Impact of intrinsic and extrinsic risk factors on early-onset lung disease in cystic fibrosis

Abstract

Background: Although respiratory pathology is known to develop in young children with cystic fibrosis (CF), the determinants of early-onset lung disease have not been elucidated.

Objective: We aimed to determine the impact of potential intrinsic and extrinsic risk factors during the first 3 years of life, testing the hypothesis that both contribute significantly to early-onset CF lung disease.

Design: We studied 104 infants born during 2012-2017, diagnosed through newborn screening by age 3 months, and evaluated comprehensively to 36 months of age. Lung disease manifestations were quantified with a new scoring system known as CFELD for Cystic Fibrosis Early-onset Lung Disease. The variants in the cystic fibrosis transmembrane conductance regulator (CFTR) gene were determined and categorized. Whole genome sequencing was performed on each subject and the data transformed to polygenic risk scores (PRS) that aggregate variants associated with lung function. Extrinsic factors included socioeconomic status (SES) indicators and environmental experiences such as exposures to smoking, pets, and daycare.

Results: We found by univariate analysis that CFTR genotype and genetic modifiers aggregated by the PRS method were significantly associated with early-onset CF lung disease. Ordinal logistic regression analysis demonstrated that high and stable SES (maternal education ≥community college, stable 2-parent home, and not receiving Medicaid) and better growth (weight-for-age and height-for-age z-scores) reduced risks, while exposure to smoking and daycare ≥20 h/week increased the risk of CFELD severity.

Conclusions: Extrinsic, modifiable determinants are influential early and potentially as important as the intrinsic risk factors in the onset of CF lung disease.

Keywords: cystic fibrosis; growth; maternal education; polygenic risk score; socioeconomic status; whole genome sequencing.

Figure 1.. Impact of genetic factors on CF Early-onset Lung Disease (CFELD) severity.

Univariate analysis of the distribution of the CF Early-onset Lung Disease (CFELD) severity categories by genetic factors. Panel A shows the CFELD categories by Cystic Fibrosis Conductance Regulator (CFTR) genotype categories; pancreatic insufficiency associated variants (PI-v) and pancreatic sufficiency associated variants (PS-v) are described in METHODS and listed in Table 2. Panel B shows the correlation between the CFELD score and the polygenic risk score (PRS) z-score, which is an aggregated genetic risk score associated with lung function as described in METHODS; note that higher PRS z-scores indicate higher lung function. Panel C shows the CFELD categories by PRS z-score categories. P-values in panels A and C were obtained by chi-square or Fisher’s exact test.

Figure 2.. Impact of socioeconomic status (SES) indicators on CF Early-onset Lung Disease (CFELD) severity.

Univariate analysis of the distribution of the CFELD severity categories by maternal education (A) and type of health insurance coverage (B). Paternal education and household income were found to be significantly associated with having stable 2-parent home in the first 3 years of life (C), which prompted us to derive a composite SES indicator we refer to as “high and stable SES” (D). P-values were obtained by chi-square or Fisher’s exact test.

Figure 3.. Risk of CF Early-onset Lung Disease (CFELD) associated with three overarching categories of factors, namely, genetic, socioeconomic status (SES) and environmental, and CF clinical care factors, as assessed by ordinal logistic regression.

The outcome variable consisted of five CFELD severity categories in the following order: asymptomatic, minimal, mild, moderate, and severe. Odds ratio (OR) with 95% confidence interval (CI) and p-values are shown for each factor. An OR of greater than one indicates a greater likelihood of having more severe CFELD. For factors that have more than 3 categories, the reference group is marked. Demographic covariates sex, race (White or Non-White), ethnicity (Hispanic or Non-Hispanic) were also adjusted in the model.