Sources of Variation in Sweat Chloride Measurements in Cystic Fibrosis

Expanding the use of cystic fibrosis transmembrane conductance regulator (CFTR) potentiators and correctors for the treatment of cystic fibrosis (CF) requires precise and accurate biomarkers. Sweat chloride concentration provides an in vivo assessment of CFTR function, but it is unknown the degree t...

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Bibliographic Details
Published in:American journal of respiratory and critical care medicine 2016-12, Vol.194 (11), p.1375-1382
Main Authors: Collaco, Joseph M, Blackman, Scott M, Raraigh, Karen S, Corvol, Harriet, Rommens, Johanna M, Pace, Rhonda G, Boelle, Pierre-Yves, McGready, John, Sosnay, Patrick R, Strug, Lisa J, Knowles, Michael R, Cutting, Garry R
Format: Article
Language:English
Subjects:
Adult
Biomarkers - metabolism
Chlorides - metabolism
Cystic Fibrosis - metabolism
Cystic Fibrosis Transmembrane Conductance Regulator - metabolism
Female
Humans
Life Sciences
Male
Original
Sweat - metabolism
Young Adult
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Summary:Expanding the use of cystic fibrosis transmembrane conductance regulator (CFTR) potentiators and correctors for the treatment of cystic fibrosis (CF) requires precise and accurate biomarkers. Sweat chloride concentration provides an in vivo assessment of CFTR function, but it is unknown the degree to which CFTR mutations account for sweat chloride variation. To estimate potential sources of variation for sweat chloride measurements, including demographic factors, testing variability, recording biases, and CFTR genotype itself. A total of 2,639 sweat chloride measurements were obtained in 1,761 twins/siblings from the CF Twin-Sibling Study, French CF Modifier Gene Study, and Canadian Consortium for Genetic Studies. Variance component estimation was performed by nested mixed modeling. Across the tested CF population as a whole, CFTR gene mutations were found to be the primary determinant of sweat chloride variability (56.1% of variation) with contributions from variation over time (e.g., factors related to testing on different days; 13.8%), environmental factors (e.g., climate, family diet; 13.5%), other residual factors (e.g., test variability; 9.9%), and unique individual factors (e.g., modifier genes, unique exposures; 6.8%) (likelihood ratio test, P 
ISSN:1073-449X
1535-4970
DOI:10.1164/rccm.201603-0459OC