Literature DB >> 33006404

Peripheral biomarkers' panel for severe COVID-19 patients.

Miriana d'Alessandro1, Laura Bergantini1, Paolo Cameli1, Giuseppe Curatola1, Lorenzo Remediani1, Piersante Sestini1, Elena Bargagli1.   

Abstract

Entities:  

Mesh:

Substances:

Year:  2020        PMID: 33006404      PMCID: PMC7536919          DOI: 10.1002/jmv.26577

Source DB:  PubMed          Journal:  J Med Virol        ISSN: 0146-6615            Impact factor:   20.693


× No keyword cloud information.
To the Editor, It is widely reported in the literature that CD4, CD8, and total T cell count are significantly reduced in critically ill patients with coronavirus disease 2019 (COVID‐19). , , , Pallotto et al. analyzed CD4/CD8 ratio in 38 hospitalized patients with COVID‐19 (reff). The authors propose an elevated CD4/CD8 ratio as a useful early predictive biomarker for development of critical illness in patients with COVID‐19. Few weeks ago, we suggested natural killer (NK) cell count as a marker of severity in 34 hospitalized patients with COVID‐19 but we did not observe any significant alterations in CD4‐ and CD8‐lymphocyte counts or CD4/CD8 ratio. On this topic, our research group firstly described a novel potential COVID‐19 severity marker, Krebs von den Lungen‐6 (KL‐6), which is a high molecular weight glycoprotein expressed by Type 2 pneumocytes and released in the bloodstream after epithelial damage and reparative proliferation processes. , , In particular, the authors observed significantly higher serum KL‐6 concentrations in patients with severe COVID‐19 than those with milder disease. This study aimed to investigate how a combination of COVID‐19 severity markers could be helpful in the clinical management of these patients. We retrospectively enrolled 54 patients (median age, interquartile range [IQR], 64 [58-74] years; 61% males), hospitalized at COVID Unit of Siena University Hospital from March to May 2020. Hospitalization criteria included diagnosis of COVID‐19, vital organ involvement, and nasopharyngeal swabs positive for severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) nucleic acid by reverse‐transcription polymerase chain reaction. The study was conducted in compliance with the principles of the Declaration of Helsinki. Peripheral blood samples were obtained on admission before starting specific pharmacological treatment for COVID‐19, and were processed by flow cytometry to lymphocyte immunophenotyping and chemiluminescence assay to KL‐6 detection. According to the need for intensive care unit (ICU) admission, mechanical ventilation, or high‐flow oxygen therapy, patients were divided into two groups: severe (n = 14) and nonsevere (n = 40). The main characteristics of our COVID‐19 population, including lymphocyte subset results and KL‐6 concentrations, are reported in Table 1.
Table 1

The main characteristics of population including, age (median, IQR), gender (%) and lymphocyte subsets at the hospital admission

ParametersSevere cases (n = 14)Nonsevere cases (n = 40) p value
Age (median, IQR)65 (59–71)64 (58–72)NS
Gender, M/F12/221/19NS
Lymphocyte subsets (median, IQR)
CD45 (cells/µl)794 (537–1203)1341 (798–2071).0110
CD3%72 (63–85)72 (66–75)NS
CD3 (cells/µl)506 (389–943)782(445–1483).0372
CD4%45 (37–52)43 (37–54)NS
CD4 (cells/µl)340 (232–556)492 (269–753)NS
CD8%21 (15–33)23 (18–32)NS
CD8 (cells/µl)132 (103–344)289 (143–537).0471
CD19%12 (7–27)13 (10–19)NS
CD19 (cells/µl)99 (58–196)140 (66–279)NS
NK cells %7.8 (3.9–13.5)12.6 (8.4–19.6).0233
NK (cells/µl)69 (25–109)139 (101–211).0009
CD4/CD82.3 (1.2–3.3)1.9 (1.2–2.8)NS
KL‐6 (U/ml)1125 (495–2034)316 (210–398)<.0001

Abbreviation: IQR, interquartile range.

The main characteristics of population including, age (median, IQR), gender (%) and lymphocyte subsets at the hospital admission Abbreviation: IQR, interquartile range. Statistical analysis was performed using GraphPad Prism 8.0 software. Nonparametric one‐way analysis of variance test (Kruskal–Wallis test) and Dunn posttests were used for multiple comparisons. The Mann–Whitney test was used to compare pairs of variables. The χ 2 test was used for categorical variables as appropriate. We also performed a logistic regression, using the severe group as dependent variable against nonsevere patients, to assess the potential of serum markers in discriminating the two groups. Sensitivity, specificity, and positive and negative predicted values (PPV and NPV, respectively) were calculated for the cut‐off of the different variables. The total number of lymphocytes (CD45+) was significantly lower in the severe than in the nonsevere group (median IQR, 794 [537-1203] vs. 1341 [798-2071], p = .0110). CD3 lymphocyte count was lower in the severe group (median IQR, 506 [389-943] vs. 782 [445-1483] cells/μl, p = .0372); likewise, CD8 count was depleted in the severe group (median IQR, 132 [103-344] vs. 289 [143-537] cells/μl, p = .0471). NK cells concentration was also significantly lower in severe than in nonsevere patients (median IQR, 69 [25-109] vs. 139 [101-211], p = .0009) and NK cell percentages showed the same pattern (median IQR, 7.8 [3.9–13.5] vs. 12.6 [8.4–19.6], p = .0233). Serum KL‐6 concentrations were more elevated in the severe group than nonsevere group (median IQR, 1125 [495-2034] vs. 316 [210-398], p < .0001). Testing the severe group as a dependent variable by logistic regression, with CD45‐, CD3‐, CD8‐, NK‐cells counts, and percentages and KL‐6 concentrations as independent variables, we obtained areas under the ROC curve of 87.9% (95% confidence interval, 73–100, NPV 71.4%, and PPV 84.6%, p = .0063; Figure 1).
Figure 1

Severe group were tested as dependent variable and CD45‐, CD3‐, CD8‐, NK‐cells counts, and percentages and KL‐6 concentrations as independent variables. AUC was 87.9% (95% CI, 73–100, NPV 71.4%, and PPV 84.6%, p = .0063). AUC, area under the curve; CI, confidence interval; NK, natural killer; NPV, negative predicted value; PPV, positive predicted value

Severe group were tested as dependent variable and CD45‐, CD3‐, CD8‐, NK‐cells counts, and percentages and KL‐6 concentrations as independent variables. AUC was 87.9% (95% CI, 73–100, NPV 71.4%, and PPV 84.6%, p = .0063). AUC, area under the curve; CI, confidence interval; NK, natural killer; NPV, negative predicted value; PPV, positive predicted value Thus, the present study confirmed that both NK cells and KL‐6 are associated with more severe COVID‐19. In fact, we observed significantly higher serum KL‐6 values, lower CD3‐, CD8‐ and CD45‐lymphocyte counts, and NK cells in severe than in nonsevere patients, in line with previous reports. , Our findings support the hypothesis that NK population plays an important role as first‐line defense with cytotoxic immune activity against SARS‐Cov2 infection. Since it was depleted in severe patients with lung respiratory involvement requiring mechanical ventilation, larger, and prospective studies would be worthwhile to confirm our results. Furthermore, the evidence of a significant increase of serum KL‐6 in more critical patients is intriguing and suggests the potential prognostic value of this biomarker on this field. The combination of these validated, reproducible, and nonexpensive bioindicators showed good accuracy in discriminating between severe and nonsevere patients, suggesting a promising value of this approach in the early prediction of a more aggressive disease phenotype. Despite its monocentric design, our study confirms the reliable dysregulation of innate immune responses, particularly involving NK cells, and suggests that surveillance of a peripheral biomarkers’ panel, including lymphocyte cell counts and KL‐6, may be useful in the clinical management of patients with severe COVID‐19.

CONFLICT OF INTERESTS

All the authors declare that there are no conflict of interests.

AUTHOR CONTRIBUTIONS

Miriana d'Alessandro conceived the study and supervised all aspects of study. Miriana d'Alessandro, Laura Bergantini, Paolo Cameli, Lorenzo Remediani, Elena Bargagli, and Giuseppe Curatola collection of data and built database. Miriana d'Alessandro, Paolo Cameli, Laura Bergantini, Elena Bargagli, and Piersante Sestini data analysis and interpretation of results. All authors drafted and revised the papers.
  7 in total

1.  Krebs von den Lungen-6 as a biomarker for disease severity assessment in interstitial lung disease: a comprehensive review.

Authors:  Miriana d'Alessandro; Laura Bergantini; Paolo Cameli; Lucia Vietri; Nicola Lanzarone; Valerio Alonzi; Maria Pieroni; Rosa M Refini; Piersante Sestini; Francesco Bonella; Elena Bargagli
Journal:  Biomark Med       Date:  2020-07-02       Impact factor: 2.851

2.  Increased CD4/CD8 ratio as a risk factor for critical illness in coronavirus disease 2019 (COVID-19): a retrospective multicentre study.

Authors:  Carlo Pallotto; Lorenzo Roberto Suardi; Sara Esperti; Roberto Tarquini; Elisa Grifoni; Simone Meini; Alice Valoriani; Stefania Di Martino; Francesco Cei; Eleonora Sisti; Fiorella Piani; Annarita Botta; Elena Salomoni; Filippo Baragli; Pierluigi Blanc
Journal:  Infect Dis (Lond)       Date:  2020-06-16

3.  Serum KL-6 concentrations as a novel biomarker of severe COVID-19.

Authors:  Miriana d'Alessandro; Paolo Cameli; Rosa Metella Refini; Laura Bergantini; Valerio Alonzi; Nicola Lanzarone; David Bennett; Giuseppe Domenico Rana; Francesca Montagnani; Sabino Scolletta; Federico Franchi; Bruno Frediani; Serafina Valente; Maria Antonietta Mazzei; Francesco Bonella; Elena Bargagli
Journal:  J Med Virol       Date:  2020-06-09       Impact factor: 2.327

4.  Risk factors for disease progression in hospitalized patients with COVID-19: a retrospective cohort study.

Authors:  Wei Hou; Wei Zhang; Ronghua Jin; Lianchun Liang; Bin Xu; Zhongjie Hu
Journal:  Infect Dis (Lond)       Date:  2020-05-06

5.  Predictive values of blood urea nitrogen/creatinine ratio and other routine blood parameters on disease severity and survival of COVID-19 patients.

Authors:  Fesih Ok; Omer Erdogan; Emrullah Durmus; Serkan Carkci; Aggul Canik
Journal:  J Med Virol       Date:  2020-07-22       Impact factor: 20.693

6.  Clinical and radiological diagnosis of non-SARS-CoV-2 viruses in the era of COVID-19 pandemic.

Authors:  Aylin O Alpaydin; Naciye S Gezer; Gokçen O Simsek; Kemal C Tertemiz; Oya O E Kutsoylu; Arzu N Zeka; Irmak Guzel; Mujde Soyturk; Ayca A Sayiner; Vildan A Oguz
Journal:  J Med Virol       Date:  2020-10-30       Impact factor: 20.693

7.  Serum concentrations of Krebs von den Lungen-6 in different COVID-19 phenotypes.

Authors:  Miriana d'Alessandro; Paolo Cameli; Laura Bergantini; Federico Franchi; Sabino Scolletta; Elena Bargagli
Journal:  J Med Virol       Date:  2020-08-25       Impact factor: 20.693

  7 in total
  14 in total

1.  Interstitial lung disease associated with psoriatic arthritis: a new disease entity?

Authors:  Elena Bargagli; Francesca Bellisai; Maria Antonietta Mazzei; Edoardo Conticini; Lorenzo Alderighi; Paolo Cameli; Giovanni Biasi; Laura Bergantini; Susanna Guerrini; Miriana d'Alessandro; Bruno Frediani
Journal:  Intern Emerg Med       Date:  2020-07-29       Impact factor: 3.397

2.  Serum Concentrations of KL-6 in Patients with IPF and Lung Cancer and Serial Measurements of KL-6 in IPF Patients Treated with Antifibrotic Therapy.

Authors:  Miriana d'Alessandro; Laura Bergantini; Paolo Cameli; Maria Pieroni; Rosa Metella Refini; Piersante Sestini; Elena Bargagli
Journal:  Cancers (Basel)       Date:  2021-02-09       Impact factor: 6.639

3.  Serum Krebs von den Lungen-6 for Predicting the Severity of COVID-19 Lung Injury: A Systematic Review and Meta-Analysis.

Authors:  Andro Pramana Witarto; Bendix Samarta Witarto; Achmad Januar Er Putra; Shidi Laras Pramudito; Alfian Nur Rosyid
Journal:  Iran Biomed J       Date:  2021-11-01

Review 4.  Perforin, COVID-19 and a possible pathogenic auto-inflammatory feedback loop.

Authors:  Louise Cunningham; Ian Kimber; David Basketter; Peter Simmonds; Sheila McSweeney; Christos Tziotzios; John P McFadden
Journal:  Scand J Immunol       Date:  2021-09-22       Impact factor: 3.487

5.  Analysis of Early Biomarkers Associated with the Development of Critical Respiratory Failure in Coronavirus Disease 2019 (COVID-19).

Authors:  Hiroyoshi Yamada; Masaki Okamoto; Yoji Nagasaki; Suzuyo Yoshio; Takashi Nouno; Chiyo Yano; Tomohiro Tanaka; Fumi Watanabe; Natsuko Shibata; Yoko Arimizu; Yukako Fukamachi; Yoshiaki Zaizen; Naoki Hamada; Atsushi Kawaguchi; Tomoaki Hoshino; Shigeki Morita
Journal:  Diagnostics (Basel)       Date:  2022-01-28

6.  Early changes in laboratory parameters are predictors of mortality and ICU admission in patients with COVID-19: a systematic review and meta-analysis.

Authors:  Szabolcs Kiss; Noémi Gede; Péter Hegyi; Dávid Németh; Mária Földi; Fanni Dembrovszky; Bettina Nagy; Márk Félix Juhász; Klementina Ocskay; Noémi Zádori; Zsolt Molnár; Andrea Párniczky; Péter Jenő Hegyi; Zsolt Szakács; Gabriella Pár; Bálint Erőss; Hussain Alizadeh
Journal:  Med Microbiol Immunol       Date:  2020-11-21       Impact factor: 3.402

Review 7.  Identification of Parameters Representative of Immune Dysfunction in Patients with Severe and Fatal COVID-19 Infection: a Systematic Review and Meta-analysis.

Authors:  Rundong Qin; Li He; Zhaowei Yang; Nan Jia; Ruchong Chen; Jiaxing Xie; Wanyi Fu; Hao Chen; Xinliu Lin; Renbin Huang; Tian Luo; Yukai Liu; Siyang Yao; Mei Jiang; Jing Li
Journal:  Clin Rev Allergy Immunol       Date:  2022-01-18       Impact factor: 10.817

Review 8.  The Potential of Lung Epithelium Specific Proteins as Biomarkers for COVID-19-Associated Lung Injury.

Authors:  Sultan Almuntashiri; Chelsea James; Xiaoyun Wang; Budder Siddiqui; Duo Zhang
Journal:  Diagnostics (Basel)       Date:  2021-09-08

9.  [Relevance of myocardial injury biomarkers to the prognosis of COVID-19 patients].

Authors:  Eric Alcaide; Laia Álvarez Bota; Rosario Salas
Journal:  Rev Esp Cardiol       Date:  2021-07-24       Impact factor: 4.753

10.  Deep learning identifies antigenic determinants of severe SARS-CoV-2 infection within T-cell repertoires.

Authors:  John-William Sidhom; Alexander S Baras
Journal:  Sci Rep       Date:  2021-07-12       Impact factor: 4.379

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.