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Alpha1-antitrypsin (AAT) is a serine protease inhibitor that is encoded by the highly polymorphic SERPINA1 gene. Mutations in this gene can lead to AAT deficiency (AATD), which is associated with an increased risk of lung and/or liver disease. On the basis of electrophoretic migration, AAT variants are named with capital letters; M (medium) signifies the normal protein. Among pathological variants, the M-like ones represent a heterogeneous group of rare allelic variants that exhibit the same electrophoretic pattern as the M wild-type protein, which makes them difficult to detect with routine methods. In order to avoid their misdiagnosis, the present study defines and validates effective methods for the detection of two pathogenic M-like variants, Mwurzburg and Mwhitstable. Comparison of protein phenotypes using isoelectric focusing of samples that presented the Mwurzburg variant, as revealed by exons 5 sequencing, identified a particular electrophoretic pattern amenable to the Mwurzburg protein. The specific phenotyping pattern was retrospectively validated, thus enabling the detection of 16 patients with Mwurzburg variant among the subjects already tested but not sequenced according to our diagnostic algorithm. The Mwhitstable allele was detected by intron 4 sequencing of SERPINA1 gene. Mwurzburg and Mwhitstable are often misdiagnosed and the introduction of diagnostic improvements can help the clinical management, especially in patients with established lung disease without any other reported risk factors. [ABSTRACT FROM AUTHOR]

Alpha1-antitrypsin deficiency (AATD) is an inherited condition that predisposes individuals to an increased risk of developing lung and liver disease. Even though AATD is one of the most widespread inherited diseases in Caucasian populations, only a minority of affected individuals has been detected. Whereas methods have been validated for AATD testing, there is no universally-established algorithm for the detection and diagnosis of the disorder. In order to compare different methods for diagnosing AATD, we carried out a systematic review of the literature on AATD diagnostic algorithms. Complete biochemical and molecular analyses of 5,352 samples processed in our laboratory were retrospectively studied using each of the selected algorithms. When applying the diagnostic algorithms to the same samples, the frequency of False Negatives varied from 1.94 to 12.9%, the frequency of True Negatives was 62.91% for each algorithm and the frequency of True Positives ranged from 24.19 to 35.15%. We, therefore, highlighted some differences among Negative Predictive Values, ranging from 0.83 to 0.97. Accordingly, the sensitivity of each algorithm ranged between 0.61 and 0.95. We also postulated 1.108 g/L as optimal AAT cut-off value, in absence of inflammatory status, which points to the possible presence of genetic AATD. The choice of the diagnostic algorithm has a significant impact on the correct diagnosis of AATD, which is essential for appropriate treatment and medical care. The fairly large number of possible false negative diagnoses revealed by the present paper should also warn clinicians of negative results in patients with clinically-suspected AATD. [ABSTRACT FROM AUTHOR]