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The replacement of standard immunofluorescence anti-nuclear antibody (ANA) methods with bead-based assays is a new clinical option. A large, multi-racial cohort of SLE patients, blood relatives and unaffected control individuals was evaluated for familial aggregation and subset clustering of autoantibodies by high-throughput serum screening technology and traditional methods.
Serum samples (1,540 SLE patients, 1,127 unaffected relatives, and 906 healthy, population-based controls) were analyzed for SLE autoantibodies using a bead-based assay, immunofluorescence, and immunodiffusion. Autoantibody prevalence, disease sensitivity, clustering, and association with standard immunodiffusion results were evaluated.
ANA frequency in SLE patient sera were 89%, 73%, and 67% by BioPlex 2200 and 94%, 84%, and 86% by immunofluorescence in African-American, Hispanic, and European-American patients respectively. 60kD Ro, La, Sm, nRNP A, and ribosomal P prevalence were compared across assays, with sensitivities ranging from 0.92 to 0.83 and specificities ranging from 0.90 to 0.79. Cluster autoantibody analysis showed association of three subsets: 1) 60kD Ro, 52kD Ro and La, 2) spliceosomal proteins, and 3) dsDNA, chromatin, and ribosomal P. Familial aggregation of Sm/RNP, ribosomal P, and 60kD Ro in SLE patient sibling pairs was observed (p ≤ 0.004). Simplex pedigree patients had a greater prevalence for dsDNA (p=0.0003) and chromatin (p=0.005) autoantibodies than multiplex patients.
ANA frequencies detected by a bead-based assay are lower in European-American SLE patients compared to immunofluorescence. These assays have strong positive predictive values across racial groups, provide useful information for clinical care, and provide unique insights to familial aggregation and autoantibody clustering.
Diverse clinical presentations of SLE create significant diagnostic difficulties. However, the common feature of autoantibodies has been shown to associate with select clinical features (1). Previous work has found that autoantibodies are often present in SLE patient sera years before diagnosis and prior to their associated clinical symptoms (2, 3). Detection of autoantibodies contributes to SLE classification (4, 5), and some may be used to monitor the potential for disease flare (6, 7).
Prevalence of autoantibodies varies among self-reported ethnic groups. Compared with European-Americans (EA), African-American (AA) SLE patients have a higher prevalence of autoantibodies targeting Sm and nRNP proteins (8–11). Autoantibody cluster analysis provides additional information about clinical symptom associations or genetic risk; however, studies to date have either relatively small patient cohorts (12–15) or use historical antibody data measured by a variety of detection methods (16). A few studies examining the prevalence of autoantibodies in blood relatives of SLE patients have shown that low levels of SLE specific autoantibodies were detectable in clinically healthy relatives (17–19).
Although autoantibodies remain paramount in lupus diagnosis and management, detection of lupus specificities vary significantly between clinically available assays. To date, detailed evaluations of newer methodologies in large multi-ethnic SLE and control collections are incomplete. Historical methods of immunofluorescence and immunodiffusion autoantibody testing require specially trained laboratory personnel and are becoming less available in many US markets. Based on variability in autoantibody detection across and within select methods, it has been difficult to consistently and accurately measure prevalence of autoantibodies in diverse SLE patient cohorts. Questions remain about the number of SLE patients that would be potentially missed based upon testing of fewer autoantibody specificities with newer methodologies; the frequency of specific autoantibody detection across different races; and if healthy SLE family members would have higher rates of autoantibody specificities when using these newer methods.
Our primary objective was to examine prevalence, specificity, clustering and family aggregation of specific autoantibodies within a large cohort of SLE patients, unaffected relatives, and unaffected controls. Additionally, we sought to compare anti-nuclear antibody (ANA) results between a multiplex bead assay and classical detection methods (indirect immunofluorescence and immunodiffusion) in a large multi-ethnic cohort.
All experiments were performed in accordance with the Helsinki Declaration and approved by the Oklahoma Medical Research Foundation and the University of Oklahoma Health Sciences Center Institutional Review Boards. SLE patient and control samples identified from the Lupus Family Registry and Repository and the Lupus Genetics Cohorts at the Oklahoma Medical Research Foundation were used to identify SLE patient and control serum samples based on serum availability. The study group included 1,540 SLE patients, 1,127 SLE-unaffected relatives, and 906 healthy, population-based controls. SLE patient status was defined by the American College of Rheumatology (the presence of at least 4 of 11 criteria for classification) (4, 5). Upon enrollment, participants self-reported ethnicity from a list including AA, non-Hispanic EA, Hispanic (HI), Gullah, American Indian (AI), Asian/Pacific Islanders, other, mixed raced, or not reported. Gullah individuals live in the sea islands and coastal plains of South Carolina and Georgia. These individuals are grouped with AA throughout the manuscript. Sex, ethnicity, and study status composition statistics are included in Table 1.
Autoantibody screening was performed by the CAP-certified and CLIA-approved Clinical Immunology Laboratory at the Oklahoma Medical Research Foundation. Each serum sample was screened for SLE-associated autoantibodies. ANA and anti-double stranded DNA (dsDNA) were measured using indirect immunofluorescence (IIF; Hep-2 Cells and Crithidia luciliae, respectively; INOVA Diagnostics, San Diego, CA) (2, 3, 20). Detection of ANA ≥ 1:120 and anti-dsDNA antibodies ≥ 1:30 were considered positive. The IIF assays were manually read by Clinical Immunology Laboratory personnel (Nikon Optiphot Fluorescence microscope, HBO blub 100w mercury lamp, 20x). Precipitating levels of autoantibodies directed against Ro/SSA, La/SSB, Sm, nRNP, and ribosomal P were detected by immunodiffusion (21). Anti-cardiolipin (aCL) antibodies were measured by enzyme-linked immunosorbent assay with titers >20 aCL (IgG or IgM) units classified as positive (22).
The BioPlex 2200 system (Bio-Rad, Hercules, CA) uses multiplex technology for fully automated, high-throughput, FDA approved serologic analysis. The BioPlex 2200 ANA kit uses fluorescently dyed magnetic beads for simultaneous detection of 13 autoantibody specificity levels within a single serum sample. This method detects antibodies against: dsDNA, chromatin, ribosomal P, SS-A 60 (60kD Ro), SS-A 52 (52kD Ro), SS-B (La), Sm, SmRNP complex, RNP A, RNP 68, Scl-70, centromere B, and Jo-1. The manufacturer lists the following antigen sources: dsDNA synthesized by polymerase chain reaction, affinity purified 60kD Ro, La, Sm/RNP complex, Sm, chromatin, and ribosomal P proteins, and recombinantly produced 52kD Ro, RNP A, RNP 68, Scl-70, centromere B, and Jo-1.
For dsDNA, the BioPlex 2200 reports IU/mL, thereby serving as a semi-quantitative assay, with a previously determined positive cutoff of 10 IU/mL. The BioPlex 2200 reports an Antibody Index (AI) value (range 0–8) depending on the fluorescence intensity of each of the other autoantibody specificities with a positive cutoff as AI=1.0 as recommended by the manufacturer. The AI scale is standardized relative to binding of calibrators and control samples provided by the manufacturer. Factor XIIIb levels were tested as quality control by serving both as a serum confirmation test and as an indicator of sample integrity. Factor XIIIb levels (an enzyme involved in blood coagulation) have minimal variation between individuals. Low Factor XIIIb levels indicate non-serum or non-plasma samples, inappropriate dilution of samples, or sample degradation. Serum samples were excluded if they contained low Factor XIIIb errors as determined by cutoff values defined by the manufacturer.
Two group comparisons using Chi-square statistics and McNemar tests identified statistically significant differences in the prevalence of autoantibodies found in sera from SLE patients, SLE-unaffected family members, and healthy, population-based controls. Analysis comparing autoantibody prevalence between patients and unaffected relatives was performed using one patient and an unaffected relative matched on sex and race. McNemar and McNemar Exact tests, used when examining smaller subgroups, were performed using SAS version 9.1.3 (SAS Institute Inc., Cary, NC). ANA information is provided for 10 of the 13 lupus-associated autoantibodies, excluding centromere B, Jo-1 and Scl-70. Data for these three autoantibodies are presented separately. Independent subgroups were used for Chi-square analysis when comparing differences in autoantibody prevalence based upon race/ethnicity and when comparing unaffected relatives and healthy, population-based controls. The potential association between simplex and multiplex families and these 10 autoantibodies was assessed with logistic regression analyses adjusted for race/ethnicity.
In addition, we analyzed the influence of familial association with SLE by comparing SLE patients with no SLE familial occurrence (simplex) to SLE patients with one or more blood relatives affected by SLE (multiplex). To compensate for multiple testing, a Bonferroni correction was applied using a comparison-wise alpha of 0.005; thus, single comparison statistical significance was indicated when a p-value was ≤ 0.005. Hierarchical variable cluster analysis with the centroid method was used to produce related groups of similar autoantibody specificities. Tetrachoric correlations between the autoantibody profiles in SLE patients were performed using SAS version 9.2 (SAS Institute Inc., Cary, NC).
Familial aggregation of autoantibody occurrence within siblings was used to explore potential genetic influence in production of autoantibodies (23). We categorized each sibling pair as either concordant positive, discordant, or concordant negative for each of the 10 lupus-associated autoantibody specificities. The total number of concordant positive (n1), discordant (n2), and concordant negative (n3) sibling pairs were determined to calculate odds ratios as 4n1n3/((n2)2−n2). Odds ratios were calculated for sibling pairs consisting of two SLE patients and with sibling pairs of one SLE patient and one unaffected sibling.
The overall prevalence of autoantibodies detected by the BioPlex 2200 varied depending upon ethnicity (Figure 1). Of the 786 AAs, 356 patients (88.8%), 81 unaffected relatives (32.9%), and 29 healthy, population-based controls (20.9%) were positive for at least one tested autoantibody. The mean number (± SD) of autoantibodies present within this group was 4.33 (± 2.30) for patients, 2.38 (± 2.09) for unaffected relatives, and 1.31 (± 0.71) for healthy, population-based controls. Of the 507 HIs, 163 patients (73.1%), 34 unaffected relatives (23.4%), and 19 healthy, population-based controls (13.7%) had at least one positive autoantibody specificity. The mean number (± SD) of autoantibodies within this group was 3.68 (± 2.21), 1.74 (± 1.08), and 1.21 (± 0.71), respectively. Of the 1,872 EAs, 483 SLE patients (67.4%), 177 unaffected relatives (26.5%), and 46 healthy, population-based controls (9.5%) were found to be positive for at least 1 of the 10 autoantibody specificities associated with SLE. The mean number (± SD) of autoantibodies present within positive samples was 2.79 (± 1.79) for patients, 1.54 (± 0.99) for unaffected relatives, and 1.41 (± 1.07) for healthy, population-based controls. For the SLE patient groups, 53.1% of AAs, 34.3% of HIs, and 18.7% of EAs had 4 or more tested autoantibody specificities (Figure 1). Using this commercially available standard antibody testing platform only 88.8% (AA), 73.1% (HI) and 67.4% (EA) of SLE established cases were positive for autoantibodies. Therefore, approximately 11–33% of these established SLE cases would be ANA-negative by this detection method.
Analysis of individual autoantibody specificities revealed significant differences among ethnic groups (Figure 2). Compared to EA SLE patients, AA patients displayed a significantly higher prevalence of autoantibodies against dsDNA, chromatin, ribosomal P, 60kD Ro, Sm, Sm/RNP, RNP A, and RNP 68 (χ2 =13.3–129.5, p ≤ 0.001 for all comparisons) (Figure 2A). HI patients also had significantly higher autoantibody prevalence than EA patients for anti-dsDNA, anti-ribosomal P, anti-Sm/RNP, and anti-RNP 68 autoantibody specificities (χ2 =8.7–11.3, p ≤ 0.004 for all comparisons). Compared to HI patients, AA patients had higher autoantibody prevalence for chromatin, Sm, Sm/RNP, RNP A, and RNP 68 specificities (χ2 =11.6–41.5, p ≤ 0.001 for all comparisons). Autoantibody prevalence in unaffected relatives displayed no effect due to self-reported ethnicity (Figure 2B). Interestingly, the prevalence of anti-52kD Ro was significantly higher in AA population-based controls than in EA controls (Figure 2C).
Using the BioPlex 2200 system, 76% of SLE patients, 28% of SLE unaffected relatives, and 12% of controls were found to have at least one lupus autoantibody specificity. As expected, prevalence of all 10 autoantibodies was significantly higher in SLE patients compared to controls (χ2 =80.9–638.1, p<0.001 for all comparisons). When comparing the SLE patients to unaffected relatives, all specificities except anti-La were significantly more common in the SLE patient subgroup (p<0.005). The mean (± SD) number of detectable autoantibody specificities was 3.58 (± 2.18) for patients, 1.78 (± 1.41) for unaffected relatives, and 1.29 (± 0.83) for healthy, population-based controls.
Prevalence of individual autoantibodies varied among SLE patients; anti-chromatin (55.8%) was the most prevalent while anti-ribosomal P (12.4%) was the least. Unaffected relatives also had varied autoantibody prevalence. Chromatin (12.5%) was also the most prevalent specificity in the SLE-unaffected relative group. Compared to unrelated controls, SLE-unaffected relatives had significantly higher prevalence for dsDNA, chromatin, 60kD Ro, 52kD Ro, Sm, and RNP A autoantibodies (p< 0.005 for all comparisons). Anti-Scl-70 prevalence was 2.2% in SLE patients and 1.2% in both SLE-unaffected relatives and population-based controls (no significant difference). Anti-centromere B responses were detected in 3.7% of SLE patients and less than 1% of both unaffected relatives and controls (p< 0.001), with females more than 4 times more likely to be positive (p< 0.001). Jo-1 antibodies were present in less than 0.5% of all samples. Inclusion of prevalence for Scl-70, centromere B, and Jo-1 did not significantly affect the overall ANA prevalence rates and therefore were not included in subsequent analyses.
Indirect immunofluorescence (IIF) is the historical standard for broad-scale ANA screening. Comparisons of results from the IIF assay to the BioPlex ANA assay indicate differences in the sensitivity and specificity of detection. We found that 88.8% of SLE patients, 34.7% of unaffected relatives, and 18.3% of healthy, population-based controls were ANA positive by IIF at a serum titer of ≥ 1:120, whereas 76.4%, 27.9%, and 12.4%, respectively, were positive by BioPlex 2200. When considering all 13 measured autoantibodies, 78.2% of SLE patients, 28.9% of unaffected relatives, and 13.8% of unrelated controls were ANA positive using the BioPlex 2200 ANA assay, extremely similar to the rates detected when analyzing only the 10 lupus-associated autoantibodies. Differences were most striking in EA patients (Table 2). IIF resulted in higher autoantibody prevalence among SLE patients, SLE-unaffected relatives and controls in all ethnic groups, suggesting that the IIF assay is able to detect a more diverse repertoire of autoantibodies than those included within the BioPlex 2200 ANA kit. Interestingly, similar prevalence rates among AA controls were found when comparing the BioPlex assay and IIF.
Further comparative analysis between diagnostic efficacy of the BioPlex assay and IIF ANA screening was performed using positive predictive value (PPV) and negative predictive value (NPV) for each assay. PPV and NPV analysis was performed within the combined group of SLE patients and healthy, population-based controls as a whole, as well as within ethnic subgroups (Table 2). Overall, the PPV for the IIF assay (89.2%) was similar to that of the BioPlex assay (91.3%), but the NPV proved to be better within the IIF assay (81.1% versus 68.6% for BioPlex). Within the individual ethnic subgroups, AAs had the highest PPV and NPV in both assays.
With established clinical associations and therapeutic implications placed on anti-dsDNA in SLE, we specifically examined the consistency of autoantibody detection between the BioPlex 2200 assay and the current standard IIF detection method. Prevalence of dsDNA detected by IIF was 24.3% of SLE patients, 0.4% of unaffected relatives, and 0.1% of unaffected unrelated controls. Examination of dsDNA based on ethnicity revealed that this specificity was greatest in AA SLE patients (28.2%) and had similar prevalence in both EA (21.5%) and HI (21.1%) SLE patients. However, dsDNA autoantibody specificity had higher prevalence in all three ethnicities using the BioPlex 2200 assay. AAs display the largest difference between IIF and BioPlex 2200 dsDNA results with 28.2% and 35.7% positive respectively. In HIs, 35.0% tested positive for anti-dsDNA antibodies using the BioPlex 2200 assay and 21.1% were positive using IIF, while EAs displayed 23.4% and 21.5% anti-dsDNA autoantibody prevalence respectively.
When considering all 13 Bio-Rad BioPlex 2200 ANA analytes, 29.7% of EAs, 10.5% of AAs and 25.1% of HI SLE patients were negative. Additionally, we have examined family members and control individuals for false negative results. About 30% from AA, EA, and HI blood relatives and about 20% from each control ethnic groups are positive by IIF but negative by BioPlex 2200 for dsDNA antibodies. Of the remaining autoantibody specificities, only the blood relative individuals were false negative for Ro, La, or nRNP autoantibodies. Here, less than 1% of AA (Ro, La, nRNP) and EA (Ro and nRNP) was false negative. We next examined the presence of false positive results. In SLE individuals, 2.11% AA, 6.3% EA, and 7.6% of HI patients were positive by BioPlex 2200 but negative by IIF. In blood relatives, 11% from each ethnicity received false positive results compared to 11.4% AA, 13.2% EA, and 13% HI of control individuals. To explore the possibility of other potential antigenic targets among the BioPlex 2200 ANA negative samples, we examined the prevalence of aCL antibodies. aCL antibodies were lower in BioPlex 2200 ANA negative samples (10.4%) compared to the BioPlex 2200 ANA positive samples (15.6%, p=0.02).
To reference our findings using the BioPlex 2200 to traditional standards for detection of SLE antibody specificities, we compared the BioPlex 2200 results with the corresponding immunodiffusion results (Table 2). Within SLE patients, the sensitivity of the BioPlex 2200 results for the Ro, La, ribosomal P, Sm, and nRNP complex (nRNP A and nRNP 70k) tests were 0.92, 0.92, 0.83, 0.89, and 0.92 respectively. The BioPlex 2200 anti-dsDNA detection had a sensitivity of 0.71 and a specificity of 0.80 when compared to IIF. The BioPlex 2200 exhibited sensitivity of 0.70 and 0.80 and specificities of 0.98 and 0.998 for centromere B and Jo-1 detection, respectively.
To evaluate the effect of familial relation on autoantibody prevalence, SLE patients were categorized as simplex (having no known familial relation to other SLE patients) or multiplex (having at least one blood-related family member with SLE). Of the 1,540 SLE patients, 53.8% were classified as multiplex, 26.5% were classified as simplex, and 19.7% were unknown. Interestingly, significantly higher prevalence of anti-dsDNA was found among simplex patients (35.0%) than among multiplex patients (25.1%) (χ2 =14.37, p<0.0002). AA and HI patients are significantly more likely than EA patients to be anti-dsDNA positive, so multiple logistic regression was performed to determine if a simplex SLE patient is more likely to be anti-dsDNA positive, regardless of ethnicity. Using dsDNA positivity as the outcome and both ethnicity (EA, AA, HI, and other) and status (simplex or multiplex) as predictors, we found that simplex patients have a significantly greater dsDNA prevalence than multiplex patients, even after adjusting for ethnicity (OR=1.59, p = 0.0003). Significantly higher prevalence of chromatin autoantibodies are also found among simplex patients (59.1%) compared to multiplex patients when adjusted for ethnicity (50.8%) (χ2 =8.33, p = 0.005). No other differences were noted comparing prevalence of other autoantibody specificities between simplex and multiplex SLE patients.
Familial aggregation analysis was performed as a measurement of genetic influence on production of certain autoantibody specificities. Autoantibody specificities with evidence of familial aggregation within SLE affected sibling pairs included ribosomal P (OR=5.34, p=0.002), Sm/RNP (OR=2.90, p=0.002) and 60kD Ro (OR=2.56, p=0.004) (Table 3). Within sibling pairs consisting of an SLE affected patient and an unaffected sibling, no significant aggregation for individual autoantibody specificity was detected. In examining the relationship between ANA positivity and family aggregation results from the BioPlex 2200 indicated familial aggregation in SLE affected sibling pairs (OR=2.51, p=0.01) as well as in the SLE patients and unaffected sibling pairs (OR=0.38, p<0.001). Immunofluorescent ANA assays showed aggregation of positivity between SLE patients and unaffected sibling pairs (OR=0.49, p=0.004).
Hierarchical cluster analysis of antibody profiles detected within all SLE patient samples showed three distinct subgroups of autoantibodies: 60kD Ro, 52kD Ro, and La; Sm, Sm/RNP, nRNP A, and nRNP 68, and chromatin; and dsDNA and ribosomal P (Figure 3). Upon performing the same analysis with SLE patients sub-grouped on the basis of ethnicity, the three clusters described above appear for the AA patients. However, in EA and HI patients, antibodies against chromatin tended to cluster with dsDNA and ribosomal P. All results accounted for approximately 90% of the variability for each set of patients.
The goal of this study was to characterize autoantibody prevalence, specificity, clustering, and familial aggregation within a large cohort of SLE patients, unaffected blood relatives, and healthy, population-based controls using the Bio-Rad BioPlex 2200 ANA screen. Our results further confirm the high sensitivity of the BioPlex 2200 ANA assay identified by other groups (24–26). Additionally, our study detected variable autoantibody prevalence in SLE patients based on ethnicity, identified a subset of autoantibodies present in unaffected family members, demonstrated an enrichment of anti-dsDNA in simplex pedigree, established familial aggregation of select autoantibodies, and explored the interrelatedness of three subsets of common SLE autoantibodies.
The BioPlex 2200 ANA assay has potential to serve as an ANA screening and detection method to identify individual antibody specificities. Autoantibody prevalence within SLE patients was found to be higher when using IIF ANA detection than when using BioPlex 2200 assay detection. Differences in autoantibody frequency may be influenced by several factors. First, the BioPlex 2200 detected the presence of only 13 defined specificities whereas IIF detects antibodies against a variety of cellular components such as Ku, Ki, Su, 4–6 S RNA, alpha actinin, and single stranded DNA (27), as well as unknown antigens. Nevertheless, specificities included within the BioPlex 2200 assay allowed for positive identification of the majority of IIF ANA positive patients. Interestingly, using this detection method, one in five AA healthy individuals would be ANA positive. Unfortunately, a number of ANA IIF positive EA patients (32.6%) have no detectable autoantibodies using the BioPlex 2200 ANA screen, thereby suggesting a number of SLE patients may be ANA negative by this method. When examining EA controls, the BioPlex 2200 ANA screen found that prevalence of antibodies was nearly 50% less than the prevalence detected by ANA IIF screening at a titer of ≥ 1:120. This difference in ANA positivity and the overall prevalence of other autoantibodies cannot be explained by medication or changes in ANA positivity over five years of disease as no significant differences were observed. Interestingly, as the length from SLE diagnosis increased to 10 years, the ANA negativity decreased significantly in AA (p=0.0281) and EA (p=0.0077). As the overall percentage of ANA positive healthy, population-based controls was still higher than optimal, the BioPlex 2200 ANA will result in more false negative results in SLE patients if used as a sole ANA test.
We examined PPV and NPV of each assay to compare their abilities to identify SLE patients. Of particular note, the PPV for both the BioPlex 2200 and IIF assays in AAs were strong (92.5% and 93.1% respectively), while the PPV for HIs (89.6%) and EAs (91.3%) was slightly better than IIF (88.3% and 87.3% respectively). However, NPV was better for IIF overall (81.1% versus 68.6% for BioPlex 2200) and within the individual ethnic subgroups: AA (82.2% and 71.0% respectively), HI (76.5% and 66.7%), and EA (79.4% and 65.3%). Our study uses a cutoff titer of 1:120 as a positive result (28), as the PPVs are decreased for the total cohort (83.7%) and the individual ethnicities (AA=90.8%; HI=82.7%; EA=85.2%). Overall, these results support the premise that the BioPlex 2200 assay has a better PPV, while the IIF assay had a stronger NPV for autoantibody detection within established SLE patients.
The BioPlex 2200 ANA assay also allowed for identification of autoantibody specificities within unaffected family members of SLE patients. This is of considerable interest as we continue to explore genetic and environmental influences leading to development of SLE and further characterization and serial monitoring of these autoantibody positive, unaffected relatives. Previous studies contribute to the idea that production of specific autoantibodies by SLE patients and family members is the result of complex genetic influence and might indicate genetic susceptibility to autoimmune disease (18, 29). Previous studies demonstrating familial aggregation of autoantibodies were done using immunodiffusion data. In those studies, Ro, La, nRNP, and ribosomal P had significant aggregation within patient/patient sibling pairs, while significant aggregation in patient/unaffected relative pairs was detected for dsDNA, Ro, La, nRNP, and ribosomal P (29). In our study using BioPlex 2200, familial aggregation of autoantibodies was detectable in patient/patient sibling pairs (ANA, ribosomal P, Sm/RNP, and 60kD Ro) and ANA only within patient/unaffected relative pairs, suggesting a more refined heritability pattern.
Previous studies have introduced the concept of antibody cluster analysis to create serologic profiles which may assist with diagnosis, prognosis, and disease subsetting (12–16). In this study we identified clusters of autoantibodies associated with previously defined physical antigen complexes or other suspected temporal associations (30–32). Our study indicated antibody clustering between Ro and La autoantibodies, Sm and nRNP antibodies, and dsDNA and ribosomal P autoantibodies. These results are in agreement with the published literature (12–16), with the exception of an association between nRNP and ribosomal P autoantibodies. Autoantibody cluster analysis of separate racial groups revealed variation of clustering within AAs as compared to the trend established by EAs and HIs. A recent study identified ANA variance in healthy adults across six different geographical regions suggesting an environmental component to autoantibody prevalence (33). Thus differing genetic or initial environmental influences may alter autoantibody prevalence, and suggests the need to analyze racial groups separately when examining etiology of disease, potential biomarkers, or any other proposed diagnostic or prognostic application of autoantibodies in SLE. Ongoing efforts to integrate these findings into a diagnostic or prognostic algorithm are underway.
In conclusion, we found the BioPlex 2200 ANA screen to be a highly informative tool for assessment of autoantibody prevalence in SLE patients. Our analysis identified a significant increase in prevalence and total number of autoantibodies in AA SLE patients compared to EA SLE patients. Analysis of unaffected relatives of SLE patients and healthy, population-based controls revealed an increased prevalence for a specific group of autoantibody specificities within unaffected relatives and requires further examination of the genetic influence of autoantibody production. Cluster analysis of autoantibody prevalence displayed association between autoantibody specificities which can be subsequently used to establish more accurate algorithms for interpretation of ANA testing results in a racial-specific way, thereby maximizing the informative potential of ANA screening. Autoantibodies play a fundamental role in diagnosis and treatment of SLE and with further characterization may provide a unique perspective into pathogenesis.
We thank Aaron Guthridge and David Wiist Jr for their technical assistance and Tony Prestigiacomo, PhD and Steve Binder, PhD for their expertise with the Bio-Rad BioPlex 2200 instrument and data interpretation. We also thank the Clinical Immunology Lab at Oklahoma Medical Research Foundation and more specifically Cathy Velte, Camille Anderson, and Sandy Long. We thank the Lupus Family Registry and Repository, including their personnel, participants and referring physicians.
Funding Sources: This work was supported by the NIH (R01AI31584 (JBH), P01AR49084 (JBH), P30AR053483 (JAJ), P20RR20143 (JMG), P30RR031152/P30GM103510 (JAJ), U19AI082714 (JAJ), U01AI101934 (JAJ), UL1RR029882 (GSG and DLK) and P60AR049459 (GSG and DLK); US Department of Veterans Affairs (RHS, JBH); OMRF Pre-Doctoral Fellowship Award (BFB); and the Lou Kerr Chair in Biomedical Research (JAJ). This work is also made possible by the Kirkland Scholar Award Program at the Hospital for Special Surgery in New York City (JBH and JAJ). The contents of this work are solely the responsibility of the authors and do not necessarily represent the official views of the NIH or its relevant institutes.
Financial Disclosures and Conflict of Interest: J.B.H. is a consultant for Bio-Rad. Bio-Rad provided the BioPlex 2200 ANA screening kits for this study. The authors report no other financial conflict of interest.