Heterogeneity of phenotypic manifestations of cystic fibrosis in children and predictors of the disease severity

Abstract

The aim of the study was to investigate the heterogeneity of phenotypic manifestations of cystic fibrosis (CF) in children depending on the CFTR gene mutation and to determine predictors of the disease severity for the personalization of treatment and prevention of complications. Materials and methods. Fifty-nine children with CF, aged 1 to 18 years (mean age was 12.0 (8.5; 15.0) years), were examined. All patients underwent general clinical, genetic, laboratory and microbiological examination. Instrumental methods of examination included spirometry, chest X-ray and computed tomography, ultrasound densitometry. According to the functional effects of CFTR mutations, the patients were divided into two groups. The group with “severe” genotypes (n = 40) included patients with two class I and/or II mutations, and the group with “mild” genotypes (n = 10) included patients with at least one class IV or V mutation. Mutations were not identified in 9 patients. Results. Analysis of the spectrum of genetic variants in the CFTR gene showed that 10 patients (20.0 %) were identified with the “mild” genotype, and 40 patients (80.0 %) with the “severe” genotype. The most common mutation was F508del predominantly in the compound heterozygous state (42.4 %). It was found that patients with the “mild” genotype were characterized by a more favorable course of the disease than patients with the “severe” genotype. No statistically significant phenotypic features of the CF course in children depending on the F508del mutation status (F508del/F508del or F508del/nonF508del) were found, except for earlier formation of pulmonary hypertension in patients with the homozygous condition (47.1% vs 16.0 %, P < 0.05). Conclusions. Examination of the relationship between CFTR genotype and phenotype has revealed associations between CFTR mutations and lesion severity of both the digestive and bronchopulmonary systems. Identification of disease severity predictors can provide a more accurate prediction of the disease course, that will determine the patient management and prevent the development of complications.