Probabilistic Statistics to Aid Novel Heavy Metal Analysis in ADHD – A Case-Control Study with Bayesian Statistics

Background and objectives: Probabilistic statistical methods with the help of Bayesian Statistics help to analyze biomedical data with a newer and wider perspective. Combined with the knowledge provided by previous data, the novel approach was undertaken to study the association of Attention Deficit Hyperactivity Disorder (ADHD) with heavy metals in children. ADHD is characterized by inattention, impulsivity, and hyperactivity. Being a widely prevalent neurodevelopmental disorder, it is attributed to both genetic and environmental etiology. Hair and urine samples were used instead of blood as two non-invasive sources for assessing heavy metals. Materials and Methods: ADHD children diagnosed as per DSM-V criteria and age-matched healthy controls were recruited in this study. Hair and urine samples were analyzed for arsenic, cadmium, copper, lead, nickel, and zinc. The levels of heavy metals were measured using ICP-OES after acid digestion and extraction. Bayesian Statistics in JASP v0.15 was used for statistical analysis. Results: The Bayes Factor, BF10 for the group with ADHD vs. healthy children, were significantly different for all heavy metals. In both hair and urine, lead, cadmium, nickel, and copper concentrations were higher in ADHD. However, zinc levels were lower in both samples for ADHD. Conclusion: By the probabilistic method, heavy metals are higher in the urine and hair of children with ADHD. Key Words: Attention Deficit Hyperactivity Disorder, Bayesian Statistics, ICP-OES, heavy metals