The Cardiometabolic Burden of Self-Perceived Obesity: A Multilevel Analysis of a Nationally Representative Sample of Korean Adults.
Yongjoo KimS Bryn AustinS V SubramanianIchiro KawachiPublished in: Scientific reports (2018)
Emerging evidence has shown that self-perception of overweight/obese status is associated with unfavorable cardiometabolic outcomes, above and beyond actual body weight. Given the lack of research among Asian populations, we examined the association between weight perception and metabolic syndrome (MetS) and cardiometabolic risks among Koreans. Data from the 2010-2015 Korea National Health and Nutrition Examination Survey, including women (N = 12,181) and men (N = 9,448) aged 19-65 years, were analyzed. Weight status perception was measured by participants' self-evaluation of their body size ("very/slightly obese," "normal," and "very/slightly thin"). Overall, 23.2% of women and 28.7% of men had MetS. Our cross-sectional multilevel logistic analyses showed a significant positive association between self-perceived obesity (vs. perceived normal weight) and MetS, independent of BMI and sociodemographic/behavioral/medical conditions, with a stronger association detected among men (OR = 1.38, p < 0.05) than women (OR = 1.22, p < 0.05), confirmed by a statistically significant interaction. Additionally, perceived obesity was associated with high blood pressure (OR = 1.27, p < 0.05) and high triglycerides (OR = 1.38, p < 0.05) among men and low high-density lipoprotein cholesterol (OR = 1.15, p < 0.05) among women. While further prospective research is needed, our findings suggest that perception of being obese may be an unfavorable indicator of cardiometabolic health among Koreans regardless of actual body weight.
Keyphrases
- weight loss
- body weight
- metabolic syndrome
- weight gain
- polycystic ovary syndrome
- physical activity
- insulin resistance
- bariatric surgery
- social support
- mental health
- body mass index
- depressive symptoms
- type diabetes
- blood pressure
- adipose tissue
- pregnancy outcomes
- cross sectional
- middle aged
- uric acid
- glycemic control
- breast cancer risk
- obese patients
- cardiovascular disease
- pregnant women
- social media
- electronic health record
- heart rate
- health information
- deep learning
- machine learning