Neurometabolic disorders are hereditary conditions mainly affect the function of the brain and the nervous system. The prevalence of these disorders is 1 in 1,000 live births. Such disorders, at different ages, could manifest as sepsis, hypoglycemia, and other neurologic disorders. Having similar manifestations leads to delayed diagnosis of neurometabolic disorders. A number of neurometabolic disorders have known treatments; however, to prevent long-term complications the key factors are early diagnosis and treatment. Although a large number of neurometabolic diseases have no treatment or cure, the correct and on-time diagnosis before death is important for parents to have plans for prenatal diagnosis. Different diagnostic procedures could be offered to parents, enzymatic procedures, and determining metabolites in plasma, urine, and CSF, and molecular genetic diagnosis. Molecular genetic diagnostic procedures are expensive and could not be offered to all parents. Therefore, we aimed to design algorithms to diagnose neurometabolic disorders according to some frequent and characteristic signs and symptoms. By designing these algorithms and using them properly, we could offer diagnostic enzymatic panels. These enzymatic panels are inexpensive; thereby reducing the financial burden on the parents. Also, having an early diagnosis according to these panels could lead to offering more accurate and less expensive molecular genetic tests.
Keywords: Neurometabolic disorders; algorithms; children; diagnosis; enzymatic panels.