Urinary N-methylhistamine in asthmatic children receiving azelastine hydrochloride

Metabolomic profiling of asthma and chronic obstructive pulmonary disease: A pilot study differentiating diseases

BACKGROUND

Differentiating asthma from other causes of chronic airflow limitation, such as chronic obstructive pulmonary disease (COPD), can be difficult in a typical outpatient setting. The inflammation of asthma typically is different than that of COPD, and the degree of inflammation and cellular damage varies with asthma severity. Metabolomics is the study of molecules created by cellular metabolic pathways.

OBJECTIVES

We hypothesized that the metabolic activity of adults with asthma would differ from that of adults with COPD. Furthermore, we hypothesized that nuclear magnetic resonance spectroscopy (NMR) would measure such differences in urine samples.

METHODS

Clinical and urine-based NMR data were collected on adults meeting the criteria of asthma and COPD before and after an exacerbation (n = 133 and 38, respectively) and from patients with stable asthma or COPD (n = 54 and 23, respectively). Partial least-squares discriminant analysis was performed on the NMR data to create models of separation (86 metabolites were measured per urine sample). Some subjects' metabolomic data were withheld from modeling to be run blindly to determine diagnostic accuracy.

RESULTS

Partial least-squares discriminant analysis of the urine NMR data found unique differences in select metabolites between patients with asthma and those with COPD seen in the emergency department and even in follow-up after exacerbation. By using these select metabolomic profiles, the model could correctly diagnose blinded asthma and COPD with greater than 90% accuracy.

CONCLUSION

This is the first report showing that metabolomic analysis of human urine samples could become a useful clinical tool to differentiate asthma from COPD.

Personalised medicine and asthma diagnostics/management. Curr Allergy Asthma Rep. 2013;13:118-129. [PMID: 23212666 DOI: 10.1007/s11882-012-0325-9]

Human beings come in all shapes and sizes. Heterogeneity makes life interesting, but leads to inter-individual variation in disease susceptibility and response to therapy. One major health challenge is to develop "personalised medicine"; therapeutic interventions tailored to an individual to ensure optimal treatment of disease. Asthma is a heterogeneous disease with several different phenotypes triggered by multiple gene-environment interactions. Inhaled corticosteroids and β2-agonists have been the mainstay asthma therapies for 30 years, but they are not effective in all patients, while high costs and side-effects also drive the need for better targeted treatment of asthma. Pharmacogenetics is the study of variations in the genetic code for proteins in signaling pathways targeted by pharmacological therapies. Biomarkers are biological markers obtained from patients that can aid in asthma diagnosis, prediction of treatment response, and monitoring of disease control. This review presents a broad discussion of the use of genetic profiling and biomarkers to better diagnose, monitor, and tailor the treatment of asthmatics. We also discuss possible future developments in personalised medicine, including the construction of artificially engineered airway tissues containing a patient's own cells for use as personalised drug-testing tools.

Clinical update on the use of biomarkers of airway inflammation in the management of asthma

Biological markers are already used in the diagnosis and treatment of cardiovascular disease and cancer. Biomarkers have great potential use in the clinic as a noninvasive means to make more accurate diagnoses, monitor disease progression, and create personalized treatment regimes. Asthma is a heterogeneous disease with several different phenotypes, generally triggered by multiple gene-environment interactions. Pulmonary function tests are most often used objectively to confirm the diagnosis. However, airflow obstruction can be variable and thus missed using spirometry. Furthermore, lung function measurements may not reflect the precise underlying pathological processes responsible for different phenotypes. Inhaled corticosteroids and β(2)-agonists have been the mainstay of asthma therapy for over 30 years, but the heterogeneity of the disease means not all asthmatics respond to the same treatment. High costs and undesired side effects of drugs also drive the need for better targeted treatment of asthma. Biomarkers have the potential to indicate an individual's disease phenotype and thereby guide clinicians in their decisions regarding treatment. This review focuses on biomarkers of airway inflammation which may help us to identify, monitor, and guide treatment of asthmatics. We discuss biomarkers obtained from multiple physiological sources, including sputum, exhaled gases, exhaled breath condensate, serum, and urine. We discuss the inherent limitations and benefits of using biomarkers in a heterogeneous disease such as asthma. We also discuss how we may modify our study designs to improve the identification and potential use of potential biomarkers in asthma.

Metabolomic profiling of asthma: diagnostic utility of urine nuclear magnetic resonance spectroscopy

BACKGROUND

The ability to diagnose and monitor asthma on the basis of noninvasive measurements of airway cellular dysfunction is difficult in the typical clinical setting.

OBJECTIVE

Metabolomics is the study of molecules created by cellular metabolic pathways. We hypothesized that the metabolic activity of children with asthma would differ from healthy children without asthma. Furthermore, children having an asthma exacerbation would be different compared with children with stable asthma in outpatient clinics. Finally, we hypothesized that (1)H-nuclear magnetic resonance (NMR) would measure such differences using urine samples, one of the least invasive forms of biofluid sampling.

METHODS

Children (135 total, ages 4-16 years) were enrolled, having met the criteria of healthy controls (C), stable asthma in the outpatient clinic (AO), or unstable asthma in the emergency department (AED). Partial least squares discriminant analysis was performed on the NMR data to create models of separation (70 metabolites were measured/urine sample). Some NMR data were withheld from modeling to be run blindly to determine possible diagnostic accuracy.

RESULTS

On the basis of the model of AO versus C, 31 of 33 AO samples were correctly diagnosed with asthma (94% accuracy). Only 1 of 20 C samples was incorrectly labeled as asthma (5% misclassification). On the basis of the AO versus AED model, 31 of the 33 AO samples were correctly diagnosed as outpatient asthma (94% accurate).

CONCLUSION

This is the first report suggesting that (1)H-NMR analysis of human urine samples has the potential to be a useful clinical tool for physicians treating asthma.