Shared Genetic Factors Contributing to the Overlap between Attention-Deficit/Hyperactivity Disorder Symptoms and Overweight/Obesity in Swedish Adolescent Girls and Boys.
Kristin N JavarasMelissa A Munn-ChernoffElizabeth W DiemerLaura M ThorntonCynthia M BulikZeynap YilmazPaul LichtensteinHenrik LarssonJessica H BakerPublished in: Twin research and human genetics : the official journal of the International Society for Twin Studies (2023)
Attention-deficit/hyperactivity disorder (ADHD) and obesity are positively associated, with increasing evidence that they share genetic risk factors. Our aim was to examine whether these findings apply to both types of ADHD symptoms for female and male adolescents. We used data from 791 girl and 735 boy twins ages 16-17 years to examine sex-specific phenotypic correlations between the presence of ADHD symptoms and overweight/obese status. For correlations exceeding .20, we then fit bivariate twin models to estimate the genetic and environmental correlations between the presence of ADHD symptoms and overweight/obese status. ADHD symptoms and height/weight were parent- and self-reported, respectively. Phenotypic correlations were .30 (girls) and .08 (boys) for inattention and overweight/obese status and .23 (girls) and .14 (boys) for hyperactivity/impulsivity and overweight/obese status. In girls, both types of ADHD symptoms and overweight/obese status were highly heritable, with unique environmental effects comprising the remaining variance. Furthermore, shared genetic effects explained most of the phenotypic correlations in girls. Results suggest that the positive association of both types of ADHD symptoms with obesity may be stronger in girls than boys. Further, in girls, these associations may stem primarily from shared genetic factors.
Keyphrases
- attention deficit hyperactivity disorder
- weight loss
- bariatric surgery
- autism spectrum disorder
- weight gain
- metabolic syndrome
- working memory
- physical activity
- type diabetes
- sleep quality
- genome wide
- risk factors
- obese patients
- adipose tissue
- insulin resistance
- young adults
- copy number
- dna methylation
- skeletal muscle
- depressive symptoms
- machine learning
- deep learning
- data analysis
- big data
- electronic health record