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BackgroundDelirium is common in COVID-19 intensive care unit (ICU) patients. Biomarkers for prediction, detection, and monitoring are missing. Unbiased omics analyses are warranted to gain a systems biology view on pathophysiology.MethodsThis prospective observational satellite study aims to investigate the proteome signatures of COVID-19 ICU patients, comparing those with delirium to those without. This study was conducted in ICUs of a university hospital between March 2020 and September 2021. ICU patients of legal age with a positive SARS-CoV-2 test were screened daily for oversedation and delirium. Blood samples were taken thrice a week. 457 samples were analyzed using data-independent acquisition mass spectrometry to determine protein levels. A mixed-effects logistic regression model was developed to identify proteins significantly influenced by delirium, accounting for sex and age as confounders. This model also aimed to determine proteins that were either up- or downregulated in association with delirium. Additionally, an enrichment analysis was conducted to examine the biological pathways linked to these delirium-associated proteins.ResultsOut of 360 ICU patients, 69 were analyzed for protein profiling. Out of these 69 patients, 42 patients (60.9%) had delirium on ICU admission, and 27 (39.1%) did not. Based on the multivariate model, the analysis of 204 proteins unfolded 125 (61.3%) to be differentially expressed. In total, 80.8% (n=101) of these 125 proteins were associated with delirium. Of these, 10 proteins were uniquely associated with delirium and were not significant in the multivariate model (SERPING1, SERPINA7, HP, TGFBI, CD5L, IGHV3-7, IGHV1-46, IGHV3-15, IGHV3-23, and “IGHV4-34;IGHV4-38-2”). In the univariate model for delirium, six out of 111 significant proteins showed increased expression with a log2FC > 0.5: PIGR, MST1, LBP, CRP, SAA1, and “SAA1;SAA2”; while three showed decreased expression with a log2FC < - 0.5: HP, PPBP, and “HP;HPR”. The enrichment analysis of delirium-influenced proteins revealed three significant pathways: “Network map of SARS-CoV-2 signaling” (M42569/WP5115), “Acute inflammatory response” (M10617), and “Regulation of defense response” (M15277).ConclusionWe identified a unique proteomic signature in COVID-19 ICU patients with delirium, including up- and downregulated proteins. These findings may provide potential biomarker candidates for the assessment of delirium risk and its underlying causes. These findings could be a further step towards the development of personalized, causative treatments for delirium and its monitoring in the ICU.Trial registrationThe study was retrospectively registered in the German Clinical Trials Register on May 13, 2020 (DRKS00021688).
BackgroundDelirium is common in COVID-19 intensive care unit (ICU) patients. Biomarkers for prediction, detection, and monitoring are missing. Unbiased omics analyses are warranted to gain a systems biology view on pathophysiology.MethodsThis prospective observational satellite study aims to investigate the proteome signatures of COVID-19 ICU patients, comparing those with delirium to those without. This study was conducted in ICUs of a university hospital between March 2020 and September 2021. ICU patients of legal age with a positive SARS-CoV-2 test were screened daily for oversedation and delirium. Blood samples were taken thrice a week. 457 samples were analyzed using data-independent acquisition mass spectrometry to determine protein levels. A mixed-effects logistic regression model was developed to identify proteins significantly influenced by delirium, accounting for sex and age as confounders. This model also aimed to determine proteins that were either up- or downregulated in association with delirium. Additionally, an enrichment analysis was conducted to examine the biological pathways linked to these delirium-associated proteins.ResultsOut of 360 ICU patients, 69 were analyzed for protein profiling. Out of these 69 patients, 42 patients (60.9%) had delirium on ICU admission, and 27 (39.1%) did not. Based on the multivariate model, the analysis of 204 proteins unfolded 125 (61.3%) to be differentially expressed. In total, 80.8% (n=101) of these 125 proteins were associated with delirium. Of these, 10 proteins were uniquely associated with delirium and were not significant in the multivariate model (SERPING1, SERPINA7, HP, TGFBI, CD5L, IGHV3-7, IGHV1-46, IGHV3-15, IGHV3-23, and “IGHV4-34;IGHV4-38-2”). In the univariate model for delirium, six out of 111 significant proteins showed increased expression with a log2FC > 0.5: PIGR, MST1, LBP, CRP, SAA1, and “SAA1;SAA2”; while three showed decreased expression with a log2FC < - 0.5: HP, PPBP, and “HP;HPR”. The enrichment analysis of delirium-influenced proteins revealed three significant pathways: “Network map of SARS-CoV-2 signaling” (M42569/WP5115), “Acute inflammatory response” (M10617), and “Regulation of defense response” (M15277).ConclusionWe identified a unique proteomic signature in COVID-19 ICU patients with delirium, including up- and downregulated proteins. These findings may provide potential biomarker candidates for the assessment of delirium risk and its underlying causes. These findings could be a further step towards the development of personalized, causative treatments for delirium and its monitoring in the ICU.Trial registrationThe study was retrospectively registered in the German Clinical Trials Register on May 13, 2020 (DRKS00021688).
Delirium, an acute change in cognition, is common, morbid, and costly, particularly among hospitalized older adults. Despite growing knowledge of its epidemiology, far less is known about delirium pathophysiology. Initial work understanding delirium pathogenesis has focused on assaying single or a limited subset of molecules or genetic loci. Recent technological advances at the forefront of biomarker and drug target discovery have facilitated application of multiple “omics” approaches aimed to provide a more complete understanding of complex disease processes such as delirium. At its basic level, “omics” involves comparison of genes (genomics, epigenomics), transcripts (transcriptomics), proteins (proteomics), metabolites (metabolomics), or lipids (lipidomics) in biological fluids or tissues obtained from patients who have a certain condition (i.e., delirium), and those who do not. Multi-omics analyses of these various types of molecules combined with machine learning and systems biology enables the discovery of biomarkers, biological pathways, and predictors of delirium, thus elucidating its pathophysiology. This review provides an overview of the most recent omics techniques, their current impact on identifying delirium biomarkers, and future potential in enhancing our understanding of delirium pathogenesis. We summarize challenges in identification of specific biomarkers of delirium, and more importantly, in discovering the mechanisms underlying delirium pathophysiology. Based on mounting evidence, we highlight a heightened inflammatory response as one common pathway in delirium risk and progression, and we suggest other promising biological mechanisms that have recently emerged. Advanced multiple omics approaches coupled with bioinformatics methodologies have great promise to yield important discoveries that will advance delirium research.
BACKGROUND: Delirium after cardiac surgery is common, morbid, and costly, but may be prevented with risk stratification and targeted intervention. In this study, we aimed to identify protein biomarkers and develop a predictive model for postoperative delirium in older patients undergoing cardiac surgery. METHODS: SomaScan analysis of 1305 proteins in the plasma from 57 older adults undergoing cardiac surgery requiring cardiopulmonary bypass was conducted to define delirium-specific protein signatures at baseline (preoperative baseline timepoint [PREOP]) and postoperative day 2 (POD2). Selected proteins were validated in 115 patients using the Enzyme-Linked Lectin Assay (ELLA) multiplex immunoassay platform. Proteins were combined with clinical and demographic variables to build multivariable models that estimate the risk of postoperative delirium and bring light to the underlying pathophysiology. RESULTS: Of the 115 patients, 21 (18.3%) developed delirium after surgery. The SomaScan proteome screening evidenced differential expression of 115 and 85 proteins in delirious patients compared to nondelirious preoperatively and at POD2, respectively (P < .05). Following biological and methodological criteria, 12 biomarker candidates (Tukey’s fold change [|tFC|] >1.4, Benjamini-Hochberg [BH]-P < .01) were selected for ELLA multiplex validation. Statistical analyses of model fit resulted in the combination of age, sex, and 3 proteins (angiopoietin-2; C-C motif chemokine 5; and metalloproteinase inhibitor 1; area under the curve [AUC] = 0.829) as the best performing predictive model for delirium. Analyses of pathways showed that delirium-associated proteins are involved in inflammation, glial dysfunction, vascularization, and hemostasis. CONCLUSIONS: Our results support the identification of patients at higher risk of developing delirium after cardiac surgery using a multivariable model that combines demographic and physiological features, also bringing light to the role of immune and vascular dysregulation as underlying mechanisms.
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