INTRODUCTION

Obesity is a chronic, progressive, and often relapsing condition that has reached epidemic levels globally [1]. It contributes to a wide spectrum of complications, chronic diseases, and imposes a substantial burden on health systems worldwide. Recent data indicate that overweight and obesity were responsible for an estimated 3.71 million deaths and 129 million disability-adjusted life-years in 2021 [2]. Furthermore, these conditions are among the leading global risk factors, with the most rapidly increasing impact in terms of attributable disease burden [2].

Although obesity is recognized as a disease, its diagnosis based solely on body mass index (BMI) departs from the manner in which most disease states are defined, typically by their capacity to cause illness through ongoing alterations in organ and tissue function, as experienced both objectively and subjectively. This disconnect has led to negative consequences at clinical, economic, and political levels, including obesity-related stigma and the potential for overdiagnosis.

To address these issues, the recently established Commission on Clinical Obesity has proposed a new framework based on clinical and biological criteria for defining clinical obesity [3]. This review aims to examine and critically evaluate these recommendations.

LIMITATIONS OF EXISTING DIAGNOSTIC CRITERIA

The World Health Organization defines obesity as an abnormal or excessive accumulation of body fat that poses a health risk, and has adopted BMI—calculated as weight in kilograms divided by height in meters squared—as the standard tool for its diagnosis and classification [4]. This approach is grounded in several key considerations: (1) facilitating meaningful comparisons of weight status across and within populations; (2) identifying individuals and groups at increased risk for morbidity and mortality; (3) supporting the prioritization of interventions at both individual and public health levels; and (4) providing a consistent framework for evaluating intervention effectiveness [4]. While BMI is a convenient and widely used screening tool for obesity in population-based settings, it has several limitations in individual clinical care. First, although BMI generally correlates with total body fat (with correlation coefficients typically around 0.80) [5,6] and with obesity-related outcomes such as cardiometabolic diseases, it does not distinguish between fat mass and lean mass, leading to potential misclassification [6]. For example, athletes or individuals with high muscle mass may have elevated BMIs that suggest overweight or obesity, despite low levels of body fat. Additionally, studies in Korean populations have shown that the relationship between BMI and body fat percentage varies according to age, sex, and ethnicity [6-9]. Second, BMI does not account for fat distribution—particularly visceral adiposity, which is more strongly linked to metabolic risk than subcutaneous fat [10-14]. Third, BMI cannot capture metabolic health status. Some individuals with high BMI may be metabolically healthy (with normal blood pressure, glucose, and lipid levels), while others with normal BMI may present with metabolic syndrome—a phenotype known as ‘metabolically obese normal weight’ (MONW). In a study of older Korean adults, those with the MONW phenotype exhibited higher all-cause mortality over a 10-year follow-up compared to metabolically healthy obese individuals [15]. Another analysis, using the 2009–2010 Korea National Health and Nutrition Examination Survey, reported a high prevalence of clustered cardiometabolic abnormalities in MONW individuals [16]. Finally, BMI-based anthropometric classifications do not account for comorbidities or disease risk. Moreover, changes in BMI or waist circumference do not necessarily reflect improvements in overall health or functional status. Notably, even modest weight loss of 5% to 10% has been shown to yield meaningful health benefits, despite limited changes in BMI classification [17-19].

INTRODUCTION OF THE NEW DIAGNOSTIC CRITERIA AND DIFFERENCES FROM PREVIOUS CRITERIA

Given the limitations of current diagnostic criteria for obesity, which rely predominantly on anthropometric measurements, the Commission on Clinical Obesity was established to redefine obesity in a manner that supports clinical decision-making and informs the prioritization of therapeutic interventions and public health strategies [3]. It proposed a staging system based on straightforward clinical assessments, including medical history, clinical and functional evaluation, as well as simple routine diagnostic investigations that are readily and widely available (Fig. 1) [3]. Obesity is categorized into clinical and preclinical stages based on the presence or absence of objective clinical manifestations, such as signs and symptoms of organ dysfunction or limitations in daily activities. Clinical obesity is defined as a chronic and systemic disease (Table 1), characterized by functional alterations in tissues, organs, the whole individual, or any combination thereof, resulting from excess adiposity [3]. Preclinical obesity refers to excess adiposity without dysfunction in other tissues or organs, although it is typically associated with an increased risk of progression to clinical obesity and the development of non-communicable diseases such as type 2 diabetes, cardiovascular disease, certain cancers, and mental health disorders [3]. Accordingly, although BMI has traditionally been the primary indicator for diagnosing obesity, it is now recommended to serve only as a screening tool. Diagnostic confirmation should involve more precise assessments of excess adiposity, either through direct measurements such as dual-energy Xray absorptiometry or bioelectrical impedance analysis, or, at minimum, by adding another anthropometric criterion (e.g., waist circumference, waist-to-hip ratio, or waist-to-height ratio) (Table 2) [3].

POTENTIAL IMPROVEMENTS

People with obesity often experience negative perceptions, stereotypes, and blame from others due to their body weight—phenomena commonly referred to as weight bias [20]. Weight bias is partly rooted in the misconception that individuals have complete control over their body weight, despite the fact that obesity results from a complex interplay of genetic, biological, social, economic, environmental, and behavioral factors. Weight stigma has been consistently and strongly linked to negative physical and mental health outcomes [21-24]. Moreover, the traditional definition of obesity, which focuses on body weight and relies on BMI derived from height and weight, has also been associated with weight stigma [25]. Under the BMI-based definition of obesity, which emphasizes body weight, treatment has traditionally focused on weight reduction. However, in cases of clinical obesity—defined by functional impairments of tissues or organs or significant limitations in daily living activities—the therapeutic approach shifts toward addressing the health problems caused by obesity. This shift may help to reduce weight stigma and enhance patient engagement and adherence to care.

The Commission on Clinical Obesity was established to redefine obesity in a way that informs clinical decision-making and guides the prioritization of therapeutic interventions and public health strategies [3]. The Commission distinguishes individuals with clinical obesity as appropriate candidates for active treatment, while those with preclinical obesity should undergo risk-based monitoring and receive preventive interventions [3]. This approach enables more accurate risk stratification, helping to identify patients most likely to benefit from medical or surgical treatment.

Importantly, obesity treatment should not focus solely on weight loss, but rather aim to improve functional impairments of organs or tissues, or significant limitations in daily living activities. This paradigm shift may help to avoid overdiagnosis and unnecessary treatment in individuals who do not require medical intervention (Table 2). Moreover, it has the potential to influence policy by shifting the focus away from weight-centric blame narratives and toward the management of obesity-related health complications. It may also serve as a guide for policies that encourage evidence-based coverage decisions, support more equitable access to care by focusing on clinical need rather than BMI thresholds, and enhance cost-effectiveness by prioritizing high-risk patients for intervention. Finally, it can provide a framework for policies that promote evidence-based coverage decisions, support more equitable access to care based on clinical need instead of BMI alone, and improve cost-effectiveness by targeting high-risk individuals for intervention.

POTENTIAL ISSUES WITH THE NEW DEFINITION

Although the proposed system offers potential improvements, it also presents several challenges. First, its reliance on clinical judgment to assess functional impairments and limitations in daily activities introduces diagnostic subjectivity. Without standardized and validated tools to objectively measure organ or tissue dysfunction, clinicians may differ in their interpretations of the severity or clinical relevance of these impairments, leading to inconsistencies in diagnosis and staging. Additionally, the broad range of obesity-related comorbidities, each with varying degrees of association and diagnostic ambiguity, adds further complexity. These factors make it difficult to consistently estimate obesity prevalence and hinder meaningful cross-country comparisons. Second, implementation in primary care may be challenging due to time constraints, limited training, and inadequate resources in routine clinical practice. The inherently broad and heterogeneous nature of obesity-related conditions may further complicate assessment, especially in the absence of a structured classification system that distinguishes, for example, between major and minor comorbidities. Incorporating a framework that assigns relative importance to obesity-related diseases, based on the strength of association and clinical relevance, could help improve usability. Furthermore, clearer definitions of each comorbid condition are needed to enhance diagnostic consistency. Third, the proposed staging system may fail to identify obesity-related complications that occur in individuals with lower body weights, particularly those who do not meet traditional BMI thresholds. For example, individuals with a MONW phenotype may fall within the non-obese BMI range but still possess relatively high body fat and frequently present with obesity-associated comorbidities. Nevertheless, they remain unrecognized under the current definition. Fourth, although the system is based on sound clinical rationale, its sensitivity, specificity, reliability, and practical utility remain unproven. In addition, most existing clinical trials and treatment guidelines are grounded in BMI-defined obesity rather than functional or biological criteria, which limits their applicability to this new model. To support evidence-based decision-making, further research is needed to validate the proposed framework, refine diagnostic definitions of obesity-related conditions, and develop tools that allow for consistent and scalable implementation in both clinical and public health settings.

FUTURE PERSPECTIVES

To facilitate the application and global adoption of the proposed diagnostic criteria for clinical obesity, several future directions may be considered. First, developing standardized tools and structured clinical frameworks for evaluating obesity-related functional impairments and comorbidities could be prioritized. This may include validated diagnostic instruments and clearly defined, evidence-based criteria for identifying obesity-associated conditions, along with their degree of association with excess adiposity. Implementing these measures may help reduce diagnostic subjectivity, promote consistency among clinicians, and support risk stratification, which can inform treatment priorities and enable international comparisons. Second, practical methods to implement the proposed definition in routine clinical settings, especially in resource-limited primary care, are needed. Simplified screening protocols, clinician training, and integration with electronic health record systems may facilitate this process without increasing provider workload. Third, aligning future clinical research and therapeutic guidelines with the proposed criteria is necessary. As most existing research is based on BMI-defined obesity, the clinical performance of new criteria requires further validation. Future studies should investigate health outcomes, treatment responses, and prognostic accuracy using the clinical definition to provide an evidence base for practice and policy. Finally, widespread engagement and international collaboration may be required for effective implementation. Educational efforts targeting healthcare professionals and the public can clarify the changes, enhance communication, and encourage consensus. Collaboration among clinicians, policymakers, and public health organizations will also help support the transition toward a function-based, patient-centered approach to obesity care.

CONCLUSIONS

The current BMI-based definition of obesity, while useful for population-level screening, has significant limitations in clinical practice, including its inability to capture body composition, fat distribution, and functional health status. In response, the Commission on Clinical Obesity has proposed a new definition based on clinical and biological criteria, distinguishing between clinical and preclinical obesity. This framework enables better risk stratification, reduces weight stigma, and supports more targeted interventions. However, its implementation faces challenges such as diagnostic subjectivity, limited validation, and feasibility concerns in routine care. Further research is required to standardize diagnostic tools, assess clinical utility, and facilitate the integration of this model into evidence-based practice.

Article information

CONFLICTS OF INTEREST

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

Fig. 1.

Diagnostic model of clinical obesity. BMI, body mass index.

References

• 1. Bray GA, Kim KK, Wilding JPH. Obesity: a chronic relapsing progressive disease process. A position statement of the World Obesity Federation. Obes Rev 2017;18:715–23.ArticlePubMedPDF
• 2. GBD 2021 Risk Factors Collaborators. Global burden and strength of evidence for 88 risk factors in 204 countries and 811 subnational locations, 1990-2021: a systematic analysis for the Global Burden of Disease Study 2021. Lancet 2024;403:2162–203.PubMedPMC
• 3. Rubino F, Cummings DE, Eckel RH, Cohen RV, Wilding JPH, Brown WA, et al. Definition and diagnostic criteria of clinical obesity. Lancet Diabetes Endocrinol 2025;13:221–62.PubMedPMC
• 4. Obesity: preventing and managing the global epidemic. Report of a WHO consultation. World Health Organ Tech Rep Ser 2000;894:1–253.
• 5. Jeong SM, Lee DH, Rezende LFM, Giovannucci EL. Different correlation of body mass index with body fatness and obesity-related biomarker according to age, sex and race-ethnicity. Sci Rep 2023;13:3472.ArticlePubMedPMCPDF
• 6. Paek JK, Kim J, Kim K, Lee SY. Usefulness of relative fat mass in estimating body adiposity in Korean adult population. Endocr J 2019;66:723–9.ArticlePubMed
• 7. Hood K, Ashcraft J, Watts K, Hong S, Choi W, Heymsfield SB, et al. Allometric scaling of weight to height and resulting body mass index thresholds in two Asian populations. Nutr Diabetes 2019;9:2.ArticlePubMedPMCPDF
• 8. Cho YG, Song HJ, Kim JM, Park KH, Paek YJ, Cho JJ, et al. The estimation of cardiovascular risk factors by body mass index and body fat percentage in Korean male adults. Metabolism 2009;58:765–71.ArticlePubMed
• 9. Hong S, Oh HJ, Choi H, Kim JG, Lim SK, Kim EK, et al. Characteristics of body fat, body fat percentage and other body composition for Koreans from KNHANES IV. J Korean Med Sci 2011;26:1599–605.ArticlePubMedPMCPDF
• 10. Stefan N. Metabolically healthy and unhealthy normal weight and obesity. Endocrinol Metab (Seoul) 2020;35:487–93.ArticlePubMedPMCPDF
• 11. Park SH, Choi SJ, Lee KS, Park HY. Waist circumference and waist-to-height ratio as predictors of cardiovascular disease risk in Korean adults. Circ J 2009;73:1643–50.ArticlePubMed
• 12. Yoo JE, Han K, Jung JH, Hur YI, Kim YH, Kim ES, et al. Body mass index, waist circumference and cardiovascular diseases in transitional ages (40 and 66 years). J Cachexia Sarcopenia Muscle 2023;14:369–81.ArticlePubMedPMCPDF
• 13. Rhee TM, Han KD, Choi EK, Kim BS, Lee KY, Choi J, et al. Combination of body mass index and waist circumference burden determines the risk of atrial fibrillation. JACC Asia 2025;5:129–39.ArticlePubMedPMC
• 14. Camhi SM, Bray GA, Bouchard C, Greenway FL, Johnson WD, Newton RL, et al. The relationship of waist circumference and BMI to visceral, subcutaneous, and total body fat: sex and race differences. Obesity (Silver Spring) 2011;19:402–8.ArticlePubMedPMCPDF
• 15. Choi KM, Cho HJ, Choi HY, Yang SJ, Yoo HJ, Seo JA, et al. Higher mortality in metabolically obese normal-weight people than in metabolically healthy obese subjects in elderly Koreans. Clin Endocrinol (Oxf) 2013;79:364–70.ArticlePubMed
• 16. Kim MK, Han K, Kwon HS, Song KH, Yim HW, Lee WC, et al. Normal weight obesity in Korean adults. Clin Endocrinol (Oxf) 2014;80:214–20.PubMed
• 17. Cardoso CRL, Leite NC, Salles GF. Effects of weight loss and interaction with physical activity on risks of cardiovascular outcomes in individuals with type 2 diabetes. Endocrinol Metab (Seoul) 2023;38:305–14.ArticlePubMedPMCPDF
• 18. Kang E, Hong YH, Kim J, Chung S, Kim KK, Haam JH, et al. Obesity in children and adolescents: 2022 update of clinical practice guidelines for obesity by the Korean Society for the Study of Obesity. J Obes Metab Syndr 2024;33:11–9.ArticlePubMedPMC
• 19. Kang S, Kang SM, Choi JH, Ko SH, Koo BK, Kwon HS, et al. 2025 Clinical practice guidelines for diabetes management in Korea: recommendation of the Korean Diabetes Association. Diabetes Metab J 2025;49:582–783.PubMedPMC
• 20. Puhl RM, Brownell KD. Psychosocial origins of obesity stigma: toward changing a powerful and pervasive bias. Obes Rev 2003;4:213–27.ArticlePubMed
• 21. Puhl RM, Himmelstein MS, Pearl RL. Weight stigma as a psychosocial contributor to obesity. Am Psychol 2020;75:274–89.ArticlePubMed
• 22. Emmer C, Bosnjak M, Mata J. The association between weight stigma and mental health: a meta-analysis. Obes Rev 2020;21:e12935.ArticlePubMedPDF
• 23. Romano KA, Heron KE, Sandoval CM, MacIntyre RI, Howard LM, Scott M, et al. Weight bias internalization and psychosocial, physical, and behavioral health: a meta-analysis of cross-sectional and prospective associations. Behav Ther 2023;54:539–56.ArticlePubMedPMC
• 24. Puhl RM, Phelan SM, Nadglowski J, Kyle TK. Overcoming weight bias in the management of patients with diabetes and obesity. Clin Diabetes 2016;34:44–50.ArticlePubMedPMCPDF
• 25. Mahase E. Stop using body mass index as measure of health, say MPs. BMJ 2021;373:n941.ArticlePubMed

Figure & Data

References

Citations

Citations to this article as recorded by  

• BMI-stratified phenotypes of polycystic ovary syndrome: advances in gut microbiota research and personalized management strategies
  Bisha Su, Yining Cao, Lin Ma, Jian Huang
  Frontiers in Endocrinology.2026;[Epub]     CrossRef
• Association of regional fat distribution indicators with infertility in women: insights from the 2013–2018 NHANES
  Luming Chen, Fangxiang Mu, Tianyu He, Fang Wang
  Gynecological Endocrinology.2026;[Epub]     CrossRef
• Beyond BMI: Reassessing the Prevalence of Obesity in Patients With MASLD Under the Lancet Commission Diagnostic Criteria
  Ru‐Tao Lin, Ren‐Qiang Zeng, Xu‐Ting Shen, Qin‐Mei Sun, Xin Xin, Jia‐Mei Chen, Yi‐Yang Hu, Qin Feng
  Diabetes, Obesity and Metabolism.2026; 28(8): 6699.     CrossRef
• The Complex Relationship Between Clinical Obesity and Heart Failure
  Jinxia Zhang, Cheng Liu
  JACC: Asia.2026; 6(7): 1261.     CrossRef
• Cuerpo vivido y ética del tratamiento en la obesidad
  Raul Sampieri Cabrera, Gustavo López-Toledo, Mireya Velázquez Paniagua
  Bioética UNAM.2026;[Epub]     CrossRef
• Does metabolically healthy obesity really exist: going toward new definitions
  Christian Rodriguez, Eric Ravussin
  The Journal of Clinical Endocrinology & Metabolism.2026;[Epub]     CrossRef
• European Association for the Study of Obesity–Defined Obesity Trajectories and Risk of Incident Cancer Among Middle-Aged and Older Chinese Adults
  Li He, Yan Song, Yalan Liu
  Nutrition and Cancer.2026; : 1.     CrossRef
• Are we measuring the same disease? Clinical obesity as a definition-dependent construct
  Jin Eui Kim, Hannah Oh
  International Journal of Obesity.2026;[Epub]     CrossRef
• Muscle Insulin Resistance Elicits Muscle Atrophy in Obesity
  Omid Razi, Nastaran Zamani, Ayoub Saeidi, Marios Hadjicharalambous, Khadija Ayed, Anthony C. Hackney, Juan Del Coso, Ismail Laher, Giovanna Muscogiuri, Hassane Zouhal
  Current Obesity Reports.2025;[Epub]     CrossRef
• Beyond BMI: Rethinking Obesity Metrics and Cardiovascular Risk in the Era of Precision Medicine
  Maria-Daniela Tanasescu, Andrei-Mihnea Rosu, Alexandru Minca, Andreea-Liana Rosu, Maria-Mihaela Grigorie, Delia Timofte, Dorin Ionescu
  Diagnostics.2025; 15(23): 3025.     CrossRef