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International Journal of Molecular Sciences logoLink to International Journal of Molecular Sciences
. 2023 Feb 3;24(3):3041. doi: 10.3390/ijms24033041

Finding the Needle in the Haystack: Serological and Urinary Biomarkers in Behçet’s Disease: A Systematic Review

Marta Arbrile 1,†, Massimo Radin 1,2,*,†, Davide Medica 1, Paolo Miraglia 1, Letizia Rilat 1, Irene Cecchi 2, Silvia Grazietta Foddai 2, Alice Barinotti 1, Elisa Menegatti 1,2, Dario Roccatello 1,2, Savino Sciascia 1,2
Editor: Isabella Russo
PMCID: PMC9917563  PMID: 36769366

Abstract

Urinary and serological markers play an essential role in the diagnostic process of autoimmune diseases. However, to date, specific and reliable biomarkers for diagnosing Behçet’s disease (BD) are still lacking, negatively affecting the management of these patients. To analyze the currently available literature on serological and urinary BD biomarkers investigated in the last 25 years, we performed a systematic literature review using the Population, Intervention, Comparison, and Outcomes (PICO) strategy. One hundred eleven studies met the eligibility criteria (6301 BD patients, 5163 controls). Most of them were retrospective, while five (5%) were prospective. One hundred ten studies (99%) investigated serological biomarkers and only two (2%) focused on urinary biomarkers. One hundred three studies (93%) explored the diagnostic potential of the biomolecules, whereas sixty-two (56%) tested their effect on disease activity monitoring. Most articles reported an increase in inflammatory markers and pro-oxidant molecules, with a decrease in antioxidants. Promising results have been shown by the omics sciences, offering a more holistic approach. Despite the vast number of investigated markers, existing evidence indicates a persistent gap in BD diagnostic/prognostic indices. While new steps have been taken in the direction of pathogenesis and disease monitoring, international efforts for the search of a diagnostic marker for BD are still needed.

Keywords: Behçet’s disease, biomarkers, diagnosis, disease activity, autoinflammatory disease

1. Introduction

Behçet’s disease (BD) is a multisystemic inflammatory condition often described as a part of the vasculitic spectrum, whose etiology, although not fully characterized, is attributed to a complex inter-relationship between the genetic background and the dysregulation of both the innate and the adaptive immune system [1]. Females and males are equally affected, with a worse disease progression in males due to ocular, vascular, and neurological involvement [2]. Diagnosis onset is collocated between 25 and 30 years old, although countries with a low disease prevalence may show a delayed time of diagnosis [3].

The distribution of BD is widespread; however, it is more prevalent in countries along the ancient “Silk Road”, from the Mediterranean area to the far east, where it is associated with the distribution of the major histocompatibility complex antigen HLA-B51 [4].Even though the first description of BD dates back to 1937, its diagnosis still relies entirely on clinical criteria [5], and a laboratory test to help identify patients with BD is still lacking. Unfortunately, the most common symptoms of BD, including oro-genital aphthae, skin lesions, arthritis, and uveitis, overlap with other autoimmune diseases, such as inflammatory bowel conditions or connective tissue diseases, and the differential diagnosis may become a real challenge [6,7]. Moreover, the disease course tends to be considerably prolonged, and it may take months or years before all the typical signs and symptoms appear. Unfortunately, for most patients, an early diagnosis of BD can be an unrealistic goal and having one or more biomarkers of BD could drastically change how BD is diagnosed and ultimately help clinical evaluation. A biomarker is a measurable characteristic of the body that may indicate a particular biological state or condition [8]. Biomarkers are employed in many fields of medicine, such as disease diagnosis, disease activity evaluation, prognosis, and therapy monitoring. Since the 1950s, new biomarkers for BD have been studied to be applicable to all populations in which the disease is prevalent. Still, there is no consensus on a shared biomarker for BD to be evaluated by testing in the diagnostic routine. Recently, omics sciences have helped solve this diagnostic gap in multiple diseases using a promising holistic approach, but at the moment, they are not yet integrated into standard clinical care [9,10]. A system based on omics sciences is also needed in BD—on the one hand, for diagnosing BD patients early; and, on the other hand, for identifying different profiles of BD patients based on their disease activity, prognosis, and response to therapy. In order to contribute to this growing field of research, this study aimed to systematically review the currently available literature on the identification and characterization of the clinical utility of serological and urinary BD biomarkers investigated in the last 25 years.

2. Methods

2.1. Literature Search Strategy

A detailed literature search screening Ovid MEDLINE, In-Process and Other Non-Indexed Citation, the National Library of Medicine’s (NLM), and the in-process database for Ovid MEDLINE, from inception to November 2021, was performed a priori to identify original articles analyzing the diagnostic role of urinary and serological biomarkers in BD. The Population, Intervention, Comparison, and Outcomes strategy (PICO) was adopted to identify the best keywords to use in database queries. The following keywords and medical subject heading (MESH) terms were used in all possible combinations using Boolean operators: Behçet’s syndrome; retinal vasculitis; biomarkers; inflammation mediators; immune checkpoint proteins; pathogen-associated molecular pattern molecules.

2.2. Selection of the Studies

We screened and selected full-text articles, analyzing the titles and abstracts. After the first screening phase, we evaluated the selected abstracts and the full texts to determine eligibility. Papers retrieved by the literature search but reporting insufficient data according to the chosen PICO strategy were excluded. The online search was limited to case-control, cohort, and case-series studies. Studies with a small sample size (n < 20), conference abstracts, reviews, and animal studies were excluded. Articles written in languages other than English were excluded. The selection and inclusion criteria were determined a priori.

We considered studies eligible if they met the following inclusion criteria:

Studies that included at least 20 patients diagnosed with BD following the current International Study Group Criteria [5];

Studies that analyzed urinary biomarkers, serological biomarkers, or both;

Ex vivo studies (in vitro studies were excluded).

Four independent reviewers (MA, DM, PM, and LR) systematically analyzed the abstracts and full texts of the articles meeting the inclusion criteria; any disagreements were resolved by consensus. If consensus could not be achieved, a third party (MR) provided an assessment of eligibility. As the data on eligibility were dichotomous (eligible: yes/no), agreement at both the title and abstract review and the full article review stages was determined by the calculation of Cohen’s kappa coefficient (k > 8). We performed the present study according to the PRISMA guidelines [11].

2.3. Data Extraction and Data Synthesis

Data were extracted in an electronic database, summarized, analyzed, and discussed. For each study, the following data were identified: study design, country of origin, type of biomarker, methods used for detection, sample size, pathergy and HLAB51 positivity, type of involvement (systemic/organ-specific), different marker concentrations in BD, and controls and measures of association. The homogeneity of studies was assessed per each diagnostic maker. Quantitative synthesis was considered inappropriate due to the heterogeneity among studies in the population set, the type of biomarker analyzed, and the methods used for the identifications used in different studies. Therefore, a qualitative narrative synthesis was performed.

3. Results and Discussion

3.1. Systematic Literature Search

We retrieved 637 articles from the initial search (Figure 1).

Figure 1.

Figure 1

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Flowchart of the literature search strategy.

Three hundred forty-four studies were excluded after the title and abstract screening because they did not fit the selection criteria described above. We further assessed twohundred ninety-three studies for eligibility. We excluded one hundred eighty-two studies because they did not meet the inclusion criteria, were not focused on biomarkers, did not reach statistically significant results, or were not in English. Finally, one hundred eleven articles were eligible for the qualitative synthesis.

Figure 2 shows the number of studies per year included in this systematic review. Furthermore, Table 1 displays the main characteristics of the analyzed studies, including the number of patients, study design, biomarkers tested, and accuracy.

Figure 2.

Figure 2

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Graphical representation of the number of studies per year included in this systematic review. The scatter plot was established using the package Ggplot2 [12] of R studio [13].

Table 1.

Main characteristics of the studies included in the analysis.

REF Year First Author Country Design BD Patients, n Controls, n Biomarker Tested Urinary/
Serologic		 Diagnostic/Activity
[14] 1995 Yosipovitch et al. Israel Retrospective 25 20 IL-1B S Diagnostic
SIL-2R S Diagnostic
[15] 1995 Deǧer et al. Turkey Retrospective 42 (20 active) 40 PMN elastase S Diagnostic/Activity
[16] 1995 Direskeneli et al. UK Retrospective 70 (56 active) 52 AECA S Diagnostic/Activity
vVF S Diagnostic
[17] 1997 Uslu et al. Turkey Retrospective 27 18 ET-1 S Diagnostic
[18] 1998 Alpsoy et al. Turkey Retrospective 32 (14 active) 20 IL-2 S Diagnostic
SIL-2R S Activity
[19] 2000 Katsantonis et al. Germany Retrospective 34 (25 active) N/A IL-8 S Activity
[20] 2000 Eksioglu-Demiralp et al. Turkey Retrospective 37 55 CD4+CD16+ S Diagnostic
CD4+CD56+ S Diagnostic
[21] 2000 Freysdottir et al. UK Retrospective 20 26 T-γδ S Diagnostic
CD56 S Diagnostic
[22] 2002 Krause et al. Israel Retrospective 27 20 IgG ASCA S Diagnostic
IgA ASCA S Diagnostic
[23] 2002 Evereklioglu et al. Turkey Retrospective 35 (18 active) 20 Leptin S Diagnostic/Activity
[24] 2002 Er et al. Turkey Retrospective 43 (20 active) 52 ET-1 S Diagnostic/Activity
Homocysteine S Diagnostic/Activity
NO S Diagnostic/Activity
[25] 2002 Saglam et al. Turkey Retrospective 44 (23 active) 30 cICAM-1 S Diagnostic/Activity
[26] 2002 Evereklioglu et al. Turkey Retrospective 52 (27 active) 32 NO S Diagnostic/Activity
[27] 2003 Evereklioglu et al. Turkey Retrospective 36 (16 active) 20 NO (urinary) U Diagnostic/Activity
NO (serum) S Diagnostic/Activity
[28] 2003 Erkiliç et al. Turkey Retrospective 35 (17 active) 20 ADA S Diagnostic/Activity
TBARS S Diagnostic
Plasmatic SOD S Diagnostic/Activity
RBC SOD S Diagnostic/Activity
Plasmatic GSHPx S Diagnostic/Activity
RBC GSHPx S Diagnostic/Activity
RBC Catalase S Diagnostic
[29] 2004 Akdeniz et al. Turkey Retrospective 27 16 IL-6 S Diagnostic
Il-2 S Diagnostic
TNF-α S Diagnostic
NO S Diagnostic
[30] 2004 Sari et al. Turkey Retrospective 23 20 E-selectine S Diagnostic
ESR S Diagnostic
PCR S Diagnostic
[31] 2004 Yazici et al. Turkey Retrospective 49 (31 active) 40 MPO S Diagnostic/Activity
AOPP S Diagnostic/Activity
Thiol S Diagnostic/Activity
[32] 2004 Duygulu et al. Turkey Retrospective 23 (11 active) 15 NO S Diagnostic/Activity
[33] 2005 Ureten et al. Turkey Retrospective 72 (37 active) 73 CD64 S Diagnostic/Activity
[34] 2005 Calis et al. Turkey Retrospective 75 (50 active) 25 ADA S Diagnostic/Activity
[35] 2005 Qiao et al. Japan Retrospective 35 (15 active) 16 CXCR2 S Diagnostic/Activity
[36] 2005 Gür-Toy et al. Turkey Retrospective 67 0 IL-8 S Activity
CRP S Diagnostic
ESR S Diagnostic
[37] 2005 Coskun et al. Turkey Retrospective 40 (25 active) 30 Neopterin S Diagnostic/Activity
ESR P Diagnostic/Activity
CRP S Diagnostic/Activity
[38] 2006 Yardim-Akaydin et al. Turkey Retrospective 23 43 Allantoin S Diagnostic
MDA S Diagnostic
Ascorbic acid S Diagnostic
[39] 2006 Kose et al. Turkey Retrospective 68 (51 active) 17 Neopterin S Diagnostic/Activity
[40] 2006 Canpolat et al. Turkey Retrospective 23 (10 active) 20 ADA S Diagnostic/Activity
Erythrocyte ADA S Diagnostic/Activity
[41] 2006 Kwon et al. South Korea Prospective 211 (92 active) N/A Protein S S Activity
[42] 2006 Briani et al. Italy Retrospective 32 118 Anti-HS igM S Diagnostic
Anti-HS igG S Diagnostic
[43] 2006 Sarican et al. Turkey Retrospective 64 (25 active) 26 Homocysteine S Diagnostic/Activity
[44] 2007 Lee et al. South Korea Retrospective 50 (26 active) UK Gal-3 S Diagnostic/Activity
G3BP S Activity
[45] 2007 Pay S et al. Turkey Retrospective 58 (23 active) 20 MMP-2 S Diagnostic
MMP-9 S Diagnostic/Activity
[46] 2008 Öztürk et al. Turkey Retrospective 21 21 VEGF S Diagnostic
ESR S Diagnostic
CRP S Diagnostic
[47] 2008 Turan et al. Turkey Prospective 35 N/A sTNFR1 S Activity
sTNFR2 S Activity
[48] 2008 Kutlay et al. Turkey Retrospective 45 (33 active) 15 CEC S Diagnostic/Activity
[49] 2008 Curnow et al. UK Retrospective 52 (24 active) 35 IL-15 S Diagnostic/Activity
CXCL-8 S Diagnostic/Activity
TNF-α S Diagnostic/Activity
[50] 2008 Polat et al. Turkey Retrospective 32 16 IL-8 S Diagnostic/Activity
[51] 2008 Durmazlar et al. Turkey Retrospective 45 (33 active) 29 IL-8 S Diagnostic/Activity
[52] 2009 Habibagah et al. Iran Retrospective 53 (15 active) 44 IL-23 S Diagnostic/Activity
E–cadherin S Diagnostic
[53] 2009 Fadini et al. Italy Retrospective 30 27 CD34+KDR+ EPCs S Diagnostic
CD34+CD133+KDR+ EPCs S Diagnostic
[54] 2010 Choe et al. South Korea Retrospective 59 (21 active) 65 Angiopoietin-1 S Diagnostic
Angiopoietin-2 S Diagnostic
[55] 2010 Donmez et al. Turkey Retrospective 89 (17 active) 86 aTAFI S Diagnostic
Thrombomodulin Diagnostic
[56] 2010 Sezer et al. Turkey Retrospective 60 (33 active) 46 MDA S Diagnostic
8-OHdG S Diagnostic/Activity
T-SH S Diagnostic
[57] 2011 Özden et al. Turkey Retrospective 70 61 Gal-3 S Diagnostic/Activity
[58] 2011 Pehlivan et al. Turkey Retrospective 45 (25 active) 30 Resistin S Diagnostic/Activity
TNF-α S Diagnostic/Activity
[59] 2011 Ahn et al. South Korea Retrospective 71 (21 active) 34 α defensin1 S Activity
αdefensin1 mRNA S Diagnostic/Activity
[60] 2011 Shin et al. South Korea Retrospective 80 23 AAEA S Diagnostic
[61] 2011 Jung et al. South Korea Retrospective 88 (30 severe, 12 moderate) 10 sTREM1 S Diagnostic/Activity
TNF-α Diagnostic
[62] 2011 Vural et al. Turkey Retrospective 20 40 STIP-1 S Diagnostic
[63] 2012 Bello et al. Spain Retrospective 30 28 sCD40L S Diagnostic
MMP-9 S Diagnostic
[64] 2012 Gündüz et al. Turkey Retrospective 40 (11 active) 20 CD4+CD25+FOXP3+Treg S Diagnostic/Activity
CD4+FOXP3+Treg S Diagnostic/Activity
[65] 2012 Wang et al. China Retrospective 49 79 Proteomic analysis S Diagnostic
[66] 2013 Örem et al. Turkey Retrospective 72 (40 active) 30 Lipoprotein-associated phospholipase A2 S Diagnostic/Activity
CRP S Diagnostic/Activity
ESR S Diagnostic/Activity
[67] 2013 Hamzaoui et al. Tunisia Retrospective 46 (20 active) 70 IL-33 S Diagnostic/Activity
IL6 S Diagnostic
IL7 S Diagnostic
[68] 2013 Vural et al. Turkey Retrospective 144 168 MTCH1 Ab S Diagnostic
[69] 2014 Shaker et al. Egypt Retrospective 30 (20 active) 20 TNF- α S Diagnostic/Activity
APRIL S Diagnostic/Activity
BCMA S Diagnostic/Activity
BAFF S Diagnostic/Activity
CRP S Diagnostic/Activity
ESR S Diagnostic/Activity
[70] 2014 Xun et al. China Retrospective 58 106 Prohibitin S Diagnostic
[71] 2014 Vayà et al. Spain Retrospective 89 94 RDW S Diagnostic
CRP S Diagnostic
Fibrinogen S Diagnostic
Leucocytes S Diagnostic
Neutrophils S Diagnostic
[72] 2014 Balta et al. Turkey Retrospective 33 (16 active) 35 Endocan S Diagnostic/Activity
CRP S Diagnostic
ESR S Diagnostic
[73] 2014 Ozuguz et al. Turkey Prospective 40 20 ADMA S Diagnostic
CRP S Diagnostic/Activity
ESR S Diagnostic/Activity
Homocysteine S Diagnostic/Activity
[74] 2014 Mejia et al. Spain Prospective 56 (17 active) 56 Prothrombin fragm. 1.2 S Diagnostic/Activity
Factor VIII S Diagnostic/Activity
vWF S Diagnostic
[75] 2015 Lopalco et al. Italy Prospective 58 32 IL-6 S Diagnostic
IL-8 S Diagnostic
IL-18 S Diagnostic
IFN-α S Diagnostic
CXCL11 S Diagnostic
[76] 2015 Yuksel et al. Turkey Retrospective 36 (17 active) 35 ADMA S Diagnostic/Activity
NLR S Diagnostic/Activity
[77] 2015 Bassyouni et al. Egypt Retrospective 47 30 Angiopoietin-1 S Diagnostic
[78] 2015 Tulunay et al. Turkey Retrospective 26 26 STAT3 S Diagnostic
[79] 2015 Belguendouz et al. Algeria Retrospective 26 (16 active) 17 IL-18 S Activity
[80] 2015 Ozturk et al. Turkey Retrospective 65 (40 active) 62 NLR S Diagnostic/Activity
[81] 2015 Turkcu et al. Turkey Retrospective 51 (25 active) 24 TNF-α S Diagnostic
Resistin S Diagnostic
Omentin S Diagnostic
[82] 2015 De Souza et al. Brazil Retrospective 26 (13 active) 20 HMGB1 S Diagnostic
[83] 2015 Seo et al. South Korea Retrospective 112 (66 active) 45 YKL-40 S Diagnostic/Activity
[84] 2016 Yolbas et al. Turkey Retrospective 53 (6 active) 55 NLR S Activity
91
51+39
[85] 2016 Hu et al. China Retrospective
Phase I		 40 (identification) 35 Protein microarray
Phase II 130 (validation) 223 Anti-CTDP1 Ab S Diagnostic
[86] 2016 Mejia et al. Spain Retrospective 55 73 Procoagulant microparticles S Diagnostic
[87] 2016 Balkarli et al. Turkey Retrospective 186 (120 active) 79 NLR S Diagnostic
ESR S Diagnostic/Activity
CRP S Diagnostic
[88] 2016 Park et al. South Korea Retrospective 51 (29 active) N/A Anti-lysozyme S Activity
[89] 2016 Cantarini et al. Italy Retrospective 27 (57 total samples: 21 from active, 36 inactive) 36 CD40L S Diagnostic
Leptin S Diagnostic
sTNFR S Diagnostic
IL-6 S Diagnostic
ESR S Activity
[90] 2017 Cure et al. Turkey Retrospective 84 84 AIP S Diagnostic/Activity
CRP S Diagnostic
[91] 2017 Jiang et al. China Retrospective 140 (108 active) 107 PLR S Diagnostic/Activity
LMR S Diagnostic
ESR S Activity
CRP S Activity
[92] 2017 Kang et al. South Korea Retrospective 110 110 AAEA IgG S Diagnostic
[93] 2017 Ahn JK et al. South Korea Retrospective 44 41 Panel of 10 urinary biomarkers: guanine, pyrrole-2-carboxylate, 3-hydroxypyroline, mannose, L-citrulline, galactonate, isothreonate, sedoheptulose, hypoxanthine, and gluconic acidlactonate U Diagnostic
Guanine U Diagnostic
Pyrrole-2-carboxylate U Diagnostic
3-hydroxypyroline U Diagnostic
Mannose U Diagnostic
L-citrulline U Diagnostic
Galactonate U Diagnostic
Isothreonate U Diagnostic
Sedoheptulose U Diagnostic
Hypoxanthine U Diagnostic
Gluconic acidlactonate U Diagnostic
[94] 2017 Lee et al. South Korea Retrospective Phase I 15 (identification) 15 Fibrin, apoliprorotein A-IV and SAA S Diagnostic
Phase II 49 (validation) 41 SAA S Diagnostic
IL-1β S Diagnostic
[95] 2017 Ha et al. South Korea Retrospective 50 (29 active) 35 IL-32 S Diagnostic
[96] 2017 Lopalco et al. Italy Retrospective 46 19 sTNFR1 S Diagnostic
sTNFR2 S Diagnostic
Chitinase3-like1 S Diagnostic
gp130/sIL-6Rb S Diagnostic
IL-26 S Diagnostic
[97] 2018 Omma et al. Turkey Retrospective 93 (57 active) 62 Calprotectin S Diagnostic
CRP S Diagnostic
IMA S Diagnostic
[98] 2018 Koca et al. Turkey Retrospective 71 75 Bilirubin S Diagnostic
[99] 2018 Enecik et al. Turkey Retrospective 45 (28 active) 25 IL-20 S Diagnostic
[100] 2018 Harmanci et al. Turkey Retrospective 30 30 VEGF gene expression levels S Diagnostic
[101] 2018 Lucherini et al. Italy Retrospective 72 29 IgD S Diagnostic
[102] 2018 Chekaoui et al. Algeria Retrospective 48 (28 active) 41 IL-1β S Diagnostic/Activity
NO S Diagnostic/Activity
AOPP S Diagnostic/Activity
MDA S Diagnostic
SOD S Diagnostic/Activity
[103] 2018 Kolahi et al. Iran Retrospective 47 61 mir-155 S Diagnostic
TNF-α expression S Diagnostic
[104] 2018 Ahn et al. South Korea Retrospective 45 45 Panel of 5 biomarkers: DA, fructose, tagatose, LA, and OA S Diagnostic
[105] 2018 Saylam et al. Turkey Retrospective 30 41 suPAR S Diagnostic
CRP S Diagnostic
[106] 2018 Ahmadi et al. Iran Retrospective 47 58 Th17 S Diagnostic
Treg S Diagnostic
RORɣt mRNA S Diagnostic
FoxP3 mRNA S Diagnostic
IL-17mRNA S Diagnostic
IL-23 mRNA S Diagnostic
TGF mRNA S Diagnostic
IL-10 mRNA S Diagnostic
IL-17 S Diagnostic
IL-23 S Diagnostic
IL-10 S Diagnostic
TFG-beta S Diagnostic
miR-93 S Diagnostic
miR-106b S Diagnostic
miR-25 S Diagnostic
miR-146° S Diagnostic
miR-155 S Diagnostic
miR-326 S Diagnostic
[104] 2018 Hassouna et al. Egypt Retrospective 30 15 miR-155 S Diagnostic
[107] 2018 Prado et al. Brazil Retrospective 97 (43 active) 123 AAEA IgM S Diagnostic/Activity
[108] 2018 Acikgoz et al. Turkey Retrospective 60 50 MHR S Diagnostic
[109] 2018 Hasan et al. UK Retrospective 60 (44 active) 60 NK S Diagnostic
CD56Dim S Diagnostic
CD56Brigh S Diagnostic
[110] 2018 Zheng et al. China Retrospective Phase I 24 (identification) 26 PC (34:3) S Diagnostic
PC (40:8) S Diagnostic
LA S Diagnostic
AA S Diagnostic
Phase II 25 (validation) 19 LA S Diagnostic
27 AA S Diagnostic
[111] 2019 Şahin et al. Turkey Retrospective 46 44 Pannexin-1 S Diagnostic
[112] 2019 Bassyouni et al. Egypt Retrospective 87 60 CCN2 S Diagnostic
[113] 2019 Arica et al. Turkey Retrospective 45 (32 active) 28 Early EPCs S Diagnostic/Activity
Late EPCs S Diagnostic/Activity
MMP9 S Diagnostic
VEGF S Diagnostic/Activity
CRP S Diagnostic/Activity
ESR S Diagnostic
[114] 2019 Sandikci et al. Turkey Retrospective 150 100 Serumnativethiol S Diagnostic
Total thiol S Diagnostic
T-SH S Diagnostic
[115] 2019 Talaat et al. Egypt Retrospective 64 20 IL-6 S Diagnostic/Activity
IL-10 S Diagnostic
IL-17 S Diagnostic
[116] 2019 Gheita et al. Egypt Retrospective 96 60 NLR S Diagnostic
PLR S Diagnostic
RDW S Diagnostic
MPV S Diagnostic
VEGF S Diagnostic
[117] 2019 El Boghdady et al. Egypt Retrospective 51 45 TNF-α S Diagnostic
IL-6 S Diagnostic
E-selectine S Diagnostic
VCAM S Diagnostic
miR-181b S Diagnostic
[118] 2019 Balbaba et al. Turkey Retrospective 48 (24 active) 24 Cortistatin S Diagnostic
[119] 2020 Hassan et al. Egypt Retrospective 42 42 Endocan S Diagnostic/Activity
[120] 2020 Hussain et al. China Retrospective 50 100 Moesin S Diagnostic
[121] 2020 Hussain et al. China Retrospective 32 64 NuMA Ab S Diagnostic
[122] 2020 Djaballah-Ider et al. Algeria Retrospective 61 (47 active) 25 NLR S Activity
NO S Activity
IL-4 S Activity
IFN-gamma S Activity
[123] 2021 Cheng et al. China Retrospective 48 (34 active) 96 Lymphocyte count S Diagnostic/Activity
White blood cell count S Diagnostic
Neutrophil count S Diagnostic
Basophil count S Diagnostic/Activity
RDW S Diagnostic/Activity
MCH S Diagnostic
MCHC S Diagnostic
Platelet count S Diagnostic/Activity
Plateletcount S Diagnostic/Activity
MPV S Diagnostic/Activity
CRS S Diagnostic
PLR S Diagnostic/Activity
NLR S Diagnostic
Monocyte S Activity
LMRcount S Diagnostic
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8-OHdG—8-hydroxy-2′-deoxyguanosine; AA—arachidonic acid; AAEA—anti-alpha-enolase antibodies; ADA—adenosine deaminase; ADMA—asymmetric dimethyl arginine; AECA—anti-endothelial cell antibodies; AIP—atherogenic index plasma, anti-HS—anti-heparin–sulfate antibodies; anti-CTDP1—anti-carboxy-terminal domain phosphatase subunit 1; AOPP—advanced oxidation protein products; APRIL—a proliferation-inducing ligand; ASCA—anti-Saccharomyces cerevisiae; aTAFI—activated thrombin activatable fibrinolysis inhibitor; BAFF—B-cell-activating factor; BCMA—B-cell maturation antigen; CEC- circulating endothelial cells; cICAM—circulating intercellular adhesion molecule-1; cNuM—anuclear mitotic apparatus protein located at the carboxyl terminus; CPR—C-reactive protein; CTGF—connective tissue growth factor;CXCL11—C-X-C motif chemokine 11; CXCR2—C-X-C motif chemokine receptor 2; DA—decanoic acid; Endocan—human endothelial cell-specific molecule-1; EPC—endothelial progenitor cells; ESR—erythrocyte sedimentation rate; ET-1—endothelin-1; ETP—endogenous thrombin potential; GAL-3—galectin-3; G3BP—galectin-3 binding protein; HMGB1—high-mobility group box 1; IgD—D immunoglobulin;IMA—ischemia-modified albumin; INFa—interferon alpha; INFg—interferon gamma; LA—linoleic acid; LMR—lymphocytes-to-monocytes ratio; LpPLA2—lipoprotein-associated phospholipase A2; MDA—manoldialdehyde; MHR—monocyte-to-high-density lipoprotein–cholesterol ratio; MMP—matrix metalloproteinase; MPO—plasma myeloperoxidase; MPV—mean platelet volume; MTCH1—mitochondrial carrier homolog 1; NLR—neutrophil-to-lymphocyte ratio; NO—nitric oxide; OA—oleic acid; PC—phosphatidylcholines; PLR—platelet-to-lymphocyte ratio; PMN—polymorph nuclear; Procoagulant MP—procoagulant microparticles; RDW—red cell distribution width; SAA—serum amyloid A; SIL-1R—Soluble interleukin-1 receptor; SIL6-RB-Soluble interleukin-6 receptor B; SOD—Superoxide dismutase; STIP1—Stress induced phosphoprotein 1; sTNFR—soluble tumor necrosis factor receptor; sTREM1—soluble triggering receptor expressed on myeloid cells; suPAR—soluble urokinase plasminogen activator receptor; TBARS—thiobarbituric acid-reactive substances TGF-b—transforming growth factor beta; TNFa—tumor necrosis factor alpha; T-SH—total sulfhydryl levels; VCAM—vascular cell adhesion molecule 1;VEGF—vascular endothelial growth factor; vWF—von Willebrand factor.

A total of 6301 patients with BD (1813 with active, 1543 with inactive BD, and 2945 cases in which the activity of BD was not addressed in the study) met the inclusion criteria and were further analyzed. There were 5163 included controls, consisting of 4171 healthy controls (HC) and 992 patients with autoimmune diseases (such as SLE, AR, SS, multiple sclerosis, and vasculitis). Most studies were retrospective, whereas six had a prospective design.

Considering the extensive geographical diffusion of BD, we analyzed the countries of origin in which all included studies was performed. The global map of Figure 3 shows the publication rate of the analyzed studies per country: Turkey and South Korea were the most represented countries. Interestingly, it is possible to identify the characteristic spread of BD studies along the ancient Silk Road.

Figure 3.

Figure 3

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Graphical representation of the global origin of the publication rate of the analyzed studies per country, colored by BD study rate. The graphical representation was computed by log-transforming the number of research papers published by each country. It is possible to recognize the Silk Road pattern. The map was created using the package Ggplot2 [124] of R studio [12].

3.2. Biomarkers and Their Roles in Diagnosis and Disease Activity

A total of 110 studies (99%) investigated serological biomarkers, while only two (2%) tested their population with urinary biomarkers. Most of the included studies (103; 93%) were designed to investigate the diagnostic potential of the biomolecules, while 62 (56%) tested their ability to differentiate between different stages of disease activity. A comprehensive view of all the examined markers is given in Table 2. The most important are cited in the following paragraphs.

Table 2.

Serological and urinary biomarkers investigated in the studies included in the systematic review.

ILs IL-1β
[14,94,102]		 IL-2
[18,29]		 IL-4
[122]		 IL-6 [29,67,75,89,115,117] IL-7
[67]		 IL-8
[19,36,49,50,51,75]		 IL-10
[106,115]		 IL-15
[49]		 IL-17
[106,115]		 IL-18
[75,79]		 IL-20
[99]		 IL-23
[52,106]
IL-26
[96]		 IL-32
[95]		 IL-33
[67]
Cytokines TNF-α
[29,49,58,61,69,81,103,117]		 TGF-β
[106]		 APRIL
[69]		 BAFF
[69]		 INF-α
[75]		 IFN-γ
[122]		 CTGF
[112]		 STAT3
[78]		 CXCL11
[75]
Surface proteins CD64
[33]		 CXCR2
[35]		 BMCA
[69]		 VCAM
[117]
Soluble proteins SIL-2R
[14,18]		 PMN leukocyte elastase
[15]		 AECA
[16]		 vWF
[16,74]		 ET-1
[17,24]		 Anti-ASCA Ab [22] Leptin
[23,89]		 Homocysteine [24,43,73] CRP [30,31,36,37,64,66,69,71,72,73,87,90,91,93,99,105,113,123] cICAM-1
[23]		 Catalase
[28]		 ADA
[28,34,40]
SOD
[28,102]		 TBARS
[28]		 E-selectine
[30,117]		 MPO
[31]		 Neopterin
[37,39]		 VEGF [46,100,113,116] Protein S
[41]		 antiHS
[42]		 Gal-3
[44,57]		 G3BP
[44]		 MMP2
[45]		 α-defensin1 [59]
sTNFR1 e 2 [47,89,96] E-Caderin [52] Angiopoietin1 [54,77] Resistin
[58,81]		 Thrombomodulin [55] aTAFI
[55]		 AAEA
[60,92,107]		 sTREM1
[61,100]		 STIP
[62]		 sCD40L
[63,89]		 MMP9
[63,113]		 Lp-PLA2
[66]
MTCH1 Ab
[68]		 Prohibitin [70] Endocan
[72,119]		 ADMA
[73,76]		 Omentin
[81]		 HMBG1
[82]		 Anti-lysozyme
[88]		 Fibrinogen
[71]		 Factor VIII [74] cNuMA Ab
[121]		 anti-CTDP1 Ab [85] SAA
[94]
sIL6-RB
[96]		 Chitinase3-like1 [83,96] Bilirubin
[98]		 Calprotectin [97] IMA
[97]		 IgD
[101]
 suPAR
[105]		 Pannexin-1
[111]		 Cortistatin
[118]		 Moesin
[120]
Cells CD4+CD16+
[19,20]		 CD4+CD56+ [19,20] T γδ [21] CEC
[48]		 CD34+KDR+EPCs [53,113] CD34+CD133+KDR+ EPCs [53] CD4+CD25+FOXP3+Treg
[64]		 CD4+FOXP3+Treg
[64]		 Treg
[106]		 Th17
[106]		 CD56 +
[109]
miRNA α-defensin 1
[59]		 miR-155
[103,104,106]		 miR-181b
[117]		 miR-93
[106]		 miR-106b
[106]		 miR-25
[106]		 miR-146a
[106]		 miR-326
[106]
Metabolomic/
proteomic markers		 DA, OA
Fructose, tagatose
[125]		 LA
[110,125]		 PC
[110]		 AA
[110]		 Panel of six proteomic biomarkers
[65]
Others ESR [30,31,36,37,46,52,66,69,72,87,89,91,99,113] NO
[24,26,27,29,32,102,122]		 Thiol
[31,114]		 AOPP
[31,102]		 Allantoin
[38]		 MDA
[38,56,102]		 Ascorbic acid
[38]		 8-OhdG
[56]		 T-SH
[56,114]		 PLR
[31,91,116]		 LMR
[91,123]		 NLR
[31,76,80,84,87,116,122]
AIP
[90]		 RDW
[71]		 ETP
[74]		 MPV
[123]		 RDW
[71,123]		 Procoagulant MP [86] MHR
[108]
Urinary markers Metabolomic panel:
Guanine
Pyrrole-2-carboxylate
3-hydroxypyroline
Mannose
L-citrulline
Galactonate
Isothreonate
Sedoheptulose
Hypoxanthine
Gluconic acidlactonate [93]		 NO
[14]
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8-OHdG—8-hydroxy-2′-deoxyguanosine; AA—arachidonic acid; AAEA—anti-alpha-enolase antibodies; ADA—adenosine deaminase; ADMA—asymmetric dimethyl arginine; AECA—anti-endothelial cell antibodies; AIP—atherogenic index plasma, anti-HS—anti-heparin–sulfate antibodies; anti-CTDP1—anti-carboxy-terminal domain phosphatase subunit 1; AOPP—advanced oxidation protein products; APRIL—a proliferation-inducing ligand; ASCA—anti-Saccharomyces cerevisiae; aTAFI—activated thrombin activatable fibrinolysis inhibitor; BAFF—B-cell-activating factor; BCMA—B-cell maturation antigen; CEC- circulating endothelial cells; cICAM—circulating intercellular adhesion molecule-1; cNuM—a nuclear mitotic apparatus protein located at the carboxyl terminus; CPR—C-reactive protein; CTGF—connective tissue growth factor; CXCL11—C-X-C motif chemokine 11; CXCR2—C-X-C motif chemokine receptor 2; DA—decanoic acid; Endocan—human endothelial cell-specific molecule-1; EPC—endothelial progenitor cells; ESR—erythrocyte sedimentation rate; ET-1—endothelin-1; ETP—endogenous thrombin potential; GAL-3—galectin-3; G3BP—galectin-3 binding protein; HMGB1—high-mobility group box 1; IgD—D immunoglobulin; IMA—ischemia-modified albumin; INFa—interferon alpha; INFg—interferon gamma; LA—linoleic acid; LMR—lymphocytes-to-monocytes ratio; LpPLA2—lipoprotein-associated phospholipase A2; MDA—manoldialdehyde; MHR—monocyte-to-high-density lipoprotein–cholesterol ratio; MMP—matrix metalloproteinase; MPO—plasma myeloperoxidase; MPV—mean platelet volume; MTCH1—mitochondrial carrier homolog 1; NLR—neutrophil-to-lymphocyte ratio; NO—nitric oxide; OA—oleic acid; PC—phosphatidylcholines; PLR—platelet-to-lymphocyte ratio; PMN—polymorph nuclear; Procoagulant MP—procoagulant microparticles; RDW—red cell distribution width; SAA—serum amyloid A; SIL-1R—Soluble interleukin-1 receptor; SIL6-RB-Soluble interleukin-6 receptor B; SOD—Superoxide dismutase; STIP1—Stress induced phosphoprotein 1; sTNFR—soluble tumor necrosis factor receptor; sTREM1—soluble triggering receptor expressed on myeloid cells; suPAR—soluble urokinase plasminogen activator receptor; TBARS—thiobarbituric acid-reactive substances TGF-b—transforming growth factor beta; TNFa—tumor necrosis factor alpha; T-SH—total sulfhydryl levels; VCAM—vascular cell adhesion molecule 1; VEGF—vascular endothelial growth factor; vWF—von Willebrand factor.

3.2.1. Conventional Inflammation Markers and Soluble Proteins

The erythrocyte sedimentation rate (ESR) and C reactive protein (CRP), two inflammation indices, have been assessed by 14 (13%) and 19 (17%) studies on BD, respectively. An increase in their values has been reported with a total agreement rate among the articles.

The neutrophil-to-lymphocyte ratio (NLR) is a parameter analyzed through a hemocytometer. It has been investigated as a biomarker in seven (6%) articles; all the studies reported a significant increase in the NLR in BD patients, especially in patients with active disease.

Tumor necrosis factor-alpha (TNF-α) is a cytokine that regulates the immune system, inflammatory response, and apoptosis. Serum TNF-α has been analyzed in eight studies (7%). Its levels were remarkably increased in BD patients compared to healthy controls, whereas there have been inconclusive results on the correlation between high TNF-α levels and BD activity [29,49,58,61,69,81,103,117].

In the sub-group of interleukins (ILs), fifteen different molecules have been studied as serological markers in 22 studies (20%). Among them, IL-8 had increased levels in BD sera compared to controls in three different studies (3%) [50,51,75], with high rates of sensibility and specificity in differentiating active and inactive patients, as reported in four articles (4%) [19,36,50,51].Moreover, IL-6 has been investigated as a serological marker in six articles (5%), highlighting a potential in BD diagnosing but not in the activity disease classification.

Adenosine deaminase (ADA) has been the main focus of three studies (3%). ADA is a marker of T-lymphocyte activation, whose serological levels were found to be markedly elevated in BD patients compared to controls [28,34,40].

Anti-alpha enolase antibodies (AAEA) have been evaluated in three studies (3%). They consist of a heterogeneous group of antibodies directed toward surface proteins in endothelial cells, which have been found to increase in many inflammatory diseases, including SLE, AR, and vasculitis. Additionally, in this case, the serological levels of both IgG and IgM AAEA seemed to be significantly elevated in BD patients, particularly during the active phase [60,92,107].

3.2.2. Oxidant and Anti-Oxidant Molecules

Reactive oxygen species (ROS), including nitric oxide (NO), are products of oxidative stress and are usually released in inflammatory sites by the innate immune response and endothelial cells. In seven articles (6%), the authors described the NO levels to be significantly enhanced in the serum and urine of BD patients compared to HC [24,26,27,29,32,102]. Significant differences were noticed in patients with active disease in comparison to inactive patients [24,26,27,102,122].

Malondialdehyde (MDA), one of the final products of lipid peroxidation triggered by the free radicals of oxidative stress, was reported to be elevated in BD sera in comparison to HC, even if it was not a promising biomarker of BD activity of disease [38,56,102].

Super oxide dismutase (SOD) and catalase are anti-inflammatory enzymes involved in oxidative stress. The dosages of SOD and RBC catalase levels [28,102] showed significant reductions in BD patients, especially in samples collected during the phase of disease activity.

3.2.3. microRNAs

Several miRNAs (including miR-93, miR-106b, miR-25, miR-146a, miR-326, and miR-181b) were assessed by three studies (3%) included in this systematic review. In particular, in a case-control study of 47 BD patients [103], the authors observed that the miR-155 levels increased in BD patients compared to HC. However, these results were not corroborated by the authors of two other studies [104,106], where a conspicuous decrease in miR-155 levels was conversely observed when testing their BD patients.

3.2.4. New “Omics” Sciences

Two studies (2%) have addressed the serum metabolomics state of BD patients, starting with an untargeted approach and subsequently validating a specific panel of biomarkers on an independent cohort.

Through the gas chromatography/time-of-flight mass spectrometry GC/TOF-MS, Ahn and colleagues isolated a panel of five metabolites (decanoic acid, fructose, tagatose, linoleic acid, and oleic acid) able to differentiate BD patients from HC with high sensitivity and specificity, at 100% and 97.1%, respectively [125]. Concurrently, Zheng et al. observed that high serum levels of two polyunsaturated fatty acids (PUFAs), linoleic acid (LA) and arachidonic acid (AA), discriminated BD patients and HC efficiently with high sensitivity (95% for PUFAs and 95% for LA) and specificity (65 for PUFAs and 88% for LA) [110].

In their previous work, Ahn and colleagues also assessed the urinary metabolomic profiles of BD patients. The authors identified a combination of metabolites (guanine, pyrrole-2-carboxylate, 3-hydroxypyridine, mannose, L-citrulline, galactonate, isothreonate, sedoheptuloses, hypoxanthine, and gluconic acid lactone) able to identify BD patients with high sensitivity (96.7%) and specificity (93.3%) [93].

Two studies (2%) have widely investigated the serum proteomic asset using matrix-assisted laser desorption ionization–time-of-flight mass spectrometry (MALDI-TOF-MS). A first model based on 39 proteins could distinguish BD and HC with a sensitivity of 83.67% and a specificity of 89.87% [65]. The second study detected significant upregulation of fibrin, apolipoprotein A-IV, and serum amyloid A (SAA) in the sera of BD patients with active disease at the intestinal level compared to controls [94].

4. Discussion

BD is a rare multisystemic vasculitis whose symptoms and signs often overlap with other autoimmune diseases, leading to delayed diagnosis and occasionally inappropriate therapy. The pathogenesis of BD has not been fully elucidated. However, the dysregulation of the innate and acquired immune systems in a facilitative environment plays a crucial role in disease development [2] (Figure 4).

Figure 4.

Figure 4

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Mechanisms underlying Behçet’s disease’s etiopathogenesis.

Further, unlike other autoimmune diseases, such as systemic lupus erythematosus (SLE), rheumatoid arthritis (AR), or other vasculitis, specific biomarkers for BD have not yet been identified, negatively affecting the early diagnosis and management of BD patients.

In this systematic review, we carefully reviewed all the relevant articles published in the current literature to identify the international efforts made in identifying specific serological and urinary BD biomarkers.

Considering the well-known inflammatory nature of BD, most studies have shown an increase in inflammatory biomarkers in BD patients, such as CRP, ESR, and numerous cytokines, including TNFα, IL-1β, IL-6, IL-8, IL-17, and IL-23 (Table 2).

Unfortunately, despite a high agreement rate among the articles, their lack of specificity makes them a nonoptimal diagnostic tool, whereas they can be helpful in disease monitoring.

To date, there is consensus on the involvement of lymphocytes in the BD pathogenesis, in particular, T helper cells that produce IL-17 (Th17) and T regulatory (Tregs) cells [106,126,127]. In the presence of IL-23, Th naive cells differentiate in the Th17 phenotype and migrate at mucosal surfaces, where, through the secretion of IL-17, they induce the recruitment of neutrophils and activate epithelial cells, mediating the inflammatory process [128]. Conversely, Treg cells play a role in inhibiting the immune response triggered by the resident microflora in the mucosa by the secretion of TGF-β and IL-10 [129]. Interestingly, Th17 and Tregs share common pathways during differentiation, and their cell count is fundamental for maintaining the balance between pro-inflammatory and anti-inflammatory conditions in mucosal tissues [128]. Multiple studies on BD have reported a decrease in Treg cells, alongside an upregulation of Th17 cells and neutrophils (both as an absolute value and NLR) [76,80,84,87,106,116,122,123]. Activated neutrophils may reach the inflammatory sites, triggering a substantial oxidative stress response by releasing ROS, which can contribute to the disease progression over time. It is worth mentioning that increased levels of numerous pro-oxidants were observed in BD sera and urines; among them, ADA NO, advanced oxidation protein products (AOPPs), and some of the final products of lipid peroxidation, such as MDA and thiobarbituric acid-reactive substances (TBARS) [28,31,34,40,102] (Table 2). On the contrary, many studies have described lower anti-oxidant levels in the sera of BD patients, such as catalase and SOD, confirming the dysregulation of the production of pro-oxidants and anti-oxidant substances in BD [28,102]. However, similarly to the inflammation biomarkers, pro- and anti-oxidants remain aspecific and could be used for monitoring BD patients but, due to their low specificity, do not have a pivotal role in BD diagnosis.

MiRNAs are small non-coding RNA (19-23-nucleotide length) that inhibit translation by binding mRNAs. Recently, some miRNAs have been investigated as putative BD markers. In particular, low serum levels of miR-155 were detected in active BD patients, and higher levels were observed during disease remission [104,106]. It is known that miR-155 is involved in switching off the inflammatory response by downregulating IL-6 and IL-1β and upregulating IL-10, an inhibitory interleukin [130]. In fact, high serum levels of IL-6 and IL-1β and low levels IL-10 were observed in active BD [14,75,102,106,115,122].

Moreover, the role of miR-155 in blocking BD progression was confirmed by the increase in Th17 and the release of IL-17. These mechanisms are mediated by the inhibition of E26 transformation-specific-1 (ETS-1), a gene upregulated in BD [106,115,131]. One could hypothesize that lower levels of miR-155 might lead to low CD4+ T cells, Th17, and IL17 and increased ETS-1 during active BD [131]. However, Kolhai et al. reported an increase in the miR-155 levels in BD patients, in concomitance with a reduction of Ets-1 and an elevation of Th17 cells, suggesting a pro-inflammatory role and a potential therapeutic target [103]. While the current scientific interest is focused on miRNAs for improving our understanding of BD pathogenesis, their potential in diagnostic testing for BD remains to be elucidated.

Considering that BD disease is often described as an ensemble of phenotypes with different clinical characteristics, a future challenge could be to test if these phenotypes exhibit different miRNA patterns [132]. This could not only improve our knowledge about pathogenic processes underlying the various phenotypes but could also represent a step toward a more tailored therapeutic approach.

To date, new “omics“ science, such as proteomics and metabolomics, has provided a comprehensive analysis of endogenous proteins and metabolites. With the use of metabolomics, one can potentially detect the alterations of physiological and pathological metabolites at the early stages of the disease due to its excellent sensitivity. In BD, two metabolomic tests have been developed and subsequently validated with reported high specificity and sensitivity [110,125]. Unfortunately, although this approach seems to be very promising, these tests are extremely expensive and complex, and therefore, are far from being routinely available for diagnostic or follow-up testing.

In addition to metabolome investigations from blood samples, many studies have recently focused on analyzing the fecal metabolome alterations, resulting from changes in the gut microbial communities in BD patients [133,134]

Since the intercorrelation between diet and gut microbiota is well known, studying intestinal altered metabolic profiles and the microbial community imbalance of BD patients is paving the way to new therapeutic approaches based on nutritional interventions [135].

We acknowledge that this study suffers from some limitations, mainly due to the vast heterogeneity of the included studies regarding the number of patients, control groups, and the types of biomarkers and assays used. Moreover, although it was possible to include only BD diagnosed following the ISGBD criteria, there was no standard disease activity score in the past. Only recently has a consensus on a common definition of Behçet’s disease activity been reached by developing and validating the Behçet’s Disease Current Activity Form (BDCAF) score [124]; however, it is not objectionable because it is a subjective score based on referred symptoms. For these reasons, a meta-analysis of the studies could not be performed.

5. Conclusions

In conclusion, despite the enormous efforts from the scientific community to identify potential biomarkers in BD, much more work must be done. While identifying novel aspecific biomarkers might help us better understand BD’s pathogenesis and might also find a place for monitoring disease activity during follow-up, we are still far from identifying potential diagnostic biomarkers for this complex and rare disease. Proteomics, metabolomics, and microbiome analysis might help in the near future to identify potential candidates to help researchers and ultimately clinicians to better identify patients suffering from BD.

Author Contributions

Conceptualization, M.A., M.R., P.M. and S.S.; methodology, M.A., M.R., D.M., P.M. and L.R.; formal analysis, M.A., M.R., S.S., A.B., S.G.F. and I.C.; data curation, M.A., M.R., D.M., P.M. and L.R.; writing—original draft preparation, M.A., M.R. and D.M.; writing—review and editing, A.B., S.G.F., I.C., E.M., D.R. and S.S.; supervision, E.M., D.R. and S.S. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

Funding Statement

This research received no external funding.

Footnotes

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