Han Na Jung; Chang Hee Jung

doi:10.4093/dmj.2026.0062

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Letter Clinical and Preclinical Obesity in Korean Adults from 2014 to 2023 (Diabetes Metab J 2026;50:127-38): Han Na Jung¹, Chang Hee Jung²; Diabetes & Metabolism Journal 2026;50(4):816-817.
DOI: https://doi.org/10.4093/dmj.2026.0062
Published online: July 1, 2026

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¹Department of Internal Medicine, Hallym University College of Medicine, Chuncheon, Korea

²Department of Internal Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea

Corresponding author: Chang Hee Jung

Department of Endocrinology and Metabolism, Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505, Korea, E-mail: chjung0204@gmail.com

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (https://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

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See the reply "Clinical and Preclinical Obesity in Korean Adults from 2014 to 2023 (Diabetes Metab J 2026;50:127-38)" in Volume 50 on page 821.

See the article "Clinical and Preclinical Obesity in Korean Adults from 2014 to 2023" on page 127.

We read with great interest the article by Choe et al. [1], ‘Clinical and preclinical obesity in Korean adults from 2014 to 2023,’ which applied the Lancet Commission framework to 57,863 Korean adults using nationally representative data from the Korea National Health and Nutrition Examination Survey (KNHANES) [1,2]. The authors reported a prevalence of clinical obesity (31.2%) and preclinical obesity (8.1%) and importantly demonstrated substantial discordance between conventional body mass index (BMI)-based categorization and a functional, complication-based definition of obesity [1]. Particularly notable was the finding that 19.4% of overweight individuals with BMI (23.0 to 24.9 kg/m²) met clinical obesity criteria, whereas a considerable proportion of individuals with BMI-defined obesity did not [1]. These observations provide compelling evidence that BMI alone is insufficient for risk stratification in Asian populations. We appreciate the authors for advancing the current discussion beyond anthropometric thresholds toward clinically meaningful disease states, highlighting that metabolic impairment accounted for 82.6% of clinical obesity cases [1]. This work is timely and has significant implications for public health surveillance and individualized treatment decision-making. However, we would like to raise several considerations that may further strengthen the interpretation and facilitate implementation of the clinical obesity framework in routine practice.

First, the operational definition of ‘excess adiposity’ remains a key determinant of classification performance. In the KNHANES- based analysis, excess adiposity was identified through surrogate anthropometric indices (i.e., waist circumference and waist-to-height ratio), and the authors showed that applying Western waist circumference cutoffs markedly reduced the estimated prevalence of clinical obesity (13.1%) [1]. This finding strongly suggests that the clinical obesity framework is highly sensitive to the population-specific thresholds embedded within it, and that ethnically tailored criteria are essential for meaningful cross-population comparisons.

Second, age-related increases in clinical obesity despite stable BMI highlight the need for careful interpretation of clinical obesity classification in aging populations. Choe et al. [1] demonstrated that clinical obesity prevalence rose sharply with age (from 10.3% in the 20s to 46.9% in the 70s), while mean BMI values remained similar across decades. Although this pattern likely reflects the increasing burden of metabolic dysfunction, organ impairment, and functional limitation with aging, longitudinal validation will be important to clarify the prognostic performance of the framework across age strata and to refine clinically actionable thresholds for intervention.

Third, while confirmatory assessment of adiposity is conceptually appealing, body fat percentage (BFP) thresholds remain insufficiently standardized across age, sex, and ethnicity [3]. Even though this challenge is often mentioned as a limitation in implementation studies, it deserves further emphasis because it directly affects diagnostic consistency and real-world applicability. In practice, BFP is increasingly measured using bioimpedance or imaging-based modalities; however, without harmonized reference standards that account for demographic and ethnic heterogeneity, applying fixed cutoffs may lead to systematic misclassification—particularly among older adults and women, where body composition changes are most pronounced [4–6]. Future work should therefore prioritize establishing validated, population-specific BFP thresholds and testing whether such thresholds improve prediction of hard outcomes beyond BMI-based approaches.

In conclusion, Choe et al. [1] provide important population-level evidence supporting a shift from BMI-based labeling to a function- and complication-based diagnosis of obesity in Koreans. We believe that developing standardized, demographic- and ethnicity-appropriate adiposity thresholds will be critical to maximize the clinical utility and reproducibility of this framework.

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CONFLICTS OF INTEREST

Chang Hee Jung has served as an associate editor of Diabetes & Metabolism Journal since 2022 but was not involved in the review process for this manuscript. The authors declare no competing interests related to this work.

REFERENCES

1. Choe HJ, Despres JP, Wilding JP, Ryan DH, Lim S. Clinical and preclinical obesity in Korean adults from 2014 to 2023. Diabetes Metab J 2026;50:127-38.Article PubMed PMC
2. Rubino F, Cummings DE, Eckel RH, Cohen RV, Wilding JP, Brown WA, et al. Definition and diagnostic criteria of clinical obesity. Lancet Diabetes Endocrinol 2025;13:221-62.PubMed PMC
3. Ho-Pham LT, Campbell LV, Nguyen TV. More on body fat cutoff points. Mayo Clin Proc 2011;86:584.Article PubMed PMC
4. Gill LE, Bartels SJ, Batsis JA. Weight management in older adults. Curr Obes Rep 2015;4:379-88.Article PubMed PMC PDF
5. Meeuwsen S, Horgan GW, Elia M. The relationship between BMI and percent body fat, measured by bioelectrical impedance, in a large adult sample is curvilinear and influenced by age and sex. Clin Nutr 2010;29:560-6.Article PubMed
6. Guo SS, Zeller C, Chumlea WC, Siervogel RM. Aging, body composition, and lifestyle: the Fels Longitudinal Study. Am J Clin Nutr 1999;70:405-11.Article PubMed

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Clinical and Preclinical Obesity in Korean Adults from 2014 to 2023 (Diabetes Metab J 2026;50:127-38)

Diabetes Metab J. 2026;50(4):816-817. Published online July 1, 2026

DOI: https://doi.org/10.4093/dmj.2026.0062

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Clinical and Preclinical Obesity in Korean Adults from 2014 to 2023 (Diabetes Metab J 2026;50:127-38)

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Jung HN, Jung CH. Clinical and Preclinical Obesity in Korean Adults from 2014 to 2023 (Diabetes Metab J 2026;50:127-38). Diabetes Metab J. 2026;50(4):816-817.

DOI: https://doi.org/10.4093/dmj.2026.0062.

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