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 Table of Contents  
Year : 2017  |  Volume : 12  |  Issue : 6  |  Page : 149-155

Biomarkers in scleroderma: Current status

1 Division of Rheumatology, Perelman School of Medicine, University of Philadelphia, Philadelphia, PA, USA
2 Department of Medicine, BJ Medical College, Pune, Maharashtra, India
3 Department of Rheumatology and Clinical Immunology, Starcare Hospital, Kozhikode, Kerala, India

Date of Web Publication23-Nov-2017

Correspondence Address:
Sukesh Edavalath
Department of Rheumatology and Clinical Immunology, Starcare Hospital, Kozhikode - 673 017, Kerala
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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/0973-3698.219087

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Scleroderma is an autoimmune disease characterized by indolent obliterative vasculopathy and widespread fibrosis. The two main morphological manifestations of the disease overlap and may make it difficult to separate activity from damage. Many patients, especially those with the limited subset of the disease, have an indolent course without clear-cut inflammatory manifestations. There is a felt need for validated biomarkers, which can differentiate activity from damage, and yet be sensitive to change with therapy. Multiplex arrays of biomarkers have ushered an era of targeted or personalized medicine based on phenotypic characteristics in an individual.

Keywords: Biomarkers, fibrosis, systemic sclerosis

How to cite this article:
Gupta L, Phatak S, Edavalath S. Biomarkers in scleroderma: Current status. Indian J Rheumatol 2017;12, Suppl S1:149-55

How to cite this URL:
Gupta L, Phatak S, Edavalath S. Biomarkers in scleroderma: Current status. Indian J Rheumatol [serial online] 2017 [cited 2022 Jun 27];12, Suppl S1:149-55. Available from:

  Introduction Top

Scleroderma is an autoimmune disease characterized by obliterative vasculopathy and irreversible, widespread fibrosis. It tends to affect all organs albeit at different pace in different individuals. The clinical course varies from asymptomatic to aggressive life-threatening multisystem disease. A clinician needs to carefully characterize each patient to understand the specific manifestations and level of disease activity to decide appropriate treatment. This is particularly important in managing a patient with scleroderma because there is no treatment that has been proven to modify the overall disease course. It is believed that organ-specific therapy initiated early, before irreversible damage occurs, may improve quality of life and survival.

A biomarker is a measurable factor whose value can be used to assess and monitor a normal or abnormal biological process. A biomarker could be a phenotypic character, measurement of particular proteins, gene products, identification of a cell or a group of them or an imaging tool. Biomarker research is an ongoing process in the field of medicine.[1],[2] The research has come of age from actionable biomarkers, based on which a clinical decision can be taken to mechanistic biomarkers, which not only delineate a pathogenic pathway but also help target the same for therapeutic benefits. This brings research from bench to bedside. The earlier era was dedicated to the study of single biomarkers, which determined the behavior of a cohort. Of late, there is increasing interest in analysis of arrays of biomarkers. The components of these could be homogenous (a panel of proteins or gene array) or heterogenous (a protein clubbed with gene and an imaging tool). These arrays seek to define all or some of the pathogenic pathways operative in a single individual. These are often put to use in choice of drugs expected to work; they may also define at the outset which patient would be refractory to therapy.[1]

The prevalence of systemic sclerosis (SSc) varies from 2 to 20 per lakh of the population in different parts of the world.[3] Epidemiologic data from India are scarce, the estimated prevalence being 12 per lakh general population.[4] Biomarkers are a valuable guide to estimate the risk of progression to SSc in patients with Raynaud's phenomenon.

A patient can be classified as having scleroderma in the presence of skin thickening proximal to the metacarpophalangeal joints and pulmonary fibrosis with or without digital pitting scars. The 1980 American Rheumatology Association criteria consisted of signs present late in the disease course. Hence, many cases of early scleroderma and 20% of limited variety (lcSSc) were missed. Demonstration of a decrease in nail fold capillaries to <8.4 per mm 2 on capillaroscopy could identify patients with >10% risk of progression to the disease over 5 years. The presence of easily recognizable giant capillaries further increased the risk to >50%.[5] The addition of capillary telangiectasias, nail fold abnormalities, and autoantibodies with pulmonary arterial hypertension (PAH) in a weighted scoring system has led to improved classification criteria for segregating patients into homogenous groups for research.[6]

Autoantibodies are the strongest predictors of phenotype in an individual with SSc [Table 1]. In a data set of 288 patients with Raynaud's phenomenon, the presence of antinuclear antibodies conferred a significant risk of evolving to scleroderma. Anti-topoisomerase (ATA) or anti-centromere antibody (ACA) positivity conferred a 2-fold additional risk. Other antibodies increasing risk include anti-RNA polymerase III, anti-Th/To, and anti-Pm-Scl.[7]
Table 1: Association of autoantibodies in systemic sclerosis with disease manifestations

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Genome-wide association studies (GWASs) have identified additional risk loci for SSc. Most of these are nonspecific and have been identified in lupus, spondyloarthropathies, rheumatoid arthritis, juvenile idiopathic arthritis, and Sjogren's syndrome as well. Apart from HLA class 2 loci, which confer the highest risk (RR 2.5), polymorphisms in others have minor contribution. The identification of interferon regulatory factor polymorphisms led to the discovery of activated interferon pathways in scleroderma.[8] Tumor necrosis factor-alpha-induced protein 3 (also called A20) polymorphisms indicate the involvement of NF-κβ in the pathogenesis [Table 2].[9],[10]
Table 2: Salient genes associated with systemic sclerosis in genome-wide association study

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A meta-analysis of four GWASs has revealed that polymorphisms in certain HLA loci associate with increased risk of diffuse cutaneous phenotype (dcSSc) and ATA positivity or lcSSc and ACA positivity. These data emphasize the differential genetic associations of subphenotypes of SSc.[8]

In a clinic, there is a felt need for biomarkers that can reliably reflect disease activity. The most common organ manifestations are skin thickening, pulmonary fibrosis, PAH, and digital ulcers (DUs). In most other connective tissue diseases (CTDs), activity is reflected by edema and damage by fibrosis. Measurement of activity in SSc is marred by the fact that fibrosis is a manifestation of activity in this disease. Therefore, there is dearth of biomarkers that can be dynamic and reflect activity while being dissociated from damage. Hence, while most studies have focused on biomarkers of skin thickening and show moderate correlation, there is a lack of markers correlating with activity on follow-up. Most studies are limited by small sample size, and analysis is difficult in the face of marked clinical heterogeneity (especially before the advent of the new classification criteria).

  Biomarkers of Skin Fibrosis Top

The gold standard for skin thickness monitoring is the modified Rodnan skin score (mRSS). Rodnan et al. proposed the original score in 1979 by estimating the exact weight of collagen in patients' skin biopsies. The original score comprised of four grades and a total of 22 sites. Three modifications later, the current mRSS came into being. It comprises three grades at 17 sites measured by the thickness of skin palpated by a physician.[11],[12]

This simple measure is a convenient tool in an outpatient clinic, useful to rapidly stratify patients with more extensive skin fibrosis and likely to develop complications. It is the single best clinical predictor of internal organ fibrosis. The skin thickness in an individual with SSc peaks within 1 year but starts declining thereafter; hence, this can confound long-term studies designed to measure clinical improvement in skin scores with therapeutic drugs. Other demerits include subjectivity (interobserver variability - 25%, intraobserver variability 5%–12%), poor sensitivity in picking up minor changes, and heterogeneity within single areas. It is difficult to perform in obese, edematous, and wasted individuals.[11],[12]

Apart from mRSS, various imaging tools, cytokines, and gene expression arrays have been studied in the quest for an ideal biomarker. Ultrasound (US) has been tried to monitor skin thickness. Techniques such as durometry, cutometry, and Plicametry have been tried over the years without much avail.

Small studies have used US Doppler and shear wave elastography using fast computational software to reflect tissue elasticity. This employs repetitive light manual compression and relaxation of the US probe against the tissue. The technique uses the conventional Doppler probes without additional hardware. It has been measured only at limited sites such as the forearm, and bone interference as well as small surface led to variable color changes in the fingers; rendering it unacceptable as a valid biomarker [Table 3]. Magnetic resonance imaging 1.5 T supercoils have also been tried, but the cost and time required are limiting factors.[13],[14]
Table 3: Biomarkers of skin fibrosis in systemic sclerosis: Current and prospective use

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Optical coherence tomography is an infrared-based imaging tool used in ophthalmology. It produces images with good resolution up to 4-μ thickness and thus often called a “virtual biopsy.” At an optical density of 300, the technique can differentiate healthy skin from normal skin areas of diseased individuals. These images can be captured after 10 s of evaluation at each site and can be saved for future analysis. Technical requirements are minimal, making it a convenient tool for skin imaging. Long-term follow-up studies are needed in larger cohorts for validation before clinical use.[15],[16]

Gene expression profiling from skin biopsies is another interesting approach to identify biomarkers for skin fibrosis. Although an invasive method, it allows a more direct insight into the ongoing fibrotic process. In 2008, Milano et al. reported a 177-gene signature that was associated with severity of skin disease in diffuse subsets of SSc.[17]

  Biomarkers of Lung Fibrosis Top

Symptomatic interstitial lung disease (ILD) is seen in 15% of SSc cases; imaging and autopsy studies have shown much higher prevalence. Pulmonary function tests (PFTs) are used most often for screening and response to therapy. The presence of cough and poor functional reserves impairs proper PFT performance in many advanced cases. The successful performance of a patient at the test depends largely on the presence of trained operating personnel. Computerized tomography (CT) scans are often used at baseline for diagnosis. By the time the patient is diagnosed, they are likely to show either honeycombing or fibrosis. Once developed, trials of standard therapy in ILD demonstrate stabilization at best; reversal is rare. Recognition of a subclinical phase may help in preventing the development of ILD and biomarkers achieving this are likely to produce clinical benefit.

Numerous biomarkers have been tested in SSc ILD [Table 4]; most come from studies in idiopathic pulmonary fibrosis (IPF) patients. Among these, Krebs Von Den Lungen 6 (KL-6) and surfactant protein D (SP-D) are in clinical use in Japan.[18]
Table 4: Biomarkers of lung fibrosis in systemic sclerosis-interstitial lung disease

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KL-6 has been studied in large cohorts of SSc patients and appears valuable in diagnosing ILD before clinical manifestations appear. It is a mucin-like, high molecular weight glycoprotein expressed on the surface membrane of alveolar and bronchial epithelial cells. Once shed into the alveolar lumen, it is chemotactic and promotes fibroblast proliferation. The leaky alveolar walls of the SSc patients permit spill over into the circulation, from where it can be detected using chemiluminescent assay in the serum within 30 min. KL-6 positivity is seen in most lung diseases; however, it appears sensitive for ILD (80%–100% cases).[18] It cannot differentiate IPF from CTD-ILD. Levels are higher in patients with more extensive fibrosis, and data on correlation with ground glass opacities (GGOs) are conflicting. It differentiates patients without pulmonary fibrosis well from those having ILD at a cutoff of 465 U/ml. SP D levels are also higher in those with ILD, but significant overlap can be seen in cases with and without pulmonary fibrosis; it seems to be inferior to KL-6 in this regard.[19] Both the biomarkers have shown moderate correlation with high-resolution computed tomography (HRCT) fibrosis scores. The problem often arises when ILD patient presents with acute exacerbation. Pneumocystis infection can be seen in these cases, wherein both the biomarkers can be elevated. Serum lactate dehydrogenase and β D glucan levels can be used to differentiate in such cases as they reflect the fungal infection load.[20]

  Biomarkers of Pulmonary Arterial Hypertension Top

PAH is seen in 15% of SSc patients. Right heart catheterization (RHC) is an invasive yet ideal method to quantify pulmonary arterial pressures. In routine practice, transthoracic echocardiographic measurement of tricuspid regurgitation and associated right ventricular systolic pressures is used as a surrogate marker for PAH. Disproportionate reduction in diffusion lung capacity for carbon monoxide (DLCO) with respect to lung volumes on PFT and exercise desaturation should lead to suspicion of underlying PAH in the clinic. These screening tools see only the tip of the iceberg, while a significant number of cases are in the preclinical phase with exertional elevation of precapillary pressures. Earlier diagnosis may facilitate targeting the pathogenic pathways before irreversible changes set in. This has led to novel screening tools and biomarkers for PAH [Table 5]. Of these, N terminal pro-brain natriuretic peptide (NT-ProBNP) is validated for clinical use in primary PAH.
Table 5: Biomarkers of pulmonary arterial hypertension in systemic sclerosis

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ProBNP is released by myocardial tissue upon strain. The levels of circulating ProBNP increase in proportion to right ventricular strain, and cleavage by the enzyme corin leads to production of inactive fragment NT-ProBNP (T1/2 2 h) and the active molecule BNP. NT-ProBNP levels could differentiate patients with established PAH (n = 15) from those at risk by RHC pressures 20–24 mmHg and those with isolated pulmonary fibrosis without PAH (n = 19). Based on this, a composite model was proposed wherein patients having DLCO <70.3% with forced vital capacity (FVC)/DLCO ≥1.82 or NT-ProBNP ≥209.8 pg/ml could be deemed high risks for PAH based on these screening measures, warranting echocardiography or catheterization.[21]

Conventionally, DLCO values are used to predict diffusion capacity of the lung, which consists of membrane conductance for CO (DmCO), which reflects the diffusion properties of the alveolar capillary membrane, and CO loading on hemoglobin (Hb), which is the product of the CO-Hb chemical reaction rate (θCO), and the mass of Hb in the alveolar capillary blood volume (Vcap). In PAH, early fall in Vcap is seen, while DmCO decreases due to fibrosis in ILD. It has been proposed by a French group that using the equation, 1/DLCO = 1/DmCO + 1/θCO × Vcap, Vcap levels <19 mL predict PAH accurately. When alveolar volumes are also included, Vcap/alveolar volume (VA) ratio was significantly lower in the isolated PAH group with higher area under curve than DLCO.[22],[23]

PAH and DUs are dominant manifestations of scleroderma vasculopathy. Imbalance between pro-angiogenic factors (e.g., fibroblast growth factor 2 [FGF 2]) and anti-angiogenic factors (pentraxin 3 [PXT3]) has been seen in these cases. In a cohort of 148 SSc patients, a PXT3:FGF2 ratio >344 could predict the development of new DU.[24]

  Composite Biomarkers of Fibrosis Top

Since fibrosis in SSc is not limited to the skin and involves internal organs as well; organ-specific fibrosis scores have been evaluated. The enhanced liver fibrosis (ELF) score was one such measure, which correlated moderately with fibrosis at any site. The ELF algorithm incorporates serum levels of pro-collagen III amino-terminal pro-peptide, tissue inhibitors of metalloproteinase-1, and hyaluronic acid. However, clinical utility is limited to a good negative predictive value in cases with low scores. These patients are likely to have vasculopathic phenotype without organ fibrosis.[25]

Another novel composite biomarker for SSc is the IFN-inducible chemokine score. It is known that transforming growth factor (TGF)-β production leads to a negative feedback loop via type 1 IFNs to regulate its production. Alterations in this regulatory mechanism may predispose to SSc. The presence of polymorphisms in IFN pathway genes in SSc patients lends further support to this theory. A recent study established the presence of IFN gene signature in early SSc as well as SSc sine scleroderma.[26] Based on these observations, in a pilot study of 266 SSc patients, plasma levels of IFN-inducible chemokines, IFN-γ-inducible protein 10 and IFN-inducible T-cell, a chemoattractant, were measured and used to calculate the IFN-inducible chemokine score. This score correlated with the IFN gene expression signature in SSc and was higher in SSc patients compared with controls. The correlation between IFN activity in monocytes, B-cell activating factor (BAFF) mRNA expression, and Type III pro-collagen N-terminal pro-peptide (PIIINP) serum levels was demonstrated. This led to the hypothesis that IFN-mediated TLR stimulation on fibroblasts may promote collagen production.[27] The role of BAFF in this model remains to be tested. This research has had implications at the bedside: A phase one study of sifalimumab (human anti-IFN-α monoclonal antibody) in SSc is ongoing.

Among the soluble factors, proteome-wide analysis in five large scleroderma cohorts has demonstrated that the chemokine CXCL4 is the only chemokine that predicts development of scleroderma. It is the predominant cytokine produced by plasmacytoid dendritic cells and levels in SSc were much higher than in other CTDs. It correlated with skin, lung fibrosis, and PAH. Elevated levels (>10 ng/ml) predicted earlier lung fibrosis (>30% decrease of FVC and HRCT), confirmed by prospective studies. In vivo, the chemokine could induce transcriptome changes of SSc.[28]

Interleukin (IL)-6 has been proposed as a potential marker to predict the development of the disease. High serum IL-6 expression early in dcSSc was associated with more severe skin involvement at 3 years and worse long-term survival. IL-6 is an independent predictor of rapid ILD progression.[29]

A group from Boston devised a four-gene biomarker panel consisting of two TGF β and two IFN pathway genes to reflect disease activity in the skin. Cartilage oligomeric matrix protein, thrombospondin-1, interferon-induced 44, and sialoadhesin mRNA levels were used to develop this score, which could longitudinally detect changes corresponding to those in the mRSS.[30] Research based on microRNAs is on the rise, with a wide variety being reported in SSc as well (miRNAs targeting SMAD7, SMAD 3, etc.). Most of these are nonspecific, and the cause-effect relationship with the disease is not clear.[31]

  Biomarkers as Proof of Concept Top

The demonstration of elevated TGF β and IFN signature genes has offered proof of concept of these cytokines in the pathogenesis of SSc. A recent study demonstrated that fresolimumab (monoclonal antibody to TGF β) could downregulate collagen-related genes. In addition, administration in mice led to altered transcriptomic signatures in fibrosis-related genes and these changes correlated with mRSS.[32]

  Biomarkers in Therapy Top

In an era of “personalized medicine,” gene expression profiles are increasingly being used to elucidate the pathways operative in an individual to predict benefit of individual therapies.

Chung et al. suggested gene expression profiles to segregate patients at baseline into responders and nonresponders to imatinib based on activation of the PDGFR and Abl pathways.[33] For the management of ILD, quantitative ILD (Q-ILD) scoring algorithms have been devised on HRCT scanning to demonstrate early reduction in GGO and fibrosis. The Q-ILD score is an amalgamation of GGO, fibrosis score, and quantitative honeycombing score. The overall scores increased over a year-long follow-up in cases without treatment, with a decline in GGO and increase in fibrosis.[34] This confirms the natural history of the disease and further establishes that GGO could indeed be a surrogate for active alveolitis, which could be reversed with cyclophosphamide.

For response in PAH, NT Pro-BNP is widely used in trials on primary PAH. Recently, a multicenter, open-label trial on 24 treatment-naive patients with SSc-PAH receiving ambrisentan and tadalafil demonstrated serial reduction in NT Pro-BNP commensurate with reduction in pressures on RHC and echocardiography.[35]

Biomarkers of disease activity can also be used as therapeutic targets offering proof of concept in pathogenesis. Recently, miRNA inhibitors have shown decline in skin thickening in bleomycin-induced fibrosis in mice.[36]

Similarly, the presence of anti-endothelin receptor type A (ETAR) and anti-angiotensin receptor type-1 (AT1R) antibodies have shown to predict reduced endothelial calcium release in response to valsartan (AT 2 receptor antagonist) and sitaxentan (endothelin receptor antagonist). Thus, these autoantibodies may predict which patients these drugs may be used on. However, this requires validation in human studies.[37]

  Biomarkers Predicting Long-Term Outcome Top

Male gender and diffuse SSc phenotype are the strongest predictors of poor outcome. Higher age contributes to disease burden and cardiovascular mortality. The presence of ATA correlates with dcSSc phenotype and ACA with lcSSc with poor and good outcome, respectively.[38] In a patient with lcSSc, the presence of anti-Th/To antibodies confers higher risk of sudden death from embolism.[39]

The mRSS on follow-up can be used to predict the outcome in SSc. In the latent linear trajectory model, patients can be divided into high baseline improvers, high baseline nonimprovers, or low baseline improvers. Those with high baseline and nonimprovement suffer lowest cumulative survival.[40]

The presence of high KL-6 levels >15,000 U/ml (three times upper limit of normal) and ETAR/AT1R antibodies is predictive of worse outcome.[37] Similarly, the four-gene biomarker panel by Farina et al. can predict the trajectory of mRSS over time by changes, which precede those in the skin.[30]

The rarity of scleroderma along with marked clinical heterogeneity makes the study of biomarkers in large cohorts difficult. Before the advent of the new classification criteria, most studies consisted of patients with varied clinical manifestations; categorization into uniform groups was not seen. The absence of a validated disease activity measure makes comparison difficult as a gold standard is lacking. There is a felt need for a biomarker of disease activity dissociate from damage. Newer insights into the pathogenic pathways bring hope for better biomarkers.

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  References Top

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  [Table 1], [Table 2], [Table 3], [Table 4], [Table 5]


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