Association Between Sleep Apnea Risk and Obesity Phenotypes in Korean Adults: A Nationwide Population-Based Study

Background/Objectives: This study analyzes the relationship between obesity phenotypes and sleep apnea risk in the Korean population. Methods: This study utilized data from the Korean National Health and Nutrition Examination Survey (KNHANES) collected between 2019 and 2021 (n = 10,970 adults; age ≥ 40 years). Obesity phenotypes were classified into four groups based on body mass index (BMI) and the presence of metabolic syndrome: metabolically healthy normal weight (MHNW), metabolically abnormal normal weight (MANW), metabolically healthy obese (MHO), and metabolically abnormal obese (MAO). Sleep apnea risk was assessed using the STOP-Bang questionnaire, and multivariate logistic regression analyses were performed to evaluate the association between obesity phenotypes and sleep apnea. Results: Among the 10,970 participants, the phenotypes were as follows: MHNW, 51.1%; MANW, 10.3%; MHO, 15.8%; and MAO, 21.8%. Baseline characteristics differed significantly across phenotypes, with the metabolically unhealthy groups (MANW and MAO) being older and exhibiting more cardiometabolic risk factors than the metabolically healthy groups. The prevalence of STOP-Bang questionnaire components differed significantly across phenotypes (all p < 0.001), and the mean STOP-Bang score increased from MHNW to MAO. In multivariate logistic regression analyses, the odds (adjusted odds ratio [95% CI]) of high sleep apnea risk were significantly elevated in all non-MHNW phenotypes: MAO (10.27 [7.71–13.68]), MHO (6.17 [4.35–8.75]), and MANW (1.91 [1.22–2.98]). Notably, MAO conferred a significantly higher risk than MHO (OR 1.69 [1.34–2.13]), highlighting the synergy of obesity and metabolic dysfunction. Obesity phenotypes, defined by BMI and metabolic health status, were differentially associated with sleep apnea risk in Korean adults. The highest risk was observed in individuals with both obesity and metabolic syndrome, while metabolically abnormal normal-weight adults also showed a significantly increased risk. Conclusions: Metabolic dysfunction may contribute to sleep apnea risk beyond body size alone and may be considered in risk stratification strategies.

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Journal
Journal of Clinical Medicine
Published
2026-04-24
DOI
https://doi.org/10.3390/jcm15093240
Primary Topic
Obstructive Sleep Apnea Research
Type
article
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0.00
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article

Association Between Sleep Apnea Risk and Obesity Phenotypes in Korean Adults: A Nationwide Population-Based Study

Jin Hwa Kim, Young Sang Lyu, Jun Lee, Sang Yong Kim et al.
Journal of Clinical Medicine
Obstructive Sleep Apnea Research
article

Association Between Sleep Apnea Risk and Obesity Phenotypes in Korean Adults: A Nationwide Population-Based Study

Jin Hwa Kim, Young Sang Lyu, Jun Lee, Sang Yong Kim, Youngmin Yoon
article en

Abstract

Background/Objectives: This study analyzes the relationship between obesity phenotypes and sleep apnea risk in the Korean population. Methods: This study utilized data from the Korean National Health and Nutrition Examination Survey (KNHANES) collected between 2019 and 2021 (n = 10,970 adults; age ≥ 40 years). Obesity phenotypes were classified into four groups based on body mass index (BMI) and the presence of metabolic syndrome: metabolically healthy normal weight (MHNW), metabolically abnormal normal weight (MANW), metabolically healthy obese (MHO), and metabolically abnormal obese (MAO). Sleep apnea risk was assessed using the STOP-Bang questionnaire, and multivariate logistic regression analyses were performed to evaluate the association between obesity phenotypes and sleep apnea. Results: Among the 10,970 participants, the phenotypes were as follows: MHNW, 51.1%; MANW, 10.3%; MHO, 15.8%; and MAO, 21.8%. Baseline characteristics differed significantly across phenotypes, with the metabolically unhealthy groups (MANW and MAO) being older and exhibiting more cardiometabolic risk factors than the metabolically healthy groups. The prevalence of STOP-Bang questionnaire components differed significantly across phenotypes (all p < 0.001), and the mean STOP-Bang score increased from MHNW to MAO. In multivariate logistic regression analyses, the odds (adjusted odds ratio [95% CI]) of high sleep apnea risk were significantly elevated in all non-MHNW phenotypes: MAO (10.27 [7.71–13.68]), MHO (6.17 [4.35–8.75]), and MANW (1.91 [1.22–2.98]). Notably, MAO conferred a significantly higher risk than MHO (OR 1.69 [1.34–2.13]), highlighting the synergy of obesity and metabolic dysfunction. Obesity phenotypes, defined by BMI and metabolic health status, were differentially associated with sleep apnea risk in Korean adults. The highest risk was observed in individuals with both obesity and metabolic syndrome, while metabolically abnormal normal-weight adults also showed a significantly increased risk. Conclusions: Metabolic dysfunction may contribute to sleep apnea risk beyond body size alone and may be considered in risk stratification strategies.

Journal of Clinical MedicineVol. 15(9)
Chosun University (KR), Chosun University Hospital (KR)
Zero hunger
Openalex Percentile: Top 7%
Obstructive Sleep Apnea Research
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