Different phenotypes of neuropsychiatric systemic lupus erythematosus are related to a distinct pattern of structural changes on brain MRI

VPS13C and STING expression in neuropsychiatric systemic lupus erythematosus: unveiling an unbreached territory

OBJECTIVES

To measure the expression level of the vacuolar protein sorting 13 (VPS13) gene and stimulator of interferon genes (STING) in patients with SLE with and without reported neuropsychiatric symptoms to establish their possible role in the pathogenesis of neuropsychiatric SLE (NPSLE).

METHODS

This study included 100 subjects: 50 patients diagnosed with SLE and 50 age-matched and sex-matched healthy participants as the control group. The patients with SLE were further subdivided into NPSLE and non-NPSLE groups. All the subjects underwent rheumatological, neurological and psychological evaluation, MRI, VPS13C gene and STING expression assessment via quantitative real-time PCR.

RESULTS

Seventy-eight per cent of the SLE group were classified as non-NPSLE, and 22% were classified as NPSLE. Positive MRI results were found in 55% of the patients with NPSLE and 7.7% of the patients without NPSLE.VPS13C expression levels were decreased in the patients with SLE compared with the control (p<0.001), while STING expression levels showed higher levels in the patients in comparison with the control (p<0.001). Both markers showed significant differences between the MRI-positive and MRI-negative groups.At a cut-off value of 0.225 for the VPS13C assessment and a cut-off value of 3.15 for STING expression, both markers were able to distinguish patients with NPSLE from those who were non-NPSLE; however, VPS13C performed better.

CONCLUSION

The VPS13C expression levels were decreased in patients with NPSLE compared with patients without NPSLE, while STING expression levels showed higher levels in NPSLE. Both were associated with the MRI findings. To distinguish patients with NPSLE from those without it, the VPS13C assessment performed better.

Perfusion-weighted MRI patterns in neuropsychiatric systemic lupus erythematosus: a systematic review and meta-analysis

BACKGROUND

Neuropsychiatric Systemic Lupus Erythematosus (NPSLE) is a complex manifestation of Systemic Lupus Erythematosus (SLE) characterized by a wide range of neurological and psychiatric symptoms. This study aims to elucidate the patterns of Perfusion-Weighted MRI (PWI) in NPSLE patients compared to SLE patients without neuropsychiatric manifestations (non-NPSLE) and healthy controls (HCs).

MATERIAL AND METHODS

A systematic search was conducted in PubMed/Medline, Embase, Web of Science, and Scopus for studies utilizing PWI in NPSLE patients published through April 14, 2024. Cerebral blood flow (CBF) data from NPSLE, non-NPSLE patients, and HCs were extracted for meta-analysis, using standardized mean difference (SMD) as an estimate measure. For studies lacking sufficient data for inclusion, CBF, cerebral blood volume (CBV), and mean transit time (MTT) were reviewed qualitatively.

RESULTS

Our review included eight observational studies employing PWI techniques, including dynamic susceptibility contrast (DSC) and arterial spin labeling (ASL). The meta-analysis of NPSLE compared to non-NPSLE incorporated four studies, encompassing 104 NPSLE patients and 90 non-NPSLE patients. The results revealed an SMD of -1.42 (95% CI: -2.85-0.00, I2: 94%) for CBF in NPSLE compared to non-NPSLE.

CONCLUSION

PWI reveals informative patterns of cerebral perfusion, showing a significant reduction in mean CBF in NPSLE patients compared to non-NPSLE patients. Our qualitative synthesis highlights these changes, particularly in the frontal and temporal lobes. However, the existing data exhibits considerable heterogeneity and limitations.

Juvenile neuropsychiatric systemic lupus erythematosus: A specific clinical phenotype and proposal of a probability score

OBJECTIVE

Juvenile systemic lupus erythematosus (j-SLE) is a rare chronic auto-immune disease involving several organs. Neuropsychiatric (NP) SLE (NPSLE) is frequent in j-SLE and associated with increased morbidity/mortality. Although NPSLE classification criteria exist, attributing NP features to j-SLE remains a major challenge. The study objective is to thoroughly describe j-NPSLE patients and assist in their diagnosis.

METHODS

This is a 4-year retrospective monocentric study of j-SLE patients. NP events were attributed to j-SLE using standardised diagnostic criteria and multidisciplinary paediatric clinical expertise. Clinical features, brain magnetic resonance imaging (MRI)s and samples analysis including cerebrospinal fluid were assessed. A risk of j-NPSLE score was developed based on multivariable logistic regression analysis.

RESULTS

Of 39 patients included, 44% were identified as having j-NPSLE. J-NPSLE diagnosis was established at the onset of j-SLE in 59% of patients. In addition to frequent kidney involvement (76%) and chilblains (65%), all j-NPSLE patients displayed psychiatric features: cognitive symptoms (82%), hallucinations (76%), depressed mood (35%), acute confused state (18%) and catatonia (12%). Neurological involvement was often mild and nonspecific, with headache (53%) in about half of the patients. The main features reported on brain MRI were nonspecific T2/FLAIR white matter hyperintensities (65%), and cerebral atrophy (88%). Upon immunosuppressive treatment, clinical improvement of NP features was observed in all j-NPSLE patients. The score developed to attribute j-NPSLE probability, guide further investigations and appropriate treatments is based on hallucinations, memory, sleep and renal involvement (Sensitivity: 0.95 Specificity: 0.85). Cerebrospinal fluid (CSF) neopterin assessment increases the score sensitivity and specificity.

CONCLUSION

Physicians should carefully and systematically assess the presence of NP features at diagnosis and early stages of j-SLE. For j-NPSLE patients with predominant psychiatric features, a multidisciplinary collaboration, including psychiatrists, is essential for the diagnosis, management and follow-up.

Implications of Inflammatory Processes on a Developing Central Nervous System in Childhood-Onset Systemic Lupus Erythematosus

Systemic lupus erythematosus (SLE) is a chronic autoimmune disease that is increasingly affecting pediatric and adult populations. Neuropsychiatric manifestations (ie, cognitive dysfunction and mood disorders) appear to occur with greater severity and poorer prognosis in childhood-onset SLE (cSLE) versus adult-onset SLE, negatively impacting school function, self-management, and psychosocial health, as well as lifelong health-related quality of life. In this review, we describe pathogenic mechanisms active in cSLE, such as maladaptive inflammatory processes and ischemia, which are hypothesized to underpin central phenotypes in patients with cSLE, and the role of alterations in protective central nervous system (CNS) barriers (ie, the blood-brain barrier) are also discussed. Recent findings derived from novel neuroimaging approaches are highlighted because the methods employed in these studies hold potential for identifying CNS abnormalities that would otherwise remain undetected with conventional multiple resonance imaging studies (eg, T2-weighted or fluid-attenuated inversion recovery sequences). Furthermore, we propose that a more robust presentation of neuropsychiatric symptoms in cSLE is in part due to the harmful impact of a chronic inflammatory insult on a developing CNS. Although the immature status of the CNS may leave patients with cSLE more vulnerable to harboring neuropsychiatric manifestations, the same property may represent a greater urgency to reverse the maladaptive effects associated with a proneuroinflammatory state, provided that effective diagnostic tools and treatment strategies are available. Finally, considering the crosstalk among the CNS and other organ systems affected in cSLE, we postulate that a finer understanding of this interconnectivity and its role in the clinical presentation in cSLE is warranted.

Juvenile Neuropsychiatric Systemic Lupus Erythematosus: Identification of Novel Central Neuroinflammation Biomarkers

INTRODUCTION

Juvenile systemic lupus erythematosus (j-SLE) is a rare chronic autoimmune disease affecting multiple organs. Ranging from minor features, such as headache or mild cognitive impairment, to serious and life-threatening presentations, j-neuropsychiatric SLE (j-NPSLE) is a therapeutic challenge. Thus, the diagnosis of NPSLE remains difficult, especially in pediatrics, with no specific biomarker of the disease yet validated.

OBJECTIVES

To identify central nervous system (CNS) disease biomarkers of j-NPSLE.

METHODS

A 5-year retrospective tertiary reference monocentric j-SLE study. A combination of standardized diagnostic criteria and multidisciplinary pediatric clinical expertise was combined to attribute NP involvement in the context of j-SLE. Neopterin and interferon-alpha (IFN-α) protein levels in cerebrospinal fluid (CSF) were assessed, together with routine biological and radiological investigations.

RESULTS

Among 51 patients with j-SLE included, 39% presented with j-NPSLE. J-NPSLE was diagnosed at onset of j-SLE in 65% of patients. No specific routine biological or radiological marker of j-NPSLE was identified. However, CSF neopterin levels were significantly higher in active j-NPSLE with CNS involvement than in j-SLE alone (p = 0.0008). Neopterin and IFN-α protein levels in CSF were significantly higher at diagnosis of j-NPSLE with CNS involvement than after resolution of NP features (respectively p = 0.0015 and p = 0.0010) upon immunosuppressive treatment in all patients tested (n = 10). Both biomarkers correlated strongly with each other (R_s = 0.832, p < 0.0001, n = 23 paired samples).

CONCLUSION

CSF IFN-α and neopterin constitute promising biomarkers useful in the diagnosis and monitoring of activity in j-NPSLE.

Tract-based white matter hyperintensity patterns in patients with systemic lupus erythematosus using an unsupervised machine learning approach

Currently, little is known about the spatial distribution of white matter hyperintensities (WMH) in the brain of patients with Systemic Lupus erythematosus (SLE). Previous lesion markers, such as number and volume, ignore the strategic location of WMH. The goal of this work was to develop a fully-automated method to identify predominant patterns of WMH across WM tracts based on cluster analysis. A total of 221 SLE patients with and without neuropsychiatric symptoms from two different sites were included in this study. WMH segmentations and lesion locations were acquired automatically. Cluster analysis was performed on the WMH distribution in 20 WM tracts. Our pipeline identified five distinct clusters with predominant involvement of the forceps major, forceps minor, as well as right and left anterior thalamic radiations and the right inferior fronto-occipital fasciculus. The patterns of the affected WM tracts were consistent over the SLE subtypes and sites. Our approach revealed distinct and robust tract-based WMH patterns within SLE patients. This method could provide a basis, to link the location of WMH with clinical symptoms. Furthermore, it could be used for other diseases characterized by presence of WMH to investigate both the clinical relevance of WMH and underlying pathomechanism in the brain.

Autoantibodies against specific post-translationally modified proteins are present in patients with lupus and associate with major neuropsychiatric manifestations

Background

Although autoantibodies are an important hallmark of systemic lupus erythematosus (SLE), most are not specific for SLE or any of its clinical manifestations. Autoantibodies against post-translationally modified (PTM) proteins have been studied extensively in rheumatoid arthritis and associate with disease progression. While PTMs have also been detected in patients with SLE, studies on anti-PTM antibodies remain scarce. We studied the presence of anti-PTM antibodies in SLE and neuropsychiatric SLE (NPSLE), a manifestation that lacks serological markers.

Methods

IgG antibody responses against six PTMs (malondialdehyde–acetaldehyde adducts (MAA), advanced glycation end-products (AGE), carbamylation (CarP), citrullination, acetylation and nitration) were tested using ELISA in sera of 349 patients with SLE (mean age 44±13 years; 87% female) and compared with 108 healthy controls. Levels and positivity were correlated with clinical features and SLE manifestations.

Results

Anti-MAA, anti-AGE and anti-CarP antibodies were more prevalent in SLE compared with controls (MAA: 29% vs 3%, AGE: 18% vs 4%, CarP: 14% vs 5%, all p≤0.0001). Anti-MAA and anti-AGE antibodies correlated with clinical manifestations and serological inflammatory markers. Patients with major NPSLE showed higher positivity of anti-MAA (39% vs 24%, p=0.01) and anti-CarP antibodies (20% vs 11%, p=0.04) than patients without major NPSLE. In addition, anti-PTM antibody levels correlated with brain volumes, an objective measure of nervous system involvement.

Conclusions

In our NPSLE cohort, a subset of patients with SLE have anti-PTM antibodies against MAA, AGE and CarP modified proteins. Interestingly, anti-MAA and anti-CarP were more prevalent in NPSLE, a manifestation for which no biomarkers exist.

Cognitive performance in systemic lupus erythematosus patients: a cross-sectional and longitudinal study

Background

Previous research has provided evidence for cognitive dysfunction as a common symptom of systemic lupus erythematosus (SLE). In light of this, the primary goal of this study was to investigate how cognitive impairment in this patient group develops over time. In addition, the present dataset contributes to delineating the specific abilities that are impaired in SLE patients as well as answering the question whether the disease affects the cognition of SLE patients with neuropsychiatric manifestations (NPSLE) and without (non-NPSLE) in distinct ways.

Methods

91 female participants (33 NPSLE, 29 non-NPSLE, 29 healthy controls (HC)) underwent standardized neurocognitive testing. A total of ten different cognitive abilities were assessed, among others executive function, memory, and attention. Some of the participants (30 NPSLE patients, 22 non-NPSLE, 13 HC) were tested twice (mean time between testing sessions: 50 months) to enable longitudinal tracking of cognitive abilities. Analyses of Variance (ANOVA) were conducted to determine whether cognitive performance differed cross-sectionally between the groups. Linear mixed effects models were fit to investigate performance differences between the groups over time.

Results

Cross-sectional analysis at follow-up demonstrated that the cognitive performance of both NPSLE and non-NPSLE was significantly lower than that of HC for the motor speed and the psychomotor speed domain. Additionally, NPSLE patients performed significantly weaker than HC in the complex attention domain. At the same time, the cross-sectional data did not yield any support for performance differences between NPSLE and non-NPSLE patients. Weak positive correlations between disease duration and psychomotor speed, motor speed and reaction time emerged. A temporal progression of cognitive dysfunction in SLE patients was not confirmed.

Conclusions

Cognitive performance is affected in both non-NPSLE and NPSLE patients. However, a linear decline in performance over time could not be verified. More in-depth longitudinal assessments of cognition in SLE patients are needed to establish how cognitive abilities in this patient population develop over time.

Supplementary Information

The online version contains supplementary material available at 10.1186/s41927-022-00253-3.

Multi-lesion radiomics model for discrimination of relapsing-remitting multiple sclerosis and neuropsychiatric systemic lupus erythematosus

OBJECTIVES

To develop an MRI-based multi-lesion radiomics model for discrimination of relapsing-remitting multiple sclerosis (RRMS) and its mimicker neuropsychiatric systemic lupus erythematosus (NPSLE).

METHODS

A total of 112 patients with RRMS (n = 63) or NPSLE (n = 49) were assigned to training and test sets with a ratio of 3:1. All lesions across the whole brain were manually segmented on T2-weighted fluid-attenuated inversion recovery images. For each single lesion, 371 radiomics features were extracted and trained using machine learning algorithms, producing Radiomics Index for Lesion (RIL) for each lesion and a single-lesion radiomics model. Then, for each subject, single lesions were assigned to one of two disease courts based on their distance to decision threshold, and a Radiomics Index for Subject (RIS) was calculated as the mean RIL value of lesions on the higher-weighted court. Accordingly, a subject-level discrimination model was constructed and compared with performances of two radiologists.

RESULTS

The subject-based discrimination model satisfactorily differentiated RRMS and NPSLE in both training (AUC = 0.967, accuracy = 0.892, sensitivity = 0.917, and specificity = 0.872) and test sets (AUC = 0.962, accuracy = 0.931, sensitivity = 1.000, and specificity = 0.875), significantly better than the single-lesion radiomics method (training: p < 0.001; test: p = 0.001) Besides, the discrimination model significantly outperformed the senior radiologist in the training set (training: p = 0.018; test: p = 0.077) and the junior radiologist in both the training and test sets (training: p = 0.008; test: p = 0.023).

CONCLUSIONS

The multi-lesion radiomics model could effectively discriminate between RRMS and NPSLE, providing a supplementary tool for accurate differential diagnosis of the two diseases.

KEY POINTS

• Radiomic features of brain lesions in RRMS and NPSLE were different. • The multi-lesion radiomics model constructed using a merging strategy was comprehensively superior to the single-lesion-based model for discrimination of RRMS and NPSLE. • The RRMS-NPSLE discrimination model showed a significantly better performance or a trend toward significance than the radiologists.

MRI-Based Classification of Neuropsychiatric Systemic Lupus Erythematosus Patients With Self-Supervised Contrastive Learning

Introduction/Purpose

Systemic lupus erythematosus (SLE) is a chronic auto-immune disease with a broad spectrum of clinical presentations, including heterogeneous neuropsychiatric (NP) syndromes. Structural brain abnormalities are commonly found in SLE and NPSLE, but their role in diagnosis is limited, and their usefulness in distinguishing between NPSLE patients and patients in which the NP symptoms are not primarily attributed to SLE (non-NPSLE) is non-existent. Self-supervised contrastive learning algorithms proved to be useful in classification tasks in rare diseases with limited number of datasets. Our aim was to apply self-supervised contrastive learning on T1-weighted images acquired from a well-defined cohort of SLE patients, aiming to distinguish between NPSLE and non-NPSLE patients.

Subjects and Methods

We used 3T MRI T1-weighted images of 163 patients. The training set comprised 68 non-NPSLE and 34 NPSLE patients. We applied random geometric transformations between iterations to augment our data sets. The ML pipeline consisted of convolutional base encoder and linear projector. To test the classification task, the projector was removed and one linear layer was measured. Validation of the method consisted of 6 repeated random sub-samplings, each using a random selection of a small group of patients of both subtypes.

Results

In the 6 trials, between 79% and 83% of the patients were correctly classified as NPSLE or non-NPSLE. For a qualitative evaluation of spatial distribution of the common features found in both groups, Gradient-weighted Class Activation Maps (Grad-CAM) were examined. Thresholded Grad-CAM maps show areas of common features identified for the NPSLE cohort, while no such communality was found for the non-NPSLE group.

Discussion/Conclusion

The self-supervised contrastive learning model was effective in capturing common brain MRI features from a limited but well-defined cohort of SLE patients with NP symptoms. The interpretation of the Grad-CAM results is not straightforward, but indicates involvement of the lateral and third ventricles, periventricular white matter and basal cisterns. We believe that the common features found in the NPSLE population in this study indicate a combination of tissue loss, local atrophy and to some extent that of periventricular white matter lesions, which are commonly found in NPSLE patients and appear hypointense on T1-weighted images.

Abnormal cortical thickness and structural covariance networks in systemic lupus erythematosus patients without major neuropsychiatric manifestations

BACKGROUND

Non-neuropsychiatric systemic lupus erythematosus (non-NPSLE) has been confirmed to have subtle changes in brain structure before the appearance of obvious neuropsychiatric symptoms. Previous literature mainly focuses on brain structure loss in non-NPSLE; however, the results are heterogeneous, and the impact of structural changes on the topological structure of patients' brain networks remains to be determined. In this study, we combined neuroimaging and network analysis methods to evaluate the changes in cortical thickness and its structural covariance networks (SCNs) in patients with non-NPSLE.

METHODS

We compare the cortical thickness of non-NPSLE patients (N=108) and healthy controls (HCs, N=88) using both surface-based morphometry (SBM) and regions of interest (ROI) methods, respectively. After that, we analyzed the correlation between the abnormal cortical thickness results found in the ROI method and a series of clinical features. Finally, we constructed the SCNs of two groups using the regional cortical thickness and analyzed the abnormal SCNs of non-NPSLE.

RESULTS

By SBM method, we found that cortical thickness of 34 clusters in the non-NPSLE group was thinner than that in the HC group. ROI method based on Destrieux atlas showed that cortical thickness of 57 regions in the non-NPSLE group was thinner than that in the HC group and related to the course of disease, autoantibodies, the cumulative amount of immunosuppressive agents, and cognitive psychological scale. In the SCN analysis, the cortical thickness SCNs of the non-NPSLE group did not follow the small-world attribute at a few densities, and the global clustering coefficient appeared to increase. The area under the curve analysis showed that there were significant differences between the two groups in clustering coefficient, degree, betweenness, and local efficiency. There are a total of seven hubs for non-NPSLE, and five hubs in HCs, the two groups do not share a common hub distribution.

CONCLUSION

Extensive and obvious reduction in cortical thickness and abnormal topological organization of SCNs are observed in non-NPSLE patients. The observed abnormalities may not only be the realization of brain damage caused by the disease, but also the contribution of the compensatory changes within the nervous system.

Neuropsychiatric systemic lupus erythematosus is associated with a distinct type and shape of cerebral white matter hyperintensities

Objectives

Advanced white matter hyperintensity (WMH) markers on brain MRI may help reveal underlying mechanisms and aid in the diagnosis of different phenotypes of SLE patients experiencing neuropsychiatric (NP) manifestations.

Methods

In this prospective cohort study, we included a clinically well-defined cohort of 155 patients consisting of 38 patients with NPSLE (26 inflammatory and 12 ischaemic phenotype) and 117 non-NPSLE patients. Differences in 3 T MRI WMH markers (volume, type and shape) were compared between patients with NPSLE and non-NPSLE and between patients with inflammatory and ischaemic NPSLE by linear and logistic regression analyses corrected for age, sex and intracranial volume.

Results

Compared with non-NPSLE [92% female; mean age 42 (13) years], patients with NPSLE [87% female; mean age 40 (14) years] showed a higher total WMH volume [B (95%-CI)]: 0.46 (0.0 7 ↔ 0.86); P = 0.021], a higher periventricular/confluent WMH volume [0.46 (0.0 6 ↔ 0.86); P = 0.024], a higher occurrence of periventricular with deep WMH type [0.32 (0.1 3 ↔ 0.77); P = 0.011], a higher number of deep WMH lesions [3.06 (1.2 1 ↔ 4.90); P = 0.001] and a more complex WMH shape [convexity: ‒0.07 (‒0.12 ↔ ‒0.02); P = 0.011, concavity index: 0.05 (0.0 1 ↔ 0.08); P = 0.007]. WMH shape was more complex in inflammatory NPSLE patients [89% female; mean age 39 (15) years] compared with patients with the ischaemic phenotype [83% female; mean age 41 (11) years] [concavity index: 0.08 (0.0 1 ↔ 0.15); P = 0.034].

Conclusion

We demonstrated that patients with NPSLE showed a higher periventricular/confluent WMH volume and more complex shape of WMH compared with non-NPSLE patients. This finding was particularly significant in inflammatory NPLSE patients, suggesting different or more severe underlying pathophysiological abnormalities.

White matter hyperintensities associate with cognitive slowing in patients with systemic lupus erythematosus and neuropsychiatric symptoms

Objective

To compare cognitive function between patients with different phenotypes of neuropsychiatric systemic lupus erythematosus (NPSLE) and assess its association with brain and white matter hyperintensity (WMH) volumes.

Methods

Patients attending the Leiden University Medical Centre NPSLE clinic between 2007 and 2015 without large brain infarcts were included (n=151; 42±13 years, 91% women). In a multidisciplinary consensus meeting, neuropsychiatric symptoms were attributed to systemic lupus erythematosus (SLE) (NPSLE, inflammatory (n=24) or ischaemic (n=12)) or to minor/non-NPSLE (n=115). Multiple regression analyses were performed to compare cognitive function between NPSLE phenotypes and to assess associations between brain and WMH volumes and cognitive function cross-sectionally.

Results

Global cognitive function was impaired in 5%, learning and memory (LM) in 46%, executive function and complex attention (EFCA) in 39% and psychomotor speed (PS) in 46% of all patients. Patients with inflammatory NPSLE showed the most cognitive impairment in all domains (p≤0.05).

Higher WMH volume associated with lower PS in the total group (B: −0.14 (95% CI −0.32 to −0.02)); especially in inflammatory NPSLE (B: −0.36 (95% CI −0.60 to −0.12). In the total group, lower total brain volume and grey matter volume associated with lower cognitive functioning in all domains (all: 0.00/0.01 (0.00;0.01)) and lower white matter volume associated with lower LM, EFCA and PS (all: 0.00/0.01 (0.00;0.01)).

Conclusion

We demonstrated that an association between brain and WMH volumes and cognitive function is present in patients with SLE, but differs between (NP)SLE phenotypes. WMHs associated with PS especially in inflammatory NPSLE, which suggests a different, potentially more severe underlying pathophysiological mechanism of cognitive impairment in this phenotype.