gov.

Comparison between parameters from maximal cycle ergometer test first without respiratory gas analysis and thereafter with respiratory gas analysis among healthy prepubertal children

It is important to distinguish true and clinically relevant changes and methodological noise from measure to measure. In the clinical practice, maximal cycle ergometer tests are typically performed first without respiratory gas analysis and thereafter, if needed, with respiratory gas analysis. Therefore, we report a comparison of parameters from maximal cycle ergometer exercise tests that were done first without respiratory gas analysis and thereafter with it in 38 prepubertal and healthy children (20 girls, 18 boys). The Bland-Altman method was used to assess agreement in maximal workload (WMAX), heart rate (HR), and systolic blood pressure (SBP) between rest and maximum. Girls achieved higher WMAX in the exercise tests with respiratory gas analysis compared with exercise tests without respiratory gas analysis (p = 0.016), whereas WMAX was similar in the tests among boys. Maximal HR (proportional offset, -1%; coefficients of variation, 3.3%) and highest SBP (proportional offset, 3%; coefficients of variation, 10.6%) were similar in the tests among children. Precision and agreement for HR improved and precision for SBP worsened with increasing exercise intensity. Heteroscedasticity was not observed for WMAX, HR, or SBP. We conclude that maximal cycle ergometer tests without and with respiratory gas analysis can be used consecutively because measurement of respiratory gases did not impair performance or have a significant effect on the maximality of the exercise tests. Our results suggest that similar references can be used for children who accept or refuse using a mask during a maximal exercise test.

Metabolic equivalents of task are confounded by adiposity, which disturbs objective measurement of physical activity

Physical activity refers any bodily movements produced by skeletal muscles that expends energy. Hence the amount and the intensity of physical activity can be assessed by energy expenditure. Metabolic equivalents of task (MET) are multiplies of the resting metabolism reflecting metabolic rate during exercise. The standard MET is defined as 3.5 ml/min/kg. However, the expression of energy expenditure by body weight to normalize the size differences between subjects causes analytical hazards: scaling by body weight does not have a physiological, mathematical, or physical rationale. This review demonstrates by examples that false methodology may cause paradoxical observations if physical activity would be assessed by body weight scaled values such as standard METs. While standard METs are confounded by adiposity, lean mass proportional measures of energy expenditure would enable a more truthful choice to assess physical activity. While physical activity as a behavior and cardiorespiratory fitness or adiposity as a state represents major determinants of public health, specific measurements of health determinants must be understood to enable a truthful evaluation of the interactions and their independent role as a health predictor.