There is a need to identify the asthma inflammatory phenotypes of patients to facilitate personalized asthma treatment. Sputum induction is time-consuming and requires expert clinical technique. This study aimed to assess the distribution and characteristics of asthma inflammatory phenotypes in Jilin Province, China; it also aimed to identify an easier method for characterization of an asthma phenotype, rather than sputum cellular analysis. In this study, 232 asthma patients underwent sputum induction following clinical assessment and blood collection. Inflammatory cell counts in sputum were used to classify asthma inflammatory phenotypes. Receiver operating characteristic curve and Spearman correlation coefficient analyses were used to identify correlations between clinical parameters. Among the included patients, there had 52.1% paucigranulocytic, 38.4% eosinophilic, 4.3% neutrophilic, and 5.2% mixed granulocytic asthma phenotypes, respectively. In total, 129 (55.6%) patients had asthma-chronic obstructive pulmonary disease (COPD) overlap (ACO); these patients had higher proportion of smokers, higher sputum neutrophil count, worse lung function, and worse asthma control, compared with patients who had asthma alone (p < 0.05). Sputum eosinophil/neutrophil counts were positively correlated with blood eosinophil/neutrophil counts (p < 0.01). To identify the presence of sputum eosinophil proportion ≥ 3%, optimal cut-off values for blood eosinophil count and fractional exhaled nitric oxide (FeNO) were 0.2 × 109/L and 30.25 ppd (area under the curve (AUC) = 0.744; AUC = 0.653, p < 0.001). AUCs did not significantly differ between FeNO and blood eosinophil count (p = 0.162), but both exhibited poor specificity (57% and 49%, respectively). To identify the presence of sputum neutrophil proportion ≥ 61%, the optimal cut-off value for blood neutrophil proportion was 69.3% (AUC = 0.691, p = 0.0003); however, this exhibited poor sensitivity (50%). Paucigranulocytic asthma was the most common phenotype, followed by eosinophilic asthma. Higher proportion of smokers, poor patient compliance, insufficient treatment, and poor asthma control may have been the main causes of high ACO proportion among patients in this study. Blood eosinophil/neutrophil counts exhibited poor specificity and sensitivity for prediction of airway eosinophilic/neutrophilic inflammation.

中文翻译：

---

哮喘患者炎症表型的特征：血细胞计数参数与痰细胞表型的关系

需要鉴定患者的哮喘炎症表型以促进个性化哮喘治疗。痰诱导很费时，需要专业的临床技术。本研究旨在评估吉林省哮喘炎症表型的分布和特征。它也旨在鉴定一种表征哮喘表型的简便方法，而不是痰细胞分析。在这项研究中，对232名哮喘患者进行了临床评估和采血后进行了痰诱导。痰中的炎症细胞计数用于分类哮喘炎症表型。接收器工作特性曲线和Spearman相关系数分析用于确定临床参数之间的相关性。在纳入的患者中，有52名。混合性粒细胞性哮喘的表型分别为1％的青藻细胞型，38.4％的嗜酸性粒细胞，4.3％的嗜中性粒细胞和5.2％的混合粒细胞性哮喘表型。共有129名（55.6％）患者患有哮喘-慢性阻塞性肺疾病（COPD）重叠（ACO）；与单纯哮喘患者相比，这些患者的吸烟者比例更高，痰中性粒细胞计数更高，肺功能更差，哮喘控制更差（p <0.05）。痰中嗜酸性粒细胞/中性粒细胞计数与血液嗜酸性粒细胞/中性粒细胞计数呈正相关（p <0.01）。为了确定痰中嗜酸性粒细胞比例≥3％的存在，血液嗜酸性粒细胞计数和呼出一氧化氮分数（FeNO）的最佳临界值为0.2×109 / L和30.25 ppd（​​曲线下面积（AUC）= 0.744; AUC = 0.653，p <0.001）。FeNO和血液嗜酸性粒细胞计数之间的AUC没有显着差异（p = 0.162），但两者均显示出较差的特异性（分别为57％和49％）。为了确定痰中性粒细胞比例≥61％的存在，血液中性粒细胞比例的最佳临界值为69.3％（AUC = 0.691，p = 0.0003）。但是，这显示出较差的灵敏度（50％）。多发性粒细胞性哮喘是最常见的表型，其次是嗜酸性哮喘。吸烟者比例较高，患者依从性差，治疗不足以及哮喘控制不佳可能是本研究患者中ACO比例高的主要原因。血液嗜酸性粒细胞/中性粒细胞计数显示出较差的特异性和敏感性，无法预测气道嗜酸性粒细胞/中性粒细胞炎症。为了确定痰中性粒细胞比例≥61％的存在，血液中性粒细胞比例的最佳临界值为69.3％（AUC = 0.691，p = 0.0003）。但是，这显示出较差的灵敏度（50％）。多发性粒细胞性哮喘是最常见的表型，其次是嗜酸性哮喘。吸烟者比例较高，患者依从性差，治疗不足以及哮喘控制不佳可能是本研究患者中ACO比例高的主要原因。血液嗜酸性粒细胞/中性粒细胞计数显示出较差的特异性和敏感性，无法预测气道嗜酸性粒细胞/中性粒细胞炎症。为了确定痰中性粒细胞比例≥61％的存在，血液中性粒细胞比例的最佳临界值为69.3％（AUC = 0.691，p = 0.0003）。但是，这显示出较差的灵敏度（50％）。多发性粒细胞性哮喘是最常见的表型，其次是嗜酸性哮喘。吸烟者比例较高，患者依从性差，治疗不足以及哮喘控制不佳可能是本研究患者中ACO比例高的主要原因。血液嗜酸性粒细胞/中性粒细胞计数显示出较差的特异性和敏感性，无法预测气道嗜酸性粒细胞/中性粒细胞炎症。肺泡性肺炎是最常见的表型，其次是嗜酸性哮喘。吸烟者比例较高，患者依从性差，治疗不足以及哮喘控制不佳可能是本研究患者中ACO比例高的主要原因。血液嗜酸性粒细胞/中性粒细胞计数显示出较差的特异性和敏感性，无法预测气道嗜酸性粒细胞/中性粒细胞炎症。多发性粒细胞性哮喘是最常见的表型，其次是嗜酸性哮喘。吸烟者比例较高，患者依从性差，治疗不足以及哮喘控制不佳可能是本研究患者中ACO比例高的主要原因。血液嗜酸性粒细胞/中性粒细胞计数显示出较差的特异性和敏感性，无法预测气道嗜酸性粒细胞/中性粒细胞炎症。