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One of the main research priorities in bronchiectasis is to address the complexity and heterogeneity of this disease.
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In the era of multi-omics approaches, there is an urgent need to generate data to better stratify patients in endotypes.
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Personalized medicine is required, and it will be achieved by the application of omics technologies and stratification tools to uncover such biomarkers.
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Future research studies involving cluster strategies and network analyses are needed to integrate data for
Future Directions in Bronchiectasis Research
Section snippets
Key points
Validated biomarkers
For years, sputum color has been the most affordable qualitative lung infection biomarker in clinical practice for patients with bronchiectasis.9 The green color of sputum reflects pulmonary neutrophilic inflammation, through the green neutrophil protein myeloperoxidase, which is increased during exacerbations.10 However, nowadays multiple studies have been published to find quantifiable biomarkers in blood and lung samples with a diagnostic, therapeutic, or even prognostic role of the disease,
Genetics and epigenetics
Recent advances in lung biology involving genetic studies have allowed to identify genetic variants associated with lung functions and to provide novel evidence for understanding the pathogenesis in airway diseases.
In 1989, genetics approaches allowed to know the mutations in the gene responsible for cystic fibrosis (CF), the most common and lethal autosomal recessive disorder in Caucasians.45 This gene encodes for a protein called cystic fibrosis transmembrane conductance regulator (CFTR), and
Microbiome
Understanding the contribution of airway infection to the pathophysiology of bronchiectasis is a critical step toward the prevention of disease progression. The traditional quantitative microbiological cultures can provide useful data in the assessment of treatment response. Novel analyses in bronchiectasis have revealed that the total airway bacterial load can classify patients depending on their response to antibiotic treatment, being the good responders to inhaled antibiotics for those
Other relevant omics
As airway diseases are complex and multifactorial, there is a need to integrate multiple aspects that may play a role in the prognosis of the disease. The available omics technologies also provide data about protein (proteomics), RNA expression (transcriptomics), metabolites (metabolomics), lipid mediators (lipidomic), and glycans level (glycomic).54 Although few studies have been reported in bronchiectasis using these approaches, an increase in their application is expected in the next few
Personalized medicine
The term precision or personalized medicine has been proposed to define treatments targeted to the needs of individual patients based on genetic, biomarker, phenotypic, or psychosocial characteristics, that distinguish a given patient from other patients with similar clinical presentations.56 The main objective of precision medicine is to improve clinical outcomes for individual patients while minimizing unnecessary side effects for those likely to respond to a given treatment. In patients with
Summary
Emerging evidence suggests that endotypes exist in the bronchiectasis population. For that reason, bronchiectasis treatment is challenging, and many recent randomized controlled trials have failed to prove any clinical improvement. In the era of multi-omics approaches, there is an urgent need to generate data to better stratify patients in endotypes for the development of personalized medicine. The integration of multiple aspects into systems biology studies, such as genomics, microbiome,
Clinics care points
Clinical and biological heterogeneity is the main cause of clinical trial failures in bronchiectasis. Research focused on the identification of endotypes and treatable traits in bronchiectasis will help us to move toward the development of a personalized medicine. NE is currently the most promising biomarker and therapeutic target in bronchiectasis. Further studies applying omics technologies and integrating data in network analyses are needed to find new local and systemic biomarkers with
Disclosure
The authors have nothing to disclose.
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