guidelines scored better on several health indicators than those meeting fewer components of the guidelines. 8 The purpose of this study was to determine the association between meeting the 24-hour movement guidelines and cardiometabolic risk factors in white and African American children and adolescents
Peter T. Katzmarzyk and Amanda E. Staiano
Jakob Tarp, Anna Bugge, Niels Christian Møller, Heidi Klakk, Christina Trifonov Rexen, Anders Grøntved and Niels Wedderkopp
youth may improve likelihood of a physically active adult life. 4 Additionally, high levels of cardiometabolic risk factors for noncommunicable diseases (ie, adiposity, blood pressure, lipids and glucose metabolism) in youth is associated with type 2 diabetes and advanced atherosclerosis in young
Stephanie L. Stockwell, Lindsey R. Smith, Hannah M. Weaver, Daniella J. Hankins and Daniel P. Bailey
Cardiometabolic disease is an uncommon occurrence or cause of death in children. However, cardiometabolic risk markers such as obesity, high blood pressure, adverse lipid profile, and impaired glucose levels can begin to develop in childhood, increasing the likelihood of cardiometabolic disease in
Rhona Martin-Smith, Duncan S. Buchan, Julien S. Baker, Mhairi J. Macdonald, Nicholas F. Sculthorpe, Chris Easton, Allan Knox and Fergal M. Grace
Participation in regular moderate to vigorous physical activity (MVPA) is known to improve cardiorespiratory fitness (CRF), body composition, cardiometabolic risk (CMR) profiles and reduce the risk of cardiometabolic disease ( 40 ). However, recent epidemiological data show that physical activity
Russell Jago, Kimberly L. Drews, Robert G. McMurray, Tom Baranowski, Pietro Galassetti, Gary D. Foster, Ester Moe and John B. Buse
This paper examined whether a two-year change in fitness, body mass index (BMI) or the additive effect of change in fitness and BMI were associated with change in cardiometabolic risk factors among youth. Cardiometabolic risk factors, BMI group (normal weight, overweight or obese) were obtained from participants at the start of 6th grade and end of 8th grade. Shuttle run laps were assessed and categorized in quintiles at both time points. Regression models were used to examine whether changes in obesity, fitness or the additive effect of change in BMI and fitness were associated with change in risk factors. There was strong evidence (p < .001) that change in BMI was associated with change in cardiometabolic risk factors. There was weaker evidence of a fitness effect, with some evidence that change in fitness was associated with change in total cholesterol, HDL-C, LDL-C and clustered risk score among boys, as well as HDL-C among girls. Male HDL-C was the only model for which there was some evidence of a BMI, fitness and additive BMI*fitness effect. Changing body mass is central to the reduction of youth cardiometabolic risk. Fitness effects were negligible once change in body mass had been taken into account.
Laura Banks, Cedric Manlhiot, Stafford W. Dobbin, Don Gibson, Karen Stearne, Jolie Davies-Shaw, Nita Chahal, Amanda Fisher and Brian McCrindle
Background: Moderate-to-vigorous physical activity (MVPA) has been negatively associated with cardiometabolic risk. We sought to determine if MVPA interacts with body-mass index (BMI) and waist circumference (WC) in determining cardiometabolic risk in adolescents. Methods: This cross-sectional study included cardiometabolic risk (blood pressure [BP], nonfasting lipids) screening and a 7-day recall physical activity questionnaire in 4,104 adolescents (51% male; mean age: 14.6 ± 0.5 years old). WC- and BMI- percentiles were used to define anthropometric categories (including obese adolescents: 390th WC, 385th BMI). Results: Obesity in adolescents was associated with lower levels of high-density lipoprotein (HDL cholesterol (Estimate [EST]: -0.28(0.07) mmol/L, p < .001) and higher non-HDL cholesterol (EST: +0.38(0.14) mmol/L, p = .008). Each additional day with 320 min of MVPA was associated with lower non-HDL cholesterol (EST: −0.014(0.005) mmol/L/days/week, p = .003), independent of anthropometric category. Each additional day with 320 min of MVPA was associated with an increased odds ratio (OR) for higher BP category in obese adolescents (OR: 1.055, 95% CI: 1.028−1.084, p < .001) and a lower odds ratio for higher BP category in presumably-muscular adolescents (OR: 0.968, 95% CI: 0.934−0.989, p = .005). Conclusions: An increase in MVPA was associated with an increased likelihood for higher BP category in obese adolescents. The dose-response relationship between physical activity and cardiometabolic risk needs to be evaluated in adolescents of varying anthropometry categories.
Paul A. McAuley, Haiying Chen, Duck-chul Lee, Enrique Garcia Artero, David A. Bluemke and Gregory L. Burke
The influence of higher physical activity on the relationship between adiposity and cardiometabolic risk is not completely understood.
Between 2000–2002, data were collected on 6795 Multi-Ethnic Study of Atherosclerosis (MESA) participants. Self-reported intentional physical activity in the lowest quartile (0–105 MET-minutes/week) was categorized as inactive and the upper three quartiles (123–37,260 MET-minutes/week) as active. Associations of body mass index (BMI) and waist circumference categories, stratified by physical activity status (inactive or active) with cardiometabolic risk factors (dyslipidemia, hypertension, upper quartile of homeostasis model assessment of insulin resistance [HOMA-IR] for population, and impaired fasting glucose or diabetes) were assessed using logistic regression analysis adjusting for age, gender, race/ethnicity, and current smoking.
Among obese participants, those who were physically active had reduced odds of insulin resistance (47% lower; P < .001) and impaired fasting glucose/diabetes (23% lower; P = .04). These associations were weaker for central obesity. However, among participants with a normal waist circumference, those who were inactive were 63% more likely to have insulin resistance (OR [95% CI] 1.63 [1.24–2.15]) compared with the active reference group.
Physical activity was inversely related to the cardiometabolic risk associated with obesity and central obesity.
Daniel P. Hatfield, Virginia R. Chomitz, Kenneth Chui, Jennifer M. Sacheck and Christina D. Economo
Associations between physical activity (PA) intensity and volume and adolescents’ cardiometabolic health have research, policy, and practice implications. This study compares associations between cardiometabolic risk factors and 1) moderate-to-vigorous PA (MVPA) minutes versus total PA volume (accelerometer-derived total activity counts, TAC) and 2) light PA volume (counts at light intensity, L-TAC) versus moderate-to-vigorous PA volume (counts at moderate-to-vigorous intensity, MV-TAC).
2105 adolescents from 2003– 2006 NHANES were included. Independent variables were MVPA minutes, TAC, L-TAC, and MV-TAC. Regression models tested associations between PA variables and continuous metabolic risk index (CMRI), waist circumference, systolic and diastolic blood pressure, HDL, insulin, and triglycerides.
TAC demonstrated a slightly stronger inverse association with CMRI (P = .004) than did MVPA (P = .013). TAC and MVPA were both associated with systolic and diastolic pressure, HDL, and insulin; associations were similar or slightly stronger for TAC. L-TAC and MV-TAC were both associated with CMRI and HDL. Only L-TAC was associated with diastolic pressure. Only MV-TAC was associated with waist circumference, systolic pressure, and insulin.
Compared with MVPA minutes, TAC demonstrates similar or slightly stronger associations with cardiometabolic risk factors. L-TAC and MV-TAC appear similarly associated with adolescents’ clustered risk but differently associated with individual risk factors.
Daniel P. Bailey, Louise A. Savory, Sarah J. Denton and Catherine J. Kerr
It is unclear whether cardiorespiratory fitness (CRF) is independently linked to cardiometabolic risk in children. This study investigated a) the association between CRF level and presence of cardiometabolic risk disorders using health-related cut points, and b) whether these associations were mediated by abdominal adiposity in children.
This was a cross-sectional design study. Anthropometry, biochemical parameters and CRF were assessed in 147 schoolchildren (75 girls) aged 10 to 14 years. CRF was determined using a maximal cycle ergometer test. Children were classified as ‘fit’ or ‘unfit’ according to published thresholds. Logistic regression was used to investigate the odds of having individual and clustered cardiometabolic risk factors according to CRF level and whether abdominal adiposity mediated these associations.
Children classified as unfit had increased odds of presenting individual and clustered cardiometabolic risk factors (P < .05), but these associations no longer remained after adjusting for abdominal adiposity (P > .05).
This study suggests that the association between CRF and cardiometabolic risk is mediated by abdominal adiposity in 10- to 14-year-old children and that abdominal adiposity may be a more important determinant of adverse cardiometabolic health in this age group.
Susan B. Sisson, Ashley E. Gibson, Kevin Short, Andrew W. Gardner, Teresa Whited, Candace Robledo and David M. Thompson
The purpose of this study was to determine if light physical activity (LPA) minimizes the impairment of cardiometabolic risk factors following a typical meal in adolescents. Eighteen adolescents (50% male, 14.8 ± 2.3 yrs) consumed a meal (32% fat, 14% protein, 53% carbohydrate), then completed a walking (1.5mph for 45 min of each hour) or sitting treatment for 3 hr in randomized order on separate days. Following the meal, HDL cholesterol declined 4.8% but remained higher during walking at 3 hr (42.1mg/dl ± 9.3) than sitting (8.4% decline; 40.5mg/dL ± 9.9; treatment × time interaction, p < .03). The 3-hr insulin was lower after walking (24.8μIU/ml ± 33.4) than sitting (37.8μIU/ml ± 34.7; treatment × time interaction, p < .0001). Triglycerides increased by ~40% above baseline at 1 and 2 hr, with higher values for walking (treatment × time interaction, p < .02). However by 3 hr, triglycerides were not different from baseline. Area under the curve (AUC) analyses were not significantly different between treatments for any outcomes. Although minor, LPA appears to mitigate the undesirable postprandial changes in HDL cholesterol and insulin but not triglycerides, following a typical meal in adolescents.