scholarly journals Relationship between disease severity and inflammatory markers in cystic fibrosis.

1996 ◽  
Vol 75 (6) ◽  
pp. 498-501 ◽  
Author(s):  
D Y Koller ◽  
M Gotz ◽  
C Wojnarowski ◽  
I Eichler
Keyword(s):  
Cystic Fibrosis ◽  
Disease Severity ◽  
Inflammatory Markers

scholarly journals DNA Methylation at ATP11A cg11702988 Is a Biomarker of Lung Disease Severity in Cystic Fibrosis: A Longitudinal Study

Genes ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 441
Author(s):  
Fanny Pineau ◽  
Davide Caimmi ◽  
Sylvie Taviaux ◽  
Maurane Reveil ◽  
Laura Brosseau ◽  
...  
Keyword(s):  
Cystic Fibrosis ◽  
Dna Methylation ◽  
Clinical Trials ◽  
Lung Disease ◽  
Disease Severity ◽  
Genome Wide ◽  
A Genome ◽  
Lung Disease Severity ◽  
Epigenetic Biomarker

Cystic fibrosis (CF) is a chronic genetic disease that mainly affects the respiratory and gastrointestinal systems. No curative treatments are available, but the follow-up in specialized centers has greatly improved the patient life expectancy. Robust biomarkers are required to monitor the disease, guide treatments, stratify patients, and provide outcome measures in clinical trials. In the present study, we outline a strategy to select putative DNA methylation biomarkers of lung disease severity in cystic fibrosis patients. In the discovery step, we selected seven potential biomarkers using a genome-wide DNA methylation dataset that we generated in nasal epithelial samples from the MethylCF cohort. In the replication step, we assessed the same biomarkers using sputum cell samples from the MethylBiomark cohort. Of interest, DNA methylation at the cg11702988 site (ATP11A gene) positively correlated with lung function and BMI, and negatively correlated with lung disease severity, P. aeruginosa chronic infection, and the number of exacerbations. These results were replicated in prospective sputum samples collected at four time points within an 18-month period and longitudinally. To conclude, (i) we identified a DNA methylation biomarker that correlates with CF severity, (ii) we provided a method to easily assess this biomarker, and (iii) we carried out the first longitudinal analysis of DNA methylation in CF patients. This new epigenetic biomarker could be used to stratify CF patients in clinical trials.

scholarly journals Neopterin predicts disease severity in hospitalized patients with COVID-19

2020 ◽  
Author(s):  
Rosa Bellmann-Weiler ◽  
Lukas Lanser ◽  
Francesco Burkert ◽  
Stefanie Seiwald ◽  
Gernot Fritsche ◽  
...  
Keyword(s):  
Mechanical Ventilation ◽  
Retrospective Analysis ◽  
Disease Severity ◽  
Predictive Value ◽  
Clinical Management ◽  
Inflammatory Markers ◽  
Predictive Marker ◽  
Hospitalized Patients ◽  
Icu Admission ◽  
Circulating Inflammatory Markers

Abstract This study evaluates the predictive value of circulating inflammatory markers, especially neopterin, in patients with COVID-19. Within this retrospective analysis of 115 hospitalized COVID-19 patients, elevated neopterin levels upon admission were significantly associated with disease severity, risk for ICU admission, need for mechanical ventilation and death. Therefore, neopterin is a reliable predictive marker in patients with COVID-19 and may help to improve the clinical management of patients.

scholarly journals 213 Study evaluating the effect of weekends on activity and inflammatory markers in adult patients with cystic fibrosis

2012 ◽  
Vol 11 ◽  
pp. S111
Author(s):  
C. O'Connor ◽  
C. Reilly ◽  
S. Kelly ◽  
A. Leeney ◽  
C. O'Farrell ◽  
...  
Keyword(s):  
Cystic Fibrosis ◽  
Inflammatory Markers ◽  
Adult Patients

ΔF508 Genotype Does Not Predict Disease Severity in an Ethnically Diverse Cystic Fibrosis Population

PEDIATRICS ◽  
1994 ◽  
Vol 93 (1) ◽  
pp. 114-118
Author(s):  
Lucille A. Lester ◽  
Jerome Kraut ◽  
John Lloyd-Still ◽  
Theodore Karrison ◽  
Carol Mott ◽  
...  
Keyword(s):  
Cystic Fibrosis ◽  
Disease Severity ◽  
Ethnically Diverse ◽  
Clinical Parameter ◽  
Population Based ◽  
Age At Diagnosis ◽  
Chest Roentgenogram ◽  
Sweat Chloride ◽  
Cystic Fibrosis Population ◽  
Cftr Mutations

Objective. As part of a study to determine population-based frequencies of CFTR mutations in an ethnically diverse, midwestern cystic fibrosis (CF) population, clinical histories were studied in 119 CF patients. Methodology. We sought to examine the association between genotype as characterized by the ΔF508 and 11 other commonly occurring mutations and clinical parameters including age at diagnosis, clinical presentation, sweat chloride level, chest roentgenogram score, clinical scores, pulmonary function test results, percent weight for height, and presence of associated CF complications. Results. Age at diagnosis of CF was significantly associated with homozygosity for ΔF508 (mean age at diagnosis ± SE: 1.7 ± 0.3 years for ΔF508/ΔF508 vs 3.9 ± 0.9 years for ΔF508/other and other/other; P = .03). No other age-adjusted clinical parameter was significantly associated with ΔF508 or any other genotype. Conclusion. These data suggest that in this sample of CF patients, ΔF508 genotype is not predictive of disease severity. The lack of association between disease severity and genotype in this ethnically diverse sample may reflect the presence of more severe undetected mutations in our sample, or the effects of modifying genes at other, non-CF loci.

scholarly journals Missense variants in CFTR nucleotide-binding domains predict quantitative phenotypes associated with cystic fibrosis disease severity

2014 ◽  
Vol 24 (7) ◽  
pp. 1908-1917 ◽  
Author(s):  
David L. Masica ◽  
Patrick R. Sosnay ◽  
Karen S. Raraigh ◽  
Garry R. Cutting ◽  
Rachel Karchin
Keyword(s):  
Cystic Fibrosis ◽  
Disease Severity ◽  
Nucleotide Binding ◽  
Missense Variants ◽  
Binding Domains

scholarly journals Unsupervised Phenotypic Clustering for Determining Clinical Status in Children with Cystic Fibrosis

2021 ◽  
pp. 2002881
Author(s):  
Nicole Filipow ◽  
Gwyneth Davies ◽  
Eleanor Main ◽  
Neil J. Sebire ◽  
Colin Wallis ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cystic Fibrosis ◽  
Lung Function ◽  
Disease Severity ◽  
Learning Algorithm ◽  
Severe Disease ◽  
Clinical Status ◽  
Oral Antibiotics ◽  
Multisystem Disease ◽  
Street Hospital

BackgroundCystic Fibrosis (CF) is a multisystem disease in which assessing disease severity based on lung function alone may not be appropriate. The aim of the study was to develop a comprehensive machine-learning algorithm to assess clinical status independent of lung function in children.MethodsA comprehensive prospectively collected clinical database (Toronto, Canada) was used to apply unsupervised cluster analysis. The defined clusters were then compared by current and future lung function, risk of future hospitalisation, and risk of future pulmonary exacerbation (PEx) treated with oral antibiotics. A K-Nearest Neighbours (KNN) algorithm was used to prospectively assign clusters. The methods were validated in a paediatric clinical CF dataset from Great Ormond Street Hospital (GOSH).ResultsThe optimal cluster model identified four (A-D) phenotypic clusters based on 12 200 encounters from 530 individuals. Two clusters (A,B) consistent with mild disease were identified with high FEV1, and low risk of both hospitalisation and PEx treated with oral antibiotics. Two clusters (C,D) consistent with severe disease were also identified with low FEV1. Cluster D had the shortest time to both hospitalisation and PEx treated with oral antibiotics. The outcomes were consistent in 3124 encounters from 171 children at GOSH. The KNN cluster allocation error rate was low, at 2.5% (Toronto), and 3.5% (GOSH).ConclusionMachine learning derived phenotypic clusters can predict disease severity independent of lung function and could be used in conjunction with functional measures to predict future disease trajectories in CF patients.

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