肺功能障碍,临床特征和全身炎症之间的关系基于大规模的人口查
Xiaojun Ma1,2,3,4,5, Yan Yu6, Wenxia Guan2
1Department of Respiratory and Critical Care Medicine, Tianjin Medical University General Hospital, Tianjin Medical University, Tianjin, China.
这项研究确定了中国肺功能障碍和保存比率损伤螺旋计 (PRISm) 的独特临床表型. 机器学习模型可以预测这些条件,特定的细胞因子表明潜在的早期生物标志物.
科学领域:
- 肺部医学 肺部医学
- 流行病学 流行病学
- 生物标志物 生物标志物
背景情况:
- 像COPD这样的慢性呼吸道疾病在中国被诊断不足.
- 保存比率受损螺旋测量 (PRISm) 可能表明早期的肺功能受损.
- 了解PRISm表型和系统性炎症对于早期检测至关重要.
研究的目的:
- 在中国人群中描述肺功能障碍的临床表型.
- 使用机器学习识别肺功能障碍的预测因素.
- 探索系统性炎症在肺功能障碍中的作用.
主要方法:
- 在中国洪通县 (2021-2024) 进行前性横截面研究.
- 螺旋测量将参与者分为空气流阻塞,PRISM和正常组.
- 开发了机器学习模型来预测肺功能障碍;测量了血清细胞因子.
主要成果:
- 在9,284名成年人中,51.0%的人有空气流阻塞,6.7%的人有PRISM,42.3%的人正常.
- PRISM组:主要是女性,吸烟率较低,冠心病风险较高.
- 空气流阻塞组:老年人,男性,较低的BMI,吸烟者,患有脏/脑血管并发症.
- 机器学习确定了年龄,性别,BMI,症状,肌素和血红蛋白作为预测因素 (AUC=0.635).
- 空气流阻塞和PRISm组显示较低的IL-2和较高的IL-5/IL-17A血清水平.
结论:
- 气流阻塞和PRISm存在不同的临床表型.
- 使用临床变量的预测模型有助于识别患有肺功能障碍风险的个体.
- 血清IL-2,IL-5和IL-17A可以作为早期肺功能障碍检测的生物标志物.
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