Blood eosinophils as predictors of asthma exacerbations in adults: converging evidence from mendelian randomization, single-cell transcriptomics, and a two-center longitudinal cohort

Abstract

Asthma is a common chronic respiratory disease that imposes a heavy burden of morbidity and mortality in most countries. Eosinophils have become major therapeutic targets for severe eosinophilic asthma and other eosinophil-associated diseases. This study investigated the role of eosinophils in asthma through three complementary approaches: (i) Mendelian randomization to test causal associations, (ii) single-cell transcriptomic analysis to explore potential biological mechanisms underlying elevated blood eosinophil counts, and (iii) clinical data analysis and machine learning modelling to evaluate their predictive value in clinical practice. These analyses demonstrated the potential value of eosinophils in clinical applications. We conducted three complementary but methodologically independent analyses in adult asthma patients. First, a two-sample Mendelian randomization analysis using publicly available GWAS summary statistics was performed to assess whether genetically predicted eosinophil count and eosinophil percentage are causally associated with asthma susceptibility. Second, a publicly available peripheral blood mononuclear cell scRNA-seq dataset (GSE248688), which contrasts asthma patients with healthy controls, was used to characterize transcriptomic differences and intercellular communication in the potential biological mechanisms for increased blood eosinophil counts in adult asthma. Third, using retrospectively collected data from two adult asthma cohorts (SWMU, n = 370, training/internal validation; Chongqing, n = 180, external validation), we developed and externally validated machine learning models for 12-month exacerbation risk. All predictors were measured at a clinically stable baseline visit preceding the 12-month outcome window. The Mendelian randomization analysis provided genetic evidence that eosinophil count is causally associated with asthma susceptibility (IVW: OR 1.005, 95% CI 1.002–1.008, P < 0.001), whereas the signal for eosinophil percentage was small and borderline (IVW: OR 0.984, 95% CI 0.970–0.998, P = 0.027) and directionally inconsistent with the clinical data, most plausibly reflecting the compositional nature of a percentage measurement. Overall, eotaxins and type 2 inflammation provide a plausible explanation for increased blood eosinophil counts in asthma patients. Further analysis revealed that the platelet subcluster in the asthma group highly expressed CCL26 (eotaxin-3). Upregulated transcriptional activity of GATA3 and MAF was observed in CD14 + monocytes. Cell-cell communication analysis indicated network remodeling. Specifically, three cell types—CD14 + monocytes, platelets, and hematopoietic stem and progenitor cells (HSPCs)—simultaneously showed upregulated sender activity for MIF signaling. The five machine learning models achieved external-cohort AUCs ranging from 0.718 to 0.769 (RF 0.763, XGBoost 0.769, LR 0.755, SVM-RBF 0.905, KNN 0.733), with substantially overlapping 95% CIs; a parsimonious three-variable benchmark model achieved a comparable AUC of 0.892. Peripheral blood eosinophils may play an important role in asthma. They are especially promising as potential clinical predictors of asthma exacerbation. However, the mechanisms underlying their increase are complex. We speculate that three pathways may be involved: bone marrow release, peripheral survival, and peripheral activation. Therefore, both the clinical utility and the specific biological mechanisms of peripheral blood eosinophils still require further validation.