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Alpha-1-antitrypsin deficiency is a genetic disorder caused by mutations in the SERPINA1 gene encoding alpha-1-antitrypsin (AAT), the major serine protease inhibitor in plasma. Reduced AAT levels are associated with elevated risk of developing emphysema mainly due to uncontrolled activity of neutrophil elastase in the lungs. The prevalent Z-AAT mutant and many rare pathogenic AAT variants also predispose to liver disease due to their accumulation as polymeric chains in hepatocytes. Part of these polymers are secreted into the bloodstream and could represent biomarkers of intra-hepatic accumulation. Moreover, being inactive, they further lower lung protection against proteases. Aim of our study is to accurately quantify the percentage of circulating polymers (CP) in a cohort of subjects with different SERPINA1 genotypes. CP concentration was measured in plasma or Dried Blood Spot (DBS) by a sensitive sandwich ELISA based on capture by the polymer-specific 2C1 monoclonal antibody. CP were significantly elevated in patients with the prevalent PI*SZ and PI*ZZ genotypes, with considerable intra-genotype variability. Notably, higher percentage of polymers was observed in association with elevated C-reactive protein. CP levels were also increased in carriers of the Mmalton variant, and of Mprocida, I, Plowell and Mherleen in heterozygosity with Z-AAT. These findings highlight the importance of implementing CP quantification in a clinical laboratory. Indeed, the variable amount of CP in patients with the same genotype may correlate with the variable severity of the associated lung and liver diseases. Moreover, CP can reveal the polymerogenic potential of newly discovered ultrarare AAT variants. [ABSTRACT FROM AUTHOR]

Background and objective: COVID‐19 remains a major cause of respiratory failure, and means to identify future deterioration is needed. We recently developed a prediction score based on breath‐holding manoeuvres (desaturation and maximal duration) to predict incident adverse COVID‐19 outcomes. Here we prospectively validated our breath‐holding prediction score in COVID‐19 patients, and assessed associations with radiological scores of pulmonary involvement. Methods: Hospitalized COVID‐19 patients (N = 110, three recruitment centres) performed breath‐holds at admission to provide a prediction score (Messineo et al.) based on mean desaturation (20‐s breath‐holds) and maximal breath‐hold duration, plus baseline saturation, body mass index and cardiovascular disease. Odds ratios for incident adverse outcomes (composite of bi‐level ventilatory support, ICU admission and death) were described for patients with versus without elevated scores (>0). Regression examined associations with chest x‐ray (Brixia score) and computed tomography (CT; 3D‐software quantification). Additional comparisons were made with the previously‐validated '4C‐score'. Results: Elevated prediction score was associated with adverse COVID‐19 outcomes (N = 12/110), OR[95%CI] = 4.54[1.17–17.83], p = 0.030 (positive predictive value = 9/48, negative predictive value = 59/62). Results were diminished with removal of mean desaturation from the prediction score (OR = 3.30[0.93–11.72]). The prediction score rose linearly with Brixia score (β[95%CI] = 0.13[0.02–0.23], p = 0.026, N = 103) and CT‐based quantification (β = 1.02[0.39–1.65], p = 0.002, N = 45). Mean desaturation was also associated with both radiological assessment. Elevated 4C‐scores (≥high‐risk category) had a weaker association with adverse outcomes (OR = 2.44[0.62–9.56]). Conclusion: An elevated breath‐holding prediction score is associated with almost five‐fold increased adverse COVID‐19 outcome risk, and with pulmonary deficits observed in chest imaging. Breath‐holding may identify COVID‐19 patients at risk of future respiratory failure. An elevated breath‐holding‐based prediction score was associated with increased COVID‐19 incident adverse outcome risk in a validation cohort of 110 hospitalized COVID‐19 patients. The prediction score was also positively associated with increasing radiological severity, per chest x‐ray and computed tomography (CT) assessment. Our prediction score performed better than the previously‐validated, biomarker‐based 4C‐score. [ABSTRACT FROM AUTHOR]