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.
Article types and fees
This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:
Brief Research Report
Case Report
Classification
Clinical Trial
Community Case Study
Conceptual Analysis
Curriculum, Instruction, and Pedagogy
Data Report
Editorial
Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.
Article types
This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:
Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.