Trends in Obesity and Overweight in Oregon Children With Down Syndrome.
Melinda J PierceKatrina RamseyJoseph PinterPublished in: Global pediatric health (2019)
Background. Although obesity is a commonly discussed issue in the medical management of children with Down syndrome, there have been no large studies published on its prevalence in the United States or associations with other common comorbidities in this population. Methods. Using a database of children from a single medical center Down syndrome specialty clinic and the standard Centers for Disease Control and Prevention definitions, we calculated rates of obesity and overweight by age group and examined possible associations with common comorbidities including cardiac disease, thyroid disease, sleep apnea, autism, and visual and hearing impairment. We also examined mean body mass index (BMI) percentile and change in BMI percentile by age. Results. Data were obtained from 823 visits from 412 unique patients ranging in age from 2 years to 23 years of age. A total of 1.2% were underweight, 55.2% were normal weight, 23% were overweight, and 20.6% were obese. BMI percentile increased with female gender, age, and height percentile for age. Sleep apnea was associated with higher BMI percentile, while autism was associated with lower BMI percentile. Conclusions. Children with Down syndrome have higher rates of obesity than the general population, with especially high risk for girls. Much of the increase in obesity occurs between ages 2 and 6 years. Further research needs to target interventions for prevention in this vulnerable population, particularly in young girls.
Keyphrases
- weight gain
- body mass index
- weight loss
- sleep apnea
- metabolic syndrome
- insulin resistance
- bariatric surgery
- physical activity
- young adults
- type diabetes
- high fat diet induced
- healthcare
- autism spectrum disorder
- obstructive sleep apnea
- adipose tissue
- electronic health record
- positive airway pressure
- end stage renal disease
- heart failure
- chronic kidney disease
- deep learning
- big data
- newly diagnosed
- machine learning
- atrial fibrillation
- body weight
- case control