Novel Biomarkers for SARS-CoV-2 Infection: A Systematic Review and Meta-Analysis
Abstract
:1. Introduction
2. Material and Methods
2.1. Inclusion and Exclusion Criteria
2.2. Search Strategy
2.3. Selection of Studies
2.4. Data Extraction and Quality Assessment
2.5. Statistical Analysis
3. Results
3.1. Characteristics of Included Studies
3.2. Biomarkers
3.2.1. MR-proADM
3.2.2. NLR
3.2.3. KL-6
3.2.4. Other Biomarkers: Amyloid A, miR-155, Galectin-3, and SuPAR
3.2.5. Receiver Operating Curve Analysis for New Biomarkers as Predictors of Poor Outcome in COVID-19 Patients
3.3. Quality Assessment
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Marks, K.M.; Gulick, R.M. COVID-19. Ann. Intern. Med. 2023, 176, ITC145–ITC160. [Google Scholar] [CrossRef] [PubMed]
- Zhu, N.; Zhang, D.; Wang, W.; Li, X.; Yang, B.; Song, J.; Zhao, X.; Huang, B.; Shi, W.; Lu, R.; et al. A Novel Coronavirus from Patients with Pneumonia in China, 2019. N. Engl. J. Med. 2020, 382, 727–733. [Google Scholar] [CrossRef] [PubMed]
- Gandhi, R.T.; Lynch, J.B.; Del Rio, C. Mild or Moderate COVID-19. N. Engl. J. Med. 2020, 383, 1757–1766. [Google Scholar] [CrossRef] [PubMed]
- Berlin, D.A.; Gulick, R.M.; Martinez, F.J. Severe COVID-19. N. Engl. J. Med. 2020, 383, 2451–2460. [Google Scholar] [CrossRef]
- Salton, F.; Confalonieri, P.; Campisciano, G.; Cifaldi, R.; Rizzardi, C.; Generali, D.; Pozzan, R.; Tavano, S.; Bozzi, C.; Lapadula, G.; et al. Cytokine Profiles as Potential Prognostic and Therapeutic Markers in SARS-CoV-2-Induced ARDS. J. Clin. Med. 2022, 11, 2951. [Google Scholar] [CrossRef]
- Li, J.; Wei, J.; Xu, Z.; Jiang, C.; Li, M.; Chen, J.; Li, Y.; Yang, M.; Gu, Y.; Wang, F.; et al. Cytokine/Chemokine Expression Is Closely Associated Disease Severity of Human Adenovirus Infections in Immunocompetent Adults and Predicts Disease Progression. Front. Immunol. 2021, 12, 691879. [Google Scholar] [CrossRef]
- Rasool, G.; Riaz, M.; Abbas, M.; Fatima, H.; Qamar, M.M.; Zafar, F.; Mahmood, Z. COVID-19: Clinical laboratory diagnosis and monitoring of novel coronavirus infected patients using molecular, serological and biochemical markers: A review. Int. J. Immunopathol. Pharmacol. 2022, 36, 03946320221115316. [Google Scholar] [CrossRef]
- Alnor, A.; Sandberg, M.B.; Gils, C.; Vinholt, P.J. Laboratory Tests and Outcome for Patients with Coronavirus Disease 2019: A Systematic Review and Meta-Analysis. J. Appl. Lab. Med. 2020, 5, 1038–1049. [Google Scholar] [CrossRef]
- Plebani, M. COVID-19 and Biomarkers: The Contribution of the Journal. J. Clin. Med. 2023, 12, 1853. [Google Scholar] [CrossRef]
- Ranjbar, M.; Cusack, R.P.; Whetstone, C.E.; Brister, D.L.; Wattie, J.; Wiltshire, L.; Alsaji, N.; Le Roux, J.; Cheng, E.; Srinathan, T.; et al. Immune Response Dynamics and Biomarkers in COVID-19 Patients. Int. J. Mol. Sci. 2024, 25, 6427. [Google Scholar] [CrossRef]
- Park, J.O.; Cho, H.K.; Jeon, C.H.; Kim, S.-H.; Park, I.H.; Kim, K.M.; Lee, J.; Wi, Y.M. Characteristics and biomarkers associated with mortality in COVID-19 patients presenting to the emergency department. Epidemiol. Infect. 2024, 152, e76. [Google Scholar] [CrossRef] [PubMed]
- Snopkowska Lesniak, S.W.; Maschio, D.; Henriquez-Camacho, C.; Moreno Cuerda, V. Biomarkers for SARS-CoV-2 infection. A narrative review. Front. Med. 2025, 12, 1563998. [Google Scholar] [CrossRef] [PubMed]
- Oblitas, C.-M.; Galeano-Valle, F.; Ramírez-Navarro, J.; López-Cano, J.; Monterrubio-Manrique, Á.; García-Gámiz, M.; Sancho-González, M.; Arenal-López, S.; Álvarez-Sala Walther, L.-A.; Demelo-Rodríguez, P. Mid-Regional Pro-Adrenomedullin, Methemoglobin and Carboxyhemoglobin as Prognosis Biomarkers in Critically Ill Patients with COVID-19: An Observational Prospective Study. Viruses 2021, 13, 2445. [Google Scholar] [CrossRef]
- Cheng, J.; Wang, H.; Li, C.; Yu, J.; Zhu, M. Characteristics of cytokines/chemokines associated with disease severity and adverse prognosis in COVID-19 patients. Front. Immunol. 2024, 15, 1464545. [Google Scholar] [CrossRef]
- Sanduzzi Zamparelli, S.; Fucci, V.; Rea, G.; Perna, F.; Bocchino, M.; Sanduzzi Zamparelli, A. The Role of SARS-CoV-2 Nucleocapsidic Antigen and Krebs von den Lungen 6 Serum Levels in Predicting COVID-19 Pneumonia Outcome. Diagnostics 2024, 14, 642. [Google Scholar] [CrossRef]
- Haroun, R.A.-H.; Osman, W.H.; Amin, R.E.; Hassan, A.K.; Abo-Shanab, W.S.; Eessa, A.M. Circulating plasma miR-155 is a potential biomarker for the detection of SARS-CoV-2 infection. Pathology 2022, 54, 104–110. [Google Scholar] [CrossRef]
- Gajovic, N.; Markovic, S.S.; Jurisevic, M.; Jovanovic, M.; Arsenijevic, N.; Mijailovic, Z.; Jovanovic, M.; Jovanovic, I. Galectin-3 as an important prognostic marker for COVID-19 severity. Sci. Rep. 2023, 13, 1460. [Google Scholar] [CrossRef]
- Page, M.J.; McKenzie, J.E.; Bossuyt, P.M.; Boutron, I.; Hoffmann, T.C.; Mulrow, C.D.; Shamseer, L.; Tetzlaff, J.M.; Akl, E.A.; Brennan, S.E.; et al. The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ 2021, 372, n71. [Google Scholar] [CrossRef]
- Lo, C.K.-L.; Mertz, D.; Loeb, M. Newcastle-Ottawa Scale: Comparing reviewers’ to authors’ assessments. BMC Med. Res. Methodol. 2014, 14, 45. [Google Scholar] [CrossRef]
- Wan, X.; Wang, W.; Liu, J.; Tong, T. Estimating the sample mean and standard deviation from the sample size, median, range and/or interquartile range. BMC Med. Res. Methodol. 2014, 14, 135. [Google Scholar] [CrossRef]
- Higgins, J.P.T.; Thompson, S.G.; Deeks, J.J.; Altman, D.G. Measuring inconsistency in meta-analyses. BMJ 2003, 327, 557–560. [Google Scholar] [CrossRef] [PubMed]
- Al-Juboori, R.S.F.; Al-Bayaa, Y.J. The role of C-reactive protein, procalcitonin, interleukin-6 and neutrophil/lymphocyte ratio as a laboratory biomarker in COVID-19. Egypt. J. Immunol. 2024, 31, 93–101. [Google Scholar] [PubMed]
- Ardestani, S.K.; Salehi, M.R.; Attaran, B.; Hashemi, S.M.; Sadeghi, S.; Ghaffarpour, S.; Tuserkani, F.; Ghazanfari, T. Neutrophil to Lymphocyte Ratio (NLR) and Derived NLR Combination: A Cost-effective Predictor of Moderate to Severe COVID-19 Progression. Iran. J. Allergy Asthma Immunol. 2022, 21, 241–253. [Google Scholar] [CrossRef] [PubMed]
- Arini, I.A.; Masyeni, S.; Widhidewi, N.W. Relationship between neutrophil-lymphocyte ratio and platelet-lymphocyte ratio with the severity of COVID-19. Narra J. 2024, 4, e262. [Google Scholar] [CrossRef]
- Bhandari, S.; Rankawat, G.; Mathur, S.; Kumar, A.; Sahlot, R.; Jain, A. Circulatory Cytokine Levels as a Predictor of Disease Severity in COVID-19: A Study from Western India. J. Assoc. Physicians India 2022, 70, 11–12. [Google Scholar]
- Bohra, G.K.; Bhatia, P.K.; Khichar, S.; Garg, M.K.; Sharma, P. Association of Inflammatory markers with COVID-19 Outcome among Hospitalized adult Patients. J. Assoc. Physicians India 2022, 70, 11–12. [Google Scholar]
- Hammad, R.; Eldosoky, M.A.E.R.; Fouad, S.H.; Elgendy, A.; Tawfeik, A.M.; Alboraie, M.; Abdelmaksoud, M.F. Circulating cell-free DNA, peripheral lymphocyte subsets alterations and neutrophil lymphocyte ratio in assessment of COVID-19 severity. Innate Immun. 2021, 27, 240–250. [Google Scholar] [CrossRef]
- Basoglu, O.K.; Ozhan, M.H.; Ekren, P.K.; Ak, G.; Tasbakan, M.S.; Sayiner, A. Communication: The Follow-Up of Biomarkers Better Predicts the Poor Outcome in COVID-19 Patients. Ann. Clin. Lab. Sci. 2020, 50, 848–851. [Google Scholar]
- Herrera-Van Oostdam, A.S.; Castañeda-Delgado, J.E.; Oropeza-Valdez, J.J.; Borrego, J.C.; Monárrez-Espino, J.; Zheng, J.; Mandal, R.; Zhang, L.; Soto-Guzmán, E.; Fernández-Ruiz, J.C.; et al. Immunometabolic signatures predict risk of progression to sepsis in COVID-19. PLoS ONE 2021, 16, e0256784. [Google Scholar] [CrossRef]
- Sayah, W.; Berkane, I.; Guermache, I.; Sabri, M.; Lakhal, F.Z.; Yasmine Rahali, S.; Djidjeli, A.; Lamara Mahammed, L.; Merah, F.; Belaid, B.; et al. Interleukin-6, procalcitonin and neutrophil-to-lymphocyte ratio: Potential immune-inflammatory parameters to identify severe and fatal forms of COVID-19. Cytokine 2021, 141, 155428. [Google Scholar] [CrossRef]
- Tufa, A.; Gebremariam, T.H.; Manyazewal, T.; Asrat, Y.; Getinet, T.; Hundie, T.G.; Webb, D.-L.; Hellström, P.M.; Genet, S. Limited value of neutrophil-to-lymphocyte ratio and serum creatinine as point-of-care biomarkers of disease severity and infection mortality in patients hospitalized with COVID-19. PLoS ONE 2022, 17, e0275391. [Google Scholar] [CrossRef] [PubMed]
- d’Alessandro, M.; Bergantini, L.; Cameli, P.; Curatola, G.; Remediani, L.; Bennett, D.; Bianchi, F.; Perillo, F.; Volterrani, L.; Mazzei, M.A.; et al. Serial KL-6 measurements in COVID-19 patients. Intern. Emerg. Med. 2021, 16, 1541–1545. [Google Scholar] [CrossRef] [PubMed]
- d’Alessandro, M.; Cameli, P.; Refini, R.M.; Bergantini, L.; Alonzi, V.; Lanzarone, N.; Bennett, D.; Rana, G.D.; Montagnani, F.; Scolletta, S.; et al. Serum KL-6 concentrations as a novel biomarker of severe COVID-19. J. Med. Virol. 2020, 92, 2216–2220. [Google Scholar] [CrossRef]
- Bergantini, L.; Bargagli, E.; d’Alessandro, M.; Refini, R.M.; Cameli, P.; Galasso, L.; Scapellato, C.; Montagnani, F.; Scolletta, S.; Franchi, F.; et al. Prognostic bioindicators in severe COVID-19 patients. Cytokine 2021, 141, 155455. [Google Scholar] [CrossRef]
- Maruyama, S.; Nakamori, Y.; Nakano, H.; Tsuyumu, K.; Kanayama, S.; Iwamura, H.; Wada, D.; Yoshihara, T.; Saito, F.; Yoshiya, K.; et al. Peak value of serum KL-6 may be useful for predicting poor prognosis of severe COVID-19 patients. Eur. J. Med. Res. 2022, 27, 69. [Google Scholar] [CrossRef]
- Scotto, R.; Pinchera, B.; Perna, F.; Atripaldi, L.; Giaccone, A.; Sequino, D.; Zappulo, E.; Sardanelli, A.; Schiano Moriello, N.; Stanziola, A.; et al. Serum KL-6 Could Represent a Reliable Indicator of Unfavourable Outcome in Patients with COVID-19 Pneumonia. Int. J. Environ. Res. Public Health 2021, 18, 2078. [Google Scholar] [CrossRef]
- García de Guadiana-Romualdo, L.; Martínez Martínez, M.; Rodríguez Mulero, M.D.; Esteban-Torrella, P.; Hernández Olivo, M.; Alcaraz García, M.J.; Campos-Rodríguez, V.; Sancho-Rodríguez, N.; Galindo Martínez, M.; Alcaraz, A.; 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, 211–218. [Google Scholar] [CrossRef]
- Gregoriano, C.; Koch, D.; Kutz, A.; Haubitz, S.; Conen, A.; Bernasconi, L.; Hammerer-Lercher, A.; Saeed, K.; Mueller, B.; Schuetz, P. The vasoactive peptide MR-pro-adrenomedullin in COVID-19 patients: An observational study. Clin. Chem. Lab. Med. 2021, 59, 995–1004. [Google Scholar] [CrossRef]
- Cameli, P.; Pordon, E.; d’Alessandro, M.; Marzi, M.L.; Galasso, L.; Biuzzi, C.; Bergantini, L.; Bargagli, E.; Scolletta, S.; Franchi, F. MR-proADM as Prognostic Factor of Outcome in COVID-19 Patients. Biomedicines 2023, 11, 1680. [Google Scholar] [CrossRef]
- García de Guadiana-Romualdo, L.; Calvo Nieves, M.D.; Rodríguez Mulero, M.D.; Calcerrada Alises, I.; Hernández Olivo, M.; Trapiello Fernández, W.; González Morales, M.; Bolado Jiménez, C.; Albaladejo-Otón, M.D.; Fernández Ovalle, H.; et al. MR-proADM as marker of endotheliitis predicts COVID-19 severity. Eur. J. Clin. Investig. 2021, 51, e13511. [Google Scholar] [CrossRef]
- de Montmollin, E.; Peoc’h, K.; Marzouk, M.; Ruckly, S.; Wicky, P.-H.; Patrier, J.; Jaquet, P.; Sonneville, R.; Bouadma, L.; Timsit, J.-F. Mid-Regional Pro-Adrenomedullin as a Prognostic Factor for Severe COVID-19 ARDS. Antibiotics 2022, 11, 1166. [Google Scholar] [CrossRef] [PubMed]
- Mangioni, D.; Oggioni, M.; Chatenoud, L.; Liparoti, A.; Uceda Renteria, S.; Alagna, L.; Biscarini, S.; Bolis, M.; Di Modugno, A.; Mussa, M.; et al. Prognostic Value of Mid-Region Proadrenomedullin and In Vitro Interferon Gamma Production for In-Hospital Mortality in Patients with COVID-19 Pneumonia and Respiratory Failure: An Observational Prospective Study. Viruses 2022, 14, 1683. [Google Scholar] [CrossRef] [PubMed]
- Montrucchio, G.; Sales, G.; Rumbolo, F.; Palmesino, F.; Fanelli, V.; Urbino, R.; Filippini, C.; Mengozzi, G.; Brazzi, L. Effectiveness of mid-regional pro-adrenomedullin (MR-proADM) as prognostic marker in COVID-19 critically ill patients: An observational prospective study. PLoS ONE 2021, 16, e0246771. [Google Scholar] [CrossRef]
- Popov, D.; Borovkova, U.; Rybka, M.; Ramnyonok, T.; Golukhova, E. Mid-regional pro-adrenomedullin as a predictor of in-hospital mortality in adult patients with COVID-19: A single-centre prospective study. Anaesthesiol. Intensive Ther. 2022, 54, 242–246. [Google Scholar] [CrossRef]
- Sozio, E.; Tascini, C.; Fabris, M.; D’Aurizio, F.; De Carlo, C.; Graziano, E.; Bassi, F.; Sbrana, F.; Ripoli, A.; Pagotto, A.; et al. MR-proADM as prognostic factor of outcome in COVID-19 patients. Sci. Rep. 2021, 11, 5121. [Google Scholar] [CrossRef]
- van Oers, J.A.H.; Kluiters, Y.; Bons, J.A.P.; de Jongh, M.; Pouwels, S.; Ramnarain, D.; de Lange, D.W.; de Grooth, H.-J.; Girbes, A.R.J. Endothelium-associated biomarkers mid-regional proadrenomedullin and C-terminal proendothelin-1 have good ability to predict 28-day mortality in critically ill patients with SARS-CoV-2 pneumonia: A prospective cohort study. J. Crit. Care 2021, 66, 173–180. [Google Scholar] [CrossRef]
- Cheng, L.; Yang, J.-Z.; Bai, W.-H.; Li, Z.-Y.; Sun, L.-F.; Yan, J.-J.; Zhou, C.-L.; Tang, B.-P. Prognostic value of serum amyloid A in patients with COVID-19. Infection 2020, 48, 715–722. [Google Scholar] [CrossRef]
- Tufa, A.; Gebremariam, T.H.; Manyazewal, T.; Getinet, T.; Webb, D.-L.; Hellström, P.M.; Genet, S. Inflammatory mediators profile in patients hospitalized with COVID-19: A comparative study. Front. Immunol. 2022, 13, 964179. [Google Scholar] [CrossRef]
- Haroun, R.A.-H.; Osman, W.H.; Eessa, A.M. Interferon-γ-induced protein 10 (IP-10) and serum amyloid A (SAA) are excellent biomarkers for the prediction of COVID-19 progression and severity. Life Sci. 2021, 269, 119019. [Google Scholar] [CrossRef]
- Soltane, R.; Almulla, N.; Alasiri, A.; Elashmawy, N.F.; Qumsani, A.T.; Alshehrei, F.M.; Keshek, D.E.-G.; Alqadi, T.; Al-Ghamdi, S.B.; Allayeh, A.K. A Comparative Analysis of MicroRNA Expression in Mild, Moderate, and Severe COVID-19: Insights from Urine, Serum, and Nasopharyngeal Samples. Biomolecules 2023, 13, 1681. [Google Scholar] [CrossRef]
- Gaytán-Pacheco, N.; Ibáñez-Salazar, A.; Herrera-Van Oostdam, A.S.; Oropeza-Valdez, J.J.; Magaña-Aquino, M.; Adrián López, J.; Monárrez-Espino, J.; López-Hernández, Y. miR-146a, miR-221, and miR-155 are Involved in Inflammatory Immune Response in Severe COVID-19 Patients. Diagnostics 2022, 13, 133. [Google Scholar] [CrossRef] [PubMed]
- Kadhim, H.; Ghareeb, A.; Alhilal, M. Expression level of non-coding (MiR-155) gene as biomarker for severity of coronaviruses infection among vaccinated and non-vaccinated Iraqi patients. Hum. Antibodies 2024, 32, 25–34. [Google Scholar] [CrossRef] [PubMed]
- Karsli, E.; Anabarli Metin, D.; Canacik, O.; Sabirli, R.; Kaymaz, B.; Kurt, O.; Koseler, A. Galectin-3 as a Potential Prognostic Biomarker for COVID-19 Disease: A Case-Control Study. Cureus 2022, 14, e28805. [Google Scholar] [CrossRef]
- Kartal Baykan, E.; Şebin, E.; Karaşahin, Ö.; Baykan, A.R.; Cerrah, S.; Göğebakan, H.; Sevinç, C.; Kahraman, M.; Yavuz, Y.C. Galectin-3: Can it be a diagnostic tool for pneumonia in COVID-19 patients? Turk. J. Med. Sci. 2021, 51, 2256–2262. [Google Scholar] [CrossRef]
- Vasbinder, A.; Padalia, K.; Pizzo, I.; Machado, K.; Catalan, T.; Presswalla, F.; Anderson, E.; Ismail, A.; Hutten, C.; Huang, Y.; et al. SuPAR, biomarkers of inflammation, and severe outcomes in patients hospitalized for COVID-19: The International Study of Inflammation in COVID-19. J. Med. Virol. 2024, 96, e29389. [Google Scholar] [CrossRef]
- Vassiliou, A.G.; Zacharis, A.; Vrettou, C.S.; Keskinidou, C.; Jahaj, E.; Mastora, Z.; Orfanos, S.E.; Dimopoulou, I.; Kotanidou, A. Comparison of the Mortality Prediction Value of Soluble Urokinase Plasminogen Activator Receptor (suPAR) in COVID-19 and Sepsis. Diagnostics 2022, 12, 1261. [Google Scholar] [CrossRef]
- Arnold, D.T.; Attwood, M.; Barratt, S.; Morley, A.; Elvers, K.T.; McKernon, J.; Donald, C.; Oates, A.; Noel, A.; MacGowan, A.; et al. Predicting outcomes of COVID-19 from admission biomarkers: A prospective UK cohort study. Emerg. Med. J. 2021, 38, 543–548. [Google Scholar] [CrossRef]
- Chalkias, A.; Skoulakis, A.; Papagiannakis, N.; Laou, E.; Tourlakopoulos, K.; Pagonis, A.; Michou, A.; Ntalarizou, N.; Mermiri, M.; Ragias, D.; et al. Circulating suPAR associates with severity and in-hospital progression of COVID-19. Eur. J. Clin. Investig. 2022, 52, e13794. [Google Scholar] [CrossRef]
- Molfino, A.; Anastasi, E.; Assanto, E.; Toccini, L.; Imbimbo, G.; Gigante, A.; Viggiani, V.; Farina, A.; Picconi, O.; Angeloni, A.; et al. Association between serum levels of GDF-15, suPAR, PIVKA-II, sdLDL and clinical outcomes in hospitalized COVID-19 patients. Intern. Emerg. Med. 2024, 19, 1557–1566. [Google Scholar] [CrossRef]
Study ID | Country | Year | Sample | Severity Definition | Design | Poor Outcome | Good Outcome | Biomarker | NOS Scale | ||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
No | Age | Female% | No | Age | Female% | ||||||||
AlJuboori 2024 [22] | Iraq | 2021 | 160 | Case-control observational study | 40 | 49 | - | 40 | 49 | - | NLR | 7 | |
Ardestani 2022 [23] | Iran | 2020 | 209 | In-hospital mortality | Retrospective observational | 44 | - | - | 110 | - | - | NLR | 7 |
Arini 2024 [24] | Indonesia | 2022 | - | Disease severity | Cross-sectional observational study | 23 | - | 74% | 81 | - | 69% | NLR | 6 |
Bhandari 2022 [25] | India | 2020 | 175 | Disease severity | Retrospective observational study | 79 | 48 | 20% | 96 | 38 | 25% | NLR | 6 |
Bohra 2022 [26] | - | - | - | Disease severity | - | - | - | - | - | - | - | NLR | - |
Hammad 2021 [27] | Egypt | - | 64 | Disease severity | Prospective observational study | 34 | - | - | 30 | - | - | NLR | - |
Basoglu 2020 [28] | Turkey | 2020 | 132 | Disease severity | Single-center observational study | 70 | 64 | 54% | 62 | 51 | 42% | NLR | 8 |
Herrera 2021 [29] | Tukey | - | 100 | Disease severity | Prospective case-control study | 60 | 66 | 63% | 40 | 64 | 55% | NLR | 7 |
Saya 2021 [30] | Algeria | 2020 | 153 | Disease severity | Prospective observational study | 80 | 65 | 29% | 73 | 57 | 37% | NLR | 8 |
Tufa 2022 [31] | Ethiopia | 2020 | 126 | In-hospital mortality | Prospective cohort study | 68 | 60 | 43% | 58 | 32 | 43% | NLR | 8 |
Alessandro 2021 [32] | Italy | 2020 | 60 | Disease severity | Prospective observational study | 14 | 62 | 14% | 12 | 64 | 42% | KL-6 | 7 |
Alessandro 2020 [33] | Italy | 2020 | 22 | Disease severity | Prospective observational study | 12 | 62 | 25% | 10 | 64 | 40% | KL-6 | 7 |
Bergantini 2021 [34] | Italy | 2020 | 41 | Disease severity | Single-center observational study | 10 | 65 | 20% | 14 | 62 | 21% | KL-6 | 8 |
Maruyama 2022 [35] | Spain | 2021 | 147 | Disease severity | Retrospective observational study | 39 | 79 | - | 108 | 69 | - | KL-6 | 8 |
Scotto 2021 [36] | Italy | 2021 | 34 | In-hospital mortality | Prospective observational study | 15 | - | - | 19 | - | - | KL-6 | 8 |
Garcia 2021 [37] | Spain | 2020 | 359 | In-hospital mortality | Prospective observational study | 32 | 76 | 34% | 327 | 57 | 36% | MR-proADM | 8 |
Gregoriano 2021 [38] | Switzerland | 2020 | 89 | In-hospital mortality | Prospective observational study | 17 | - | - | 72 | 67 | 35% | MR-proADM | 8 |
Cameli 2023 [39] | Italy | 2020 | 74 | In-hospital mortality | Prospective observational study | 12 | 86 | - | 62 | 65 | - | MR-proADM | 7 |
GarciaDe 2021 [40] | Spain | 2020 | 99 | In-hospital mortality | Prospective observational study | 14 | 76 | 29% | 85 | 64 | 40% | MR-proADM | 8 |
Montmollin 2022 [41] | France | 2021 | 135 | In-hospital mortality | Prospective observational study | 46 | 71 | 28% | 89 | 58 | - | MR-proADM | 8 |
Mangioni 2022 [42] | Italy | 2021 | 100 | In-hospital mortality | Prospective observational study | 13 | 77 | 39% | 87 | 63 | 36% | MR-proADM | 8 |
Montrucchio 2021 [43] | Italy | 2020 | 57 | In-hospital mortality | Prospective observational study | 31 | 67 | 10% | 26 | 59 | 15% | MR-proADM | 8 |
Popov 2022 [44] | Russia | 2020 | 135 | In-hospital mortality | Prospective observational study | 20 | 73 | - | 115 | 62 | - | MR-proADM | 8 |
Sozio 2021 [45] | Italy | 2020 | 111 | In-hospital mortality | Retrospective observational study | 28 | - | - | 83 | - | - | MR-proADM | 8 |
Van Oers 2021 [46] | The Netherlands | 2020 | 105 | In-hospital mortality | Prospective observational study | 30 | 72 | 20% | 75 | 65 | 25 | MR-proADM | 8 |
Cheng 2020 [47] | China | 2020 | 89 | In-hospital mortality | Retrospective observational study | 36 | 69 | 44% | 53 | 54 | 45% | Amyloid A | 7 |
Tufa 2022 [48] | Ethiopia | 2021 | 126 | Disease severity | Comparative cross-sectional study | 68 | 60 | 57% | 58 | 32 | 57% | Amyloid A | 7 |
Harouni 2021 [49] | Egypt | 2020 | 150 | Disease severity | Case-control observational study | 52 | 50 | 37% | 98 | 48 | 43% | Amyloid A | 8 |
Soltane 2023 [50] | Egypt | 2021 | 273 | Disease severity | Cross-sectional observational study | 39 | 62 | 33% | 177 | 43 | 36% | miR-155 | 8 |
Gaytan 2022 [51] | Mexico | 2022 | 41 | Disease severity | Cross-sectional observational study | 41 | 53 | 15% | 42 | 39 | 55% | miR-155 | 7 |
Kadhim 2024 [52] | Iraq | 2023 | 105 | Disease severity | Case-control observational study | 35 | 46 | 37% | 35 | 46 | 37% | miR-155 | 8 |
Karsli 2022 [53] | Turkey | 2022 | 100 | Disease severity | Prospective case-control study | 60 | 66 | - | 40 | 64 | - | Galectin-3 | 8 |
Kartal 2021 [54] | Turkey | 2021 | 176 | Disease severity | Cross-sectional observational study | 64 | - | - | 72 | - | - | Galectin-3 | 8 |
Vasbinder 2024 [55] | United States | 2022 | 1962 | In-hospital mortality | Prospective observational study | - | - | - | - | - | - | SuPAR | 8 |
Vasiliou 2022 [56] | Greece | - | 95 | In-hospital mortality | Prospective observational study | - | - | - | - | - | - | SuPAR | 8 |
Arnold 2021 [57] | United Kingdom | 2020 | 187 | Disease severity | Prospective cohort study | 39 | 66 | 44% | 148 | 55 | 47% | SuPAR | 7 |
Chalkias 2022 [58] | Greece, Spain, and Denmark | 2021 | 767 | Disease severity | Prospective observational study | 154 | - | - | 613 | 64 | - | SuPAR | 8 |
Molfino 2024 [59] | Italy | 2021 | 93 | In-hospital mortality | Prospective observational study | 21 | - | - | 72 | - | - | SuPAR | 8 |
Biomarker | Study | Outcome | AUC (95% CI) | Cut-off | Sensitivity (%) | Specificity (%) |
---|---|---|---|---|---|---|
NLR | Ardestani 2022 [23] | In-hospital mortality | 0.726 (0.674–0.806) | 6.830 | 82.8% | 56.2% |
NLR | Arini 2024 [24] | Disease severity | - | 3.8 | 95% | 74% |
NLR | Bohra 2022 [26] | Disease severity | - | 6.44 | 85.7% | 96.8% |
NLR | Hammad 2021 [27] | Disease severity | 0.98 | 3.1 | 91% | 93% |
NLR | Basoglu 2020 [28] | Disease severity | 0.834 (0.766–0.902) | 2.8 | 75% | 74% |
NLR | Herrera 2021 [29] | Disease severity | 0.98 | 3.1 | 91% | 93% |
NLR | Saya 2021 [30] | Disease severity | 0.831 | 7.4 | 75% | 84% |
NLR | Tufa 2022 [31] | In-hospital mortality | 0.75 (0.60–0.91) | 4.63 | 86.7% | 55.9% |
KL-6 | Alessandro 2021 [32] | Disease severity | 0.85 | 455 | 75% | 80% |
KL-6 | Alessandro 2020 [33] | Disease severity | 0.85 | 455 | 75% | 80% |
KL-6 | Bergantini 2021 [34] | Disease severity | 0.95 | - | 85.7% | 85.7% |
KL-6 | Maruyama 2022 [35] | Disease severity | 0.89 (0.83–0.96) | 966 | 81.6% | 84.3% |
MR-proADM | Garcia 2021 [37] | In-hospital mortality | 0.832 | 0.80 | 96.9% | 58.4% |
MR-proADM | Gregoriano 2021 [38] | In-hospital mortality | 0.78 | 0.93 | 92.9% | 60% |
MR-proADM | Cameli 2023 [39] | In-hospital mortality | 0.958 | 1.02 | 82% | 87% |
MR-proADM | GarciaDe 2021 [40] | In-hospital mortality | 0.905 (0.829–0.955) | 1.01 | 85.7% | 84.7% |
MR-proADM | Montmollin 2022 [41] | In-hospital mortality | 0.744 | 1 | 77.5% | 68.8% |
MR-proADM | Mangioni 2022 [42] | In-hospital mortality | 0.87 (0.79–0.94) | 1.04 | 92.3% | 75% |
MR-proADM | Popov 2022 [44] | In-hospital mortality | - | 0.895 | 75% | 69% |
MR-proADM | Sozio 2021 [45] | In-hospital mortality | 0.849 | 0.895 | 85.7% | 68.7% |
MR-proADM | vanOers 2021 [46] | In-hospital mortality | 0.84 (0.76–0.92) | 1.57 | 88% | 67% |
Amyloid A | Cheng 2020 [47] | In-hospital mortality | 0.947 | 183.6 | 96.1% | 94.3% |
Amyloid A | Tufa 2022 [48] | Severity | 0.703 (0.611–0.794) | 1074 | 5.9% | 98.3% |
miR-155 | Soltane 2023 [50] | Severity | 0.927 | - | 96.6% | 87.3% |
miR-155 | Kadhim 2024 [52] | Severity | 0.91 | 4.06 | 82% | 88% |
Galectin-3 | Karsli 2022 [53] | Severity | 0.701 (0.582–0.819) | 11.3 | 75% | 50% |
Galectin-3 | Kartal 2021 [54] | Severity | 0.89 (0.83–0.94) | 18.9 | 87% | 73% |
suPAR | Vasbinder 2024 [55] | Severity | 0.712 | 4 | 95.4% | 11.8% |
suPAR | Vassiliou 2022 [56] | Severity | 0.81 (0.71–0.91) | 6.3 | 74.2% | 85.9% |
suPAR | Arnold 2021 [57] | Severity | 0.81 (0.72–0.88) | 5.2 | 82% | 65% |
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Snopkowska Lesniak, S.W.; Maschio, D.; Neria, F.; Rey-Delgado, B.; Moreno Cuerda, V.; Henriquez-Camacho, C. Novel Biomarkers for SARS-CoV-2 Infection: A Systematic Review and Meta-Analysis. J. Pers. Med. 2025, 15, 225. https://doi.org/10.3390/jpm15060225
Snopkowska Lesniak SW, Maschio D, Neria F, Rey-Delgado B, Moreno Cuerda V, Henriquez-Camacho C. Novel Biomarkers for SARS-CoV-2 Infection: A Systematic Review and Meta-Analysis. Journal of Personalized Medicine. 2025; 15(6):225. https://doi.org/10.3390/jpm15060225
Chicago/Turabian StyleSnopkowska Lesniak, Sara Weronika, Diego Maschio, Fernando Neria, Beatriz Rey-Delgado, Victor Moreno Cuerda, and Cesar Henriquez-Camacho. 2025. "Novel Biomarkers for SARS-CoV-2 Infection: A Systematic Review and Meta-Analysis" Journal of Personalized Medicine 15, no. 6: 225. https://doi.org/10.3390/jpm15060225
APA StyleSnopkowska Lesniak, S. W., Maschio, D., Neria, F., Rey-Delgado, B., Moreno Cuerda, V., & Henriquez-Camacho, C. (2025). Novel Biomarkers for SARS-CoV-2 Infection: A Systematic Review and Meta-Analysis. Journal of Personalized Medicine, 15(6), 225. https://doi.org/10.3390/jpm15060225