Cao Shurui, Li Huiqin, Xin Junyi, Jin Zhenghao, Zhang Zhengyu, Li Jiawei, Zhu Yukun, Su Li, Huang Peipei, Jiang Lei, Du Mulong, Christiani David C
School of Medicine, Nanjing Medical University, Nanjing, Jiangsu, China.
Department of Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, 101 Longmian Avenue, Nanjing, 211166, Jiangsu, China.
Intensive Care Med. 2024 Jan;50(1):46-55. doi: 10.1007/s00134-023-07248-9. Epub 2023 Nov 3.
The purpose of this study was to profile genetic causal factors of acute respiratory distress syndrome (ARDS) and early predict patients at high ARDS risk.
We performed a phenome-wide Mendelian Randomization analysis through summary statistics of an ARDS genome-wide association study (1250 cases and 1583 controls of European ancestry) and 33,150 traits. Transcriptomic data from human blood and lung tissues of a preclinical mouse model were used to validate biomarkers, which were further used to construct a prediction model and nomogram.
A total of 1736 traits, including 1223 blood RNA, 159 plasma proteins, and 354 non-gene phenotypes (classified by Biochemistry, Anthropometry, Disease, Nutrition and Habit, Immunology, and Treatment), exhibited a potentially causal relationship with ARDS development, which were accessible through a user-friendly interface platform called CARDS (Causal traits for Acute Respiratory Distress Syndrome). Regarding candidate blood RNA, four genes were validated, namely TMEM176B, SLC2A5, CDC45, and VSIG8, showing differential expression in blood of ARDS patients compared to controls, as well as dynamic expression in mouse lung tissues. Importantly, the addition of four blood genes and five immune cell proportions significantly improved the prediction performance of ARDS development, with 0.791 of the area under the curve from receiver-operator characteristic, compared to 0.725 for the basic model consisting of Acute Physiology and Chronic Health Evaluation (APACHE) III Score, sex, body mass index, bacteremia, and sepsis. A model-based nomogram was also developed for the clinical practice.
This study identifies a wide range of ARDS relevant factors and develops a promising prediction model, enhancing early clinical management and intervention for ARDS development.
本研究旨在剖析急性呼吸窘迫综合征（ARDS）的遗传因果因素，并早期预测ARDS高风险患者。
我们通过一项ARDS全基因组关联研究（1250例病例和1583例欧洲血统对照）以及33150个特征的汇总统计数据，进行了全表型孟德尔随机化分析。利用临床前小鼠模型的人血液和肺组织的转录组数据来验证生物标志物，这些生物标志物进一步用于构建预测模型和列线图。
总共1736个特征，包括1223个血液RNA、159种血浆蛋白和354种非基因表型（按生物化学、人体测量学、疾病、营养与习惯、免疫学和治疗分类），与ARDS的发生呈现潜在因果关系，可通过一个名为CARDS（急性呼吸窘迫综合征的因果特征）的用户友好界面平台获取。关于候选血液RNA，验证了四个基因，即跨膜蛋白176B（TMEM176B）、溶质载体家族2成员5（SLC2A5）、细胞分裂周期蛋白45（CDC45）和V-set和免疫球蛋白结构域8（VSIG8），与对照组相比，这些基因在ARDS患者血液中表现出差异表达，并且在小鼠肺组织中呈现动态表达。重要的是，添加四个血液基因和五个免疫细胞比例显著提高了ARDS发生的预测性能，受试者工作特征曲线下面积为0.791，而由急性生理学与慢性健康状况评估（APACHE）III评分、性别、体重指数、菌血症和脓毒症组成的基本模型的曲线下面积为0.725。还为临床实践开发了基于模型的列线图。
本研究确定了广泛的ARDS相关因素，并开发了一个有前景的预测模型，增强了对ARDS发生的早期临床管理和干预。
