<b>Body Composition-Driven Phenotyping Reveals Obesity and Cardiometabolic Heterogeneity in Children Using a Tree-Like Representation</b>

<jats:p>&lt;p dir="ltr"&gt;Traditional measures such as body mass index do not fully capture obesity-related metabolic risks. Here, we examined dual-energy X-ray absorptiometry-derived body composition patterns in children (n = 11,238) using Discriminative Dimensionality Reduction Tree algorithm, which was validated in an independent cohort (n = 2,001). The tree structure revealed a continuous landscape of body composition phenotypes, with distinct spatial gradients for fat and lean mass. Within the phenotypic tree, fat-dominant phenotypes clustered in the region of low Dimension 1 and low Dimension 2 (corresponding to the lower-left branches), where they demonstrated significant spatial overlap with high-risk clusters for hypertension, elevated LDL-C, TC, and TG (Moran’s I &gt; 0.3, all p &lt; 0.001). Meanwhile, phenotypes characterized by elevated lean mass coupled with increased adiposity were localized in the region of low Dimension 1 and high Dimension 2 (upper-left branches), demonstrating an increased risk of low HDL-C and high TG (Moran’s I &gt; 0.6, all p &lt; 0.001). In contrast, lean-dominant phenotypes clustered in the region of high Dimension 1 and high Dimension 2 (upper-right branches), which were associated with a relatively low risk of hyperglycemia, insulin resistance, and high LDL-C (Moran’s I &gt; 0.1, all p &lt; 0.001). For practical utility, we developed a publicly available web application to map individual data on the reference tree architecture, allowing for assessment of cardiometabolic risk. Our findings highlight the value of more precise body composition measures for early identification and prevention of obesity-related health problems.&lt;/p&gt;</jats:p>