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Identifying metabolically obese but normal-weight (MONW) individuals in a nondiabetic Korean population: the Chungju Metabolic disease Cohort (CMC) study


Yong-Moon Park, Department of Preventive Medicine, College of Medicine, The Catholic University of Korea, 505 Banpo-dong, Seocho-gu, Seoul 137-701, Korea. Tel.: +82 2 2258 7369; Fax: +82 2 532 3820; E-mail:
Hyuk-Sang Kwon, Department of Internal Medicine, Yeouido St. Mary’s Hospital, 62 Yeouido-dong, Youngdeungpo-gu, Seoul 150-713, Korea. Tel.: +82 2 3779 1039; Fax: +82 2 785 5655; E-mail:


Objective  To investigate the prevalence and identify the phenotype of individuals suspected to be metabolically obese but normal weight (MONW).

Design and subjects  Eight thousand nine hundred and eighty-seven nondiabetic subjects aged over 40 years were selected from the Chungju Metabolic disease Cohort study performed in 2003–2006 in Korea. Those within the highest quartile in the homeostasis model assessment of insulin resistance (HOMA-IR) with a normal body mass index (BMI) between 18·5 and 23 kg/m2 were classified as MONW.

Measurements  Data on anthropometry, lipid profiles and HOMA-IR values were analysed.

Results  The prevalence of MONW was 14·2% for men and 12·9% for women amongst normal-weight subjects. Multiple logistic regression analysis showed that total cholesterol (TC) levels over 5·17 mm (odds ratio, OR = 1·481; 95% confidence intervals, CI 1·086–2·021), triglyceride (TG) levels over 1·69 mm (OR = 1·507; 95% CI 1·093–2·077) and high-density lipoprotein-cholesterol levels lower than 1·03 mm (OR = 1·580; 95% CI 1·053–2·371) independently had higher odds of diagnosing MONW amongst men. For women, a BMI over 21·5 kg/m2 (OR = 1·405; 95% CI 1·034–1·909), TC levels over 5·17 mm (OR = 1·524; 95% CI 1·112–2·090) and TG levels over 1·69 mm (OR = 1·799; 95% CI 1·302–2·487) were independently associated with a diagnosis of MONW.

Conclusions  More than 10% of normal-weight subjects were classed as MONW in this cohort. Identification of these subjects based on lipid profiles could aid in the early detection of a high risk group of developing cardiometabolic diseases.