BACKGROUND: There is an increasing demand for easy to measure biomarkers in clinical practice. We created the relational database Utrecht Patient Oriented Database (UPOD) to develop tools for identifying new biomarkers for disease. In this study, we used UPOD to identify better biomarkers for discriminating different asthma phenotypes. METHODS: We performed a prospective study at the University Medical Center (UMC) Utrecht using blood from patients with asthma and a healthy reference group. Since asthma is an inflammatory disease, absolute leukocyte counts and leukocyte differential parameters were analyzed using raw data files and a logistic regression model. RESULTS: We compared 17 difficult-to-treat asthma (DTA) cases, 13 non-difficult-to-treat asthma cases, and 19 healthy volunteers. Absolute leukocyte counts and differential parameters for leukocytes were able to discriminate asthma patients from healthy volunteers. However, among patients with asthma, difficult-to-treat cases could be more accurately defined with a neutrophil morphology change (OR 8.0; 95% CI 1.5-42.0), compared to the absolute neutrophil count (OR 4.0; 95% CI 0.8-21.0). CONCLUSIONS: In this asthma patient population, we were able to define asthma phenotypes more precisely using neutrophil morphology parameters, compared to absolute counts. Using UPOD with differential parameters, it is possible to conduct larger scale biomarker studies, combining clinical, laboratory medicine, and epidemiological techniques.
BACKGROUND: There is an increasing demand for easy to measure biomarkers in clinical practice. We created the relational database Utrecht Patient Oriented Database (UPOD) to develop tools for identifying new biomarkers for disease. In this study, we used UPOD to identify better biomarkers for discriminating different asthma phenotypes. METHODS: We performed a prospective study at the University Medical Center (UMC) Utrecht using blood from patients with asthma and a healthy reference group. Since asthma is an inflammatory disease, absolute leukocyte counts and leukocyte differential parameters were analyzed using raw data files and a logistic regression model. RESULTS: We compared 17 difficult-to-treat asthma (DTA) cases, 13 non-difficult-to-treat asthma cases, and 19 healthy volunteers. Absolute leukocyte counts and differential parameters for leukocytes were able to discriminate asthmapatients from healthy volunteers. However, among patients with asthma, difficult-to-treat cases could be more accurately defined with a neutrophil morphology change (OR 8.0; 95% CI 1.5-42.0), compared to the absolute neutrophil count (OR 4.0; 95% CI 0.8-21.0). CONCLUSIONS: In this asthmapatient population, we were able to define asthma phenotypes more precisely using neutrophil morphology parameters, compared to absolute counts. Using UPOD with differential parameters, it is possible to conduct larger scale biomarker studies, combining clinical, laboratory medicine, and epidemiological techniques.
Authors: Karin J Velthove; Hubert Gm Leufkens; Patrick C Souverein; René C Schweizer; Wouter W van Solinge Journal: Emerg Themes Epidemiol Date: 2010-05-14