As shown in , the sample that was included in the analysis was 58.6% Hispanic, 20.8% black, and 20.7% white and included a spread of pubertal stages and socioeconomic groups (). A little more than half (50.6%) of the participants were normal weight (BMI <85th percentile) with 30.0% obese (BMI ≥95th percentile). One-way ANOVA tests indicated that there were gender differences for both fitness (P < 0.0001) and BMI categories (P < 0.0013), with higher levels of obesity among the males and lower levels of fitness among the girls. The distribution of fitness laps and the associated quintile thresholds are presented separately for males and females in . There was an approximately even distribution of girls (631, 554, 385, 573, and 471) and boys (560, 438, 433, 458, and 452) within each quintile, with the slight variations in quintile size because of ties in the number of laps. The gender-specific fitness quintiles were as follows: level 1 = 0–11 laps for both males and females, level 2 = 12–16 laps for males and 12–15 laps for females, level 3 = 17–24 laps for males and 16–18 laps for females, level 4 = 25–35 laps for males and 19–26 laps for females, and level 5 = 36–89 laps for males and 27–74 laps for females.
Fitness and quintile break points for males and female adolescents in the HEALTHY Study.
Means and 95% confidence intervals for each of the cardiometabolic risk factors are presented for BMI groups for both genders in . The mean values for each risk factor (except HDL which was the reverse) increased across the obesity groups (P < 0.001), with values highest for the obese group (BMI ≥95th percentile) and lowest for the normal weight group (BMI <85th percentile). Follow-up tests indicated that waist circumference, HDL-cholesterol, triglycerides, diastolic blood pressure, and insulin were different among each of the three BMI groups for both genders.
Means and 95% confidence intervals for each of the cardiometabolic risk factors are presented stratified by gender-specific fitness quintiles for both genders in . Waist circumference, LDL-cholesterol, triglycerides, diastolic blood pressure, and insulin were all inversely associated with fitness quintiles, with higher levels of fitness associated with lower risk factor means in both boys and girls. HDL-cholesterol levels were positively associated with fitness quintiles in both boys and girls, with higher levels of HDL associated with higher quintiles of fitness. Fitness quintiles were not associated with girls’ total cholesterol or the systolic blood pressure and with glucose levels of either gender. Follow-up tests indicated that, with the exception of male waist circumference, there were no significant differences between the cardiometabolic risk factor levels of all quintiles. For example, for girls’ LDL-cholesterol, there was only a significant difference between the lowest and top two highest quintiles of fitness. Moreover, only waist circumference and insulin were significantly different between the first and second quintiles of fitness. Similarly, very few of the comparisons between adjacent fitness quintiles (e.g., 2 vs 3) were significant for most of the cardiometabolic risk factors. Collectively, these findings demonstrate a pattern whereby, although cardio-metabolic risk factors differed by quintiles of fitness, differences tended to be statistically significant only when nonadjacent groups with greater differences in fitness levels were compared.
presents a comparison of models when BMI group, fitness group, or both BMI group and fitness group were included in models to predict cardiometabolic risk factors. When both BMI group and fitness were included in the model together, BMI group was a significant predictor of all cardiometabolic risk factors except girls’ total cholesterol. In the same models, fitness was associated only with waist circumference (both genders), LDL-cholesterol (males), systolic blood pressure (males), and insulin (both genders). Models that included BMI alone or both fitness and BMI had lower AIC values than models that just included fitness. In four models, AIC did not improve with the addition of fitness to models that included BMI; these models were HDL in males, triglycerides in males, triglycerides in females, and glucose in females. Because lower AIC values indicate improved model fit, the analyses demonstrate that, in all instances, including both BMI group and fitness quintiles in the analysis improved the prediction of cardiometabolic risk factors.