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Abstract

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. SUBJECTS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. AUTHOR CONTRIBUTIONS
  8. Acknowledgements
  9. REFERENCES

Objective

To evaluate serum free light chains (FLC) as a putative biomarker of systemic lupus erythematosus (SLE) activity.

Methods

Seventy-five SLE patients and 41 age- and sex-matched rheumatoid arthritis (RA) controls were enrolled. Disease activity was assessed using the Safety of Estrogens in Lupus Erythematosus: National Assessment version of the Systemic Lupus Erythematosus Disease Activity Index (SLEDAI) definition and physician global assessments for SLE and the Disease Activity Score in 28 joints for RA. Serum FLC levels were compared against other biomarkers (IgG, C3, C4, double-stranded DNA [dsDNA] antibody). Nonparametric tests were used to compare 1) FLC and IgG in SLE versus RA and healthy controls, 2) FLC and IgG among different levels of activity in SLE, and 3) FLC in active versus nonactive RA. Correlation of FLC, C3, C4, dsDNA antibody, and IgG with the SLEDAI and modified SLEDAI (M-SLEDAI) were obtained.

Results

FLC was higher in SLE than in RA; both were higher than referent healthy controls. Total FLC was significantly higher in subjects with greater SLE disease activity than lower/no activity. There were no significant differences in IgG, C4, or dsDNA antibody stratified by disease activity. Total FLC and C3 showed moderate to strong correlation with the SLEDAI and M-SLEDAI. In RA, no differences were seen in FLC levels for different levels of disease activity. Similar results were seen after controlling for renal function, age, and sex. In multiple linear regression, FLC significantly explained 50% variance of the SLEDAI after adjusting for renal function, age, and sex.

Conclusion

Serum FLC levels correlate strongly with disease activity in SLE, but not in RA. Serum FLC may be used as a biomarker of SLE disease activity.


INTRODUCTION

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. SUBJECTS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. AUTHOR CONTRIBUTIONS
  8. Acknowledgements
  9. REFERENCES

Systemic lupus erythematosus (SLE) is a chronic multisystem autoimmune disease primarily affecting young women (1). Patients with SLE frequently experience flares, often requiring hospitalizations and initiation or escalation of immunosuppressive medications (1, 2). The ability to detect a flare directly depends on our ability to correctly measure disease activity. Currently, active disease in SLE is identified only after it has become clinically apparent, since the available methods rely mainly on the recognition of clinical manifestations (3–5). Sometimes, organ damage has already set in by the time the patient presents clinically. We have an array of SLE-associated autoantibodies that are currently used to measure disease activity in SLE; however, disease activity may not always correlate with these known immunologic parameters (complement and double-stranded DNA [dsDNA] antibody) (3–5). Therefore, it is important to identify sensitive and specific “biomarkers” of disease activity that could aid in the detection and assess the severity of active disease, and thus may provide a window of opportunity for therapeutic intervention to prevent onset of irreversible damage.

B cell activation has a pivotal role in the pathogenesis of SLE, and an exaggerated polyclonal synthesis of immunoglobulin is seen during active disease in SLE (6–9). During normal immunoglobulin synthesis by plasma cells, free light chains (FLC) are produced in excess of heavy chains (10, 11). These excess FLC are released into the serum, from which they are rapidly removed by the kidneys with a half-life of 2–6 hours (10, 11). Recently, quantitative immunoassays for serum FLC are being used in monitoring plasma cell dyscrasias to evaluate monoclonal increase in FLC (12, 13). Hopper et al found elevated levels of urinary FLC in SLE during periods of active disease that tended to normalize with disease quiescence (14). They showed that a dramatic increase in urinary FLC precedes clinical manifestation of SLE flare by 4–8 weeks, thus suggesting that measurements of FLC in urine may represent quantitative markers of in vivo polyclonal B cell activation in SLE patients (14, 15). The increase in FLC can be explained by a dysregulated or inefficient process of receptor editing reported in patients with SLE (16, 17). Receptor editing refers to the secondary light chain immunoglobulin gene rearrangement in which a heavy chain combines with a new light chain, trying to induce tolerance in an autoreactive B cell (18). However, these were small case series and urinary FLC levels may be confounded by proteinuria due to renal involvement in SLE. With advances in technology, highly sensitive and specific serum FLC assays have become available. Using the same argument as for the use of urinary FLC, it is plausible that serum FLC might serve as a potential surrogate marker for disease activity in SLE without the disadvantages of urinary FLC. This hypothesis has been briefly explored in a European case series where serum FLC was found to be associated with lupus disease activity (19).

In this proof-of-concept study, our aim was to evaluate the associations between serum FLC levels and disease activity in SLE and compare it with another autoimmune disease model: rheumatoid arthritis (RA) and referent controls from a healthy population. We hypothesized that quantitative measurements of serum FLC will correlate with disease activity in SLE, and this correlation will be stronger in SLE than in RA.

SUBJECTS AND METHODS

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. SUBJECTS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. AUTHOR CONTRIBUTIONS
  8. Acknowledgements
  9. REFERENCES

The present study was conducted from October 2007 to January 2009 at two academic centers: Rush University Medical Center and Cook County Hospital, Chicago, Illinois. We enrolled consecutive consenting SLE patients based on predefined criteria and simultaneously enrolled sex- and age-matched (± 10 years) RA patients as controls.

Inclusion criteria.

Inclusion criteria for SLE and RA subjects were age ≥18 years and fulfillment of the American College of Rheumatology (ACR) criteria for the diagnosis of SLE or RA (20, 21). Also, RA patients were enrolled by matching the sex and age (± 10 years) of enrolled SLE patients in batches of 10 patients.

Exclusion criteria.

Patients with 1) concurrent diagnosis of overlap or mixed connective tissue disease were excluded to improve the distinctions between the study populations (20), 2) severe infection and comorbidity requiring hospitalization were excluded because they can potentially influence B cell activation, and 3) severe renal insufficiency defined by a serum creatinine level >2.0 mg/dl or hemodialysis were excluded, since serum FLC levels can be influenced by decreasing renal function (22).

Protocol.

Participants underwent detailed history and physical examinations, and their medical records and laboratory data were reviewed. Clinical information relevant to calculating SLE disease activity/damage scores were collected prospectively for each SLE patient, including: 1) Safety of Estrogens in Lupus Erythematosus: National Assessment (SELENA) version of the Systemic Lupus Erythematosus Disease Activity Index (SLEDAI) (23), 2) physician global assessment (PGA) by visual analog scale (VAS) with anchors for SLE disease activity (range 0–3, where 0 = none and 3 = severe), as defined by the SELENA–SLEDAI (23), and 3) Systemic Lupus International Collaborating Clinics/ACR Damage Index (SDI) (24). For the RA patients' tender and swollen joint counts, a VAS of patient's global health and overall disease activity was prospectively calculated by the Disease Activity Score in 28 joints (DAS28) (25). For each subject, complete blood cell count, comprehensive metabolic profile, and C-reactive protein (CRP) and IgG levels were obtained. In addition, Westergren erythrocyte sedimentation rate (ESR) was obtained in RA patients and high-sensitivity dsDNA antibody titers, complements (C3, C4), urinalysis, spot urine protein, and creatinine ratio as a surrogate for 24-hour proteinuria were obtained for SLE patients. Appropriate ethics committee approval and patient consents were obtained.

Serologic tests: FLC.

Two hundred microliters of serum from each patient was stored at −20°C and was transferred monthly to an external laboratory (The Binding Site Group) to quantify serum FLC (κ and λ light chains) by quantitative nephelometric assay using a reference range from 282 healthy adults (26). The serum FLC assay (Freelite, The Binding Site Group) was centrally performed using a SPA PLUS turbidimeter using 0.05 ml of stored thawed serum. This assay consists of two separate measurements: κ and λ FLC. The total FLC is equal to κ + λ light chains. Serum FLC has been shown to remain stable for 1–2 years at −20°C (27, 28). We used the decision limits of <0.26 and >1.65 for an abnormal κ:λ ratio, and we applied the reference ranges (95% range of normal healthy individuals) of 3.3–19.4 mg/liter and 5.7–26.1 mg/liter for κ and λ FLC, respectively (26). dsDNA was measured by enzyme-linked immunosorbent assay (Trinity Biotech). Complement proteins were measured using an immunoturbidimetric assay (Abbott Diagnostic). ESR was determined by the Westergren method. CRP levels were measured by a nephelometric immunoassay (Minineph, The Binding Site Group). IgG was measured by a turbidimetric immunoassay method using an SPA analyzer (The Binding Site Group).

Disease activity definition.

PGA was defined as either “no activity” (PGA = 0), “mild” (PGA >0 to ≤1.5), “moderate” (PGA >1.5 to ≤2.5), or “severe” (PGA >2.5). Disease activity for RA subjects was divided into “remission” (DAS28-ESR <2.6), “low activity” (DAS28-ESR ≤3.2), “moderate activity” (DAS28-ESR >3.2 and ≤5.1), and “severe activity” (DAS28-ESR >5.1) using the disease activity and remission criteria of RA disease activity reported by Welsing and van Riel (29). A DAS28 based on CRP (DAS28-CRP) was also calculated in patients for whom CRP levels were available. Modified SLEDAI (M-SLEDAI) was defined as the SELENA–SLEDAI score after removing score for complement and dsDNA component in the SELENA–SLEDAI, a method that is consistent with previous studies (30). All SLE patients were divided into clinically (and/or laboratory) quiescent disease (M-SLEDAI = 0 and PGA = 0) and clinically (and/or laboratory) active disease (M-SLEDAI >0 or PGA >0). Another separate definition of clinically (and/or laboratory) quiescent (CQ1) disease (M-SLEDAI ≤2 and PGA = 0) and clinically (and/or laboratory) active (CA1) disease (M-SLEDAI >2 or PGA >0) was used as well. Also, SLE patients were divided into serologically quiescent disease (dsDNA below the lower limit of normal range and complement above the upper limit of normal range) and serologically active disease (dsDNA above the upper limit of normal range or complement below the lower limit of the reference range). In this manner, all SLE patients were divided into 4 categories of disease: serologically and clinically quiescent (SQCQ), serologically and clinically active (SACA), serologically quiescent and clinically active (SQCA), and serologically active and clinically quiescent (SACQ).

Statistical analysis.

Chi-square test or Student's t-test was used to compare baseline characteristics of SLE and RA. Appropriate transformations were undertaken when feasible to make the data normative. Wilcoxon's signed rank test was used to compare FLC levels in SLE and RA with standard healthy normal data (26). Since the data on FLC were mostly nonparametric, Mann-Whitney test/Kruskal-Wallis test was used to compare the FLC levels (κ, λ, and total FLC [κ + λ]) as well as IgG levels between 1) patients with SLE and RA, 2) SLE patients with different levels of PGA, 3) SLE patients with different groups based on serologically and clinical activity, i.e., SQCQ, SACA, SQCA, SACQ, and 4) RA patients with low activity (DAS28-ESR ≤3.2) versus active disease (DAS28-ESR >3.2). Analysis of covariance (with Bonferroni correction, as needed) was used to compare the FLC between various groups mentioned above for controlling for covariates, including glomerular filtration rate (GFR), age, and sex, as they may influence the serum FLC levels. Estimated GFR was calculated using the Modification of Diet in Renal Disease Study equation (31).

Correlation analysis.

Spearman's correlation was used to examine the correlation of 1) SELENA–SLEDAI and 2) M-SLEDAI with the following: dsDNA antibody, CRP, C3, C4, IgG, and total FLC levels. M-SLEDAI was used, as complement and dsDNA antibody are components of the SELENA–SLEDAI and thus would falsely give high correlation with the SELENA–SLEDAI. For RA participants, the DAS28 was correlated with total FLC. The strength of the correlation was defined as per Cohen: strong (r = >0.5), moderate (0.3 ≤ r < 0.5), weak (0.1 ≤ r < 0.3), or absent (r = <0.1) (32).

Regression analysis.

Multiple linear regression analysis was used to evaluate the association between the SELENA–SLEDAI score and serum total FLC levels after adjusting for age, sex, and GFR. A hierarchical regression model was used to evaluate the relationship between the M-SLEDAI and serum total FLC after controlling for known immunologic parameters such as CRP, C3, IgG and dsDNA antibody.

RESULTS

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. SUBJECTS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. AUTHOR CONTRIBUTIONS
  8. Acknowledgements
  9. REFERENCES

Seventy-five SLE patients and 41 RA patients were enrolled. The mean ± SD ages at study entry for the SLE and RA groups were 40.0 ± 12.7 and 45.9 ± 13.9 years, respectively (P = 0.02). There were 7.9% men in the SLE group and 9.7% men in the RA group (P = 0.74). In the SLE group, a plurality of the subjects were African American (29 [42.6%] of 68), Hispanic (23 [33.8%] of 68), and white (12 [17.6%] of 68). In the RA group, a plurality of subjects were African American (13 [37.1%] of 35), Hispanic (12 [34.3%] of 35), and white (8 [22.8%] of 35), similar to SLE (P = 0.5). The mean ± SD and median SELENA–SLEDAI score was 4.96 ± 4.58 and 4.0 (interquartile range [IQR] 2–6), and the mean ± SD and median SDI score was 1.54 ± 1.72 and 1.0 (IQR 0–2). In the RA cohort, the mean ± SD DAS28-ESR and DAS28-CRP were 3.97 ± 1.61 (median 3.74, IQR 2.6–5.5) and 3.35 ± 1.53 (median 3.2, IQR 1.8–4.7), respectively.

Serum FLC and IgG levels in SLE, RA, and healthy controls.

Total serum FLC (κ + λ) levels were significantly higher in both RA and SLE as compared to healthy controls (P < 0.01) (Figure 1). The median level of total FLC in SLE was 48.3 mg/liter (IQR 30.0–70.1), in RA was 32.3 mg/liter (IQR 22.0–44.9), and in healthy controls was 20.0 mg/liter (IQR 17.5–28.0) (26). Total serum FLC levels were significantly higher in SLE than in RA (P < 0.001). Serum FLC levels remained significantly different between SLE and RA even after adjusting for GFR, age, and sex (log total FLC: F[1,84] = 12.87, P < 0.004). The serum IgG levels were significantly higher in SLE than in RA (median 18.2 gm/liter, IQR 14.9–22.7 in SLE versus median 15.4 gm/liter, IQR 11.8–18.24 in RA; P < 0.01). Similar results were seen with κ or λ FLC individually. The ratio of κ:λ was within the normal range in SLE (mean ± SD κ:λ ratio 0.97 ± 0.39) as well as in RA (mean ± SD κ:λ ratio 0.96 ± 0.27).

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Figure 1. Comparison of serum free light chains (total FLC) in systemic lupus erythematosus (SLE), rheumatoid arthritis (RA), and healthy controls (Normal).

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Serum FLC levels by SLE disease activity by PGA.

Serum total FLC levels were highest in severe PGA (median 179.8 mg/liter, IQR 122.1–211.5), followed by moderate (median 57.03 mg/liter, IQR 50–113), mild (median 50.8 mg/liter, IQR 28.6–66.3), and no activity (median 36.2 mg/liter, IQR 29.8–48.1), decreasing in a stepwise manner from severe to no disease activity (Figure 2). A statistically significant difference was observed between severe activity and no activity, as well as between severe activity and mild activity (P < 0.001). Serum FLC levels remained significantly different between the groups after adjusting for covariates GFR, age, and sex (log total FLC F[1,71] = 32.69, P < 0.001). There were no significant differences in the IgG levels between the different levels of PGA. There was a moderate correlation between PGA and total FLC (ρ = 0.49, P < 0.002). Similar associations with PGA were seen with κ or λ FLC. The Spearman's correlation between SDI and FLC levels was ρ = 0.26.

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Figure 2. Free light chain (total FLC) by physician global assessment in systemic lupus erythematosus patients.

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Serum FLC levels by serologic and clinical activity.

The median total FLC levels were highest for SACA (67.7 mg/liter, IQR 51.9–139.1) and lowest for SQCQ (30.1 mg/liter, IQR 23.4–38.2), and both were significantly different (P < 0.001) (Figure 3). Intermediate total FLC levels were seen for SQCA (median 37.93 mg/liter, IQR 32.54–56.51) and SACQ (median 43.26 mg/liter, IQR 37.79–59.05). SACA was significantly different than all other groups after adjusting for covariates GFR, age, and sex (log total FLC F[1,70] = 25.15, P < 0.001). Similar results were obtained for κ or λ FLC. We found similar results using the alternate definitions of clinically quiescent and active disease (CQ1 and CA1).

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Figure 3. Serum free light chain levels (total FLC) by clinical and serologic activity. SQCQ = serologically and clinically quiescent disease; SACQ = serologically active and clinically quiescent disease; SQCA = serologically quiescent and clinically active disease; SACA = serologically and clinically active disease.

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Serum FLC in RA patients.

There was no significant difference seen between total FLC levels in RA patients with low activity disease (median 32.3 mg/liter, IQR 17.9–39.2) as compared to active disease (median 32.3 mg/liter, IQR 25.3–47.1; P < 0.05). Serum total FLC levels were compared between RA patients in remission (median 31.9 mg/liter, IQR 17.9–39.2) and with low disease activity (median 37.2 mg/liter, IQR 16.2–49.4), moderate disease (median 26.7 mg/liter, IQR 21.7–39.2), and severe disease (median 46.3 mg/liter, IQR 31.8–67.1), but there was no significant difference between them. Similar results were obtained for κ or λ FLC.

Correlations between disease activity and FLC level.

Serum total FLC correlated strongly with the SELENA–SLEDAI in SLE patients (Figure 4). Serum total, κ, and λ FLC and C3 showed moderate correlation with the M-SLEDAI (P < 0.001), whereas dsDNA, C4, and CRP level showed weak correlation (P = not significant) (Table 1). The serum IgG levels showed weak correlation with the SELENA–SLEDAI. For RA patients, serum total FLC showed moderate correlation with DAS28-ESR (total FLC ρ = 0.35, P = 0.037) and weak correlation with DAS28-CRP (total FLC ρ = 0.26, P = 0.10).

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Figure 4. Correlation between total free light chain (FLC) and Safety of Estrogens in Lupus Erythematosus: National Assessment version of the Systemic Lupus Erythematosus Disease Activity Index (SLEDAI) in systemic lupus erythematosus patients.

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Table 1. Spearman's correlation (ρ) between SELENA–SLEDAI, M-SLEDAI, and immunologic biomarkers (C3, C4, dsDNA, CRP level, and FLC)*
 Total FLCλ FLCκ FLCdsDNAC3C4CRP levelIgG
ρPρPρPρPρPρPρPρP
  • *

    SELENA–SLEDAI = Safety of Estrogens in Lupus Erythematosus: National Assessment version of the Systemic Lupus Erythematosus Disease Activity Index; M-SLEDAI = modified Systemic Lupus Erythematosus Disease Activity Index (SELENA–SLEDAI − [dsDNA and complement components]); dsDNA = double-stranded DNA; CRP = C-reactive protein; FLC = free light chain.

SELENA–SLEDAI0.66< 0.010.68< 0.0010.580.0020.45< 0.001−0.56< 0.001−0.370.0010.290.010.270.06
M-SLEDAI0.45< 0.010.49< 0.0010.390.0020.160.20−0.320.005−0.180.120.180.100.140.23

Multiple linear regression model.

In a univariate model, serum log total FLC explained nearly 50% of the variance in the SELENA–SLEDAI (R2 = 0.48, β = 4.62, P < 0.001). In a multiple linear regression model, log total FLC significantly explained the variance of the SELENA–SLEDAI even after adjusting for GFR, age, and sex (model R2 = 0.52, log total FLC β = 4.63, P < 0.001). To examine the association of serum FLC with the M-SLEDAI after adjusting for complements, we did multiple linear regressions in hierarchical models. First we ran the model with the M-SLEDAI as the dependent variable and serum C3 as the independent variable. Only serum C3 was used as a marker for complement levels due to colinearity between C3 and C4. Other immunologic markers such as dsDNA, CRP level, and IgG were not included, as they were not significant in the univariate model. In model 1, C3 significantly explained 11% variance of the M-SLEDAI (model R2 = 0.11, C3 coefficient = −0.036, SE 0.012, P = 0.004). With the addition of log total FLC in the above model, i.e., model 2 as the independent variable, the combined model explained 20% additional variance (R2 = 0.31) of the M-SLEDAI than explained by C3 alone. In fact, C3 lost its association with the M-SLEDAI when total FLC was added to the model.

DISCUSSION

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. SUBJECTS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. AUTHOR CONTRIBUTIONS
  8. Acknowledgements
  9. REFERENCES

This study evaluates a potential biomarker for SLE disease activity: serum FLC. The role of serum FLC in SLE has not been well described; to date, the literature contains a single abstract of 45 German patients where serum FLC appeared to correlate with lupus disease activity assessed by the European Consensus Lupus Activity Measure Score (19). This study did not compare serum FLC with other immunologic markers or compare it with another rheumatic disease.

Our results show that serum FLC are elevated in patients with rheumatic diseases, such as SLE and RA, as compared to healthy normal individuals. Also, they are significantly higher in patients with SLE than in patients with RA. Both κ and λ FLC were elevated, but their ratio was normal, indicating polyclonal rather than monoclonal elevation. In SLE, FLC levels correlated strongly with disease activity measured by PGA as well as by the SELENA–SLEDAI. SLE patients with serologic or clinically active disease had significantly higher values of FLC. These results can be explained by the strong relationship between B cell hyperactivity and disease activity of SLE in humans (6, 8, 9, 33), as well as in murine models (7). Immunoglobulin light chains and heavy chains are combined during the synthesis of immunoglobulins by B cells; however, there is normally a relative overproduction of light chains. The in vitro data suggest that only 60% of newly synthesized light chains are incorporated into complete immunoglobulin, and the remaining 40% are released into serum as FLC (10, 11). Therefore, elevated serum FLC seen in our study may represent B cell hyperactivity with increased formation of immune complexes. Another plausible explanation of elevated FLC in SLE is an abnormal increase in receptor editing in SLE. B cells can undergo secondary heavy and light chain immunoglobulin rearrangements at various stages of their development, a process termed receptor editing (34). In the periphery, secondary rearrangements of the light chain immunoglobulin gene lead to new light chains combining with heavy chains, trying to induce tolerance in an autoreactive B cell (18). On one hand, decreased editing may not eliminate the self-reactive specificities that emerge during B cell development in the bone marrow. On the other hand, excessive secondary rearrangements, especially in the periphery where tolerance mechanisms are less effective, can result in production of autoantibodies by edited B cells. This receptor or light chain editing is thought to be increased and dysregulated in SLE, which may explain the increase in serum FLC in our cohort (16, 17).

Recently, elevated serum FLC were observed in one-third of patients with RA and one-fifth of patients with systemic sclerosis (35), supporting the results seen in our study. It is likely that serum FLC levels are elevated in these diseases secondary to the role of B cells and antibody formation in their pathogenesis. The strong association of serum FLC levels in SLE, as shown by our results, is plausible, since FLC is a byproduct of antibody formation and SLE is primarily a B cell–mediated disease, causing production of various autoantibodies. However, the weaker association of FLC to RA disease activity is less clearly understood, since the clinical response to rituximab has recently suggested a B cell–mediated process in RA (36). Evidence in the literature suggests that despite the pivotal role B cells play in the pathogenesis of RA, T cells may still be the primary cell responsible for driving chronic inflammatory processes in RA (37). Further studies are needed to explain the weak to moderate association of serum FLC with the disease activity in RA patients, seen in our study as well as in prior reports (35, 38).

Our results also indicate that serum FLC performs better than currently known immunologic parameters such as complement, dsDNA antibodies, etc. This difference may relate to the fact that serum FLC are known to be devoid of significant antibody activity and thus, unlike dsDNA antibodies, immune complexes, complements, or IgGs, which may be consumed in causing immunologic injury to various organs, FLC accompanying the hypersecretion of SLE-associated antibody would be unaffected by the constituent immune reactions and acute inflammatory processes (39). Moreover, FLC have a half-life of only 2–6 hours, so their levels would indicate instantaneously occurring B cell activity (10, 11). Serum FLC also correlate with other markers of polyclonal B cell activation, including gamma globulins (35).

A new automated immunoassay provides sensitive and specific FLC quantification in serum using antibodies that recognize epitopes on FLC molecules (12, 26). This assay has been used to assess the monoclonal expansion using the κ:λ ratio in monitoring nonsecretory myeloma and light chain myeloma (40–43). Serum FLC measurements may be useful in SLE disease monitoring in an analogous manner, as it is a disease of polyclonal rather than monoclonal elevations of κ and λ. Serum FLC has superior sensitivity to urine FLC (42), and may be more “patient friendly” (44). Finally, the fast turnover of serum FLC (half-life of 2–6 hours) may make it useful as an early marker of response to therapy in addition to being an early surrogate marker for relapse (45). The test is now readily available in most tertiary care centers.

There were limitations to this study. First, due to the small sample size, only a few potential confounders could be controlled, and mainly those variables thought to influence the levels of FLC were selected. Second, a long-term longitudinal followup of these patients with the goal of defining the predictive role of FLC in SLE flare would be desirable. Third, an active control group consisting of patients with infection was not evaluated. Our goal, however, was to find a marker that correlates with disease activity and not to identify a marker specific for SLE. Fourth, serum FLC levels are influenced by serum creatinine and they may be filtered by hemodialysis. This causes some concern regarding the reliability of serum FLC in patients with severe renal insufficiency. Our results indicate that serum FLC showed a strong association with disease activity even after controlling for renal function; however, patients with severe renal dysfunction were not studied. Finally, we could not evaluate change in the SELENA–SLEDAI or PGA score to define flare and evaluate FLC use for prediction of flare, due to the cross-sectional nature of our data. However, this was a pilot study and we are currently conducting a longitudinal study to evaluate the role of FLC in SLE flare. We also acknowledge that trends reported in our study cannot be broken down into subcategories such as different ethnic subgroups, as it may obscure insights that could be obtained with larger studies of patient subgroups. We used the M-SLEDAI for some of our analysis in which we had to compare serum FLC with known immunologic markers such as C3 and C4. Our method was consistent with previously reported studies (30), but we acknowledge that this may not be the most optimal instrument.

We conclude that serum FLC may be a useful biomarker of SLE disease activity, and that it performs better than conventional biomarkers such as complement, dsDNA, and immunoglobulins. Serum FLC levels, although elevated in RA patients, correlate only modestly with RA disease activity. Further studies using longitudinal measurements of serum FLC in SLE are needed to evaluate its potential as a predictive marker for SLE flare.

AUTHOR CONTRIBUTIONS

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. SUBJECTS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. AUTHOR CONTRIBUTIONS
  8. Acknowledgements
  9. REFERENCES

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. Aggarwal 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. Aggarwal, Sequeira, Block, Jolly.

Acquisition of data. Aggarwal, Sequeira, Kokebie, Mikolaitis, Jolly.

Analysis and interpretation of data. Aggarwal, Sequeira, Fogg, Finnegan, Plaas, Block, Jolly.

Acknowledgements

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. SUBJECTS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. AUTHOR CONTRIBUTIONS
  8. Acknowledgements
  9. REFERENCES

We thank Drs. Sairah Alvi and Lakhvir Assi, The Binding Site, UK, for providing the serum free light chain test for our study.

REFERENCES

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. SUBJECTS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. AUTHOR CONTRIBUTIONS
  8. Acknowledgements
  9. REFERENCES
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