Rosero-Revelo Ricardo, Tamayo Mateo, Correa Ricardo, Pantalone Kevin M, Creel David, Burguera Bartolome, Griebeler Marcio L
Department of Endocrinology, Diabetes and Metabolism, Cleveland Clinic, Cleveland, OH, USA.
Obesity Center, Fundación Santa Fe de Bogotá University Hospital, Bogotá D.C, Colombia.
Rev Endocr Metab Disord. 2025 Jun 18. doi: 10.1007/s11154-025-09976-3.
Traditional reliance on Body Mass Index (BMI) as a diagnostic tool for obesity is increasingly challenged due to its inability to differentiate between fat and lean mass and to capture fat distribution. Emerging evidence-including findings from our longitudinal study in Latino patients with obesity and insights from the 2025 Lancet Commission on Obesity-suggests that a comprehensive evaluation of body composition is essential for accurate risk stratification. This review synthesizes historical perspectives and recent developments in obesity phenotyping, detailing how the field has evolved from simple BMI-based assessments to multifaceted approaches incorporating bioelectrical impedance analysis (BIA) and supplementary anthropometric measures such as waist circumference and waist-to-hip ratio. We also examine the metabolic, genetic, and hormonal mechanisms underlying phenotypic variability, which help explain why individuals with similar BMIs may exhibit markedly different health risks. By integrating our original data with an extensive review of current literature, we demonstrate that refined obesity phenotyping can serve as an early indicator of progression from preclinical to clinical obesity. Such nuanced classifications offer the potential for more personalized therapeutic interventions aimed at optimizing weight loss outcomes and reducing cardiometabolic risk. Overall, our findings advocate for a multidimensional approach to obesity assessment that promises to improve clinical outcomes through tailored, phenotype-based strategies.
传统上依赖体重指数（BMI）作为肥胖的诊断工具正日益受到挑战，因为它无法区分脂肪和瘦体重，也无法反映脂肪分布情况。越来越多的证据——包括我们对肥胖拉丁裔患者的纵向研究结果以及《柳叶刀》2025年肥胖问题委员会的见解——表明，对身体成分进行全面评估对于准确的风险分层至关重要。这篇综述综合了肥胖表型分析的历史观点和最新进展，详细阐述了该领域如何从基于简单BMI的评估发展到纳入生物电阻抗分析（BIA）以及腰围和腰臀比等补充人体测量指标的多方面方法。我们还研究了表型变异背后的代谢、遗传和激素机制，这有助于解释为什么BMI相似的个体可能表现出明显不同的健康风险。通过将我们的原始数据与对当前文献的广泛综述相结合，我们证明，精细化的肥胖表型分析可以作为从临床前肥胖发展到临床肥胖的早期指标。这种细致入微的分类为更个性化的治疗干预提供了可能性，旨在优化减肥效果并降低心脏代谢风险。总体而言，我们的研究结果倡导采用多维方法进行肥胖评估，有望通过基于表型的定制策略改善临床结果。
