Skip to main content
Download PDF

Predictive biological markers of systemic lupus erythematosus flares: a systematic literature review

Arthritis Research & Therapy volume 19, Article number: 238 (2017) Cite this article

Abstract

Background

The aim of this study was to identify the most reliable biomarkers in the literature that could be used as flare predictors in systemic lupus erythematosus (SLE).

Methods

A systematic review of the literature was performed using two databases (MEDLINE and EMBASE) through April 2015 and congress abstracts from the American College of Rheumatology and the European League Against Rheumatism were reviewed from 2010 to 2014. Two independent reviewers screened titles and abstracts and analysed selected papers in detail, using a specific questionnaire. Reports addressing the relationships between one or more defined biological test(s) and the occurrence of disease exacerbation were included in the systematic review.

Results

From all of the databases, 4668 records were retrieved, of which 69 studies or congress abstracts were selected for the systematic review. The performance of seven types of biomarkers performed routinely in clinical practice and nine types of novel biological markers was evaluated. Despite some encouraging results for anti-double-stranded DNA antibodies, anti-C1q antibodies, B-lymphocyte stimulator and tumour necrosis factor-like weak inducer of apoptosis, none of the biomarkers stood out from the others as a potential gold standard for flare prediction. The results were heterogeneous, and a lack of standardized data prevented us from identifying a powerful biomarker.

Conclusions

No powerful conclusions could be drawn from this systematic review due to a lack of standardized data. Efforts should be undertaken to optimize future research on potential SLE biomarkers to develop validated candidates. Thus, we propose a standardized pattern for future studies.

Background

Systemic lupus erythematosus (SLE) is a systemic autoimmune disease characterized by a relapsing–remitting course or flare pattern. Flares, defined by an increase in disease activity over a defined amount of time, can be measured using various scores. Flares might lead to substantial organ damage, increasing morbidity and mortality rates and resulting in higher healthcare costs [1]. Flares are unpredictable in frequency and severity. It is important to identify patients at greater risk for flares to follow them up closely, to make early diagnoses and to initiate rapid treatment or even to consider preventive therapies [2]. Because of a better understanding of SLE pathogenesis, an increasing number of biomarkers have emerged. Close relationships of serum, plasma or urinary profiles with the course of the disease have been explored. Since the 1970s, no investigators have succeeded in identifying a biomarker with the potential to predict efficiently the occurrence of new flares, despite great clinical necessity. We conducted a systematic review of the literature to identify all of the data available on biological SLE flare predictors.

Methods

We registered our protocol in PROSPERO (an international prospective registry of systematic reviews) under registration number CRD42015026141. The systematic review was written in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines [3].

Search strategy

Two investigators (NG and AM) conducted a systematic hand search of the literature in collaboration with a research librarian (EM) using two electronic databases, Medline and EMBASE, from inception to 30 April 2015. In order to cover any research that was not yet published as a manuscript, congress abstracts of the European League Against Rheumatism (EULAR) and the American College of Rheumatology (ACR) from 2010 to 2014 were considered. The search was restricted to the English and French languages and to human subjects. The search keywords in Medline were: “biological markers”, “lupus erythematosus, systemic”, “severity of illness index”, “flare(s)”, “exacerbation(s)”, “predictive value of tests” and "disease progression". Search terms in EMBASE were: “systemic lupus erythematosus”, “biological markers”, “flare(s)”, “exacerbation(s)”, “predictive value of tests” and “disease progression”. The exact search strategies are provided in Additional file 1. Abstract lists from the EULAR and the ACR were searched using the keyword “systemic lupus erythematosus”. Reference lists of selected papers were hand searched for other relevant publications. We also searched clinicaltrials.gov in May 2015 for unpublished studies on this topic.

Eligibility criteria and study selection

The two investigators independently screened the titles and abstracts of references, selected articles for full-text review using the inclusion criteria and assessed the methodological quality. Any discrepancies were resolved through consensus. Two supervisors (PD and CR) participated in resolving disagreements.

Interventional studies (randomized or non-randomized, controlled trials) and observational studies (case–control or cohort studies) were included, whereas case reports, literature reviews and editorials were not included. We considered publications involving adults with SLE, addressing the relationships between one or more defined biological test(s) and the occurrence of disease exacerbation. The exclusion criteria were paediatric subjects, other autoimmune diseases and assessment of the role of genetic markers. When duplicate reports were published on the same study, the report with more complete information was extracted.

Data extraction

The two investigators independently extracted data from each study using a systematic data extraction form (available on request) developed for this specific purpose, including sample size, socio-demographic data and SLE disease characteristics (duration, treatment(s)), follow-up duration and frequency, disease activity measurement (activity indices, definition of flare) and biomarker characteristics (type, measurement method, cut-off values for positivity and increase). After extracting data independently for every study, discrepancies were resolved through consensus.

Data synthesis

Biomarkers were allocated into one of the following two groups: biomarkers traditionally performed in clinical practice; and experimental and newly developed biological markers. Raw data are available upon request from the first authors.

Results

From both databases, 4668 records were retrieved, and we added 20 studies identified from the reference lists of papers (Fig. 1). A total of 4126 studies failed to meet the required criteria and 135 full-text articles were retained for complete screening. A total of 69 publications were finally included. No ongoing or unpublished trials relative to this topic were found in the www.clinicaltrials.gov database. The detailed characteristics of the included studies appear in Additional file 2.

Fig. 1
figure 1

Study selection process. ACR American College of Rheumatology, EULAR European League Against Rheumatism

Full size image

Predictors of flares: biomarkers traditionally performed

Anti-double-stranded DNA antibodies

From a 1979 study by Swaak et al. [4], changes in levels of anti-double-stranded DNA antibodies (anti-dsDNA ab) during the course of the disease were supposed to be related to SLE exacerbations. Table 1 reviews 28 studies, highlighting the major findings [4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31].

Table 1 Predictivity of anti-dsDNA antibodies in SLE flares
Full size table

Six studies examined anti-dsDNA ab at baseline without follow-up measurements [5,6,7,8,9,10]. Four studies failed to show any association between baseline anti-dsDNA ab and subsequent flares [5,6,7,8]. Two studies of larger size showed that the elevated baseline antibody level was an independent predictor of moderate-to-severe SLE flares (HR = 1.83 (95% confidence interval (CI) 1.29–2.60)) for any new British Isles Lupus Assessment Group (BILAG) A domain at week 52 [9] or a risk factor only for haematologic flares (OR = 2.33 (95% CI 1.34–4.04), p = 0.0033) [10].

An increase in anti-dsDNA ab during the course of the disease was found to precede general flares in nine studies [4, 7, 11,12,13,14,15,16,17], whereas six studies [6, 18,19,20,21, 31] failed to prove such an association.

Interestingly, focusing on renal flares, patients with positive anti-dsDNA ab who had persistent or increasingly levels were at greater risk for subsequent SLE nephritis [22,23,24].

The results were expressed in terms of sensitivity, specificity and predictive values in six studies [25,26,27,28,29,30] (Additional file 3). The conclusions were heterogeneous: sensitivity ranged from 27.7% [27] to 100% [30], specificity from 13% [26] to 89.1% [28], positive predictive value (PPV) from 4.1% [28] to 59% [25] and negative predictive value (NPV) from 67% [26] to 97.5% [28].

The choice of a higher anti-dsDNA ab threshold (>300 IU/ml vs 50–300 IU/ml) led to higher specificity (89.1% vs 57.1% for mild/moderate flares) and lower sensitivity (28.4% vs 51.8% for mild/moderate flares) [27, 28].

Data concerning the delay between the elevation of anti-dsDNA ab and subsequent flares were not always available. When available, they were heterogeneous, ranging from once per month [7, 9, 13, 17, 24] to every 6 weeks [4, 11, 12], every 3 or 4 months [6, 8, 15, 16, 20, 22, 23, 27, 28], every 6 months [10, 14] and up to 1 year or 18 months [18, 26]. In addition to data concerning delays, those concerning the amount of increase of anti-dsDNA ab titres were frequently missing [5, 6, 8,9,10,11,12, 14, 19, 20, 22, 23]. The threshold most frequently chosen to define a significant rise was an increase greater than 25% of the preceding value [7, 13, 16, 26, 29].

Complement and complement split products

Complement and/or complement split products were analysed in 19 studies [6,7,8,9,10, 12, 19, 20, 23, 27, 28, 31,32,33,34,35,36,37,38] (Table 2). The first study assessing the predictivity of complement consumption in SLE flares was conducted by Lloyd and Schur in 1981 [19], and reports the importance of complement depression before exacerbations. Low baseline complement levels could be associated with subsequent SLE flares according to seven studies [7,8,9,10, 35, 36, 38] but these results were not consistent with each other, depending on the complement fraction studied (C3 and/or C4 and/or CH50): C3 was found to be associated with flares in four studies [7, 9, 10, 36], whereas C4 was found to be associated in three studies [8, 10, 35] and CH50 in two studies [7, 38]. The occurrence of complement decrease during the course of the disease as revealed by serial measurements was associated with a subsequent flare in two studies [12, 32], whereas three other studies did not prove such an association [6, 20, 34]. Persistently low C3 was predictive of renal flares in two independent studies [10, 23].

Table 2 Predictivity of complement in SLE flares
Full size table

Results were expressed in terms of sensitivity, specificity and predictive values in four studies [27, 28, 33, 37] (Additional file 4). The results were heterogeneous: decreased C3 sensitivity ranged from 28.7% [27] to 45% [33], and decreased C3 specificity ranged from 63.1% [27] to 87.5% [28]. Decreased C4 sensitivity ranged from 19.1% [28] to 64.0% [33], and decreased C4 specificity ranged from 45.0% [33] to 79.0% [27]. CH50 sensitivity and specificity were evaluated only once, with the respective results of 71.0% and 29.0% [33]. Assessments of NPV were always satisfactory, with values superior to 95% (ranging from 95.8% for low C4 [28] to 98.3% for very low C3 [28]).

Some complement split products (C3a, C4d, Ba, Bb, SC5b9) were found to be informative in predicting lupus flares, particularly C3a (1–2 months prior to disease flare, C3a levels increased significantly for all 10 patients studied who experienced flares later), C4d (highest sensitivity 86.0%) and Bb (highest specificity 81.0%) [32, 33].

Anti-C1q antibodies

Authors reported very good NPV for lupus nephritis [30, 39, 40], ranging from 97.0% (95% CI 88.0–99.0%) [40] to 100.0% [30, 39]. For instance, in one study, none of the 50 patients with negative anti-C1q antibodies developed any sign of renal involvement during follow-up (median duration 24 months, range 1–60 months) [39]. NPV was less impressive in one study (70.0%) [29] (Table 3). PPV was always unsatisfactory (ranging from 50 to 56%). The high NPV of anti-C1q antibodies, especially for nephritis [30, 39], seemed to be of particular interest, suggesting that the occurrence of severe nephritis is quite improbable in the absence of anti-C1q antibodies. These results seemed promising for clearly identifying patients who are at low risk for flares or renal involvement.

Table 3 Predictivity of anti-C1q antibodies in SLE flares
Full size table

Anti-nuclear antibodies, antibodies against extractable nuclear antigens and antibodies against nucleosomes

Antibodies against extractable nuclear antigens (anti-ENA) and anti-nucleosomes were studied in eight reports [5, 6, 9, 22, 36, 41,42,43] (Table 4). Associations between anti-ENA and the occurrence of a flare were found in six studies, with the important limitation that these results were reported in only one study each, and none of them has been reproduced: anti-nuclear antibodies (ANA) [6], baseline anti-ENA [36], anti-Sm [5, 9], anti-histone [22] and anti-nucleosome [42]. No correlations with disease activity were found with anti-Ro [41, 43], anti-La, anti-Sm and anti-ribonucleoprotein (anti-RNP) [41]. Repetition of the measurement of anti-ENA antibodies appeared not to be useful in assessing disease activity in SLE, and the determination of anti-ENA antibody profiles should be limited to the diagnosis period.

Table 4 Predictivity of anti-ENA in SLE flares
Full size table

Circulating immune complexes

Two reports [18, 19], published in 1980 and 1981, studied the associations of circulating immune complexes with the occurrence of flares. In the study by Abrass et al. [18], circulating immune complexes were measured by both solid-phase (SC1q) and fluid-phase C1q (FC1q) binding assays. An increase in SC1q binding assay results correctly predicted a change in the manifestations of SLE 82% of the time. In comparison, changes in FC1q binding assay failed to predict a change in disease activity correctly. In the other study, immune complexes were measured by C1q binding assay C1qBA and ADCC (antibody-dependent cell-mediated cytotoxicity) inhibition assay [19]. Only 50% of the patients had increased levels of C1qBA prior to clinical exacerbation. These tests are no longer used in clinical practice.

Erythrocyte sedimentation rate and C-reactive protein

No statistically significant association between change in erythrocyte sedimentation rate (ESR) between two visits and a future change in disease activity was found [38, 44]. In another study, ESR elevations were associated with flares [6].

Petri et al. [9] demonstrated that, according to univariate analysis, elevated C-reactive protein (CRP) at baseline predicted SLE flares by three indices (BILAG, Safety of Estrogens in Lupus Erythematosus National Assessment–Systemic Lupus Erythematosus Disease Activity Index (SELENA-SLEDAI), SLEDAI Flare Index (SFI)) during the course of the study, but this association was no longer persistent in multivariate analysis.

Predictors of flares: experimental and newly developed biological markers, a new hope?

Cytokines, chemokines and their receptors

Several cytokines and chemokines or their soluble receptors were examined in 14 studies [9, 45,46,47,48,49,50,51,52,53,54,55,56,57] (Table 5). The ability of B-lymphocyte stimulating factor (BLyS), also known as B-cell activating factor from the TNF family (BAFF), to predict a subsequent SLE flare was dismissed in two studies [47, 52] but confirmed in two others [9, 48]. Three studies revealed an increase in the plasma levels of soluble IL-2R or sCD25 (which is the alpha chain of IL-2R) prior to disease exacerbation [52, 55, 57], while another study revealed a higher expression of CD25 on the surface of circulating lymphocytes [56]. The results concerning other cytokines, chemokines and receptors were single reports; consequently, generalization of these data did not seem suitable.

Table 5 Predictivity of cytokines and chemokines in SLE flares
Full size table

Expression of specific markers by T cells

Five studies [38, 55,56,57,58] assessed the relationship between the expression of specific antigens or specific transcription factors by T cells and disease flares. Markers testifying to the activation of T lymphocytes were the most studied, by measurement of serum levels of specific activation antigens or by flow cytometry. Levels of sCD27 increased before exacerbation in the three patients studied [55]. HLA-DR expression by CD8+ T lymphocytes [38] or by CD4+ lymphocytes [56] appeared to be associated with the occurrence of a lupus flare. Expression of CD25 was also considered a marker of lymphocyte activation, and the results achieved were presented in the preceding section [56].

Another study examined the expression of the specific transcription factor FoxP3 in different subsets of CD4+ T cells (naïve T-regulatory (Treg) cells, effector Treg cells and FoxP3+ non-Treg cells) in a small cohort of SLE patients [58]. Most of the patients who developed flares had anomalies in FoxP3+CD4+ T-cell subsets before flares (the most prevalent anomaly observed before flares was an increase in FoxP3+ non-Treg cells), while those who maintained the absence of anomalies did not develop flares.

Markers of endothelial activation

Three cellular adhesion molecules, required for cell-to-cell interactions, were evaluated in two studies [59, 60]: the results were contradictory regarding soluble vascular cell adhesion molecule-1 (sVCAM-1) in the two reports and were clearly negative for soluble intercellular adhesion molecule-1 (sICAM-1) and soluble E-selectin (sE-selectin).

Urinary markers

Seven records studied biomarkers in the urine of SLE patients [61,62,63,64,65,66,67]. Five molecules, namely tumour necrosis factor-like weak inducer of apoptosis (TWEAK), macrophage colony-stimulating factor (M-CSF), neopterin, regulated on activation, normal T-cell expressed and secreted (RANTES) and urinary neutrophil gelatinase-associated lipocalin (uNGAL), were measured in urine by ELISA (or by reverse-phase high-performance liquid chromatography for neopterin) in five studies [63,64,65,66,67]. These markers were all positively correlated with subsequent SLE renal flares. TWEAK seemed of particular interest because the results were consistent through three different studies, and this marker is considered a potentially promising therapeutic target for lupus nephritis [68]. While other reports evaluated single molecules measured in urine, two studies assessed the expression of transcription factors or the transcriptional expression of cytokines [61, 62]. One study evaluated the expression of T-bet by urinary sediment cells and revealed that a high urinary T-bet expression level was an independent predictor of a lupus flare [61]. In the other study, a significant increase was found in the mRNA levels of monocyte chemotactic protein (MCP)-1 and FoxP3 before disease flares, along with decreases in IL-17 and GATA-3 [62].

Other experimental biomarkers

In 1991, ter Borg et al. [69] evaluated the ability of anti-70-kDa and anti-A polypeptides antibodies to predict SLE flares but failed.

Plasma adiponectin did not change significantly before flares, whereas longitudinal testing revealed that urine adiponectin increases began in the 2 months prior to renal flares [70].

Plasma cell peaks (CD27++, CD20– cells) preceded the increase in disease activity [71].

Patients with circulating anti-dsDNA ab-secreting cells had significantly lower cumulative rates for remaining disease flare-free than patients without these cells in the circulation [72]. Nearly all of the patients with circulating anti-dsDNA ab-secreting cells relapsed within 12 months.

Discussion

Despite the clinical interest in and numerous publications on biomarkers in SLE, there is no validated and widely accepted biomarker for flare prediction in SLE to date. In this systematic review, none of the newly studied biomarkers stood out, and the routinely performed biomarkers appeared to be deceiving, with contradictory results. Data concerning some biomarkers, such as anti-C1q antibodies, BLyS or TWEAK, seemed promising and could be useful in identifying SLE patients who are at high risk for flares and especially at high risk for renal disease, but these results require confirmation in larger studies. Clinicians must be aware that, at this time, none of these biological markers is completely reliable in diagnosing exacerbations, and none of them can be considered a serologic gold standard. The use of some laboratory parameters, such as anti-dsDNA ab, complement and anti-C1q, and their close follow-up are still considered the most powerful tools in predicting disease flares, even if limited. Thus, they are included in the current EULAR recommendations [73] and should not be abandoned easily.

This systematic review was, to the best of our knowledge, the first aiming to compile all of the available data on biomarkers predicting SLE flares. The strengths of this study included a comprehensive review of the reports on predictive biomarkers in SLE with well-defined inclusion criteria, performed with the help of a research librarian and with data extraction performed by two independent reviewers.

Our conclusions must be considered in the presence of possible limitations. One of the main limitations of this work was the high heterogeneity between the study designs, reflecting the heterogeneity of the disease itself. The best study design to emphasize the predictivity of a biomarker is a prospective study. Nevertheless, 14 studies were retrospective and might have been biased. Another design issue was follow-up frequencies: patients were either observed monthly [13, 17] or every 3 months [20, 22]. The risk of missing an increase in antibody levels is negligible with monthly measurements. Therefore, studies with monthly follow-ups might detect correlations more often. However, the clinical utility of monthly measurements has not yet been assessed, and the economic burden of close monitoring must be justified. The study populations were heterogeneous by ethnicity, time from diagnosis, sample size, treatment and disease activity at baseline. Above all, the heterogeneity in flare definitions and disease activity measurements was the most important limiting factor and could prevent comparison of different studies. The concept of a flare in this disease is very complex, and there is no universally accepted definition to date. The absence of a standardized definition complicates the interpretation and comparison of findings. Clinicians use many indices (SLEDAI, SELENA-SLEDAI, BILAG, Physician Global Assessment (PGA), SFI, European Consensus Lupus Activity Measurement (ECLAM)), which, although valid and sensitive, do not evaluate the disease in the same manner [74]. None of them has emerged yet as a gold standard, which led to inconsistent results depending on the index used [17]. Flare rates, which are different between reports, can also vary within a report depending on the index used [9]. The choice to include different degrees of severity (mild/moderate or severe) might have modified the total number of flares and thus affected the sensitivity and specificity of biomarkers. Concerning the biomarkers themselves, the assay techniques could first lead to heterogeneity due to their different performance characteristics. The sensitivity, specificity, PPV and NPV were different if the Farr assay, C. luciliae assay or ELISA was used for measuring anti-dsDNA ab [25, 26, 29, 30]. The Farr radioimmunoassay is believed to detect high-avidity antibodies, C. luciliae assays detect antibodies of intermediate avidity and ELISA detects both high-avidity and low-avidity antibodies [75, 76]. We could not determine with certainty whether one of these assays is more performant than another due to the low number of studies. The results can also vary according to the class of Ig considered for the measurement of anti-dsDNA ab (positive results with IgG and negative results with IgM) [16], which could explain, in part, the discrepancies in the results between studies. Moreover, the use of plasma or serum samples for the assay can be important: some authors believe that it is necessary to use plasma instead of serum to measure anti-dsDNA ab to avoid the possible binding of antibodies to DNA from disrupted blood cells [76]. Finally, the threshold levels chosen for positive test results could also be a source of discrepancy. To increase the ability of biomarkers to predict flares, some authors have combined traditional ones. Values (especially PPV) obtained in this manner were often higher than those of each marker obtained separately [28,29,30], but these data are scarce. Development of prediction models for outcomes of the disease using multiple biomarkers that can be measured at the same time with commercial kits would actually be of great interest [77] and a study of the transcriptome profile could also be promising [78]. Last but not least, it is of particular interest to underline the concept of “serologically active, clinically quiescent” (SACQ) SLE, with discordance between clinical and serologic features, which adds another level of complexity for the prediction of flare in some patients. A large international task force reached recent consensus on the definition of SACQ, which corresponds to the presence of anti-dsDNA ab and/or hypocomplementemia [79]. In this group of patients, fluctuations in anti-dsDNA ab and complement levels cannot predict flares and no consensus was obtained by the task force regarding the definition of remission in those patients [79].

Clinicians need more data to help them to choose the correct biomarker or biomarker combination to predict flares, and the quest for a predictive marker of disease activity should be a major focus of SLE clinical research. With the advent of personalized medicine, with an increasing number of targeted therapies, reliable non-invasive predictors of flares are of great interest. There is a need to conduct prospective studies with standardized guidelines about severity indices and flare definitions to validate potentially relevant biomarkers and to bring them into the field of daily clinical practice, in the same manner as has been performed for therapeutic trials [73].

To homogenize study patterns, we propose conducting multicentre, longitudinal, prospective, controlled studies, including patients with SLE who fulfil the revised ACR criteria and who are ethnically diverse. The most appropriate SLE flare index must be chosen among BILAG, SLEDAI or ECLAM, as encouraged by EULAR recommendations [73], and only one score should be used. Follow-up visits should occur every 3 months over at least 3 years. Thresholds for increases and decreases in each biological marker should be defined clearly. Biomarkers should be validated with assessments of sensitivity, specificity and predictive values. Candidate biomarkers with promising results in small patient cohorts must to be validated in large populations. Biomarker panels must be developed.

Conclusions

No conclusions could be drawn from this systematic review due to the lack of standardized data. Efforts should be undertaken to optimize future research on potential SLE biomarkers to develop validated candidates. Thus, we propose a standardized pattern for future studies.

Abbreviations

ACR:

American College of Rheumatology

ANA:

Anti-nuclear antibodies

Anti-dsDNA ab:

Anti-double-stranded DNA antibodies

Anti-ENA:

Antibodies against extractable nuclear antigens

Anti-RNP:

Anti-ribonucleoprotein

BAFF:

B-cell activating factor from the TNF family

BILAG:

British Isles Lupus Assessment Group

BLyS:

B-lymphocyte stimulating factor

CRP:

C-reactive protein

ECLAM:

European Consensus Lupus Activity Measurement

ESR:

Erythrocyte sedimentation rate

EULAR:

European League Against Rheumatism

MCP:

Monocyte chemotactic protein

M-CSF:

Macrophage colony-stimulating factor

NPV:

Negative predictive value

PGA:

Physician Global Assessment

PPV:

Positive predictive value

PRISMA:

Preferred Reporting Items for Systematic Reviews and Meta-Analyses

RANTES:

Regulated on activation, normal T-cell expressed and secreted

SACQ:

Serologically active, clinically quiescent

SELENA-SLEDAI:

Safety of Estrogens in Lupus Erythematosus National Assessment–Systemic Lupus Erythematosus Disease Activity Index

sE-selectin:

Soluble E-selectin

sICAM-1:

Soluble intercellular adhesion molecule-1

SLE:

Systemic lupus erythematosus

sVCAM-1:

Soluble vascular cell adhesion molecule-1

Treg:

T-regulatory

TWEAK:

Tumour necrosis factor-like weak inducer of apoptosis

uNGAL:

Urinary neutrophil gelatinase-associated lipocalin

References

  1. Zhu TY, Tam L-S, Lee VW-Y, Lee KK-C, Li EK. The impact of flare on disease costs of patients with systemic lupus erythematosus. Arthritis Rheum. 2009;61:1159–67.

    Article  PubMed  Google Scholar 

  2. Tseng C-E, Buyon JP, Kim M, Belmont HM, Mackay M, Diamond B, et al. The effect of moderate-dose corticosteroids in preventing severe flares in patients with serologically active, but clinically stable, systemic lupus erythematosus: findings of a prospective, randomized, double-blind, placebo-controlled trial. Arthritis Rheum. 2006;54:3623–32.

    CAS  Article  PubMed  Google Scholar 

  3. Moher D, Liberati A, Tetzlaff J, Altman DG, PRISMA Group. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. PLoS Med. 2009;6:e1000097.

    Article  PubMed Central  PubMed  Google Scholar 

  4. Swaak AJ, Aarden LA, van Statius Eps LW, Feltkamp TE. Anti-dsDNA and complement profiles as prognostic guides in systemic lupus erythematosus. Arthritis Rheum. 1979;22:226–35.

    CAS  Article  PubMed  Google Scholar 

  5. Swaak AJ, Nossent JC, Bronsveld W, van Rooyen A, Nieuwenhuys EJ, Theuns L, et al. Systemic lupus erythematosus. II. Observations on the occurrence of exacerbations in the disease course: Dutch experience with 110 patients studied prospectively. Ann Rheum Dis. 1989;48:455–60.

    CAS  Article  PubMed Central  PubMed  Google Scholar 

  6. Mirzayan MJ, Schmidt RE, Witte T. Prognostic parameters for flare in systemic lupus erythematosus. Rheumatol Oxf Engl. 2000;39:1316–9.

    CAS  Article  Google Scholar 

  7. Tomioka R, Tani K, Sato K, Suzuka C, Toyoda Y, Kishi J, et al. Observations on the occurrence of exacerbations in clinical course of systemic lupus erythematosus. J Med Investig. 2008;55:112–9.

    Article  Google Scholar 

  8. El Hachmi M, Jadoul M, Lefèbvre C, Depresseux G, Houssiau FA. Relapses of lupus nephritis: incidence, risk factors, serology and impact on outcome. Lupus. 2003;12:692–6.

    Article  PubMed  Google Scholar 

  9. Petri MA, van Vollenhoven RF, Buyon J, Levy RA, Navarra SV, Cervera R, et al. Baseline predictors of systemic lupus erythematosus flares: data from the combined placebo groups in the phase III belimumab trials. Arthritis Rheum. 2013;65:2143–53.

    CAS  Article  PubMed  Google Scholar 

  10. Petri M, Singh S, Tesfasyone H, Malik A. Prevalence of flare and influence of demographic and serologic factors on flare risk in systemic lupus erythematosus: a prospective study. J Rheumatol. 2009;36:2476–80.

    Article  PubMed  Google Scholar 

  11. Swaak AJ, Groenwold J, Aarden LA, van Statius Eps LW, Feltkamp EW. Prognostic value of anti-dsDNA in SLE. Ann Rheum Dis. 1982;41:388–95.

    CAS  Article  PubMed Central  PubMed  Google Scholar 

  12. Swaak AJ, Groenwold J, Bronsveld W. Predictive value of complement profiles and anti-dsDNA in systemic lupus erythematosus. Ann Rheum Dis. 1986;45:359–66.

    CAS  Article  PubMed Central  PubMed  Google Scholar 

  13. ter Borg EJ, Horst G, Hummel EJ, Limburg PC, Kallenberg CG. Measurement of increases in anti-double-stranded DNA antibody levels as a predictor of disease exacerbation in systemic lupus erythematosus. A long-term, prospective study. Arthritis Rheum. 1990;33:634–43.

    Article  PubMed  Google Scholar 

  14. Oelzner P, Abendroth K, Hein G, Stein G. Predictors of flares and long-term outcome of systemic lupus erythematosus during combined treatment with azathioprine and low-dose prednisolone. Rheumatol Int. 1996;16:133–9.

    CAS  Article  PubMed  Google Scholar 

  15. Coremans IE, Spronk PE, Bootsma H, Daha MR, van der Voort EA, Kater L, et al. Changes in antibodies to C1q predict renal relapses in systemic lupus erythematosus. Am J Kidney Dis. 1995;26:595–601.

    CAS  Article  PubMed  Google Scholar 

  16. Bootsma H, Spronk PE, Ter Borg EJ, Hummel EJ, de Boer G, Limburg PC, et al. The predictive value of fluctuations in IgM and IgG class anti-dsDNA antibodies for relapses in systemic lupus erythematosus. A prospective long-term observation. Ann Rheum Dis. 1997;56:661–6.

    CAS  Article  PubMed Central  PubMed  Google Scholar 

  17. Ho A, Magder LS, Barr SG, Petri M. Decreases in anti-double-stranded DNA levels are associated with concurrent flares in patients with systemic lupus erythematosus. Arthritis Rheum. 2001;44:2342–9.

    CAS  Article  PubMed  Google Scholar 

  18. Abrass CK, Nies KM, Louie JS, Border WA, Glassock RJ. Correlation and predictive accuracy of circulating immune complexes with disease activity in patients with systemic lupus erythematosus. Arthritis Rheum. 1980;23:273–82.

    CAS  Article  PubMed  Google Scholar 

  19. Lloyd W, Schur PH. Immune complexes, complement, and anti-DNA in exacerbations of systemic lupus erythematosus (SLE). Medicine (Baltimore). 1981;60:208–17.

    CAS  Article  Google Scholar 

  20. Esdaile JM, Abrahamowicz M, Joseph L, MacKenzie T, Li Y, Danoff D. Laboratory tests as predictors of disease exacerbations in systemic lupus erythematosus. Why some tests fail. Arthritis Rheum. 1996;39:370–8.

    CAS  Article  PubMed  Google Scholar 

  21. Steiman A, Urowitz MB, Wither JE, Ibanez D, Li T, Gladman DD. Prolonged serologically active clinically quiescent (SACQ) systemic lupus erythematosus (SLE): novel predictors of flare? [Abstract]. Arthritis Rheum. 2010;62(Suppl 10):1874.

  22. Cortés-Hernández J, Ordi-Ros J, Labrador M, Buján S, Balada E, Segarra A, et al. Antihistone and anti-double-stranded deoxyribonucleic acid antibodies are associated with renal disease in systemic lupus erythematosus. Am J Med. 2004;116:165–73.

    Article  PubMed  Google Scholar 

  23. Mok CC, Ying KY, Tang S, Leung CY, Lee KW, Ng WL, et al. Predictors and outcome of renal flares after successful cyclophosphamide treatment for diffuse proliferative lupus glomerulonephritis. Arthritis Rheum. 2004;50:2559–68.

    Article  PubMed  Google Scholar 

  24. Linnik MD, Hu JZ, Heilbrunn KR, Strand V, Hurley FL, Joh T, et al. Relationship between anti-double-stranded DNA antibodies and exacerbation of renal disease in patients with systemic lupus erythematosus. Arthritis Rheum. 2005;52:1129–37.

    CAS  Article  PubMed  Google Scholar 

  25. Pan N, Amigues I, Lyman S, Duculan R, Aziz F, Crow MK, et al. A surge in anti-dsDNA titer predicts a severe lupus flare within six months. Lupus. 2014;23:293–8.

    CAS  Article  PubMed  Google Scholar 

  26. Hillebrand JJG, Bernelot Moens HJ, Mulder AHL. Changes in Farr radioimmunoassay and EliA fluorescence immunoassay anti-dsDNA in relation to exacerbation of SLE. Lupus. 2013;22:1169–73.

    CAS  Article  PubMed  Google Scholar 

  27. To CH, Yu KL, Mok CC. Performance of complement C3, C4 and anti-dsDNA antibody in predicting disease flare in individual organ systems in systemic lupus erythematosus: an analysis of data from 6035 clinical visits [abstract]. Arthritis Rheum. 2011;63(Suppl 10):2296.

    Google Scholar 

  28. To CH, Yu KL, Mok CC. Performance of complement C3, C4 and anti-dsDNA antibody in predicting disease flare in systemic lupus erythematosus: an analysis of data from 6035 clinical visits [abstract]. Arthritis Rheum. 2011;63(Suppl 10):2295.

    Google Scholar 

  29. Matrat A, Veysseyre-Balter C, Trolliet P, Villar E, Dijoud F, Bienvenu J, et al. Simultaneous detection of anti-C1q and anti-double stranded DNA autoantibodies in lupus nephritis: predictive value for renal flares. Lupus. 2011;20:28–34.

    CAS  Article  PubMed  Google Scholar 

  30. Meyer OC, Nicaise-Roland P, Cadoudal N, Grootenboer-Mignot S, Palazzo E, Hayem G, et al. Anti-C1q antibodies antedate patent active glomerulonephritis in patients with systemic lupus erythematosus. Arthritis Res Ther. 2009;11:R87.

    Article  PubMed Central  PubMed  Google Scholar 

  31. Walz LeBlanc BA, Gladman DD, Urowitz MB. Serologically active clinically quiescent systemic lupus erythematosus—predictors of clinical flares. J Rheumatol. 1994;21:2239–41.

    CAS  PubMed  Google Scholar 

  32. Hopkins P, Belmont HM, Buyon J, Philips M, Weissmann G, Abramson SB. Increased levels of plasma anaphylatoxins in systemic lupus erythematosus predict flares of the disease and may elicit vascular injury in lupus cerebritis. Arthritis Rheum. 1988;31:632–41.

    CAS  Article  PubMed  Google Scholar 

  33. Buyon JP, Tamerius J, Belmont HM, Abramson SB. Assessment of disease activity and impending flare in patients with systemic lupus erythematosus. Comparison of the use of complement split products and conventional measurements of complement. Arthritis Rheum. 1992;35:1028–37.

    CAS  Article  PubMed  Google Scholar 

  34. Ho A, Barr SG, Magder LS, Petri M. A decrease in complement is associated with increased renal and hematologic activity in patients with systemic lupus erythematosus. Arthritis Rheum. 2001;44:2350–7.

    CAS  Article  PubMed  Google Scholar 

  35. Illei GG, Takada K, Parkin D, Austin HA, Crane M, Yarboro CH, et al. Renal flares are common in patients with severe proliferative lupus nephritis treated with pulse immunosuppressive therapy: long-term followup of a cohort of 145 patients participating in randomized controlled studies. Arthritis Rheum. 2002;46:995–1002.

    CAS  Article  PubMed  Google Scholar 

  36. Ng KP, Cambridge G, Leandro MJ, Edwards JCW, Ehrenstein M, Isenberg DA. B cell depletion therapy in systemic lupus erythematosus: long-term follow-up and predictors of response. Ann Rheum Dis. 2007;66:1259–62.

    CAS  Article  PubMed Central  PubMed  Google Scholar 

  37. Birmingham DJ, Irshaid F, Nagaraja HN, Zou X, Tsao BP, Wu H, et al. The complex nature of serum C3 and C4 as biomarkers of lupus renal flare. Lupus. 2010;19:1272–80.

    CAS  Article  PubMed Central  PubMed  Google Scholar 

  38. Viallard JF, Bloch-Michel C, Neau-Cransac M, Taupin JL, Garrigue S, Miossec V, et al. HLA-DR expression on lymphocyte subsets as a marker of disease activity in patients with systemic lupus erythematosus. Clin Exp Immunol. 2001;125:485–91.

    CAS  Article  PubMed Central  PubMed  Google Scholar 

  39. Marto N, Bertolaccini ML, Calabuig E, Hughes GRV, Khamashta MA. Anti-C1q antibodies in nephritis: correlation between titres and renal disease activity and positive predictive value in systemic lupus erythematosus. Ann Rheum Dis. 2005;64:444–8.

    CAS  Article  PubMed  Google Scholar 

  40. Siegert CE, Daha MR, Tseng CM, Coremans IE, van Es LA, Breedveld FC. Predictive value of IgG autoantibodies against C1q for nephritis in systemic lupus erythematosus. Ann Rheum Dis. 1993;52:851–6.

    CAS  Article  PubMed Central  PubMed  Google Scholar 

  41. Agarwal S, Harper J, Kiely PDW. Concentration of antibodies to extractable nuclear antigens and disease activity in systemic lupus erythematosus. Lupus. 2009;18:407–12.

    CAS  Article  PubMed  Google Scholar 

  42. Ng KP, Manson JJ, Rahman A, Isenberg DA. Association of antinucleosome antibodies with disease flare in serologically active clinically quiescent patients with systemic lupus erythematosus. Arthritis Rheum. 2006;55:900–4.

    CAS  Article  PubMed  Google Scholar 

  43. Praprotnik S, Bozic B, Kveder T, Rozman B. Fluctuation of anti-Ro/SS-A antibody levels in patients with systemic lupus erythematosus and Sjögren’s syndrome: a prospective study. Clin Exp Rheumatol. 1999;17:63–8.

    CAS  PubMed  Google Scholar 

  44. Stojan G, Fang H, Magder L, Petri M. Erythrocyte sedimentation rate is a predictor of renal and overall SLE disease activity. Lupus. 2013;22:827–34.

    CAS  Article  PubMed  Google Scholar 

  45. Spronk PE, ter Borg EJ, Limburg PC, Kallenberg CG. Plasma concentration of IL-6 in systemic lupus erythematosus; an indicator of disease activity? Clin Exp Immunol. 1992;90:106–10.

    CAS  Article  PubMed Central  PubMed  Google Scholar 

  46. Tokano Y, Morimoto S, Kaneko H, Amano H, Nozawa K, Takasaki Y, et al. Levels of IL-12 in the sera of patients with systemic lupus erythematosus (SLE)—relation to Th1- and Th2-derived cytokines. Clin Exp Immunol. 1999;116:169–73.

    CAS  Article  PubMed Central  PubMed  Google Scholar 

  47. Becker-Merok A, Nikolaisen C, Nossent HC. B-lymphocyte activating factor in systemic lupus erythematosus and rheumatoid arthritis in relation to autoantibody levels, disease measures and time. Lupus. 2006;15:570–6.

    CAS  Article  PubMed  Google Scholar 

  48. Petri M, Stohl W, Chatham W, McCune WJ, Chevrier M, Ryel J, et al. Association of plasma B lymphocyte stimulator levels and disease activity in systemic lupus erythematosus. Arthritis Rheum. 2008;58:2453–9.

    CAS  Article  PubMed  Google Scholar 

  49. Bauer JW, Petri M, Batliwalla FM, Koeuth T, Wilson J, Slattery C, et al. Interferon-regulated chemokines as biomarkers of systemic lupus erythematosus disease activity: a validation study. Arthritis Rheum. 2009;60:3098–107.

    CAS  Article  PubMed Central  PubMed  Google Scholar 

  50. Andrade F, Akhter E, Fang H, Petri M. Elevated plasma levels of CXCL2 and CXCL10 have distinct predictive value in systemic lupus erythematosus [abstract]. Arthritis Rheum. 2012;64 Suppl 10:619.

    Google Scholar 

  51. Rose T, Grützkau A, Hirseland H, Huscher D, Dähnrich C, Dzionek A, et al. IFNα and its response proteins, IP-10 and SIGLEC-1, are biomarkers of disease activity in systemic lupus erythematosus. Ann Rheum Dis. 2013;72:1639–45.

    CAS  Article  PubMed  Google Scholar 

  52. Munroe ME, Vista ES, Guthridge JM, Thompson LF, Merrill JT, James JA. Proinflammatory adaptive cytokine and shed tumor necrosis factor receptor levels are elevated preceding systemic lupus erythematosus disease flare. Arthritis Rheumatol. 2014;66:1888–99.

    CAS  Article  PubMed Central  PubMed  Google Scholar 

  53. Lauwerys BR, Husson SN, Maudoux AL, Badot V, Houssiau FA. sIL7R concentrations in the serum reflect disease activity in the lupus kidney. Lupus Sci Med. 2014;1:e000036.

    CAS  Article  PubMed Central  PubMed  Google Scholar 

  54. Guthridge JM, Lou R, Kamp S, Munroe ME, Bean K, Macwana SR, et al. THU0046 Predictive modeling of immunologic and inflammatory markers of impending disease flare in patients with systemic lupus erythematosus not taking immunosuppressive medications. Ann Rheum Dis. 2014;73(Suppl 2):192.

  55. Swaak AJ, Hintzen RQ, Huysen V, van den Brink HG, Smeenk JT. Serum levels of soluble forms of T cell activation antigens CD27 and CD25 in systemic lupus erythematosus in relation with lymphocytes count and disease course. Clin Rheumatol. 1995;14:293–300.

    CAS  Article  PubMed  Google Scholar 

  56. Spronk PE, Horst G, Van Der Gun BT, Limburg PC, Kallenberg CG. Anti-dsDNA production coincides with concurrent B and T cell activation during development of active disease in systemic lupus erythematosus (SLE). Clin Exp Immunol. 1996;104:446–53.

    CAS  Article  PubMed Central  PubMed  Google Scholar 

  57. Spronk PE, ter Borg EJ, Huitema MG, Limburg PC, Kallenberg CG. Changes in levels of soluble T-cell activation markers, sIL-2R, sCD4 and sCD8, in relation to disease exacerbations in patients with systemic lupus erythematosus: a prospective study. Ann Rheum Dis. 1994;53:235–9.

    CAS  Article  PubMed Central  PubMed  Google Scholar 

  58. Miyara M, Mathian A, Haroche J, Arnaud L, Chader D, Musset L, et al. The monitoring of FoxP3 expressing CD4 T cell subsets may be helpful for the prediction of systemic lupus erythematosus flares [abstract]. Arthritis Rheum. 2011;63(Suppl 10):658.

    Google Scholar 

  59. Landolt-Marticorena C, Prokopec S, Morrison S, Reich H, Scholey J, Gladman D, et al. Fluctuations in sVCAM-1 and adiponectin mirror fluctuations in disease activity in lupus, but cannot be use to accurately predict impending changes in disease state [abstract]. Arthritis Rheum. 2013;65(Suppl 10):2557.

    Google Scholar 

  60. Spronk PE, Bootsma H, Huitema MG, Limburg PC, Kallenberg CG. Levels of soluble VCAM-1, soluble ICAM-1, and soluble E-selectin during disease exacerbations in patients with systemic lupus erythematosus (SLE); a long term prospective study. Clin Exp Immunol. 1994;97:439–44.

    CAS  Article  PubMed Central  PubMed  Google Scholar 

  61. Chan RW-Y, Lai FM-M, Li EK-M, Tam L-S, Chow K-M, Li PK-T, et al. Expression of T-bet, a type 1 T-helper cell transcription factor, in the urinary sediment of lupus patients predicts disease flare. Rheumatol Oxf Engl. 2007;46:44–8.

    CAS  Article  Google Scholar 

  62. Szeto C-C, Tam L-S, Kwan BC-H, Lai K-B, Wang G, Li EK-M, et al. Monitoring of urinary messenger RNA levels for the prediction of flare in systemic lupus erythematosus. Clin Chim Acta. 2012;413:448–55.

    CAS  Article  PubMed  Google Scholar 

  63. Tian S, Li J, Wang L, Liu T, Liu H, Cheng G, et al. Urinary levels of RANTES and M-CSF are predictors of lupus nephritis flare. Inflamm Res. 2007;56:304–10.

    CAS  Article  PubMed  Google Scholar 

  64. Rubinstein T, Pitashny M, Levine B, Schwartz N, Schwartzman J, Weinstein E, et al. Urinary neutrophil gelatinase-associated lipocalin as a novel biomarker for disease activity in lupus nephritis. Rheumatol Oxf Engl. 2010;49:960–71.

    CAS  Article  Google Scholar 

  65. Wisniacki N, Stebbins J, Bienkowska J, Gawlak S, Bennett D, Xiang Y, Dearth A, Ranger A, Burkly L, Petri M. Urinary TWEAK predicts renal disease activity in patients with systemic lupus erythematosus over a 1 year period. Ann Rheum Dis. 2013;72(Suppl 3):474.

  66. Schwartz N, Rubinstein T, Burkly LC, Collins CE, Blanco I, Su L, et al. Urinary TWEAK as a biomarker of lupus nephritis: a multicenter cohort study. Arthritis Res Ther. 2009;11:R143.

    Article  PubMed Central  PubMed  Google Scholar 

  67. Lim KL, Muir K, Powell RJ. Urine neopterin: a new parameter for serial monitoring of disease activity in patients with systemic lupus erythematosus. Ann Rheum Dis. 1994;53:743–8.

    CAS  Article  PubMed Central  PubMed  Google Scholar 

  68. Michaelson JS, Wisniacki N, Burkly LC, Putterman C. Role of TWEAK in lupus nephritis: a bench-to-bedside review. J Autoimmun. 2012;39:130–42.

    CAS  Article  PubMed Central  PubMed  Google Scholar 

  69. ter Borg EJ, Horst G, Limburg PC, van Venrooij WJ, Kallenberg CG. Changes in levels of antibodies against the 70 kDa and a polypeptides of the U1RNP complex in relation to exacerbations of systemic lupus erythematosus. J Rheumatol. 1991;18:363–7.

    PubMed  Google Scholar 

  70. Rovin BH, Song H, Hebert LA, Nadasdy T, Nadasdy G, Birmingham DJ, et al. Plasma, urine, and renal expression of adiponectin in human systemic lupus erythematosus. Kidney Int. 2005;68:1825–33.

    CAS  Article  PubMed  Google Scholar 

  71. Boekel ET, Prins M, Vrielink G-J, de Kieviet W, Siegert CEH. Longitudinal studies of the association between peripheral CD27++ plasma cells and systemic lupus erythematosus disease activity: preliminary results. Ann Rheum Dis. 2011;70:1341–2.

    Article  PubMed  Google Scholar 

  72. Hanaoka H, Okazaki Y, Satoh T, Kaneko Y, Yasuoka H, Seta N, et al. Circulating anti-double-stranded DNA antibody-secreting cells in patients with systemic lupus erythematosus: a novel biomarker for disease activity. Lupus. 2012;21:1284–93.

    CAS  Article  PubMed  Google Scholar 

  73. Bertsias G, Ioannidis JPA, Boletis J, Bombardieri S, Cervera R, Dostal C, et al. EULAR recommendations for the management of systemic lupus erythematosus. Report of a Task Force of the EULAR Standing Committee for International Clinical Studies Including Therapeutics. Ann Rheum Dis. 2008;67:195–205.

    CAS  Article  PubMed  Google Scholar 

  74. Petri M. Disease activity assessment in SLE: do we have the right instruments? Ann Rheum Dis. 2007;66(Suppl 3):iii61–4.

  75. Smeenk R, van der Lelij G, Aarden L. Avidity of antibodies to dsDNA: comparison of IFT on Crithidia luciliae, Farr assay, and PEG assay. J Immunol (Baltim Md 1950). 1982;128:73–8.

  76. Pisetsky DS. Anti-DNA antibodies—quintessential biomarkers of SLE. Nat Rev Rheumatol. 2016;12:102–10.

    CAS  Article  PubMed  Google Scholar 

  77. Wolf BJ, Spainhour JC, Arthur JM, Janech MG, Petri M, Oates JC. Development of biomarker models to predict outcomes in lupus nephritis. Arthritis Rheumatol. 2016;68:1955–63.

    CAS  Article  PubMed Central  PubMed  Google Scholar 

  78. Banchereau R, Hong S, Cantarel B, Baldwin N, Baisch J, Edens M, et al. Personalized immunomonitoring uncovers molecular networks that stratify lupus patients. Cell. 2016;165:1548–50.

    CAS  Article  PubMed  Google Scholar 

  79. van Vollenhoven R, Voskuyl A, Bertsias G, Aranow C, Aringer M, Arnaud L, et al. A framework for remission in SLE: consensus findings from a large international task force on definitions of remission in SLE (DORIS). Ann Rheum Dis. 2017;76:554–61.

    Article  PubMed  Google Scholar 

Download references

Acknowledgements

The authors would like to acknowledge E. Mouillet, a research librarian, and H. Maisonneuve for their advice and support.

Funding

This research did not receive any specific grant from funding agencies in the public, commercial or not-for-profit sectors.

Availability of data and materials

The data analysed during the current study are available from the corresponding author on reasonable request.

Author information

Authors and Affiliations

  1. ImmunoConcept, UMR CNRS 5164, Université de Bordeaux, Bordeaux, France

    Noémie Gensous, Patrick Blanco, Estibaliz Lazaro, Marie-Elise Truchetet, Pierre Duffau & Christophe Richez

  2. Department of Internal Medicine and Clinical Immunology, Saint-Andre Hospital, Bordeaux, France

    Noémie Gensous & Pierre Duffau

  3. Department of Dermatology, Saint-Andre Hospital, Bordeaux, France

    Aurélie Marti & Julien Seneschal

  4. Department of Rheumatology, Pellegrin Hospital, Place Amélie Raba Léon, 33076, Bordeaux, France

    Thomas Barnetche, Marie-Elise Truchetet & Christophe Richez

  5. Department of Internal Medicine and Infectious Diseases, Haut-Leveque Hospital, Pessac, France

    Estibaliz Lazaro

Authors
  1. Noémie Gensous
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Aurélie Marti
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Thomas Barnetche
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Patrick Blanco
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Estibaliz Lazaro
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Julien Seneschal
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Marie-Elise Truchetet
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Pierre Duffau
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Christophe Richez
    View author publications

    You can also search for this author in PubMed Google Scholar

Consortia

on behalf of the FHU ACRONIM

Contributions

NG and AM contributed equally to this work. All authors were involved in drafting the article or revising it critically for important intellectual content. All authors approved the final manuscript. NG, AM, TB, PB, EL, JS, M-ET, PD and CR were responsible for study conception and design. NG and AM were responsible for acquisition of data. NG, AM, TB, PB, EL, JS, M-ET, PD and CR were responsible for analysis and interpretation of data.

Corresponding author

Correspondence to Christophe Richez.

Ethics declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Additional information

Noémie Gensous and Aurélie Marti are equally first authors.

Pierre Duffau and Christophe Richez share co-senior authorship.

Additional files

Additional file 1:

Presenting search strategies. (DOC 201 kb)

Additional file 2:

Study characteristics. (DOC 300 kb)

Additional file 3:

Anti-dsDNA antibody sensitivity, specificity, PPV and NPV. (DOC 43 kb)

Additional file 4:

Complement sensitivity, specificity, PPV and NPV. (DOC 47 kb)

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Gensous, N., Marti, A., Barnetche, T. et al. Predictive biological markers of systemic lupus erythematosus flares: a systematic literature review. Arthritis Res Ther 19, 238 (2017). https://doi.org/10.1186/s13075-017-1442-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s13075-017-1442-6

Keywords

  • Systemic lupus erythematosus
  • Biomarker
  • Flare
  • Exacerbation
  • Systematic review