Exploring Biomarkers in Asthma: Insights From Serum Metabolomics

Asthma, a highly heterogeneous disease with various clinical phenotypes, arises from complex interactions among genetic, epigenetic, and environmental factors.¹,² Despite extensive clinical research endeavors, the pathogenesis of asthma remains unclear.³ Given the substantial global burden of asthma, affecting nearly 300 million people, the importance of personalized and precise treatment is paramount.⁴ Therefore, innovative approaches are necessary to elucidate the molecular characteristics involved in asthma pathogenesis.

Metabolomics provides a snapshot of systemic physiology, representing the metabolic state of the entire body. Levels of metabolites in body fluid can rapidly fluctuate in response to various stimuli, including dietary intake, exercise, medication, disease states, or environmental factors.⁵ Monitoring metabolites enables real-time assessment of metabolic dynamics and responses to interventions. Therefore, metabolomics can more accurately reflect the phenotypes of complex diseases and their pathophysiological changes, shedding light on disease pathogenesis from a metabolic perspective.⁶,⁷

In the current issue, Zhu et al.⁸ aimed to elucidate the underlying mechanisms of asthma and identify novel biomarkers through the application of metabolomics. Serum samples from 102 asthmatic patients and 18 healthy controls were analyzed using liquid chromatography tandem mass spectrometry (LC-MS/MS) and ultra-high performance liquid chromatography tandem mass spectrometry (UHPLC-MS/MS) systems, coupled with sophisticated statistic methods such as Principal Component Analysis (PCA), Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA) and Weighted Gene Co-expression Network Analysis (WGCNA). Furthermore, clinically promising biomarkers, such as myristoleic acid and dodecanoylcarnitine, were also identified in an independent validation cohort.

Remarkably, key metabolites were selected by integrating the results of various statistical approaches. Zhu et al. ⁸ utilized powerful tools to investigate metabolic changes in asthma. By combining multivariate analysis with WGCNA, which can identify metabolite modules associated with specific clinical outcomes, the authors were able to pinpoint hub metabolites. Among 12 hub metabolites identified, 5 were carnitines. Carnitine is a compound crucial for energy metabolism, facilitating the transportation of fatty acids into mitochondria for oxidation to produce energy. Some studies have reported elevated carnitine levels in asthma patients, especially during exacerbations or in specific subtypes of the disease.⁹,¹⁰ However, contrasting research has observed reduced carnitine levels in asthma patients compared to healthy controls, implying that a deficiency of carnitine may play a role in asthma pathology.¹¹,¹² The relationship between carnitine levels and asthma is complex, requiring further research to elucidate the underlying mechanisms and implications for asthma pathogenesis. Additionally, recent studies have identified forced expiratory volume in 1 second or serum immunoglobulin E as significant parameters for classifying asthma. These parameters have also been reported to influence lipid or nucleotide metabolites in asthma.¹³,¹⁴,¹⁵ If such clinical information of patients is analyzed together with metabolomic analysis conducted in this study, it could be utilized to identify asthma endotypes that still require further classification in clinical practice.

Recently, studies on asthma metabolomics have focused on elucidating how metabolic pathways may vary across different severities of the disease and in response to various treatments. Given the current trend toward precision medicine, however, the study by Zhu et al.⁸ has some limitations. First, there needs to be more data regarding asthma medication usage and disease severity. Secondly, despite using two independent cohorts, the analysis was conducted with samples from a single center. Lifestyle factors, including dietary intake, can significantly affect the metabolite profile. Therefore, collaborating with multiple centers representing medical history, diverse demographic profiles, and dietary intake can provide a more comprehensive perspective.

Although serum metabolomics may not directly reflect lung responses, it still provides valuable insights into the systemic manifestations of asthma. Addressing these limitations in future investigations will enhance our understanding of asthma pathophysiology and facilitate the development of improved diagnostic and management strategies.