Theo Wirth
Nathalie Balandraud
Laurent Boyer
Pierre Lafforgue1
Thao Pham- 1Rheumatology Department, Sainte Marguerite Hospital, Aix-Marseille University, APHM, Marseille, France
- 2Autoimmune Arthritis Laboratory, INSERM UMRs1097, Aix Marseille University, Marseille, France
- 3School of Medicine, EA 3279, CEReSS, Research Center on Health Services and Quality of Life, Aix Marseille University, Marseille, France
Introduction: Psoriatic arthritis (PsA) is a chronic inflammatory disease that frequently develops in patients with psoriasis (PsO) but can also occur spontaneously. As a result, PsA diagnosis and treatment is commonly delayed, or even missed outright due to the manifold of clinical presentations that patients often experience. This inevitably results in progressive articular damage to axial and peripheral joints and entheses. As such, patients with PsA frequently experience reduced expectancy and quality of life due to disability. More recently, research has aimed to improve PsA diagnosis and prognosis by identifying novel disease biomarkers.
Methods: Here, we conducted a systematic review of the published literature on candidate biomarkers for PsA diagnosis and prognosis in MEDLINE(Pubmed), EMBase and the Cochrane library with the goal to identify clinically applicable PsA biomarkers. Meta-analyses were performed when a diagnostic bone and cartilage turnover biomarker was reported in 2 or moredifferent cohorts of PsA and control.
Results: We identified 1444 publications and 124 studies met eligibility criteria. We highlighted bone and cartilage turnover biomarkers, genetic markers, and autoantibodies used for diagnostic purposes of PsA, as well as acute phase reactant markers and bone and cartilage turnover biomarkers for activity or prognostic severity purposes. Serum cartilage oligometrix metalloproteinase levels were significantly increased in the PsA sera compared to Healthy Control (HC) with a standardized mean difference (SMD) of 2.305 (95%CI 0.795-3.816, p=0.003) and compared to osteoarthritis (OA) with a SMD of 0.783 (95%CI 0.015-1.551, p=0.046). The pooled serum MMP-3 levels were significantly higher in PsA patients than in PsO patients with a SMD of 0.419 (95%CI 0.119-0.719; p=0.006), but no significant difference was highlighted when PsA were compared to HC. While we did not identify any new genetic biomarkers that would be useful in the diagnosis of PsA, recent data with autoantibodies appear to be promising in diagnosis, but no replication studies have been published.
Conclusion: In summary, no specific diagnostic biomarkers for PsA were identified and further studies are needed to assess the performance of potential biomarkers that can distinguish PsA from OA and other chronic inflammatory diseases.
1 Introduction
Psoriatic arthritis (PsA) is a chronic inflammatory disease that develops in up to 30% of patients with psoriasis (PsO), and can affect up to 0.7% of the general population (1, 2). PsA is characterized as affecting axial and peripheral joints and entheses, which can present clinically with diverse symptoms, often resulting in delayed diagnosis and treatment. PsA can lead to progressive articular damage, thus can be a source of impaired function, permanent disability, quality of life, and an increase in mortality (3, 4).
Through the Biomarkers Project, the Group for Research and Assessment of Psoriasis and Psoriatic Arthritis (GRAPPA) places critical emphasis on the research of biomarkers in its development strategy (5). A biomarker is defined as a characteristic that is objectively measured and evaluated as an indicator of pharmacologic responses, or normal or pathogenic biological processes, for a therapeutic intervention (6). Identification of specific biomarkers would improve early diagnosis and management of PsA in patients with joint pain and/or skin psoriasis. Although PsA can develop in up to 30% of PsO patients, the prevalence of undiagnosed PsA in patients with psoriasis is still estimated to be 10-15% (4). Although classification criteria are sometimes used by default, there are currently no diagnostic criteria or specific biomarkers available for PsA (4). Therefore, we sought to identify biomarkers for determining diagnosis and prognosis of PsA by conducting a systematic review and pairwise meta-analysis.
2 Methods
To conduct this research, we followed the guidelines and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement for reporting on studies evaluating healthcare interventions (7). PRISMA Checklist is provided in supplementary file 1. Ethics approval was not required under local legislation for this study.
2.1 Study selection
A systematic search of English-language literature was conducted in MEDLINE (via PubMed), EMBase, and the Cochrane Library dating from inception to March 1, 2022. We designed the search algorithm according to the Patient-Intervention-Comparison-Outcome-Time (PICOT) format. Search terms corresponded to MeSH or Emtree terms for “psoriatic arthritis” and “biomarkers” or “pharmacological biomarkers”. A manual search was also performed.
After searching with the pre-determined PICOT algorithm, study eligibility was ascertained after reading the title, keywords, and abstract. Once the articles of interest were identified, the full text was read to evaluate it according to the exclusion criteria, and to subsequently extract the necessary data. Inclusion criteria required the following: study must be an observational or interventional clinical trial published in English before March 2022; include an assessment of biomarker(s) in serum (including genetic biomarkers), synovial fluid, urine, or feces as diagnostic or prognostic factors; cohort must include patients with PsA according to a rheumatologist diagnosis, Moll and Wright criteria, or Classification Criteria for Psoriatic Arthritis (CASPAR).
Exclusion criteria were applied in a sequential order, and included an editorial or congress abstract; duplicates (between electronic databases or journals); non-English language full text; non-human, non-PsA, or pediatric (≤18 years old) populations; off-topic; not relevant for diagnosis and prognosis in PsA. We focused on prognostic factors of disease severity, regardless of treatment, and did not include prognostic factors of response to treatment, out of the scope.
Study results were highlighted in the main text if select biomarkers were mentioned in at least 2 publications. In addition, all included articles are presented in tabular form (Tables 1, 2).
2.2 Data extraction
All data was extracted into a standardized spreadsheet. For each article, we collected the data according to a pre-specified strategy. Collected information included the year of publication, name of the first author, geographical area, study design, population age and sex, disease duration, how the PsA population was determined (classification criteria used or therapist diagnosis), biomarkers investigated, primary study methodology, proportion of patients using corticosteroid and/or non-steroidal anti-inflammatory drugs (NSAIDs), proportions of patients using conventional disease modifying anti-rheumatic drugs (DMARDs; i.e., methotrexate, salazopyrine or leflunomide), and biological DMARDs. Study objectives (diagnosis or prognosis), primary outcomes, and control groups (i.e. cutaneous psoriasis, rheumatoid arthritis, spondyloarthritis, systemic lupus erythematosus, undifferentiated arthritis, osteoarthritis or healthy control (HC)) were also recorded. For all extracted data, a central value (mean or median) and variability (standard deviation or interquartile range) was collected. Study quality and risk of bias was assessed using the Newcastle Ottawa Scale for assessing the quality of non-randomized studies in meta-analysis (131).
2.3 Statistical analysis
Meta-analyses were performed when a diagnostic bone and cartilage turnover biomarker was reported in 2 or more different cohorts of PsA and control. Levels of biomarkers in PsA and control populations, means differences (MD) and standard deviation (SD) were extracted. If necessary, we converted median and interquartile in MD and SD using previously published methods (132). To perform sensitivity analyses, we applied a random effects models using the “one-study-removed” method as soon as there were more than two publications. Difference effect sizes were ascertained with the standardized mean difference (SMD) and its 95% confidence intervals (CI). A positive SMD confirmed a higher biomarker level in PsA than the control population. Magnitude of SMD was characterized as small (< 0.40), moderate (0.41 to 0.69) or large (> 0.70) (133).
3 Results
We identified 1495 records extracted from the PubMed/MEDLINE (n=514), EMBASE (n=919), and Cochrane Library (n=62) databases. After a manual search, 4 additional publications were included.
After removal of 111 duplicates, 1388 articles were screened and 559 met inclusion criteria. Subsequently, 346 publications were excluded because they did not report specific diagnostic or prognostic data for the biomarkers. Ultimately, a total of 124 studies, published from 1993 to 2022, met the eligibility criteria and were included in the qualitative analysis (Figure 1).
Figure 1 Flowchart of the study selection.
Sixty-eight studies evaluated biomarkers for diagnostic purposes, 48 evaluated biomarkers for activity or prognostic severity purposes, and 8 publications studied both diagnostic and prognostic biomarkers (Tables 1, 2). An assessment of bias risk was performed for each study and is available in Tables 3, 4.
Table 1 Included Diagnostic Biomarkers Studies.
3.1 Diagnostic biomarkers
3.1.1 Bone and cartilage turnover biomarkers
Sixteen articles evaluated biomarkers associated with bone and cartilage turnover for their potential as PsA diagnostic biomarkers. All the studies assessing diagnostic biomarkers in our systematic review, including those assessing biomarkers panels, are listed in chronological order of publication in Table 1. For meta-analyses, we considered only clearly identified individual biomarkers, and not panels of biomarkers. The two mainly assessed biomarkers were Cartilage Oligometrix MetalloProteinase (COMP) and Matrix MetalloProteinase-3 (MMP3).
3.1.1.1 Cartilage oligometrix metalloproteinase (COMP)
Increases of serum COMP levels by one unit resulted in an increased PsO odds ratio (OR) of 1.001 (95% CI=1.000-1.002, p=0.04), but not for PsA (OR=1.00; 95% CI=0.999-1.002, p=0.47) when comparing PsA to both PsO and HC (10). More recently, a cross-sectional study including patients with PsA, osteoarthritis (OA) and HC demonstrated in its primary discovery phase that COMP levels were significantly higher in sera of the PsA population than that of the OA population (OR=1.24; 95% CI=1.06-1.46, p=0.0062) (15). In the validation phase, serum COMP levels were not significantly different between PsA and OA populations (217.3 ng/mL vs 210.3 ng/mL, respectively p=0.344) (15). The diagnostic value of COMP was assessed in two cross-sectional studies. In the first study, serum COMP levels were significantly higher in patients with PsA (2645.3 ± 489.5 ng/mL) than in the HC population (835.9 ± 434.6 ng/mL) and clearly distinguished the 2 populations (Area Under the Curve [AUC]=0.96, 95% CI Not Available [NA]) (17). The second study compared the biomarker in PsA, OA, and HC and reported significantly higher levels in the sera of PsA patients than in the other two populations (18). COMP levels were also reported to be significantly higher in PsA synovial fluid compared to rheumatoid arthritis (RA), even in the presence of joint destruction (8).
The four studies comparing serum COMP levels between PsA and HC were included in a meta-analysis (10, 15, 17, 18). COMP was significantly increased in the serum of the PsA population, and the effect size measured by SMD was 2.305 (95% CI=0.80-3.81, p=0.003; Figure 2). When COMP levels were compared between PsA and OA, the 3-study meta-analysis reported a significant SMD of 0.78 (95% CI=0.02-1.55, p=0.046) (15, 18).
Figure 2 Meta-analysis of COMP levels. (A) Forest plot of difference in COMP levels means between PsA and HC in meta-analysis. (B) Forest plot of difference in serum COMP levels means between PsA and OA.
Due to the heterogeneity of the results, sensitivity analyses were also performed. The results remained unchanged after each study was excluded serially. The difference in COMP levels between PsA and PsO was tested in only one study (10).
3.1.1.2 Matrix metalloproteinase-3 (MMP3)
The results are more discordant when considering serum levels of MMP3, also known as stromelysin-1. Five cross-sectional studies revealed that MMP3 levels were significantly higher in patients with PsA than in HC (12–14, 17, 18) or in patients with PsO (13). The accuracy of MMP3 to distinguish PsA from PsO (AUC=0.70, 95% CI=0.65-0.75) and PsA from HC (AUC=0.66, 95% CI=0.59-0.74) was moderate. Other studies did not report any difference in MMP3 levels between PsA and HC (16) or PsO (14). The latter study described an increased concentration of MMP3 in PsA sera compared to PsO, with a significant OR of 1.59 (95% CI=1.21-2.11); however, this disappeared after multivariate regression (14). In a recent study, serum MMP3 levels from PsA compared to PsO and HC were not significantly different. Only one study compared PsA and OA, and did not demonstrate any difference in serum levels of MMP3 (18).
In total, 6 studies compared serum MMP3 levels between PsA and HC (10, 13, 14, 16–18) and 4 studies compared levels between PsA and PsO (10, 13, 14, 16). The pooled results displayed a higher, but not significant, level of MMP3 in sera of PsA patients compared to HC (SMD=0.450, 95% CI=-0.080-0.981, p=0.096; Figure 3). MMP3 was significantly higher in PsA compared to PsO (SMD=0.419, 95% CI=0.119-0.719; p=0.006). Results were confirmed with sensitivity analyses. No significant SMD was found when pooled MMP3 levels of PsO and HC were compared (SMD=-0.118 [95% CI=-0.693-0.457], p=0.689).
Figure 3 Meta-analysis of serum MMP3 levels. (A) Forest plot of difference in serum MMP3 levels means between PsA and HC. (B) Forest plot of difference in serum MMP3 levels means between PsA and PsO.
3.1.1.3 Receptor activator of nuclear kappa-B ligand (RANK-L)
RANK-L was also assessed as a diagnostic biomarker in three studies. Two studies reported significantly higher serum RANK-L levels in PsA patients compared with HC (59, 134), whereas the remaining study observed the opposite (16). The same three studies also compared RANK-L levels between PsA and PsO, with little or no difference detected between the two populations. The only study reporting significantly higher levels in PsA patients concluded that RANK-L was inaccurate for differentiating PsA from PsO (AUC=0.66 [95% CI NA]) (59). In the meta-analysis of these three publications, comparison of RANKL levels between PsA and HC and between PsA and PsO (10, 16, 59), the SMDs were -0.106 (95% CI=-3.75 -3.36, p=0.952) and 0.315 (95% CI=-0.391-1.021, p=0.382), respectively (Figure 4). Results did not change upon performance of sensitivity analyses.
Figure 4 Meta-analysis of circulating biomarkers. (A) Forest plot of difference in serum RANK-L levels means between PsA and HC. (B) Forrest plot of difference in mean levels of OPG between PsA and HC. (C) Forrest plot of difference in mean levels of OPG between PsA and PsO.
3.1.1.4 Osteoprotegerin (OPG)
OPG serum levels were significantly increased in a small PsA cohort (n=26 per group) compared with PsO and HC (10). However, levels were not different in a larger PsA cohort (n=200 per group) compared to PsO and HC (13), nor in another smaller PsA cohort (n=50) compared to PsO (n=50) and HC (n=20) (16). In our meta-analysis, we calculated an SMD of 0.405 (95%CI -0.962-1.773, p=0.562) when PsA was compared to HC, and an SMD of 0.644 (95%CI 0.036-1.252, p=0.038) when PsA was compared to PsO (Figure 4).
3.1.1.5 Dickkopf-1 (Dkk-1)
Dkk-1 serum levels were studied in two publications with opposing results (13, 16). After meta-analysis, SMD in Dkk-1 levels between PsA and HC was 3.22 (95% CI=-5.974-12.412, p=0.493; Figure 5). When patients with PsA were compared to patients with PsO, the SMD was 0.992 (95% CI=-0.89-2.87, p=0.301).
Figure 5 Forest plot of difference in Dkk-1 levels means between PsA and HC in meta-analysis.
3.1.2 Genetic biomarkers
Human Leucocyte Antigen (HLA) was studied in 6 publications (19, 27–29, 35, 36). A family-based study reported an association between PsA and various HLA alleles: HLA-B*27, HLA-B*38, HLA-B*39, and HLA-C*12 (27). HLA-B*27 association with PsA was additionally described in a large cross-sectional study, while HLA-C*06 was found to be associated with skin damage and less prevalent musculoskeletal developmental phenotypes (28). Another study described an association between PsA and HLA-B*27, HLA haplotype C*12/B*38m and HLA-C*06/B*57 (29). Interestingly, HLA-B27 was not associated with PsA in the Jewish Israeli population (19), though HLA-A*01/A*01 and HLA-C*06/C*02 were risk alleles for PsA in the Chinese Han population (35).
Genetic polymorphisms were also explored in 9 different articles (20, 21, 24–26, 30, 31, 33, 41). Polymorphisms of the gene Il-13 were examined in two studies, which reported that rs1800925*C and rs20641*G were significantly associated with PsA in a PsO population (25, 26). However, one study reported the association between rs1800925 polymorphism and PsA only in the smoker population (26).
3.1.3 Autoantibodies
In our systematic literature review, autoantibodies were explored in 8 articles (43–50). They focused on different autoantibodies, preventing meta-analysis. Among these autoantibodies, those of interest are outlined below. It should be noted that none of these autoantibodies have been evaluated or validated in any additional PsA cohorts beyond those described here.
Anti-Cyclic citrullinated peptides (CCP) were present in some PsA patients (7.9%) (43). Anti-Mutated citrullinated vimentin (MCV) levels were significantly higher in PsA sera (24%) than PsO (8%) (45). The novel autoantibody named anti-PsA peptide was identified after screening of a random synthetic peptide library with pooled immunoglobulins derived from 30 patients with recent onset PsA. Anti-PsA shares a sequence homology with Nebullin Related Anchoring Protein (N-RAP) (46). A peptide corresponding to N-RAP sequence was synthetized and tested by ELISA. Anti-NRAP autoantibodies were recognized by 83% of PsA sera, versus 7% of rheumatoid arthritis (RA) anti-CCP positive, 4% of RA anti-CCP negative, 3.3% of PsO, and none of other rheumatic diseases included in this study (46).
Anti-A Disintegrin and MetalloproteinaSe with ThromboSpondin motifs 5 (ADAMSTS5) and anti-Cathelicidin LL37 (LL37) IgG autoantibodies were assessed for differentiating PsA from PsO. The ROC analysis reported an AUC of 0.84 (95% CI=NA, p<0.1) for anti-ADAMSTS5 autoantibodies and 0.87 (95% CI=NA, p<0.01) for anti-LL37 autoantibodies (49). Expression of Carbamethylated anti-LL37 was significantly higher in PsA sera (median=0.66, IQR=0.439) than PsO (median=0.43, IQR=0.47, p=0.02) and HC (median=0.158, IQR=0.099, p=0.0001) (48). Finally, higher IgA anti-oxidized collagen type II (oxPTMCII) autoantibodies were detected in PsA (84%, n=33/39) and axial spondyloarthritis (SpA, 47%, n=79/165) sera compared to HC (0%, n=0/28) (50).
3.1.4 Other biomarkers
Two additional biomarkers unrelated to the above categories were also assessed in at least 2 publications.
3.1.4.1 C-X-C motif chemokine ligand 10 (CXCL10)
CXCL10 rates were overexpressed in synovial fluid of PsA versus gout or SpA, but rates were similar to those of RA (61). A prospective follow-up of patients with PsO reported both higher baseline serum CXCL10 levels in patients who subsequently developed PsA as compared with those who did not, and a significant decrease in CXCL10 levels from the year before to the year after PsA onset (70).
3.1.4.2 Calprotectin S100A8/S100A9
A serum calprotectin S100A8/S100A9 with a cut-off of 475 ng/mL was able to discriminate PsA from HC with a 93.3% specificity and 75.0% sensitivity (54). Serum calprotectin levels were increased in PsA but also in other inflammatory arthritis (RA, axial SpA) compared to controls (i.e., OA, fibromyalgia, and undifferentiated arthralgia), with good accuracy for distinguishing inflammatory arthritis from controls (AUC=0.964, 95% CI=NA) (65). Calprotectin levels in each subpopulation were not available for a meta-analysis.
3.2 Prognostic biomarkers
Prognostic biomarkers, including markers of disease activity, were less frequently assessed than diagnostic biomarkers (Table 2). As such, the data collected were insufficient to perform meta-analyses.
Table 2 Included Prognostic Biomarkers Studies.
3.2.1 Acute phase reactant biomarkers
One study concluded that erythrocyte sedimentation rate (ESR) was better correlated with Ritchie’s index, tender joint count (TJC) and swollen joint count (SJC) than C-Reactive Protein (CRP) in a 36 patient PsA cohort (84). These potential prognosis markers were explored in a 5-year follow-up prospective study. The 36 patients with PsA demonstrated a disease duration ranging from 1-40 years, and baseline ESR was associated with structural progression (85). Both ESR and CRP were also associated with UltraSonography (US) which are signs of active synovitis (86).
3.2.2 Bone and cartilage turnover biomarkers
Serum COMP levels were correlated with acute phase reactants and disease activity (TJC, r=0.60, p<0.001) and SJC, r=0.75, p<0.0001) (18, 88). A decrease in MMP3 serum levels in PsA patients receiving adalimumab was correlated with Disease Activity Score (DAS)-28 improvement (90).
3.2.3 Autoantibodies
In a cross-sectional study, positivity for anti-CCP antibodies was associated with more radiographic damage and polyarticular phenotypes (95). Anti-LL37 autoantibodies were also described to correlate with disease activity in PsA (48). A strong correlation between Anti-Carbamethylated Protein (CarP) antibody levels and both clinical and ultrasonographic activity was described (correlation between anti-CarP and DAS-28 (r=0.96), CRP (r=0.97), ESR (r=0.97) and US power Doppler+synovitis with a Pearson coefficient >0.97) (96).
3.2.4 Other biomarkers
3.2.4.1 Serum calprotectin S100A8/A9
Calprotectin S100A8/A9 plasma levels were not correlated with disease activity (122, 126). Although, one study reported calprotectin S100A8/A9 levels significantly increased in the polyarticular phenotype of PsA, and were correlated with SJC (54). Contradictory results were reported regarding correlation with US synovitis in greyscale and power Doppler analyses (115, 122). High levels of calprotectin were also associated with relapse at 1-year (118).
3.2.4.2 YKL40
Serum concentration of YKL40, also named Chitinase like-3 protein (Chi3L1) was significantly correlated with disease activity (r=0.848, p<0.001) (109). It was also sensitive to changes, with a significant decrease in serum of good responders to TNF inhibitor (106).
Table 3 Assessment of bias risk using NOS scale for case-control studies.
Table 4 Assessment of bias risk using NOS scale for cohort study.
4 Discussion
Our systematic review of the literature shows that few biomarkers are currently available to guide clinical practice in the diagnosis and prognosis of PsA. This review has highlighted COMP and MMP3 as two potential serum biomarkers for the diagnosis of PsA. However, the discriminative qualities of these bone and cartilage remodeling markers have been revealed as insufficient for clinical use.
The meta-analysis showed that COMP was able to differentiate patients with PsA from those with OA or from healthy subjects. However, serum levels varied widely between studies. One study reported mean COMP rates were 10-fold lower in the PsA, OA, and HC populations compared with those observed in the other studies, while methodologically, only the commercial ELISA kits differed between studies (15). The success of COMP in distinguishing PsA from PsO was different across studies, and the absence of numerical data prevented performance of a meta-analysis and to deduce its efficacy in this application.
The pooled serum MMP-3 levels were significantly higher in PsA patients than in PsO patients. However, the meta-analysis showed that they did not appropriately distinguish patients with PsA from healthy subjects. Similarly, the only publication that studied MMP3 serum levels in PsA and OA reported no difference between the two (134). MMP3 levels were increased in OA when compared to HC, but do not appear to be a reliable biomarker, particularly as their assessment in a multiplex system reported results contrary to those found in this analysis (i.e., lower MMP3 serum levels in patients with PsA and PsO than in healthy subjects) (16). None of the remaining bone remodeling or cartilage markers demonstrated any ability to differentiate patients with PsA from controls. However, all the studies included in the analysis were cross-sectional studies, with patients whose diagnosis was pre-existing and whose disease duration was not considered.
We have not identified any new genetic biomarkers useful in the diagnosis of PsA since the last meta-analyses on the subject, and these analyses on genetic polymorphisms had not identified any useful biomarkers for the practical diagnosis of PsA (135, 136). Our systematic review did not identify all publications on HLA and PsA association, which may be due to the fact that our search algorithm was not specifically focused on genetics. The major loci of interest have historically been MHC region and HLA genes, of which certain alleles, primarily HLA-C*06 and HLA-B*27, are carried by about 20-35% of PsA patients (137). Recently, HLA-B27 has been identified as a marker of the axial PsA phenotype, and HLA-C*06 as a marker of the peripheral PsA phenotype (138). Furthermore, both systematic review for PsA biomarkers and a recent meta-analysis examining HLA association in PsA patients confirmed a significant increase in the risk of PsA in HLA-C*02 and C*12 populations (139, 140). Specific non-HLA PsA variants have been identified in GWAS studies, including in the IL12B, NOS2 and IFIH1 regions as reported in a systematic review published in 2015 (141). Several signaling pathways possibly implicated in the pathogenesis of PsA were presented in a recent systematic review published in 2020 (142).
Data concerning autoantibodies in PsA remains sparse in the literature. While some data appears promising, no replication studies have been published. Anti-CCP antibodies are associated with polyarticular phenotypes and structural lesions, and have been shown to be markers of severity rather than diagnosis. Indeed, a recent study reported a correlation between anti-CCP antibodies in PsA and pulmonary manifestations (143).
This systematic review of the literature has several limitations. Only patients with PsA were included while studies involving patients with SpA were excluded, which may have incidentally excluded patients with psoriatic forms of axial disease. The search algorithm also did not include imaging biomarkers, although combining imaging with other biomarkers might help to better define PsA (144). In addition, we choose to focus on diagnosis and prognosis biomarkers unrelated to treatment and did not include predictive biomarkers of treatment response. Finally, our search equation did not allow us to highlight the numerous studies concerning genetics biomarkers in psoriatic arthritis either and should be a focus of a future systemic review.
In summary, this review was broad, with more than 50 studies included since the prior systematic review on diagnostic and prognostic biomarkers in PsA (139). No specific diagnostic biomarkers for PsA were identified, despite the fact that this was the first meta-analyses to assess COMP and MMP3. The search for autoantibodies in PsA appears promising but requires additional confirmatory studies. Further studies are also needed to assess the performance of potential biomarkers that can distinguish PsA from OA and other chronic inflammatory diseases.
Data availability statement
The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author/s.
Author contributions
TW: conception and design of the study, acquisition of data, analysis of data, original draft preparation, data curation, reviewing and editing. NB: conception and design of the study, editing LB: analysis of data, stats PL: editing, supervision TP: conception and design of the study, analysis of data, drafting manuscript, supervision, reviewing and editing All authors reviewed and approved the final draft of the manuscript.
Acknowledgments
Mrs. Catherine Weill assisted with the Embase bibliographic research. JetPub Scientific Communications LLC provided editorial assistance to the authors during preparation of this manuscript.
Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Publisher’s note
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Keywords: psoriatic arthritis, psoriatic biomarker, psoriasis, arthritis, meta-analysis
Citation: Wirth T, Balandraud N, Boyer L, Lafforgue P and Pham T (2022) Biomarkers in psoriatic arthritis: A meta-analysis and systematic review. Front. Immunol. 13:1054539. doi: 10.3389/fimmu.2022.1054539
Received: 26 September 2022; Accepted: 07 November 2022;
Published: 30 November 2022.
Edited by:
Durga Prasanna Misra, Sanjay Gandhi Post Graduate Institute of Medical Sciences (SGPGI), IndiaReviewed by:
Ajesh Maharaj, University of KwaZulu-Natal, South AfricaVinod Chandran, University of Toronto, Canada
Copyright © 2022 Wirth, Balandraud, Boyer, Lafforgue and Pham. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Theo Wirth, wirththeo@gmail.com