KEY FIGURE

Obesity is currently defined as excessive fat accumulation that poses health risks. Traditionally, body mass index (BMI) has been the primary diagnostic tool for obesity in clinical and public health settings. However, BMI has critical limitations— it does not differentiate between fat and lean mass, ignores fat distribution, and fails to capture metabolic health status [1]. These limitations have led to significant misclassification of obesity-related health risks, particularly in metabolically healthy obesity (MHO) and metabolically obese normal weight (MONW) individuals [2]. Individuals with MHO and MONW exhibit distinct fat distribution patterns, with excessive visceral and hepatic fat contributing to increased metabolic risk even among those with normal BMI [3]. Moreover, the limitations of BMI-based diagnosis are particularly pronounced in Asian populations, where many individuals exhibit a unique obesity phenotype characterized by higher visceral fat accumulation at lower BMI levels compared to Western populations [4]. This phenotype is associated with an increased risk of type 2 diabetes mellitus, hypertension, and cardiovascular diseases at lower BMI cutoffs than those used in Western populations. The WHO has acknowledged these ethnic differences and suggested a lower BMI threshold (≥25 kg/m²) for obesity in Asians [5], but even individuals within the BMI range of 23–24.9 kg/m² exhibit significantly elevated metabolic risk [6]. A Korean study found that 32% of individuals with a BMI of 18.5–22.9 kg/m² had body fat levels exceeding 26% (men) or 36% (women), placing them at higher risk for metabolic syndrome [7].

Recognizing these gaps, the Lancet Diabetes & Endocrinology Commission has introduced a transformative framework that redefines obesity as a spectrum of disease states (Fig. 1) [8]. This consensus introduces the concepts of preclinical obesity and clinical obesity, moving away from a simplistic BMI threshold toward a functional approach that evaluates organ and tissue dysfunction. Before classifying individuals into preclinical or clinical obesity, excess adiposity must first be assessed, as it serves as the common diagnostic foundation for both categories. While BMI has traditionally been used as a surrogate marker, this new framework incorporates both anthropometric and direct fat measures to improve diagnostic accuracy. Anthropometric measures, such as waist circumference, waist-to-height ratio, and waist-to-hip ratio, provide indirect assessments of central adiposity. Although these measures have limitations in directly assessing body fat distribution and organ dysfunction, they remain essential diagnostic tools due to their accessibility, cost-effectiveness, and strong correlation with obesity-related health risks [9]. In addition to anthropometric assessments, direct fat measures such as dualenergy X-ray absorptiometry (DXA) and bioelectrical impedance analysis (BIA) offer more precise evaluations of total body fat percentage and fat distribution [10,11]. While BMI remains a useful screening tool, these additional assessments offer a more precise evaluation of body fat distribution and metabolic risk. This approach is particularly relevant for Asian populations for the reasons discussed above [4].

Preclinical obesity refers to individuals with excess adiposity but without evident organ dysfunction or significant physical limitations. Preclinical obesity is often a precursor to clinical obesity, although it is not necessarily a pre-disease state. Some individuals may remain in this stage without progression, while others may develop clinical obesity and its associated non-communicable diseases, such as type 2 diabetes mellitus and cardiovascular disease. Not all individuals with preclinical obesity require immediate intervention. The Lancet commission framework suggests that individuals considered high-risk based on metabolic risk factors, fat distribution, and genetic predisposition may benefit from early intervention, whereas others may only require regular monitoring.

Clinical obesity is diagnosed when excess adiposity leads to at least one of the following: reduced organ or tissue function (e.g., cardiovascular dysfunction, respiratory impairments, musculoskeletal limitations, renal dysfunction, liver dysfunction), or substantial limitations in daily activities such as mobility or self-care tasks (Fig. 1). This concept highlights the clinical implications of dysfunctional adipose tissue in increased risk of cardiometabolic risk, metabolic syndrome, and impairment in the daily activities [12]. Unlike previous classifications that primarily focused on cardiometabolic risk, this new framework acknowledges that obesity affects multiple organ systems. By shifting the focus from weight-based cutoffs to functional health impairments, this model enables clinicians to prioritize treatment for individuals who are experiencing obesity-induced disease states.

Implementation of this organ dysfunction-based framework will require adjustments in clinical guidelines, insurance policies, and public health initiatives. Diagnosis now prioritizes functional impairments over BMI, necessitating new screening tools like DXA, BIA, and metabolic assessments. Treatment goals shift from simple weight loss to remission of obesity-related dysfunction, requiring healthcare providers to adopt a broader therapeutic approach. This mirrors the recent reclassification of nonalcoholic fatty liver disease to metabolic dysfunction-associated steatotic liver disease (MASLD), which moved from a diagnosis of exclusion to one rooted in metabolic dysfunction [13]. The MASLD transition reshaped clinical guidelines, broadened treatment eligibility, and refined public health messaging, mirroring the changes that obesity classification must now undergo with the shift to the Lancet commission criteria. Just as MASLD adoption required new policy frameworks, obesity management must adjust insurance coverage and public health campaigns to reflect functional health rather than weight-centric models.

Further research is needed to validate the clinical utility of function-based obesity classification and assess its long-term impact on health outcomes. Studies should evaluate the reliability and feasibility of new diagnostic tools in diverse populations and determine how early intervention strategies in preclinical obesity affect disease progression. Additionally, the economic and policy implications of shifting from BMI-centric criteria to functional impairment-based assessments must be explored, ensuring accessibility, insurance coverage, and cost-effectiveness.

The Lancet commission’s redefinition of obesity represents a critical shift from weight-based definitions to functional health assessments, which is especially applicable to Asian populations, where BMI-based classification often underestimates cardiometabolic risk. Collaborative efforts among researchers, clinicians, and policymakers will be essential in shaping a more effective and inclusive obesity management framework. By embracing a more precise and clinically meaningful approach to obesity classification, healthcare systems across Asia can improve patient outcomes, optimize treatment accessibility, and advance metabolic health equity. Future research should focus on long-term validation of function-based obesity classification and its impact on healthcare accessibility, patient adherence, and disease prevention strategies.

NOTES

CONFLICTS OF INTEREST

No potential conflict of interest relevant to this article was reported.

Fig. 1.

Comparison of the current obesity criteria and new Lancet commission obesity criteria. BMI, body mass index; WC, waist circumference; WHR, waist-to-hip ratio; WHtR, waist-to-height ratio; DXA, dual-energy X-ray absorptiometry; BIA, bioimpedance; CNS, central nervous system; DVT, deep vein thrombosis; HDL, high-density lipoprotein; NAFLD, nonalcoholic fatty liver disease; eGFR, estimated glomerular filtration rate; PCOS, polycystic ovary syndrome.

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Sung K, Lee SH, Lim S. Beyond Body Mass Index: New Criteria for a Holistic Approach to Clinical Obesity. Diabetes Metab J. 2025;49(2):165-168.

DOI: https://doi.org/10.4093/dmj.2025.0097.

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