Supplementary Material for: Allergic Polysensitization Clusters: Newly Recognized Severity Marker in Urban Asthmatic Adults

Introduction: While reliable, quantitative in vitro testing for sensitivity to aeroallergens has been available for decades, such information has largely been ignored in clustering analyses of asthma. Our aim is to explore allergic polysensitization as a possible marker of asthma severity and, as such, to be considered as an integral marker in future asthma clustering analyses. Methods: We constructed a database of sensitizations to the 25 aeroallergens in our geographic area (zone 1, Northeastern US) using the ImmunoCAP® in vitro assay. We used the Scikit-Learn® machine learning library for model-based clustering to identify allergic polysensitization clusters. Clusters were compared for differences in common office-based clinical markers of asthma. Results: The database consisted of 509 patients. Unbiased machine learning identified ten clusters of increasing allergic polysensitization of varying sizes (n = 1–339) characterized by significant increases in mean serum immunoglobulin E (p p LCO (p = 0.02). There was a significant decline in mean age at presentation (p 1/FVC (p = 0.01), and FEF25-75 (p = 0.002) with increasing allergic polysensitization. Finally, we identified two divergent paths for the poly-atopic march, one driven by perennial and the other by seasonal allergens. Conclusion: This pilot study showed that allergic polysensitization, using readily available qualitative and quantitative in vitro sensitization data, largely ignored in cluster analyses to date, may add further clinical precision in cluster analyses of asthma. We suggest the methods used here can be applied and tested using larger databases and aeroallergens present in diverse geographic regions.

研究背景：尽管针对气传过敏原（aeroallergens）敏感性的定量体外检测（in vitro assay）技术已问世数十年且性能可靠，但此类信息在哮喘的聚类分析（clustering analysis）中长期未得到重视。本研究旨在探讨过敏性多敏化（allergic polysensitization）作为哮喘严重程度潜在标志物的可行性，并推动其成为未来哮喘聚类分析中的核心整合指标。
研究方法：本研究采用ImmunoCAP®体外检测技术，构建了针对美国东北部1区本地区25种气传过敏原的致敏状态数据库。我们借助Scikit-Learn®机器学习（machine learning）库开展基于模型的聚类分析，以识别过敏性多敏化聚类亚组，并比较各组间门诊常用哮喘临床标志物的差异。
研究结果：本数据库共纳入509例患者。通过无偏倚机器学习分析，我们识别出10个过敏性多敏化程度逐渐升高的聚类亚组，各组样本量跨度为1~339例。这些亚组的平均血清免疫球蛋白E（immunoglobulin E，IgE）水平显著升高，LCO（p=0.02）存在显著差异。随着过敏性多敏化程度升高，患者首次就诊时的平均年龄显著降低，FEV1/FVC（p=0.01）与FEF25-75（p=0.002）水平则显著下降。最后，我们明确了多敏特应性进程的两条不同路径：一条由常年性过敏原驱动，另一条由季节性过敏原驱动。
研究结论：本先导研究表明，尽管此前聚类分析长期忽视了基于便捷获取的定性与定量体外致敏数据开展的过敏性多敏化分析，但该指标可进一步提升哮喘聚类分析的临床精准度。我们认为，本研究采用的分析方法可在更大规模的数据库中开展应用与验证，并可推广至包含不同地理区域气传过敏原的研究场景。