A multiplex protein panel assay for severity prediction and outcome prognosis in patients with COVID-19: An observational multi-cohort study

The Circulating Proteome─Technological Developments, Current Challenges, and Future Trends

Recent improvements in proteomics technologies have fundamentally altered our capacities to characterize human biology. There is an ever-growing interest in using these novel methods for studying the circulating proteome, as blood offers an accessible window into human health. However, every methodological innovation and analytical progress calls for reassessing our existing approaches and routines to ensure that the new data will add value to the greater biomedical research community and avoid previous errors. As representatives of HUPO's Human Plasma Proteome Project (HPPP), we present our 2024 survey of the current progress in our community, including the latest build of the Human Plasma Proteome PeptideAtlas that now comprises 4608 proteins detected in 113 data sets. We then discuss the updates of established proteomics methods, emerging technologies, and investigations of proteoforms, protein networks, extracellualr vesicles, circulating antibodies and microsamples. Finally, we provide a prospective view of using the current and emerging proteomics tools in studies of circulating proteins.

Targeted MRM-analysis of plasma proteins in frozen whole blood samples from patients with COVID-19: a retrospective study

OBJECTIVES

The COVID-19 pandemic has exposed a number of key challenges that need to be urgently addressed. Mass spectrometric studies of blood plasma proteomics provide a deep understanding of the relationship between the severe course of infection and activation of specific pathophysiological pathways. Analysis of plasma proteins in whole blood may also be relevant for the pandemic as it requires minimal sample preparation.

METHODS

The frozen whole blood samples were used to analyze 203 plasma proteins using multiple reaction monitoring (MRM) mass spectrometry and stable isotope-labeled peptide standards (SIS). A total of 131 samples (FRCC, Russia) from patients with mild (n=41), moderate (n=39) and severe (n=19) COVID-19 infection and healthy controls (n=32) were analyzed.

RESULTS

Levels of 94 proteins were quantified and compared. Significant differences between all of the groups were revealed for 44 proteins. Changes in the levels of 61 reproducible COVID-19 markers (SERPINA3, SERPING1, ORM1, HRG, LBP, APOA1, AHSG, AFM, ITIH2, etc.) were consistent with studies performed with serum/plasma samples. The best-performing classifier built with 10 proteins achieved the best combination of ROC-AUC (0.97-0.98) and accuracy (0.90-0.93) metrics and distinguished patients from controls, as well as patients by severity.

CONCLUSIONS

Here, for the first time, frozen whole blood samples were used for proteomic analysis and assessment of the status of patients with COVID-19. The results obtained with frozen whole blood samples are consistent with those from plasma and serum.

Delineating protein biomarkers for gastric cancers: A catalogue of mass spectrometry-based markers and assessment of their suitability for targeted proteomics applications

Gastric cancer (GC) is a global health concern. To facilitate improved management of GCs, protein biomarkers have been identified through mass spectrometry-based proteomics platforms. In order to exhibit clinical utility of such data, we congregated over 6800 differentially regulated proteins in GCs from proteomics studies and recorded the mass spectrometry platforms, association of the protein with infectious agents, protein identifiers, sample size and clinical characters of samples used with details on validation. Development of targeted proteomics methods is the cornerstone for pursuing these markers into clinical utility. Therefore, we developed Protein Biomarker Matrix for Gastric Cancer (PBMGC), a simple catalogue of robustness of each protein. This analysis yielded the identification of robust tissue, serum, and urine diagnostic and prognostic protein biomarker panels which can be further tested for their clinical utility. We also ascertained proteotypic tryptic peptides of 5631 proteins suitable for developing multiple reaction monitoring (MRM) assays. Extensive characterization of these peptides was carried out to record peptide ions, mass/charge and enhanced specific peptide features. With the vision of catering to proteomics researchers, the data generated through this analysis has been catalogued at Gastric Cancer Proteomics DataBase (GCPDB) (https://ciods.in/gcpdb/). Users can browse and download the data and improve GCPDB by submitting recently published data. SIGNIFICANCE: Mass spectrometry-based proteomics platforms have accumulated substantial data on proteins differentially regulated in gastric cancer (GC) clinical samples. The utility of such data in clinical applications is limited by search for suitable biomarker panels for assessment of GCs. We assembled over 6800 differentially regulated proteins in GCs from proteomics studies and recorded the corresponding details including mass spectrometry platforms, status on the association of the protein with infectious agents, protein identifiers from different databases, sample size and clinical characters of samples used in test and control conditions along with details on their validation. Towards the vision of utilizing these markers in clinical assays, Protein Biomarker Matrix for Gastric Cancer (PBMGC) was developed and clinically relevant multi-protein panels were identified. We also demonstrated identification and characterization of tryptic proteotypic tryptic peptides of 5631 proteins biomarkers of GCs which are suitable for development of MRM assays in a SCIEX QTRAP instrument. Aimed to caterproteomics researchers, the data generated through this analysis has been catalogued at Gastric Cancer Proteomics DataBase (GCPDB) (https://ciods.in/gcpdb/). The users can browse and download details on different markers and improve GCPDB by submitting recently published data. Such an analysis could lay a cornerstone for building more such resources or conduct such analysis in different clinical conditions to uptake and develop targeted proteomics as the method of choice for clinical applications.

Blood proteomics of COVID-19 infection: An update

Despite significant progress in understanding SARS-CoV-2 and its impact, a deeper comprehension of the molecular changes in the host following infection is still lacking. Proteomic analysis of COVID-19 infected patients can provide valuable data about the events and mechanisms related to the initial, progression, and terminal stages of the disease. Such information can lead to better prevention, treatment, and convalescence strategies. In this review, we discuss blood proteomic studies based on mass spectrometry in COVID-19 patients. The analysis focuses on various blood components including plasma, platelets, serum, red blood cells, and peripheral blood mononuclear cells. Scientific papers published since 2022 are covered, with approaches categorised based on whether they use in-gel, in-solution, or on-beads/on-filter digestion modes. Additionally, candidate protein biomarkers related to COVID-19 are surveyed and discussed.

The Impact of Serum/Plasma Proteomics on SARS-CoV-2 Diagnosis and Prognosis

While COVID-19's urgency has diminished since its emergence in late 2019, it remains a significant public health challenge. Recent research reveals that the molecular intricacies of this virus are far more complex than initially understood, with numerous post-translational modifications leading to diverse proteoforms and viral particle heterogeneity. Mass spectrometry-based proteomics of patient serum/plasma emerges as a promising complementary approach to traditional diagnostic methods, offering insights into SARS-CoV-2 protein dynamics and enhancing understanding of the disease and its long-term consequences. This article highlights key findings from three years of pandemic-era proteomics research. It delves into biomarker discovery, diagnostic advancements, and drug development efforts aimed at monitoring COVID-19 onset and progression and exploring treatment options. Additionally, it examines global protein abundance and post-translational modification profiling to elucidate signaling pathway alterations and protein-protein interactions during infection. Finally, it explores the potential of emerging multi-omics analytic strategies in combatting SARS-CoV-2.

Longitudinal plasma proteomic analysis of 1117 hospitalized patients with COVID-19 identifies features associated with severity and outcomes

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is characterized by highly heterogeneous manifestations ranging from asymptomatic cases to death for still incompletely understood reasons. As part of the IMmunoPhenotyping Assessment in a COVID-19 Cohort study, we mapped the plasma proteomes of 1117 hospitalized patients with COVID-19 from 15 hospitals across the United States. Up to six samples were collected within ~28 days of hospitalization resulting in one of the largest COVID-19 plasma proteomics cohorts with 2934 samples. Using perchloric acid to deplete the most abundant plasma proteins allowed for detecting 2910 proteins. Our findings show that increased levels of neutrophil extracellular trap and heart damage markers are associated with fatal outcomes. Our analysis also identified prognostic biomarkers for worsening severity and death. Our comprehensive longitudinal plasma proteomics study, involving 1117 participants and 2934 samples, allowed for testing the generalizability of the findings of many previous COVID-19 plasma proteomics studies using much smaller cohorts.

Plasma Proteins Associated with COVID-19 Severity in Puerto Rico

Viral strains, age, and host factors are associated with variable immune responses against SARS-CoV-2 and disease severity. Puerto Ricans have a genetic mixture of races: European, African, and Native American. We hypothesized that unique host proteins/pathways are associated with COVID-19 disease severity in Puerto Rico. Following IRB approval, a total of 95 unvaccinated men and women aged 21-71 years old were recruited in Puerto Rico from 2020-2021. Plasma samples were collected from COVID-19-positive subjects (n = 39) and COVID-19-negative individuals (n = 56) during acute disease. COVID-19-positive individuals were stratified based on symptomatology as follows: mild (n = 18), moderate (n = 13), and severe (n = 8). Quantitative proteomics was performed in plasma samples using tandem mass tag (TMT) labeling. Labeled peptides were subjected to LC/MS/MS and analyzed by Proteome Discoverer (version 2.5), Limma software (version 3.41.15), and Ingenuity Pathways Analysis (IPA, version 22.0.2). Cytokines were quantified using a human cytokine array. Proteomics analyses of severely affected COVID-19-positive individuals revealed 58 differentially expressed proteins. Cadherin-13, which participates in synaptogenesis, was downregulated in severe patients and validated by ELISA. Cytokine immunoassay showed that TNF-α levels decreased with disease severity. This study uncovers potential host predictors of COVID-19 severity and new avenues for treatment in Puerto Ricans.

Harnessing the power of proteomics in precision diabetes medicine

Precision diabetes medicine (PDM) aims to reduce errors in prevention programmes, diagnosis thresholds, prognosis prediction and treatment strategies. However, its advancement and implementation are difficult due to the heterogeneity of complex molecular processes and environmental exposures that influence an individual's disease trajectory. To address this challenge, it is imperative to develop robust screening methods for all areas of PDM. Innovative proteomic technologies, alongside genomics, have proven effective in precision cancer medicine and are showing promise in diabetes research for potential translation. This narrative review highlights how proteomics is well-positioned to help improve PDM. Specifically, a critical assessment of widely adopted affinity-based proteomic technologies in large-scale clinical studies and evidence of the benefits and feasibility of using MS-based plasma proteomics is presented. We also present a case for the use of proteomics to identify predictive protein panels for type 2 diabetes subtyping and the development of clinical prediction models for prevention, diagnosis, prognosis and treatment strategies. Lastly, we discuss the importance of plasma and tissue proteomics and its integration with genomics (proteogenomics) for identifying unique type 2 diabetes intra- and inter-subtype aetiology. We conclude with a call for action formed on advancing proteomics technologies, benchmarking their performance and standardisation across sites, with an emphasis on data sharing and the inclusion of diverse ancestries in large cohort studies. These efforts should foster collaboration with key stakeholders and align with ongoing academic programmes such as the Precision Medicine in Diabetes Initiative consortium.

Predicting outcomes of the acute phase of COVID-19. High sensitive prognostic model, based on the results of the international registry "analysis of chronic non-infectious diseases dynamics after COVID-19 infection in adult patients" (ACTIV)

The aim of this study is to investigate the course of the acute period of COVID-19 and devise a prognostic scale for patients hospitalized.

Materials and methods

The ACTIV registry encompassed both male and female patients aged 18 years and above, who were diagnosed with COVID-19 and subsequently hospitalized. Between June 2020 and March 2021, a total of 9364 patients were enrolled across 26 medical centers in seven countries. Data collected during the patients' hospital stay were subjected to multivariate analysis within the R computational environment. A predictive mathematical model, utilizing the "Random Forest" machine learning algorithm, was established to assess the risk of reaching the endpoint (defined as in-hospital death from any cause). This model was constructed using a training subsample (70% of patients), and subsequently tested using a control subsample (30% of patients).

Results

Out of the 9364 hospitalized COVID-19 patients, 545 (5.8%) died. Multivariate analysis resulted in the selection of eleven variables for the final model: minimum oxygen saturation, glomerular filtration rate, age, hemoglobin level, lymphocyte percentage, white blood cell count, platelet count, aspartate aminotransferase, glucose, heart rate, and respiratory rate. Receiver operating characteristic analysis yielded an area under the curve of 89.2%, a sensitivity of 86.2%, and a specificity of 76.0%. Utilizing the final model, a predictive equation and nomogram (termed the ACTIV scale) were devised for estimating in-hospital mortality amongst COVID-19 patients.

Conclusion

The ACTIV scale provides a valuable tool for practicing clinicians to predict the risk of in-hospital death in patients hospitalized with COVID-19.

The Role and Value of Professional Rapid Testing of Acute Respiratory Infections (ARIs) in Europe: A Special Focus on the Czech Republic, Poland, and Romania

This review aims to explore the role of professional diagnostic rapid testing of acute respiratory infections (ARIs), especially COVID-19 and influenza, ensuring proper disease management and treatment in Europe, and particularly in Czech Republic, Poland, and Romania. The paper was constructed based on a review of scientific evidence and national and international policies and recommendations, as well as a process of validation by four experts. The development of new testing technologies, treatment options, and increased awareness of the negative multidimensional impact of ARI profiles transformed differential diagnosis into a tangible and desirable reality. This review covers the following topics: (1) the multidimensional impact of ARIs, (2) ARI rapid diagnostic testing platforms and their value, (3) the policy landscape, (4) challenges and barriers to implementation, and (5) a set of recommendations illustrating a path forward. The findings indicate that rapid diagnostic testing, including at the point of care (POC), can have a positive impact on case management, antimicrobial and antibiotic stewardship, epidemiological surveillance, and decision making. Integrating this strategy will require the commitment of governments and the international and academic communities, especially as we identified room for improvement in the access and expansion of POC rapid testing in the focus countries and the inclusion of rapid testing in relevant policies.

Multiple reaction monitoring assays for large-scale quantitation of proteins from 20 mouse organs and tissues

Mouse is the mammalian model of choice to study human health and disease due to its size, ease of breeding and the natural occurrence of conditions mimicking human pathology. Here we design and validate multiple reaction monitoring mass spectrometry (MRM-MS) assays for quantitation of 2118 unique proteins in 20 murine tissues and organs. We provide open access to technical aspects of these assays to enable their implementation in other laboratories, and demonstrate their suitability for proteomic profiling in mice by measuring normal protein abundances in tissues from three mouse strains: C57BL/6NCrl, NOD/SCID, and BALB/cAnNCrl. Sex- and strain-specific differences in protein abundances are identified and described, and the measured values are freely accessible via our MouseQuaPro database: http://mousequapro.proteincentre.com . Together, this large library of quantitative MRM-MS assays established in mice and the measured baseline protein abundances represent an important resource for research involving mouse models.

Cross-Linking Mass Spectrometry Uncovers Interactions Between High-Density Lipoproteins and the SARS-CoV-2 Spike Glycoprotein

High-density lipoprotein (HDL) levels are reduced in patients with coronavirus disease 2019 (COVID-19), and the extent of this reduction is associated with poor clinical outcomes. While lipoproteins are known to play a key role during the life cycle of the hepatitis C virus, their influence on coronavirus (CoV) infections is poorly understood. In this study, we utilize cross-linking mass spectrometry (XL-MS) to determine circulating protein interactors of the severe acute respiratory syndrome (SARS)-CoV-2 spike glycoprotein. XL-MS of plasma isolated from patients with COVID-19 uncovered HDL protein interaction networks, dominated by acute-phase serum amyloid proteins, whereby serum amyloid A2 was shown to bind to apolipoprotein (Apo) D. XL-MS on isolated HDL confirmed ApoD to interact with SARS-CoV-2 spike but not SARS-CoV-1 spike. Other direct interactions of SARS-CoV-2 spike upon HDL included ApoA1 and ApoC3. The interaction between ApoD and spike was further validated in cells using immunoprecipitation-MS, which uncovered a novel interaction between both ApoD and spike with membrane-associated progesterone receptor component 1. Mechanistically, XL-MS coupled with data-driven structural modeling determined that ApoD may interact within the receptor-binding domain of the spike. However, ApoD overexpression in multiple cell-based assays had no effect upon viral replication or infectivity. Thus, SARS-CoV-2 spike can bind to apolipoproteins on HDL, but these interactions do not appear to alter infectivity.

The Impact of Acute Nutritional Interventions on the Plasma Proteome

CONTEXT

Humans respond profoundly to changes in diet, while nutrition and environment have a great impact on population health. It is therefore important to deeply characterize the human nutritional responses.

OBJECTIVE

Endocrine parameters and the metabolome of human plasma are rapidly responding to acute nutritional interventions such as caloric restriction or a glucose challenge. It is less well understood whether the plasma proteome would be equally dynamic, and whether it could be a source of corresponding biomarkers.

METHODS

We used high-throughput mass spectrometry to determine changes in the plasma proteome of i) 10 healthy, young, male individuals in response to 2 days of acute caloric restriction followed by refeeding; ii) 200 individuals of the Ely epidemiological study before and after a glucose tolerance test at 4 time points (0, 30, 60, 120 minutes); and iii) 200 random individuals from the Generation Scotland study. We compared the proteomic changes detected with metabolome data and endocrine parameters.

RESULTS

Both caloric restriction and the glucose challenge substantially impacted the plasma proteome. Proteins responded across individuals or in an individual-specific manner. We identified nutrient-responsive plasma proteins that correlate with changes in the metabolome, as well as with endocrine parameters. In particular, our study highlights the role of apolipoprotein C1 (APOC1), a small, understudied apolipoprotein that was affected by caloric restriction and dominated the response to glucose consumption and differed in abundance between individuals with and without type 2 diabetes.

CONCLUSION

Our study identifies APOC1 as a dominant nutritional responder in humans and highlights the interdependency of acute nutritional response proteins and the endocrine system.

Immunohematologic Biomarkers in COVID-19: Insights into Pathogenesis, Prognosis, and Prevention

Coronavirus disease 2019 (COVID-19) has had profound effects on the health of individuals and on healthcare systems worldwide. While healthcare workers on the frontlines have fought to quell multiple waves of infection, the efforts of the larger research community have changed the arch of this pandemic as well. This review will focus on biomarker discovery and other efforts to identify features that predict outcomes, and in so doing, identify possible effector and passenger mechanisms of adverse outcomes. Identifying measurable soluble factors, cell-types, and clinical parameters that predict a patient's disease course will have a legacy for the study of immunologic responses, especially stimuli, which induce an overactive, yet ineffectual immune system. As prognostic biomarkers were identified, some have served to represent pathways of therapeutic interest in clinical trials. The pandemic conditions have created urgency for accelerated target identification and validation. Collectively, these COVID-19 studies of biomarkers, disease outcomes, and therapeutic efficacy have revealed that immunologic systems and responses to stimuli are more heterogeneous than previously assumed. Understanding the genetic and acquired features that mediate divergent immunologic outcomes in response to this global exposure is ongoing and will ultimately improve our preparedness for future pandemics, as well as impact preventive approaches to other immunologic diseases.

Fighting Post-COVID and ME/CFS - development of curative therapies

The sequela of COVID-19 include a broad spectrum of symptoms that fall under the umbrella term post-COVID-19 condition or syndrome (PCS). Immune dysregulation, autoimmunity, endothelial dysfunction, viral persistence, and viral reactivation have been identified as potential mechanisms. However, there is heterogeneity in expression of biomarkers, and it is unknown yet whether these distinguish different clinical subgroups of PCS. There is an overlap of symptoms and pathomechanisms of PCS with postinfectious myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS). No curative therapies are available for ME/CFS or PCS. The mechanisms identified so far provide targets for therapeutic interventions. To accelerate the development of therapies, we propose evaluating drugs targeting different mechanisms in clinical trial networks using harmonized diagnostic and outcome criteria and subgrouping patients based on a thorough clinical profiling including a comprehensive diagnostic and biomarker phenotyping.

Mass spectrometry-based high-throughput proteomics and its role in biomedical studies and systems biology

There are multiple reasons why the next generation of biological and medical studies require increasing numbers of samples. Biological systems are dynamic, and the effect of a perturbation depends on the genetic background and environment. As a consequence, many conditions need to be considered to reach generalizable conclusions. Moreover, human population and clinical studies only reach sufficient statistical power if conducted at scale and with precise measurement methods. Finally, many proteins remain without sufficient functional annotations, because they have not been systematically studied under a broad range of conditions. In this review, we discuss the latest technical developments in mass spectrometry (MS)-based proteomics that facilitate large-scale studies by fast and efficient chromatography, fast scanning mass spectrometers, data-independent acquisition (DIA), and new software. We further highlight recent studies which demonstrate how high-throughput (HT) proteomics can be applied to capture biological diversity, to annotate gene functions or to generate predictive and prognostic models for human diseases.

Quantitative protein biomarker panels: a path to improved clinical practice through proteomics

The utilisation of protein biomarker panels, rather than individual protein biomarkers, offers a more comprehensive representation of human physiology. It thus has the potential to improve diagnosis, prognosis and the differentiation of responders from nonresponders in the context of precision medicine. Although several proteomic techniques exist for measuring biomarker panels, the integration of proteomics into clinical practice has been limited. In this Commentary, we highlight the significance of quantitative protein biomarker panels in clinical medicine and outline the challenges that must be addressed in order to identify the most promising panels and implement them in clinical routines to realise their medical potential. Furthermore, we argue that the absolute quantification of protein panels through targeted mass spectrometric assays remains the most promising technology for translating proteomics into routine clinical applications due to its high flexibility, low sample costs, independence from affinity reagents and low entry barriers for its integration into existing laboratory workflows.

The human host response to monkeypox infection: a proteomic case series study

The rapid rise of monkeypox (MPX) cases outside previously endemic areas prompts for a better understanding of the disease. We studied the plasma proteome of a group of MPX patients with a similar infection history and clinical manifestation typical for the current outbreak. We report that MPX in this case series is associated with a strong plasma proteomic response among nutritional and acute phase response proteins. Moreover, we report a correlation between plasma proteins and disease severity. Contrasting the MPX host response with that of COVID-19, we find a range of similarities, but also important differences. For instance, CFHR1 is induced in COVID-19, but suppressed in MPX, reflecting the different roles of the complement system in the two infectious diseases. Of note, the spatial overlap in response proteins suggested that a COVID-19 biomarker panel assay could be repurposed for MPX. Applying a targeted protein panel assay provided encouraging results and distinguished MPX cases from healthy controls. Hence, our results provide a first proteomic characterization of the MPX human host response and encourage further research on protein-panel assays in emerging infectious diseases.

Levels of soluble complement regulators predict severity of COVID-19 symptoms

The SARS-CoV-2 virus continues to cause significant morbidity and mortality worldwide from COVID-19. One of the major challenges of patient management is the broad range of symptoms observed. While the majority of individuals experience relatively mild disease, a significant minority of patients require hospitalisation, with COVID-19 still proving fatal for some. As such, there remains a desperate need to better understand what drives this severe disease, both in terms of the underlying biology, but also to potentially predict at diagnosis which patients are likely to require further interventions, thus enabling better outcomes for both patients and healthcare systems. Several lines of evidence have pointed to dysregulation of the complement cascade as a major factor in severe COVID-19 outcomes. How this is underpinned mechanistically is not known. Here, we have focussed on the role of the soluble complement regulators Complement Factor H (FH), its splice variant Factor H-like 1 (FHL-1) and five Factor H-Related proteins (FHR1-5). Using a targeted mass spectrometry approach, we quantified these proteins in a cohort of 188 plasma samples from controls and SARS-CoV-2 patients taken at diagnosis. This analysis revealed significant elevations in all FHR proteins, but not FH, in patients with more severe disease, particularly FHR2 and FHR5 (FHR2: 1.97-fold, p<0.0001; FHR5: 2.4-fold, p<0.0001). Furthermore, for a subset of 77 SARS-CoV-2 +ve patients we also analysed time course samples taken approximately 28 days post-diagnosis. Here, we see complement regulator levels drop in all individuals with asymptomatic or mild disease, but regulators remain high in those with more severe outcomes, with elevations in FHR2 over baseline levels in this group. These data support the hypothesis that elevation of circulating levels of the FHR family of proteins could predict disease severity in COVID-19 patients, and that the duration of elevation (or lack of immune activation resolution) may be partly responsible for driving poor outcomes in COVID-19.

Early Prediction of COVID-19 Patient Survival by Targeted Plasma Multi-Omics and Machine Learning

The recent surge of coronavirus disease 2019 (COVID-19) hospitalizations severely challenges healthcare systems around the globe and has increased the demand for reliable tests predictive of disease severity and mortality. Using multiplexed targeted mass spectrometry assays on a robust triple quadrupole MS setup which is available in many clinical laboratories, we determined the precise concentrations of hundreds of proteins and metabolites in plasma from hospitalized COVID-19 patients. We observed a clear distinction between COVID-19 patients and controls and, strikingly, a significant difference between survivors and nonsurvivors. With increasing length of hospitalization, the survivors' samples showed a trend toward normal concentrations, indicating a potential sensitive readout of treatment success. Building a machine learning multi-omic model that considers the concentrations of 10 proteins and five metabolites, we could predict patient survival with 92% accuracy (area under the receiver operating characteristic curve: 0.97) on the day of hospitalization. Hence, our standardized assays represent a unique opportunity for the early stratification of hospitalized COVID-19 patients.