Laboratory Biomarkers for Diagnosis and Prognosis in COVID-19

Authors: Denise Battaglini, 1 , 2 , 3 Miquéias Lopes-Pacheco, 4 Hugo C. Castro-Faria-Neto, 5 Paolo Pelosi, 1 , 2 and Patricia R. M. Rocco 4 , 6 , 7 , * Front Immunol. 2022; 13: 857573.  Apr 27.  2022 doi: 10.3389/fimmu.2022.857573 PMCID: PMC9091347 PMID: 35572561


Severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) causes a wide spectrum of clinical manifestations, with progression to multiorgan failure in the most severe cases. Several biomarkers can be altered in coronavirus disease 2019 (COVID-19), and they can be associated with diagnosis, prognosis, and outcomes. The most used biomarkers in COVID-19 include several proinflammatory cytokines, neuron-specific enolase (NSE), lactate dehydrogenase (LDH), aspartate transaminase (AST), neutrophil count, neutrophils-to-lymphocytes ratio, troponins, creatine kinase (MB), myoglobin, D-dimer, brain natriuretic peptide (BNP), and its N-terminal pro-hormone (NT-proBNP). Some of these biomarkers can be readily used to predict disease severity, hospitalization, intensive care unit (ICU) admission, and mortality, while others, such as metabolomic and proteomic analysis, have not yet translated to clinical practice. This narrative review aims to identify laboratory biomarkers that have shown significant diagnostic and prognostic value for risk stratification in COVID-19 and discuss the possible clinical application of novel analytic strategies, like metabolomics and proteomics. Future research should focus on identifying a limited but essential number of laboratory biomarkers to easily predict prognosis and outcome in severe COVID-19.


Severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) causes a wide spectrum of clinical manifestations, from mild respiratory symptoms to pneumonia and, in more severe cases, multiple organ failure (1). The mechanisms underlying multisystem involvement may include an unbalanced immune response that facilitates the progression of coronavirus disease-2019 (COVID-19). This hypothesis has been confirmed by laboratory biomarker alterations, showing greater potential for abnormal immune response, mainly an increase in neutrophil counts and a substantial reduction in lymphocyte counts, thus altering the neutrophil-to-lymphocyte ratio. Such an abnormal immune response is driven by an increased serum concentration of many pro-inflammatory mediators. These include interleukin (IL)-1β, IL-2, IL-6, IL-8, interferon (IFN)-γ-induced protein 10, granulocyte colony-stimulating factor, monocyte chemoattractant protein 1, macrophage inflammatory protein-1α, and tumor necrosis factor-α, among others (25). Nevertheless, the inflammatory cytokine storm in patients with COVID-19 is less injurious than that observed in patients with sepsis or acute respiratory distress syndrome (ARDS) but without COVID-19 (6), thus raising questions regarding the mechanisms underlying multiorgan involvement in COVID-19.

Several biomarkers other than cytokines have been found altered in COVID-19, and are associated with diagnosis, prognosis and outcomes (7). Some of these biomarkers can be easily used to predict disease severity, hospitalization, intensive care unit (ICU) admission, and mortality, while others, like metabolomic and proteomic analysis, are still of purely investigational concern and difficult to translate into clinical practice, despite their prognostic potential (810).

The aim of this narrative review is to identify laboratory biomarkers that have shown significant diagnostic and prognostic value for risk stratification in COVID-19 and to discuss the possible clinical application of novel analytic strategies, such as metabolomics and proteomics.

Go to:

Potential for Multiorgan Involvement in COVID-19

SARS-CoV-2 is an enveloped, single-stranded ribonucleic acid (ssRNA) virus. The SARS-CoV-2 genome is composed of two polypeptides encoded between two open-reading frames that are processed by viral proteases to produce nonstructural proteins (11). These proteins are involved in viral replication and suppression of host innate immune defense. On the other hand, structural proteins of SARS-CoV-2 include the spike (S), envelope (E), and nucleocapsid (N) protein, as well as the membrane (M) glycoprotein. The S protein is a transmembrane glycoprotein that is located on the viral surface and cleaved by host-cell proteases. After anchoring the S protein, SARS-CoV-2 enters host cells via angiotensin receptor-2 (ACE2), thus activating transmembrane serine protease 2 (TMPRSS2), cathepsin B and L. The E protein is a glycoprotein involved in virion maturation and pathogenesis, while the M protein is involved in viral assembly and delineates the shape of the viral envelope; finally, the N protein binds directly to viral RNA (11). The pathogenic mechanisms of SARS-CoV-2 include 1) direct epithelial damage, 2) dysregulated immune response, 3) ACE2 dysregulation and downregulation of the renin-angiotensin- aldosterone system (RAAS), 4) direct endothelial damage, and, possibly, 5) tissue fibrosis (11). Hence, patients with severe COVID-19 are at high risk of multiple organ involvement and, ultimately, death. Indeed, the virus has been identified in multiple tissues, including endothelial, liver, kidney, pulmonary, and neuronal cells, suggesting direct invasion as possible pathological mechanism underlying systemic effects (1). Therefore, laboratory biomarkers of organ damage play a key role in the diagnosis, prediction, and prognosis of patients at high risk of multiorgan involvement, and their use should be implemented in clinical practice (1). Table 1 summarizes the most investigated biomarkers in COVID-19, while Figure 1 depicts possible multiorgan involvement in COVID-19. In the following section, we will describe individual organ systems and how they can be affected by severe COVID-19, associated laboratory and clinical biomarkers of damage, severity, and outcome, and their potential utility for patient management.

Table 1

Laboratory biomarkers in COVID-19.

BiomarkersClinical significance
Pulmonary functionNSEDyspnea
LDH, ASTMortality at admission, longer IMV
Surfactant protein-D, angiopoietin-2, TREM-1, TREM-2Severity
Thiol, ferritin, LDHARDS development
Platelet count, neutrophils/lymphocyte ratio, CRP, D-dimer, ferritinSurvival at extubation
Kynurenine, p-cresol sulphateLonger IMV
Metabolomic/proteomic: PPAR, D-arginine, D-ornithine, TRP, alpha linoleicFibrosis
Inflammation and infectionPCTSeverity, mortality
Neutrophil countClinical outcome, mortality
Neutrophil/lymphocyte ratioSeverity, mortality
Lymphocyte count, CD3+, 4+, 8+, 25+, 127-, NK cellsSeverity, mortality
Cardiovascular functionNPs, troponinsCV disease, inflammation, mortality
CK-MB, myoglobin, D-dimer, BNP, NT-proBNP, neutrophil/lymphocyte ratioPrognosis
Coagulation and hemostasisD-dimerMortality
Plasma fibrinogenHyperinflammation, severity
sVCAM-1, vWF, thrombomodulin, sTNFRI, HS, C5b9, PAI-1, alpha-2 antiplasminSeverity
vWF, ADAMTS13Mortality
Endothelial dysfunctionSeverity of pulmonary impairment
Metabolic systemHDL cholesterolRisk of hospitalization
LDL cholesterolInflammation
Vitamin AARDS development, mortality
Metabolomic/proteomic: cAMPMortality
Thyroid hormonesSeverity, mortality
Neurological manifestationsGFAP, NfL, tau, S100B, NSE, inflammatory markersInflammation, severity
D-dimer, LDH, ESR, CRP, lymphocytes, PCT, creatinineOccurrence of ischemic stroke
Kidney and liver functionUrine 11-dehydro-thromboxane B2, 8-hydroxy-2’-deoxyguanosine, L-FABPHospitalization
N-acetyl-β-D-glucosaminidase, β2-microglobulin, α1-microglobulin, L-FABPHyperinflammation
PCT, arterial saturation of oxygen, blood urea nitrogenAcute kidney injury
CreatinineAcute kidney injury, mortality
Urine blood, urine weightMortality
Albumin, direct albumin, neutrophils, lymphocytes, mean corpuscular hemoglobinSeverity

Open in a separate window

ADAMTS, a disintegrin and metalloproteinase with thrombospondin motifs, ARDS, acute respiratory distress syndrome, AST, aspartate aminotransferase, BNP, brain natriuretic peptide, cAMP, adenosine cyclic monophosphate, CD, cluster differentiation, CK-MB, creatine kinase, CRP, C-reactive protein, CV, cardiovascular, ESR, erythrocyte sedimentation rate, GFAP, glial fibrillary acidic protein, HDL, high density lipoproteins, HS, heparan sulfate, IMV, invasive mechanical ventilation, L-FABP, liver-type fatty acid binding protein, LDH, lactate dehydrogenase, LDL, low density lipoproteins, MR-proADM, mid-regional pro-adrenomedullin, NfL, neurofilament light polypeptide, NK, natural killer, NPs, natriuretic peptides, NSE, neuron specific enolase, NT-proBNP, N-terminal pro-hormone, PAI, plasminogen activator inhibitor, PCT, procalcitonin, PPAR, peroxisome proliferator-activated receptors, sTNFRI soluble tumor necrosis factor receptor I, sVCAM-1, vascular cells adhesion molecule-1, TREM, triggering receptor expressed on myeloid cells, TRP, transient receptor potential channel, vWF, von Willebrand.

An external file that holds a picture, illustration, etc.
Object name is fimmu-13-857573-g001.jpg

Figure 1

COVID-19 multiple organ dysfunction. This figure shows the potential for multiorgan involvement in COVID-19. Respiratory (AIP, acute interstitial pneumonia; ARDS, acute respiratory distress syndrome; DAD, diffuse alveolar damage), renal, cardiovascular, coagulative/hemostatic, liver, gastrointestinal, metabolic/endocrine, and cerebral functions and systems, as well as their possible alterations, are presented.

Go to:

Diagnostic and Prognostic Value of Biomarkers

Biomarkers reflecting multiple organ involvement and/or pharmacological effects have been widely examined in critically ill patients. Some of these biomarkers are also used to monitor dysfunction in distinct organs at the same time, due to their redundancy or non-specificity. However, the most appropriate biomarkers to be studied in critically ill patients with COVID-19 have yet to be defined. Figure 2 depicts a proposed algorithm for critical care management which includes the investigation of biomarkers in severe COVID-19 patients at ICU admission.

An external file that holds a picture, illustration, etc.
Object name is fimmu-13-857573-g002.jpg

Figure 2

Proposed algorithm for the management of patients with COVID-19 at ICU admission. This figure shows a potential algorithm for initial patient management at ICU admission, including the most useful biomarkers to be used in the COVID-19 critical care setting. Neurological system: sequential transcranial doppler (TCD) and/or optic nerve sheath diameter (ONSD) in sedated patients for whom conventional neurological evaluation is impossible. Cardiovascular system: electrocardiogram and echocardiography, as well as continuous monitoring of mean arterial pressure (MAP) and heart rate (HR), are suggested on ICU admission. Respiratory system: computed tomography (CT) scan is the gold standard; if not feasible, chest X-ray, CT angiography, and/or lung ultrasound should be performed. Lactate dehydrogenase (LDH), C-reactive protein (CRP), neuron specific enolase (NSE), neurofilament light polypeptide (NfL), glial fibrillary acidic protein (GFAP), thyrotropic stimulating hormone (TSH), NGAL, aspartate transaminase (AST), alanine aminotransferase (ALT), gamma-glutamyl transferase (γGT), interleukin-6 (IL-6). BNP, brain natriuretic peptide; UN, urea nitrogen; NT-proBNP, N-terminal pro-hormone.

Respiratory System

The lungs are usually the organs affected primarily by SARS-CoV-2, due to their large and highly vascularized surface area (11). The pathogenesis of COVID-19 in the lung includes an initial phase of local inflammation, endothelial cell damage, and antifibrinolytic activation in the upper and lower respiratory tracts, followed by repair mechanisms that can elicit the restoration of normal pulmonary architecture. Inflammation is followed by platelet recruitment with degranulation, clot formation, altered vessel permeability, and accumulation of leukocytes in the injury site, leading to the recruitment of other inflammatory cells with the involvement of specific cytokines (i.e., IL-4, IL-13, transforming growth factor-β) that are also responsible for pro-fibrotic activity (12).

SARS-CoV-2 lung infection causes a wide variety of clinical manifestations and symptoms, from asymptomatic, mild, and moderate disease to severe COVID-19. Severe and critical illness accounts for up to 14% and 5% of cases, respectively, with the ARDS occurring in 10-20% of patients; multiorgan failure and death may supervene (1314). Various phenotypes have been identified by computed tomography (CT) (1516), including phenotype L or 1, which is characterized by low compliance, altered ventilation and perfusion, and shunting with focal hypo/hyper-perfused ground-glass opacities; and phenotype H or 2, which is identified by an inhomogeneous distribution of atelectasis with a patchy ARDS-like pattern (1718). Progressive evolution of COVID-19 (19) may lead to phenotype F, caused by mechanical stretch of lung epithelial cells and pathological fibro-proliferation and remodeling of the extracellular matrix, with increased expression of pro-fibrotic markers, as is mainly typical of severe forms of lung disease (20).

Although not specific to pulmonary disease, several biomarkers of different stages of lung involvement in COVID-19 have been identified and have been associated with pulmonary and systemic hyperinflammation and fibrotic damage (12). In the early disease course, neuron-specific enolase (NSE) can be used to differentiate patients who are going to develop dyspnea (21). On admission, higher lymphocyte and platelet counts and lower ferritin, D-dimer, lactate dehydrogenase (LDH), and aspartate transaminase (AST) have all been associated with lower risk of mortality in COVID-19 patients who ultimately required intubation and mechanical ventilation (22). Surfactant protein-D, angiopoietin-2, triggering receptor expressed on myeloid cell (TREM)-1, and TREM-2 levels were found to be higher in mild/moderate and severe/critical COVID-19 pneumonia than in asymptomatic and uncomplicated cases. Moreover, these biomarkers correlated well with clinical severity (2324). In severe COVID-19 cases, total thiol, ferritin, and LDH were identified as prognostic biomarkers for ARDS development (25). At extubation, COVID-19 survivors had higher platelet counts and neutrophil-to-lymphocyte ratios and lower C-reactive protein (CRP), D-dimer, ferritin, LDH, and AST (22).

Infection and Systemic Inflammatory Response

Following SARS-CoV-2 invasion of the host cells, the virus replicates at the infection site, thus triggering activation of the innate and adaptive immune responses (26). Neutrophils are rapidly recruited to infection foci, while innate cells recognize the virus and secrete multiple cytokines. Antigen-presenting cells recognize viral antigens which are carried to the local lymph nodes, while activating the T-helper cell response, which is also responsible for stimulating B cells to secrete antibodies (27). The systemic immune-inflammatory response is activated; if left unchecked, this may progress to multiorgan illness (28).

Patients with severe COVID-19 are highly susceptible to superimposed bacterial, fungal, and viral infections, including ventilator-associated pneumonia and bloodstream infection, among others (2930). As for systemic biomarkers of infection, procalcitonin is a predictor of disease severity (31), and can be useful to guide antimicrobial stewardship (3233). Another study found an association between procalcitonin and mortality in COVID-19 patients more than 75 years old (34). Neutrophil count was also predictive of clinical outcome in hospitalized COVID-19 patients (35), while the neutrophil-to-lymphocyte ratio was strongly associated with severity and mortality in COVID-19 (36). Additionally, total lymphocyte count, cluster differentiation (CD)3+, CD4+, CD8+, CD25+, CD127 T cells, and natural killer (NK) cells were found to be depressed in severe COVID-19 (37), whereas C-reactive protein, erythrocyte sedimentation rate, and IL-6 – common markers of inflammation – were elevated (38).

Cardiovascular System

SARS-CoV-2 can directly trigger endothelial dysfunction, causing a status known as COVID-19-associated coagulopathy. After viral entry into the cells, increased vascular permeability and tissue factor expression in subendothelial cells, with activation of platelets and leukocytes, may trigger the coagulation cascade. Endothelial damage and a generalized inflammatory state are drivers of thrombosis, which can contribute to cardiovascular manifestations (39).

Cardiovascular manifestations of COVID-19 are frequently reported (240). Acute heart failure and exacerbation of chronic heart failure are reported in up to 20-30% of hospitalized patients, and carry high mortality rates, especially in patients with severe comorbidities (4143). Acute coronary syndrome has been reported in a high proportion of patients, probably because of plaque rupture, coronary spasm, or microthrombi triggered by systemic inflammation and cytokine storm (44). In general, the mechanisms underlying cardiovascular manifestations include increased cardiac workload, hypoxemia, hypervolemia, myocardial injury, arrhythmias, myocarditis, stress-induced cardiomyopathy, acute kidney injury, and, as noted above, systemic inflammatory response with the release of several cytokines and chemokines (45). Triggering mechanisms may be attributed to an imbalance between heightened cardiac workload and reduced oxygen supply secondary to systemic conditions, with possible type-2 myocardial infarction (46).

Cardiac biomarkers (47), electrocardiography (ECG), and transthoracic echocardiography (TTE) play a pivotal role in risk stratification and early detection of cardiovascular complications, as well as to guide treatment (4849). Recent evidence confirmed that cardiac biomarkers, including natriuretic peptides (NPs) and troponins, may reflect cardiovascular involvement and inflammation in COVID-19, and are strongly associated with poor prognosis and mortality (415053). In some cases, troponin elevation in COVID-19 has been associated with ECG changes (54), ICU admission, and in-hospital death (5556). However, despite the confirmed prognostic impact of troponins, routine testing is still a matter of debate, because of several other variables that have been associated with outcome and prognosis (48). Additionally, pre-existing cardiac disease and/or acute stress injury may justify mild elevations in cardiac troponins, while myocarditis, Takotsubo syndrome, type 2 myocardial infarction triggered by severe respiratory failure, systemic hypoxemia, or shock are mostly associated with more marked increase in troponins (445758). Other cardiac and non-cardiac biomarkers are common findings in COVID-19-associated cardiovascular disease, including creatine kinase (CK)-MB, myoglobin, D-dimer, brain natriuretic peptide (BNP) and its N-terminal pro-hormone (NT-proBNP), and neutrophil-to-lymphocyte ratio (555961). Myoglobin seems to offer higher prognostic accuracy than other cardiac-specific biomarkers (troponins and CK-MB) in COVID-19 (62). Moreover, mid-regional pro-adrenomedullin (MR-proADM) levels were found to be associated with endothelial dysfunction and mortality in COVID-19, potentially making it an optimal biomarker for the prediction of survival in this patient population (63). Nevertheless, only limited evidence exists so far to define any of these biomarkers as an independent predictor of prognosis in COVID-19 (4864).

Coagulation and Hemostasis

Coagulation derangement is a well-known systemic effect of COVID-19 that can originate from direct or indirect viral impact on the endothelium, or from immunothrombosis (65). COVID-19 can cause alterations in the coagulation cascade, with imbalance of the regulatory mechanisms of coagulation and fibrinolysis, altered platelet function, and a hyperinflammatory response (1165). In this context, D-dimer has been identified among the first altered coagulation biomarkers in COVID-19, and is predictive of mortality on admission (66). Similarly, plasma fibrinogen appears to be associated with hyperinflammation and disease severity in COVID-19 (67). A coagulopathy signature diagnostic of COVID-19 has been identified, including elevated levels of soluble vascular cell adhesion molecule (sVCAM)-1 (68), von Willebrand Factor (vWF), thrombomodulin, soluble tumor necrosis factor (TNF) receptor I (sTNFRI), heparan sulfate, C5b9 complement, plasminogen activator inhibitor (PAI)-1, and alpha-2 antiplasmin, among others. Some of these markers, such as sVCAM-1, vWF, sTNFRI, and heparan sulfate, were also associated with disease severity (69). Fibrinogen, thrombin peak, vWF, and ADAMTS13 at admission and elevated vWF : Ag to ADAMTS13 activity ratio were associated with severity and higher risk of death (7071). Endothelial dysfunction seems to be persistent after resolution of COVID-19, and directly associated with the severity of pulmonary impairment (72).

Metabolic Function

Sphingolipid metabolism regulates the inflammation and immune response through the conversion of sphingosine to sphingosine 1-phosphate, increasing the release of lymphocytes into the blood, with subsequent systemic inflammation and release of cytokines and chemokines in COVID-19 (73). Like lipid metabolism, fat-soluble vitamins such as vitamin D have been implicated in suppressing the cytokine storm and enhancing the immune response (74). Investigating lipid metabolism and its biomarkers could thus be of diagnostic and prognostic value in COVID-19.

Metabolic comorbidities including obesity, diabetes, cardiovascular, and hypertension have been associated with poor prognosis in COVID-19 (75). A certain degree of metabolic dysregulation has been found in COVID-19, possibly due to immune-triggered inflammation and hypercoagulability, as well as microbial changes in host physiology (1076). Indeed, COVID-19 patients with lower levels of high-density lipoprotein (HDL) cholesterol are more susceptible to hospitalization, while low-density lipoprotein (LDL) cholesterol was associated with higher inflammation (77). Critically ill patients with COVID-19 showed significantly lower levels of vitamin A than non-critical ones, and this was associated with higher inflammation (78). Vitamin A levels below 0.2 mg/L were significantly associated with the developments of ARDS and higher mortality (78). Vitamin D, a well-known regulator of phosphate and calcium metabolism with immunomodulatory functions, seems to not influence mortality or hospital length of stay in COVID-19 (7980). Finally, thyroid hormones showed marked association with disease severity and mortality, suggesting the importance of early assessment of thyroid function – and, when necessary, initiation of treatment – in hospitalized COVID-19 patients (81).

Neurologic Involvement

Pathogenetic mechanisms of SARS-CoV-2 neurologic manifestations include possible spreading of the virus across the blood-brain barrier via leukocyte migration or sluggish movement of blood within the microcirculation, thus binding to endothelial cells. Cells which may present ACE2 receptors, including neurons, astrocytes, and oligodendrocytes, can all be affected directly by viral entry and activate the local immune response. As a consequence of neuronal involvement, several biomarkers of neuroinflammation and damage can be detected (82).

Although COVID-19 rarely affects the brain as a primary manifestation, neurological complications are common in this patient population (8284). Patients with neurological complications, compared to those without, may experience longer hospital stays, and the duration of mechanical ventilation can be associated with the risk of developing new neurological complications (8485). CT and magnetic resonance imaging (MRI) are considered the gold standard for detecting cerebral derangements, although the use of methods which involve exposure to ionizing radiation in non-primarily brain-injured patients can only be justified in case of high suspicion of neurological complications (86). The use of multimodal neuromonitoring has received increasing attention as a means of identifying patients at higher risk of brain derangement because of its low cost, speed, safety, and ready availability. However, the use of neuromonitoring tools is still mainly limited to specific settings (i.e., ICU) and patient populations (i.e., those with primary brain injury) (84).

Other than imaging, blood biomarkers can detect brain damage and predict prognosis efficiently. Blood biomarkers for the study of brain derangements include glial fibrillary acidic protein (GFAP), neurofilament light polypeptide (NfL), tau, S100B calcium binding protein, NSE, and inflammatory markers. Increased GFAP staining has been found in postmortem analysis of brain tissue from patients with COVID-19 (87), and NfL was significantly associated with COVID-19 status (88). Another study reported that GFAP was increased in both moderate and severe COVID-19 cases, whereas serum NfL was increased only in severe cases compared to controls (89). However, another study reported that serum NfL, although elevated across patients hospitalized with COVID-19, was not associated with neurological manifestations. Additionally, the usual close correlation between cerebrospinal fluid and serum NfL was not found, suggesting serum NfL elevation in the non-neurological patients may reflect peripheral nerve damage in response to severe illness (90). In COVID-19 patients with altered NfL and GFAP, values of these markers had normalized in all individuals at 6-month follow-up, suggesting that post-COVID-19 neurological sequelae may be not accompanied by ongoing brain injury (91). Inflammatory and coagulatory markers like D-dimer, LDH, erythrocyte sedimentation rate (ESR), and CRP were independently associated with the occurrence of ischemic stroke in COVID-19 (9293), while higher age, diabetes mellitus, and hypertension were found not to be significant predictors of stroke in this population, despite being known predictors of non-COVID-19 stroke (93). Levels of lymphocytes, procalcitonin, and creatinine were higher in COVID-19 stroke patients (94). S100B was higher in patients with mild and severe COVID-19 than in healthy controls, and may be a marker of disease severity (95). Antiphospholipid antibodies (i.e., anti-phosphatidylserine/prothrombin) were higher in COVID-19 patients, particularly those with neurological manifestations, than in controls. In contrast, anticardiolipin antibodies were not associated with neurologic involvement in COVID-19 (96).

Kidney and Liver

COVID-19 may cause kidney and liver injury by either direct infection of cells, via host immune clearance and immune tolerance disorders, endothelium-associated vasculitis, thrombus formation, metabolism and glucose disorder, or tissue hypoxia. As a consequence, biomarkers of endothelial, renal, hepatic, vascular, or hypoxic damage can help in the detection of new organ involvement and assist in determining prognosis (97).

As part of multiorgan involvement in COVID-19, kidney function might be altered directly by viral invasion or may occur secondary to multiple organ failure due to systemic inflammation or aggressive therapies (98). Around 25% of patients hospitalized with COVID-19 were reported to develop acute kidney injury, including low molecular weight proteinuria, Fanconi syndrome, and tubular injury (98). Moreover, regional inflammation, endothelial injury, and microthrombi have been identified as major causative factors of renal pathology in COVID-19. This is also sustained by the fact that anti-inflammatory drugs, such as steroids, play a key role in limiting renal disease progression (98). Classic diagnostic biomarkers of kidney damage include creatinine, neutrophil gelatinase-associated lipocalin (NGAL), cystatin C, kidney injury molecule-1 (KIM-1), blood and urinary urea nitrogen, and urinary proteins (99100).

Novel urinary biomarkers have been proposed in COVID-19, including urine 11-dehydro-thromboxane B2, 8-hydroxy-2′-deoxyguanosine, and liver-type fatty acid binding protein (L-FABP) levels, all of which were higher in this patient cohort at the time of hospitalization (101). N-acetyl-β-D-glucosaminidase, β2-microglobulin, α1-microglobulin, and L-FABP, which are all markers of tubular injury, were significantly associated with inflammation, as were IL-6 levels (102). Indeed, another observational study confirmed the association between pro-inflammatory cytokines, urinary cytokines, and urinary kidney injury markers (103). Procalcitonin was associated with acute kidney injury in COVID-19, and a score including simple and easily accessible variables such as procalcitonin, arterial saturation of oxygen, and blood urea nitrogen was shown to be predictive of acute kidney injury (104).

Altered serum creatinine levels with decreased kidney function at admission and up to 24 hours thereafter were significantly associated with acute kidney injury and in-hospital mortality (105). Additionally, urine blood >0.03 mg/dL and urine specific gravity >1.026 were associated with acute kidney injury, ICU admission, and higher mortality (106).

Abnormal liver and hepatobiliary function have been also identified in COVID-19 (107). A systematic review and meta-analysis showed a cumulative prevalence of liver disease of 24% in COVID-19, with possible alterations in albuminemia, liver enzymes, and total bilirubin (108). Recent findings showed that some liver and renal biomarkers, including albumin, direct bilirubin, neutrophil and lymphocyte counts, and mean corpuscular hemoglobin, are associated with risk of developing severe COVID-19 (107). Moreover, the presence of pre-existing liver fibrosis with silent liver injury significantly influenced mortality in COVID-19 (109)

Future Perspectives: Metabolomic and Proteomic Biomarkers and Machine Learning Models

Given the significant immune dysregulation of COVID-19 patients, the interplay between metabolism and immunity may play a pivotal role in the disease course (110). Additionally, oxygen deprivation may affect homeostasis in tissues and organs such as the lung, brain, kidney, and liver. The modulation of oxygen homeostasis and response to hypoxia is mainly mediated by glycolysis and the lactate cycle. This has increased research interest in proteomic and metabolomic methods to investigate pathways linked to energy production and amino acid metabolism in patients with SARS-CoV-2 infections (110). Metabolomic analyses in COVID-19 patients with and without pulmonary fibrosis revealed that pathways including the peroxisome proliferator-activated receptor (PPAR), D-arginine and D-ornithine metabolism, inflammatory tryptophan metabolic pathway (TRP), and alpha-linolenic acid metabolism were significantly increased in fibrotic lungs, thus suggesting that PPAR signaling is one of the main pathways involved in the formation and development of lung fibrosis in COVID-19 (9). A proteomic and metabolomic analysis identified hypoxanthine and betaine as predictors of ICU stay, and early ICU admission, elevated creatinine, and D-dimer were found to be associated with these pathways (8). Longer duration of invasive mechanical ventilation was associated with the kynurenine and p-cresol sulfate pathways (8). Several markers of metabolic function identified via metabolomic analysis were associated with in-hospital mortality, including cyclic adenosine monophosphate (cAMP), which plays a role in SARS-CoV2 endocytosis in the initial phase of the disease (10). Another major signature of the serum metabolome in COVID-19 was lactic acid, as well as spermidine and spermine. Many other metabolites were commonly increased, including glutamate, aspartate, phenylalanine, β-alanine, ornithine, arachidonic acid, choline, and xanthine (110). Recent machine learning models have been developed to support decision making and risk stratification in COVID-19. Most predictive models rely on demographic and clinical variables. However, biomarkers have recently shown good correlation with severity of disease and mortality in COVID-19 modeling (111). One example was a large study of 2,895 consecutive patients with COVID-19 in whom three biomarkers measured at admission were found to reflect pathobiological axes of myocardial injury, altered coagulation, and inflammation. The machine learning model concluded that patients with low levels of these biomarkers were at lower risk of critical disease and in-hospital mortality (112). In conclusion, the alterations found in the serum metabolome of patients with COVID-19 may reflect a more complex systemic derangement affecting carbon and nitrogen liver metabolism, but further research is needed to completely understand the impact of these alterations on routine clinical practice. Machine learning models can be promising in risk stratification in COVID-19. However, further investigations are needed to develop mathematical models that can help clinicians select the right parameters and interpret results.


Laboratory biomarkers have shown significant diagnostic and prognostic value for risk stratification in COVID-19. Furthermore, novel analytic strategies including metabolomics and proteomics offer interesting insights for early detection of patients at higher risk of severe disease and death. However, their limited availability restricts their widespread clinical use. Further investigations are warranted to identify a core set of laboratory biomarkers which can be used in daily clinical practice to easily predict prognosis and outcome in hospitalized patients with severe COVID-19.

Author Contributions

DB and ML-P: review, design, writing, editing. HC-F-N and PP: editing. PR: review, design, editing, senior contribution. All authors contributed to the article and approved the submitted version.


This work was supported by the Brazilian Council for Scientific and Technological Development (COVID-19-CNPq; 401700/2020-8 and 403485/2020-7); Rio de Janeiro State Research Foundation (COVID-19-FAPERJ; E-26/210.181/2020); and Funding Authority for Studies and Projects (01200008.00), Brazil.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Go to:


The authors express their gratitude to Mrs. Moira Elizabeth Schottler and Mr Filippe Vasconcellos for their assistance in editing the paper.

Go to:


ACE2, angiotensin receptor 2; ARDS, acute respiratory distress syndrome; AST, aspartate transaminase; BNP, brain natriuretic peptide; cAMP, cyclic adenosine monophosphate; CD, cluster differentiation; CK, creatine kinase; COVID-19, coronavirus disease 2019; CRP, C-reactive protein; CT, computed tomography; ECG, electrocardiography; ESR, erythrocyte sedimentation rate; GFAP, glial fibrillary acidic protein; HDL, high-density lipoprotein; ICU, intensive care unit; IL, interleukin; KIM, kidney injury molecule; L-FABP, liver-type fatty acid binding protein; LDH, lactate dehydrogenase; LDL, low-density lipoprotein; MR-proADM, mid-regional pro-adrenomedullin; MRI, magnetic resonance imaging; NfL, neurofilament light polypeptide; NGAL, neutrophil gelatinase-associated lipocalin; NK, natural killer; NP, natriuretic peptides; NSE, neuron-specific enolase; NT-proBNP, N-terminal pro-hormone BNP; PPAR, peroxisome proliferator-activated receptor; RASS, renin-angiotensin- aldosterone system; SARS-CoV-2, severe acute respiratory syndrome-coronavirus-2; ssRNA, single-stranded ribonucleic acid; sTNFRI, soluble TNF receptor I; sVCAM, soluble vascular cell adhesion molecule; TMPRSS2, transmembrane serine protease 2; TNF, tumor necrosis factor; TREM, triggering receptor expressed on myeloid cell; TRP, tryptophan metabolic pathway; TTE, transthoracic electrocardiography; vWF, von Willebrand Factor.

Go to:


1. Robba C, Battaglini D, Pelosi P, Rocco PRM. Multiple Organ Dysfunction in SARS-CoV-2: MODS-CoV-2. Expert Rev Respir Med (2020) 14:865–8. doi:  10.1080/17476348.2020.1778470 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

2. Huang C, Wang Y, Li X, Ren L, Zhao J, Hu Y, et al.. Clinical Features of Patients Infected With 2019 Novel Coronavirus in Wuhan, China. Lancet (2020) 395:497–506. doi: 10.1016/S0140-6736(20)30183-5 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

3. Zhang C, Wu Z, Li J-W, Zhao H, Wang G-Q. Cytokine Release Syndrome in Severe COVID-19: Interleukin-6 Receptor Antagonist Tocilizumab May Be the Key to Reduce Mortality. Int J Antimicrob Agents (2020) 55:105954. doi:  10.1016/j.ijantimicag.2020.105954 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

4. Liu J, Li S, Liu J, Liang B, Wang X, Wang H, et al.. Longitudinal Characteristics of Lymphocyte Responses and Cytokine Profiles in the Peripheral Blood of SARS-CoV-2 Infected Patients. EBioMedicine (2020) 55:102763. doi:  10.1016/j.ebiom.2020.102763 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

5. Ackermann M, Verleden SE, Kuehnel M, Haverich A, Welte T, Laenger F, et al.. Pulmonary Vascular Endothelialitis, Thrombosis, and Angiogenesis in Covid-19. N Engl J Med (2020) 383:120–8. doi:  10.1056/NEJMoa2015432 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

6. Leisman DE, Ronner L, Pinotti R, Taylor MD, Sinha P, Calfee CS, et al.. Cytokine Elevation in Severe and Critical COVID-19: A Rapid Systematic Review, Meta-Analysis, and Comparison With Other Inflammatory Syndromes. Lancet Respir Med (2020) 8:1233–44. doi:  10.1016/S2213-2600(20)30404-5 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

7. Andrianto, Al-Farabi MJ, Nugraha RA, Marsudi BA, Azmi Y. Biomarkers of Endothelial Dysfunction and Outcomes in Coronavirus Disease 2019 (COVID-19) Patients: A Systematic Review and Meta-Analysis. Microvasc Res (2021) 138:104224. doi:  10.1016/j.mvr.2021.104224 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

8. Taleb S, Yassine HM, Benslimane FM, Smatti MK, Schuchardt S, Albagha O, et al.. Predictive Biomarkers of Intensive Care Unit and Mechanical Ventilation Duration in Critically-Ill Coronavirus Disease 2019 Patients. Front Med (2021) 8:733657. doi:  10.3389/fmed.2021.733657 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

9. Yang J, Chen C, Chen W, Huang L, Fu Z, Ye K, et al.. Proteomics and Metabonomics Analyses of Covid-19 Complications in Patients With Pulmonary Fibrosis. Sci Rep (2021) 11:14601. doi:  10.1038/s41598-021-94256-8 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

10. Saccon E, Bandera A, Sciumè M, Mikaeloff F, Lashari AA, Aliberti S, et al.. Distinct Metabolic Profile Associated With a Fatal Outcome in COVID-19 Patients During the Early Epidemic in Italy. Microbiol Spectr (2021) 9:e00549–21. doi:  10.1128/Spectrum.00549-21 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

11. Lopes-Pacheco M, Silva PL, Cruz FF, Battaglini D, Robba C, Pelosi P, et al.. Pathogenesis of Multiple Organ Injury in COVID-19 and Potential Therapeutic Strategies. Front Physiol (2021) 12:593223. doi:  10.3389/fphys.2021.593223 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

12. Vianello A, Guarnieri G, Braccioni F, Lococo S, Molena B, Cecchetto A, et al.. The Pathogenesis, Epidemiology and Biomarkers of Susceptibility of Pulmonary Fibrosis in COVID-19 Survivors. Clin Chem Lab Med (2022) 60:307–16. doi:  10.1515/cclm-2021-1021 [PubMed] [CrossRef] [Google Scholar]

13. Chen N, Zhou M, Dong X, Qu J, Gong F, Han Y, et al.. Epidemiological and Clinical Characteristics of 99 Cases of 2019 Novel Coronavirus Pneumonia in Wuhan, China: A Descriptive Study. Lancet (2020) 395:507–13. doi:  10.1016/S0140-6736(20)30211-7 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

14. Zhou M, Zhang X, Qu J. Coronavirus Disease 2019 (COVID-19): A Clinical Update. Front Med (2020) 14:126–35. doi:  10.1007/s11684-020-0767-8 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

15. Orlandi D, Battaglini D, Robba C, Viganò M, Bergamaschi G, Mignatti T, et al.. COVID-19 Phenotypes, Lung Ultrasound, Chest Computed Tomography, and Clinical Features in Critically Ill Mechanically Ventilated Patients. Ultrasound Med Biol (2021) 41(12):3323–32. doi:  10.1016/j.ultrasmedbio.2021.07.014. [PMC free article] [PubMed] [CrossRef] [Google Scholar]

16. Pelosi P, Ball L, Barbas CSV, Bellomo R, Burns KEA, Einav S, et al.. Personalized Mechanical Ventilation in Acute Respiratory Distress Syndrome. Crit Care (2021) 25:250. doi:  10.1186/s13054-021-03686-3 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

17. Gattinoni L, Chiumello D, Caironi P, Busana M, Romitti F, Brazzi L, et al.. COVID-19 Pneumonia: Different Respiratory Treatments for Different Phenotypes? Intensive Care Med (2020) 46:1099–102. doi:  10.1007/s00134-020-06033-2 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

18. Robba C, Battaglini D, Ball L, Patroniti N, Loconte M, Brunetti I, et al.. Distinct Phenotypes Require Distinct Respiratory Management Strategies in Severe COVID-19. Respir Physiol Neurobiol (2020) 279:103455. doi: 10.1016/j.resp.2020.103455 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

19. Tonelli R, Marchioni A, Tabbì L, Fantini R, Busani S, Castaniere I, et al.. Spontaneous Breathing and Evolving Phenotypes of Lung Damage in Patients With COVID-19: Review of Current Evidence and Forecast of a New Scenario. J Clin Med (2021) 10:975. doi:  10.3390/jcm10050975 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

20. George PM, Wells AU, Jenkins RG. Pulmonary Fibrosis and COVID-19: The Potential Role for Antifibrotic Therapy. Lancet Respir Med (2020) 8:807–15. doi:  10.1016/S2213-2600(20)30225-3 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

21. Cione E, Siniscalchi A, Gangemi P, Cosco L, Colosimo M, Longhini F, et al.. Neuron-Specific Enolase Serum Levels in COVID-19 Are Related to the Severity of Lung Injury. PLoS One (2021) 16:e0251819. doi:  10.1371/journal.pone.0251819 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

22. Topp G, Bouyea M, Cochran-Caggiano N, Ata A, Torres P, Jacob J, et al.. Biomarkers Predictive of Extubation and Survival of COVID-19 Patients. Cureus (2021) 13:e15462. doi:  10.7759/cureus.15462 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

23. Alay H, Laloglu E. The Role of Angiopoietin-2 and Surfactant Protein-D Levels in SARS-CoV-2-Related Lung Injury: A Prospective, Observational, Cohort Study. J Med Virol (2021) 93:6008–15. doi:  10.1002/jmv.27184 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

24. Kerget F, Kerget B, İba Yılmaz S, Kızıltunç A. Evaluation of the Relationship Between TREM-1/TREM-2 Ratio and Clinical Course in COVID-19 Pneumonia. Int J Clin Pract (2021) 75(10):e14697. doi:  10.1111/ijcp.14697 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

25. Martinez Mesa A, Cabrera César E, Martín-Montañez E, Sanchez Alvarez E, Lopez PM, Romero-Zerbo Y, et al.. Acute Lung Injury Biomarkers in the Prediction of COVID-19 Severity: Total Thiol, Ferritin and Lactate Dehydrogenase. Antioxidants (2021) 10:1221. doi:  10.3390/antiox10081221 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

26. V’kovski P, Kratzel A, Steiner S, Stalder H, Thiel V. Coronavirus Biology and Replication: Implications for SARS-CoV-2. Nat Rev Microbiol (2021) 19:155–70. doi:  10.1038/s41579-020-00468-6 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

27. Rouse BT, Sehrawat S. Immunity and Immunopathology to Viruses: What Decides the Outcome? Nat Rev Immunol (2010) 10:514–26. doi:  10.1038/nri2802 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

28. Hamming I, Timens W, Bulthuis M, Lely A, Navis G, van Goor H. Tissue Distribution of ACE2 Protein, the Functional Receptor for SARS Coronavirus. A First Step Understanding SARS Pathogen J Pathol (2004) 203:631–7. doi:  10.1002/path.1570 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

29. Ippolito M, Misseri G, Catalisano G, Marino C, Ingoglia G, Alessi M, et al.. Ventilator-Associated Pneumonia in Patients With COVID-19: A Systematic Review and Meta-Analysis. Antibiotics (2021) 10:545. doi:  10.3390/antibiotics10050545 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

30. Ippolito M, Simone B, Filisina C, Catalanotto FR, Catalisano G, Marino C, et al.. Bloodstream Infections in Hospitalized Patients With COVID-19: A Systematic Review and Meta-Analysis. Microorganisms (2021) 9:2016. doi:  10.3390/microorganisms9102016 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

31. Shen Y, Cheng C, Zheng X, Jin Y, Duan G, Chen M, et al.. Elevated Procalcitonin Is Positively Associated With the Severity of COVID-19: A Meta-Analysis Based on 10 Cohort Studies. Med (B Aires) (2021) 57:594. doi:  10.3390/medicina57060594 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

32. Waris A, Din M, Iqbal N, Yar L, Khalid A, Nawaz M, et al.. Evaluation of Serum Procalcitonin Level as a Biomarker for Disease Severity in COVID-19 Patients. New Microbes New Infect (2021) 43:100922. doi:  10.1016/j.nmni.2021.100922 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

33. Calderon M, Li A, Bazo-Alvarez JC, Dennis J, Baker KF, Schim van der Loeff I, et al.. Evaluation of Procalcitonin-Guided Antimicrobial Stewardship in Patients Admitted to Hospital With COVID-19 Pneumonia. JAC-Antimicrobial Resist (2021) 3(3):dlab133. doi:  10.1093/jacamr/dlab133 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

34. Ticinesi A, Nouvenne A, Prati B, Guida L, Parise A, Cerundolo N, et al.. The Clinical Significance of Procalcitonin Elevation in Patients Over 75 Years Old Admitted for COVID-19 Pneumonia. Mediators Inflamm (2021) 2021:1–10. doi:  10.1155/2021/5593806 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

35. Kåsine T, Dyrhol-Riise AM, Barratt-Due A, Kildal AB, Olsen IC, Nezvalova-Henriksen K, et al.. Neutrophil Count Predicts Clinical Outcome in Hospitalized COVID-19 Patients: Results From the NOR-Solidarity Trial. J Intern Med (2021) 291(12):241–3. doi:  10.1111/joim.13377 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

36. Ulloque-Badaracco JR, Ivan Salas-Tello W, Al-kassab-Córdova A, Alarcón-Braga EA, Benites-Zapata VA, Maguiña JL, et al.. Prognostic Value of Neutrophil-to-Lymphocyte Ratio in COVID-19 Patients: A Systematic Review and Meta-Analysis. Int J Clin Pract (2021) 75:e14596. doi:  10.1111/ijcp.14596 [PubMed] [CrossRef] [Google Scholar]

37. Liu K, Yang T, Peng X, Lv S, Ye X, Zhao T, et al.. A Systematic Meta-Analysis of Immune Signatures in Patients With COVID-19. Rev Med Virol (2021) 31:e2195. doi:  10.1002/rmv.2195 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

38. Iwamura APD, Tavares da Silva MR, Hümmelgen AL, Soeiro Pereira PV, Falcai A, Grumach AS, et al.. Immunity and Inflammatory Biomarkers in COVID-19: A Systematic Review. Rev Med Virol (2021) 31:e2199. doi:  10.1002/rmv.2199 [PubMed] [CrossRef] [Google Scholar]

39. Gorog DA, Storey RF, Gurbel PA, Tantry US, Berger JS, Chan MY, et al.. Current and Novel Biomarkers of Thrombotic Risk in COVID-19: A Consensus Statement From the International COVID-19 Thrombosis Biomarkers Colloquium. Nat Rev Cardiol (2022), 1–21. doi:  10.1038/s41569-021-00665-7 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

40. Wang D, Hu B, Hu C, Zhu F, Liu X, Zhang J, et al.. Clinical Characteristics of 138 Hospitalized Patients With 2019 Novel Coronavirus–Infected Pneumonia in Wuhan, China. JAMA (2020) 323:1061. doi:  10.1001/jama.2020.1585 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

41. Zhou F, Yu T, Du R, Fan G, Liu Y, Liu Z, et al.. Clinical Course and Risk Factors for Mortality of Adult Inpatients With COVID-19 in Wuhan, China: A Retrospective Cohort Study. Lancet (2020) 395:1054–62. doi:  10.1016/S0140-6736(20)30566-3 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

42. Yang J, Zheng Y, Gou X, Pu K, Chen Z, Guo Q, et al.. Prevalence of Comorbidities and its Effects in Patients Infected With SARS-CoV-2: A Systematic Review and Meta-Analysis. Int J Infect Dis (2020) 94:91–5. doi:  10.1016/j.ijid.2020.03.017 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

43. Zhu Z, Wang M, Lin W, Cai Q, Zhang L, Chen D, et al.. Cardiac Biomarkers, Cardiac Injury, and Comorbidities Associated With Severe Illness and Mortality in Coronavirus Disease 2019 (COVID-19): A Systematic Review and Meta-Analysis. Immun Inflammation Dis (2021) 9(4):1071–100. doi:  10.1002/iid3.471 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

44. Schiavone M, Gobbi C, Biondi-Zoccai G, D’Ascenzo F, Palazzuoli A, Gasperetti A, et al.. Acute Coronary Syndromes and Covid-19: Exploring the Uncertainties. J Clin Med (2020) 9:1683. doi:  10.3390/jcm9061683 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

45. Nishiga M, Wang DW, Han Y, Lewis DB, Wu JC. COVID-19 and Cardiovascular Disease: From Basic Mechanisms to Clinical Perspectives. Nat Rev Cardiol (2020) 17:543–58. doi:  10.1038/s41569-020-0413-9 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

46. Jabri A, Kalra A, Kumar A, Alameh A, Adroja S, Bashir H, et al.. Incidence of Stress Cardiomyopathy During the Coronavirus Disease 2019 Pandemic. JAMA Netw Open (2020) 3:e2014780. doi:  10.1001/jamanetworkopen.2020.14780 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

47. De Marzo V, Di Biagio A, Della Bona R, Vena A, Arboscello E, Emirjona H, et al.. Prevalence and Prognostic Value of Cardiac Troponin in Elderly Patients Hospitalized for COVID-19. J Geriatr Cardiol (2021) 18:338–45. doi:  10.11909/j.issn.1671-5411.2021.05.004 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

48. The European Society for Cardiology. ESC Guidance for the Diagnosis and Management of CV Disease During the COVID-19 Pandemic. (2020). Avalable at: 10 June 2020). [Google Scholar]

49. Gilad V, De Marzo V, Guglielmi G, Della BR, Giovinazzo S, Pescetelli F, et al.. Cardiac Point-of-Care Ultrasound in Hospitalized Coronavirus Disease-2019 Patients: Findings and Association With Outcome. J Cardiovasc Med (2022) 23:e3–7. doi:  10.2459/JCM.0000000000001177 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

50. Metkus TS, Sokoll LJ, Barth AS, Czarny MJ, Hays AG, Lowenstein CJ, et al.. Myocardial Injury in Severe COVID-19 Compared With Non–COVID-19 Acute Respiratory Distress Syndrome. Circulation (2021) 143:553–65. doi:  10.1161/CIRCULATIONAHA.120.050543 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

51. Shi S, Qin M, Shen B, Cai Y, Liu T, Yang F, et al.. Association of Cardiac Injury With Mortality in Hospitalized Patients With COVID-19 in Wuhan, China. JAMA Cardiol (2020) 5:802. doi:  10.1001/jamacardio.2020.0950 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

52. Guan W, Ni Z, Hu Y, Liang W, Ou C, He J, et al.. Clinical Characteristics of Coronavirus Disease 2019 in China. N Engl J Med (2020) 382:1708–20. doi:  10.1056/NEJMoa2002032 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

53. Ni W, Yang X, Liu J, Bao J, Li R, Xu Y, et al.. Acute Myocardial Injury at Hospital Admission Is Associated With All-Cause Mortality in COVID-19. J Am Coll Cardiol (2020) 76:124–5. doi:  10.1016/j.jacc.2020.05.007 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

54. Doyen D, Moceri P, Ducreux D, Dellamonica J. Myocarditis in a Patient With COVID-19: A Cause of Raised Troponin and ECG Changes. Lancet (2020) 395:1516. doi:  10.1016/S0140-6736(20)30912-0 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

55. Cipriani A, Capone F, Donato F, Molinari L, Ceccato D, Saller A, et al.. Cardiac Injury and Mortality in Patients With Coronavirus Disease 2019 (COVID-19): Insights From a Mediation Analysis. Intern Emerg Med (2021) 16:419–27. doi:  10.1007/s11739-020-02495-w [PMC free article] [PubMed] [CrossRef] [Google Scholar]

56. Lombardi CM, Carubelli V, Iorio A, Inciardi RM, Bellasi A, Canale C, et al.. Association of Troponin Levels With Mortality in Italian Patients Hospitalized With Coronavirus Disease 2019. JAMA Cardiol (2020) 5:1274. doi:  10.1001/jamacardio.2020.3538 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

57. Cameli M, Pastore MC, Mandoli GE, D’Ascenzi F, Focardi M, Biagioni G, et al.. COVID-19 and Acute Coronary Syndromes: Current Data and Future Implications. Front Cardiovasc Med (2021) 7:593496. doi:  10.3389/fcvm.2020.593496 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

58. Chieffo A, Stefanini GG, Price S, Barbato E, Tarantini G, Karam N, et al.. EAPCI Position Statement on Invasive Management of Acute Coronary Syndromes During the COVID-19 Pandemic. Eur Heart J (2020) 41:1839–51. doi:  10.1093/eurheartj/ehaa381 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

59. Chung MK, Zidar DA, Bristow MR, Cameron SJ, Chan T, Harding CV, et al.. COVID-19 and Cardiovascular Disease. Circ Res (2021) 128:1214–36. doi:  10.1161/CIRCRESAHA.121.317997 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

60. Zinellu A, Sotgia S, Fois AG, Mangoni AA, Serum CK-MB. COVID-19 Severity and Mortality: An Updated Systematic Review and Meta-Analysis With Meta-Regression. Adv Med Sci (2021) 66:304–14. doi:  10.1016/j.advms.2021.07.001 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

61. Zhan L, Liu Y, Cheng Y, Guo W, Yang J. Predictive Value of Neutrophil/Lymphocyte Ratio (NLR) on Cardiovascular Events in Patients With COVID-19. Int J Gen Med (2021) 14:3899–907. doi:  10.2147/IJGM.S317380 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

62. Yu J-S, Chen R-D, Zeng L-C, Yang H-K, Li H. Myoglobin Offers Higher Accuracy Than Other Cardiac-Specific Biomarkers for the Prognosis of COVID-19. Front Cardiovasc Med (2021) 8:686328. doi:  10.3389/fcvm.2021.686328 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

63. García de Guadiana-Romualdo L, Martínez Martínez M, Rodríguez Mulero MD, Esteban-Torrella P, Hernández Olivo M, Alcaraz García MJ, et al.. Circulating MR-proADM Levels, as an Indicator of Endothelial Dysfunction, for Early Risk Stratification of Mid-Term Mortality in COVID-19 Patients. Int J Infect Dis (2021) 111:211–8. doi:  10.1016/j.ijid.2021.08.058 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

64. Sorrentino S, Cacia M, Leo I, Polimeni A, Sabatino J, Spaccarotella CAM, et al.. B-Type Natriuretic Peptide as Biomarker of COVID-19 Disease Severity—A Meta-Analysis. J Clin Med (2020) 9:2957. doi:  10.3390/jcm9092957 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

65. Robba C, Battaglini D, Ball L, Valbusa A, Porto I, Della Bona R, et al.. Coagulative Disorders in Critically Ill COVID-19 Patients With Acute Distress Respiratory Syndrome: A Critical Review. J Clin Med (2021) 10:140. doi:  10.3390/jcm10010140 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

66. Poudel A, Poudel Y, Adhikari A, Aryal BB, Dangol D, Bajracharya T, et al.. D-Dimer as a Biomarker for Assessment of COVID-19 Prognosis: D-Dimer Levels on Admission and Its Role in Predicting Disease Outcome in Hospitalized Patients With COVID-19. PLoS One (2021) 16:e0256744. doi:  10.1371/journal.pone.0256744 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

67. Sui J, Noubouossie DF, Gandotra S, Cao L. Elevated Plasma Fibrinogen Is Associated With Excessive Inflammation and Disease Severity in COVID-19 Patients. Front Cell Infect Microbiol (2021) 11:734005. doi:  10.3389/fcimb.2021.734005 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

68. Blot M, de Maistre E, Bourredjem A, Quenot J-P, Nguyen M, Bouhemad B, et al.. Specific Features of the Coagulopathy Signature in Severe COVID-19 Pneumonia. Front Med (2021) 8:675191. doi:  10.3389/fmed.2021.675191 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

69. Fernández S, Moreno-Castaño AB, Palomo M, Martinez-Sanchez J, Torramadé-Moix S, Téllez A, et al.. Distinctive Biomarker Features in The Endotheliopathy of COVID-19 and Septic Syndromes. Shock (2021) 57(1):95–105. doi:  10.1097/SHK.0000000000001823. Online ahead of print. [PMC free article] [PubMed] [CrossRef] [Google Scholar]

70. Billoir P, Alexandre K, Duflot T, Roger M, Miranda S, Goria O, et al.. Investigation of Coagulation Biomarkers to Assess Clinical Deterioration in SARS-CoV-2 Infection. Front Med (2021) 8:670694. doi:  10.3389/fmed.2021.670694 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

71. Joly BS, Darmon M, Dekimpe C, Dupont T, Dumas G, Yvin E, et al.. Imbalance of Von Willebrand Factor and ADAMTS13 Axis Is Rather a Biomarker of Strong Inflammation and Endothelial Damage Than a Cause of Thrombotic Process in Critically Ill COVID-19 Patients. J Thromb Haemost (2021) 19:2193–8. doi:  10.1111/jth.15445 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

72. Ambrosino P, Calcaterra I, Molino A, Moretta P, Lupoli R, Spedicato GA, et al.. Persistent Endothelial Dysfunction in Post-Acute COVID-19 Syndrome: A Case-Control Study. Biomedicines (2021) 9:957. doi:  10.3390/biomedicines9080957 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

73. Janneh AH, Kassir MF, Dwyer CJ, Chakraborty P, Pierce JS, Flume PA, et al.. Alterations of Lipid Metabolism Provide Serologic Biomarkers for the Detection of Asymptomatic Versus Symptomatic COVID-19 Patients. Sci Rep (2021) 11:14232. doi:  10.1038/s41598-021-93857-7 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

74. Razdan K, Singh K, Singh D. Vitamin D Levels and COVID-19 Susceptibility: Is There Any Correlation? Med Drug Discov (2020) 7:100051. doi:  10.1016/j.medidd.2020.100051 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

75. Fakhroo AD, Al Thani AA, Yassine HM. Markers Associated With COVID-19 Susceptibility, Resistance, and Severity. Viruses (2020) 13:45. doi:  10.3390/v13010045 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

76. Battaglini D, Robba C, Fedele A, Trancǎ S, Sukkar SG, Di Pilato V, et al.. The Role of Dysbiosis in Critically Ill Patients With COVID-19 and Acute Respiratory Distress Syndrome. Front Med (2021) 8:671714. doi:  10.3389/fmed.2021.671714 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

77. Alcántara-Alonso E, Molinar-Ramos F, González-López JA, Alcántara-Alonso V, Muñoz-Pérez MA, Lozano-Nuevo JJ, et al.. High Triglyceride to HDL-Cholesterol Ratio as a Biochemical Marker of Severe Outcomes in COVID-19 Patients. Clin Nutr ESPEN (2021) 44:437–44. doi:  10.1016/j.clnesp.2021.04.020 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

78. Tepasse P-R, Vollenberg R, Fobker M, Kabar I, Schmidt H, Meier JA, et al.. Vitamin A Plasma Levels in COVID-19 Patients: A Prospective Multicenter Study and Hypothesis. Nutrients (2021) 13:2173. doi:  10.3390/nu13072173 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

79. Murai IH, Fernandes AL, Sales LP, Pinto AJ, Goessler KF, Duran CSC, et al.. Effect of a Single High Dose of Vitamin D 3 on Hospital Length of Stay in Patients With Moderate to Severe COVID-19. JAMA (2021) 325:1053. doi:  10.1001/jama.2020.26848 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

80. Zelzer S, Prüller F, Curcic P, Sloup Z, Holter M, Herrmann M, et al.. Vitamin D Metabolites and Clinical Outcome in Hospitalized COVID-19 Patients. Nutrients (2021) 13:2129. doi:  10.3390/nu13072129 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

81. Beltrão FE de L, Beltrão DC de A, Carvalhal G, Beltrão FE de L, Brito A da S, da Capistrano KHR, et al.. Thyroid Hormone Levels During Hospital Admission Inform Disease Severity and Mortality in COVID-19 Patients. Thyroid (2021) 31:1639–49. doi:  10.1089/thy.2021.0225 [PubMed] [CrossRef] [Google Scholar]

82. Battaglini D, Brunetti I, Anania P, Fiaschi P, Zona G, Ball L, et al.. Neurological Manifestations of Severe SARS-CoV-2 Infection: Potential Mechanisms and Implications of Individualized Mechanical Ventilation Settings. Front Neurol (2020) 11:845. doi:  10.3389/fneur.2020.00845 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

83. Huth SF, Cho S-M, Robba C, Highton D, Battaglini D, Bellapart J, et al.. Neurological Manifestations of Coronavirus Disease 2019: A Comprehensive Review and Meta-Analysis of the First 6 Months of Pandemic Reporting. Front Neurol (2021) 12:664599. doi:  10.3389/fneur.2021.664599 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

84. Battaglini D, Santori G, Chandraptham K, Iannuzzi F, Bastianello M, Tarantino F, et al.. Neurological Complications and Noninvasive Multimodal Neuromonitoring in Critically Ill Mechanically Ventilated COVID-19 Patients. Front Neurol (2020) 11:602114. doi:  10.3389/fneur.2020.602114 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

85. Barbosa-Silva MC, Lima MN, Battaglini D, Robba C, Pelosi P, Rocco PRM, et al.. Infectious Disease-Associated Encephalopathies. Crit Care (2021) 25:236. doi:  10.1186/s13054-021-03659-6 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

86. Lee B, Newberg A. Neuroimaging in Traumatic Brain Imaging. NeuroRX (2005) 2:372–83. doi:  10.1602/neurorx.2.2.372 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

87. Reichard RR, Kashani KB, Boire NA, Constantopoulos E, Guo Y, Lucchinetti CF. Neuropathology of COVID-19: A Spectrum of Vascular and Acute Disseminated Encephalomyelitis (ADEM)-Like Pathology. Acta Neuropathol (2020) 140:1–6. doi:  10.1007/s00401-020-02166-2 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

88. Ameres M, Brandstetter S, Toncheva AA, Kabesch M, Leppert D, Kuhle J, et al.. Association of Neuronal Injury Blood Marker Neurofilament Light Chain With Mild-to-Moderate COVID-19. J Neurol (2020) 267:3476–8. doi:  10.1007/s00415-020-10050-y [PMC free article] [PubMed] [CrossRef] [Google Scholar]

89. Kanberg N, Ashton NJ, Andersson L-M, Yilmaz A, Lindh M, Nilsson S, et al.. Neurochemical Evidence of Astrocytic and Neuronal Injury Commonly Found in COVID-19. Neurology (2020) 95:e1754–9. doi:  10.1212/WNL.0000000000010111 [PubMed] [CrossRef] [Google Scholar]

90. Paterson RW, Benjamin LA, Mehta PR, Brown RL, Athauda D, Ashton NJ, et al.. Serum and Cerebrospinal Fluid Biomarker Profiles in Acute SARS-CoV-2-Associated Neurological Syndromes. Brain Commun (2021) 3:fcab099. doi:  10.1093/braincomms/fcab099 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

91. Kanberg N, Simrén J, Edén A, Andersson L-M, Nilsson S, Ashton NJ, et al.. Neurochemical Signs of Astrocytic and Neuronal Injury in Acute COVID-19 Normalizes During Long-Term Follow-Up. EBioMedicine (2021) 70:103512. doi:  10.1016/j.ebiom.2021.103512 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

92. Esenwa C, Cheng NT, Luna J, Willey J, Boehme AK, Kirchoff-Torres K, et al.. Biomarkers of Coagulation and Inflammation in COVID-19–Associated Ischemic Stroke. Stroke (2021) 52:e706–709. doi:  10.1161/STROKEAHA.121.035045 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

93. Goyal N, Sodani AK, Jain R, Ram H. Do Elevated Levels of Inflammatory Biomarkers Predict the Risk of Occurrence of Ischemic Stroke in SARS-CoV2 ?: An Observational Study. J Stroke Cerebrovasc Dis (2021) 30:106063. doi:  10.1016/j.jstrokecerebrovasdis.2021.106063 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

94. Yassin A, Ghzawi A, Al-Mistarehi A-H, El-Salem K, Y Benmelouka A, M Sherif A, et al.. Mortality Rate and Biomarker Expression Within COVID-19 Patients Who Develop Acute Ischemic Stroke: A Systematic Review and Meta-Analysis. Futur Sci OA (2021) 7:FSO713. doi:  10.2144/fsoa-2021-0036 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

95. Mete E, Sabirli R, Goren T, Turkcuer I, Kurt O, Koseler A. Association Between S100b Levels and COVID-19 Pneumonia: A Case Control Study. Vivo (Brooklyn) (2021) 35:2923–8. doi:  10.21873/invivo.12583 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

96. Benjamin LA, Paterson RW, Moll R, Pericleous C, Brown R, Mehta PR, et al.. Antiphospholipid Antibodies and Neurological Manifestations in Acute COVID-19: A Single-Centre Cross-Sectional Study. EClinicalMedicine (2021) 39:101070. doi:  10.1016/j.eclinm.2021.101070 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

97. Faour WH, Choaib A, Issa E, El Choueiry F, Shbaklo K, Alhajj M, et al.. Mechanisms of COVID-19-Induced Kidney Injury and Current Pharmacotherapies. Inflammation Res (2022) 71:39–56. doi:  10.1007/s00011-021-01520-8 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

98. Legrand M, Bell S, Forni L, Joannidis M, Koyner JL, Liu K, et al.. Pathophysiology of COVID-19-Associated Acute Kidney Injury. Nat Rev Nephrol (2021) 17:751–64. doi:  10.1038/s41581-021-00452-0 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

99. Liu Y, Xia P, Cao W, Liu Z, Ma J, Zheng K, et al.. Divergence Between Serum Creatine and Cystatin C in Estimating Glomerular Filtration Rate of Critically Ill COVID-19 Patients. Ren Fail (2021) 43:1104–14. doi:  10.1080/0886022X.2021.1948428 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

100. Yasar E, Ozger HS, Yeter HH, Yildirim C, Osmanov Z, Cetin TE, et al.. Could Urinary Kidney Injury Molecule-1 be a Good Marker in Subclinical Acute Kidney Injury in Mild to Moderate COVID-19 Infection? Int Urol Nephrol (2021) 54(3):627–36. doi:  10.1007/s11255-021-02937-0 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

101. Tantry US, Bliden KP, Cho A, Walia N, Dahlen JR, Ens G, et al.. First Experience Addressing the Prognostic Utility of Novel Urinary Biomarkers in Patients With COVID-19. Open Forum Infect Dis (2021) 8:ofab274. doi:  10.1093/ofid/ofab274 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

102. Fukao Y, Nagasawa H, Nihei Y, Hiki M, Naito T, Kihara M, et al.. COVID-19-Induced Acute Renal Tubular Injury Associated With Elevation of Serum Inflammatory Cytokine. Clin Exp Nephrol (2021) 25:1240–6. doi:  10.1007/s10157-021-02101-z [PMC free article] [PubMed] [CrossRef] [Google Scholar]

103. Gradin A, Andersson H, Luther T, Anderberg SB, Rubertsson S, Lipcsey M, et al.. Urinary Cytokines Correlate With Acute Kidney Injury in Critically Ill COVID-19 Patients. Cytokine (2021) 146:155589. doi:  10.1016/j.cyto.2021.155589 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

104. Wang RR, He M, Kang Y. A Risk Score Based on Procalcitonin for Predicting Acute Kidney Injury in COVID-19 Patients. J Clin Lab Anal (2021) 35:e23805. doi:  10.1002/jcla.23805 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

105. Alfano G, Ferrari A, Fontana F, Mori G, Ligabue G, Giovanella S, et al.. Twenty-Four-Hour Serum Creatinine Variation Is Associated With Poor Outcome in the Novel Coronavirus Disease 2019 (COVID-19) Patients. Kidney Res Clin Pract (2021) 40:231–40. doi:  10.23876/j.krcp.20.177 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

106. Morell-Garcia D, Ramos-Chavarino D, Bauça JM, Argente del Castillo P, Ballesteros-Vizoso MA, García de Guadiana-Romualdo L, et al.. Urine Biomarkers for the Prediction of Mortality in COVID-19 Hospitalized Patients. Sci Rep (2021) 11:11134. doi:  10.1038/s41598-021-90610-y [PMC free article] [PubMed] [CrossRef] [Google Scholar]

107. Wang K, Qu M, Ding L, Shi X, Wang C, Cheng S, et al.. Liver and Kidney Function Biomarkers, Blood Cell Traits and Risk of Severe COVID-19: A Mendelian Randomization Study. Front Genet (2021) 12:647303. doi:  10.3389/fgene.2021.647303 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

108. Kumar- MP, Mishra S, Jha DK, Shukla J, Choudhury A, Mohindra R, et al.. Coronavirus Disease (COVID-19) and the Liver: A Comprehensive Systematic Review and Meta-Analysis. Hepatol Int (2020) 14:711–22. doi:  10.1007/s12072-020-10071-9 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

109. Romero-Cristóbal M, Clemente-Sánchez A, Piñeiro P, Cedeño J, Rayón L, del Río J, et al.. Possible Unrecognised Liver Injury Is Associated With Mortality in Critically Ill COVID-19 Patients. Ther Adv Gastroenterol (2021) 14:175628482110234. doi:  10.1177/17562848211023410 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

110. Caterino M, Costanzo M, Fedele R, Cevenini A, Gelzo M, Di Minno A, et al.. The Serum Metabolome of Moderate and Severe COVID-19 Patients Reflects Possible Liver Alterations Involving Carbon and Nitrogen Metabolism. Int J Mol Sci (2021) 22:9548. doi:  10.3390/ijms22179548 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

111. Lasso G, Khan S, Allen SA, Mariano M, Florez C, Orner EP, et al.. Longitudinally Monitored Immune Biomarkers Predict the Timing of COVID-19 Outcomes. PLoS Comput Biol (2022) 18:e1009778. doi:  10.1371/journal.pcbi.1009778 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

112. Smilowitz NR, Nguy V, Aphinyanaphongs Y, Newman JD, Xia Y, Reynolds HR, et al.. Multiple Biomarker Approach to Risk Stratification in COVID-19. Circulation (2021) 143:1338–40. doi:  10.1161/CIRCULATIONAHA.120.053311 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

Leave a Reply

Your email address will not be published. Required fields are marked *