ALPHA-1 antitrypsin deficiency (AATD) remains a frequently underdiagnosed condition that predisposes individuals to chronic lung and liver diseases. A comprehensive new diagnostic approach, combining serum AAT levels, genotyping, and functional activity, offers a promising solution to this challenge.
Current Diagnostic Gaps
AATD is caused by mutations in the SERPINA1 gene, leading to dysfunctional AAT, an essential protein that regulates inflammation. Traditional diagnostic methods, such as serum AAT testing and phenotyping, often fail to detect many cases due to the variability in serum AAT levels and the complexity of the SERPINA1 gene. These limitations prompted researchers to develop a more reliable testing algorithm.
The New AATD Testing Algorithm
The newly proposed diagnostic algorithm combines serum AAT measurement with genotyping for known pathogenic single nucleotide polymorphisms (SNPs) and functional AAT activity assays. This algorithm starts with a genotyping assay that targets 19 SNPs and the normal M allele, providing a broad detection of AATD-associated mutations. If no pathogenic SNP is found, next-generation sequencing (NGS) of SERPINA1 exons or whole gene sequencing (WGS) is employed as second-tier testing.
Key Findings
The new testing approach has proven successful in identifying previously undetected cases. For example, WGS revealed a potential splicing mutation in the 3’ UTR of the SERPINA1 gene in four subjects, where three individuals were genotyped as MM (normal) and one as MmaltonS. Although serum AAT levels were moderately low in these individuals, only one showed reduced anti-neutrophil elastase (anti-NE) activity, suggesting that certain mutations do not affect anti-NE function.
Implications for AATD Diagnosis
The researchers found that the traditional approach, which focuses on serum AAT levels alone, could be misleading, especially in cases involving the pathogenic Z, S, or F alleles. These genotypes displayed reduced anti-NE activity even when serum levels were within normal ranges. The findings highlight that AATD diagnosis should consider both AAT expression and functional activity to provide a more accurate clinical picture.
Conclusion
This comprehensive AATD testing algorithm offers significant potential to enhance the diagnosis of this underrecognized condition. By integrating multiple assays that evaluate AAT genotype, expression, and functional activity, the algorithm could help identify individuals with AATD who might otherwise be missed by standard tests. The full results of the study will be presented at the CHEST Annual Meeting 2025, providing further insights into the clinical impact of these findings.
Reference: CHEST. Improving Diagnosis for AATD through Testing Algorithm. 2025. Available at: https://www.chestnet.org/-/media/documents/news/improving-diagnosis-for-aatd-through-testing-algorithm.ashx. Last accessed: 21 October 2025.
