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Localization, Identification, and Excision of Murine Adipose Depots
Published on: December 4, 2014
Decoding Obesity Through the Lens of Adipose Tissue Inflammatory Regulation: A Comparative Study of Obesity Subtypes
Yinuo Chen1, Yuchen Zhang1, Yuang Song1
1Department of Plastic and Cosmetic Surgery, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, China.
Abstract:
Obesity is increasingly recognized as a heterogeneous condition rather than a single disease entity. Although body mass index remains widely used in clinical practice, it does not adequately reflect differences in fat distribution, metabolic status, inflammatory activity, or body composition. As a result, individuals with similar body mass index values may have markedly different cardiometabolic risk profiles and clinical trajectories. This review examines four clinically relevant obesity-related phenotypes, namely metabolically healthy obesity, metabolically unhealthy obesity, metabolically obese normal weight, and sarcopenic obesity, within an integrated framework linking inflammation, metabolism, and structural tissue remodeling. Across these phenotypes, adipose dysfunction and chronic low-grade inflammation represent a common biological background. However, important differences arise from the extent to which inflammatory stress is buffered, amplified, or coupled to maladaptive remodeling in adipose tissue and skeletal muscle. Metabolically healthy obesity is characterized by relatively preserved metabolic function and a more favorable fat distribution, although this state is often unstable over time. Metabolically unhealthy obesity reflects overt metabolic decompensation driven by visceral adiposity, ectopic fat deposition, and amplified inflammatory signaling. Metabolically obese normal weight highlights concealed metabolic risk despite normal body weight, whereas sarcopenic obesity represents the coexistence of excess adiposity with impaired muscle mass and function. Taken together, these phenotypes are best viewed as dynamic and partially overlapping states. A phenotype-informed approach may improve risk stratification and support more precise strategies for prevention and treatment.

