Omega-3 Fatty Acids are Related to Abnormal Emotion Processing in Adolescent Boys with Attention Deficit Hyperactivity Disorder

Background In addition to the core symptoms, attention deficit hyperactivity disorder (ADHD) is associated with poor emotion regulation. There is some evidence that children and young adults with ADHD have lower omega-3 levels and that supplementation with omega-3 can improve both ADHD and affective symptoms. We therefore investigated differences between ADHD and non-ADHD children in omega-3/6 fatty acid plasma levels and the relationship between those indices and emotion-elicited event-related potentials (ERPs). Methods Children/adolescents with (n=31) and without ADHD (n=32) were compared in their plasma omega-3/6 indices and corresponding ERPs during an emotion processing task. Results Children with ADHD had lower mean omega-3/6 and ERP abnormalities in emotion processing, independent of emotional valence relative to control children. ERP abnormalities were significantly associated with lower omega-3 levels in the ADHD group. Conclusions The findings reveal for the first time that lower omega-3 fatty acids are associated with impaired emotion processing in ADHD children.

Validation of Electronic Health Record Phenotyping of Bipolar Disorder Cases and Controls

Objective: The study was designed to validate use of elec-tronic health records (EHRs) for diagnosing bipolar disorder and classifying control subjects. Method: EHR data were obtained from a health care system of more than 4.6 million patients spanning more than 20 years. Experienced clinicians reviewed charts to identify text features and coded data consistent or inconsistent with a diagnosis of bipolar disorder. Natural language processing was used to train a diagnostic algorithm with 95% specificity for classifying bipolar disorder. Filtered coded data were used to derive three additional classification rules for case subjects and one for control subjects. The positive predictive value (PPV) of EHR-based bipolar disorder and subphenotype di- agnoses was calculated against diagnoses from direct semi- structured interviews of 190 patients by trained clinicians blind to EHR diagnosis. Results: The PPV of bipolar disorder defined by natural language processing was 0.85. Coded classification based on strict filtering achieved a value of 0.79, but classifications based on less stringent criteria performed less well. No EHR- classified control subject received a diagnosis of bipolar dis- order on the basis of direct interview (PPV=1.0). For most subphenotypes, values exceeded 0.80. The EHR-based clas- sifications were used to accrue 4,500 bipolar disorder cases and 5,000 controls for genetic analyses. Conclusions: Semiautomated mining of EHRs can be used to ascertain bipolar disorder patients and control subjects with high specificity and predictive value compared with diagnostic interviews. EHRs provide a powerful resource for high-throughput phenotyping for genetic and clinical research.