Obesity is a well-established risk factor for a broad spectrum of chronic diseases, including cardiovascular disease, diabetes, cancer, and liver disease. However, recent evidence indicates that traditional anthropometric indices—such as body mass index (BMI)—do not capture the full complexity of body fat distribution, metabolic health, or muscle–fat interactions. As new evaluation methods emerge, including visceral adiposity indices, body composition analysis, metabolic phenotyping, and imaging-derived fat metrics, an increasing number of individuals with “normal-weight obesity” or a “metabolically unhealthy normal weight” are being identified.

This Research Topic aims to explore how diverse obesity assessment strategies are reshaping our understanding of obesity-related disease risk and seeks to provide a more precise and multidimensional understanding of obesity-related health risks.

We welcome contributions that can, but are not limited to:
• Evaluate the limitations of conventional obesity metrics;
• Introduce novel measurement or modeling approaches e.g. DXA, MRI, bioimpedance, epigenetic or metabolomic biomarkers;
• Investigate their implications for disease prediction, prevention, and clinical decision-making by integrating epidemiology, omics, and artificial intelligence;
• Critical evaluations of the limitations and consequences of conventional obesity metrics;
• Development and validation of novel anthropometric, imaging, or omics-based assessment tools;
• Insights from advanced data analytics, machine learning, and artificial intelligence in obesity phenotyping;
• Identification and clinical relevance of “hidden” high-risk phenotypes such as metabolically unhealthy normal weight and normal-weight obesity;
• Exploration of novel biomarkers—epigenetic, metabolomic, proteomic—that improve risk prediction and treatment response.

Are you interested in contributing a manuscript to this Research Topic?

Rethinking Obesity: Hidden Risks, New Metrics, and Disease Burden Beyond BMI