Changes in novel biomarkers of disease activity in juvenile and adult dermatomyositis are sensitive biomarkers of disease course




Muscle enzyme levels are insensitive markers of disease activity in juvenile and adult dermatomyositis (DM), especially during the active treatment phase. To improve our ability to monitor DM disease activity longitudinally, especially in the presence of immunomodulating agents, we prospectively evaluated whether interferon (IFN)–dependent peripheral blood gene and chemokine signatures could serve as sensitive and responsive biomarkers for change in disease activity in adult and juvenile DM.


Peripheral blood and clinical data were collected from 51 patients with juvenile or adult DM prospectively over 2 study visits. We performed disease activity measurements and calculated whole-blood type I IFN gene and chemokine scores. We also measured serum levels of other proinflammatory cytokines, including interleukin-6 (IL-6).


Changes in juvenile and adult DM global disease activity correlated positively and significantly with changes in the type I IFN gene score before adjustment for medication use (r = 0.33, P = 0.023) and with changes in the IFN chemokine score before and after adjustment for medication use (r = 0.53, P < 0.001 and r = 0.50, P < 0.001, respectively). Changes in muscle and extramuscular visual analog scale (VAS) scores correlated positively with changes in IFN gene and chemokine scores (P = 0.002, P < 0.001, P = 0.095, P < 0.001). Serum levels of IL-6, IL-8, and tumor necrosis factor α (TNFα) correlated positively with changes in global, muscle, and extramuscular VAS scores (P < 0.05).


Our findings suggest that changes in type I IFN gene and chemokine scores as well as in levels of IL-6, IL-8, and TNFα may serve as sensitive and responsive longitudinal biomarkers of change in disease activity in juvenile and adult DM, even in the presence of immunomodulating agents.

Juvenile and adult dermatomyositis (DM) are autoimmune disorders characterized by proximal muscle weakness, muscle inflammation, and a characteristic skin rash. Despite advances in our understanding of the pathogenesis of DM, disease activity monitoring is heavily dependent upon the physician's clinical assessment. Few reliable indicators of disease prognosis, disease activity, or response to treatment have been identified. Traditionally, manual muscle strength testing (MMT) and serum levels of muscle enzymes have been used as markers of disease activity; however, muscle strength may be impaired by disease damage (chronic scarring, fibrosis, or atrophy) rather than ongoing disease activity (1), while muscle enzymes are insensitive markers of disease activity (2).

Accumulating data from our group and others suggest that cells from the muscle tissue and blood of patients with DM carry distinct immune “fingerprints” (3). These studies have shown up-regulation of genes related to type I interferon (IFNα/β) in both muscle tissue and peripheral blood of DM patients with active disease (4). Using a subset of type I IFN–up-regulated genes and chemokines, we developed scores that were strongly correlated with DM disease activity based on cross-sectional study (5). In aggregate, these data suggest that type I IFN–inducible genes and chemokines may be sensitive measures of disease activity in DM. In this study we tested the hypothesis that changes in prominent type I IFN “signatures,” reflecting both transcript up-regulation and elevated serum proteins, will reflect changes in DM disease activity when studied longitudinally, independent of use of immunosuppressive agents.


Study patients.

The study protocol was approved by the Human Subjects Institutional Review Boards at the University of Minnesota and the Mayo Clinic, and informed consent was obtained from each participant. Study participants were recruited among attendees at myositis clinics at the Mayo Clinic between 2005 and 2010; all patients met Bohan and Peter criteria for probable or definite juvenile DM (n = 21) or adult DM (n = 30) (6, 7). Juvenile DM patients were age <18 years at enrollment and age <16 years at diagnosis. There were no exclusions based on disease activity or medication use. We excluded individuals with overlapping connective tissue disease, including systemic lupus erythematosus (SLE), scleroderma, Sjögren's syndrome, or mixed connective tissue disease. All study participants had their disease activity assessed and blood samples collected at the time of enrollment and at a followup visit. Thirty-four of the 51 patients (67%) had been included in a previously reported cross-sectional study (5).

Assessment of disease activity.

Disease activity was evaluated by rheumatologists (AMR,SRY,SA,FE) who were blinded to IFN gene signature and IFN chemokine scores or cytokine/chemokine levels, using established disease activity tools for use in myositis clinical trials described by the International Myositis Assessment and Clinical Studies group (8, 9). Muscle strength was assessed using MMT of 8 muscle groups (10). The Childhood Myositis Assessment Scale (CMAS) was performed on all juvenile DM patients age >4 years (11). Serum levels of muscle enzymes were measured, including creatine kinase (CK), aldolase, aspartate aminotransferase (AST), alanine aminotransferase (ALT), and lactate dehydrogenase (LDH). Disease activity measures included the Myositis Disease Activity portion of the Myositis Disease Activity Assessment Tool (11), which comprises assessment of the physician's measures of global disease activity and organ-specific involvement, including using separate 100-mm visual analog scales (VAS) to gauge the physician's evaluation of disease activity in several discrete domains including muscle disease (muscle VAS score). Involvement of all nonmuscle organ systems (cardiac, pulmonary, gastrointestinal, skeletal, and cutaneous) was also evaluated using a composite score (composite extramuscular VAS score). Physician's and patient's global assessments of disease activity were rated using VAS scores (8). Antinuclear antibodies, anti–Jo-1 antibodies, and other myositis-specific autoantibodies as well as erythrocyte sedimentation rate and C-reactive protein level were all assayed in the clinical laboratory.

Gene expression measurements.

Whole blood was drawn into PAXgene tubes (Qiagen/Becton Dickinson). Total RNA was isolated according to the manufacturer's protocol with on-column DNase treatment. RNA yield and integrity were assessed using an Agilent Lab-on-a-Chip Bioanalyzer. The whole-blood type I IFN gene expression signature was defined by expression levels of 3 IFN-regulated genes (IFIT1, G1P2, and IRF7) as measured by TaqMan real-time quantitative reverse transcription–polymerase chain reaction using the ABI Prism 7900HT Sequence Detection System (Applied Biosystems). Relative quantification of expression levels was performed following the manufacturer's guidelines with normalization against GAPDH and comparison against a calibrator sample (PAXgene whole-blood RNA from a healthy control subject). Calculation of IFN gene scores was performed as previously reported (5). Briefly, expression levels were truncated at the 95th percentile value for each gene to reduce the effects of outliers and then normalized so that the maximum value for each gene was 1.0. The normalized expression values for the 3 genes were summed for each patient, and the sums were normalized to a 100-point scale for visualization (12).

Serum protein measurements.

Serum was isolated from blood drawn into serum-separator Vacutainer tubes (Becton Dickinson). A protease inhibitor (aprotinin; 1 μg/ml) was added to each sample, and aliquots were immediately frozen at –80°C. Multiplexed sandwich immunoassays (Meso Scale Discovery) were used to quantify serum levels of IFN-regulated chemokines (13) and other proinflammatory cytokines (monokine induced by IFNγ [CXCL9], macrophage inflammatory protein 1α [MIP-1α/CCL3], MIP-1β/CCL4, tumor necrosis factor α [TNFα], TNF receptor type I, interleukin-10 [IL-10], IL-6, and IL-8). Samples were run in duplicate, and calibrated recombinant proteins were used to generate standard curves. A summary chemokine score based on serum levels of IFN-inducible T cell α chemoattractant (I-TAC), IFNγ-inducible 10-kd protein (IP-10), and monocyte chemotactic protein 1 (MCP-1) was calculated for each participant in a manner similar to the calculation of the type I IFN gene score (12).

Statistical analysis.

Descriptive statistics were used to summarize the characteristics of the study patients. Comparisons between age groups were performed using chi-square and rank sum tests. Changes in disease activity between visits 1 and 2 were compared using paired t-tests. Our primary hypothesis was that the previously defined IFN chemokine and gene scores would be useful for assessing disease activity longitudinally, and the remainder of chemokines/cytokines in our panel would not be correlated with changes in disease activity. Given this focused hypothesis, adjustment for multiple comparisons was not necessary and was not performed.

Spearman's rank correlation coefficient was calculated to assess the relationship between biomarkers and continuous variables (disease activity scores, blood biochemical measurements). Spearman partial correlation methods were used to adjust the correlation coefficients for medication use at both study visits. Changes were calculated as visit 2 minus visit 1, so changes <0 indicated improvement and changes >0 indicated worsening. Similarly, for cytokine values, changes <0 indicated decreases in cytokine levels between visits 1 and 2 and changes >0 indicated increases in cytokine levels. Hence, positive correlations indicated that biomarker levels and disease activity were either both decreasing from visit 1 to visit 2 or both increasing from visit 1 to visit 2. Significance levels were set at P < 0.05 for all tests. While we do not believe adjustment for multiple comparisons is warranted in this study, with 22 chemokines/cytokines and 2 comparisons each (visit 1 and change from visit 1 to visit 2) for each disease activity measure, the Bonferroni adjustment would consider P < 0.05/44 = 0.0011 to be statistically significant. Note that due to the correlated nature of chemokines and cytokines, this adjustment would be overly conservative.

Responsiveness to change was assessed by correlating the change in biomarker levels with the change in disease activity measures. Another way to assess sensitivity to change is to determine whether the measure detects meaningful change when it occurs and stays stable when no change has occurred. To assess this, we compared the change in biomarker levels between patients who improved (decrease in disease activity of ≥10 units), remained stable (increase or decrease in disease activity of <10 units), or worsened (increase in disease activity of ≥10 units) using rank sum tests.

Finally, we performed exploratory correlations between cytokine values and medication use. Multiple definitions of medication use were utilized to ensure comprehensive assessment of medication effects on cytokine values. For these analyses, medications were defined as yes/no at visit 1, visit 2, and ever (either visit). In addition, each medication was assessed individually, and due to small numbers for some medications, we also assessed combinations of medications, such as any disease-modifying antirheumatic medication.


The study population consisted of 51 participants with juvenile DM (n = 21) or adult DM (n = 30) who had 2 study visits. The mean age of juvenile DM patients was 8 years (range 2 to 17 years), and that of adult DM patients was 45.5 years (range 18 to 77 years); 35 of the 51 patients (69%) were female (Table 1). The majority of patients had short disease duration at visit 1 (median <3 months). Of the 51 participants, 18 were not receiving immunomodulatory medications at visit 1 (16 with untreated new-onset disease and 2 who were not receiving medications because their disease was in remission). Medication use at visit 1 for the remaining 33 participants included azathioprine (AZA) (n = 4), methotrexate (MTX) (n = 20), mycophenolate mofetil (MMF) (n = 3), hydroxychloroquine (n = 6), and corticosteroids (n = 18). Only children with juvenile DM underwent CMAS evaluations at visits 1 and 2. The CMAS score was abnormal (<52, range 32 to 51) in 15 of 21 juvenile DM patients despite near-normal MMT assessments.

Table 1. Characteristics of the 51 patients with myositis at visit 1*
VariableAge <18 years (n = 21)Age ≥18 years (n = 30)Total (n = 51)P
  • *

    MMT8 = 8 muscle group manual muscle strength testing; VAS = visual analog scale; ALT = alanine aminotransferase; AST = aspartate aminotransferase; CK = creatine kinase; LDH = lactate dehydrogenase; anti-RBP = anti–RNA binding protein.

Age, median (range) years8.0 (2, 17)45.5 (18, 77)22.0 (2, 77)<0.001
Female, no. (%)15 (71)20 (67)35 (69)0.72
Race, no. (%)   0.18
 Caucasian16 (76)28 (93)44 (86)
 Other1 (5)0 (0)1 (2)
 Unknown4 (19)2 (7)6 (12)
Time between visits, median (range) months9.9 (3.6, 18.0)7.7 (2.9, 25.7)9.2 (2.9, 25.7)0.57
Myositis disease duration, median (range) years0.2 (0, 8)0.2 (0, 12)0.2 (0, 12)0.49
MMT8 score (0–80), median (range)80 (31, 80)76 (52, 80)78 (31, 80)0.97
Physician's global assessment of disease activity (0–100-mm VAS), median (range)25.5 (0, 80)26.5 (0, 80)26.0 (0, 80)0.56
Muscle score (0–100-mm VAS), median (range)0.5 (0, 80)2.5 (0, 73)1.0 (0, 80)0.87
Extramuscular score (0–100-mm VAS), median (range)26.5 (0, 80)27.0 (0, 84)27.0 (0, 84)0.71
Laboratory values, no. (%) of tested patients with abnormal results    
 Aldolase9 (50)7 (32)16 (40)0.24
 ALT4 (24)8 (35)12 (30)0.44
 AST10 (48)10 (38)20 (43)0.53
 CK11 (61)9 (36)20 (47)0.10
 LDH2 (40)9 (56)11 (52)0.53
Autoantibodies, no. (%) of patients presenting    
 Anti–Ro 601 (5)1 (3)2 (4)0.82
 Anti–Ro 522 (10)2 (14)6 (12)0.65
 Anti-SSB0 (0)1 (3)1 (2)0.39
 Anti-Sm1 (5)1 (3)2 (4)0.82
 Anti-RNP2 (10)1 (3)3 (6)0.37
 Anti–Scl-701 (5)0 (0)1 (2)0.24
 Anti–Jo-11 (5)2 (7)3 (6)0.75
 Anti–ribosomal P0 (0)0 (0)0 (0)
 Antichromatin1 (5)1 (3)2 (4)0.82
 Any anti-RBP3 (14)5 (17)8 (16)0.78
Medications, no. (%) of patients receiving    
 Azathioprine0 (0)4 (13)4 (8)0.08
 Methotrexate10 (48)10 (33)20 (39)0.30
 Hydroxychloroquine0 (0)6 (20)6 (12)0.029
 Corticosteroids5 (24)13 (43)18 (35)0.15
 Any medication12 (57)21 (70)33 (65)0.34

Changes in disease activity measures between visit 1 and visit 2 are shown in Table 2. Overall, these patients improved significantly on all disease activity measures between the 2 study visits. However, the median change in muscle VAS score was zero, indicating that some patients showed no change, despite a mean decrease in the muscle VAS score of ∼14 mm.

Table 2. Changes in disease activity between visits (visit 2 − visit 1) among the 51 patients with myositis*
Disease activity measureVisit 1Visit 2ChangeP
  • *

    Values are the mean ± SD, median (range). See Table 1 for definitions.

  • By paired t-test.

MMT8 score (0–80)73.8 ± 8.4, 78 (31, 80)77.4 ± 5.3, 80 (60, 80)3.6 ± 9.3, 0 (–20, 24)0.019
Physician's global assessment of disease activity (0–100-mm VAS)31.5 ± 26.9, 26.0 (0, 80)10.7 ± 14.3, 3 (0, 55)−20.8 ± 26.0,–13 (–69, 45)<0.001
Muscle score (0–100-mm VAS)19.0 ± 26.2, 1.0 (0, 80)5.0 ± 10.3, 0.0 (0, 36)−13.9 ± 23.0, 0 (–76, 17)<0.001
Extramuscular score (0–100-mm VAS)27.6 ± 24.7, 27.0 (0, 84)10.1 ± 14.7, 4.5 (0, 57)−17.4 ± 24.9,–7 (–72, 43)<0.001

Changes in IFN gene scores and IFN chemokine scores, as well as in some of the individual cytokine/chemokine measures, were positively correlated with changes in global VAS score (physician's global assessment of disease activity), muscle VAS score, and extramuscular VAS score (Table 3). Positive correlations were seen between IL-6, IP-10, I-TAC, and MCP-1 levels and changes in global VAS scores, before and after adjustment for medication use (Figure 1). Changes in serum IL-8 and TNFα levels correlated positively with changes in global and muscle VAS scores regardless of medication use (Table 3).

Table 3. Correlations between changes in cytokine and chemokine levels, IFN gene score, and IFN chemokine score and changes in disease activity measures*
Parameter (n)Correlation (r) with change in physician's global assessment of disease activity (0–100-mm VAS), PCorrelation (r) with change in muscle score (0–100-mm VAS), PCorrelation (r) with change in extramuscular score (0–100-mm VAS), P
UnadjustedAdjusted for medicationsUnadjustedAdjusted for medicationsUnadjustedAdjusted for medications
  • *

    IFN = interferon; VAS = visual analog scale; IL-6 = interleukin-6; MIP-1α = macrophage inflammatory protein 1α; IP-10 = IFNγ-inducible 10-kd protein; I-TAC = IFN-inducible T cell α chemoattractant; MCP-1 = monocyte chemotactic protein 1; MIG = monokine induced by IFNγ; TNFα = tumor necrosis factor α.

  • Includes IP-10, I-TAC, and MCP-1.

IFNα (23)0.20, 0.370.34, 0.170.30, 0.170.34, 0.190.22, 0.340.18, 0.48
IL-6 (51)0.46, <0.0010.39, 0.0090.51, <0.0010.49, 0.0010.36, 0.0140.31, 0.049
IL-17 (23)−0.33, 0.12−0.45, 0.064−0.20, 0.54−0.16, 0.54−0.20, 0.37−0.23, 0.37
MIP-1α (23)0.11, 0.63−0.002, 0.99−0.10, 0.66−0.11, 0.680.13, 0.550.12, 0.66
IP-10 (51)0.53, <0.0010.48, 0.0010.44, 0.0020.45, 0.0030.52, <0.0010.48, 0.002
MIP-1β (51)0.08, 0.580.01, 0.930.10, 0.510.10, 0.540.27, 0.070.21, 0.19
I-TAC (51)0.44, 0.0010.42, 0.0040.45, 0.0010.53, <0.0010.44, 0.0020.45, 0.003
MCP-1 (51)0.42, 0.0030.32, 0.0320.46, 0.0010.46, 0.0020.42, 0.0030.33, 0.036
MIG (51)0.15, 0.290.13, 0.390.25, 0.0850.32, 0.0410.12, 0.420.14, 0.39
IFNγ (50)−0.08, 0.60−0.11, 0.48−0.16, 0.30−0.26, 0.10−0.09, 0.56−0.06, 0.72
IL-1β (50)−0.10, 0.52−0.22, 0.160.03, 0.85−0.12, 0.47−0.02, 0.90−0.14, 0.38
IL-10 (50)−0.01, 0.960.04, 0.800.17, 0.260.18, 0.25−0.14, 0.35−0.03, 0.86
IL-12 (50)−0.04, 0.810.02, 0.890.09, 0.530.11, 0.47−0.20, 0.19−0.13, 0.42
IL-13 (50)−0.18, 0.21−0.13, 0.42−0.06, 0.71−0.08, 0.60−0.34, 0.022−0.26, 0.11
IL-2 (50)−0.02, 0.90−0.05, 0.760.05, 0.73−0.04, 0.82−0.03, 0.85−0.02, 0.92
IL-4 (50)−0.11, 0.45−0.12, 0.44−0.01, 0.96−0.09, 0.57−0.24, 0.11−0.23, 0.15
IL-5 (50)−0.01, 0.97−0.01, 0.950.21, 0.170.18, 0.25−0.17, 0.25−0.16, 0.33
IL-8 (50)0.32, 0.0260.37, 0.0160.35, 0.0160.50, <0.0010.29, 0.0550.31, 0.055
TNFα (50)0.37, 0.0090.32, 0.0400.52, <0.0010.48, 0.0010.31, 0.0410.28, 0.085
MCP-2 (41)0.20, 0.0760.29, 0.0950.51, <0.0010.51, 0.0020.21, 0.210.16, 0.39
IFN gene score (49)0.33, 0.0230.26, 0.0960.44, 0.0020.41, 0.0070.25, 0.0950.19, 0.25
IFN chemokine score (51)0.53, <0.0010.50, <0.0010.50, <0.0010.55, <0.0010.55, <0.0010.52, <0.001
Figure 1.

Whole-blood interferon (IFN) gene and chemokine scores correlate positively and significantly with changes in physician's global assessment of disease activity (score on 0–100-mm visual analog scale [VAS]), as do levels of interleukin-6 (IL-6), IFNγ-inducible 10-kd protein (IP-10), IFN-inducible T cell α chemoattractant (I-TAC), and monocyte chemotactic protein 1 (MCP-1).

The potential influence of medication use on cytokine values was also examined. Participants who were not taking medications at visit 1 were somewhat younger (median age 16 years) than patients who were taking medications (median age 37 years) (P = 0.09). Patients not taking medications at visit 1 had significantly higher disease activity (median global VAS score 52 versus 19; P = 0.013). Despite these differences, the close agreement between correlations without and with adjustment for medications suggests that medication use had little influence on cytokine values.

In order to determine whether changes in cytokine levels and IFN scores were sensitive to changes in disease activity, we compared changes in IFN gene and chemokine scores between patients who remained clinically stable between visits 1 and 2 (n = 11) and patients whose disease improved (n = 33). We found that patients who had no change in disease activity generally had little or no change (defined as increase or decrease in global disease activity of <10 units) in IFN chemokine and gene scores, whereas patients whose disease improved (defined as decrease of ≥10 units) also had decreased IFN chemokine and gene scores (P = 0.04 for change in IFN chemokine score and P = 0.11 for change in IFN gene score) (Figure 2).

Figure 2.

Changes in IFN chemokine score and IFN gene score are specific and sensitive measures of change, as demonstrated by lack of change in scores among patients with stable disease (defined as increase or decrease in global disease activity of <10 units) and decreases in scores among patients with improved disease (defined as decreases in global disease activity of ≥10 units). Data are shown as box plots. Each box represents the 25th to 75th percentiles. Lines inside the boxes represent the median. Lines outside the boxes represent the 10th and the 90th percentiles. Circles indicate outliers. P = 0.04 for change in IFN chemokine score; P = 0.11 for change in IFN gene score. See Figure 1 for definitions.

Despite the fact that the chemokine score reached significance in this comparison while the gene score did not, overall we found that the chemokine score was correlated with the IFN gene score (r = 0.65, P < 0.001 at visit 1; r = 0.60, P < 0.001 at visit 2; and r = 0.54, P < 0.001 for change in IFN chemokine scores versus change in IFN gene scores). Furthermore, we found that differences in CK were correlated with differences in both IFN chemokine scores (r = 0.46, P = 0.006) and IFN gene scores (r = 0.58, P < 0.001) and also global VAS scores (r = 0.54, P < 0.001) and muscle VAS scores (r = 0.62, P < 0.001). Results were similar using the fold change instead of the absolute difference in CK (for IFN chemokine score, r = 0.49, P = 0.003; for IFN gene score, r = 0.55, P < 0.001; for global VAS score, r = 0.57, P < 0.001; for muscle VAS score, r = 0.61, P < 0.001).

Finally, we tested whether cytokine levels at the first visit were associated with impending changes in disease activity. Serum levels of several cytokines measured at visit 1 were associated with change in disease activity between visits 1 and 2, including IL-6, IP-10, I-TAC, MCP-1, IL-8, TNFα, and MCP-2, as well as the IFN gene score and IFN chemokine score (Table 4). A negative correlation indicates that participants with higher levels of cytokines at visit 1 were more likely to have improvements in disease activity from visit 1 to visit 2, while participants with lower cytokine levels at visit 1 were more likely to have worsening disease activity. The values of individual cytokines and chemokines, such as IL-6, IP-10, I-TAC, and MCP-1, as well as the IFN gene and chemokine scores, were lower at visit 1 if medications were in use. This suggests a suppression of the type I IFN response with concurrent medication, but when these values were compared to disease activity measures they remained strongly correlated.

Table 4. Correlations between cytokine and chemokine levels, IFN gene score, and IFN chemokine score at visit 1 and changes in disease activity measures*
Parameter (n)Correlation (r) with change in physician's global assessment of disease activity (0–100-mm VAS), PCorrelation (r) with change in muscle score (0–100-mm VAS), PCorrelation (r) with change in extramuscular score (0–100-mm VAS), P
  • *

    All associations were unadjusted for medication use. See Table 3 for definitions.

  • Includes IP-10, I-TAC, and MCP-1.

IFNα (29)−0.14, 0.460.17, 0.38−0.22, 0.26
IL-6 (51)−0.54, <0.001−0.51, <0.001−0.51, <0.001
IL-17 (29)0.08, 0.670.04, 0.820.15, 0.46
MIP-1α (29)−0.19, 0.32−0.19, 0.33−0.09, 0.67
IP-10 (51)−0.61, <0.001−0.51, <0.001−0.64, <0.001
MIP-1β (51)0.02, 0.880.13, 0.39−0.02, 0.89
I-TAC (51)−0.58, <0.001−0.47, <0.001−0.61, <0.001
MCP-1 (51)−0.46, 0.001−0.46, 0.001−0.48, <0.001
MIG (51)0.11, 0.47−0.01, 0.96−0.17, 0.27
IFNγ (50)−0.11, 0.250.33, 0.0220.15, 0.31
IL-1β (50)−0.06, 0.68−0.12, 0.44−0.10, 0.52
IL-10 (50)−0.20, 0.17−0.24, 0.12−0.07, 0.66
IL-12 (50)0.14, 0.330.01, 0.950.26, 0.09
IL-13 (50)0.24, 0.10−0.05, 0.750.30, 0.045
IL-2 (50)−0.11, 0.440.06, 0.71−0.13, 0.39
IL-4 (50)0.17, 0.240.05, 0.750.29, 0.051
IL-5 (50)0.07, 0.64−0.09, 0.550.24, 0.15
IL-8 (50)−0.43, 0.002−0.35, 0.016−0.35, 0.016
TNFα (50)−0.39, 0.005−0.37, <0.001−0.39, 0.007
MCP-2 (41)−0.39, 0.005−0.41, 0.004−0.46, 0.001
IFN gene score (49)−0.43, 0.003−0.56, <0.001−0.30, 0.048
IFN chemokine score (51)−0.59, <0.001−0.50, <0.0010.62, <0.001

In addition, associations between medications and cytokine values adjusted for changes in disease activity were also examined longitudinally. Addition of use of MMF (n = 3) was associated with decreases in serum levels of IFNγ (r = –0.39, P = 0.01) and IL-10 (r = –0.43, P = 0.004) and with increases in serum MCP-1 (r = 0.30, P = 0.04). Use of MTX (n = 20) was associated with decreases in IP-10 levels (r = –0.33, P = 0.03) and IFN chemokine scores (r = –0.42, P = 0.005). Use of corticosteroids (n = 18) was associated with decreases in IL-1β (r = –0.46, P = 0.002), and use of AZA (n = 4) was associated with elevations in IL-17 (r = 0.55, P = 0.01), suggesting that while varying potential immune pathways are altered with treatment, the IFN chemokine and gene scores remain sensitive measures of disease activity.


Several autoimmune diseases, including juvenile DM, adult DM, and SLE, exhibit elevated expression of type I IFN–regulated gene transcripts, chemokines, and cytokines in both peripheral blood and target tissues (14–16). Traditional biomarkers of juvenile and adult DM include serum levels of muscle-derived enzymes such as CK, aldolase, AST, ALT, and LDH. Elevation of muscle-derived enzymes including CK, which is only elevated in 50–70% of patients at disease onset, becomes less sensitive as a marker of disease activity with chronic disease (2). We undertook this study to evaluate the disease-monitoring utility of novel biomarkers of disease based on peripheral blood IFN gene and chemokine scores and measurement of serum cytokine levels. Our major finding was that changes in juvenile and adult DM global disease activity correlated positively and significantly with changes in the type I IFN gene score before adjustment for medication use, and with changes in the type I IFN chemokine score before and after adjustment for medication use. Assessment of nonmuscle involvement has been challenging in DM. We demonstrated that changes in muscle and extramuscular VAS subscale scores correlated positively with changes in IFN gene and chemokine scores, suggesting that these markers may be useful as noninvasive tools to assess extramuscular disease activity.

Several soluble factors in serum have been evaluated cross-sectionally as biomarkers of disease activity in juvenile DM. Neopterin, a product released primarily by macrophages and monocytes upon stimulation with IFNγ, was found in increased concentrations in plasma and urine of children with DM compared with healthy controls (17) but generally did not correlate with serum-derived muscle enzyme levels (18). Another marker used to measure disease activity in juvenile DM is von Willebrand factor (vWF), a cleaved product of ADAMTS-13 activity which is present on activated endothelial cells and is elevated in a subset of patients with vasculitis, including juvenile DM; however, no correlation of vWF was found with active skin disease or with muscle strength, creatine phosphokinase, or aldolase (13).

Studies of phenotyping of peripheral blood lymphocytes as markers of disease activity have demonstrated lymphopenia in both adult and juvenile DM at the onset of active disease (19). Lymphocyte enumeration has not been fully studied in relation to disease activity, and existing data are inconsistent. In adult DM, a decrease in total lymphocyte (CD3+) numbers as well as in the number of cell subsets (CD4+ and CD8+) has been reported prior to the onset of treatment, with normalization after treatment is initiated. However, CD19+ B cells have been reported to be both decreased and increased in adult DM (19). Fewer data exist for juvenile DM; however, cross-sectional studies found an association between disease activity and increased numbers of CD19+ B cells and decreased numbers of CD3–CD16+ and/or CD56+ natural killer cells. Prospective studies are lacking, and the effects of medication on lymphocyte phenotype are completely unknown, with little data to suggest usefulness in monitoring of disease activity.

Due to poor sensitivity and specificity of conventional blood-based biomarkers of disease activity in DM, we previously conducted a cross-sectional study seeking candidate DM disease activity markers (5). We found that factors such as type I IFN–regulated genes, cytokines (IL-6 and IL-1), and chemokines (I-TAC; CXCL10, also known as IP-10; CCL2, also known as MCP-1; and CCL8, also known as MCP-2) are overexpressed in peripheral blood of juvenile and adult patients with DM and that they correlate with clinical measures of disease activity (5). MCP-1 previously was shown to be up-regulated in the muscle tissue of adult DM patients (20). Both the IFN gene and chemokine scores were associated with the global VAS score. The present study was initiated to examine the longitudinal responsiveness and sensitivity of these markers and their performance under conditions of treatment with immunomodulating agents. This study demonstrated that both the IFN gene and chemokine scores were responsive and sensitive measures longitudinally even when we adjusted for medication use. The IFN chemokine score correlated with clinical global, muscle-specific, and extramuscular (primarily including skin, lung, and joint) disease activity measures, before and after adjusting for immunomodulating agents in both juvenile and adult DM.

Results of the present study suggest that both the chemokine and gene scores would be useful for monitoring patients with DM over time, even in the context of different immunosuppressive agents that might be used. We found many candidate biomarkers for prediction of changes in disease activity. Assessment of disease activity is sometimes difficult. We found that these candidate biomarkers were associated with muscle disease activity (i.e., muscle fatigue, inability to perform activities of daily living, and elevated muscle enzyme levels, even with a normal MMT score) and with active lung disease, arthritis, and skin disease. In the juvenile DM population we also used the CMAS, finding that many children have abnormal CMAS scores when the MMT score is normal. The CMAS incorporates functional assessments with strength measures, with the potential of being more sensitive in this population. The CMAS has not been validated in adults and is not used universally as a muscle assessment tool.

Evaluation of these disease activity markers prospectively is key to defining their clinical utility. We have demonstrated that changes in both the IFN gene and chemokine scores correlate well with changes in disease activity over time, as do levels of selected serum chemokines (IP-10, I-TAC, MCP-1, and MCP-2) and cytokines (IL-6, IL-8, and TNFα). That IL-8 and TNFα each correlate with disease activity suggests further engagement of plasmacytoid dendritic cells and Th1 cells, and each of these cytokines was significantly correlated with global and muscle disease activity measures in particular. Increases in IFNγ levels correlated over time with increased muscle disease activity and may represent more of a Th1 pathway of continued disease activity. Furthermore, our finding that the levels of several cytokines and chemokines at the first visit were correlated with changes in disease activity at the second visit suggests that these may be candidate biomarkers for prediction of disease course. Additional controlled, prospective studies will be required to assess the utility of these markers in predicting impending changes in disease activity.

Direct detection of type I IFN (IFNα/β) is not routinely seen in the peripheral blood in juvenile or adult DM. Additional reports of the importance of serum IFNα in juvenile DM include a functional measure of IFNα activity by measuring the correlation of the downstream products (IFN-induced protein with tetratricopeptide repeats 1, myxovirus resistance protein 1, and RNA-dependent protein kinase) with new-onset disease (21). Levels of these IFNα-induced proteins are reported to be higher in untreated patients than in treated patients. However, these IFN-inducible proteins become less sensitive markers by 36 months of treatment and are not associated with disease activity but were weakly associated with elevation of serum muscle enzyme levels (P < 0.05) prior to the introduction of therapy.

One study did directly measure IFNα levels in adult DM and polymyositis (PM) patients and showed that they were higher in patients with anti–Jo-1 antibodies and were not significantly affected by medications. A negative correlation was found between IFNα levels and the intensity of magnetic resonance imaging signal in muscle (22). Additional peripheral blood studies of IFN concentrations (IFNα, IFNβ, and IFNω) measured by enzyme-linked immunosorbent assay in adults with DM found that IFNβ elevation was seen in 9 of 26 DM patients (35%) compared with 3 of 48 patients with other inflammatory myopathies (inclusion body myositis and PM) (6%), and 2 of 36 healthy volunteers (6%) (23). The highest levels were in those patients prior to treatment or with minimal treatment (prednisone dose of ≤15 mg/day or treatment duration ≤7 days). There is a concern that direct measurement of IFNα or IFNβ does not always reflect the full effect of type I IFN since it is seen in low levels and may be short lived.

The use of the type I IFN–inducible gene and chemokine signatures reflects the cellular infiltrates seen in the muscle and skin tissue in patients with active juvenile and adult DM, including plasmacytoid dendritic cells and IL-17–producing Th17 cells (24–26). Coupled with the longstanding identification of CD4+ T cells and B cells in tissue of DM patients, the proposed IFN gene and chemokine scores reflect the ongoing inflammatory process in the target tissue and thus may allow a more nuanced assessment of ongoing disease activity when used in conjunction with muscle enzyme levels and clinical measures.

In conclusion, this prospective evaluation of the IFN gene and chemokine scores supports the utility of these measures of disease activity in juvenile and adult DM. Of key importance is that unlike traditional disease activity measures, the IFN-based markers reflect changing disease activity over the duration of the disease even when adjusted for medication use.


All authors were involved in drafting the article or revising it critically for important intellectual content, and all authors approved the final version to be published. Dr. Reed had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Study conception and design. Reed, Peterson, Bilgic, Gillespie.

Acquisition of data. Reed, Bilgic, Ytterberg, Amin, Hein, Ernste.

Analysis and interpretation of data. Reed, Bilgic, Crowson, Ernste, Gillespie.