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Background <p>Acute respiratory failure (ARF) is common, exhibits variable outcomes, and arises from diverse causes that incompletely reflect underlying biology. Although the pandemic accelerated molecular investigation of ARF in COVID-19, whether these biologic signals generalize to ARF in hospital populations of varying etiology and severity remains unknown.</p> Methods <p>We analyzed aptamer-based plasma proteomics collected within 48 hours of admission across two multihospital studies of varying etiology and severity, including individuals aged ≥14 years with acute viral or respiratory illness, stratified by COVID-19 and non-COVID-19 status (<i>N</i> = 1,096 total across four cohorts). The primary outcome was critical ARF, defined as advanced respiratory support or death by day 7. We identified proteins reproducibly associated with critical ARF in all cohorts, then contextualized protein associations with pathway analysis. Additionally, we developed proteomics-based models for two clinical purposes: predicting critical ARF and identifying heterogeneity of treatment response.</p> Results <p>We identify 9 proteins associated with critical ARF across cohorts (FDR &lt; 0.05). Seven proteins related to innate immunity, oxidative stress, and barrier disruption are higher in critical ARF, while two proteins linked to immune signaling are lower. Pathway analysis identifies dysregulated inflammatory and fibrotic signaling pathways. Protein-based models improve discrimination of critical ARF beyond clinical variables in non-COVID-19 cohorts. A causal forests model finds elevated IL1RL1 (soluble ST2) is associated with greater benefit from imatinib.</p> Conclusions <p>A proteomic signature linked to ARF-related outcomes is reproducible across four cohorts with a breadth of etiologies and severity. The proteins may inform design of novel treatments and enriched clinical trials.</p>

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Plasma proteomics reveals shared biologic signatures of acute respiratory failure across etiology and severity in COVID-19 and non-COVID-19 cohorts

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月22日

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  • Neha A. Sathe,
  • Eric D. Morrell,
  • Ian B. Stanaway,
  • F. Linzee Mabrey,
  • Sarah E. Holton,
  • Carmen Mikacenic,
  • George L. Anesi,
  • Clea R. Barnett,
  • David M. Brett-Major,
  • M. Jana Broadhurst,
  • J. Perren Cobb,
  • Amy Irwin,
  • Vishakha K. Kumar,
  • Douglas P. Landsittel,
  • Richard A. Lee,
  • Janice M. Liebler,
  • Karen Lutrick,
  • Vikramjit Mukherjee,
  • Radu Postelnicu,
  • Leopoldo N. Segal,
  • Jonathan E. Sevransky,
  • Avantika Srivastava,
  • Timothy M. Uyeki,
  • David Wyles,
  • Laura E. Evans,
  • Pavan K. Bhatraju,
  • Mark M. Wurfel

摘要

Background

Acute respiratory failure (ARF) is common, exhibits variable outcomes, and arises from diverse causes that incompletely reflect underlying biology. Although the pandemic accelerated molecular investigation of ARF in COVID-19, whether these biologic signals generalize to ARF in hospital populations of varying etiology and severity remains unknown.

Methods

We analyzed aptamer-based plasma proteomics collected within 48 hours of admission across two multihospital studies of varying etiology and severity, including individuals aged ≥14 years with acute viral or respiratory illness, stratified by COVID-19 and non-COVID-19 status (N = 1,096 total across four cohorts). The primary outcome was critical ARF, defined as advanced respiratory support or death by day 7. We identified proteins reproducibly associated with critical ARF in all cohorts, then contextualized protein associations with pathway analysis. Additionally, we developed proteomics-based models for two clinical purposes: predicting critical ARF and identifying heterogeneity of treatment response.

Results

We identify 9 proteins associated with critical ARF across cohorts (FDR < 0.05). Seven proteins related to innate immunity, oxidative stress, and barrier disruption are higher in critical ARF, while two proteins linked to immune signaling are lower. Pathway analysis identifies dysregulated inflammatory and fibrotic signaling pathways. Protein-based models improve discrimination of critical ARF beyond clinical variables in non-COVID-19 cohorts. A causal forests model finds elevated IL1RL1 (soluble ST2) is associated with greater benefit from imatinib.

Conclusions

A proteomic signature linked to ARF-related outcomes is reproducible across four cohorts with a breadth of etiologies and severity. The proteins may inform design of novel treatments and enriched clinical trials.