Dynamics of circulating calprotectin accurately predict the outcome of moderate COVID-19 patients

Nicolas Chapuis¹, Nusaibah Ibrahimi², Thibaut Belmondo³, Claire Goulvestre⁴, Anne-Emmanuelle Berger⁵, Alice-Andrée Mariaggi⁶, Muriel Andrieu⁷, Camille Chenevier-Gobeaux⁸, Arnaud Bayle⁹, Lydia Campos¹⁰, Cherifa Cheurfa¹¹, Richard Chocron¹², Jean-Luc Diehl¹³, Benoît Doumenc¹⁴, Jérôme Duchemin¹⁵, Manon Duprat⁵, Fabien François⁵, Nicolas Gendron¹⁶, Tristant Mirault¹⁷, Frédéric Pène¹⁸, Aurélien Philippe¹⁹, Fanny Pommeret²⁰, Olivier Sanchez²¹, David M Smadja²², Tali-Anne Szwebel²³, Aymeric Silvin²⁴, Florent Ginhoux²⁴, Ludovic Lacroix²⁵, Gérôme Jules-Clément²⁶, Sarobidy Rapeteramana²⁶, Colette Mavier²⁷, Laura Steller²⁸, Barbara Perniconi²⁹, Fabrice André²⁰, Damien Drubay², Michaela Fontenay³⁰, Sophie Hüe³, Stéphane Paul⁵, Eric Solary³¹

Affiliations

¹ Laboratory of Hematology, Assistance Publique-Hôpitaux de Paris, Cochin Hospital, Paris, France; Université de Paris, Institut Cochin, CNRS UMR8104, INSERM U1016, Paris, France.
² Department of Biostatistics and Epidemiology, Gustave Roussy Cancer Center, Villejuif, France; Université Paris-Saclay, INSERM U1018, Gustave Roussy Cancer Center, Villejuif, France.
³ Laboratory of Immunology, Assistance Publique-Hôpitaux de Paris, Groupe Hospitalier Henri Mondor, Créteil, France.
⁴ Laboratory of Immunology, Assistance Publique-Hôpitaux de Paris, Cochin Hospital, Paris, France.
⁵ Department of Immunology, University Hospital of Saint Etienne, CIC1408, GIMAP EA3064, Saint Etienne, France.
⁶ Université de Paris, Institut Cochin, CNRS UMR8104, INSERM U1016, Paris, France; Assistance Publique-Hôpitaux de Paris, Laboratory of Virology, Cochin hospital, Paris, France.
⁷ Université de Paris, Institut Cochin, CNRS UMR8104, INSERM U1016, Paris, France.
⁸ Department of automated biological diagnosis, Assistance Publique-Hôpitaux de Paris, Cochin Hospital, Paris, France.
⁹ Department of Biostatistics and Epidemiology, Gustave Roussy Cancer Center, Villejuif, France; Université Paris-Saclay, INSERM U1018, Gustave Roussy Cancer Center, Villejuif, France; Drug Development Department, Gustave Roussy Cancer Center, Villejuif, France.
¹⁰ Department of Hematology, University Hospital of Saint Etienne, Saint Etienne, France.
¹¹ Department of Anesthesiology and Intensive care, Assistance Publique-Hôpitaux de Paris, Cochin hospital, Paris, France.
¹² Department of Emergency, Assistance Publique-Hôpitaux de Paris, European Georges Pompidou Hospital, Paris, France; Paris Cardiovascular Research Center (PARCC), Université de Paris, INSERM U970, European Georges Pompidou Hospital, Paris, France.
¹³ Medical Intensive Care Department and Biosurgical Research Lab (Carpentier Foundation), Assistance Publique-Hôpitaux de Paris, Georges Pompidou European Hospital, Paris, France; Université de Paris, Innovative Therapies in Haemostasis, INSERM U1140, Paris France.
¹⁴ Department of Emergencies, Assistance Publique-Hôpitaux de Paris, Cochin Hospital, Paris, France.
¹⁵ Laboratory of Hematology, Assistance Publique-Hôpitaux de Paris, Cochin Hospital, Paris, France.
¹⁶ Université de Paris, Innovative Therapies in Haemostasis, INSERM U1140, Paris France; Department of Emergencies, Assistance Publique-Hôpitaux de Paris, Cochin Hospital, Paris, France.
¹⁷ Paris Cardiovascular Research Center (PARCC), Université de Paris, INSERM U970, European Georges Pompidou Hospital, Paris, France.
¹⁸ Université de Paris, Institut Cochin, CNRS UMR8104, INSERM U1016, Paris, France; Assistance Publique-Hôpitaux de Paris, Medical Intensive Care, Cochin hospital, Paris, France.
¹⁹ Université de Paris, Innovative Therapies in Haemostasis, INSERM U1140, Paris France; Department of Biological Hematology, Assistance Publique-Hôpitaux de Paris, Georges Pompidou European Hospital, Paris France; Laboratory of Hematology and Biosurgical Research (Carpentier Foundation)Assistance Publique-Hôpitaux de Paris, European Georges Pompidou Hospital, Paris, France.
²⁰ Medical Oncology Department, Gustave Roussy Cancer Center, Villejuif, France.
²¹ Université de Paris, Innovative Therapies in Haemostasis, INSERM U1140, Paris France; Department of Pneumology and Intensive Care, Assistance Publique-Hôpitaux de Paris, European Georges Pompidou Hospital, Paris, France.
²² Université de Paris, Innovative Therapies in Haemostasis, INSERM U1140, Paris France; Department of Biological Hematology, Assistance Publique-Hôpitaux de Paris, Georges Pompidou European Hospital, Paris France.
²³ Internal Medicine Department, Assistance Publique-Hôpitaux de Paris, Cochin hospital, Paris, France.
²⁴ University Paris-Saclay, INSERM U1015, Gustave Roussy Cancer Center, Villejuif, France.
²⁵ Department of Biopathology, Gustave Roussy Cancer Center, Villejuif, France.
²⁶ Gustave Roussy Cancer Center, Bioinformatics platform, Université Paris-Saclay, INSERM US23, CNRS UMS 3655, Villejuif, France.
²⁷ Thermofisher immunodiagnostics, Dardilly, France.
²⁸ Thermo Fisher Scientific ImmunoDiagnostics, Phadia GmbH, Freiburg, Germany.
²⁹ CerbaXpert Alliance, Saint Ouen L'Aumone, France.
³⁰ Laboratory of Hematology, Assistance Publique-Hôpitaux de Paris, Cochin Hospital, Paris, France; Université de Paris, Institut Cochin, CNRS UMR8104, INSERM U1016, Paris, France; Laboratory of Immunology, Assistance Publique-Hôpitaux de Paris, Cochin Hospital, Paris, France.
³¹ Université Paris-Saclay, INSERM U1287, Gustave Roussy Cancer Center, 114 rue Edouard Vaillant, Villejuif 94805, France; Department of Hematology, Gustave Roussy Cancer Center, Villejuif, France. Electronic address: eric.solary@gustaveroussy.fr.

PMID: 35644124
PMCID: PMC9132728
DOI: 10.1016/j.ebiom.2022.104077

Dynamics of circulating calprotectin accurately predict the outcome of moderate COVID-19 patients

Nicolas Chapuis et al. EBioMedicine. 2022 Jun.

. 2022 Jun:80:104077.

doi: 10.1016/j.ebiom.2022.104077. Epub 2022 May 26.

Authors

Affiliations

¹ Laboratory of Hematology, Assistance Publique-Hôpitaux de Paris, Cochin Hospital, Paris, France; Université de Paris, Institut Cochin, CNRS UMR8104, INSERM U1016, Paris, France.
² Department of Biostatistics and Epidemiology, Gustave Roussy Cancer Center, Villejuif, France; Université Paris-Saclay, INSERM U1018, Gustave Roussy Cancer Center, Villejuif, France.
³ Laboratory of Immunology, Assistance Publique-Hôpitaux de Paris, Groupe Hospitalier Henri Mondor, Créteil, France.
⁴ Laboratory of Immunology, Assistance Publique-Hôpitaux de Paris, Cochin Hospital, Paris, France.
⁵ Department of Immunology, University Hospital of Saint Etienne, CIC1408, GIMAP EA3064, Saint Etienne, France.
⁶ Université de Paris, Institut Cochin, CNRS UMR8104, INSERM U1016, Paris, France; Assistance Publique-Hôpitaux de Paris, Laboratory of Virology, Cochin hospital, Paris, France.
⁷ Université de Paris, Institut Cochin, CNRS UMR8104, INSERM U1016, Paris, France.
⁸ Department of automated biological diagnosis, Assistance Publique-Hôpitaux de Paris, Cochin Hospital, Paris, France.
⁹ Department of Biostatistics and Epidemiology, Gustave Roussy Cancer Center, Villejuif, France; Université Paris-Saclay, INSERM U1018, Gustave Roussy Cancer Center, Villejuif, France; Drug Development Department, Gustave Roussy Cancer Center, Villejuif, France.
¹⁰ Department of Hematology, University Hospital of Saint Etienne, Saint Etienne, France.
¹¹ Department of Anesthesiology and Intensive care, Assistance Publique-Hôpitaux de Paris, Cochin hospital, Paris, France.
¹² Department of Emergency, Assistance Publique-Hôpitaux de Paris, European Georges Pompidou Hospital, Paris, France; Paris Cardiovascular Research Center (PARCC), Université de Paris, INSERM U970, European Georges Pompidou Hospital, Paris, France.
¹³ Medical Intensive Care Department and Biosurgical Research Lab (Carpentier Foundation), Assistance Publique-Hôpitaux de Paris, Georges Pompidou European Hospital, Paris, France; Université de Paris, Innovative Therapies in Haemostasis, INSERM U1140, Paris France.
¹⁴ Department of Emergencies, Assistance Publique-Hôpitaux de Paris, Cochin Hospital, Paris, France.
¹⁵ Laboratory of Hematology, Assistance Publique-Hôpitaux de Paris, Cochin Hospital, Paris, France.
¹⁶ Université de Paris, Innovative Therapies in Haemostasis, INSERM U1140, Paris France; Department of Emergencies, Assistance Publique-Hôpitaux de Paris, Cochin Hospital, Paris, France.
¹⁷ Paris Cardiovascular Research Center (PARCC), Université de Paris, INSERM U970, European Georges Pompidou Hospital, Paris, France.
¹⁸ Université de Paris, Institut Cochin, CNRS UMR8104, INSERM U1016, Paris, France; Assistance Publique-Hôpitaux de Paris, Medical Intensive Care, Cochin hospital, Paris, France.
¹⁹ Université de Paris, Innovative Therapies in Haemostasis, INSERM U1140, Paris France; Department of Biological Hematology, Assistance Publique-Hôpitaux de Paris, Georges Pompidou European Hospital, Paris France; Laboratory of Hematology and Biosurgical Research (Carpentier Foundation)Assistance Publique-Hôpitaux de Paris, European Georges Pompidou Hospital, Paris, France.
²⁰ Medical Oncology Department, Gustave Roussy Cancer Center, Villejuif, France.
²¹ Université de Paris, Innovative Therapies in Haemostasis, INSERM U1140, Paris France; Department of Pneumology and Intensive Care, Assistance Publique-Hôpitaux de Paris, European Georges Pompidou Hospital, Paris, France.
²² Université de Paris, Innovative Therapies in Haemostasis, INSERM U1140, Paris France; Department of Biological Hematology, Assistance Publique-Hôpitaux de Paris, Georges Pompidou European Hospital, Paris France.
²³ Internal Medicine Department, Assistance Publique-Hôpitaux de Paris, Cochin hospital, Paris, France.
²⁴ University Paris-Saclay, INSERM U1015, Gustave Roussy Cancer Center, Villejuif, France.
²⁵ Department of Biopathology, Gustave Roussy Cancer Center, Villejuif, France.
²⁶ Gustave Roussy Cancer Center, Bioinformatics platform, Université Paris-Saclay, INSERM US23, CNRS UMS 3655, Villejuif, France.
²⁷ Thermofisher immunodiagnostics, Dardilly, France.
²⁸ Thermo Fisher Scientific ImmunoDiagnostics, Phadia GmbH, Freiburg, Germany.
²⁹ CerbaXpert Alliance, Saint Ouen L'Aumone, France.
³⁰ Laboratory of Hematology, Assistance Publique-Hôpitaux de Paris, Cochin Hospital, Paris, France; Université de Paris, Institut Cochin, CNRS UMR8104, INSERM U1016, Paris, France; Laboratory of Immunology, Assistance Publique-Hôpitaux de Paris, Cochin Hospital, Paris, France.
³¹ Université Paris-Saclay, INSERM U1287, Gustave Roussy Cancer Center, 114 rue Edouard Vaillant, Villejuif 94805, France; Department of Hematology, Gustave Roussy Cancer Center, Villejuif, France. Electronic address: eric.solary@gustaveroussy.fr.

PMID: 35644124
PMCID: PMC9132728
DOI: 10.1016/j.ebiom.2022.104077

Abstract

Background: Severe COVID-19 is associated with a high circulating level of calprotectin, the S100A8/S100A9 alarmin heterodimer. Baseline calprotectin amount measured in peripheral blood at diagnosis correlates with disease severity. The optimal use of this biomarker along COVID-19 course remains to be delineated.

Methods: We focused on patients with a WHO-defined moderate COVID-19 requiring hospitalization in a medical ward. We collected plasma and serum from three independent cohorts (N = 626 patients) and measured calprotectin amount at admission. We performed longitudinal measures of calprotectin in 457 of these patients (1461 samples) and used a joint latent class mixture model in which classes were defined by age, body mass index and comorbidities to identify calprotectin trajectories predicting the risk of transfer into an intensive care unit or death.

Findings: After adjustment for age, sex, body mass index and comorbidities, the predictive value of baseline calprotectin in patients with moderate COVID19 could be refined by serial monitoring of the biomarker. We discriminated three calprotectin trajectories associated with low, moderate, and high risk of poor outcome, and we designed an algorithm available as online software (https://calpla.gustaveroussy.fr:8443/) to monitor the probability of a poor outcome in individual patients with moderate COVID-19.

Interpretation: These results emphasize the clinical interest of serial monitoring of calprotectin amount in the peripheral blood to anticipate the risk of poor outcomes in patients with moderate COVID-19 hospitalized in a standard care unit.

Funding: The study received support (research grants) from ThermoFisher immunodiagnostics (France) and Gustave Roussy Foundation.

Keywords: Biomarker; COVID-19; Calprotectin; Dynamics; S100A8/A9; Serial measurement.

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Conflict of interest statement

This study (NI, TB, CG, AEB, MA, CCG, AB, LC, RC, BD, JD, MD, FF, AP, FP, OS, DMS, TAS, AS, LL, GJC, SR, BP, DD, MF, SH, SP, ES) received support of a research grant provided by ThermoFisher Immunodiagnostics; CM and LS are employees of Thermo Fisher Scientific; FA received institutional grants from Roche, Astra Zeneca, Daichy Sankyo, Pfizer, Novartis, and Lilly; NG received consultation fees from Bayer, Leo Pharma, Aspen, Sanofi and honoraria from Boehringer Ingelheim, Bristol Myers Squib, Pfizer, and Leo Pharma; TM received honoraria and supports for attending meetings from BAYER HealthCare and Incyte Biosciences France; FP received a grant from Alexion, consulting fees and honoraria from Gilead.

Figures

**Figure 1**
**Flowchart of the study. a.** Patient screening; b. Samples collected and tested.

**Figure 2**
**Calprotectin dosage and sampling method effects. a.** Correlation between calprotectin circulating level measured in plasma (in red, Spearman correlation 0.96; linear regression slope 2.64) and serum (in blue, Spearman correlation 0.92; linear regression slope 1.10) using the Thermo Fisher (TF) and the MesoScale Diagnostics (MSD) methods, respectively. b. Calprotectin circulating levels measured using the Thermo Fisher (TF) method (red, EDTA plasma, Saint-Etienne, n = 135; blue, EDTA plasma, Ile de France, n = 160; green, serum, n = 102); Student's t-test, ****, p-value < 0.0001; c. Calprotectin circulating levels measured using the MesoScale Diagnostics (MSD) (red left citrate plasma, n = 65; red middle EDTA plasma, n = 256; yellow serum, n = 102). Student's t-test, ****, p-value < 0.0001.

**Figure 3**
**Prognostic impact of baseline calprotectin level** for each cohort adjusted for age, sex, body mass index and comorbidities, including cancer, diabetes, cardio-vascular and lung diseases.

**Figure 4**
**Dynamic assessment of circulating calprotectin level.** Mean predicted trajectory of calprotectin level (a) and cumulative incidence of ICU admission or death (b) since COVID-19 diagnostic (day) for each class.

See this image and copyright information in PMC

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Dynamics of circulating calprotectin accurately predict the outcome of moderate COVID-19 patients

Affiliations

Dynamics of circulating calprotectin accurately predict the outcome of moderate COVID-19 patients

Authors

Affiliations

Abstract

Conflict of interest statement

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