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Rheumatology Advance Access published online on July 24, 2007
Rheumatology, doi:10.1093/rheumatology/kem153
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© The Author 2007. Published by Oxford University Press on behalf of the British Society for Rheumatology. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Systemic lupus erythematosus in a multiethnic US cohort LUMINA LI: Anaemia as a predictor of disease activity and damage accrual

A. M. Bertoli, L. M. Vilá, M. Apte1, B. J. Fessler1, H. M. Bastian1, J. D. Reveille2, G. S. Alarcón1 for the LUMINA Study Group

Department of Medicine (Division of Rheumatology), The University of Puerto Rico Medical Sciences Campus, San Juan, Puerto Rico, 1Department of Medicine (Division of Clinical Immunology and Rheumatology), School of Medicine, The University of Alabama at Birmingham, Birmingham, AL and 2Department of Medicine (Division of Rheumatology), The University of Texas Health Science Center at Houston, Houston, TX.

Correspondence to: Luis M. Vilá, Division of Rheumatology, Department of Medicine, University of Puerto Rico Medical Sciences Campus, PO Box 365067, San Juan, PR 00936-5067. E-mail: lvila{at}rcm.upr.edu


   Abstract
Top
 Abstract
Introduction
 Patients and methods
 Results
 Discussion
 Acknowledgements
 References
 
Objective: To examine if anaemia (and its severity) is associated with disease activity and damage accrual in systemic lupus erythematosus (SLE).

Methods: Four thousand four-hundred study visits in 613 SLE patients enrolled in LUMINA were studied. Anaemia was expressed in four categories of haematocrit (Hct) as defined by the Systemic Lupus Activity Measure-Revised (SLAM-R): no anaemia (Hct >35%), mild (Hct = 30–35%), moderate (Hct = 25–29%) and severe (Hct <25%). Anti-dsDNA antibodies were measured at baseline. Disease activity was assessed with the SLAM-R and damage with the Systemic Lupus International Collaborating Clinics Damage Index (SDI). The relationship between anaemia and anti-dsDNA antibodies with the SLAM and SDI scores was examined by univariate (one-way ANOVA) and multivariate (generalized linear models and generalized estimating equation regression) analyses.

Results: All categories of anaemia and anti-ds DNA were significantly associated with the SLAM-R at baseline and over time. However, only moderate and severe anaemia were associated with the SDI at baseline and over time, while the presence of anti-ds DNA was only associated with the SDI over time but not at baseline. Several clinical domains of the SLAM-R and SDI were associated with anaemia at baseline and over time.

Conclusions: Mild, moderate and marked anaemia are strongly associated with disease activity in SLE. Moderate and marked anaemia are associated with damage accrual. These associations are observed both early and during the course of SLE. Different levels of anaemia could be used to monitor disease activity and predict organ/system damage in SLE.

KEY WORDS: Systemic lupus erythematosus, Anaemia, Disease activity, Disease damage, Clinical manifestations


   Introduction
Top
 Abstract
 Introduction
Patients and methods
 Results
 Discussion
 Acknowledgements
 References
 
In the general population, anaemia has been associated with higher morbidity in a variety of clinical conditions [1–5]. For example, increased perinatal risk, developmental abnormalities in infants, changes in the immune status, increase risk of infections and altered patterns of hormone production and metabolism have been linked to anaemia [1]. Anaemia has also been shown to be an independent risk factor for the development of adverse cardiovascular outcomes in different patient populations [2–5].

Haematological manifestations affecting one or more blood cell lineages are frequent in systemic lupus erythematosus (SLE), and anaemia is the most common finding [6–8]; most studies in SLE have, however, examined haemolyticanaemia [9–12], but not anaemia in general. Little is known about the role of anaemia in the course of the disease and on its long-term outcome. It is possible that in SLE patients, like the general population, anaemia be a marker of unfavourable outcomes.

As a chronic disease, SLE exhibits an unpredictable course. It is well recognized that inadequate treatment during periods of disease activity leads to progressive morbidity and higher mortality rates. Therefore, ascertaining biomarkers that can reliably measure the extent of disease activity and predict disease outcomes would be of interest.

We have now examined the impact of anaemia and its severity on disease activity and damage accrual and compared it with anti-dsDNA antibodies in LUMINA (LUpus in MInorities, NAture vs Nurture), a large multiethnic SLE cohort. We hypothesized that anaemia (and its degree) is associated with a more severe disease in terms of disease activity and damage accrued over time.


   Patients and methods
Top
 Abstract
 Introduction
 Patients and methods
Results
 Discussion
 Acknowledgements
 References
 
As previously described [13, 14], LUMINA is a longitudinal study of outcome in lupus established in 1994. To be eligible for enrolment (T0), patients must meet the American College of Rheumatology (ACR) criteria for the classification of SLE [15, 16], have disease duration ≤5 yrs, be ≥16 yrs of age, have a defined ethnicity [self stated and the same for the four grand-parents: African-American, Hispanic (from Texas and from the Island of Puerto Rico) and Caucasian] and live in the geographical recruitment area of the participating centres [University of Alabama at Birmingham (UAB), The University of Texas Health Science Center at Houston (UTH) and The University of Puerto Rico Medical Sciences Campus (UPR)]. Over 90% of patients eligible for the study, agreed to participate. The Institutional Review Board of each participating centre approved the LUMINA study, and written informed consent was obtained from each participating subject according to the declaration of Helsinki. At the time these analyses were conducted, 613 patients constituted the cohort.

Prior to enrolment, all medical records are reviewed to confirm the patient's eligibility and to gather socio-economic-demographic and relevant clinical data from the time of diagnosis (TD) to T0. Every patient has a baseline visit at T0; follow-up visits are conducted every 6 months for the first year (T0.5 and T1, respectively), and yearly thereafter (T2, Tn). A LUMINA study visit consists of an interview, a physical examination and laboratory tests. Data for missed study visits are obtained, whenever possible, by review of all available medical records. TD is defined as the time at which patients meet four ACR criteria for the classification of SLE. Follow-up time is defined as the interval between T0 and last study visit (TL).

Variables
As previously described [17], the LUMINA database includes variables from the following domains: socio-economic-demographic, clinical, immunological, genetic, behavioural and psychological. These variables are measured at T0 and at every subsequent visit. For the purpose of this study, variables recorded at T0 and during follow-up time were examined. Only the variables included in these analyses will be described.

Variables from the socio-economic-demographic domain included were age, gender and ethnicity. Clinical variables included were disease manifestations as per ACR classification criteria [15, 16], disease activity and damage accrual.

Disease activity was assessed using the Systemic Lupus Activity Measure-Revised (SLAM-R) [18]. The SLAM is a validated instrument, which includes 23 clinical manifestations and seven laboratory parameters, present some time during the preceding month and attributable to SLE. Anaemia, our variable of interest, was defined as per the SLAM-R, where four categories according to the haematocrit (Hct) level are identified: no anaemia (Hct >35%), mild anaemia (Hct = 30–35%), moderate anaemia (Hct = 25–29%) and severe anaemia (Hct <25%). Because anaemia is one of the parameters evaluated in the SLAM-R, it was excluded from the total SLAM-R score. Damage was measured with the Systemic Lupus International Collaborating Clinics/American College of Rheumatology Damage Index (SDI) [19].

In order to assess the value of anaemia as a predictor of disease activity and damage accrual, the presence of anti-double stranded DNA (anti-dsDNA), by immunofluorescence against Crithidia luciliae (abnormal ≥ 1: 10) was also analysed and the results compared with those of anaemia. Anti-dsDNA was expressed as either positive (+) or negative (–). The SLAM-R (total score and individual items), the SDI (total score and individual domains) and anaemia were examined at every patient's visit, whereas anti-dsDNA antibodies were examined only at T0.

Statistical analyses
The relationship between the different anaemia categories and anti-dsDNA antibodies with the SLAM-R and the SDI at T0 was examined by one-way ANOVA. Then, multivariate generalized lineal regression models were examined in which the dependent variables were the SLAM-R and the SDI; age, gender and ethnicity were entered into these models. Second, in order to account for the longitudinal nature of the study, Generalized Estimating Equations (GEE) analyses were used to determine the association between the different anaemia categories and anti-ds-DNA antibodies with the SLAM-R and the SDI over time (at each visit). Third, the association between clinical manifestations and laboratory abnormalities (per domains of the SLAM-R) and anaemia as a categorical variable (Hct ≥ 35% and Hct < 35%) were examined by Chi-square tests. Similar analyses were performed for the different domains of the SDI. Finally, the scores of the SLAM-R and the SDI's domains as per above anaemia categories were compared using Students’ t-tests. All statistical analyses were performed using SAS, version 9.1 (SAS Institute, Cary, NC, USA).


   Results
Top
 Abstract
 Introduction
 Patients and methods
 Results
Discussion
 Acknowledgements
 References
 
At the time these analyses were performed, 613 patients were enrolled in the LUMINA cohort providing 4400 visits. At T0, 25.2%, 8.0% and 4.4% of patients had mild, moderate and severe anaemia, respectively. When all visits were studied, 25.9%, 7.6%, 3.2% of patients’ visits corresponded to the mild, moderate and severe anaemia categories, respectively. Patients were predominantly women (89.8%) and middle age [mean (± standard deviation, S.D.) 36.6 (12.4) yrs]. Nineteen per cent of the patients were Hispanics from Texas, 16.7% were Hispanics from Puerto Rico, 35.8% were African-Americans and 28.5% were Caucasians. The mean (S.D.) total follow-up time (T0–TL) was 3.81 (3.11) yrs.

Anaemia and SLAM-R and SDI scores
Univariate analyses
By ANOVA, all categories of anaemia were strongly associated with higher SLAM-R and SDI scores, both at T0 and for all visits. Anti-dsDNA antibodies at baseline were associated with higher SLAM-R scores at T0 and for all visits, and higher SDI scores for all visits, but not at T0. These data are shown in Tables 1 and 2.


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  		TABLE 1. Univariate analyses of disease activity and damage accrual as a function of the level of anaemia and the presence of anti-dsDNA antibodies at T0

 

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  		TABLE 2. Univariate analyses of disease activity and damage accrual as a function of the level of anaemia and the presence of anti-dsDNA antibodies at all visits

 
Multivariate analyses
In these analyses, all categories of anaemia and anti-ds-DNA antibodies were significantly associated with higher SLAM-R scores at T0 and over time after adjusting for age, gender and ethnicity. In contrast, only moderate and severe anaemia were associated with higher SDI scores at T0 and over time, while the presence of anti-dsDNA was only associated with higher SDI over time but not at T0. These data are depicted in Table 3.


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  		TABLE 3. Multivariate analyses of disease activity and damage accrual as a function of anaemia and the presence of anti-dsDNA antibodiesa

 
Anaemia and domains of the SLAM-R
Since all categories of anaemia (Hct ≤ 35%) were associated with disease activity at T0 and over time, we examined the relationship between any degree of anaemia and the specific domains of the SLAM-R. Anaemia was associated with almost all domains and laboratory parameters of the SLAM-R and their respective scores at T0 except for the integument, neuromotor and articular domains (Table 4). When all visits were examined, the majority of domains was associated with anaemia; however, no differences were observed in the frequency of neuromotor abnormalities and the scores of the integument and ocular domains (Table 5).


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  		TABLE 4. Association between anaemia (Hct <35%) and organ/system activitya at T0 by (A) proportions (patients) and (B) by domain scores

 

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  		TABLE 5. Association between anaemia (Hct <35%) and organ/system activitya over time by (A) proportions (patients’ visits) and (B) by domain scores

 
Anaemia and domains of the SDI
Since moderate and severe anaemia (Hct ≤ 30%) were associated with damage accrual at T0 and over time, we examined the relationship between them and the specific domains of the SDI. Moderate and severe anaemia were only associated with the neuropsychiatric and renal domains (frequency and scores) and the pulmonary domain (only score) at T0, whereas over time, they were associated with most domains, except for the musculoskeletal, skin, gastrointestinal and diabetes domains. These data are depicted in Tables 6 (T0) and 7 (over time).


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  		TABLE 6. Association between moderate and severe anaemia (Hct <30%) and organ/system damagea at T0 by (A) proportions (patients) and (B) by domain scores

 

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  		TABLE 7. Association between anaemia (Hct <30%) and organ/system damagea over time by (A) proportions (patients’ visits) and (B) by domain scores

 

   Discussion
Top
 Abstract
 Introduction
 Patients and methods
 Results
 Discussion
Acknowledgements
 References
 
As a chronic disease, SLE can exhibit a wax and wane course that, oftentimes, cannot be predicted and/or corroborated by serological tests [20–23]. The predictive value that many biomarkers have in the disease course, including anti-dsDNA antibodies [23, 24], anti-Smith antibodies [22], complement levels [25], prolactin levels [26], among others, has been shown in several studies. However, some of these tests are not uniformly available worldwide, universal standardized techniques are still missing and, overall, these tests are relatively expensive. In this study, we are addressing the relevance anaemia (independently of its cause) may have as a prognostic factor for the short (disease activity) and long-term (damage accrual) disease outcome. We are also describing its relationship with individual SLAM-R and SDI domains.

Although anaemia is one of the most common features of SLE, most studies are limited to haemolytic anaemia [9, 10, 11]. This is not surprising as haemolytic anaemia in not only a SLE classification criterion [12, 16], but it can also be a clinical life-threatening event [27]. On the other hand, anaemia in SLE, other than the haemolytic form, can be secondary to many other aetiologies; anaemia of chronic disease, iron deficiency, drug myelotoxicity and end-stage renal disease, among others [7].

Several pathogenic mechanisms have been proposed to explain anaemia in SLE; these include inadequate erythropoietin response due to anti-erythropoietin antibodies in the anaemia of chronic disease [28, 29], as well as raised levels of interleukin-6 [30] and tumour necrosis factor-{alpha} [31] and the presence of anticardiolipin antibodies in the case of haemolytic anaemia [32]. Furthermore, Fc{gamma} receptor IIa and IIIa polymorphisms have also been associated with anaemia [33]. Given that these abnormalities very likely represent disease activity, it is not surprising that we found an association between anaemia and disease activity. Of interest and in agreement with our data, Mirzayan et al. [34] found anaemia as a predictor of the occurrence of disease flares in a cohort of 120 SLE Caucasian patients. In contrast, an association between anaemia and damage accrual has not been reported to date. Since disease activity is a strong predictor of damage accrual [35], this finding is not too unexpected.

Anti-dsDNA antibodies showed a different performance; while the presence of these antibodies was strongly associated with disease activity at study entry and over time, it was associated with damage accrual only over the course of the disease. This antibody, is a well-recognized marker of disease activity, however, this is not the same for damage, as other investigators have reported the lack of association between the presence of anti-DNA antibodies and damage [21, 23]. We do not have an explanation for the discrepancies observed relative to damage. It is possible; however, that we have uncovered this association because of the analytical method used (GEE), which maximizes the utilization of longitudinal data.

Our study also demonstrates the association between anaemia and SLAM-R and SDI domains, especially those indicating more severe disease. This, again, is not surprising as each individual domain is a piece within a major instrument that fully defines the patients’ status. However, it is essential to underscore the relationship between anaemia and some specific SDI domains such as the neurocognitive, cardiovascular and the renal domains as anaemia has been implicated as a contributing factor to the cognitive impairment in the elderly [36] and with higher mortality rates in patients with acute myocardial infarction and end-stage renal disease [3, 37]; hence, the use of supportive measures for anaemia that have shown to be effective in patients with end-stage renal disease such as the erythropoietin [38, 39] should be considered in the lupus patient.

This study is not without limitations. Firstly, we did not assess the precise aetiology of anaemia; on the contrary, anaemia was included in the analyses as recorded in the SLAM-R, thus by default it must be interpreted as due to lupus. Nevertheless, the aetiology of anaemia in SLE is oftentimes multi-factorial and therefore difficult to attribute to only one aetiology. Secondly, only baseline anti-dsDNA antibodies were included in these analyses; although serial measurements of these antibodies could have strengthened a possible association, such values are not available for all patients and at all times. Thirdly, other markers of disease activity such as complement levels were not measured; thus a head-to-head comparison between anaemia and complement levels as indicative of disease activity in lupus cannot be inferred with the data presented. Fourthly, we used a categorical rather than a continuous scale for the variable of interest (anaemia), thus we may have lost some precision in our analyses. Finally, a number of other possible confounders known to be associated with activity and damage accrual were not included in the analyses presented. An example of such confounder is, renal involvement that can cause anaemia and at the same time is a predictor of disease activity and damage accrual.

In summary, as shown in our study, anaemia is strongly associated with disease activity and damage accrual early in the disease course as well as over time; this association is even stronger than with anti-dsDNA antibodies. Furthermore, anaemia is associated with several clinical manifestations, including, but not limited to, those reflecting more severe disease such as the neuropsychiatric and renal involvement. Ideally, a biomarker should be standardized and widely available; testing Hct, a very simple and accessible test, can thus be regarded as a non-expensive indicator of the disease course allowing clinicians to anticipate the intermediate and long-term outcomes of the disease. Finally, the results of this study can have important therapeutic implications for lupus patients because a prompt and adequate treatment of anaemia may result in less disease activity and damage. However, it would be necessary to determine the levels at which haemoglobin should be aimed before recommendations can be made.


   Acknowledgements
Top
 Abstract
 Introduction
 Patients and methods
 Results
 Discussion
 Acknowledgements
References
 
The authors would like to acknowledge all LUMINA patients without whom this study would have not been possible, our supporting staff (Martha L. Sanchez M.D., M.P.H., and Ellen Sowell at UAB, Carmine Pinilla-Diaz, M.T. at UPR and Robert Sandoval B.A. and Binh Vu, B.S. at UTH) for their efforts in securing our patients’ follow up and performing other LUMINA-related tasks.

Supported by grants from the National Institute of Arthritis and Musculoskeletal and Skin Diseases #R01-AR42503 (UAB, UTH-HSC), General Clinical Research Centers #M01-RR02558 (UTH-HSC) and M01-RR00032 (UAB), the National Center for Research Resources (NCRR/NIH) RCMI Clinical Research Infrastructure Initiative (RCRII) award #1P20 RR11126 (UPR-MSC) and by an unrestricted educational grant from Bristol-Myers Squibb Company (UPR-MSC).

The authors have declared no conflicts of interest.


   References
Top
 Abstract
 Introduction
 Patients and methods
 Results
 Discussion
 Acknowledgements
 References
 

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Submitted 29 January 2007; revised version accepted 20 May 2007.
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