High BMI z-Scores from Different Growth References Are Not Comparable: An Example from a Weight Management Trial with an Anti-Obesity Medication in Pubertal Adolescents with Obesity.

Background: The BMI z-score is a standardized measure of weight status and weight change in children and adolescents. BMI z-scores from various growth references are often considered comparable, and differences among them are underappreciated. Methods: This study reanalyzed data from a weight management clinical study of liraglutide in pubertal adolescents with obesity using growth references from CDC 2000, CDC Extended, World Health Organization (WHO), and International Obesity Task Force. Results: BMI z-score treatment differences varied 2-fold from -0.13 (CDC 2000) to -0.26 (WHO) overall and varied almost 4-fold from -0.05 (CDC 2000) to -0.19 (WHO) among adolescents with high baseline BMI z-score. Conclusions: Depending upon the growth reference used, BMI z-score endpoints can produce highly variable treatment estimates and alter interpretations of clinical meaningfulness. BMI z-scores cited without the associated growth reference cannot be accurately interpreted.

Screening Accuracy of BMI for Adiposity Among 8- to 19-Year-Olds.

BACKGROUND AND OBJECTIVES: Although the limitations of BMI have long been recognized, there are recent concerns that it is not a good screening tool for adiposity. We therefore examined the cross-sectional relation of BMI to adiposity among 6923 8- to 19-year-olds in the National Health and Nutrition Survey from 2011 through 2018. METHODS: Participants were scanned with dual-energy x-ray absorptiometry. Adiposity was expressed as fat mass index (FMI, fat mass kg ÷ m2) and percentage of body fat (%fat). Lean mass was expressed as lean mass index (LMI, lean mass ÷ m2). Regression models and 2 × 2 tables were used to assess the relation of BMI to FMI, %fat, and LMI. RESULTS: Age and BMI accounted (R2) for 90% to 94% of the variability of FMI and LMI in each sex. Associations with %fat were weaker (R2s ∼0.70). We also examined the screening abilities of a BMI ≥ Centers for Disease Control and Prevention 95th percentile for high levels of adiposity and LMI. Cut points were chosen so that prevalences of high values of these variables would be similar to that for high BMI. Of participants with a high BMI, 88% had a high FMI, and 76% had a high %fat. Participants with a high BMI were 29 times more likely to have a high FMI than those with lower BMIs; comparable relative risks were 12 for high %fat and 14 for high LMI. CONCLUSIONS: Despite its limitations, a high BMI is a very good screening tool for identifying children and adolescents with elevated adiposity.

Trends in Severe Obesity Among Children Aged 2 to 4 Years in WIC: 2010 to 2020.

OBJECTIVES: To examine the prevalence and trends in severe obesity among 16.6 million children aged 2 to 4 years enrolled in the Special Supplemental Nutrition Program for Women, Infants, and Children (WIC) from 2010 to 2020. METHODS: Severe obesity was defined as a sex-specific BMI for age ≥120% of the 95th percentile on the Centers for Disease Control and Prevention growth charts or BMI ≥35 kg/m2. Joinpoint regression was used to identify when changes occurred in the overall trend. Logistic regression was used to compute the adjusted prevalence differences between years controlling for sex, age, and race and ethnicity. RESULTS: The prevalence of severe obesity significantly decreased from 2.1% in 2010 to 1.8% in 2016 and then increased to 2.0% in 2020. From 2010 to 2016, the prevalence decreased significantly among all sociodemographic subgroups except for American Indian/Alaska Native (AI/AN) children. The largest decreases were among 4-year-olds, Asian/Pacific Islander and Hispanic children, and children from higher-income households. However, from 2016 to 2020, the prevalence increased significantly overall and among sociodemographic subgroups, except for AI/AN and non-Hispanic white children. The largest increases occurred in 4-year-olds and Hispanic children. Among 56 WIC agencies, the prevalence significantly declined in 17 agencies, and 1 agency (Mississippi) showed a significant increase from 2010 to 2016. In contrast, 21 agencies had significant increases, and only Alaska had a significant decrease from 2016 to 2020. CONCLUSIONS: Although severe obesity prevalence in toddlers declined from 2010 to 2016, recent trends are upward. Early identification and access to evidence-based family healthy weight programs for at-risk children can support families and child health.

Probability of 5% or Greater Weight Loss or BMI Reduction to Healthy Weight Among Adults With Overweight or Obesity.

Importance: Information on the probability of weight loss among US adults with overweight or obesity is limited. Objective: To assess the probability of 5% or greater weight loss, 10% or greater weight loss, body mass index (BMI) reduction to a lower BMI category, and BMI reduction to the healthy weight category among US adults with initial overweight or obesity overall and by sex and race. Design, Setting, and Participants: This cohort study obtained data from the IQVIA ambulatory electronic medical records database. The sample consists of US ambulatory patients 17 years or older with at least 3 years of BMI information from January 1, 2009, to February 28, 2022. Minimum age was set at 17 years to allow for the change in BMI or weight starting at 18 years. Maximum age was censored at 70 years. Exposures: Initial BMI (calculated as weight in kilograms divided by height in meters squared) category was the independent variable of interest, and the categories were as follows: lower than 18.5 (underweight), 18.5 to 24.9 (healthy weight), 25.0 to 29.9 (overweight), 30.0 to 34.9 (class 1 obesity), 35.0 to 39.9 (class 2 obesity), and 40.0 to 44.9 and 45.0 or higher (class 3 or severe obesity). Main Outcomes and Measures: The 2 main outcomes were 5% or greater weight loss (ie, a ≥5% reduction in initial weight) and BMI reduction to the healthy weight category (ie, BMI of 18.5-24.9). Results: The 18 461 623 individuals in the sample had a median (IQR) age of 54 (40-66) years and included 10 464 598 females (56.7%) as well as 7.7% Black and 72.3% White patients. Overall, 72.5% of patients had overweight or obesity at the initial visit. Among adults with overweight and obesity, the annual probability of 5% or greater weight loss was low (1 in 10) but increased with higher initial BMI (from 1 in 12 individuals with initial overweight to 1 in 6 individuals with initial BMI of 45 or higher). Annual probability of BMI reduction to the healthy weight category ranged from 1 in 19 individuals with initial overweight to 1 in 1667 individuals with initial BMI of 45 or higher. Both outcomes were generally more likely among females than males and were highest among White females. Over the 3 to 14 years of follow-up, 33.4% of persons with overweight and 41.8% of persons with obesity lost 5% or greater of their initial weight. At the same time, 23.2% of persons with overweight and 2.0% of persons with obesity reduced BMI to the healthy weight category. Conclusions and Relevance: Results of this cohort study indicate that the annual probability of 5% or greater weight loss was low (1 in 10) despite the known benefits of clinically meaningful weight loss, but 5% or greater weight loss was more likely than BMI reduction to the healthy weight category, especially for patients with the highest initial BMIs. Clinicians and public health efforts can focus on messaging and referrals to interventions that are aimed at clinically meaningful weight loss (ie, ≥5%) for adults at any level of excess weight.

Children's rates of BMI change during pre-pandemic and two COVID-19 pandemic periods, IQVIA Ambulatory Electronic Medical Record, January 2018 Through November 2021.

OBJECTIVE: Many US youth experienced accelerated weight gain during the early COVID-19 pandemic. Using an ambulatory electronic health record data set, the authors compared children's rates of BMI change in three periods: pre-pandemic (January 2018-February 2020), early pandemic (March-December 2020), and later pandemic (January-November 2021). METHODS: This study used mixed-effects models to examine differences in rates of change in BMI, weight, and obesity prevalence among the three periods. Covariates included time as a continuous variable, a variable indicating in which period each BMI was taken, sex, age, and initial BMI category. RESULTS: In a longitudinal cohort of 241,600 children aged 2 through 19 years with ≥4 BMI measurements, the monthly rates of BMI change (kilograms per meters squared) were 0.056 (95% CI: 0.056-0.057) in the pre-pandemic period, 0.104 (95% CI: 0.102-0.106) in the early pandemic, and 0.035 (95% CI: 0.033-0.036) in the later pandemic. The estimated prevalence of obesity in this cohort was 22.5% by November 2021. CONCLUSIONS: In this large, geographically diverse cohort of US youth, accelerated rates of BMI change observed during 2020 were largely attenuated in 2021. Positive rates indicate continued weight gain rather than loss, albeit at a slower rate. Childhood obesity prevalence remained high, which raises concern about long-term consequences of excess weight and underscores the importance of healthy lifestyle interventions.

Cleaning of anthropometric data from PCORnet electronic health records using automated algorithms.

Objective: To demonstrate the utility of growthcleanr, an anthropometric data cleaning method designed for electronic health records (EHR). Materials and Methods: We used all available pediatric and adult height and weight data from an ongoing observational study that includes EHR data from 15 healthcare systems and applied growthcleanr to identify outliers and errors and compared its performance in pediatric data with 2 other pediatric data cleaning methods: (1) conditional percentile (cp) and (2) PaEdiatric ANthropometric measurement Outlier Flagging pipeline (peanof). Results: 687 226 children (<20 years) and 3 267 293 adults contributed 71 246 369 weight and 51 525 487 height measurements. growthcleanr flagged 18% of pediatric and 12% of adult measurements for exclusion, mostly as carried-forward measures for pediatric data and duplicates for adult and pediatric data. After removing the flagged measurements, 0.5% and 0.6% of the pediatric heights and weights and 0.3% and 1.4% of the adult heights and weights, respectively, were biologically implausible according to the CDC and other established cut points. Compared with other pediatric cleaning methods, growthcleanr flagged the most measurements for exclusion; however, it did not flag some more extreme measurements. The prevalence of severe pediatric obesity was 9.0%, 9.2%, and 8.0% after cleaning by growthcleanr, cp, and peanof, respectively. Conclusion: growthcleanr is useful for cleaning pediatric and adult height and weight data. It is the only method with the ability to clean adult data and identify carried-forward and duplicates, which are prevalent in EHR. Findings of this study can be used to improve the growthcleanr algorithm.

Longitudinal Changes in Various BMI Metrics and Adiposity in 3- to 7-Year-Olds.

BACKGROUND AND OBJECTIVES: Changes in BMI z score (BMIz) are widely used in weight control programs and interventions to monitor changes in body fatness, but this metric may not be optimal. We examined the ability of 3 BMI metrics to assess adiposity change among children with a wide range of BMIs. METHODS: The sample comprised 343 3-year-old children with serial measurements of BMI and body fatness every 4 months over 4 years. We compared correlations between changes in body fatness, calculated with dual-energy-x-ray absorptiometry, and changes in 3 BMI metrics: BMIz and percentage of the 50th (%50th) and 95th (%95th) percentiles in the CDC growth charts. RESULTS: About 21% of the participants were Black and 79% were white. Changes in body fatness over 4 years were more strongly associated with changes in %50th and %95th than with changes in BMIz. Correlations with %body fat among all children were r = 0.64 for BMIz versus r = 0.77 to 0.78 for %50th and %95th (P < .001 for differences between the correlations). Stratified analyses showed the difference between the correlations were similar among boys and girls, among white children and Black children, and among children without obesity and those with obesity. CONCLUSIONS: Changes in adiposity among young children are better captured by expressing changes in BMI as a percentage of the 50th or 95th percentiles instead of BMIz change. Using the best BMI metric will allow pediatricians to better assess a child's change in body fatness over time.

Weight gain among US adults during the COVID-19 pandemic through May 2021.

OBJECTIVE: There have been conflicting reports concerning weight gain among adults during the COVID-19 epidemic. Although early studies reported large weight increases, several of these analyses were based on convenience samples or self-reported information. The objective of the current study is to examine the pandemic-related weight increase associated with the pandemic through May 2021. METHODS: A total of 4.25 million adults (18 to 84 years) in an electronic health record database who had at least two weight measurements between January 2019 and February 2020 and one after June 2020 were selected. Weight changes before and after March 2020 were contrasted using mixed-effects regression models. RESULTS: Compared with the pre-pandemic weight trend, there was a small increase (0.1 kg) in weight in the first year of the pandemic (March 2020 through March 2021). Weight changes during the pandemic varied by sex, age, and initial BMI, but the largest mean increase across these characteristics was < 1.3 kg. Weight increases were generally greatest among women, adults with BMI of 30 or 35 kg/m2 , and younger adults. CONCLUSIONS: The results indicate that the mean weight gain among adults during the COVID-19 pandemic may be small.

Measuring BMI change among children and adolescents.

BACKGROUND: Weight control programs for children monitor BMI changes using BMI z-scores that adjust BMI for the sex and age of the child. It is, however, uncertain if BMIz is the best metric for assessing BMI change. OBJECTIVE: To identify which of 6 BMI metrics is optimal for assessing change. We considered a metric to be optimal if its short-term variability was consistent across the entire BMI distribution. SUBJECTS: 285 643 2- to 17-year-olds with BMI measured 3 times over a 10- to 14-month period. METHODS: We summarized each metric's variability using the within-child standard deviation. RESULTS: Most metrics' initial or mean value correlated with short-term variability (|r| ~ 0.3 to 0.5). The metric for which the within-child variability was largely independent (r = 0.13) of the metric's initial or mean value was the percentage of the 50th expressed on a log scale. However, changes in this metric between the first and last visits were highly (r ≥ 0.97) correlated with changes in %95th and %50th. CONCLUSIONS: Log %50 was the metric for which the short-term variability was largely independent of a child's BMI. Changes in log %50th, %95th, and %50th are strongly correlated.

Interrelationships among age at adiposity rebound, BMI during childhood, and BMI after age 14 years in an electronic health record database.

OBJECTIVE: This study compared the importance of age at adiposity rebound versus childhood BMI to subsequent BMI levels in a longitudinal analysis. METHODS: From the electronic health records of 4.35 million children, a total of 12,228 children were selected who were examined at least once each year between ages 2 and 7 years and reexamined after age 14 years. The minimum number of examinations per child was six. Each child's rebound age was estimated using locally weighted regression (lowess), a smoothing technique. RESULTS: Children who had a rebound age < 3 years were, on average, 7 kg/m2 heavier after age 14 years than were children with a rebound age ≥ 7 years. However, BMI after age 14 years was more strongly associated with BMI at the rebound than with rebound age (r = 0.57 vs. -0.44). Furthermore, a child's BMI at age 3 years provided more information on BMI after age 14 years than did rebound age. In addition, rebound age provided no information on subsequent BMI if a child's BMI at age 6 years was known. CONCLUSIONS: Although rebound age is related to BMI after age 14 years, a child's BMI at age 3 years provides more information and is easier to obtain.

Evaluation of Alternative Body Mass Index (BMI) Metrics to Monitor Weight Status in Children and Adolescents With Extremely High BMI Using CDC BMI-for-age Growth Charts.

In the United States, obesity and severe obesity in children and adolescents are defined using threshold values from the 2000 Centers for Disease Control and Prevention (CDC) sex-specific body mass index (BMI)- for-age growth charts. BMI z-scores and percentiles from the 2000 CDC BMI-for-age growth charts are also used to monitor children's weight status over time and to evaluate obesity treatments. Parameters to calculate percentiles and corresponding z-scores (BMIz) were derived from selected percentiles between the 3rd and 97th. Use of the BMI-for-age growth charts for children and adolescents with extremely high BMI requires extrapolation beyond the 97th percentile, which leads to compression of BMIz values into a very narrow range and is not recommended. This report evaluates eight alternative BMI metrics for monitoring weight status in children and adolescents with extremely high BMI.

Longitudinal Trends in Body Mass Index Before and During the COVID-19 Pandemic Among Persons Aged 2-19 Years - United States, 2018-2020.

Obesity is a serious health concern in the United States, affecting more than one in six children (1) and putting their long-term health and quality of life at risk.* During the COVID-19 pandemic, children and adolescents spent more time than usual away from structured school settings, and families who were already disproportionally affected by obesity risk factors might have had additional disruptions in income, food, and other social determinants of health.† As a result, children and adolescents might have experienced circumstances that accelerated weight gain, including increased stress, irregular mealtimes, less access to nutritious foods, increased screen time, and fewer opportunities for physical activity (e.g., no recreational sports) (2,3). CDC used data from IQVIA's Ambulatory Electronic Medical Records database to compare longitudinal trends in body mass index (BMI, kg/m2) among a cohort of 432,302 persons aged 2-19 years before and during the COVID-19 pandemic (January 1, 2018-February 29, 2020 and March 1, 2020-November 30, 2020, respectively). Between the prepandemic and pandemic periods, the rate of BMI increase approximately doubled, from 0.052 (95% confidence interval [CI] = 0.051-0.052 to 0.100 (95% CI = 0.098-0.101) kg/m2/month (ratio = 1.93 [95% CI = 1.90-1.96]). Persons aged 2-19 years with overweight or obesity during the prepandemic period experienced significantly higher rates of BMI increase during the pandemic period than did those with healthy weight. These findings underscore the importance of efforts to prevent excess weight gain during and following the COVID-19 pandemic, as well as during future public health emergencies, including increased access to efforts that promote healthy behaviors. These efforts could include screening by health care providers for BMI, food security, and social determinants of health, increased access to evidence-based pediatric weight management programs and food assistance resources, and state, community, and school resources to facilitate healthy eating, physical activity, and chronic disease prevention.

Changes in High Weight-for-Length among Infants Enrolled in Special Supplemental Nutrition Program for Women, Infants, and Children during 2010-2018.

Background: Infants and young children with high weight-for-length are at increased risk for obesity in later life. This study describes prevalence of high weight-for-length and examines changes during 2010-2018 among 11,366,755 infants and young children 3-23 months of age in the Special Supplemental Nutrition Program for Women, Infants, and Children (WIC). Methods: Children's weights and lengths were measured. High weight-for-length was defined as ≥2 standard deviations above sex and age-specific median on World Health Organization growth charts. Adjusted prevalence differences (APDs) between years were calculated as 100 times marginal effects from logistic regression models. APD was statistically significant if 95% confidence interval did not include 0. Results: Adjusted prevalence of high weight-for-length decreased from 2010 to 2014, and leveled off through 2018 overall, in boys and girls, those 6-11 and 18-23 months of age, and non-Hispanic whites, non-Hispanic blacks, Hispanics, and Asians/Pacific Islanders. For 12-17 months old and American Indian/Alaska Native infants and young children, adjusted prevalence decreased from 2010 to 2014, and then increased slightly through 2018. Among 56 WIC state or territorial agencies, 33 had significant decreases between 2010 and 2018, whereas 8 had significant increases. Between 2014 and 2018, prevalence decreased significantly in 12 agencies and increased significantly in 23. Conclusions: The results indicate overall declines in prevalence of high weight-for-length from 2010 to 2018, with a prevalence stabilization since 2014. Continued surveillance is needed. Obesity prevention strategies in WIC and multiple settings are important for ensuring healthy child growth.

A Longitudinal Comparison of Alternatives to Body Mass Index Z-Scores for Children with Very High Body Mass Indexes.

OBJECTIVE: The current Centers for Disease Control and Prevention (CDC) body mass index (BMI) z-scores are inaccurate for BMIs of ≥97th percentile. We, therefore, considered 5 alternatives that can be used across the entire BMI distribution: modified BMI-for-age z-score (BMIz), BMI expressed as a percentage of the 95th percentile (%CDC95th percentile), extended BMIz, BMI expressed as a percentage of the median (%median), and %median adjusted for the dispersion of BMIs. STUDY DESIGN: We illustrate the behavior of the metrics among children of different ages and BMIs. We then compared the longitudinal tracking of the BMI metrics in electronic health record data from 1.17 million children in PEDSnet using the intraclass correlation coefficient to determine if 1 metric was superior. RESULTS: Our examples show that using CDC BMIz for high BMIs can result in nonsensical results. All alternative metrics showed higher tracking than CDC BMIz among children with obesity. Of the alternatives, modified BMIz performed poorly among children with severe obesity, and %median performed poorly among children who did not have obesity at their first visit. The highest intraclass correlation coefficients were generally seen for extended BMIz, adjusted %median, and %CDC95th percentile. CONCLUSIONS: Based on the examples of differences in the BMI metrics, the longitudinal tracking results and current familiarity BMI z-scores and percentiles. Both extended BMIz and extended BMI percentiles may be suitable replacements for the current z-scores and percentiles. These metrics are identical to those in the CDC growth charts for BMIs of <95th percentile and are superior for very high BMIs. Researchers' familiarity with the current CDC z-scores and clinicians with the CDC percentiles may ease the transition to the extended BMI scale.