Relationship Between Trajectories of Trunk Fat Mass Development in Adolescence and Cardiometabolic Risk in Young Adulthood

Abstract

To examine developmental trajectories of trunk fat mass (FM) growth of individuals categorized as either low or high for cardiometabolic risk at 26 years, a total of 55 males and 76 females from the Saskatchewan Pediatric Bone Mineral Accrual Study (1991–2007) were assessed from adolescence (11.5 ± 1.8 years) to young adulthood (26.2 ± 2.2 years) (median of 11 visits per individual) and had a measure of cardiometabolic risk in young adulthood. Participants were categorized as low or high for blood pressure and cardiometabolic risk as adults using a sex‐specific median split of continuous standardized risk scores. Individual trunk FM trajectories of participants in each risk group were analyzed using multilevel random effects models. Males and females in the high blood pressure group had significantly steeper (accelerated) trajectories of trunk FM development (0.61 ± 0.14 and 0.52 ± 0.10 log g, respectively) than those in the low blood pressure group for females in the high cardiometabolic risk group trajectory of trunk FM was significantly steeper (0.52 ± 0.10 log g) than those females in the low cardiometabolic risk group. Dietary fat was positively related (0.01 ± 0.003 g/1,000 kcal) and physical activity negatively related (−0.16 ± 0.05 physical activity score) to trunk FM development in males. Young adults with high cardiometabolic risk, compared to low, have greater trunk FM as early as 8 years of age, which supports the need for early intervention.

Height-based Indices of Pubertal Timing in Male Adolescents

“…Since then, many studies have used a number of different methods to calculate APHV, ranging from simple mid-year estimations (e.g., Lindgren, 1976) to spline interpolants (e.g., Sherar et al, 2011). Our script is automated and efficient, allowing for robust and consistent estimates in large datasets.…”

“…First, the longitudinal measurements of height and the respective age in months are used to plot the participant’s height over time. The cubic spline-interpolation function from the MATLAB Basic Fitting toolbox (Mathworks, Natick, MA USA) is used to generate a curve through the data-points (see Supplementary Section; Supplementary Figure 1) as reported previously (Ramsay, Altman, & Bock, 1994; Sherar et al, 2011). Plotting the derivative of this height curve produces the participant’s growth curve, and indicates height velocity at each month (Supplementary Figure 2).…”

Trajectories of Body Mass Index from Young Adulthood to Middle Age among Canadian Men and Women

“…Typically, cross-sectional studies defined body weight status based on a one-time measurement [7], and many longitudinal studies used a short period of followup and/or assessed the BMI changes based on a limited time frame [8][9][10][11]. Additionally, multiple studies have utilized conventional growth modelling (CGM) to model BMI trajectories [12][13][14][15][16]. CGM is an umbrella term for multilevel (hierarchical) modelling [17] and latent curve analysis [18].…”

Adipocytokines, Metabolic Syndrome, and Exercise

“…Y. Kim and H. Park review the effects of exercise training programs for alleviating insulin resistance in children. This is an important population to investigate as the proportion of adolescents with metabolic syndrome is estimated at between 6.5 and 7.8% [14] and metabolic syndrome risk in adolescence tracks into adulthood in longitudinal studies [15]. As outlined by Y. Kim and H. Park, there is good evidence that exercise training independent of weight loss is effective for reducing insulin resistance in adults but limited evidence that aerobic or resistance training is effective in children and adolescents.…”