A large biobank study reveals that subtle imbalances between inflammatory lipids and steroids in the blood can identify people with asthma who are most likely to suffer future exacerbations, outperforming traditional clinical markers.
Study: The ratio of circulatory levels of sphingolipids to steroids predicts asthma exacerbations. Image Credit: Prostock-studio / Shutterstock
In a recent study in the journal Nature Communications, researchers analyzed medical records and blood samples from over 2,500 biobank participants with asthma to identify a novel predictive biomarker for future asthma exacerbations. The study used a metabolomics approach and found that the ratio of circulating sphingolipids (a type of fat) to steroids (hormones) is a strong discriminator of subsequent exacerbation risk.
The identified metabolite ratio was found to predict the five-year risk of asthma exacerbations with approximately 89–90% discriminative performance (area under the curve, AUC) when combined with selected clinical variables, significantly outperforming current clinical standards such as lung function tests and blood eosinophil counts when used alone. These findings may advance risk stratification for asthma management, enabling clinicians to identify high-risk individuals for future exacerbations long before clinical deterioration becomes evident, although clinical benefit from early intervention was not directly tested.
Limitations of Current Asthma Risk Assessment
Asthma is a chronic lung disease characterized by severely inflamed, narrow, or excessively mucus-producing airways, which result in wheezing, coughing, and difficulty breathing. Despite decades of research, asthma remains notoriously heterogeneous (patient-specific variation in disease expression).
While some individuals manage symptoms with occasional inhaler use, others suffer from recurrent “exacerbations”, severe flare-ups that lead to progressive lung damage, airway remodeling, and emergency room visits.
Alarmingly, despite asthma exacerbations being a leading cause of disease morbidity and hospitalizations, clinicians currently lack reliable biomarkers to prospectively predict exacerbation risk. Currently, a patient’s risk is determined using clinical tools such as Forced Expiratory Volume (FEV1) tests, which measure how much air a person can exhale in one second, or by counting eosinophils (a type of white blood cell) and measuring immunoglobulin E (IgE) antibodies in the blood.
Unfortunately, while these metrics capture the current state of the disease, they have been shown to fall short when forecasting future instability. While previous research has established these limitations and flagged disruptions in metabolic pathways associated with respiratory ailments, mechanistic investigations of the interactions between these pathways and their contributions to future asthma exacerbations remain lacking.
Study Design and Metabolomics Framework
The present study aims to address these knowledge gaps and aid future asthma interventions by leveraging metabolomics, the study of metabolites left behind by cellular processes. Metabolites have been shown to offer a unique snapshot of health, reflecting the combined influence of a patient’s genetics and environment.
The study specifically utilized metabolomics data from three independent cohorts comprising 2,513 adults from the Mass General Brigham Biobank. The study design linked longitudinal Electronic Medical Records (EMR) to participants’ blood serum and plasma samples, with follow-up extending up to 25 years for some individuals, within a single integrated healthcare system.
Analytical Strategy and Predictive Modeling
Study analyses were conducted in three stages.
Global profiling: First, an untargeted “global” analysis of the discovery cohort (MGBB-KAS, n = 1,080) was performed to identify metabolic pathways that were generally disrupted in asthmatics with a history of exacerbations.
Targeted assays: Based on the initial hits, targeted mass spectrometry was performed to quantify 166 specific metabolites (77 sphingolipids, 18 steroids, and 71 microbial-derived metabolites). These included sphingolipids (bioactive lipids involved in cell signaling), steroids (endogenous hormones), and metabolites derived from gut microbes.
Predictive modeling: Finally, advanced statistical methods (elastic net and Cox regression) were used to build a predictive model capable of forecasting incident asthma exacerbations over 5 years, defined using EMR-documented oral corticosteroid treatments, a pragmatic but indirect proxy for exacerbation events.
Contrasting previous attempts at asthma biomarker identification, which prioritized absolute concentrations of potential biomarkers, the current analyses computed and evaluated metabolite ratios, operating on the hypothesis that the balance between biological pathways is more indicative of disease state than any single molecule alone.
Key Metabolic Signatures Associated With Risk
The study analyses revealed sphingolipid-to-steroid ratios as a consistent biological imbalance in participants prone to asthma attacks. Specifically, high levels of sphingolipids (such as ceramides and sphingomyelins) combined with low levels of steroids (such as dehydroepiandrosterone sulfate, DHEAS, or cortisone) accurately signaled elevated risk of future exacerbations.
When leveraging 21 identified sphingolipid-to-steroid ratios in a multivariable 5-year predictive model, the study revealed that these ratios were capable of forecasting future asthma exacerbation risk with high discrimination (AUC = 0.90 for the discovery cohort and 0.89 for the replication cohort), substantially higher than current “gold standard” clinical approaches when those clinical markers were used without metabolomic ratios. The authors note that prior exacerbation history remains a strong predictor and may partially overlap with metabolomic risk signals.
The model was also capable of successfully differentiating the time to first exacerbation. Patients identified as high-risk were shown to experience their first attack significantly sooner, often by a margin of more than 100 days, compared to those in the low-risk group.
Notably, the study also identified microbial-derived metabolites as associated with asthma exacerbations, but their relative contributions were significantly lower than those of sphingolipids and the body’s endogenous steroid pathways.
Implications for Precision Asthma Management
The present study represents significant progress in precision medicine for respiratory health, establishing that the interaction between inflammatory lipid signaling (sphingolipids) and hormonal regulation (steroids) is critical to understanding asthma exacerbation susceptibility.
Future research should aim to leverage these findings to develop a novel clinical assay that enables clinicians to identify high-risk individuals for future exacerbations months or years in advance and potentially guide earlier risk-tailored management strategies, pending prospective validation, assessment of clinical utility, and confirmation of generalisability across diverse populations and healthcare settings.
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