Novel plasma protein biomarkers from critically ill sepsis patients

Nynatten, Logan R. Van; Slessarev, Marat; Martin, Claudio M.; Leligdowicz, Aleks; Miller, Michael R.; Patel, Maitray A.; Daley, Mark; Patterson, Eric K.; Cepinskas, Gediminas; Fraser, Douglas D.

doi:10.1186/s12014-022-09389-3

Cited by 10 publications

(7 citation statements)

References 116 publications

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“…Identification of accurate Long-COVID-specific biomarkers allows for early disease detection, accurate diagnosis, prognosis and/or targeted therapeutics. Advanced proteomic techniques, such as proximity extension assays [PEA; (Assarsson et al 2014 ; Lundberg et al 2011 ))] have great potential for an efficient and holistic approach to identifying disease and injury biomarkers (Fraser et al 2020a ; Fraser et al 2021 ; Iosef et al 2023 ; Van Nynatten et al 2022 ).…”

Section: Introductionmentioning

confidence: 99%

Organ and cell-specific biomarkers of Long-COVID identified with targeted proteomics and machine learning

Patel

Knauer

Nicholson

et al. 2023

Mol Med

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Background Survivors of acute COVID-19 often suffer prolonged, diffuse symptoms post-infection, referred to as “Long-COVID”. A lack of Long-COVID biomarkers and pathophysiological mechanisms limits effective diagnosis, treatment and disease surveillance. We performed targeted proteomics and machine learning analyses to identify novel blood biomarkers of Long-COVID. Methods A case–control study comparing the expression of 2925 unique blood proteins in Long-COVID outpatients versus COVID-19 inpatients and healthy control subjects. Targeted proteomics was accomplished with proximity extension assays, and machine learning was used to identify the most important proteins for identifying Long-COVID patients. Organ system and cell type expression patterns were identified with Natural Language Processing (NLP) of the UniProt Knowledgebase. Results Machine learning analysis identified 119 relevant proteins for differentiating Long-COVID outpatients (Bonferonni corrected P < 0.01). Protein combinations were narrowed down to two optimal models, with nine and five proteins each, and with both having excellent sensitivity and specificity for Long-COVID status (AUC = 1.00, F1 = 1.00). NLP expression analysis highlighted the diffuse organ system involvement in Long-COVID, as well as the involved cell types, including leukocytes and platelets, as key components associated with Long-COVID. Conclusions Proteomic analysis of plasma from Long-COVID patients identified 119 highly relevant proteins and two optimal models with nine and five proteins, respectively. The identified proteins reflected widespread organ and cell type expression. Optimal protein models, as well as individual proteins, hold the potential for accurate diagnosis of Long-COVID and targeted therapeutics.

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Section: Introductionmentioning

confidence: 99%

Organ and cell-specific biomarkers of Long-COVID identified with targeted proteomics and machine learning

Patel

Knauer

Nicholson

et al. 2023

Mol Med

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“…Other studies based on sepsis‐3 diagnostic criteria also pointed out that MDW and WBC did not have an obvious predictive effect 16 . Cytokines such as IL10, CCL23, TGFα1, ST2, VSIG4, CNTN5 and ITGAV have also been mentioned in earlier research 17 . Due to the limited test conditions in this study, we did not examine other relevant data.…”

Section: Discussionmentioning

confidence: 69%

“… 16 Cytokines such as IL10, CCL23, TGFα1, ST2, VSIG4, CNTN5 and ITGAV have also been mentioned in earlier research. 17 Due to the limited test conditions in this study, we did not examine other relevant data. We anticipate that more sepsis markers will be identified in the future to facilitate the early diagnosis of sepsis.…”

Section: Discussionmentioning

confidence: 99%

Developing an early warning system for detecting sepsis in patients with trauma

Guo,

Pan,

Zhang

et al. 2024

International Wound Journal

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The purpose of this study was to analyse the risk factors for sepsis in patients with trauma and develop a new scoring system for predicting sepsis in patients with trauma based on these risk factors. This will provide a simple and effective early warning method for the rapid and accurate detection and evaluation of the probability of sepsis in patients with trauma to assist in planning timely clinical interventions. We undertook a retrospective analysis of the clinical data of 216 patients with trauma who were admitted to the emergency intensive care unit of the emergency medicine department of the Hebei Medical University Third Hospital, China, between November 2017 and October 2022. We conducted a preliminary screening of the relevant factors using univariate logistic regression analysis and included those factors with a p value of <0.075 in the multivariate logistic regression analysis, from which the risk factors were screened and assigned, and obtained a total score, which was the sepsis early warning score. The incidence of sepsis in patients in the intensive care unit with trauma was 36.9%, and the mortality rate due to sepsis was 19.4%. We found statistically significant differences in several factors for patients with sepsis. The risk factors for sepsis in patients with trauma were the activated partial thromboplastin time, the New Injury Severity Score, growth differentiation factor‐15 levels, shock, mechanical ventilation and the Acute Physiology and Chronic Health Evaluation II score. The area under the receiver operating characteristic curve of the sepsis early warning score for predicting sepsis in patients with trauma was 0.725. When the cutoff value of the early warning score was set at 5.0 points, the sensitivity was 69.9% and the specificity was 60.3%. The incidence of sepsis in patients with trauma can be reduced by closely monitoring patients' hemodynamics, implementing adequate fluid resuscitation promptly and by early removal of the catheter to minimize the duration of unnecessary invasive mechanical ventilation. In this study, we found that the use of the sepsis early warning score helped in a more accurate and effective evaluation of the prognosis of patients with trauma.

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“…sST2 inhibits immune responses and results in myocardial injury by targeting ST2L/IL-33 interaction ( 14 ). Although sST2 is an inflammation biomarker in other conditions ( 16 , 17 , 30 , 31 ), its role in SIC is unclear. A previous study found sST2 elevation on days 1 and 3 of septic patients and correlated with lactate,which indicating illness severity ( 15 ).…”

Section: Discussionmentioning

confidence: 99%

Testosterone and soluble ST2 as mortality predictive biomarkers in male patients with sepsis-induced cardiomyopathy

Wang,

Dai,

Zhu

et al. 2024

Front. Med.

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Sepsis-induced cardiomyopathy (SIC) is characterized by high mortality and poor outcomes. This study aimed to explore the relationship between testosterone and soluble ST2 (sST2) and all-cause mortality in patients with SIC. Clinical data from SIC patients at Renmin Hospital of Wuhan University from January 2021 and March 2023 were reviewed. Serum testosterone and sST2 were measured at admission. Kaplan–Meier analysis and receiver operative characteristic curve (ROC) were used to estimate the predictive values of testosterone and sST2 on 28 days and 90 days mortality of SIC. A total of 327 male subjects with SIC were enrolled in this study. During the 28 days and 90 days follow-up, 87 (26.6%) and 103 deaths (31.5%) occurred, respectively. Kaplan–Meier analysis showed significantly higher 28 days and 90 days survival in patients with higher testosterone and decreased sST2 levels (p < 0.001). Testosterone, sST2, and N-terminal pro-B-type natriuretic peptide (NT-proBNP) were significantly associated with 28 days and 90 days mortality (p < 0.05). Partial correlation analysis showed strong positive correlation between testosterone and left ventricular ejection fraction (LVEF) (p < 0.001), and negative correlation between testosterone and sST2 (p < 0.001), high-sensitivity troponin I (hs-TnI) levels (p < 0.001) and smoke history (p < 0.01). The concentrations of sST2 were positively related with E/e′ ratio (p < 0.001), and negatively correlated with TAPSE (p < 0.001). The combination of testosterone and sST2 enhanced the prediction of both 28 days [area under the ROC curve (AUC), 0.805] and 90 days mortality (AUC, 0.833). Early serum testosterone and sST2 levels could predict mortality of SIC independently and jointly. Further research is needed to determine the utility of biochemical markers in identifying high-risk patients with SIC.

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Novel plasma protein biomarkers from critically ill sepsis patients

Cited by 10 publications

References 116 publications

Organ and cell-specific biomarkers of Long-COVID identified with targeted proteomics and machine learning

Organ and cell-specific biomarkers of Long-COVID identified with targeted proteomics and machine learning

Developing an early warning system for detecting sepsis in patients with trauma

Testosterone and soluble ST2 as mortality predictive biomarkers in male patients with sepsis-induced cardiomyopathy

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