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Longitudinal multiomic signatures of ARDS and sepsis inflammatory phenotypes identify pathways associated with mortality
Narges Alipanah-Lechner, Lucile Neyton, Pratik Sinha, Carolyn Leroux, Kim Bardillon, Sidney A. Carrillo, Suzanna Chak, Olivia Chao, Taarini Hariharan, Carolyn Hendrickson, Kirsten Kangelaris, Charles R. Langelier, Deanna Lee, Chelsea Lin, Kathleen Liu, Liam Magee, Angelika Ringor, Aartik Sarma, Emma Schmiege, Natasha Spottiswoode, Kathryn Sullivan, Melanie F. Weingart, Andrew Willmore, Hanjing Zhuo, Angela J. Rogers, Kathleen A. Stringer, Michael A. Matthay, Carolyn S. Calfee
Narges Alipanah-Lechner, Lucile Neyton, Pratik Sinha, Carolyn Leroux, Kim Bardillon, Sidney A. Carrillo, Suzanna Chak, Olivia Chao, Taarini Hariharan, Carolyn Hendrickson, Kirsten Kangelaris, Charles R. Langelier, Deanna Lee, Chelsea Lin, Kathleen Liu, Liam Magee, Angelika Ringor, Aartik Sarma, Emma Schmiege, Natasha Spottiswoode, Kathryn Sullivan, Melanie F. Weingart, Andrew Willmore, Hanjing Zhuo, Angela J. Rogers, Kathleen A. Stringer, Michael A. Matthay, Carolyn S. Calfee
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Clinical Research and Public Health Clinical Research Inflammation Pulmonology

Longitudinal multiomic signatures of ARDS and sepsis inflammatory phenotypes identify pathways associated with mortality

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Abstract

BACKGROUND Critically ill patients with acute respiratory distress syndrome (ARDS) and sepsis exhibit distinct inflammatory phenotypes with divergent clinical outcomes, but the underlying molecular mechanisms remain poorly understood. These phenotypes, derived from clinical data and protein biomarkers, were associated with metabolic differences in a pilot study.METHODS We performed integrative multiomics analysis of blood samples from 160 patients with ARDS in the ROSE trial, randomly selecting 80 patients from each latent class analysis–defined inflammatory phenotype (hyperinflammatory and hypoinflammatory) with phenotype probability greater than 0.9. Untargeted plasma metabolomics and whole-blood transcriptomics at day 0 and day 2 were analyzed using multimodal factor analysis (MEFISTO). The primary outcome was 90-day mortality, with validation in an independent critically ill sepsis cohort (EARLI).RESULTS Multiomics integration revealed 4 molecular signatures associated with mortality: (a) enhanced innate immune activation coupled with increased glycolysis (associated with hyperinflammatory phenotype), (b) hepatic dysfunction and immune dysfunction paired with impaired fatty acid β-oxidation (associated with hyperinflammatory phenotype), (c) interferon program suppression coupled with altered mitochondrial respiration (associated with hyperinflammatory phenotype), and (d) redox impairment and cell proliferation pathways (not associated with inflammatory phenotype). These signatures persisted through day 2 of trial enrollment. Within-phenotype analysis revealed distinct mortality-associated pathways in each group. All molecular signatures were validated in the independent EARLI cohort.CONCLUSION Inflammatory phenotypes of ARDS reflect distinct underlying biological processes with both phenotype-specific and phenotype-independent pathways influencing patient outcomes, all characterized by mitochondrial dysfunction. These findings suggest potential therapeutic targets for precise treatment strategies in critical illness.FUNDING NIH National Heart, Lung, and Blood Institute and National Institute of General Medical Sciences.

Authors

Narges Alipanah-Lechner, Lucile Neyton, Pratik Sinha, Carolyn Leroux, Kim Bardillon, Sidney A. Carrillo, Suzanna Chak, Olivia Chao, Taarini Hariharan, Carolyn Hendrickson, Kirsten Kangelaris, Charles R. Langelier, Deanna Lee, Chelsea Lin, Kathleen Liu, Liam Magee, Angelika Ringor, Aartik Sarma, Emma Schmiege, Natasha Spottiswoode, Kathryn Sullivan, Melanie F. Weingart, Andrew Willmore, Hanjing Zhuo, Angela J. Rogers, Kathleen A. Stringer, Michael A. Matthay, Carolyn S. Calfee

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Figure 7

Hyperinflammatory MEFISTO and mortality-associated signature.

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Hyperinflammatory MEFISTO and mortality-associated signature.
Analysis o...
Analysis of 137 samples from 80 patients at 2 time points. (A) Proportion of total variance explained per data modality (gene, metabolite). (B) Proportion of total variance explained per MEFISTO factor. (C) Association of MEFISTO factors with clinical variables at day 0 as determined via Spearman’s correlation for continuous predictors and linear regression for categorical predictors (FDR < 0.05). (D) The slope of change in factor 3 over time by 90-day mortality. P value derived from interaction term of a linear mixed effects regression model with 90-day mortality, time point, and their interaction as fixed effects and patient as random effect. (E) Top genes in factor 3 by relative scaled weight. (F) Top metabolites in factor 3 by relative scaled weight. (G) Top enriched gene expression pathways by normalized enrichment score (NES) in factor 3. (H) Top enriched metabolic pathways by NES in factor 3. ESR, estrogen receptor; plt, platelet; APACHE, Acute Physiology and Chronic Health Evaluation III score; BMI, body mass index; Carn, carnitine; FA, fatty acid; GFR, glomerular filtration rate; HSC, hematopoietic stem cell; LC, long chain; metab, metabolism; MUFA, monounsaturated fatty acid; PUFA, polyunsaturated fatty acid; Sat, saturated.

Copyright © 2026 American Society for Clinical Investigation
ISSN: 0021-9738 (print), 1558-8238 (online)

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