Predictive formulas to estimate body composition in children have been explored for some time, to this date, the most accurate obesity diagnostic tool is to determine fat mass. The aim of this study is to establish cutoff points that allow for a precise interpretation of nutritional status using the Fat Mass Index (FMI) in a Mexican pediatric population. A literature review using PubMed and Cochrane databases was made, searching for research articles on childhood obesity that compared BMI, FM percentage, and FMI, as well as those proposing diagnostic cutoff points. Mathematic formulas and linear regressions were then used to make a proposal on accurate cutoff points for this population. Our findings show that FM percentage is less precise than BMI and FMI in diagnosing obesity, and FMI seems to be a more complete tool for assessment as it differentiates fat and muscle mass of the total body weight. Both BMI and FMI should be considered when clinical evaluations regarding weight, with BMI complementing FMI by establishing fat-free mass. Our proposed cutoff points need to be validated in this population.

Enterohepatic circulation (EHC) is a complex process where drugs undergo secretion and reabsorption from the intestinal lumen multiple times, resulting in pharmacokinetic profiles with multiple peaks. The impact of EHC on area under the curve (AUC) has been a topic of extensive debate, questioning the suitability of conventional AUC estimation methods. Moreover, a universal model for accurately estimating AUC in EHC scenarios is lacking. To address this gap, we conducted a simulation study evaluating five empirical models under various sampling strategies to assess their performance in AUC estimation. Our results identify the most suitable model for EHC scenarios and underscore the critical role of meal-based sampling strategies in accurate AUC estimation. Additionally, we demonstrate that while the trapezoidal method performs comparably to other models with a large number of samples, alternative models are essential when sample numbers are limited. These findings not only illuminate how EHC influences AUC but also pave the way for the application of empirical models in real-world drug studies.