Longitudinal changes in BMI<i>z</i>-scores among 45 414 2–4-year olds with severe obesity

Freedman, David S.; Butte, Nancy F.; Taveras, Elsie M.; Goodman, Alyson B.; Blanck, Heidi M.

doi:10.1080/03014460.2017.1388845

Cited by 17 publications

(11 citation statements)

References 32 publications

(51 reference statements)

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“…1,16,[34][35][36][37][38] Additionally, we found that fat mass varied widely in children of healthy body weights and the application of BMI/BMI95 performed as well, if not better, than weight or BMI. 37 Finally, the utility of the BF% in clinical PK trials requires experimental verification. Obesity will not be associated with altered PK for every drug, yet studies done primarily in adults have identified some physical drug properties, as well as mechanisms of metabolism and elimination, that are more likely associated with altered PK in children and adolescents with obesity.…”

Section: Body Fat Percentagementioning

confidence: 63%

Estimation of Body Fat Percentage for Clinical Pharmacokinetic Studies in Children

Green

Binns

et al. 2020

Clinical Translational Sci

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Obesity is a prevalent childhood condition and the degree of adiposity appears likely to be an important covariate in the pharmacokinetics (PKs) of many drugs. We undertook these studies to facilitate the evaluation and, where appropriate, quantification of the covariate effect of body fat percentage (BF%) on PK parameters in children. We examined two large databases to determine the values and variabilities of BF% in children with healthy body weights and in those with obesity, comparing the accuracy and precision of BF% estimation by both clinical methods and demographically derived techniques. Additionally, we conducted simulation studies to evaluate the utility of the several methods for application in clinical trials. BF% was correlated with body mass index (BMI), but was highly variable among both children with healthy body weights and those with obesity. Bio-impedance and several demographically derived techniques produced mean estimates of BF% that differed from dual x-ray absorptiometry by < 1% (accuracy) and a SD of 5% or less (precision). Simulation studies confirmed that when the differences in precision among the several methods were small compared with unexplained between-subject variability of a PK parameter, the techniques were of similar value in assessing the contribution of BF%, if any, as a covariate for that PK parameter. The combination of sex and obesity stage explained 68% of the variance of BF% with BMI. The estimation of BF% from sex and obesity stage can routinely be applied to PK clinical trials to evaluate the contribution of BF% as a potential covariate. Approximately one-third of children in the United States are overweight or obese, and this proportion has continued to increase over the last decade. 1,2 Fat and nonfat tissues have many distinct physical, chemical, and metabolic characteristics, so differences in drug disposition can occur relative to a child's obesity status. Nevertheless, differences in drug pharmacokinetics (PKs) between children with and without obesity have been described for only a few drugs. 3-10 For most drugs, the effects of obesity on PKs in children are poorly characterized, 11-14 leaving clinicians with inadequate

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Section: Body Fat Percentagementioning

confidence: 63%

Estimation of Body Fat Percentage for Clinical Pharmacokinetic Studies in Children

Green

Binns

et al. 2020

Clinical Translational Sci

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“…In our sample, 26.7% of children have BMI over 95th percentile. Second, in longitudinal and intervention studies the changes in L (normality) and S (dispersion) parameters are particularly large in very high BMI children and cause even more instability in zBMI [29, 30, 37].…”

Section: Discussionmentioning

confidence: 99%

A three-year multifaceted intervention to prevent obesity in children of Mexican-heritage

Sadeghi¹,

Kaiser

Hanbury

et al. 2019

BMC Public Health

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Background Obesity and overweight have increased dramatically in the United States over the last decades. The complexity of interrelated causal factors that result in obesity needs to be addressed within the cultural dynamic of sub-populations. In this study, we sought to estimate the effects of a multifaceted, community-based intervention on body mass index (BMI) among Mexican-heritage children. Methods Niños Sanos, Familia Sana (Healthy Children, Healthy Family) was a quasi-experimental intervention study designed to reduce the rate of BMI growth among Mexican-heritage children in California’s Central Valley. Two rural communities were matched based on demographic and environmental characteristics and were assigned as the intervention or comparison community. The three-year intervention included parent workshops on nutrition and physical activity; school-based nutrition lessons and enhanced physical education program for children; and a monthly voucher for fruits and vegetables. Eligible children were between 3 and 8 years old at baseline. Intent-to-treat analyses were estimated using linear mixed-effect models with random intercepts. We ran a series of models for each gender where predictors were fixed except interactions between age groups and obesity status at baseline with intervention to determine the magnitude of impact on BMI. Results At baseline, mean (SD) BMI z-score (zBMI) was 0.97 (0.98) in the intervention group ( n = 387) and 0.98 (1.02) in the comparison group ( n = 313) (NS). The intervention was significantly associated with log-transformed BMI (β = 0.04 (0.02), P = 0.03) and zBMI (β = 0.25 (0.12), P = 0.04) among boys and log-transformed BMI among obese girls (β = − 0.04 (0.02), P = 0.04). The intervention was significantly and inversely associated with BMI in obese boys and girls across all age groups and normal weight boys in the oldest group (over 6 years) relative to their counterparts in the comparison community. Conclusions A community-based, multifaceted intervention was effective at slowing the rate of BMI growth among Mexican-heritage children. Our findings suggest that practitioners should consider strategies that address gender disparities and work with a variety of stakeholders to target childhood obesity. Trial registration clinicaltrials.gov Identifier: NCT01900613 . Registered 16th July 2013.

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“…Another study used data for adolescents with obesity to demonstrate that change in BMI had greater statistical power than change in BMIz (11). Among a cohort of preschool children with severe obesity who had two measures (mean interval of 11 months), the tracking, as measured by intraclass correlations of the two measures, was better for %BMI p95 and BMImz than for BMIz (12). Longitudinal data from the Bogalusa Heart Study of nearly 7,000 children with two or more measurements found that among the 247 children with severe obesity, BMIz tracked weakly and that repeated measures of %BMI p95 and of BMImz both had correlations of > 0.6 (13).…”

Section: Pediatric Obesitymentioning

confidence: 99%

Evaluation of BMI Metrics to Assess Change in Adiposity in Children with Overweight and Moderate and Severe Obesity

Barlow

Salahuddin

Durand

et al. 2020

Obesity

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This study aimed to examine the relation between change in different BMI metrics and change in adiposity over a 12-month weight management intervention. Methods: Baseline and 12-month weights and heights from 399 children aged 2 to 12 years with BMI ≥ 85th percentile were used to calculate BMI, %BMI p95 , %BMI p50 , BMI z-score (BMIz), and modified BMI z-score (BMImz). Changes (Δ) in these measures were compared with changes in body fat percent (Δfat%) from bioelectrical impedance assessment. Correlation and regression models predicting associations between ΔBMI metrics and Δfat% were examined. Results: A total of 89% of the cohort was Hispanic, and 34% had class 2 or 3 obesity. In models predicting Δfat% adjusting for age, sex, and weight category, R 2 for ΔBMI, ΔBMIz, BMImz, Δ%BMI p95 , and Δ%BMI p50 were 0.53, 0.38, 0.45, 0.53, and 0.54, respectively (all P < 0.001). Only the ΔBMIz model had an interaction with weight status. Among the models with the highest R 2 , age group and sex interacted with the Δ%BMI p95 model but not ΔBMI or ΔBMI p50 models. Conclusions: Longitudinal analyses demonstrate the utility of several BMI metrics other than z-score in capturing adiposity change consistently across a range of obesity severity. Characteristics of studied groups and interpretability could influence metric choice.

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Longitudinal changes in BMIz-scores among 45 414 2–4-year olds with severe obesity

Cited by 17 publications

References 32 publications

Estimation of Body Fat Percentage for Clinical Pharmacokinetic Studies in Children

Estimation of Body Fat Percentage for Clinical Pharmacokinetic Studies in Children

A three-year multifaceted intervention to prevent obesity in children of Mexican-heritage

Evaluation of BMI Metrics to Assess Change in Adiposity in Children with Overweight and Moderate and Severe Obesity

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