Weight fluctuation during childhood and cardiometabolic risk at young adulthood

Modeling BMI z score lability during childhood as a function of child temperament and genetic risk for obesity

OBJECTIVE

This study aimed to examine whether child genetic risk for obesity and temperament (i.e., negative affectivity, effortful control) accounted for stability versus lability in children's weight status (BMI z score) over time.

METHODS

A total of 561 adopted children (42% female; 56% Caucasian, 13% African American, 11% Latino, and 20% other) and their birth and adoptive parents were followed from birth to age 9 years. The multilevel location-scale model was used to examine whether child genetic risk for obesity and temperament were related to differences in level and lability in child BMI z scores over time.

RESULTS

For the full sample, higher levels of child negative affectivity were associated with greater BMI z score lability, whereas higher levels of effortful control and children's mean-level BMI z scores were related to less lability across childhood. Additional analyses examined associations within groups of children with healthy versus overweight/obesity weight statuses. Within the healthy weight status group only, better effortful control was associated with more stable BMI z scores, whereas genetic risk for higher BMI was associated with more labile BMI z scores.

CONCLUSIONS

These findings provide insights into factors that can be harnessed to redirect unhealthy trajectories as well as factors that may challenge redirection or maintain a healthy trajectory.

Perception of moderate-intensity physical activity by onset of obesity: A randomized crossover trial

Moderate-intensity physical activity is recommended for inactive adults with overweight/obesity (OW/OB). The objective of this study is to determine if differences exist in the selection of moderate intensity between inactive adults with juvenile-onset (JO) and adult-onset (AO) OW/OB. Participants (JO = 18, AO = 20) were stratified by onset and completed two separate 20-minute moderate-intensity exercise sessions on a treadmill and cycle ergometer (randomized order). Multiple linear regression was used to determine whether exercise intensity (average METS, % age-predicted HR_max ), self-reported pleasure or exertion differed by onset, controlling for age and gender. On the treadmill, JO and AO participants selected an average intensity of (mean [SD]) 3.5 (0.9) and 3.7 (0.9) METS, and 64.0 (7.7) and 64.9 (7.5) % of age-predicted HR_max , respectively. On the cycle, JO and AO participants selected an average intensity of 3.3 (0.9) and 3.3 (1.0) METS, and 65.2 (8.8) and 60.7 (7.2) % of age-predicted HR_max . After adjustment, participant intensity selection did not significantly differ by obesity onset when walking or cycling. There were no significant differences in pleasure or perceived exertion by onset, however, perception of exertion was on the high-end of moderate for both the cycle (13.0, 12.5) and treadmill (12.0, 12.1), in JO and AO participants, respectively. Perception of moderate intensity did not differ by obesity onset. Self-selected intensity was at the low end of moderate for walking and cycling.

Repeatedly measured predictors: a comparison of methods for prediction modeling

BACKGROUND

In literature, not much emphasis has been placed on methods for analyzing repeatedly measured independent variables, even less so for the use in prediction modeling specifically. However, repeated measurements could especially be interesting for the construction of prediction models. Therefore, our objective was to evaluate different methods to model a repeatedly measured independent variable and a long-term fixed outcome variable into a prediction model.

METHODS

Six methods to handle a repeatedly measured predictor were applied to develop prediction models. Methods were evaluated with respect to the models' predictive quality (explained variance R ² and the area under the curve (AUC)) and their properties were discussed. The models included overweight and BMI-standard deviation score (BMI-SDS) at age 10 years as outcome and seven BMI-SDS measurements between 0 and 5.5 years as longitudinal predictor. Methods for comparison encompassed developing models including: all measurements; a single (here: the last) measurement; a mean or maximum value of all measurements; changes between subsequent measurements; conditional measurements; and growth curve parameters.

RESULTS

All methods, except for using the maximum or mean, resulted in prediction models for overweight of similar predictive quality, with adjusted Nagelkerke R ² ranging between 0.230 and 0.244 and AUC ranging between 0.799 and 0.807. Continuous BMI-SDS prediction showed similar results.

CONCLUSIONS

The choice of method depends on hypothesized predictor-outcome associations, available data, and requirements of the prediction model. Overall, the growth curve method seems to be the most flexible method capable of incorporating longitudinal predictor information without loss in predictive quality.