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Review

Molecular diagnostics of acute intermittent porphyria

Pages 243-249 | Published online: 09 Jan 2014
 

Abstract

Acute intermittent porphyria (AIP) is an inherited metabolic disease with an autosomal dominant pattern of inheritance. The disease is caused by a partial deficiency of porphobilinogen deaminase (PBGD) in heme biosynthesis. Since biochemical measurements of patients and their healthy relatives overlap, the diagnosis of AIP may remain undetermined at the symptom-free phase. Mutation detection in AIP, which provides 95% sensitivity and around 100% specificity, has quickly been incorporated into good clinical practice. During an acute attack, which includes various neurovisceral symptoms, measurement of urinary porphobilinogen (PBG) is a method of choice to confirm diagnosis, and DNA testing is unnecessary at that stage. DNA testing has revealed many new patients and excluded AIP from many healthy relatives despite slightly increased excretions of porphyrin precursors and erythrocyte PBGD in the low or borderline zone. Thus, quality-assured DNA testing is accurate enough to confirm or exclude the diagnosis of AIP. The clinical utility of DNA testing is limited for those individuals whose mutation is currently unknown, in which biochemical analyses are essential and the majority of the patients can be identified using urinary PBG and erythrocyte PBGD measurements. The measurement of urinary PBG can be used to evaluate the prognosis for symptom-free individuals. Currently, DNA testing of AIP at the population level is not recommended unless the frequency of gene carriers is locally very high and large-scale population-based mutation screening is reasonable. In the future, the knowledge of gene–gene and gene–environment interactions and protein networks using gene array and proteomics technologies may provide more precise information about pathogenetic mechanisms and novel therapeutic strategies for an acute attack and the long-term complications of AIP. Increasing knowledge of pharmacogenetics may identify the patients who are at high risk for clinical manifestations.

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