Pulmonary MRI and Cluster Analysis Help Identify Novel Asthma Phenotypes. Issue 5 (12th March 2022)

• Record Type: Journal Article
• Authors: Eddy, Rachel L.; McIntosh, Marrissa J.; Matheson, Alexander M.; McCormack, David G.; Licskai, Christopher; Parraga, Grace
• Is Part Of: Journal of magnetic resonance imaging. Volume 56:Issue 5(2022)
• Page Start: 1475
• Page End: 1486
• Publication Date: 2022-03-12
• Subjects: asthma -- 129Xe MRI -- cluster analysis -- asthma phenotypes; Magnetic resonance imaging -- Periodicals; 616
• DOI: 10.1002/jmri.28152
• Languages: English
• ISSNs: 1053-1807

Abstract

Abstract : Background: Outside eosinophilia, current clinical asthma phenotypes do not show strong relationships with disease pathogenesis or treatment responses. While chest x‐ray computed tomography (CT) phenotypes have previously been explored, functional MRI measurements provide complementary phenotypic information. Purpose: To derive novel data‐driven asthma phenotypic clusters using functional MRI airway biomarkers that better describe airway pathologies in patients. Study Type: Retrospective. Population: A total of 45 patients with asthma who underwent post‐bronchodilator 129 Xe MRI, volume‐matched CT, spirometry and plethysmography within a 90‐minute visit. Field Strength/Sequence: Three‐dimensional gradient‐recalled echo 129 Xe ventilation sequence at 3 T. Assessment: We measured MRI ventilation defect percent (VDP), CT airway wall‐area percent (WA%), wall‐thickness (WT, WT* [*normalized for age/sex/height]), lumen‐area (LA), lumen‐diameter (D, D*) and total airway count (TAC). Univariate relationships were utilized to select variables for k‐means cluster analysis and phenotypic subgroup generation. Spirometry and plethysmography measurements were compared across imaging‐based clusters. Statistical Tests: Spearman correlation ( ρ ), one‐way analysis of variance (ANOVA) or Kruskal–Wallis tests with post hoc Bonferroni correction for multiple comparisons, significance level 0.05. Results: Based on limited common variance (Kaiser–Meyer–Olkin‐measure = 0.44), four unique clusters were generated using MRI VDP, TAC, WT* and D* (52 ± 14 years, 27 female). Imaging measurements were significantly different across clusters as was the forced expiratory volume in 1‐second (FEV1 %pred ), residual volume/total lung capacity and airways resistance. Asthma‐control ( P = 0.9), quality‐of‐life scores ( P = 0.7) and the proportions of severe‐asthma ( P = 0.4) were not significantly different. Cluster1 ( n = 15/8 female) reflected mildly abnormal CT airway measurements and FEV1 with moderately abnormal VDP. Cluster2 ( n = 12/12 female) reflected moderately abnormal TAC, WT and FEV1 . In Cluster3 and Cluster4 ( n = 14/6 female, n = 4/1 female, respectively), there was severely reduced TAC, D and FEV1, but Cluster4 also had significantly worse, severely abnormal VDP (7 ± 5% vs. 41 ± 12%). Data Conclusion: We generated four proof‐of‐concept MRI‐derived clusters of asthma with distinct structure–function pathologies. Cluster analysis of asthma using 129 Xe MRI in combination with CT biomarkers is feasible and may challenge currently used paradigms for asthma phenotyping and treatment decisions. Evidence Level: 3 Technical Efficacy: Stage