PDF(Pubmed)
Abstract：
OBJECTIVE: 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.
METHODS: 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.
RESULTS: 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.
CONCLUSIONS: 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.
METHODS: 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.
RESULTS: 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.
CONCLUSIONS: 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.
摘要：
目的：这项研究的目的是描述急性呼吸窘迫综合征（ARDS）的遗传原因，并早期预测高ARDS风险患者。
方法：我们通过对ARDS全基因组关联研究（欧洲血统的1250例和1583例对照）和33,150个性状进行了全表型孟德尔随机化分析。来自临床前小鼠模型的人类血液和肺组织的转录组数据用于验证生物标志物，进一步用于构建预测模型和列线图。
结果：共1736个性状，包括1223个血液RNA,159血浆蛋白，和354种非基因表型(按生物化学分类，人体测量学,疾病,营养和习惯，免疫学,和治疗），表现出与ARDS发展的潜在因果关系，可以通过称为CARDS（急性呼吸窘迫综合征的原因特征）的用户友好界面平台访问。关于候选血液RNA，四个基因被验证，即TMEM176B，SLC2A5、CDC45和VSIG8在ARDS患者血液中的差异表达与对照组相比，以及在小鼠肺组织中的动态表达。重要的是,4种血液基因和5种免疫细胞比例的加入显著提高了ARDS发生的预测性能,根据接受者-操作者特征曲线下的面积为0.791，与由急性生理学和慢性健康评估（APACHE）III评分组成的基本模型的0.725相比，性别,身体质量指数,菌血症,还有败血症.还为临床实践开发了基于模型的列线图。
结论：这项研究确定了广泛的ARDS相关因素，并开发了一个有前途的预测模型，加强ARDS发展的早期临床管理和干预。
方法：我们通过对ARDS全基因组关联研究（欧洲血统的1250例和1583例对照）和33,150个性状进行了全表型孟德尔随机化分析。来自临床前小鼠模型的人类血液和肺组织的转录组数据用于验证生物标志物，进一步用于构建预测模型和列线图。
结果：共1736个性状，包括1223个血液RNA,159血浆蛋白，和354种非基因表型(按生物化学分类，人体测量学,疾病,营养和习惯，免疫学,和治疗），表现出与ARDS发展的潜在因果关系，可以通过称为CARDS（急性呼吸窘迫综合征的原因特征）的用户友好界面平台访问。关于候选血液RNA，四个基因被验证，即TMEM176B，SLC2A5、CDC45和VSIG8在ARDS患者血液中的差异表达与对照组相比，以及在小鼠肺组织中的动态表达。重要的是,4种血液基因和5种免疫细胞比例的加入显著提高了ARDS发生的预测性能,根据接受者-操作者特征曲线下的面积为0.791，与由急性生理学和慢性健康评估（APACHE）III评分组成的基本模型的0.725相比，性别,身体质量指数,菌血症,还有败血症.还为临床实践开发了基于模型的列线图。
结论：这项研究确定了广泛的ARDS相关因素，并开发了一个有前途的预测模型，加强ARDS发展的早期临床管理和干预。
