Advancing Pediatric Obesity Risk Assessment: The Role of Imaging in Developing Body Composition Reference Charts

Concordia University researchers have developed age‑ and sex‑specific DXA body‑composition charts that map fat and muscle trajectories and better stratify pediatric cardiometabolic risk than BMI, according to a recent report.

BMI, derived from weight and height, cannot distinguish adipose from lean tissue; DXA‑derived percentiles do. In a large U.S. pediatric cohort, investigators used DXA scans to generate age‑ and sex‑specific percentiles for fat and muscle and constructed growth curves that map developmental change.

Since DXA quantifies compartments rather than aggregate mass, these charts allow phenotype classification and clearer evaluation of tissue‑specific risk signals described in the report.

The charts identify children with high adiposity but low muscle (HA‑LM) who may be missed by BMI. The HA‑LM phenotype was associated with higher triglycerides and lower HDL—biochemical markers consistent with early cardiometabolic risk—allowing the charts to flag a subgroup with adverse lipid patterns despite normal weight‑for‑height.

Unlike BMI’s blunt, population‑level screening, body‑composition percentiles provide tissue‑specific insight into adipose distribution and lean‑mass deficits.

These data may prompt targeted counseling, prioritize metabolic screening (lipid panel and fasting glucose or HbA1c) for HA‑LM children, support tailored physical‑activity and nutrition plans to increase lean mass, and inform referrals to pediatric endocrinology or dietetics for high‑risk phenotypes—actions tied to the report’s observed lipid and insulin‑resistance signals.

Key Takeaways:

• DXA‑based, age‑ and sex‑specific fat and muscle percentiles quantify developmental trajectories and move beyond BMI’s weight‑for‑height construct to tissue‑level risk stratification.
• Children with a high fat–low muscle (HA‑LM) phenotype—often overlooked by BMI—show adverse lipid and insulin‑resistance signals and warrant closer metabolic evaluation.
• Consider integrating DXA‑based percentiles into specialty workflows and research protocols to validate prognostic utility and guide targeted metabolic screening and interventions.