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03.02.2024 | Original Research Paper

CCL18, CHI3L1, ANG2, IL-6 systemic levels are associated with the extent of lung damage and radiomic features in SARS-CoV-2 infection

verfasst von: Ilaria Ferrigno, Laura Verzellesi, Marta Ottone, Martina Bonacini, Alessandro Rossi, Giulia Besutti, Efrem Bonelli, Rossana Colla, Nicola Facciolongo, Elisabetta Teopompi, Marco Massari, Pamela Mancuso, Anna Maria Ferrari, Pierpaolo Pattacini, Valeria Trojani, Marco Bertolini, Andrea Botti, Alessandro Zerbini, Paolo Giorgi Rossi, Mauro Iori, Carlo Salvarani, Stefania Croci

Erschienen in: Inflammation Research | Ausgabe 4/2024

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Abstract

Objective and design

We aimed to identify cytokines whose concentrations are related to lung damage, radiomic features, and clinical outcomes in COVID-19 patients.

Material or subjects

Two hundred twenty-six patients with SARS-CoV-2 infection and chest computed tomography (CT) images were enrolled.

Methods

CCL18, CHI3L1/YKL-40, GAL3, ANG2, IP-10, IL-10, TNFα, IL-6, soluble gp130, soluble IL-6R were quantified in plasma samples using Luminex assays. The Mann–Whitney U test, the Kruskal–Wallis test, correlation and regression analyses were performed. Mediation analyses were used to investigate the possible causal relationships between cytokines, lung damage, and outcomes. AVIEW lung cancer screening software, pyradiomics, and XGBoost classifier were used for radiomic feature analyses.

Results

CCL18, CHI3L1, and ANG2 systemic levels mainly reflected the extent of lung injury. Increased levels of every cytokine, but particularly of IL-6, were associated with the three outcomes: hospitalization, mechanical ventilation, and death. Soluble IL-6R showed a slight protective effect on death. The effect of age on COVID-19 outcomes was partially mediated by cytokine levels, while CT scores considerably mediated the effect of cytokine levels on outcomes. Radiomic-feature-based models confirmed the association between lung imaging characteristics and CCL18 and CHI3L1.

Conclusion

Data suggest a causal link between cytokines (risk factor), lung damage (mediator), and COVID-19 outcomes.

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Ochani RK, Asad A, Yasmin F, Shaikh S, Khalid H, Batra S, et al. COVID-19 pandemic: from origins to outcomes. A comprehensive review of viral pathogenesis, clinical manifestations, diagnostic evaluation, and management. Infez Med. 2021;29:20–36.PubMed

Alsharif W, Qurashi A. Effectiveness of COVID-19 diagnosis and management tools: a review. Radiography. 2021. https://doi.org/10.1016/j.radi.2020.09.010.CrossRefPubMed

Sharif PM, Nematizadeh M, Saghazadeh M, Saghazadeh A, Rezaei N. Computed tomography scan in COVID-19: a systematic review and meta-analysis. Pol J Radiol. 2022. https://doi.org/10.5114/pjr.2022.112613.CrossRefPubMedPubMedCentral

Verzellesi L, Botti A, Bertolini M, Trojani V, Carlini G, Nitrosi A, et al. Machine and deep learning algorithms for COVID-19 mortality prediction using clinical and radiomic features. Electronics. 2023. https://doi.org/10.3390/electronics12183878.CrossRef

Haralick RM, Shanmugam K, Dinstein I. Textural features for image classification. IEEE Trans Syst Man Cybern. 1973. https://doi.org/10.1109/TSMC.1973.4309314.CrossRef

Castellano G, Bonilha L, Li LM, Cendes F. Texture analysis of medical images. Clin Radiol. 2004. https://doi.org/10.1016/j.crad.2004.07.008.CrossRefPubMed

Tourassi GD. Journey toward computer-aided diagnosis: role of image texture analysis. Radiology. 1999. https://doi.org/10.1148/radiology.213.2.r99nv49317.CrossRefPubMed

van Timmeren JE, Cester D, Tanadini-Lang S, Alkadhi H, Baessler B. Radiomics in medical imaging—“how-to” guide and critical reflection. Insights Imaging. 2020. https://doi.org/10.1186/s13244-020-00887-2.CrossRefPubMedPubMedCentral

Liu X, Li Y, Qian Z, Sun Z, Xu K, Wang K, et al. A radiomic signature as a non-invasive predictor of progression-free survival in patients with lower-grade gliomas. Neuroimage Clin. 2018. https://doi.org/10.1016/j.nicl.2018.10.014.CrossRefPubMedPubMedCentral

10.

Iori M, Di Castelnuovo C, Verzellesi L, Meglioli G, Lippolis DG, Nitrosi A, et al. Mortality prediction of COVID-19 patients using radiomic and neural network features extracted from a wide chest X-ray sample size: a robust approach for different medical imbalanced scenarios. Appl Sci. 2022. https://doi.org/10.3390/app12083903.CrossRef

11.

Chen M, Copley SJ, Viola P, Lu H, Aboagye EO. Radiomics and artificial intelligence for precision medicine in lung cancer treatment. Semin Cancer Biol. 2023. https://doi.org/10.1016/j.semcancer.2023.05.004.CrossRefPubMed

12.

Castello A, Castellani M, Florimonte L, Urso L, Mansi L, Lopci E. The role of radiomics in the era of immune checkpoint inhibitors: a new protagonist in the jungle of response criteria. J Clin Med. 2022. https://doi.org/10.3390/jcm11061740.CrossRefPubMedPubMedCentral

13.

Wen Q, Zhu J, Meng X, Ma C, Bai T, Sun X, et al. The value of CBCT-based tumor density and volume variations in prediction of early response to chemoradiation therapy in advanced NSCLC. Sci Rep. 2017. https://doi.org/10.1038/s41598-017-14548-w.CrossRefPubMedPubMedCentral

14.

Wen Q, Yang Z, Zhu J, Qiu Q, Dai H, Feng A, et al. Pretreatment CT-based radiomics signature as a potential imaging biomarker for predicting the expression of PD-L1 and CD8+TILs in ESCC. Onco Targets Ther. 2020. https://doi.org/10.2147/OTT.S261068.CrossRefPubMedPubMedCentral

15.

Wen Q, Yang Z, Dai H, Feng A, Li Q. Radiomics study for predicting the expression of PD-L1 and tumor mutation burden in non-small cell lung cancer based on CT images and clinicopathological features. Front Oncol. 2021. https://doi.org/10.3389/fonc.2021.620246.CrossRefPubMedPubMedCentral

16.

Qiu Q, Xing L, Wang Y, Feng A, Wen Q. Development and validation of a radiomics nomogram using computed tomography for differentiating immune checkpoint inhibitor-related pneumonitis from radiation pneumonitis for patients with non-small cell lung cancer. Front Immunol. 2022. https://doi.org/10.3389/fimmu.2022.870842.CrossRefPubMedPubMedCentral

17.

Venerito V, Manfredi A, Lopalco G, Lavista M, Cassone G, Scardapane A, et al. Radiomics to predict the mortality of patients with rheumatoid arthritis-associated interstitial lung disease: a proof-of-concept study. Front Med (Lausanne). 2023. https://doi.org/10.3389/fmed.2022.1069486.CrossRefPubMed

18.

Yang CC, Chen CY, Kuo YT, Ko CC, Wu WJ, Liang CH, et al. Radiomics for the prediction of response to antifibrotic treatment in patients with idiopathic pulmonary fibrosis: a pilot study. Diagnostics (Basel). 2022. https://doi.org/10.3390/diagnostics12041002.CrossRefPubMedPubMedCentral

19.

Schniering J, Maciukiewicz M, Gabrys HS, Brunner M, Blüthgen C, Meier C, et al. Computed tomography-based radiomics decodes prognostic and molecular differences in interstitial lung disease related to systemic sclerosis. Eur Respir J. 2022. https://doi.org/10.1183/13993003.04503-2020.CrossRefPubMedPubMedCentral

20.

Rabaan AA, Al-Ahmed SH, Muhammad J, Khan A, Sule AA, Tirupathi R, et al. Role of inflammatory cytokines in COVID-19 patients: a review on molecular mechanisms, immune functions, immunopathology and immunomodulatory drugs to counter cytokine storm. Vaccines. 2021. https://doi.org/10.3390/vaccines9050436.CrossRefPubMedPubMedCentral

21.

Zanza C, Romenskaya T, Manetti AC, Franceschi F, La Russa R, Bertozzi G, et al. Cytokine storm in COVID-19: immunopathogenesis and therapy. Medicina (Kaunas). 2022. https://doi.org/10.3390/medicina58020144.CrossRefPubMed

22.

Rizzi M, Costanzo M, Tonello S, Matino E, Casciaro FG, Croce A, et al. Prognostic markers in hospitalized COVID-19 patients: the role of IP-10 and C-reactive protein. Dis Markers. 2022. https://doi.org/10.1155/2022/3528312.CrossRefPubMedPubMedCentral

23.

Potere N, Batticciotto A, Vecchié A, Porreca E, Cappelli A, Abbate A, et al. The role of IL-6 and IL-6 blockade in COVID-19. Expert Rev Clin Immunol. 2021. https://doi.org/10.1080/1744666X.2021.1919086.CrossRefPubMed

24.

Gao YD, Ding M, Dong X, Zhang J, Kursat Azkur A, Azkur D, et al. Risk factors for severe and critically ill COVID-19 patients: a review. Allergy. 2021. https://doi.org/10.1111/all.14657.CrossRefPubMed

25.

Dorgham K, Quentric P, Gökkaya M, Marot S, Parizot C, Sauce D, et al. Distinct cytokine profiles associated with COVID-19 severity and mortality. J Allergy Clin Immunol. 2021. https://doi.org/10.1016/j.jaci.2021.03.047.CrossRefPubMedPubMedCentral

26.

Kassianidis G, Siampanos A, Poulakou G, Adamis G, Rapti A, Milionis H, et al. Calprotectin and imbalances between acute-phase mediators are associated with critical illness in COVID-19. Int J Mol Sci. 2022. https://doi.org/10.3390/ijms23094894.CrossRefPubMedPubMedCentral

27.

Yuan Y, Wang Q, Sun D, Wu Z, Peng H, Liu X, et al. Differences in immune responses between children and adults with COVID-19. Curr Med Sci. 2021. https://doi.org/10.1007/s11596-021-2318-1.CrossRefPubMedPubMedCentral

28.

Wu Z, Liu X, Liu J, Zhu F, Liu Y, Liu Y, et al. Correlation between ground-glass opacity on pulmonary CT and the levels of inflammatory cytokines in patients with moderate-to-severe COVID-19 pneumonia. Int J Med Sci. 2021. https://doi.org/10.7150/ijms.56683.CrossRefPubMedPubMedCentral

29.

Liu Y, Zhang C, Huang F, Yang Y, Wang F, Yuan J, et al. Elevated plasma levels of selective cytokines in COVID-19 patients reflect viral load and lung injury. Natl Sci Rev. 2020. https://doi.org/10.1093/nsr/nwaa037.CrossRefPubMedPubMedCentral

30.

Antoniou KM, Margaritopoulos GA, Tomassetti S, Bonella F, Costabel U, Poletti V. Interstitial lung disease. Eur Respir Rev. 2014. https://doi.org/10.1183/09059180.00009113.CrossRefPubMedPubMedCentral

31.

Bonella F, Ulrich C. Biomarkers in connective tissue disease-associated interstitial lung disease. Semin Respir Crit Care Med. 2014. https://doi.org/10.1055/s-0034-1371527.CrossRefPubMed

32.

Jee A, Sahhar J, Youssef P, Bleasel J, Adelstein S, Nguyen M, et al. Review: Serum biomarkers in idiopathic pulmonary fibrosis and systemic sclerosis associated interstitial lung disease—frontiers and horizons. Pharmacol Ther. 2019. https://doi.org/10.1016/j.pharmthera.2019.05.014.CrossRefPubMed

33.

Maher TM, Oballa E, Simpson JK, Porte J, Habgood A, Fahy WA, et al. An epithelial biomarker signature for idiopathic pulmonary fibrosis: an analysis from the multicentre PROFILE cohort study. Lancet Respir Med. 2017. https://doi.org/10.1016/S2213-2600(17)30430-7.CrossRefPubMedPubMedCentral

34.

MacKinnon AC, Gibbons MA, Farnworth SL, Leffler H, Nilsson UJ, Delaine T, et al. Regulation of transforming growth factor-β1-driven lung fibrosis by galectin-3. Am J Respir Crit Care Med. 2012. https://doi.org/10.1164/rccm.201106-0965OC.CrossRefPubMedPubMedCentral

35.

Kameda M, Otsuka M, Chiba H, Kuronuma K, Hasegawa T, Takahashi H, et al. CXCL9, CXCL10, and CXCL11; biomarkers of pulmonary inflammation associated with autoimmunity in patients with collagen vascular diseases-associated interstitial lung disease and interstitial pneumonia with autoimmune features. PLoS ONE. 2020. https://doi.org/10.1371/journal.pone.0241719.CrossRefPubMedPubMedCentral

36.

Wynn TA. Integrating mechanisms of pulmonary fibrosis. J Exp Med. 2011. https://doi.org/10.1084/jem.20110551.CrossRefPubMedPubMedCentral

37.

Wollin L, Wex E, Pautsch A, Schnapp G, Hostettler K, Stowasser S, et al. Mode of action of nintedanib in the treatment of idiopathic pulmonary fibrosis. Eur Respir J. 2015. https://doi.org/10.1183/09031936.00174914.CrossRefPubMedPubMedCentral

38.

Fiedler U, Augustin HG. Angiopoietins: a link between angiogenesis and inflammation. Trends Immunol. 2006. https://doi.org/10.1016/j.it.2006.10.004.CrossRefPubMed

39.

Besutti G, Giorgi Rossi P, Iotti V, Spaggiari L, Bonacini R, Nitrosi A, et al. Accuracy of CT in a cohort of symptomatic patients with suspected COVID-19 pneumonia during the outbreak peak in Italy. Eur Radiol. 2020. https://doi.org/10.1007/s00330-020-07050-x.CrossRefPubMedPubMedCentral

40.

Hansell DM, Bankier AA, MacMahon H, McLoud TC, Müller NL, Remy J. Fleischner Society: glossary of terms for thoracic imaging. Radiology. 2008. https://doi.org/10.1148/radiol.2462070712.CrossRefPubMed

41.

Ho T, Park J, Kim T, Park B, Lee J, Kim J, et al. Deep learning models for predicting severe progression in covid-19-infected patients: retrospective study. JMIR Med Inform. 2021. https://doi.org/10.2196/24973.CrossRefPubMedPubMedCentral

42.

VanderWeele T. Explanation in Causal Inference. Methods for Mediation and Interaction. New York: Oxford University Press; 2015.

43.

Richiardi L, Bellocco R, Zugna D. Mediation analysis in epidemiology: methods, interpretation and bias. Int J Epidemiol. 2013. https://doi.org/10.1093/ije/dyt127.CrossRefPubMed

44.

Tsicopoulos A, Chang Y, Ait Yahia S, de Nadai P, Chenivesse C. Role of CCL18 in asthma and lung immunity. Clin Exp Allergy. 2013. https://doi.org/10.1111/cea.12065.CrossRefPubMed

45.

Mothes R, Pascual-Reguant A, Koehler R, Liebeskind J, Liebheit A, Bauherr S, et al. Distinct tissue niches direct lung immunopathology via CCL18 and CCL21 in severe COVID-19. Nat Commun. 2023. https://doi.org/10.1038/s41467-023-36333-2.CrossRefPubMedPubMedCentral

46.

Luzina IG, Tsymbalyuk N, Choi J, Hasday JD, Atamas SP. CCL18-stimulated upregulation of collagen production in lung fibroblasts requires Sp1 signaling and basal Smad3 activity. J Cell Physiol. 2006. https://doi.org/10.1002/jcp.20452.CrossRefPubMed

47.

Tizaoui K, Yang JW, Lee KH, Kim JH, Kim M, Yoon S, et al. The role of YKL-40 in the pathogenesis of autoimmune diseases: a comprehensive review. Int J Biol Sci. 2022. https://doi.org/10.7150/ijbs.67587.CrossRefPubMedPubMedCentral

48.

Zhou Y, Peng H, Sun H, Peng X, Tang C, Gan Y, et al. Chitinase 3-like 1 suppresses injury and promotes fibroproliferative responses in Mammalian lung fibrosis. Sci Transl Med. 2014. https://doi.org/10.1126/scitranslmed.3007096.CrossRefPubMedPubMedCentral

49.

Lee SY, Lee CM, Ma B, Kamle S, Elias JA, Zhou Y, et al. Targeting chitinase 1 and chitinase 3-Like 1 as novel therapeutic strategy of pulmonary fibrosis. Front Pharmacol. 2022. https://doi.org/10.3389/fphar.2022.826471.CrossRefPubMedPubMedCentral

50.

Sun X, Nakajima E, Norbrun C, Sorkhdini P, Yang AX, Yang D, et al. Chitinase 3 like 1 contributes to the development of pulmonary vascular remodeling in pulmonary hypertension. JCI Insight. 2022. https://doi.org/10.1172/jci.insight.159578.CrossRefPubMedPubMedCentral

51.

Schoneveld L, Ladang A, Henket M, Frix AN, Cavalier E, Guiot J, et al. YKL-40 as a new promising prognostic marker of severity in COVID infection. Crit Care. 2021. https://doi.org/10.1186/s13054-020-03383-7.CrossRefPubMedPubMedCentral

52.

Kamle S, Ma B, He CH, Akosman B, Zhou Y, Lee CM, et al. Chitinase 3-like-1 is a therapeutic target that mediates the effects of aging in COVID-19. JCI Insight. 2021. https://doi.org/10.1172/jci.insight.148749.CrossRefPubMedPubMedCentral

53.

De Lorenzo R, Sciorati C, Lorè NI, Capobianco A, Tresoldi C, Cirillo DM, et al. Chitinase-3-like protein-1 at hospital admission predicts COVID-19 outcome: a prospective cohort study. Sci Rep. 2022. https://doi.org/10.1038/s41598-022-11532-x.CrossRefPubMedPubMedCentral

54.

Ebihara T, Matsubara T, Togami Y, Matsumoto H, Tachino J, Matsuura H, et al. Combination of WFDC2, CHI3L1, and KRT19 in plasma defines a clinically useful molecular phenotype associated with prognosis in critically Ill COVID-19 patients. J Clin Immunol. 2023. https://doi.org/10.1007/s10875-022-01386-3.CrossRefPubMed

55.

Kimura Y, Nakai Y, Shin J, Hara M, Takeda Y, Kubo S, et al. Identification of serum prognostic biomarkers of severe COVID-19 using a quantitative proteomic approach. Sci Rep. 2021. https://doi.org/10.1038/s41598-021-98253-9.CrossRefPubMedPubMedCentral

56.

Rosenberger CM, Wick KD, Zhuo H, Wu N, Chen Y, Kapadia SB, et al. Early plasma angiopoietin-2 is prognostic for ARDS and mortality amongcritically ill patients with sepsis. Crit Care. 2023. https://doi.org/10.1186/s13054-023-04525-3.CrossRefPubMedPubMedCentral

57.

Dawson RE, Jenkins BJ, Saad MI. IL-6 family cytokines in respiratory health and disease. Cytokine. 2021. https://doi.org/10.1016/j.cyto.2021.155520.CrossRefPubMed

58.

Bivona G, Agnello L, Ciaccio M. Biomarkers for prognosis and treatment response in COVID-19 patients. Ann Lab Med. 2021. https://doi.org/10.3343/alm.2021.41.6.540.CrossRefPubMedPubMedCentral

59.

Bhaskaran K, Bacon S, Evans SJ, Bates CJ, Rentsch CT, MacKenna B, et al. Factors associated with deaths due to COVID-19 versus other causes: population-based cohort analysis of UK primary care data and linked national death registrations within the OpenSAFELY platform. Lancet Reg Health Eur. 2021. https://doi.org/10.1016/j.lanepe.2021.100109.CrossRefPubMedPubMedCentral

60.

Besutti G, Pellegrini M, Ottone M, Cantini M, Milic J, Bonelli E, et al. The impact of chest CT body composition parameters on clinical outcomes in COVID-19 patients. PLoS ONE. 2021. https://doi.org/10.1371/journal.pone.0251768.CrossRefPubMedPubMedCentral

61.

Michaud M, Balardy L, Moulis G, Gaudin C, Peyrot C, Vellas B, et al. Proinflammatory cytokines, aging, and age-related diseases. J Am Med Dir Assoc. 2013. https://doi.org/10.1016/j.jamda.2013.05.009.CrossRefPubMed

62.

Zanatta E, Martini A, Depascale R, Gamba A, Tonello M, Gatto M, et al. CCL18 as a biomarker of interstitial lung disease (ILD) and progressive fibrosing ILD in Patients with idiopathic inflammatory myopathies. Diagnostics. 2023. https://doi.org/10.3390/diagnostics13101715.CrossRefPubMedPubMedCentral

63.

Utsunomiya A, Oyama N, Hasegawa M. Potential biomarkers in systemic sclerosis: a literature review and update. J Clin Med. 2020. https://doi.org/10.3390/jcm9113388.CrossRefPubMedPubMedCentral

64.

Arron JR. Biomarkers in systemic sclerosis: mechanistic insights into pathogenesis and treatment. Curr Opin Rheumatol. 2021. https://doi.org/10.1097/BOR.0000000000000827.CrossRefPubMed

65.

Tong X, Ma Y, Liu T, Zhenzhen L, Sitong L, Guihui W, et al. Can YKL-40 be used as a biomarker for interstitial lung disease?: a systematic review and meta-analysis. Medicine. 2021. https://doi.org/10.1097/MD.0000000000025631.CrossRefPubMedPubMedCentral

66.

Montero P, Milara J, Roger I, Cortijo J. Role of JAK/STAT in interstitial lung diseases; molecular and cellular mechanisms. Int J Mol Sci. 2021. https://doi.org/10.3390/ijms22126211.CrossRefPubMedPubMedCentral

67.

Uehara M, Enomoto N, Mikamo M, Oyama Y, Kono M, Fujisawa T, et al. Impact of angiopoietin-1 and -2 on clinical course of idiopathic pulmonary fibrosis. Respir Med. 2016. https://doi.org/10.1016/j.rmed.2016.03.001.CrossRefPubMed

Titel: CCL18, CHI3L1, ANG2, IL-6 systemic levels are associated with the extent of lung damage and radiomic features in SARS-CoV-2 infection
verfasst von: Ilaria Ferrigno
Laura Verzellesi
Marta Ottone
Martina Bonacini
Alessandro Rossi
Giulia Besutti
Efrem Bonelli
Rossana Colla
Nicola Facciolongo
Elisabetta Teopompi
Marco Massari
Pamela Mancuso
Anna Maria Ferrari
Pierpaolo Pattacini
Valeria Trojani
Marco Bertolini
Andrea Botti
Alessandro Zerbini
Paolo Giorgi Rossi
Mauro Iori
Carlo Salvarani
Stefania Croci
Publikationsdatum: 03.02.2024
Verlag: Springer International Publishing
Erschienen in: Inflammation Research / Ausgabe 4/2024
Print ISSN: 1023-3830
Elektronische ISSN: 1420-908X
DOI: https://doi.org/10.1007/s00011-024-01852-1

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Abstract

Objective and design

Material or subjects

Methods

Results

Conclusion

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