Dataset Information

Classifying severity of cystic fibrosis lung disease using longitudinal pulmonary function data.

ABSTRACT:

Rationale

The study of genetic modifiers in cystic fibrosis (CF) lung disease requires rigorous phenotyping. One type of genetic association study design compares polymorphisms in patients at extremes of phenotype, requiring accurate classification of pulmonary disease at varying ages.

Objective

To evaluate approaches to quantify severity of pulmonary disease and their ability to discriminate between patients with CF at the extremes of phenotype.

Methods

DeltaF508 homozygotes (n = 828) were initially classified as "severe" (approximate lowest quartile of FEV(1) (% pred) for age, 8-25 yr) or "mild" disease (highest quartile of FEV(1) for age, > or = 15 yr). FEV(1) measurements from the 5 yr before enrollment (total = 18,501 measurements; average 23 per subject) were analyzed with mixed models, and patient-specific estimates of FEV(1) (% pred) at ages 5, 10, 15, 20, and 25 yr and slope of FEV(1) versus age were examined for their ability to discriminate between groups using receiver operating characteristics (ROC) curve areas.

Results

Logistic regression of severity group on mixed model (empirical Bayes) estimates of intercept and slope of FEV(1) (% pred) versus age discriminated better than did classification using FEV(1) slope alone (ROC area = 0.995 vs. 0.821) and was equivalent to using estimated FEV(1) at 20 yr of age as a single discriminator. The estimated survival percentile from a joint survival/longitudinal model provided equally good classification (ROC area = 0.994).

Conclusions

In CF, estimated FEV(1) (% pred) at 20 yr of age and the estimated survival percentile are useful indices of pulmonary disease severity.

SUBMITTER: Schluchter MD

PROVIDER: S-EPMC2648062 | biostudies-literature | 2006 Oct

REPOSITORIES: biostudies-literature

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Publications

Classifying severity of cystic fibrosis lung disease using longitudinal pulmonary function data.

Schluchter Mark D MD Konstan Michael W MW Drumm Mitchell L ML Yankaskas James R JR Knowles Michael R MR

American journal of respiratory and critical care medicine 20060720 7

<h4>Rationale</h4>The study of genetic modifiers in cystic fibrosis (CF) lung disease requires rigorous phenotyping. One type of genetic association study design compares polymorphisms in patients at extremes of phenotype, requiring accurate classification of pulmonary disease at varying ages.<h4>Objective</h4>To evaluate approaches to quantify severity of pulmonary disease and their ability to discriminate between patients with CF at the extremes of phenotype.<h4>Methods</h4>DeltaF508 homozygot ...[more]

PMID: 16858011

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Project description:The Rationale: Molecular markers of disease activity that are predictive of forced vital capacity (FVC) progression in idiopathic pulmonary fibrosis are needed. Objectives: Develop a predictor using longitudinal within-patient gene expression differences (ΔGE) in peripheral blood mononuclear cells (PBMC) to predict of FVC progression. Methods: Patients in the training cohort (n=74) experiencing ≥10% relative reduction in FVC% of predicted over 12 months were categorized as progressors in contrast to the remaining stable patients. Baseline to 4-month within-patient ΔGE were correlated with FVC status. FVC-predictor genes were prioritized by two-group comparison with FDR<5%, logistic LASSO regression with p<0.05, and 10-Fold Cross-Validation with ≥50% support. Receiver operating characteristic with area under the curve (AUC) analyses were conducted in training subsets and independent validation cohorts from UChicago (n=27), UPMC (n=35), and Imperial (n=24) where different transcriptome assay platforms and varying transcriptome sampling times were used to derive ΔGE. Results: Intra-subject Compared to cross-sectional analysis of baseline GE, our longitudinal ΔGE variation approach demonstratedlargely reduced within-group sample variation and increased statistic power fin progressor and stable groups for prediction model development. A 25-gene FVC-predictor separated “progressors” from “stable” by Principal Component Analysis in the training and subsets of the training cohort. The resulting FVC-predictor consistently demonstrated high discriminatory performance independent of transcriptome assay platforms and varying sampling times in validation cohorts (AUC= 0.77-0.80). TGF beta was the highest-ranking canonical pathway by Gene Set Enrichment Analysis. Conclusions: Our novel short-term longitudinal within-patient ΔGE approach identified a FVC-predictor which may reflect disease activity and prove to be a reliable biomarker predictive of future FVC decline.

Project description:BackgroundChronic infection and concomitant airway inflammation is the leading cause of morbidity and mortality for people living with cystic fibrosis (CF). Although chronic infection in CF is undeniably polymicrobial, involving a lung microbiota, infection surveillance and control approaches remain underpinned by classical aerobic culture-based microbiology. How to use microbiomics to direct clinical management of CF airway infections remains a crucial challenge. A pivotal step towards leveraging microbiome approaches in CF clinical care is to understand the ecology of the CF lung microbiome and identify ecological patterns of CF microbiota across a wide spectrum of lung disease. Assessing sputum samples from 299 patients attending 13 CF centres in Europe and the USA, we determined whether the emerging relationship of decreasing microbiota diversity with worsening lung function could be considered a generalised pattern of CF lung microbiota and explored its potential as an informative indicator of lung disease state in CF.ResultsWe tested and found decreasing microbiota diversity with a reduction in lung function to be a significant ecological pattern. Moreover, the loss of diversity was accompanied by an increase in microbiota dominance. Subsequently, we stratified patients into lung disease categories of increasing disease severity to further investigate relationships between microbiota characteristics and lung function, and the factors contributing to microbiota variance. Core taxa group composition became highly conserved within the severe disease category, while the rarer satellite taxa underpinned the high variability observed in the microbiota diversity. Further, the lung microbiota of individual patient were increasingly dominated by recognised CF pathogens as lung function decreased. Conversely, other bacteria, especially obligate anaerobes, increasingly dominated in those with better lung function. Ordination analyses revealed lung function and antibiotics to be main explanators of compositional variance in the microbiota and the core and satellite taxa. Biogeography was found to influence acquisition of the rarer satellite taxa.ConclusionsOur findings demonstrate that microbiota diversity and dominance, as well as the identity of the dominant bacterial species, in combination with measures of lung function, can be used as informative indicators of disease state in CF. Video Abstract.

Dataset Information

Classifying severity of cystic fibrosis lung disease using longitudinal pulmonary function data.

Rationale

Objective

Methods

Results

Conclusions

Publications

Classifying severity of cystic fibrosis lung disease using longitudinal pulmonary function data.

Similar Datasets

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