TY - JOUR T1 - European pulmonary alveolar proteinosis network (EuPAPNet): Results from biomarkers´ investigation JF - European Respiratory Journal JO - Eur Respir J VL - 44 IS - Suppl 58 SP - 1736 AU - Francesco Bonella AU - Ilaria Campo AU - Coline H.M. van Moorsel AU - Matthias Griese AU - Jan C. Grutters AU - Ulrich Costabel AU - Maurizio Luisetti Y1 - 2014/09/01 UR - http://erj.ersjournals.com/content/44/Suppl_58/1736.abstract N2 - Background Pulmonary Alveolar Proteinosis (PAP) is an orphan disease with a prevalence of six cases per million. EuPAPNet project was aimed at creating a network among four European PAP reference centers in three countries (Italy, Germany and the Netherlands), in order to establish a database of PAP patients, to investigate biomarkers for predicting disease outcome, and to identify candidate genes for PAP susceptibility.Patients and Methods 165 patients have been included in EuPAPNet between 2010 and 2013 (81 from Italy, 75 from Germany and 9 from the Netherlands). KL-6, CCL-18, GM-CSF, LDH, YKL-40, SP-A have been measured in serum and bronchoalveolar lavage (BAL) samples by ELISA. Single nucleotide polymorphisms (SNPs) for MUC 1 and CHI3L1 genes were genotyped using polymerase chain reaction. The correlation between biomarkers, disease outcome and SNPs was evaluated.Main Results Differences in KL-6 serum levels but not YKL-40, CCL18 or SP-A were found among the three cohorts, with the highest levels in the Italian patients (p<0.01). Serum KL-6 has been found to predict the disease outcome and the necessity of treatment with whole lung lavage. MUC1 rs4072037 genotype A/A seems to be associated with higher serum KL-6 levels (p<0.05 vs A/A patients and healthy controls) while genotype A/G may predict clinical remission (HR 5.4, p=0.03). Serum YKL-40 and CHI3L1 SNPs showed a similar predicting profile as KL-6 for disease outcome.Conclusions Through a proof-of-concept approach it has been found that serum KL-6 and YKL-40 can predict outcome in PAP. Differences in serum levels of the biomarkers can be explained by different SNPs genotypes and their distribution in the studied cohorts. ER -