Soluble and Tissue Biomarkers in Ankylosing Spondylitis

Best Practice & Research Clinical Rheumatology 24 (2010) 671–682

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Soluble and Tissue Biomarkers in Ankylosing Spondylitis Kurt de Vlam, M.D., Ph.D * Department of Musculoskeletal sciences, Division of Rheumatology, Katholieke Universiteit Leuven, Belgium

Keywords: biomarkers ankylosing spondylitis disease activity damage

The study of biomarkers has become a very important field of research in spondyloarthropathy. Biomarkers are useful for different aspects of the disease such as diagnosis, assessment of disease activity and outcome, including damage. The most commonly used biomarkers in spondyloarthropathies are HLA-B27 for diagnosis and erythrocyte sedimentation rate (ESR) and C-reactive protein (CRP) for disease activity. HLA-B27 is very sensitive but has a low specificity. ESR and CRP have both low sensitivity and specificity. The introduction of new and very expensive therapies is another reason for analysis of biomarkers. Clinicians need tools to predict more accurately disease activity, disease progression and response to therapy. This article focusses on the several known and new biomarkers of promise, including markers for cartilage and bone damage, and discusses some of the problems encountered during the search and development of new biomarkers. Biomarkers, soluble and tissue-related, reflecting structural damage and disease activity, constitute a high priority for the drug discovery process and the understanding of the pathogenesis of a particular disease. The identification of relevant tools to evaluate the natural course, disease activity, treatment response and outcome of ankylosing spondylitis is of increasing relevance since the raised awareness and development of new therapeutic options. Until now these different aspects are monitored by artificial patient-centred or physician-centred constructs. Very often, their approach is indirect and is not free from disease-unrelated influences. The Outcome Measures in Rheumatology Soluble Biomarker Working Group has taken several major steps towards the development and implementation of such assessment methods. The major drawback is that these tools do not directly reflect biological and pathological processes. Serological biomarkers objectively

* Division of Rheumatology, University Hospitals Leuven, Herestraat 49, B-3000 Leuven, Belgium. Tel.: þ32 16 342541; Fax: þ32 16 342547. E-mail address: [email protected]. 1521-6942/$ – see front matter Ó 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.berh.2010.05.009

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measure different aspects of the biological and pathological process and may contribute to a major advance in the assessments of patients. The ultimate goal is the use of biomarkers in a personalised approach for disease management in clinical practice. Ó 2010 Elsevier Ltd. All rights reserved.

The National Institutes of Health (NIH) Biomarkers and Surrogate Endpoint Working Group defines a biological biomarker as a characteristic that is objectively measured and evaluated as an indicator of normal biological processes, pathogenic processes or pharmacological responses to therapeutic intervention [1]. Biological biomarkers include markers for drug effect or response, as well as for diagnosis, prognosis or physiological status information, such as disease activity or damage not related to drug effect. The Food and Drug Administration (FDA) distinguishes three context-specific types of biomarkers: known valid biomarker, probable valid biomarker and possible valid biomarker based on the available scientific data for the biomarker (http://www.fda.gov/cber/gdlns/pharmdtasub.htm) [2]. Biomarkers allow quantitative assessment of diagnosis, disease processes and treatment response and therefore are important in clinical practice. They enable appropriate choice of therapy and drug dosage to maximise effect, minimise toxicity and monitor disease outcomes, and thus are the foundation of evidence-based medicine. Conservative thinking, lack of quality control and methodological issues such as inappropriate statistical analysis and validation seriously hamper the development of new biomarkers. For this reason, the development of biomarkers lags significantly behind that of drug development. The absence of new and appropriate markers may slow down the evolution of patient-tailored targeted therapies. Within the rheumatology community under the umbrella of OMERACT (Outcome Measures in Rheumatology), a Working Group has developed clinical validation criteria for soluble biomarkers reflecting structural damage in rheumatoid arthritis (RA) and spondyloarthropathies (SpA) [3,4].(http://www.omeract.org/). The techniques used to develop and measure biomarkers are diverse, for example, in vitro analyses such as protein expression, gene patterns or gene expression, and in vivo analyses such as in functional imaging that are still in early development in humans. Technologies available today may evaluate biochemical and chemical markers by proteomics and metabolomics; genetic markers using pharmacogenomics, gene expression profiles, systems biology and single nucleotide polymorphisms (SNPs) and structural markers using classical molecular imaging techniques.

Possible use of biomarkers in ankylosing spondylitis Outcome or clinical endpoints in ankylosing spondylitis (AS) include diagnosis, inflammation, prognosis, disease activity, disease severity, damage, disability and quality of life (QoL). Recently, specific instruments have been developed to measure disease activity or damage but for other domains the instruments are still under development or not yet widely validated [5,6]. Up till now there is a reasonable consensus about the definition of spondyloarthropathies and AS and about the use of bath ankylosing spondylitis activity index (BASDAI) as the gold standard for disease activity. Several sets of classification criteria for AS (modified New York criteria) and SpA (ESSG and Amor criteria) are available [7,8,9]. Recently, new classification criteria were developed for axial SpA under the umbrella of Assessment of SpondyloArthritis International Society (ASAS) [10,11]. Criteria for peripheral SpA are under development. Moreover, there is general agreement that SpA responds well to tumour necrosis factor (TNF) blockade. In general, erythrocyte sedimentation rate (ESR) and C-reactive protein (CRP) correlate poorly with disease activity evaluated by BASDAI or patient global assessment of disease activity, especially in patients with exclusive axial disease [12]. ESR, CRP and even BASDAI cover the concept of disease activity only partly. Spinal inflammation detected by magnetic resonance imaging (MRI) cannot predict BASDAI scores in patients treated with anti-TNF. Different scoring systems to characterise inflammatory spinal lesions have been developed, including ASspiMRI (the AS spinal MRI score) and the spondyloarthritis research consortium of canada (SPARCC) MRI spinal inflammation

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index, but are recommended by ASAS only for study purposes [13,14].Moreover, it is not yet clear how MRI changes relate to changes in clinical variables. Therefore, MRI is not recommended for the evaluation of disease activity and treatment response in patients with AS [15]. It is agreed that HLA-B27 is a biomarker for diagnosis with high sensitivity but low specificity. Instrument development for disease assessment must fulfil the OMERACT filter for validity (truth, feasibility and discrimination). For some domains, no gold-standard instrument has been identified. Additionally, different studies have used different instruments making cross-comparisons difficult. A better approach might be to define domains based on the biology of the disease and then to look for the outcome of different biological processes such as the inflammatory process or the tissue response including damage. Biomarkers in AS can mainly be determined in blood (serum/cellular compartment). Synovial fluid and synovial tissue are useful only in patients with concomitant peripheral involvement. In patients with axial involvement or enthesial involvement, tissue from the spine or enthesis is difficult to obtain and subsequently not useful for regular evaluation. Recent evolution in the understanding of AS and SpA emphasises the need for biomarkers that facilitate understanding of the underlying pathophysiological processes, diagnostic ascertainment, prediction of prognosis, assessment of burden of disease and, last but not least, prediction of treatment response. During the last decade, the development of TNF-blocking agents with beneficial symptomatic responses has dramatically changed the therapeutic approach in these patients. The cost and possible serious side effects are the major drawbacks of these therapeutic options. Biomarkers, which can predict the degree and duration of response and can detect possible side effects in an early stage, are needed. Biomarkers for diagnosis Diagnosing AS is a clinically driven process based on the observation of clinical signs and structural changes on X-rays. Classification criteria are useful for constituting uniform groups of patients but not for diagnosis. The modified New York criteria for AS are widespread and have good sensitivity and specificity but the presence of radiographic sacroiliitis is mandatory. The structural changes on X-rays are the consequence of the inflammatory process but do not reflect the inflammatory process itself. It takes several years before these structural changes are visible on conventional X-rays. These criteria only capture established forms and lack sensitivity for the earlier forms. Recently, a new set of criteria have been developed to facilitate the identification of earlier forms. A diagnostic algorithm was developed based on the available sensitivities, specificities and likelihood ratio for SpA-related features. This approach has been validated by the ASAS in an international study [11]. These criteria were shown to be superior to the older criteria such as the ESSG and Amor criteria. The high association of HLA-B27 with AS, combined with a relatively high population prevalence, does not make it a good candidate for use as a screening marker alone but in a combined model, it can be very useful. Biomarkers for the diagnosis of AS must be evaluated in cohorts of patients with an established diagnosis based on the current gold standard, that is, the modified New York criteria for AS and radiographic sacroiliitis, in particular. An appropriate control population, including normal subjects or patients with mechanical low back or disc herniation, is essential. A statistically significant difference in frequency or level of biomarker between patients and controls must be combined with additional calculations for likelihood or specificity and sensitivity for selection of candidate biomarkers. For this purpose, the receiver operator characteristic (ROC) is often used. ROC curves combine sensitivity and specificity and are expressed by the area under the curve (AUC) and a score above 0.75 is acceptable. The choice of the biomarker depends on the current understanding of disease susceptibility or pathogenic mechanisms. Since the genetic contribution to disease susceptibility is very high in AS, assessment of genetic markers (genotypes or SNPs) may be useful. The strong association with HLAB27 has been known for a long time but is not very useful for diagnosis due to its low specificity. Recent advances in the genetics of AS have shown that it is a multigenic disease in which HLA-B27 contributes only about 20–30% of the genetic risk raising the possibility that additional genes such as IL1, ERAP-1 and IL23R may improve diagnostic accuracy in AS by combination approaches [16].

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CRP failed as a diagnostic biomarker due to its low sensitivity that ranges from 38% to 75% [17]. This is in contrast with the requirements for a diagnostic tool which requires maximal sensitivity without sacrificing specificity. An alternative approach is to look for proteins or biological products as biomarkers reflecting distinct disease processes in AS as compared to other forms of chronic arthritis. Since AS is characterised by bone remodelling, specific markers for bone formation may be promising candidates that help differentiate AS from other chronic arthritides [18]. Osteopontin, a protein with an important role in bone remodelling, induces anchoring of osteoclasts to the bone matrix, and is also involved in inflammation. It shows increased levels in patients with AS compared to healthy controls. It also correlates with other proteins such as serum alkaline phosphatase (ALP), TNFa and IL 6 but not with disease activity and reflects the bone remodelling process rather than disease activity in AS [19]. Runx2 is a transcription factor related to bone formation and is involved in osteoblast differentiation. The increased expression of Runx2 in peripheral blood cells of patients with AS discriminates them from those with RA, who express lower levels of Runx2 [20]. Biomarkers for the stage of disease may also be of interest, especially since there is still a mean delay of 7 years between the first symptoms and diagnosis [21]. A combination of markers for diagnosis and stage of disease may support the goals of early treatment. It is hypothesised but not yet proven that early treatment of AS may prevent the development of ankylosis. Levels of STREM-1, a soluble triggering receptor expressed on myeloid cells-1 ( 15 pg ml1), are significantly higher in patients with AS compared to controls (31.3% vs. 10%; p < 0.027) but this level of sensitivity is insufficient for a diagnostic marker. Within the groups of patients with AS, STREM-1 correlates inversely with disease duration in AS and may be a marker for early disease in AS but is not related to disease activity [22]. Several groups have evaluated synovial tissue as a biomarker in spondyloarthritis or AS patients with peripheral involvement. Although peripheral arthritis in AS differs clinically, serologically and radiographically from other forms of chronic arthritis, some patients have an atypical presentation in clinical practice and may be difficult to label with a definite diagnosis [9,23,24]. In a diagnostic setting, no single histological feature of the synovium discriminates SpA from other forms of chronic arthritis. Baeten et al. developed a multi-parameter algorithm enabling the identification of the majority of patients with SpA. The characteristics that made the diagnosis of SpA likely were: (1) pronounced vascularisation, (2) moderate hyperplasia of the intimal lining layer, (3) increased presence of the scavenger receptor CD 163 in the lining and sublining layer and (4) the relative abundance of polymorphonuclear cells in the synovial tissue [25]. Using a multi-parameter setting including synovial histopathology, staining with anti-citrulline, staining with MHC class II-HC gp39 peptide complex and detection for crystal deposition in patients with an atypical clinical presentation can be very useful. Only microscopic vascularity score >2 and tortuous vascular pattern has a high predictive value for SpA [25]. Biomarkers for disease activity AS is a complex chronic rheumatic disease affecting articular and extra-articular sites. The concept of disease activity is a complex concept and may cover a wide spectrum of measures and concepts. ASAS has defined a core set of instruments covering most aspects of the disease, including disease activity [26]. But single variable parameters such as ESR, CRP, patient pain, patient global assessment or constructs such as BASDAI are insufficient. They do not appropriately cover the entire spectrum of disease activity and lack face and content validity. A disease activity biomarker may capture these different aspects. Recently, a new disease activity score was developed and endorsed by ASAS: the AS Disease Activity Score (ASDAS). This construct is now under validation and may replace the existing tools for disease activity. Till then the BASDAI is still considered as the principal outcome measure. Since inflammation is the major driver of disease activity, different inflammatory parameters have been studied. Inflammatory parameters such as CRP or ESR have a poor correlation with BASDAI and have a poor predictive value in longitudinal studies of patients with AS [12,27,28]. Only a few other acute phase reactants have been evaluated. Serum amyloid A (SAA) is increased in patients with AS. Patients with elevated levels of SAA have higher BASDAI than those with normal levels ((5.6  1.3 vs. 4.4  1.5, p < 0.05). Furthermore, SAA levels correlate well with ESR (r ¼ 0.521, p ¼ 0.001) CRP

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(r ¼ 0.648, p < 0.001) and BASDAI (r ¼ 0.431, p ¼ 0.007) and show higher levels in patients with increased BASDAI. In patients with normal CRP or ESR but increased SAA levels, BASDAI was significantly elevated [29]. Other possible biomarkers include MMP-3, cytokines and growth factors such as IL-6, GM-CSF and Vascular Endothelial Growth Factor (VEGF), and bone- and cartilage-related factors including Bone Alkaline Phosphatase (BALP), C2C neo-epitopes and collagen degradation products. The use of MMP-3 seems to be more useful than ESR and CRP in AS. The interest behind evaluating MMP-3 in AS was initiated by the microarray finding of increased expression in synovial tissue of patients with SpA. MMP-3 and MMP-9 were found in increased amounts in the synovial tissue and synovial fluid of patients with SpA, especially in patients with peripheral arthritis. A recent study from Taiwan found only increased amounts of MMP-3. AS patients with axial involvement showed a higher correlation between MMP-3 and BASDAI, as compared to that between ESR or CRP and BASDAI [30]. The usefulness of MMP-3 was confirmed by ROC analysis. In general, the data revealed that MMP-3, and not MMP-1, MMP-9 or TIMP1 and 2, was invariably increased in AS patients. MMP-3 levels were also higher in AS patients with high disease activity compared to those with low disease activity, and correlated significantly with BASDAI (r ¼ 0.366, p ¼ 0.017) and functional indices (r ¼ 0.344, p ¼ 0.026). Nevertheless, the correlation between MMP-3 and disease activity as recorded by the BASDAI, ESR or CRP is not uniform in all studies. Differences in clinical phenotype may explain this variability [31–33]. In an ROC plot, MMP-3 was more accurate than ESR and CRP in detecting AS patients with high disease activity (p ¼ 0.01 and p ¼ 0.009, respectively) [32,33]. Serum MMP-3 levels correlated with disease activity longitudinally as well. In a small cohort, the correlation coefficient between change in MMP-3 and change in BASDAI was still high (0.464), but because of the small sample size the p value was not significant. In a cohort of AS patients, serum levels of granulocyte-macrophage colony stimulating factors (GMCSF) were significantly increased compared to controls and correlated well with the BASDAI (r ¼ 0.62, p < 0.05) and other markers such as ESR and IgA (respectively r ¼ 0.61,p < 0.05; r ¼ 0.68, p < 0.05) in AS patients [30]. In another study, serum GM-CSF correlated well with BASDAI but was not different in healthy controls [33,34]. IL-6 is a multifunctional cytokine that regulates immune responses, inflammation, acute phase responses and haematopoiesis. High levels of IL -6 have been found in sacroiliac biopsies of AS patients and may be partly responsible for the inflammatory response [35]. Earlier studies of IL-6 and its correlation with disease activity were contradictory, which could be due to patient heterogeneity, but generally showed correlations with BASDAI, ESR, CRP, spinal mobility and morning stiffness [36–38]. Visvanathan et al. showed correlations between IL-6 and BASDAI, (r ¼ 0.134, p ¼ 0.0327) and ESR and CRP in a large group of patients with AS participating in the ankylosing spondylitis study for the evaluation of recombinant infliximab therapy (ASSERT) trial [38]. Appel et al. found correlations between VEGF and ESR(r ¼ 0.370, p ¼ 0.009) and CRP (r ¼ 0.307, p ¼ 0.009) [39]. VEGF may be of interest in new bone formation since neoangiogenesis is a key event in the formation of new bone and thus related to disease activity. Peripheral disease Global disease activity in SpA correlated significantly with lining-layer hyperplasia as well as inflammatory infiltration with macrophages, especially the CD163 þ subset, and with polymorphonuclear cells (PMNs). However, multi-parameter models based on synovial histopathology were relatively poor predictors of disease activity in individual patients [25]. The serum biomarkers of current interest and their correlation with BASDAI and other markers for disease activity are listed in Table 1. Biomarkers for structural damage Radiographical damage in AS results from bone destruction and new bone formation and is considered an important outcome in the disease. The modified Stoke ankylosing spondylitis spine score (mSASSS) is the current gold standard for the evaluation of structural damage in AS. Usually, AS is a slowly progressive disease and radiological change appears gradually: evaluation of radiographs with

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Table 1 Serological biomarkers of current interest for assessment of disease activity in AS. Biomarkers

Statistical test

P value

Comparator

References

Matrix metalloproteinases 3

Spearman rank correlation

r ¼ 0.48, p ¼ 0.0007 r ¼ 0.366, p ¼ 0.017 auc ¼ 0.74, p ¼ 0.01 auc ¼ 0.75, p ¼ 0.009 R ¼ 0.291, p ¼ 0.014 R ¼ 0.333, p ¼ 0.005 r ¼ 0.48, p ¼ 0.048 r ¼ 0.51, p ¼ 0.04 r ¼ 0.693, p<0.05 with bonferoni corr r ¼ 0.693, p<0.05 with bonferoni correction r ¼ 0.41, p ¼ 0.004

BASDAI BASDAI BASDAI BASDAI CRP CRP CRP ESR ESR

[33] [32] [32] [32,33] [39] [39] [57] [41] [41]

BASDAI

[41]

BASDAI

[33,34]

r ¼ 0.431, p ¼ 0.007 r ¼ 0.78, p < 0.0001 r ¼ 0.005, p < 0.0011 r ¼ 0.57, p < 0.0011 r ¼ 2537, p ¼ 0.034 r ¼ 0.3854, p ¼ 0.001 r ¼ 0.134, p ¼ 0.0327 R ¼ 0.307, p ¼ 0.030 R ¼ 0.370, p ¼ 0.009 R ¼ 0.340, p ¼ 0.018 R ¼ 0.361, p ¼ 0.010 R ¼ 0.372, p ¼ 0.011 R ¼ 0.496, p ¼ 0.003

BASDAI BASDAI CRP ESR CRP ESR BASDAI CRP ESR BASDAI CRP ESR BASDAI

[29] [29] [37] [37] [36] [36] [38] [39] [39] [39] [39] [39] [39]

ROC

BALP at baseline C2C neoepitope

C-propeptide of Type II collagen (CII) Macrophage colony stimulating factor Amyloid A Interleukin 6

Spearman correlation Spearman correlation Linear regression Spearman rank correlation Spearman rank correatlion? Spearman rank correlation Spearman rank correlation Spearman rank correlation Pearson correlation Spearman rank correlation Spearman rank correlation

VEGF at baseline

VEGF at 2 yrs

Spearman rank correlation Spearman rank correlation

an interval of less than two years is unreliable [40]. Therefore, biomarkers predicting structural damage in AS are attractive because of their availability early in the disease course. They can help decide whether and how to proceed with respect to management. From the clinician’s perspective, availability of such biomarkers may permit targeting of pre-radiographic disease and the identification of subgroups at particular risk of disease progression. This may then lead to earlier and more aggressive therapeutic intervention in routine clinical practice. Damage in AS is reflected not only in bone and cartilage loss but also by new formation of bone, distinguishing it from most other chronic arthritides. Biomarkers for damage in AS are likely to originate from cartilage or bone. For a long time most biomarker studies in AS focussed only on bone loss. Cartilage turnover, including cartilage synthesis and degradation, and the process of new bone formation were not studied. Articular cartilage is composed primarily of a type II collagen network complexed with the large proteoglycan aggrecan. In degenerative and inflammatory joint disease, cleavage of type II collagen by collagenases generates the neoepitopes Col2-3/4Clong mono (C2C) and Col2-3/4Cshort (C1–2C), followed by up-regulation of the biosynthesis of procollagen in chondrocytes. The rate of synthesis of type II collagen is directly proportional to the content of the C-propeptide of type II collagen (CPII) in cartilage. The main cartilage proteoglycan is aggrecan and changes in aggrecan matrix turnover are reflected by an increase in a distinctive epitope of aggrecan, the 846 epitope. The CPII-to-C2C (CII:C2C) ratio reflects the balance between type II collagen synthesis and degradation. If the disease process were associated with synthesis, this would favour more anabolic changes and vice versa. The balance between synthesis and degradation appears to be more informative than either biomarker alone. Many studies have documented changes in cartilage collagen and proteoglycan biomarkers in RA and osteoarthritis. Several studies have evaluated biomarkers for cartilage turnover and bone changes, including erosions and new bone formation. Comparison is often difficult since different studies used different read-outs for these processes. Possible biomarkers for structural damage that are of current interest are listed in Table 2.

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Table 2 Serum and urine biomarkers of current interest for evaluation of cartilage and bone turnover. Aggrecan 846 epitope (present on intact aggrecan molecule) C-propeptide of type II collagen (CPII) Col2-3/4 long mono (C2C) Col2-3/4 short (C1-C2 neoepitopes) CII:C2C ratio Cartilage oligmeric matrix protein (COMP) Human cartilage GP-39 (YKL-40) Matrix metalloproteinase-3 (MMP-3) Urinary C-terminal cross-linking telopeptide of type I collagen (CTX-I) Urinary C-terminal cross-linking telopeptide of type II collagen (CTX-II)

Cartilage turnover (hyalin cartilage & intervertebral disc) Biosynthesis marker Collagen cleavage epitope of type II collagen (hyalin cartilage & intervertebral disc) Collagen cleavage epitope of type II collagen Balance between collagen II synthesis and degradation Cartilage remodeling Synovial hyperplasia Synovial inflammation Bone degradation Cartilage degradation

Baseline damage (cross-sectional evaluation) In an interventional observational study with infliximab, patients with AS demonstrated significant elevations in serum levels of CPII, the 846 epitope and the CPII-to-C2C (CPII:C2C) ratio (but not C2C or C1–2C) compared with normal controls at baseline. Of the biomarkers examined, only CPII:C2C showed a correlation with the CRP level. Among the biomarker–cytokine relationships, TGF-b demonstrated a trend towards a positive correlation with the 846 epitope [41]. Patients with AS have higher levels of CTX-I and CTX-II. CTX-I correlates with disease activity [42], while the level of CTX-II correlates with radiographic spinal damage [42]. In a longitudinal observational cohort of AS patients (OASIS), baseline radiographical damage correlated with CTX-II but not with CTX-I. Moreover, CTX-II correlated significantly with ESR (r ¼ 0.29) and CRP (r ¼ 0.30) (p < 0.01) but not with BASDAI or BMD [43]. CTX-II significantly explained the differences in radiographic progression. Cartilage degradation seems to play an important role in radiographic damage and progression in AS. The exact mechanism is unknown but it is hypothesised that ossification of zygapophyseal joints, ligaments and intervertebral discs goes along with cartilage destruction. Mean bone alkaline phosphatase and osteocalcin in patients do not differ from normal and have no significant correlation with bath ankylosing spondylitis radiology Index (BASRI) or bone mineral density (BMD) [39,42]. Patients with AS also showed an increased serum level of osteopontin, which is also involved in bone remodelling. The level of osteopontin not only correlated with serum alkaline phosphatase and osteocalcin as markers of bone formation but also with CTX-1, reflecting bone resorption, even though there was no correlation with radiographic score (BASRI). In general, osteopontin did not correlate with inflammatory parameters or disease activity measured by BASDAI. This suggests that osteopontin might be involved in bone remodelling rather than in the inflammatory process in AS [19]. Finally, sclerostin levels seem to be decreased in patients with AS compared to patients with RA and healthy volunteers [44]. The decreased sclerostin expression in osteocytes in AS patients may reflect a specific alteration of osteocyte function which is associated with the suppression of bone formation in AS patients. Radiographic progression – longitudinal data Baseline damage has been identified as the sole independent predictor of radiographic progression in AS amongst clinical parameters. YKL-40 and MMP-3 showed weak to moderate correlation with the radiographic 2-year progression measured by the mSASSS. However, after adjustment for sex, age, CRP and baseline mSASSS, only MMP-3 was significantly associated (b 0.29, p ¼ 0.004) [45]. It seems that MMP-3 is the only independent predictor of disease progression in patients with pre-existing radiographic damage. Combination of MMP-3 > 70 ng/ml and mSASSS > 10 gave an odds ratio for

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progression of 70; progression was observed in 2/3 of these patients which could constitute a clinically useful risk prediction model if the findings are replicated [45]. Serum levels of sclerostin over time are significantly higher in AS patients without growth of syndesmophytes compared to those with syndesmophyte growth (p ¼ 0.007), independently of radiographic damage at baseline (p ¼ 0.001) [44]. No biomarker has been shown to predict damage in AS patients without any prior radiographic damage. Osteoporosis and osteopaenia in lumbar spine and femoral neck is found in a substantial number of patients with AS as shown by a reduced BMD (up to 75%). Osteoporosis in AS increases the risk for vertebral fractures, often an unrecognised complication in patients with established AS. It is known that inflammatory activity (i.e., pro-inflammatory cytokines) may play a role in the pathophysiology of bone loss and links this form of damage directly to disease activity. Serum levels of 1.25 OH vitamin D3 and parathormone (PTH), essential regulators of bone metabolism, are negatively correlated with disease activity and TNF-abut positively correlated with bone BALP [46]. In active AS, sRANKL and SRANKL/OPG ratio are up-regulated in AS patients. The sRANKL/OPG ratio tended to increase in patients with reduced BMD and radiological findings of active inflammation [47]. An imbalance between RANKL and OPG may underlie the development of osteoporosis in AS. Elevated CTX-I correlates significantly with BMD measured at the trochanter but not at the femoral neck (r ¼ -0.31, p < 0.05) [47]. Prediction of treatment response Visual Analogue Scale (VAS) pain, VAS general health, BASDAI and inflammatory parameters and composite response criteria are used to evaluate treatment effect in AS. ASAS developed standard criteria for improvement in patients with AS treated with non-steroidal anti-inflammatory drugs and TNF blockade [48]. Three levels of response are defined and validated: ASAS 20, ASAS 40 improvement criteria and ASAS partial remission criteria. An additional response criterion, the ASAS 5/6 improvement criterion, was also developed that also includes CRP and spinal mobility. Since current clinical assessment of therapeutic response relies heavily on subjective self-evaluation, biomarkers with high sensitivity and specificity for treatment response are highly desirable. Short-term randomised controlled trials indicate that treatment of AS with biological agents (infliximab and etanercept) is safe and efficacious. However, a subset of patients fails to respond or does not sustain initial responses. It is thus imperative to define variability in clinical response in order to guide the optimal use of these agents. Serum biomarkers that are of current interest for response to therapy are summarised in Table 2. Prediction of treatment effect on disease activity ESR and CRP are poor predictors due to low sensitivity and specificity. Only 40–50% of all AS patients have elevated inflammatory parameters. However, in patients usually treated with TNF blockade, the percentage is higher and ESR and CRP may be useful biomarkers. In all randomised controlled phase 2 and 3 trials for etanercept, infliximab and adalimumab, a significant decrease was documented in CRP levels after treatment as compared to the placebo group [49–53]. A combination of ESR, CRP and platelet count is able to distinguish responders from non-responders within 2 weeks of treatment with TNF blockade with a sensitivity of 81.3% and specificity of 72.7% [54]. Interleukin1-a also discriminated between responders and non-responders with a sensitivity of 84.9% but a lower specificity of 53.8% [54]. Early reductions in IL-6 were significantly associated with improvement in disease activity and spinal inflammation detected by MRI upon treatment with infliximab [37]. MMP-3 decreases significantly upon anti-TNF treatment together with other conventional variables of disease activity such as BASDAI, CRP and ESR, but there is no correlation between changes in ESR, CRP, BASDAI and MMP-3 [33,55]. Moreover, MMP-3 is not able to distinguish responders from nonresponders [54]. Detailed sequential analysis of combinations of cytokines may also be of interest. Stone et al. evaluated serum levels of different cytokines (interleukin (IL) 1, TNFa, interferon gamma (IFNg), transforming growth factor b (TGFb) and IL10) in a cohort of AS patients treated with infliximab to determine predictors of response. Sequential analysis of cytokine profiles was not very informative, but baseline CRP and TNF-a may be useful markers for clinical response in AS patients treated with TNF blockade [56].

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Prediction of treatment effect on damage Prevention of damage is another important outcome of therapy. Slow radiographic progression of the disease and the relatively small fraction of patients evolving over a time period of 2–3 years makes radiographic evaluation less sensitive for damage evaluation. The major predictor of progression is existing radiographic damage. Serum levels of BALP and osteocalcin significantly increase after 12 weeks of treatment with etanercept (p < 0.05) possibly reflecting bone formation [55]. Patients treated with etanercept showed a significant reduction in levels of C2C (p ¼ 0.005) and a significant increase in the 846 epitope (p ¼ 0.01) compared to controls but these changes in C2C were not evident in an infliximab observational cohort [57,58]. Changes in cartilage turnover markers, such as C2C, correlated significantly with changes in ESR (r ¼ 0.51, p ¼ 0.04) and CRP (r ¼ 0.48, p ¼ 0.048) reflecting a possible correlation between inflammatory activity and damage. It is clear that TNF blocking agents may have a structural effect indicated by the changes in different serum biomarkers for cartilage and bone turnover but an effect on radiographic damage remains to be demonstrated. Biomarkers of treatment response that are of current interest are listed in Table 3. Problems A number of problems are encountered in the development of biomarkers for AS and other chronic arthritides. Most identified candidates are not specific to AS but rather to the structures involved such as bone, cartilage or tendon. This implies that other causes of damage to these structures also influence the levels of these biomarkers and may interfere with the interpretation of these assays. The holy grail of disease-specific biomarkers for a particular process such as inflammation or damage may be unrealistic since this implies that the underlying processes of inflammation and damage are only specific to AS. To date, there are quantitative rather than qualitative differences in the inflammatory and damage processes between different chronic arthritides. Second, there is a conceptual problem due to the different disease processes in AS. Inflammation is a dynamic process with variation in intensity and is bidirectional and can improve or worsen over time. At some point in time, it can also disappear. A biomarker assessment that reflects the inflammatory process which, in turn, reflects disease activity constitutes a point estimate and must correlate highly with the underlying process at the same time and be very sensitive to change. The biomarker should not reflect previous or future inflammation. Actual candidate biomarkers for the inflammatory process in AS partly fulfil these conditions. CRP and ESR have problems of content and construct validity. Only

Table 3 Serological erum biomarkers of current interest for evaluation of response to therapy. Biomarkers

Treatment

Statistical test

P value

Comparator

References

Matrix metalloproteinases 3

Infliximab Infliximab

Pearson correlation Wilcoxon rank-sum test, 2-tailed Wilcoxona Spearman rank correlation Wilcoxon Spearman rank Wilcoxona Wilcoxona Paired t-test Spearman rank correlation Wilcoxon

R ¼ 0.80, p < 0.01 P ¼ 0.04

BASDAI BASDAI

[58] [57]

P ¼ 0.013 R ¼ 0.446, p ¼ 0.022 P ¼ 0.022 R ¼ 0.454, p ¼ 0.009 P ¼ 0.005 P ¼ 0.01 P ¼ 0.035 R ¼ 0.689, p < 0.001 P < 0.001

BASDAI CRP CRP CRP CRP CRP CRP CRP Baseline vs wk 12 CRP Baseline vs wk 52

[33] [55] [39] [39] [57] [57] [55] [38] [39]

C2C neoepitope Aggrecan 846 epitope Amyloid A Interleukin 6 VEGF dVEGF BALP a

Infliximab Etanercept Adalimumab Adalimumab Etanercept Etanercept Infliximab Adalimumab Adalimumab Adalimumab

Spearman rank correlation

Wilcoxon matched pairs signed rank test.

R ¼ 0.498, p ¼ 0.004 P < 0.001

[39] [39]

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about 40–50% of AS patients with clinical inflammation have increased CRP or ESR. It raises the question if these parameters truly reflect the inflammatory process in AS. Damage behaves differently and is a cumulative process. Point estimates will not truly reflect the degree of damage in these patients. New approaches with sequential analysis of markers and data analysis over time may better reflect the accumulated damage. Conclusion The development of biomarkers has only reached the stage of the discovery process. A number of candidates have been identified but not yet validated. From the above survey of biomarkers in AS it is clear that the field is still in its earliest phase of development. There is a lack of agreement regarding assay methodology and approach to statistical analysis. There is a necessity for collaborative research in setting up large longitudinal cohorts with standardised data capture and collection of biological material. Better insight in the underlying pathophysiological mechanism of inflammation and tissue response will increase our understanding and form the basis for the discovery of new biomarkers. This discovery will also rely on the introduction of new screening techniques including epigenetic control, microRNA arrays and analysis of signalling pathways. Furthermore, it is likely that combinations of different biomarkers rather than a single biomarker will be important in predicting the evolution of disease and treatment outcome.

Practice points 1. Biomarkers can be useful tools for evaluating different aspects of the disease: diagnosis, damage, disease progression and treatment response. 2. Currently, there are no clinically useful biomarkers available for AS.

Research agenda 1. Collaborative research that compiles large longitudinal cohorts of AS patients with standardised data capture and collection of biological samples. 2. Development of optimal statistical tools for the analysis of longitudinal data. 3. Analysis of combinations of biomarkers for disease evaluation. 4. Development of biomarkers for treatment response and disease progression.

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