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Keywords:

  • Systemic lupus erythematosus;
  • Cognitive impairment;
  • Magnetic resonance imaging;
  • Antiphospholipid antibodies

Abstract

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. PATIENTS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. AUTHOR CONTRIBUTIONS
  8. REFERENCES
  9. AAPPENDIX A
  10. AAPPENDIX B

Objective

To determine factors affecting the severity of cognitive impairment in systemic lupus erythematosus (SLE) and to analyze its anatomic location.

Methods

Fifteen cognitive functions grouped into 8 domains were evaluated in 52 patients with SLE and 20 with rheumatoid arthritis. Patients were classified according to severity of impairment as normal, mild, or moderate/severe. Multivariate analysis was performed to identify the main factors affecting severity of cognitive deficits. The most likely anatomic site of damage according to neuropsychological performance was compared with the lesion's location on magnetic resonance imaging (MRI).

Results

In SLE patients, a stepwise regression analysis showed that the number of impaired functions (dependent variable) was associated with antiphospholipid antibody positivity (aPL+; P = 0.04), the Systemic Lupus International Collaborating Clinics/American College of Rheumatology Damage Index (SDI; P = 0.001), hypertension (P = 0.032), and was inversely related to educational level (P = 0.021). Including MRI, the number of impaired functions was associated with severity of MRI (P < 0.001), the SDI (P = 0.013), and the presence of Raynaud's phenomenon (P = 0.04). The contemporary presence of aPL+ and Raynaud's phenomenon resulted in a higher probability to develop moderate/severe cognitive deficits (P = 0.015). Two logistic multiple regression analyses identified hypertension (P < 0.05), the SDI (P < 0.01), and moderate/severe MRI findings as main predictors of moderate/severe impairment (dependent variable). The damage site hypothesized through neuropsychological testing corresponded with MRI findings in 71.7% of SLE patients K = 0.42, P = 0.005).

Conclusion

Hypertension, aPL+, accumulated damage, and MRI lesions are the main factors affecting severity of cognitive impairment in SLE. The hypothesized sites of central nervous system involvement according to neuropsychological testing correlated with MRI findings in most patients.


INTRODUCTION

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. PATIENTS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. AUTHOR CONTRIBUTIONS
  8. REFERENCES
  9. AAPPENDIX A
  10. AAPPENDIX B

The prevalence of cognitive impairment in systemic lupus erythematosus (SLE) ranges between 14% and 90% according to several reports (1–4). Some prospective studies have confirmed the role of antiphospholipid antibodies (aPL) in the pathogenesis of cognitive impairment, especially if present at persistently high titers (5–8). Most studies failed to find an association between radiologic findings and cognitive deficits (9, 10) or a clear correlation between aPL positivity and specific neuroradiologic lesions (10–14). No previous reports identified a role of the generic cardiovascular risk factors in the pathogenesis of cognitive deficits in SLE, even though a recent study underlined that regular use of aspirin is associated with improved cognitive functions in older patients with SLE (15). Finally, no data are available about the possible role of Raynaud's phenomenon in neuropsychological functions. Because cerebral vasospasm has been implied in neurologic manifestations as a migraine with aura (16, 17) and seems to be more frequent in patients with SLE and peripheral Raynaud's phenomenon (18), a possible role of cerebral vasospastic phenomena in the development of some neuropsychiatric syndromes in patients with SLE and Raynaud's phenomenon could be hypothesized.

In view of these observations, we aimed to determine the main factors affecting the severity of cognitive deficits in consecutive patients with SLE with particular attention to the role of aPL, Raynaud's phenomenon, and the common cardiovascular risk factors. Second, we evaluated the correlation between the possible sites of central nervous system (CNS) involvement hypothesized according to neuropsychological testing and the CNS damage location as detected by magnetic resonance imaging (MRI), in order to verify the contribution of structural damage to neuropsychological impairment in each given patient with SLE.

PATIENTS AND METHODS

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. PATIENTS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. AUTHOR CONTRIBUTIONS
  8. REFERENCES
  9. AAPPENDIX A
  10. AAPPENDIX B

Patients.

A total of 54 consecutive patients with SLE, recruited at the Division of Rheumatology of Udine, all fulfilling the American College of Rheumatology (ACR) 1982 revised criteria for SLE (19), were asked to participate in the study; 52 patients agreed. One patient refused because she was a neuropsychologist and the other refused because of the inability to take time off work. Positivity for Raynaud's phenomenon, disease activity according to the Systemic Lupus Erythematosus Disease Activity Index (SLEDAI), accumulated damage according to the Systemic Lupus International Collaborating Clinics/ACR Damage Index (SDI), clinical history of neuropsychiatric involvement according to the ACR nomenclature (20), and immunologic evaluation were recorded for all patients. The SDI was calculated excluding the item concerning cognitive deficits to make it an independent factor.

We studied 20 patients with rheumatoid arthritis (RA) as controls: the purpose was to have a comparison group with similar possible confounding factors influencing neuropsychological performance, such as the distress linked to a chronic disease and steroid therapy (21, 22). These patients were selected based on similar age and steroid daily dose.

Exclusion criteria included age <18 years and/or >60 years and insufficient premorbid cognitive level estimated through the methods described below. Demographic and education data, disease duration, risk factors for cognitive deficits (head injury, hypothyroidism, drugs, vision/hearing problems), cardiovascular risk factors, and mean daily steroid dose were recorded for all the patients.

Neuropsychological assessment.

All patients underwent a 3-hour battery of neuropsychological tests sampling 15 cognitive functions grouped into 8 cognitive domains, as defined by the ACR Committee (20). The neuropsychological battery included Raven's Progressive Matrices; Comprehension, Similarities, Block Design, and Digit Symbol subtests of the Wechsler Adult Intelligence Scale-Revised (WAIS-R); Wechsler Memory Scale; Rey Auditory-Verbal Learning Test; Trail-Making A and B; Corsi Block Test; Number Cancellation Test; Reverse Numerical Sequence from Mini Mental State Examination; Stroop Word, Color, and Word-Color Test; Semantic and Phonemic Verbal Fluency Test; Denomination of Aachener Aphasie Test; and Token Test. From these 13 tests, 26 raw scores were derived and converted into percentile and Z scores using normative Italian population data (23–26); cutoff for abnormality was defined as a score <1 SD from the population mean or below the tenth percentile. The Z scores were grouped into 15 different summary scores, each one depicting a different neurocognitive process; because some Z scores were used for testing more than 1 function, a qualitative rating, e.g., based on the presence of confabulation and perseveration, was introduced to avoid a single test result influencing too many functions. The 15 functions were grouped into the 8 domains defined by the ACR Committee (Appendix A).

To respect the diagnostic criteria established by the ACR Committee, impairment for each patient was defined as the presence of at least 1 function and therefore 1 domain impaired. Patients were classified into 3 groups according to severity of impairment: normal (0 function impaired), mildly impaired (<3 functions impaired, placed in ≤2 domains), or moderately and severely impaired (≥3 functions impaired, placed in ≥2 domains).

Premorbid level of neuropsychological functioning was estimated with the best performance method (average of the 2 highest WAIS-R scores apart from Digit Span and Digit Symbol [27]) and with the Raven's Progressive Matrices (26).

Serologic tests.

Anticardiolipin antibodies (aCL) and anti–β2-glycoprotein I were quantified by enzyme-linked immunosorbent assay; aCL positivity was defined as levels of aCL >15 IgG phospholipid units/ml and levels of anti–β2-glycoprotein I IgG >20 IU/ml. Lupus anticoagulant (LAC) was determined by the presence of a prolonged activated partial thromboplastin time (APTT), not normalized by the addition of normal plasma. Prolongation of APTT was measured with ellagic acid with addition of synthetic phospholipid and with micronized silica reagent with addition of synthetic phospholipid. It was confirmed through Russell's viper venom test. Positive aPL was defined as aCL IgG or anti–β2-glycoprotein I IgG or LAC positivity in at least 2 evaluations within 3 months.

According to the presence of Raynaud's phenomenon and/or aPL, the patients were divided into 3 subsets: absence of Raynaud's phenomenon and aPL, presence of Raynaud's phenomenon or aPL, and presence of Raynaud's phenomenon and aPL.

Psychiatric evaluation.

The Hospital Anxiety and Depression Scale (HADS) (28) and the Medical Outcomes Study 36-item Short Form Health Survey (SF-36), for measurement of health-related quality of life (29), were administered to determine possible factors confounding cognitive performances as psychiatric disorders and distress due to chronic disease. A score ≥8 for each subscale was required to establish cases for anxiety and depression.

Neuroradiologic assessment.

A total of 46 patients with SLE underwent MRI scan within 3 months of the neuropsychological tests. Two patients refused to undergo MRI because they felt asymptomatic and 4 patients were excluded because they underwent MRI after 3 months since neuropsychological testing. MRI was performed without a contrast medium, using spin-echo proton density and T2-weighted sequences in the axial plane. Sections acquired were 5 mm thick. For T2-weighted sequences, repetition time was 2,200 msec and echo time was 12 msec and 80 msec. Cerebral MRI was performed by an SP-Siemens 1.5T scanner (Siemens, Erlangen, Finland).

Imaging was evaluated by a neuroradiologist unaware of the patient's cognitive and clinical status. MRI images were analyzed for the presence of 1) cortical atrophy, based on sulcal and ventricular enlargement (mild, moderate, or severe) and 2) size and location of areas of increased signal intensity according to McCune et al (30) and Molad et al (31). The size of the lesions was defined as micro (<6 mm), macro (6–10 mm), or large (>10 mm). The following locations were considered: frontal, parietal, temporal, and occipital lobes (cortical, juxtacortical, and subcortical), as well as other locations (cerebellum, basal ganglia, diencephalon). Severity of MRI scans was defined according to the degree of cortical atrophy and number and size of focal lesions (Table 1). A scan that revealed 2 small focal lesions was considered a normal scan.

Table 1. Factors associated with severity of cerebral MRI: univariate analysis and bivariate analysis*
 Univariate analysisBivariate analysis: severity of cerebral MRI
No. of patients with moderate/severe MRIOR (95% CI)PrP
  • *

    MRI severity: normal MRI (absence of atrophy and/or ≤2 micro-ischemic lesions), mild (mild atrophy and/or ≤5 micro-ischemic lesions), moderate (moderate atrophy and/or >5 micro-ischemic lesions and/or ≤2 macro-ischemic lesions [6–10 mm]), severe (severe atrophy and/or >2 macro-ischemic lesions and/or single large lesions [1 cm]). MRI = magnetic resonance imaging; OR = odds ratio; 95% CI = 95% confidence interval; aPL = antiphospholipid antibodies; SDI = Systemic Lupus International Collaborating Clinics/American College of Rheumatology Damage Index.

Age, years 3.9 (1.08–13.9)0.0330.410.0044
 <40 (n = 30)9    
 ≥40 (n = 16)10    
Educational level 0.10 (0.02–0.42)0.0013−0.6< 0.0001
 ≤8 (n = 16)12    
 >8 (n = 30)7    
Hypertension 6.4 (1.6–25.7)0.0090.410.0044
 No (n = 32)9    
 Yes (n = 14)10    
aPL positivity 7.9 (1.5–41)0.010.420.004
 No (n = 15)2    
 Yes (n = 31)17    
Raynaud's phenomenon positivity  0.350.060.7
 No (n = 29)10    
 Yes (n = 17)9    
SDI 12.9 (3.1–52.6)0.00010.51< 0.0003
 0 (n = 27)4    
 ≥1 (n = 19)14    
Severity of cognitive impairment   0.66< 0.0001
Number of functions impaired   0.71< 0.0001
Number of domains impaired   0.69< 0.0001

Positive agreement was defined as a concordance between the presence and possible site of CNS involvement, hypothesized through the neuropsychological tests (according to the cerebral locations described in Appendix B), and the presence and site of damage identified by MRI. This means that in patients whose MRI and neuropsychological tests were both abnormal, if the site of CNS involvement hypothesized on testing was different from the site of the MRI lesions, the classification was “not concordant.”

Study design.

The primary objective of the study was to evaluate factors affecting the presence and severity of cognitive impairment in patients with SLE (outcome measures). The severity of cognitive impairment was evaluated in terms of 1) number of functions and domains impaired and 2) presence of moderate/severe impairment according to the definition given before. The possible factors affecting the severity of impairment considered in the analysis were 1) demographic and educational data (age, sex, education), 2) SLE disease-related factors (SLEDAI, SDI, daily steroid dose, disease duration, aPL, Raynaud's phenomenon), 3) risk factor for neuropsychological deficits, 4) cardiovascular risk factors (primary hypertension, obesity, ischemic cardiopathy, tobacco smoking, dyslipidemia), 5) psychosocial parameters (anxiety, depression), and 6) imaging data. Another goal of the study was to compare the anatomic location of CNS damage hypothesized according to the neuropsychological performance with the site of damage observed on the MRI.

Statistical analysis.

Because the continuous variables were not normally distributed, comparisons between medians were made using Mann-Whitney and Kruskal-Wallis tests. The differences in proportions between groups were analyzed using Pearson's chi-square test (or, where applicable, Fisher's exact test and Yates' correction). Bivariate analysis was conducted using Spearman's correlation coefficient. The statistical level of significance was set at P less than 0.05. Four models of multivariate analysis were used to evaluate the effects of the following independent variables on cognitive performance: age, sex, years of education, daily steroid dose, level of anxiety and depression, risk factors for neuropsychological deficits and cardiovascular events, SLEDAI score, SDI score, disease duration, aPL, and Raynaud's phenomenon. In the first model, the number of functions impaired at the neuropsychological performance was included as the dependent variable in a multiple linear regression analysis, performed with the stepwise method (backward elimination procedure). In the second model, the independent variables were the same as the first model, while the dependent variable was the number of domains impaired. The model validity was assessed using R2 statistics. In the third and fourth models, a multiple logistic regression was applied considering the presence of cognitive deficits (mild/moderate/severe) and the presence of moderate/severe impairment as dependent variables, respectively. The model validity was assessed with the Hosmer and Lemeshow test. In these models the results are expressed as odds ratios (ORs) and 95% confidence intervals (95% CIs).

These four models were also applied to the group of 46 patients who underwent MRI, adding as an independent variable the severity of MRI. Agreement between neuropsychological performance and cerebral MRI was evaluated with K measure of concordance. The statistical analysis was performed using SPSS software, release 12.0 (SPSS, Chicago, IL).

RESULTS

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. PATIENTS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. AUTHOR CONTRIBUTIONS
  8. REFERENCES
  9. AAPPENDIX A
  10. AAPPENDIX B

Patient demographics and clinical features.

SLE patients and RA controls were similar in demographic features, cardiovascular risk factors, and mean daily steroid dosage (Table 2). Among SLE patients, 35 were aPL positive and 21 had Raynaud's phenomenon. Sixteen patients were positive for aPL and Raynaud's phenomenon, 12 were negative for both, and 24 were positive for either aPL or Raynaud's phenomenon.

Table 2. Demographics, clinical features, and neuropsychological performance in SLE and RA patients*
 SLE (n = 52)RA (n = 20)POR (95% CI)
  • *

    Values are the number (percentage) unless otherwise indicated. SLE = systemic lupus erythematosus; RA = rheumatoid arthritis; OR = odds ratio; 95% CI = 95% confidence interval; NA = not applicable.

Age, mean ± SD years36.3 ± 9.741 ± 100.08 
Education, mean ± SD years11.7 ± 3.89.6 ± 2.70.035 
Sex    
 Female47 (90.4)15 (75)0.13 
 Male5 (9.6)5 (25)0.13 
Steroid dose, mean ± SD mg/day7 ± 102 ± 1.70.44 
Cardiovascular risk factors    
 Total no. (%) of patients28 (53.8)10 (50)0.08 
  Hypertension15 (29)3 (15)0.36 
  Smoking13 (25)4 (20)0.76 
  Obesity2 (3.8)2 (10)0.31 
  Ischemic cardiopathy1 (1.9)0 (0)NA 
  Dyslipidemia3 (5.8)1 (5)1 
Cognitive deficit risk factors    
 Total no. (%) of patients10 (19)2 (10)0.49 
  Hypothyroidism9 (17)1 (5)0.26 
  Vision/hearing problems0 (0)1 (5)NA 
  Head injury1 (2)0 (0)NA 
Neuropsychological performance   0.22 (0.07–0.72)
 Normal21 (40.4)15 (75)0.017 
 No. of patients with at least 1 function and   1 domain impaired31 (59.6)5 (25)0.0174.4 (1.4–14)
 No. of patients with moderate/severe   deficits (≥3 functions and ≥2   domains impaired)21 (40.4)0 (0)0.000328 (1.6–488)

Patients positive for aPL and those negative for aPL, as far as the Raynaud's positive and Raynaud's negative subsets, did not significantly differ in demographic features, mean daily steroid dose, SLEDAI score, SDI score, and proportion of patients with history of neuropsychiatric involvement other than cognitive impairment and migraine (Table 3).

Table 3. Demographics and clinical features in aPL positive (aPL+) and aPL negative (aPL−) patients*
 aPL+ (n = 35)aPL− (n = 17)POR (95% CI)
  • *

    Values are the number (percentage) unless otherwise indicated. aPL = antiphospholipid antibody; OR = odds ratio; 95% CI = 95% confidence interval; SLEDAI = Systemic Lupus Erythematosus Disease Activity Index; SDI = Systemic Lupus International Collaborating Clinics/American College of Rheumatology Damage Index; SLE = systemic lupus erythematosus; TIA = transient ischemic attack; NA = not applicable.

Age, mean ± SD years37 ± 10.134.9 ± 9.10.52 
Education, mean ± SD years11.2 ± 3.712.8 ± 3.80.81 
Sex    
 Female31 (88.6)16 (94.1)1.0 
 Male4 (11.4)1 (5.9)1.0 
Steroid dosage, mean ± SD mg/day6.5 ± 11.67.9 ± 8.30.1 
SLEDAI, mean ± SD score3.9 ± 3.75.3 ± 5.90.47 
SDI, mean ± SD score0.7 ± 0.80.35 ± 0.80.12 
Cardiovascular risk factors, no.    
 Hypertension1140.7 
 Smoking850.6 
 Obesity111 
 Ischemic cardiopathy101 
 Dyslipidemia300.54 
Neuropsychiatric SLE0 (0)1 (5.9)0.33 
 Acute confusional state    
 Cerebrovascular disease10 (28.5)2 (11.8)0.29 
  Stroke2 (5.7)1 (5.9)1 
  TIA4 (11.4)1 (5.9)1 
  Intracranial hemorrhage2 (5.7)0 (0)NA 
  Subarachnoid hemorrhage1 (2.8)0 (0)NA 
  Chronic multivascular disease1 (2.8)0 (0)NA 
Seizures7 (20)0 (0)NA 
Myelopathy1 (2.8)0 (0)NA 
Psychosis0 (0)0 (0)NA 
Paresthesias0 (0)1 (5.9)NA 
Headache26 (72.2)8 (47)0.053 
 Migraine26 (72.2)7 (41.2)0.024.2 (1.2–14.1)
Mood disorders12 (34.3)8 (47)0.37 
Anxiety18 (51.4)12 (71)0.19 
Cognitive deficits24 (68.6)7 (41.2)0.059 
 Moderate/severe18 (51.4)3 (14.7)0.0344.9 (1.2–20.3)

Psychiatric assessment.

Prevalence of anxiety and depression in SLE patients was 57.7% and 38.5%, respectively, and in RA controls was 45% (not significant [NS]) and 50% (NS), respectively. SLE and RA patients presented similar mean scores of anxiety and depression, assessed using the HADS. Significant differences were detected in the physical activity (P = 0.0035) and physical pain (P = 0.0078) subscales of the SF-36; these scores were lower in RA patients, as expected. Prevalence of anxiety and depression in the aPL positive and aPL negative subsets and in the Raynaud's positive and Raynaud's negative subsets were similar (Table 3).

Neuropsychological evaluation.

All patients had a sufficient premorbid level, estimated with the best performance method, and none of them had deficits in reasoning/problem-solving functions on the Raven's Progressive Matrices. Thirty-one of the 52 SLE patients (59.6%) versus 5 of the 20 RA controls (25%) were impaired in at least 1 function (P = 0.017) (Table 2). All the 5 RA controls were mildly impaired, 4 in only 1 function, 1 in 2 functions, and all in the memory domain. With a cutoff of ≥3 impaired functions (≥3 functions and ≥2 domains impaired), prevalence decreased to 40.4% in SLE patients and 0% in RA controls (P = 0.0003, OR 28, 95%CI 1.6–488).

The median number of functions and domains impaired in SLE patients was significantly different for the following variables: aPL positive (P = 0.007 and P = 0.009, respectively) and hypertension (P = 0.003 and P = 0.001, respectively). The number of functions impaired was significantly correlated with age (r = 0.316, P = 0.022), SDI (r = 0.555, P < 0.001), and aPL positivity (r = 0.38, P = 0.005) and was inversely related to years of education (r = −0.59, P < 0.001) (Table 4).

Table 4. Factors associated with severity of cognitive deficits: univariate and bivariate analysis*
 Univariate analysisBivariate analy- sis: no. of functions  impaired Bivariate analy- sis: severity of cognitive deficits
No. of patients with moderate/severe deficitsPOR (95% CI)rPrP
  • *

    OR = odds ratio; 95% CI = 95% confidence interval; SDI = Systemic Lupus International Collaborating Clinics/American College of Rheumatology Damage Index; HAS = Hospital Anxiety Scale; HDS = Hospital Depression Scale; aPL = antiphospholipid antibody; NA = not applicable.

  • SDI scores: eye involvement (3.8%), central nervous system involvement other than cognitive deficits (28.8%), renal involvement (5.8%), heart involvement (5.8%), musculoskeletal involvement (3.8%), skin involvement (1.9%).

Age, years 0.491.5 (0.8–4.5)0.320.0220.270.05
 <40 (n = 35)13      
 >40 (n = 17)8      
Educational level, years 0.0060.09 (0.023–0.36)−0.59< 0.001−0.58< 0.0001
 ≤8 (n = 17)13      
 >8 (n = 35)8      
Hypertension 0.0274.7 (1.3–17.1)0.420.0020.430.002
 No (n = 37)11      
 Yes (n = 15)10      
SDI 0.00156.8 (2–23.6)0.56< 0.00010.470.008
 0 (n = 31 pts)7      
 ≥1 (n = 21)14      
HAS score 0.90.96 (0.9–2.3)0.010.920.040.76
 <8 (n = 22)9      
 ≥8 (n = 30)12      
HDS score 0.61.4 (0.4–4.2)0.110.440.130.35
 <8 (n = 32)12      
 ≥8 (n = 20)9      
aPL positivity 0.00344.94 (1.2–20.3)0.380.0060.320.023
 No (n = 17)3      
 Yes (n = 35)18      
Raynaud's positivity 0.0433.2 (1.02–10.4)0.240.080.220.12
 No (n = 31)9      
 Yes (n = 21)12      
aPL neg./Raynaud's neg. (n = 12)10.015  NA NA
aPL pos. or Raynaud's pos. (n = 24)10      
aPL pos./Raynaud's pos. (n = 16)10      

In a univariate analysis the presence of a moderate/severe degree of impairment (≥3 functions and ≥2 domains impaired) was associated with aPL positivity (OR 4.9, 95% CI 1.2–20.3, P = 0.034), Raynaud's phenomenon (OR 3.2, 95% CI 1.02–10.4, P = 0.043), hypertension (OR 4.7, 95% CI 1.3–17.1, P = 0.027), and SDI score ≥1 (OR 6.8, 95% CI 2–23.6, P = 0.0015) (Table 4). Dividing the patients according to positivity for aPL and/or Raynaud's phenomenon, the absence of Raynaud's phenomenon and aPL was associated with a lower probability of developing moderate/severe deficits with respect to positivity for Raynaud's phenomenon, aPL, or both (8.3%, 41.7%, and 62.5%, respectively; χ2 = 8.38 with Yates' correction, P = 0.015) (Table 4); such a difference was due to the contemporary presence of Raynaud's phenomenon and aPL (aPL negative/Raynaud's negative versus aPL positive/Raynaud's positive; OR 0.05, 95% CI 0.005–0.53, P = 0.006), since no statistically significant variation in the proportions of patients with moderate/severe deficits was observed between aPL negative/Raynaud's negative patients and those with either aPL or Raynaud's, or between aPL positive/Raynaud's positive patients and those with either aPL or Raynaud's.

In the bivariate analysis, severity of impairment was significantly associated with SDI (P = 0.008), aPL positivity (P = 0.023), and hypertension (P = 0.002), and inversely related to education level (P < 0.0001) (Table 4).

A stepwise regression analysis showed that the number of functions impaired (dependent variable) was directly associated with aPL positivity (P = 0.04), SDI (P = 0.001), and hypertension (P = 0.032), and inversely related to education level (P = 0.021). The number of domains impaired was directly correlated with the same variables and with obesity (P = 0.035) (Table 5).

Table 5. Effect of demographics, clinical, serologic, and psychiatric features on cognitive performance: multiple linear regression analysis of 52 patients*
Multiple linear regression analysisNo. of functions impairedNo. of domains impaired
BTPBtP
  • *

    NS = not significant; aPL = antiphospholipid antibody; SDI = Systemic Lupus International Collaborating Clinics/American College of Rheumatology Damage Index.

Independent variables      
 (Constant)2.171.790.081.562.170.035
 Educational level (>8 years)−0.19−2.390.02−0.12−2.530.015
 Obesity  NS1.952.170.035
 Hypertension1.372.220.031.082.970.005
 aPL1.262.120.040.940.350.011
 SDI1.333.480.0010.723.160.003
 Goodness-of-fit model (R2)0.58  0.63  

A logistic multiple regression analysis, applied with the presence of moderate/severe impairment as the dependent variable, identified hypertension (P < 0.05) and SDI (P < 0.01) as 2 predictors of severe cognitive deficits (Table 6). Any other independent variable was associated with neuropsychological performance in both models.

Table 6. Effect of demographics, clinical, serologic, and psychiatric features on cognitive performance: logistic regression analysis of 52 patients*
Logistic regression analysisPresence of moderate/severe deficits versus mild/absentPresence of cognitive deficits (mild/moderate/severe) versus absence
OR (95% CI)POR (95% CI)P
  • *

    OR = odds ratio; 95% CI = 95% confidence interval; NS = not significant; SDI = Systemic Lupus International Collaborating Clinics/American College of Rheumatology Damage Index.

Independent variables    
 AgeNS  < 0.01
  <40 yrs  1 
  >40 yrs  11.35 (2.15–59.80) 
Hypertension < 0.05NS 
 No1   
 Yes5.03 (1.02–24.88)   
SDI score < 0.01NS 
 01   
 ≥14.6 (1.75–12.09)   
Hosmer-Lemeshow test0.83 0.89 

In SLE patients, memory (50%), complex attention (42.3%), and executive functions (26.9%) were the domains more frequently altered; memory was affected in a similar proportion of aPL positive (54.3%) and aPL negative (41.2%) patients, whereas executive functions (37.1% versus 5.95%; OR 9.4, 95%CI 1.1–80, P = 0.02) and complex attention (57.1% versus 17.6%; OR 6.22, 95%CI 1.5–25.6, P = 0.009) were more frequently impaired in aPL positive patients, suggesting a prevalent involvement of prefrontal and frontal areas. According to the neuropsychological performance, parietal and temporal areas were affected in similar proportions of aPL positive and aPL negative patients while frontal and prefrontal areas were involved in a greater proportion of aPL positive patients (OR 4.9, 95% CI 1.3–18.05, P = 0.019) (data not shown).

Neuropsychological evaluation and MRI.

Twenty-nine patients of 46 (63%) had an abnormal MRI scan; the lesions were mild in 10 patients (21.7%), moderate in 12 (26.1%), and severe in 7 (15.2%). In a univariate analysis, the presence of an abnormal MRI was associated with the presence of hypertension (OR 13, 95% CI 1.5–111, P = 0.007) and with aPL positivity (OR 4.3, 95% CI 1.2–16, P = 0.024).

MRI severity was significantly correlated with the following variables: aPL positivity (r = 0.42, P = 0.004), hypertension (r = 0.41, P = 0.0044), SDI (r = 0.51, P < 0.0003), age (r = 0.41, P = 0.0044), education level (r = −0.6, P < 0.0001), severity of cognitive impairment (r = 0.66, P < 0.0001), number of functions impaired (r = 0.71, P < 0.0001), and domains impaired (r = 0.69, P < 0.0001) (Table 1).

The kinds of MRI lesions detected more frequently in aPL positive patients versus aPL negative patients were macro-ischemic lesions (6–10 mm; 38.7% versus 6.7%; OR 8.8, 95% CI 1–76, P = 0.035). Most of the MRI lesions detected were localized in the subcortical and juxtacortical white matter of the frontal and parietal lobes, thus reflecting the topographical areas of prevalent impairment detected through the neuropsychological tests. No differences in severity, dimension, and location of MRI lesions were detected between Raynaud's positive and Raynaud's negative patients (data not shown).

The stepwise regression analysis on the group of 46 patients who underwent MRI, including the MRI data as an additional independent variable, showed that the number of functions impaired (dependent variable) was directly associated with the severity of MRI (P < 0.001), SDI (P = 0.013), obesity (P = 0.003), and Raynaud's phenomenon (P = 0.04); the number of domains impaired was related to severity of MRI (P < 0.001) and obesity (P = 0.005) (Table 7).

Table 7. Effect on cognitive performance of demographics, clinical, serological, psychiatric, and MRI features: multiple linear regression analysis of 46 patients*
Multiple linear regression analysisNo. of functions impairedNo. of domains impaired
BTPBtP
  • *

    SDI = Systemic Lupus International Collaborating Clinics/American College of Rheumatology Damage Index; MRI = magnetic resonance imaging.

Independent variables      
 (Constant)−0.25−0.640.520.0610.230.81
 Obesity5.253.120.0033.352.930.005
 Raynaud's positivity1.062.110.040.651.920.06
 SDI1.012.610.010.511.960.05
 MRI1.304.910.0010.804.45< 0.001
 Goodness-of-fit model (R2)0.72  0.66  

The logistic multiple regression analysis, performed including the result of MRI and applied considering the presence of moderate/severe deficits as the dependent variable, identified the presence of a moderate/severe MRI as a predictor of severe impairment (OR 33.5, 95% CI 3.2–348.3, P < 0.01); the same model, applied with the presence of cognitive deficits (mild/moderate/severe), identified hypertension and age >40 years as the main predictors for developing cognitive impairment (Table 8).

Table 8. Effect on cognitive performance of demographics, clinical, serological, psychiatric, and MRI features: logistic regression analysis of 46 patients*
Logistic regression analysisPresence of moderate/severe deficits versus mild/absentPresence of cognitive deficits (mild/moderate/severe) versus absence
OR (95% CI)POR (95% CI)P
  • *

    MRI = magnetic resonance imaging; OR = odds ratio; 95% CI = 95% confidence interval.

Independent variables    
 Age, yearsNS  < 0.05
  <40  1 
  >40  7.03 (1.29–38.45) 
HypertensionNS  < 0.01
 No  1 
 Yes  26.46 (2.44–286.7) 
MRI < 0.01NS 
 Normal/mild1   
 Moderate and severe33.53 (3.23–348.3)   
Hosmer-Lemeshow test0.829 0.836 

Negative and positive agreement between neuropsychological performance and MRI scan was detected in 33 (71.7%) of 46 SLE patients (measure K of concordance 0.42, P = 0.005). Among the 13 patients with disagreement, 5 were unimpaired with positive MRI (3 mild and 2 moderate) and 5 had negative MRI with cognitive deficits (3 mild and 2 moderate). In 3 patients there was disagreement between site of involvement at neuropsychological evaluation and site of damage at MRI; all had mild cognitive impairment and mild MRI damage.

DISCUSSION

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. PATIENTS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. AUTHOR CONTRIBUTIONS
  8. REFERENCES
  9. AAPPENDIX A
  10. AAPPENDIX B

This study analyzed the main factors affecting the severity of cognitive deficits in patients with SLE. Hypertension resulted in the most important generic risk factor affecting both the presence and the severity of cognitive impairment in our SLE cohort. The negative effect of hypertension, even when controlled, on cognitive performance is well known, especially in the elderly general population (32, 33), but it has never been reported before in SLE. We also observed an independent effect of age on the development of impairment and a protective effect of a high education level on the number of functions impaired. The protective effect of educational attainment, noticed in several types of dementia (34), may be due to a greater functional brain reserve that delays the onset of clinical manifestations. In addition, Brey et al (4) identified an association of age and education with some measures of cognitive impairment. These data, considering that we did not find any differences between SLE and RA patients regarding demographics and cardiovascular risk factors, suggest that generic risk factors for impairment, such as hypertension and aging, play a more relevant role in a context such as SLE disease, characterized by accelerated atherosclerosis.

Among the SLE disease-related factors, the presence of chronic damage, measured by the SDI, arose as the main factor affecting the severity of impairment. Other studies highlighted the association of cumulative damage measured by the SDI with the development of general neuropsychiatric involvement (35, 36), but not specifically with cognitive impairment. In our stepwise regression analysis, aPL positivity was also an independent factor associated with the number of functions and domains impaired. The role of aPL in worsening cognitive performance, especially if persistently present, has already been highlighted in the literature (5–7), even though it has not been widely confirmed (37, 38). Both in our univariate analysis and in the multivariate analysis performed including the MRI data, the presence of Raynaud's phenomenon was an independent factor associated with the severity of cognitive deficits and with the number of functions impaired. No data are available in the literature regarding the possible effect of Raynaud's phenomenon on cognitive performance in patients with SLE. Ferraccioli et al (18) demonstrated that cerebral vasospasm, identified at the single-photon–emission computed tomography analysis, is more frequent in patients with SLE and peripheral Raynaud's phenomenon with respect to patients without peripheral vasospasm. Furthermore, cerebral vasospasm seems to be involved in some neurologic manifestations as a migraine with aura (16, 17) and might be a possible cause of reversible or irreversible ischemia (39). In our cohort, the prevalence of severe/moderate deficits was significantly higher in patients with the contemporary presence of aPL and Raynaud's phenomenon. A study by Hirashima et al (40) was the first to highlight the additive effect of aPL and cerebral vasospasm; the authors reported how the outcome of cerebral vasospasm after subarachnoid hemorrhage was much worse in a group of aPL positive patients versus aPL negative patients. The possible role of cerebral vasospasm in affecting the neuropsychological performance might explain the temporal fluctuating aspects of cognitive impairment observed in some longitudinal studies (8).

As far as imaging is concerned, the severity of cerebral MRI appears to be the main factor associated, in an independent fashion, with the severity of cognitive impairment in a subgroup of 46 SLE patients; the result of the logistic regression analysis was confirmed by the correlation identified between the severity of MRI and cognitive dysfunctions. In addition, the sites of CNS involvement that were hypothesized according to the neuropsychological performance were correlated to the location of damage seen at cerebral MRI in most patients. In previous studies MRI proved to be not so sensitive in identifying cognitive dysfunctions (9, 10). Kozora et al (9) did not find any association between the number of hyperintense white matter T2 lesions and cortical atrophy and the presence of cognitive deficits in 20 patients who never had neuropsychiatric SLE, but associations were searched without any anatomic localization for MRI lesions and neuropsychological dysfunctions. Our results are in agreement with a recent study (41) that identified an association between cerebral atrophy and T2-weighted lesions on MRI with cognitive dysfunctions, suggesting that structural damage, mainly consisting of white matter micro-ischemic lesions, may be responsible for the development of cognitive impairment in patients with SLE. Furthermore, in our study, the severity of cerebral MRI correlated with all the factors that were independently related to the severity of cognitive performance in the analysis without imaging data, such as aPL positivity, age, and hypertension, suggesting that they influence the severity of cerebral MRI and this, in turn, may determine the severity of cognitive performance. A recent prospective study indirectly confirmed these observations, demonstrating that the regular use of aspirin is associated with improved cognitive functions in older patients with SLE (15).

In our multiple regression analysis including imaging data, Raynaud's phenomenon, the SDI, and obesity were associated, in an independent manner, with the number of functions impaired, confirming the importance in the SLE-related chronic damage, of generic cardiovascular risk factors, and the possible role of cerebral vasospasm in affecting the severity of neuropsychological performances.

To our knowledge, this is the first study that analyzed cognitive dysfunctions in SLE patients with respect to a wide number of factors. This allowed us to identify some generic cardiovascular risk factors, aPL positivity, accrued damage, and cerebral MRI lesions as the main factors affecting the severity of neuropsychological performance. A high education level may prevent the presentation of severe impairment. Finally, this is the first study demonstrating a possible role of cerebral vasospasm in worsening cognitive deficits, given aPL positivity.

A limitation of this study is the lack of a longitudinal analysis, now ongoing, likely helpful to better define some predictors for the development of neuropsychological deficits. Furthermore, the multivariate analyses with imaging data have been performed in a subgroup of patients. Despite this caveat the final results appear to be clearcut.

The final message of the study for clinical practice is that antiaggregant therapy, a tight control of hypertension, and vasodilators, in patients with Raynaud's phenomenon, might be relevant therapies to prevent the development of severe cognitive impairment in SLE. Additional studies should be conducted.

AUTHOR CONTRIBUTIONS

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. PATIENTS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. AUTHOR CONTRIBUTIONS
  8. REFERENCES
  9. AAPPENDIX A
  10. AAPPENDIX B

Dr. Ferraccioli had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Study design. Tomietto, Annese, Ferraccioli.

Acquisition of data. Tomietto, Annese, D'Agostini, Venturini.

Analysis and interpretation of data. Tomietto, Annese, De Vita, Ferraccioli.

Manuscript preparation. Tomietto, Ferraccioli.

Statistical analysis. Tomietto, La Torre.

REFERENCES

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. PATIENTS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. AUTHOR CONTRIBUTIONS
  8. REFERENCES
  9. AAPPENDIX A
  10. AAPPENDIX B
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AAPPENDIX A

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. PATIENTS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. AUTHOR CONTRIBUTIONS
  8. REFERENCES
  9. AAPPENDIX A
  10. AAPPENDIX B
Table  . NEUROPSYCHOLOGICAL EVALUATION*
DomainCognitive functionTests
  • *

    WMS = Wechsler Memory Scale; WAIS-R = Wechsler Adult Intelligence Scale-Revised; AAT = Aachener Aphasie Test; RAVL = Rey Auditory-Verbal Learning Test; MMSE = Mini Mental State Examination; SPM = Standard progressive matrixes.

Executive functionsExecutive functionsDigit Span Backward (WMS)
  Paired Difficult Associates (WMS)
  Verbal Phonemic Fluency
  Stroop Interference and Color-Word Naming Test
  Trail Making B
Psychomotor speedPsychomotor speedDigit Symbol (WAIS-R)
  Stroop Word-Naming and Color-Naming Test
LanguageLanguageDenomination of AAT
  Token Test
  Verbal Semantic Fluency
  Verbal Phonemic Fluency
Visual-spatial processingVisual-spatial processingBlock Design (WAIS-R)
MemoryVisual-spatial short-term memoryCorsi Block Test
 Visual memoryVisual Reproduction (WMS)
 Verbal short-term memoryDigit Span Forward (WMS) RAVL Trial A–I
  Paired Associates Trial A–I (WMS)
 Thematic memoryLogical Memory (WMS)
 Auditory verbal long-term memory (encoding)RAVL: Trials A–I to A–V Paired Associates Total (WMS)
 Recall from auditory long-term memoryRAVL: Trial A–VI Delayed
Simple attentionSimple attentionDigit Span Forward (WMS)
  Number Cancellation Test
Complex attentionSustained attentionNumber Cancellation Test
  Stroop Word and Color-Naming
 Complex attention with verbal stimuliReverse Numerical Sequence (MMSE) 100–7 Digit Span Backward (WMS) Direct Numerical Sequence (WMS)
 Complex attention with visual stimuliDigit Symbol (WAIS-R) Stroop Interference and Color-Word Naming Test Trail Making B
Reasoning/problem solvingVerbal and visual-spatial reasoningComprehension and Similarities (WAIS-R) Raven's Progressive Matrices (SPM)

AAPPENDIX B

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. PATIENTS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. AUTHOR CONTRIBUTIONS
  8. REFERENCES
  9. AAPPENDIX A
  10. AAPPENDIX B
Table  . MAIN HYPOTHESIS FOR CEREBRAL LOCATION OF SOME NEUROPSYCHOLOGICAL FUNCTIONS (42)
Attentional and executive functions, psychomotor speed 
• System of tonic excitation: locus coeruleus, intralaminar nucleus of the thalamus, suprachiasmatic nucleus of hypothalamus [RIGHTWARDS ARROW]regulation of attentional levelMesulam 1985
• System of executive behavior of attention:
  • — circuit of the prefrontal dorsolateral cortex[RIGHTWARDS ARROW] complex attention, answer flexibility, genesis of alternative answers

  • — circuit prefrontal orbital-medial [RIGHTWARDS ARROW] regulation of impulses and mood

  • — circuit of the anterior cingulate gyrus [RIGHTWARDS ARROW]motivation

 
Alexander et al 1986 Cummings 1984
Mega and Cummings 1994
Salloway and Cummings 1994
Memory functions 
• Short-term verbal memory: parietal posterior-inferior left region (gyro supramarginal)Vallar and Papagno 1995
• Short-term visual-spatial memory: visual associative cortex (areas 19), parietal posterior-inferior right associative cortex, prefrontal cortex (areas 47)De Renzi, Nichelli 1975
Goldman-Rakic 1987
Hauley 1990, Perani 1993
• Visual memory: visual associative cortex, occipital-parietal left associative cortex; temporal-inferior region and right thalamus, prefrontal inferior regionJonides 1993
Warrington and Rabin 1971
McCarthy and Warrington 1990
• Working memory: prefrontal dorsolateral areas (9–10–46), posterior parietal areas (7–40–39), ventral-anterior and nucleus dorsomedial nucleus of thalamus, head of caudate nucleus, globus pallidusKinsbourne and Warrington 1963
Smith 1995, Stein et al 1995
• Long-term verbal memory:
  • — storage: hippocampus, mammillary bodies, mammillo-thalamic tracts, anterior and dorsomedial nucleus of thalamus, cingulated gyrus, frontal-basal median region

  • — archive: parahippocampal and entorhinal cortex

  • — recall: prefrontal dorsolateral cortex, left caudate nucleus and white matter above

Baddeley 1986
Alexander et al
1990 Petrides et al 1993 a,b
Barbizet 1981, Golberg 1984
Weiskrantz 1985
Hyman et al 1984–1986
Phillips et al 1987
Press 1989, Squire 1990–1992
Aggleton and Sahgal 1993
Shallice 1994
Language 
• Phonemic codification: temporal-parietal left cortexBroca 1861, Wernicke 1874
• Processing of motor output: infero-anterior frontal cortex, premotor areas (4s of Broca)Dejerine 1914, Benson 1977
Kertesz 1979, Damasio 1981
• Auditory-motor integration tracts: subcortical structures as left thalamus, left caudate nucleus, and the near white matterBasso 1985, Kirshner 1989
Alexander 1990, Gordon 1974
• Language semantic and phonemic recall: prefrontal and premotor cortex (mainly left)Petersen 1990, Wise 1991
Howard 1992, Demonet 1992
Miozzo 1994, Daniele et al 1994
Visual-spatial processing 
• Parietal region, prefrontal and premotor cortex (mainly right), basal gangliaHeilman 1983, 1993
Robertson-Marshall 1993