Abstract
Objective. Thrombosis is an important cause of morbidity and mortality in SLE. We have explored the factors associated with time to the occurrence of thrombotic events in SLE patients to expand our cohort’s previous observations.
Method. SLE patients (ACR criteria), age ≥16 years, disease duration ≤5 years at enrolment (T0), African-American, Hispanic (Texan or Puerto Rican) or Caucasian ethnicity, from LUMINA, a longitudinal cohort, were studied. An event was defined as the presence of arterial or venous thrombosis. Time to the first thrombotic event was examined by univariable and multivariable (MV) Cox models adjusting for pertinent baseline clinical and socio-demographic variables.
Results. A total of 643 patients were studied; mean (s.d.) age was 36.4 (12.6) years and disease duration at T0 was 1.4 (1.3) years; 90% were female. After T0, 81 (12.6%) patients had developed a thrombotic event. In the MV model, age [hazard ratio (HR) = 1.06; 95% CI 1.03, 1.08; P < 0.0001], health insurance (HR = 0.53; 95% CI 0.30, 0.94; P = 0.029), smoking (HR = 1.85; 95% CI 1.01, 3.40; P = 0.048), damage (T0) (HR = 1.44; 95% CI 1.20, 1.71; P < 0.0001), aPL (HR = 2.12; 95% CI 1.19, 3.76; P = 0.011) and glucocorticoid (highest dose) (HR = 1.01; 95% CI 1.01, 1.02; P < 0.0001) were significant.
Conclusions. Age, poverty, smoking, damage accrual, aPL and higher doses of glucocorticoids were independently associated with a shorter time to the first thrombotic event; health insurance had a protective effect. Acting upon modifiable risk factors at the personal (smoking, high-dose glucocorticoids) and societal (poverty, health insurance) levels may prevent these events and improve the long-term outcome of SLE patients.
Keywords: Systemic lupus erythematosus, Thrombotic events, Risk factors
Introduction
Thrombosis has been reported with a higher frequency (from 9 to 37%) and at a younger age in SLE patients and 3–15% of SLE patients experience a non-fatal thrombotic stroke [1]; this variability is probably a function of the type of patients included and the methods used in the different published studies. Thrombotic events are important causes of associated morbidity and increased mortality in SLE patients [2, 3]. The frequency of less well-known events such as transient ischaemic attacks and silent ischaemic cardiac manifestations is difficult to ascertain and thus it is largely unknown.
In addition to the risk factors for thrombosis identified in the general population, other predisposing factors have been recognized in patients with SLE; of them, the most important is APS but others such as older age at diagnosis, ethnicity (Caucasian and African-American), smoking, thrombophilias, the presence of hypertension and of nephritis and the use of some medications have also been identified [4, 5]. On the other hand, anti-malarial medications may have a protective thrombotic effect in these patients [6].
We have previously examined the predictive factors of thrombotic events in SLE patients from LUMINA (LUpus in MInorities: NAture vs Nurture), a multi-ethnic US cohort and found that smoking and disease activity were important determinants of their occurrence in these patients [4]. With one-third more patients in the cohort and a longer follow-up time (4.6 years), we have now re-examined the factors associated with clinically evident thrombotic events over the duration of the disease. We hypothesized that the presence of aPL and HCQ use will be associated with an increased and a decreased predisposition to the occurrence of thrombotic events in SLE, respectively.
Patients and methods
Patients
LUMINA is a longitudinal study of outcome in lupus [7]. All patients who meet the ACR criteria for the classification of SLE [8], have disease duration of ≤5 years, are ≥16 years of age, of defined ethnicity [African-American, Hispanic (from TX, USA and Puerto Rico, Spain) or Caucasian], and live in the geographic recruitment area of the participating centres (the University of Alabama at Birmingham, the University of Texas Heath Science Center at Houston and the University of Puerto Rico Medical Sciences Campus). The Institutional Review Board of these centres approved the study; written informed consent was obtained from each subject according to the Declaration of Helsinki.
Every patient had a baseline or enrolment visit (T0); follow-up visits were conducted every 6 months during the first year (T0.5 and T1, respectively) and yearly thereafter. At each visit, the patients were interviewed, and physical examination and laboratory tests were performed. Data for missed study visits were obtained by the review of all available medical records.
Disease duration was defined as time elapsing from the date patients met four ACR classification (TD) criteria for SLE to T0. Duration of follow-up was defined as the period between T0 and the last visit (TL).
Variables
As previously reported [9], the LUMINA database includes variables from the following domains: socio-economic–demographic, clinical, immunologic, genetic, behavioural and psychological. These variables are measured at T0 and at every subsequent visit. Only the variables included in these analyses will be described.
Recorded thrombotic events were defined as either arterial [myocardial infarction, angina, stroke, intermittent claudication or peripheral arterial thrombosis (or both)] or venous [visceral or peripheral (or both)] occurring after T0 as recorded in the SLICC Damage Index (SDI) [10]. No specific imaging or ancillary studies were performed at the time of thrombotic event unless it was clinically indicated.
Variables included from the socio-economic–demographic domain were age, ethnicity, education, poverty (as defined by the US Federal government adjusted for the number of subjects in the household) [11] and current smoking (by self-reporting). Clinical variables included were the number of ACR criteria at T0, onset type, disease duration, disease manifestations, disease activity and damage accrual, immunologic variables, diabetes [fasting plasma glucose ≥126 mg/dl or random (or 2-h value in an oral glucose tolerance test) ≥200 mg/dl or physician diagnosed or patient self-reported intake of pharmacological treatment (requiring either insulin or oral hypoglycaemic agent)], hypertension [systolic blood pressure (BP) >140 mmHg or diastolic BP > 90 mmHg on two or more occasions or patients self-reported intake of anti-hypertensive medications regardless of cause] and medications such as glucocorticoids and HCQ. The current and the highest dose of glucocorticoids were recorded at each visit; the highest prednisone dose was computed for each patient based on the data available for all the intervals between visits (per month).
Disease activity was assessed with the SLAM revised (SLAM-R) [12] at T0 and at every visit; the average SLAM-R score for all visits (from TD to TL) was calculated as a measurement of disease activity over time. Damage accrual at T0 was ascertained with the SDI score; thrombotic events were removed from the SDI in these analyses. The following laboratory variables were recorded at T0: immunoglobulin G (IgG) and immunoglobulin M (IgM) aPL (abnormal >13 IgG phospholipid U/ml, by ELIZA), or LAC (Staclot test; Diagnostica Stago 92600, Asnieres-Sur-Seine, France).
Statistical analyses
Features from the different domains were compared between those patients who developed thrombotic events and those who did not using Student’s t-tests and Chi-square tests for continuous and categorical variables, respectively. Variables with P ≤ 0.10 in these analyses and clinically important variables were entered into univariable (UV) Cox models. From there, we used the following approach for multivariable (MV) modelling: the candidate variables were iteratively entered/removed from the model until a group of those that were statistically/clinically significant remained in this MV Cox (parsimonious) model. The hazard ratio (HR) is the measure of association in these analyses; an HR > 1 indicates a shorter time to the event and a HR < 1 indicates a longer time. Age, gender and ethnicity were entered in all models. The level of statistical significance was set at P ≤ 0.05; all statistical analyses were performed using the SAS software version 9.1 (SAS Institute, Cary, NC, USA).
Results
At the time this study was conducted, the LUMINA cohort was constituted by a total of 643 patients with a mean (s.d.) follow-up time of 4.6 (3.5) years (range 0–14.3 years); of the 643 patients 81 (12.6%) had developed a thrombotic event after T0 (stroke 7.5%; claudication 0.6%; myocardial infarction 2.3%; angina 2.8%; and deep venous thrombosis 1.7%). There were no thrombotic events recorded in the SDI at T0. As expected, 90% of the patients were women; the mean age and disease duration were 36.4 (12.6) and 1.4 (1.3) years, respectively. All ethnic groups were represented.
UV analyses
From the socio-economic–demographic variables, age was associated with the occurrence of thrombotic events. Although thrombotic events did not uniformly occur among the different ethnic groups, statistical significance was not observed. Within the clinical features, neurological manifestations and aPL were more common at baseline in the patients who had developed a thrombotic event than in those who had not. Likewise, the SDI score was higher among those who had developed an event than those who had not. These data are shown in Table 1.
Table 1.
Baseline socio-economic–demographic and clinical features as a function of the occurrence of thrombotic events
| Variables | Thrombosis |
P-value | |
|---|---|---|---|
| Yes (n = 81) | No (n = 562) | ||
| Age, mean (s.d.), years | 43.5 (14.8) | 35.4 (11.9) | <0.0001 |
| Gender, female, % | 69 (85.2) | 508 (90.4) | 0.149 |
| Ethnicity, % | |||
| Hispanic-Texan | 13 (16.1) | 106 (18.9) | 0.194 |
| Caucasian | 27 (33.3) | 155 (27.6) | 0.194 |
| African-American | 34 (42) | 206 (36.7) | 0.194 |
| Hispanic-Puerto Rican | 7 (8.6) | 95 (16.9) | 0.194 |
| Heath insurance, % present | 60 (74.1) | 447 (81.3) | 0.128 |
| Povertya, % | 31 (40.3) | 175 (32.7) | 0.190 |
| Disease duration, mean (s.d.), years | 1.7 (1.5) | 1.4 (1.3) | 0.112 |
| Smoking, current, yes, % | 16 (19.8) | 71 (12.8) | 0.089 |
| Diabetes, % | 5 (6.3) | 14 (2.5) | 0.069 |
| Hypertension, % | 42 (51.9) | 187 (33.3) | 0.001 |
| Cholesterol, mean (s.d.), mg/dl | 181.1 (64.9) | 174.7 (64.1) | 0.414 |
| SLAM-R score, mean (s.d.) | 10.0 (5.1) | 9.3 (5.9) | 0.358 |
| SDI score, mean (s.d.) | 1.5 (1.5) | 0.6 (1.1) | <0.0001 |
| ACR criteria, % | |||
| Malar rash | 38 (46.9) | 280 (49.8) | 0.625 |
| Discoid lupus | 10 (12.4) | 66 (11.7) | 0.875 |
| Photosensitivity | 39 (48.2) | 295 (39) | 0.465 |
| Ulcers | 33 (40.7) | 231 (41.1) | 0.951 |
| Arthritis | 62 (76.5) | 425 (75.6) | 0.857 |
| Serositis | 38 (46.9) | 227 (40.4) | 0.265 |
| Neurological disorder | 14 (17.3) | 45 (8.0) | 0.007 |
| Renal disorder | 27 (33.3) | 177 (31.5) | 0.740 |
| Haematologic disorder | 55 (67.9) | 384 (68.3) | 0.939 |
| Immunologic disorder | 61 (75.3) | 393 (69.9) | 0.320 |
| ANA | 80 (98.8) | 544 (96.8) | 0.494 |
| aPLb | 17 (23.0) | 63 (12.0) | 0.009 |
| Anti-DNA antibodies | 18 (24.3) | 136 (27.8) | 0.537 |
| HCQ use, % | 60 (74.1) | 404 (71.9) | 0.681 |
| Cyclophosphamide use, % | 12 (14.8) | 65 (11.6) | 0.400 |
| Glucocorticoids, highest dose, mean (s.d.) | 32.0 (43.1) | 26.3 (30.8) | 0.146 |
| Glucocorticoids, current dose, mean (s.d.) | 11.2 (17.3) | 10.0 (16.9) | 0.560 |
aPoverty was determined according to US government guidelines and was adjusted for the number of persons in the household. bIgG and/or IgM aPL and/or LAC.
UV and MV Cox analyses
While in the UV Cox analyses age, smoking, hypertension, damage accrual at T0, neurological disorder and glucocorticoid use (highest dose) were significant, the only variables retained in the parsimonious MV Cox model were age (HR = 1.06; 95% CI 1.03, 1.08; P < 0.0001), health insurance (HR = 0.53; 95% CI 0.30, 0.94; P = 0.029), smoking (HR = 1.85; 95% CI 1.01, 3.40; P = 0.048), damage accrual (SDI) at T0 (HR = 1.44; 95% CI 1.20, 1.71; P < 0.0001), aPL (HR = 2.12; 95% CI 1.19, 3.76; P = 0.011) and glucocorticoid use (highest dose) (HR = 1.01; 95% CI 1.01, 1.02; P < 0.0002). These data are depicted in Table 2.
Table 2.
UV and parsimonious MV Cox proportional hazard models for baseline variables predictive of a thrombotic event
| Variables | UV |
MV |
||
|---|---|---|---|---|
| HR (95% CI) | P-value | HR (95% CI) | P-value | |
| Age, years | 1.04 (1.03, 1.06) | <0.0001 | 1.06 (1.03, 1.08) | <0.0001 |
| Gender | 0.70 (0.38, 1.31) | 0.265 | ||
| Ethnicity | ||||
| Hispanic-Texan | 0.76 (0.39, 1.47) | 0.414 | ||
| Caucasian | Reference group | |||
| African-American | 1.04 (0.63, 1.72) | 0.884 | ||
| Hispanic-Puerto Rican | 0.52 (0.22, 1.20) | 0.124 | ||
| Health insurance | 0.69 (0.42, 1.14) | 0.150 | 0.53 (0.30, 0.94) | 0.029 |
| Povertya | 1.52 (0.97, 2.41) | 0.069 | 1.60 (0.95, 2.70) | 0.078 |
| Disease duration | 1.08 (0.93, 1.26) | 0.320 | ||
| Smoking | 1.80 (1.04, 3.11) | 0.036 | 1.85 (1.01, 3.40) | 0.048 |
| Diabetes | 2.30 (0.93, 5.70) | 0.072 | ||
| Hypertension | 2.16 (1.39, 3.34) | 0.0006 | ||
| Cholesterol | 1.00 (1.00, 1.01) | 0.198 | ||
| SLAM-R at enrolment | 1.04 (1.00, 1.07) | 0.065 | ||
| SDI at enrolment | 1.39 (1.19, 1.62) | <0.0001 | 1.44 (1.20, 1.71) | <0.0001 |
| ACR criteria | ||||
| Malar rash | 0.83 (0.54, 1.30) | 0.415 | ||
| Lupus discoid | 1.15 (0.59, 2.22) | 0.688 | ||
| Photosensitivity | 0.84 (0.55, 1.31) | 0.462 | ||
| Ulcers | 1.00 (0.64, 1.56) | 0.985 | ||
| Arthritis | 0.92 (0.55, 1.53) | 0.735 | ||
| Serositis | 1.27 (0.82, 1.96) | 0.289 | ||
| Neurological disorder | 2.16 (1.21, 3.85) | 0.009 | ||
| Renal disorder | 1.17 (0.73, 1.87) | 0.515 | ||
| Haematologic disorder | 1.07 (0.67, 1.71) | 0.779 | ||
| Immunologic disorder | 1.43 (0.86, 2.37) | 0.168 | ||
| ANA | 2.39 (0.33, 17.21) | 0.386 | ||
| aPLb | 1.69 (0.98, 2.92) | 0.058 | 2.12 (1.19, 3.76) | 0.011 |
| Anti-DNA antibodies | 0.77 (0.44, 1.32) | 0.334 | 0.63 (0.34, 1.16) | 0.138 |
| HCQ use | 1.05 (0.64, 1.72) | 0.859 | ||
| Cyclophosphamide use | 1.70 (0.92, 3.17) | 0.092 | ||
| Glucocorticoids, highest dose | 1.01 (1.00, 1.02) | 0.0009 | 1.01 (1.01, 1.02) | <0.0001 |
| Glucocorticoids, current dose | 1.01 (0.99, 1.02) | 0.427 | ||
aAs per the US Federal government guidelines adjusted for the number of persons in the household. bIgG and/or IgM aPL and/or LAC.
Discussion
In addition, to corroborate the fact that smoking is an important risk factor for the occurrence of thrombotic events, we have now expanded our previous observations by examining the factors associated with thrombosis occurring over the course of the disease utilizing our entire longitudinal database that has now nearly 2950 person-years of observation from the enrolment or baseline visit and 4.5 years of follow-up. Thus, we have now found that age, damage accrual at enrolment, aPL and use of higher dosages of glucocorticoids are associated with a shorter time to thrombotic events, while health insurance is associated with a longer time to thrombotic events or a protective effect over the course of disease in these SLE patients; however, disease activity was not retained in the MV model as we had shown before.
As expected, smoking has consistently been associated not only with the development of lupus, but also with worse outcomes and with the occurrence of thrombotic events in general and venous events in particular [13, 14]. The possible mechanism by which smoking favours the occurrence of thrombotic events is by inflicting endothelial damage that results in its activation and the initiation of a cascade of events leading to thrombosis [15]. Likewise, age has been consistently demonstrated to be a risk factor given that endothelial injury from diverse sources tends to accumulate over time [16].
Another strong clinical variable associated with thrombotic events was the occurrence of damage accrual early in the course of the disease, which is not unexpected since an important component of damage accrual in lupus relates to accelerated atherosclerosis, endothelial injury being an important component of it. Otherwise, the presence of health insurance seems to have a protective effect in thrombotic events and is probably not only a proxy for a better socio-economic status, but also probably associates with the implementation of preventive measures in the presence of pro-coagulant states.
As we hypothesized based on published literature, the presence of aPL was associated with thrombotic events. This group of autoantibodies is directed against phospholipids of the cell membranes; APS is characterized by their presence and either recurrent thrombotic events, recurrent fetal loss or thrombocytopenia. APS can be detected in ∼5–21% of patients with recurrent venous thrombotic events and these events are a common manifestation of APS in patients with SLE, occurring more frequently in the venous territories [17, 18]. The underlying mechanisms favouring thrombosis in the presence of aPL include an acquired resistance to activated protein C, platelet activation, prevention of the binding of coagulation inhibitory factors occupying these receptors on endothelial surfaces, disruption of the protective shield of anti-coagulant protein Annexin V and activation of endothelial cells [19].
Glucocorticoids are commonly used in SLE patients; higher dosages as well as cumulative dosages have been associated with thrombosis probably mediated by endothelial damage, accelerated atherosclerosis and abnormalities in the coagulation cascade [20, 21]. Furthermore, higher dosages of glucocorticoids are associated with disease activity, and therefore they are probably a proxy for this variable.
In terms of other variables explored, we were surprised about the lack of association between some variables that have been previously shown to be associated with the occurrence of vascular and thrombotic events such as disease activity and, particularly, the use of HCQ [4, 22]. As noted above, higher dosages of glucocorticoids could be a proxy for disease activity, but we do not have a good explanation for not finding a protective effect from HCQ. Possible explanations for not corroborating the protective effect of anti-malarials may include the different ethnic composition of our cohort or the fact that the majority of events examined were arterial in nature, which may differ in their pathogenesis from venous events in which these compounds’ protective effect may be more evident; however, there are insufficient data in the literature to address whether this, in fact, is the case and is completely speculative for now [23]. Finally, it is possible that a longer period of observation may be needed to demonstrate this protective effect.
An important limitation of our study is that the LUMINA protocol does not include some potentially important data elements such as homocysteine levels, ICs and folic acid use, which contribute or modulate the outcome of interest. It should also be noted that our data may not be applicable to patients with more established disease or longer disease duration (well-known risk factor) since our cohort had only ∼4.5 years of follow-up; however, the distribution of this variable within the cohort is such that in some patients it was >14 years. Finally, although we suspect that a thrombotic event predisposes to further thrombotic events, we could not validate this assertion since there were no early events, as defined, recorded in our patients.
In conclusion, in addition to the risk factors identified in the general population, SLE patients exhibit other pro-thrombotic factors. Preventive measures should include smoking cessation programmes and screening for the presence of aPL regardless of the patients’ clinical manifestations. Physicians should be particularly vigilant in patients who have other risk factors such as evidence of endothelial damage, the presence of damage accrual early in the course of the disease and the use of high dosages of glucocorticoids.
Acknowledgements
Funding: Supported by grants from the National Institute of Arthritis and Musculoskeletal and Skin Disease P01 AR49084, General Clinical Research Centers M01-RR02558 (University of Texas Health Science Center, Houston) and M01-RR00032 (University of Alabama, Birmingham) and from the National Center for Research Resources (NCRR/HIH) RCMI Clinical Research Infrastructure Initiative (RCRII) 1P20RR11126 (University of Puerto Rico). The work of P.I.B. was also supported by Programa de Postgrado Becas Chile.
Disclosure statement: The authors have declared no conflicts of interest.
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