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Identification of genetic profile and biomarkers involved in acute respiratory distress syndrome

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Abstract

Purpose

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.

Conclusion

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.

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Data availability

For the iSPAAR consortium dataset, the genotype data and relevant covariate information (age, sex, ancestry, principal components, etc.) are deposited in dbGaP under accession code phs000631.v1.p1. For MESSI and the African American dataset, dbGaP submission is forthcoming in accordance with the NIH genomic data sharing policy. In advance of their availability on dbGaP, full summary statistics are available on reasonable request to the authors.

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Acknowledgements

The authors acknowledge the patients of the ARDS cohorts who graciously agreed to participate in this study. They also acknowledge the nurses, physicians, and staff in the medical and surgical ICUs who participated in the clinical care of the enrolled patients. The authors also thank Joe G. N. Garcia (University of Arizona, Tucson, AZ, USA) for his data sharing of preclinical mouse model.

Funding

This study was supported by US NIH # R01HL060710 and P30ES000002.

Author information

Author notes

  1. Shurui Cao, Huiqin Li, and Junyi Xin have contributed equally to this work. Peipei Huang and Lei Jiang have co-supervised this work.

Authors and Affiliations

  1. School of Medicine, Nanjing Medical University, Nanjing, Jiangsu, China

    Shurui Cao, Zhenghao Jin, Zhengyu Zhang, Jiawei Li & Yukun Zhu

  2. Department of Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, 101 Longmian Avenue, Nanjing, 211166, Jiangsu, China

    Huiqin Li & Mulong Du

  3. Department of Bioinformatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, Jiangsu, China

    Junyi Xin

  4. Department of Environmental Health, Harvard T.H. Chan School of Public Health, 655 Huntington Avenue, Boston, MA, 02115, USA

    Li Su, Peipei Huang, Lei Jiang, Mulong Du & David C. Christiani

  5. Department of Emergency, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China

    Peipei Huang & Lei Jiang

  6. Pulmonary and Critical Care Unit, Department of Medicine, Massachusetts General Hospital/Harvard Medical School, Boston, MA, USA

    David C. Christiani

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  1. Shurui Cao
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Contributions

SRC, HQL and JYX performed formal analysis, data visualization, and draft. LS performed the data collection. ZHJ, ZYZ, JWL and YKZ joined the programming and data curation. PPH and LJ administrated the project. MLD and DCC designed and supervised the project. MLD and DCC reviewed and edited the manuscript. DCC acquired the funding support.

Corresponding authors

Correspondence to Mulong Du or David C. Christiani.

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Cao, S., Li, H., Xin, J. et al. Identification of genetic profile and biomarkers involved in acute respiratory distress syndrome. Intensive Care Med 50, 46–55 (2024). https://doi.org/10.1007/s00134-023-07248-9

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