A new method to improve the clinical evaluation of cystic fibrosis patients by mucus viscoelastic properties

Abstract

In cystic fibrosis (CF) patients airways mucus shows an increased viscoelasticity due to the concentration of high molecular weight components. Such mucus thickening eventually leads to bacterial overgrowth and prevents mucus clearance. The altered rheological behavior of mucus results in chronic lung infection and inflammation, which causes most of the cases of morbidity and mortality, although the cystic fibrosis complications affect other organs as well. Here, we present a quantitative study on the correlation between cystic fibrosis mucus viscoelasticity and patients clinical status. In particular, a new diagnostic parameter based on the correlation between CF sputum viscoelastic properties and the severity of the disease, expressed in terms of FEV1 and bacterial colonization, was developed. By using principal component analysis, we show that the types of colonization and FEV1 classes are significantly correlated to the elastic modulus, and that the latter can be used for CF severity classification with a high predictive efficiency (88%). The data presented here show that the elastic modulus of airways mucus, given the high predictive efficiency, could be used as a new clinical parameter in the prognostic evaluation of cystic fibrosis.

Figure 1. The steady state viscosity of CF sputum sample from 33 donors.

a) Average viscosity from donors with different values of FEV1 (grey circle: very severe; white circle: severe; and black circle: mild/moderate) and b) Average viscosity from donors with different bacterial colonization (grey triangle: B. cepacia and S. maltophila, white triangle: P. aeruginosa and black triangle: S. aureus). Both for FEV1 and bacterial colonization, there is a significative difference between S. aureus and P. aeruginosa (P<0.05) at viscosity values below 10 Pa. c) Hysteresis loop of CF sputum.

Figure 2. G’ and G’’ as a function of angular frequency.

G’ and G’’ are almost parallel lines, typical of a cross-linked gel.

Figure 3. The elastic and loss modulus of CF sputum sample from 33 donors.

a) G’ and b) G’’ as a function of angular frequency for the three levels of CF severity (i.e. FEV1 values corresponding to mild/moderate, severe and very severe level); c) G’ and d) G’’ as a function of angular frequency for different types of bacterial colonization (i.e. S. aureus, P. aeruginosa and B. cepacia and S. maltophila).

Figure 4. The loss modulus G’’(ω) vs the elastic modulus G’(ω).

ω values range from 0.134 rad/s to 18.7 rad/s. G’’ vs G’ for a) FEV1 (grey circle: very severe; white circle: severe; black circle: mild/moderate) and b) bacterial colonization (grey triangle: B. cepacia and S. maltophila; white triangle: P. aeruginosa; black triangle: S. aureus).

Figure 5. The loss tangent tan δ and the log value of the magnitude of the complex modulus log G* as a function of bacterial colonization and FEV1.

a) tan δ (mean±SD) at 1 rad/s as a function of bacterial colonization (black) and FEV1 severity (grey). In both cases the three set of data are not significantly different one from each other; b) log G* (mean±SD) at 1 rad/s as a function of bacterial colonization (black) and FEV1 severity (grey). Regarding FEV1, the three set of data are not significantly different one from each other, while for bacterial colonization there is a significative difference between S. aureus and B. cepacia and S. maltophila (P=0.035).

Figure 6. MCI (mucociliary clearance index) and CCI (cough clearance index) as a function of bacterial colonization and FEV1.

a) MCI (mean±SD) at 1 rad/s as a function of bacterial colonization (black) and FEV1 severity (grey). In both cases the three set of data are not significantly different one from each other; b) CCI (mean±SD) at 10 rad/s as a function of bacterial colonization (black) and FEV1 severity (grey). Regarding FEV1, the three set of data are not significantly different one from each other, while for bacterial colonization there is a significative difference between S. aureus and B. cepacia and S. maltophila (P=0.014).

Figure 7. Loading plot for the first two PCs, taking globally into account 98.2% of data variability.

Figure 8. Principal Components Scatter Plot for the first two PCs.

Points corresponding to the different patients are colored according to the patient bacterial colonization (a) or FEV1 class (b).