ABSTRACT
Neurometabolic disorders such as tyrosinemia type 1 (TYRSN1) may interfere with brain metabolism and show symptoms of attention-deficit hyperactivity disorder (ADHD) in patients treated with the enzyme inhibitor nitisinone [2-(2-nitro-4-trifluoromethylbenzoyl)-1,3-cyclohexanedione, NTBC]. It has been reported that ADHD treatment improves treatment compliance, which is imperative for the long-term prognosis of patients with TYRSN1. In this study, we report the case of a male patient who was diagnosed with TYRSN1 at 3 months of age and was subsequently treated with NTBC, restricted protein intake, and amino acids supplementation. At 7 years of age, he was referred for neuropsychiatric assessment, diagnosed with ADHD, and treated with methylphenidate. The effects of the treatment were monitored via parental interviews, questionnaires covering ADHD symptoms, and a continuous performance test. A reduction in ADHD symptoms, particularly inattentiveness, was observed across all measures. The early identification of ADHD and the treatment of neurometabolic disorders, such as TYRSN1, may be important from a lifetime perspective as this may improve the prognosis of the medical condition as well.
ABSTRACT
Recombinant human growth hormone (r-hGH) is used as a therapeutic agent for disorders of growth including growth hormone deficiency (GHD) and Turner syndrome (TS). Treatment is costly and current methods to model response are inexact. GHD (n = 71) and TS patients (n = 43) were recruited to study response to r-hGH over 5 years. Analysis was performed using 1219 genetic markers and baseline (pre-treatment) blood transcriptome. Random forest was used to determine predictive value of transcriptomic data associated with growth response. No genetic marker passed the stringency criteria for prediction. However, we identified an identical set of genes in both GHD and TS whose expression could be used to classify therapeutic response to r-hGH with a high accuracy (AUC > 0.9). Combining transcriptomic markers with clinical phenotype was shown to significantly reduce predictive error. This work could be translated into a single genomic test linked to a prediction algorithm to improve clinical management. Trial registration numbers: NCT00256126 and NCT00699855.
