Clustering of Metabolic Risk Components and Associated Lifestyle Factors: A Nationwide Adolescent Study in Taiwan.
Wei-Ting LinChun-Ying LeeSharon TsaiHsiao-Ling HuangPei-Wen WuYu-Ting ChinDavid W SealTed ChenYu-Ying ChaoChien-Hung LeePublished in: Nutrients (2019)
Clustering of metabolic syndrome (MetS) risk components in childhood has been linked to a higher risk of diabetes and cardiovascular diseases in adulthood. By using data from the 2010⁻2011 Nutrition and Health Survey in Taiwan, this study investigated epidemic patterns and correlates for the clustering of MetS risk components. A total of 1920 adolescents aged 12⁻18 years were included in this study. The MetS diagnostic criteria defined by the Taiwan Pediatric Association (TPA) and International Diabetes Federation (IDF) for adolescents and the criteria defined by the Joint Interim Statement for adults (JIS-Adult) were used to evaluate MetS and its abnormal components. The prevalence of TPA-, IDF-, and JIS-Adult-defined MetS was 4.1%, 3.0%, and 4.0%, with 22.1%, 19.3%, and 17.7%⁻18.1% of adolescents having high fasting glucose, low high-density lipoprotein cholesterol, and central obesity, respectively. A 0.4-to-0.5-fold decreased risk of having ≥2 MetS abnormal components was detected among adolescents who consumed ≥1 serving/week of dairy products and fresh fruits. Boys who consumed ≥7 drinks/week of soda and girls who consumed ≥7 drinks/week of tea had a 4.6- and 5.2-fold risk of MetS, respectively. In conclusion, our findings revealed significant dimensions of adolescent MetS, including detecting population-specific prevalent patterns for MetS risk components and their clustering, and emphasized on health promotion activities that reduce sugar-sweetened beverage intake.
Keyphrases
- young adults
- metabolic syndrome
- cardiovascular disease
- physical activity
- type diabetes
- single cell
- insulin resistance
- childhood cancer
- mental health
- rna seq
- blood glucose
- health promotion
- randomized controlled trial
- depressive symptoms
- body mass index
- machine learning
- adipose tissue
- weight gain
- blood pressure
- risk factors
- mass spectrometry
- electronic health record
- high resolution
- cardiovascular risk factors
- early life