BMI is widely used clinically and its application was recently reinforced by the first clinical practice guideline for childhood obesity from the American Academy of Pediatrics.1 The first key action statement in this guideline is for pediatric providers to assess age- and sex-specific BMI percentiles at least annually for youth 2 to 18 years old (Grade B).

The relationship of weight (in kg) divided by height (in meters) squared was originally derived by Belgian mathematician Adolphe Quetelet in 1832 (originally termed the Quetelet Index) as a way to describe vertical and transverse human growth, particularly outside of infancy and puberty.2 In 1972, physiologist Ancel Keys proposed this same equation, renamed the “body mass index,” as a preferred way to describe relative weight as it pertains to “body fatness” on the basis of validation data from a multinational sample of men.3 Showing foresight in this seminal article, Keys cautions against attaching value judgments about human behavior to changes in body metrics.

Over the last 50 years, including recently, BMI has been criticized as a rudimentary, antiquated metric with a major flaw being its detachment from a direct measure of adiposity.4,5 Compared with absolute BMI cutoffs used for adults, pediatrics leverages age- and sex-specific growth charts from the Centers for Disease Control and Prevention to define underweight, average, overweight, obesity, and severe obesity.6,7 Newer approaches to body composition analysis, including digital anthropometry and associated machine learning (ie, generating a three-dimensional model of the body using a digital scanner and pairing with a database that matches the body shape/measurements to body composition), aim to be fast, accessible, cost-effective, and personalized.8 But to date, none have proven better than BMI in the clinical setting.

In this issue of Pediatrics, Freedman et al evaluate the relationship between BMI and adiposity within a contemporary cohort of 6928 8- to 19-year-olds in the United States using National Health and Nutrition Examination Survey data.9 A key finding is that the combination of age and BMI predicts the vast majority (90%–94%) of the variability in dual-energy x-ray absorptiometry-measured fat mass and lean mass indexed to height within each sex. And, although BMI ≥95th percentile is associated with both increased adiposity and increased lean mass, the likelihood of increased adiposity was about twice as high as the likelihood of increased lean mass (relative risk 29 vs 15). Notably, the association between elevated BMI and increased adiposity is very strong for youth with BMI ≥95th percentile, but is not for youth with lower BMIs, including in the overweight range (BMI 90th percentile). Stark sex differences in the pattern of lean versus adipose tissue accretion over puberty are also redemonstrated in their analysis, where a steady increase in the fat mass index is seen only in girls across the entire age range studied, whereas a near-linear increase in lean mass index over this age range is seen only in boys.

This study by Freedman et al serves to reinforce the clinical utility of BMI, particularly in identifying increased adiposity in the obesity range. Strengths of this study include the use of a gold standard for body composition (dual-energy x-ray absorptiometry), evaluation across the full BMI/adiposity spectrum, and inclusion of practical screening characteristics (eg, positive predictive value) to describe associations between BMI ≥95th percentile and body composition.

Limitations of the study by Freedman et al include the cross-sectional study design, exclusion of youth <8 years of age, and lack of data on regional distribution of adiposity (eg, visceral versus subcutaneous fat). Longitudinal data are needed to clarify how the relationship between BMI and body composition is affected by tailoring variables including onset and duration (dose) of elevated BMI, and effects of treatment interventions (eg, lifestyle versus pharmacotherapy versus surgery). A higher proportion of visceral to subcutaneous fat has been shown for example to strongly predict hepatic fat accumulation and dysglycemia during adolescence.10 The current study did not assess differences by race or ethnicity, but references the 2022 study by Martin et al, which used the same 2011–2018 National Health and Nutrition Examination Survey data set and found that the relationship between adiposity and race or ethnicity is both sex- and BMI-category dependent.11 Race and ethnicity are social constructs, not genetic or biologic categories, but are relevant to population-based studies because these characteristics can be used to identify groups disproportionately affected by obesity.1 Martin et al found that, among youth with underweight/healthy BMI, percentage of body fat and fat mass index were both lower among non-Hispanic Black males and females versus non-Hispanic white males and females. In this same BMI category, percentage of body fat only was higher among non-Hispanic Asian American and Hispanic males and females. However, among youth with obesity, the only significant finding was that non-Hispanic Asian American females had lower percentage of body fat and fat mass index than non-Hispanic white females. The physiologic mechanisms for these differences are unknown and these data do not support using different BMI thresholds on the basis of race and ethnicity to define pediatric obesity. Complementary tools including reliable population-based standards for adipose distribution are still needed and could be useful to refine risk assessment. Finally, the authors of the current study leave an opportunity to further articulate how BMI can be used as 1 piece of a more holistic approach to assess and treat obesity in a way that is respectful, reduces bias/stigma, and is personalized.

Overall, on the basis of these results, pediatricians can feel confident that identification of elevated BMI (≥95th percentile) continues to be an effective way to screen for increased adiposity. Elevated BMI in childhood persists into adulthood in 4 out of 5 children and is strongly linked to the development of numerous, life-limiting cardiometabolic complications.12,13 In fact, improved standardization for the identification of pediatric obesity and severe obesity using BMI, when paired with equitable delivery of treatment, could help to counteract weight bias and reduce disparities in obesity-related health outcomes.14 We remind practitioners that the standardized use of BMI to identify patients with obesity is a first step. Additional risk stratification for how a specific BMI affects an individual’s health should be person-centered and include additional data from the history and diagnostic testing. Further, treatment goals should be focused on health and quality-of-life outcomes, as opposed to a number or percentile on a growth chart.

Drs Moore and Daniels drafted the commentary, reviewed it critically for important intellectual content, approved the final manuscript as submitted, and agree to be accountable for all aspects of the work.

COMPANION PAPER: A companion to this article can be found online at www.pediatrics.org/cgi/doi/10.1542/peds.2024-065960.

FUNDING: Dr Moore’s effort was supported by the National Institutes of Health grant K23HL163480. Dr Daniels received no additional funding. The funder had no role in the design or conduct of this commentary.

CONFLICT OF INTEREST DISCLOSURES: The authors have indicated they have no conflicts of interest relevant to this article to disclose.

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