Pediatric growth patterns in youth‐onset type 2 diabetes mellitus: Implications for physiologically‐based pharmacokinetic models

Abstract

An accurate understanding of the changes in height and weight of children with age is critical to the development of models predicting drug concentrations in children (i.e., physiologically‐based pharmacokinetic models). However, curves describing the growth of a typical population of children may not accurately characterize growth of children with various conditions, such as obesity. Therefore, to develop height and weight versus age growth curves for youth who were diagnosed with type 2 diabetes, we extracted data from electronic medical records. Robust nonlinear models were parameterized to the equations describing height and weight versus age as defined by the Centers for Disease Control and Prevention (CDC). CDC z‐scores were calculated using an internal program. The growth curves and z‐scores were compared to CDC norms. Youth with type 2 diabetes were increasingly heavier than CDC norms from early childhood. Except for a period around puberty, youth with type 2 diabetes were, on average, shorter than CDC norms, resulting in shorter average adult height. Deviations in growth were apparent in youth who develop type 2 diabetes; such deviations may be expected for other conditions as well, and disease‐specific growth curves should be considered during development of model‐informed drug development for pediatric conditions.

Limitations of BMI z scores for assessing weight change: A clinical tool versus individual risk

“…Some guidance exists for pediatric clinical trial weight reduction outcomes (e.g., mean change in BMI, proportion of patients who lose greater than or equal to 5% of baseline), but there is no common consensus. There are valid concerns regarding the use of raw BMI for pediatric longitudinal analyses, but statistical models can account for age-and sex-growth patterns, like modeling continuous norming [14,15]. In adults, growing evidence suggests that a 5% change in actual weight may be a good surrogate marker to capture longitudinal changes in adiposity and cardiometabolic risk [16,17].…”