Assessing adiposity using BMI z-Score in children with severe obesity

An electronic medical record (EMR) prompt improves screening rates for metabolic conditions among children with obesity

OBJECTIVE

This study evaluated the effectiveness of an automated prompt in the electronic medical record (EMR) to increase screening rates for metabolic conditions and referrals to health education and to improve BMI percentile among children with obesity.

METHODS

The intervention used an EMR reminder that provided a panel of lab orders (hemoglobin A1c, lipid panel, and alanine aminotransferase) during clinical encounters among pediatric patients ages 10 to 18 with BMI ≥ 95th percentile. Data on demographics, comorbid conditions, BMI, lab results, and health education referrals were analyzed. Cohorts during the year before (n = 3479) and after (n = 3439) workflow implementation were compared.

RESULTS

The distribution of race/ethnicity among children with obesity was 56% Hispanic, 19% White, 11% Filipino, 4% Pacific Islander, 3% Black, and 1% East/South Asian. Orders for metabolic lab tests increased from 2% in the pre-workflow period to 52% in the post-workflow period (p < 0.0001). Completed screening rates improved from 1% to 27% (p < 0.0001). Health education referrals increased from 0.4% to 7% (p < 0.0001). We observed a mean change in BMI percentile of -0.13% (p = 0.06).

CONCLUSIONS

This study validates the use of an EMR-based prompt to improve metabolic lab screening and health education referrals among children with obesity. During the limited period of follow-up, we found no significant change in BMI percentile.

Measuring adolescents' eating in the absence of hunger in the home environment using subjective and objective measures

Eating in the absence of hunger (EAH) has been identified as a behavioral phenotype for obesity. Few studies have reported on objective measures of EAH in adolescents, and fewer yet have objectively measured EAH in a naturalistic, home setting. The purpose of this paper was to examine relations between objective, adolescent-report and parent-report measures of EAH, and to examine variation by sex and race. Participants included 295 predominantly low-income and rural adolescents (mean age = 14.2 ± 0.6 years) and their parents, drawn from the Family Life Project. An EAH task was administered in the home following an ad-libitum meal and compulsory milkshake; EAH was also reported on a web-based survey (both adolescent and parent reports) and adolescents' BMIz was calculated from height and weight, measured in the home or self-reported on the web survey. A high degree of variability in EAH intake was observed (range = 8-741 kcals). Parent and adolescent reports of EAH were weakly correlated and unrelated to observed EAH consumption; only adolescent reports of EAH were related to their BMIz. Several relations varied by sex and race. Positive associations between reported and observed EAH was only observed in girls, and positive associations between observed EAH and BMI was only observed in boys and in white adolescents. Overall EAH consumption was significantly greater in boys and in white adolescents. These findings suggest that EAH can be measured in adolescents in the home. In this sample of youth experiencing rural poverty, this home-based measure appears most valid for white adolescent girls.

Biobehavioral Dysregulation and its Association with Obesity and Severe Obesity Trajectories from 2 to 15 Years of Age: A Longitudinal Study

OBJECTIVE

This study aimed to identify obesity trajectories from childhood to adolescence (2-15 years of age) and investigate differences in behavioral, eating, and adrenocortical regulation by trajectory membership.

METHODS

A total of 1,077 households from the National Institute of Child Health and Human Development Study of Early Child Care and Youth Development were included. Anthropometrics were measured 11 times between ages 15 months and 15 years. Behavioral self-regulation was assessed at ages 3 and 4 years. Disordered eating behaviors and awakening cortisol were assessed at age 15 years.

RESULTS

Latent growth curve modeling identified four BMI trajectories: two nonoverweight trajectories based on average BMI at the 40th and 70th percentiles and overweight/obesity and severe obesity trajectories. Youth in the severe obesity trajectory exhibited lower behavioral self-regulation in early childhood and lower awakening cortisol at age 15 years compared with youth in the nonoverweight trajectories. Youth in the overweight/obesity and severe obesity trajectories showed higher levels of disordered eating behaviors at age 15 years.

CONCLUSIONS

Obesity trajectories were associated with biobehavioral markers of dysregulation in early childhood and adolescence. Dysregulation across biobehavioral domains was particularly apparent among youth who developed severe obesity. Further work is needed to better understand resilience factors that distinguish youth who develop obesity and severe obesity from those who do not.

What change in body mass index is associated with improvement in percentage body fat in childhood obesity? A meta-regression

OBJECTIVE

Using meta-regression this paper sets out the minimum change in body mass index-SD score (BMI-SDS) required to improve adiposity as percentage body fat for children and adolescents with obesity.

DESIGN

Meta-regression.

SETTING

Studies were identified as part of a large-scale systematic review of the following electronic databases: AMED, Embase, MEDLINE via OVID, Web of Science and CENTRAL via Cochrane library.

PARTICIPANTS

Individuals aged 4-19 years with a diagnosis of obesity according to defined BMI thresholds.

INTERVENTIONS

Studies of lifestyle treatment interventions that included dietary, physical activity and/or behavioural components with the objective of reducing obesity were included. Interventions of <2 weeks duration and those that involved surgical and/or pharmacological components (eg, bariatric surgery, drug therapy) were excluded.

PRIMARY AND SECONDARY OUTCOME MEASURES

To be included in the review, studies had to report baseline and post-intervention BMI-SDS or change measurements (primary outcome measures) plus one or more of the following markers of metabolic health (secondary outcome measures): adiposity measures other than BMI; blood pressure; glucose; inflammation; insulin sensitivity/resistance; lipid profile; liver function. This paper focuses on adiposity measures only. Further papers in this series will report on other outcome measures.

RESULTS

This paper explores the potential impact of BMI-SDS reduction in terms of change in percentage body fat. Thirty-nine studies reporting change in mean percentage body fat were analysed. Meta-regression demonstrated that reduction of at least 0.6 in mean BMI-SDS ensured a mean reduction of percentage body fat mass, in the sense that the associated 95% prediction interval for change in mean percentage body fat was wholly negative.

CONCLUSIONS

Interventions demonstrating reductions of 0.6 BMI-SDS might be termed successful in reducing adiposity, a key purpose of weight management interventions.

TRIAL REGISTRATION NUMBER

CRD42016025317.

Optimisation of children z-score calculation based on new statistical techniques

Background

Expressing anthropometric parameters (height, weight, BMI) as z-score is a key principle in the clinical assessment of children and adolescents. The Centre for Disease Control and Prevention (CDC) growth charts and the CDC-LMS method for z-score calculation are widely used to assess growth and nutritional status, though they can be imprecise in some percentiles.

Objective

To improve the accuracy of z-score calculation by revising the statistical method using the original data used to develop current z-score calculators.

Design

A Gaussian Process Regressions (GPR) was designed and internally validated. Z-scores for weight-for-age (WFA), height-for-age (HFA) and BMI-for-age (BMIFA) were compared with WHO and CDC-LMS methods in 1) standard z-score cut-off points, 2) simulated population of 3000 children and 3) real observations 212 children aged 2 to 18 yo.

Results

GPR yielded more accurate calculation of z-scores for standard cut-off points (p<<0.001) with respect to CDC-LMS and WHO approaches. WFA, HFA and BMIFA z-score calculations based on the 3 different methods using simulated and real patients, showed a large variation irrespective of gender and age. Z-scores around 0 +/- 1 showed larger variation than the values above and below +/- 2.

Conclusion

The revised z-score calculation method was more accurate than CDC-LMS and WHO methods for standard cut-off points. On simulated and real data, GPR based calculation provides more accurate z-score determinations, and thus, a better classification of patients below and above cut-off points. Statisticians and clinicians should consider the potential benefits of updating their calculation method for an accurate z-score determination.