ArticleScientific reports2026
Comparing multiple definitions of obesity in a large nationwide health program.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Obesity represents a major global health challenge, yet its prevalence and clinical significance may vary substantially depending on the definition applied. While the Body Mass Index (BMI) remains the most widely used indicator, alternative frameworks such as the European Association for the Study of Obesity (EASO) classification, the metabolically healthy obesity (MHO) concept, and the recent Lancet functional framework offer broader, clinically oriented perspectives. We conducted a cross-sectional, population-based analysis of 108,350 adults. BMI was calculated from measured height and weight, with overweight defined as 25.0-29.9 kg/m² and obesity as ≥ 30.0 kg/m². EASO obesity classification incorporated both BMI and the presence of obesity-related complications. Metabolic health was assessed using established cut-offs for hypertension, diabetes, and dyslipidaemia, and MHO was defined as BMI ≥ 30.0 kg/m² without metabolic abnormalities. By conventional BMI cut-offs 77,393 participants were overweight or obese, within which 47.2% were obese and 52.8% were overweight, respectively. In contrast, the EASO classification identified 29,044 respondents of the total population as obese in the overweight strata, leaving only 11,815 as non-obese. Therefore, among individuals with BMI 25.0-29.9, 71.1% were classified as obese by EASO criteria. Among participants with BMI-defined obesity, 12,151 (35.0%) were metabolically healthy obese and 22,585 (65.0%) were considered metabolically unhealthy. In terms of comorbidities, 82.8% (n = 24,521) of obese were clinically obese and 17.2% (n = 5,094) were pre-clinical obese. Obesity prevalence and the identification of metabolically healthy subgroups depend on the definition applied. While BMI alone provides a conservative estimate, other frameworks capture a much broader population. These findings highlight the need for unified definitions that may better reflect clinical risk and guide both public health surveillance and individual patient management.
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