Serum S100A8/A9 Concentrations are Associated with Neuropsychiatric Involvement and Fatigue in Systemic Lupus Erythematosus: A Cross-Sectional Study

doi:10.21203/rs.3.rs-1110583/v1

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Serum S100A8/A9 Concentrations are Associated with Neuropsychiatric Involvement and Fatigue in Systemic Lupus Erythematosus: A Cross-Sectional Study

https://doi.org/10.21203/rs.3.rs-1110583/v1

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Background

Neuropsychiatric (NP) involvement and fatigue are major problems in systemic lupus erythematosus (SLE). S100A8/A9 is a marker of inflammation and responds to therapy in SLE patients. S100A8/A9 has an immunopathogenic role in various neurological diseases. We investigated S100A8/A9 in relation to NP-involvement and fatigue in SLE.

Methods

72 consecutive SLE outpatients at a tertiary centre and 26 healthy controls were included in this cross-sectional study. NPSLE was determined by specialists in rheumatology and neurology and defined according to three attribution models: “ACR”, “SLICC A” and “SLICC B”. Cerebral MRI was assessed by a neuroradiologist and neurocognitive testing by a neuropsychologist. The individuals were assessed by scores of pain (VAS), fatigue (VAS and FSS), depression (MADRS-S), and health-related life quality (EQ5D). Concentrations of S100A8/A9 in serum and cerebrospinal fluid were measured with ELISA. Statistical calculations were performed using non-parametric methods.

Results

Serum concentrations of S100A8/A9 were higher in NPSLE patients compared with non-NPSLE patients when applying the ACR and SLICC A models, but not significant when applying the SLICC B model (medians 1400 ng/ml; 920 ng/ml, p=0.011; 1560 ng/ml; 1090 ng/ml, p=0.050; 1460 ng/ml; 1090 ng/ml, p=0.083, respectively). The concentrations were higher in SLE patients compared with controls (medians 1230 ng/ml; 790 ng/ml, p=0.023). No differences of CSF S100A8/A9 concentrations were observed between NPSLE and non-NPSLE patients. SLE patients with depression or cognitive dysfunction as an ACR NPSLE manifestation had higher serum S100A8/A9 concentrations than non-NPSLE patients (median 1460 ng/ml, p=0.007 and 1380 ng/ml, p=0.013, respectively). Higher serum S100A8/A9 correlated with higher VAS fatigue (r=0.31; p=0.008), VAS pain (r=0.27, p=0.021), and lower EQ5D (r=-0.29, p=0.014) in SLE patients. NPSLE was associated with higher plasma CRP concentrations, higher scores of fatigue and pain, lower EQ5D, and fewer weekly work hours. Serum S100A8/A9 was not independently associated with NPSLE when adjusting for plasma CRP, and scores of fatigue (FSS) and pain (VAS), (OR 1.70, 95% CI 0.82-3.53, p=0.15).

Conclusions

Serum S100A8/A9 concentrations may be associated with NPSLE and fatigue. S100A8/A9 may be of interest in evaluating NPSLE, although further investigations are needed.

Rheumatology

Systemic lupus erythematosus

neuropsychiatric systemic lupus erythematosus

S100A8/A9

fatigue

inflammation

serum

cerebrospinal fluid

biomarker.

Systemic lupus erythematosus (SLE) is a relapsing-remitting chronic autoimmune inflammatory disease which can affect several organs, such as the skin, joints, kidneys, and nervous system. SLE mostly affects women (85%) and is on average diagnosed before the age of 50 (1). Neuropsychiatric (NP) involvement is estimated to affect 12-95% of SLE-patients depending on the criteria used, and is associated with poorer prognosis, reduced quality of life, and lower work capability (2, 3). The NP symptoms vary from severe central nervous system (CNS) manifestations, such as stroke, seizures, myelopathy and psychosis, to more common manifestations such as cognitive disturbances, affective disorders and headache (4, 5). The pathophysiology of NPSLE is heterogeneous and not fully understood. It includes inflammation-mediated tissue injury and ischemic vessel injury with abnormalities of the blood-brain barrier and autoantibody-mediated production of pro-inflammatory cytokines (6). One potential manifestation of NPSLE is fatigue, which remains one of the most salient, poorly understood and addressed patient complaints, and has been designated the most disabling symptom of a majority of SLE patients, often severely impacting quality of life and work capability (7, 8). Understanding the mechanisms of fatigue can help guide the interventions to improve health outcomes in SLE. The aetiology of fatigue in SLE is multifactorial, however, peripheral inflammation may cause fatigue directly by various neuroimmune pathways including microglial activation and toll-like receptor 4 (TLR4) upregulation (7, 9). The inflammatory biomarker S100A8/A9 may have a role in NPSLE and fatigue immunopathology and will be investigated in this paper.

S100A8 and S100A9 are calgranulins, Ca²⁺-binding proteins, members of the S100 family, and often exist in an active heterodimeric complex form named S100A8/A9, MRP8/14, or calprotectin. S100A8/A9 is abundantly expressed by phagocytes and acts as an endogenous damage-associated molecular pattern. During inflammation, S100A8/A9 is released passively and actively and exerts a critical role in modulating the inflammatory response by interacting with various membrane receptors, including TLR4 and Receptor of Advanced Glycation Endproducts (RAGE), resulting in leukocyte recruitment and cytokine secretion via the NFκB-pathway (10, 11). S100A8/A9 has been linked to the pathology of arteriosclerosis, and of various chronic inflammatory diseases such as SLE, rheumatoid arthritis, and inflammatory bowel disease (12–14). SLE patients have higher serum S100A8/A9 levels compared with healthy controls, even when their disease is clinically inactive, and serum S100A8/A9 concentrations have been correlated with disease activity and decrease after immunosuppressive treatment (13–17). S100A8/A9 concentrations are higher in the serum in multiple sclerosis (MS) patients(18) and in the cerebrospinal fluid (CSF) of Alzheimer’s disease (AD) patients(19) compared with healthy controls. Studies have revealed a neuroinflammatory role of S100A8/A9 in various neurological and psychiatric disorders such as AD (20, 21), Parkinson’s disease (22), depression (23), anxiety (24), MS (18), traumatic brain injury (12), and stroke (25). The underlying mechanisms of S100A8/A9 in CNS pathology remain unclear, however, treatment with S100A8/A9 in vitro induces the activation, proliferation and migration of microglia in mice to switch from an anti-inflammatory M2 phenotype to a pro-inflammatory activated M1 phenotype, activating the NFκB-pathway leading to the production of pro-inflammatory cytokines and chemokines, consequently resulting in injury of cells in the CNS (26).

The associations between S100A8/A9 and SLE, and the fact that S100A8/A9 is expressed in the CNS and has been associated with neurological disorders, prompted us to investigate the possible association between NPSLE and S100A8/A9. In this cross-sectional study we investigate NP involvement in SLE patients and explore its associations with S100A8/A9 in serum and CSF, together with SLE disease activity, organ damage, fatigue and pain, as well as analysing the effect of NPSLE on health-related quality of life (HRQL) and participation in the work force.

Patients and data collection

SLE out-patients attending the Department of Rheumatology in Lund, Skåne University Hospital, Sweden were asked consecutively to participate in this cross-sectional study. To reduce study group heterogeneity to facilitate interpretation of results, only female patients and patients below an age threshold of 55 were included. The latter specifically to reduce age-related cognitive decline and MRI abnormalities. Patients with any contraindication to magnetic resonance imaging (MRI) or pregnancy were not asked to participate in the study. A specialist in rheumatology and a specialist in neurology evaluated all 72 patients. NP symptoms were assessed and attributed to either SLE or other causes and required consensus between the rheumatologist and neurologist. Individual NPSLE manifestations were defined using the NPSLE American College of Rheumatology (ACR) case definitions (27). Patients with a history of any NPSLE manifestation are herein classified as NPSLE patients according to the ACR model. In addition, patients were classified according to the more stringent NPSLE attribution models “Systemic Lupus Erythematosus International Collaborating Clinics (SLICC) A” and “SLICC B” defined by Hanly et al(3) when applying the NPSLE ACR case definitions. In the most stringent model, SLICC A, all NP events more than 6 months prior to SLE diagnosis are not considered to be caused by SLE. In SLICC B, all NP events more than 10 years prior to SLE diagnosis are not considered to be caused by SLE. In both SLICC A and B, non-SLE factors responsible for an NP event, and all minor NP events are not considered to be caused by SLE. Minor NP events are defined by Ainiala et al as events found to be as common in the population as among SLE patients, namely mild cognitive dysfunction, mild depression, anxiety, headache and neuropathy without objective findings (5, 28).

Organ damage was recorded according to the SLICC/ACR-Damage Index (SLICC/ACR-DI) (29). SLE disease activity was assessed using the SLE Disease Activity Index 2000 (SLEDAI-2K) (30). All patients fulfilled the SLICC classification criteria for SLE (31). Measurements of fatigue were recorded using the Fatigue Severity Scale (FSS) (sum of score 9-63) and the Visual Analogue Scale 100mm (VAS). For FSS, fatigue was defined clinically significant with FSS total ≥ 36, with moderate fatigue between 36 and 52, and severe fatigue more than 52 (32). HRQL was registered using the EQ5D (UK tariff). Pain was recorded using the VAS. Depression was evaluated by the Montgomery-Åsberg Depression Rating Scale – Self-rated version (MADRS-S). Participation in the work force was recorded by self-reported work hours per week. Cognitive dysfunction was determined by using the CNS Vital Signs Battery (CNS-VS) (33). Cerebral MR Imaging was performed on a MAGNETOM Skyra 3T system (Siemens Healthcare, Erlangen, Germany), and the images were evaluated for white matter abnormalities and cerebral atrophy (34). CSF samples were obtained from 33 patients who consented and had no contraindications to lumbar puncture. CSF-measurements included isoelectric focusing of IgG, albumin index and concentrations of leukocytes and erythrocytes. Serum and plasma samples were obtained from patients and from 26 age-matched female controls recruited from health care personnel. The controls also underwent MRI and completed questionnaires, notably excluding VAS fatigue and VAS pain because these ask the patient to assess fatigue and pain due to a specific illness. Plasma, serum and CSF were stored in -80 degrees Celsius. The concentrations of S100A8/A9 in CSF and serum were measured with the Bühlmann MRP8/14 ELISA kit, Switzerland, according to the manufacturers’ instructions (14). The concentrations of S100A8/A9 in CSF were below the detection limit of 20ng/ml in this kit, instead we used the ThermoFisher Human Calprotectin L1/S100-A8/A9 Complex ELISA Kit (EH62RB) (standard curve range 32.77-8000 pg/ml) for the CSF analyses. Routine biochemical and immunological analyses were performed at the Departments of Laboratory Medicine and Immunology, Skåne University Hospital.

All methods were carried out in accordance with relevant guidelines and regulations.

Statistics

The comparisons of medians between groups were performed using the Mann-Whitney U test. Spearman’s Rank Correlation test was used to compare continuous variables. Chi-Square, or Fisher’s Exact Test for small samples, was applied for comparing categorical data. Binary Logistic Regression was used for the multivariate analysis and the clinical parameters included here were considered as continuous covariates with the presence or absence of NPSLE manifestations as the binary dependent. To avoid multicollinearity, covariates with a mutual correlation coefficient above 0.4 were not included in the multivariate model. All p-values were considered significant at p < 0.05. IBM SPSS Statistics version 25 was used for all statistical analyses.

Clinical characteristics

An overview of the clinical characteristics of the 72 SLE patients is presented in Table 1. Most patients had ongoing immunosuppression such as prednisolone (79%), hydroxychloroquine (79%), or any other disease-modifying anti-rheumatic drug (DMARD) (60%). SLE disease activity (SLEDAI-2K median 2, range 0-18) and organ damage (SLICC/ACR-DI median 0, range 0-5) were low in general. The median disease duration was 10 years (range 0-32), and the median age was 38 years (range 18-52).

SLE patients had a higher degree of fatigue, worked less hours per week, and had lower health-related quality of life compared with the 26 healthy controls (median (range) FSS score 45 (0-63) and 20 (11–42), p<0.001; weekly hours 20 (0-48) and 40 (0-50), p<0.001; EQ5D score 0.73 (0.03-1.0) and 1.0 (0.73-1.0), p<0.001, respectively). The age of controls did not differ from the SLE group (median (range) 40 (23-52), p=0.38). The majority of the SLE patients had clinically significant fatigue according to the FSS: 46% had moderate fatigue and 23% had severe fatigue, as opposed to 4% and 0% of the healthy controls.

An overview of the NPSLE manifestations is presented in Table 2. Forty-four (61%) SLE patients had NPSLE according to the ACR model. The most frequent NPSLE-manifestations were cognitive dysfunction (36%), headache (31%), depression (18%), anxiety (17%), and autonomic neuropathy (14%) in this model. When applying the more stringent SLICC A and SLICC B models, 16 (22%) and 23 (32%) SLE patients, respectively, had NPSLE. The most frequent NPSLE-manifestations were autonomic neuropathy and cranial neuropathy in both SLICC models.

Table 1

Clinical characteristics and ongoing treatment of 72 SLE patients.
Clinical characteristics
Age at study, median (range), years	38 (18-52)
Disease duration, median (range), years	10 (0-32)
SLICC/ACR-Damage Index, median (range)	0 (0-5)
SLEDAI-2K, median (range)	2 (0-18)
SLICC classification criteria
Acute cutaneous lupus, n (%)	53 (74%)
Chronic cutaneous lupus, n (%)	18 (25%)
Oral or nasal ulcers, n (%)	31 (43%)
Nonscarring alopecia, n (%)	24 (33%)
Joint disease, n (%)	62 (86%)
Serositis, n (%)	29 (40%)
Renal manifestations, n (%)	29 (40%)
Neurologic manifestations, n (%)	13 (18%)
Haemolytic anaemia, n (%)	4 (6%)
Leukopenia/lymphopenia, n (%)	42 (58%)
Thrombocytopenia, n (%)	20 (28%)
ANA, n (%)	71 (99%)
Anti-dsDNA, n (%)	44 (61%)
Anti-Sm, n (%)	11 (15%)
Anti-PL, n (%)	24 (33%)
Low complement, n (%)	43 (60%)
Positive Direct Coombs test, n (%)	2 (3%)
Medication, ongoing
Cyclophosphamide, n (%)	1 (1%)
Azathioprine, n (%)	23 (32%)
Mycophenolate, n (%)	16 (22%)
Rituximab, n (%)	1 (1%)
Methotrexate, n (%)	1 (1%)
Belumimab, n (%)	8 (11%)
Hydroxychloroquine, n (%)	57 (79%)
Any DMARD except hydroxychloroquine, n (%)	43 (60%)
Intravenous immunoglobulin, n (%)	2 (3%)
Prednisolone, n (%)	57 (79%)
Prednisolone daily dose, median (range), mg/day	5 (0-25)
SLICC: Systemic Lupus Erythematosus International Collaborating Clinics. ACR: American College of Rheumatology. SLEDAI-2K: SLE Disease Activity Index 2000. ANA: Antinuclear Antibody. Anti-dsDNA: Anti-double stranded DNA. Anti-Sm: Anti-Smith. Anti-PL: Anti-Phospholipid Antibody. DMARD: Disease-modifying antirheumatic drug.

Table 2

Overview of NPSLE-manifestations in 72 SLE patients when applying three NPSLE attribution models.
NPSLE-manifestation	ACR	SLICC A	SLICC B
Any NPSLE-manifestation, n (%)	44 (61%)	16 (22%)	23 (32%)
Guillain-Barré syndrome, n (%)	0 (0%)	0 (0%)	0 (0%)
Aseptic meningitis, n (%)	1 (1%)	1 (1%)	1 (1%)
Autonomic neuropathy, n (%)	10 (14%)	7 (10%)	8 (11%)
Cerebrovascular disease, n (%)	5 (7%)	1 (1%)	5 (7%)
Demyelinating disease, n (%)	3 (4%)	3 (4%)	3 (4%)
Headache, n (%)	22 (31%)	-	-
Mononeuritis, n (%)	2 (3%)	1 (1%)	2 (3%)
Myelitis, n (%)	3 (4%)	2 (3%)	3 (4%)
Chorea, n (%)	1 (1%)	0 (0%)	1 (1%)
Myasthenia gravis, n (%)	0 (0%)	0 (0%)	0 (0%)
Cranial neuropathy, n (%)	7 (10%)	7 (10%)	7 (10%)
Plexopathy, n (%)	0 (0%)	0 (0%)	0 (0%)
Polyneuropathy, n (%)	3 (4%)	1 (1%)	1 (1%)
Seizures, n (%)	2 (3%)	1 (1%)	2 (3%)
Confusion, n (%)	3 (4%)	1 (1%)	2 (3%)
Anxiety disorder, n (%)	12 (17%)	-	-
Cognitive dysfunction, n (%)	26 (36%)	0 (0%)	5 (7%)
Depression, n (%)	13 (18%)	0 (0%)	1 (1%)
Psychosis, n (%)	1 (1%)	1 (1%)	1 (1%)
One patient may have more than one NPSLE-manifestation. NPSLE: Neuropsychiatric Systemic Lupus Erythematosus. SLICC: Systemic Lupus Erythematosus International Collaborating Clinics. ACR: American College of Rheumatology

S100A8/A9 and NPSLE

Serum S100A8/A9 concentrations were higher in SLE patients compared with healthy controls (median (range) 1230 (50-3540) ng/ml; 790 (170-2650) ng/ml, p=0.023, Figure 1A). Serum S100A8/A9 concentrations were higher in NPSLE patients compared with non-NPSLE patients when applying the ACR model (median (range) 1400 (140-3540) ng/ml; 920 (50-3500) ng/ml, p=0.011, Figure 1B) and when applying the SLICC A model (median (range) 1560 (280-3520) ng/ml; 1090 (50-3540) ng/ml, p=0.050, Figure 1C), but not significant when applying the SLICC B model (median (range) 1460 (260-3520) ng/ml; 1090 (50-3540) ng/ml, p=0.084, Figure 1D). Table 3 depicts the S100A8/A9 concentrations between groups.

SLE patients with the NPSLE manifestations “depression” or “cognitive dysfunction” had higher serum S100A8/A9 concentrations than non-NPSLE patients when applying the ACR model (median (range) 1460 (140-2790) ng/ml, p=0.007 and 1380 (260-3540) ng/ml, p=0.013, respectively, Table 4).

S100A8/A9 was detectable in the CSF of SLE patients (median (range) 351 (<35-2045) pg/ml). No differences in CSF S100A8/A9 concentrations were observed between the non-NPSLE and NPSLE patients, regardless of attribution model, or when analysing specific NPSLE-manifestations (Tables 3 and 4).

Table 3

Clinical characteristics and laboratory analyses between NPSLE and non-NPSLE patients applying three NPSLE attribution models.
	ACR model			SLICC A model			SLICC B model
	NPSLE	Non-NPSLE	p-value	NPSLE	Non-NPSLE	p-value	NPSLE	Non-NPSLE	p-value
Clinical characteristics
Age at study, median (range), years	40 (18-50)	35 (19-52)	0.28	41 (24-48)	36 (18-52)	0.23	39 (23-48)	37 (18-52)	0.90
Disease duration, median (range), years	10 (0-32)	10 (0-25)	0.43	10.5 (1-24)	9.5 (0-32)	0.18	10 (1-29)	10 (0-32)	0.90
SLICC/ACR-DI, median (range)	0 (0-5)	0 (0-3)	0.18	1 (0-5)	0 (0-4)	0.008	1 (0-5)	0 (0-4)	0.003
SLEDAI-2K, median (range)	2 (0-18)	2 (0-12)	0.65	2 (0-12)	2 (0-18)	0.63	2 (0-12)	2 (0-18)	0.70
Patient reported information
FSS, median (range)	50 (0-63)	39 (14-60)	0.007	48 (13-63)	44 (0-63)	0.30	51 (0-63)	43 (0-60)	0.12
VAS fatigue, median (range)	74 (1-100)	33 (1-99)	<0.001	72 (1-100)	58 (1-100)	0.26	77 (1-100)	51 (1-100)	0.018
VAS pain, median (range)	33 (1-99)	10 (1-84)	0.014	42 (1-99)	17 (1-85)	0.028	33 (1-99)	18 (1-85)	0.069
MADRS-S, median (range)	15 (0-36)	6 (0-25)	<0.001	15 (2-35)	11 (0-36)	0.13	14 (0-35)	10 (0-36)	0.075
Work hours per week, median (range)	20 (0-48)	40 (0-45)	0.006	0 (0-40)	30 (0-48)	0.007	0 (0-40)	30 (0-48)	0.003
EQ5D, median (range)	0.69 (0.03-1)	0.80 (0.29-1)	<0.001	0.62 (0.03-1)	0.80 (0.05-1)	0.001	0.62 (0.03-1)	0.80 (0.05-1)	0.003
Laboratory analyses
Serum S100A8/A9, ng/ml, median (range)	1400 (140-3540)	920 (50-3500)	0.011	1560 (280-3520)	1090 (50-3540)	0.050	1460 (260-3520)	1090 (50-3540)	0.084
CSF S100A8/A9, pg/ml, median (range)	319 (<35-1703)	416 (<35-2045)	0.43	316 (<35-854)	351 (<35-2045)	0.83	316 (<35-1703)	(<35-2045)	1.0
Anti-dsDNA IgG antibodies, titer ≥10, n (%)	11 (25%)	4 (14%)	0.38	3 (19%)	12 (21%)	1.0	5 (22%)	10 (20%)	1.0
Complement factor 3, g/L, below lower limit, n (%)	22 (50%)	15 (54%)	0.77	7 (44%)	30 (54%)	0.49	12 (52%)	21 (51%)	0.93
Complement factor 4, g/L, below lower limit, n (%)	29 (66%)	23 (82%)	0.18	11 (69%)	41 (73%)	0.76	23 (65%)	37 (75%)	0.36
C-Reactive Protein, mg/L, median (range)	1.05 (<0.6-13)	<0.6 (<0.6-2.80)	0.023	1.85 (<0.6-11)	0.63 (<0.6-13)	0.002	1.6 (<0.6-11)	0.66 (<0.6-13)	0.030
SLICC: Systemic Lupus Erythematosus International Collaborating Clinics. ACR: American College of Rheumatology. DI: Damage Index. SLEDAI-2K: SLE Disease Activity Index 2000. FSS: Fatigue Severity Scale. VAS: Visual Analogue Scale 100mm. MADRS-S: Montgomery-Åsberg Depression Rating Scale – Self-rated version. CSF: cerebrospinal fluid. Anti-dsDNA: Anti-double stranded DNA.

Table 4

Comparison of S100A8/A9 concentrations between patients with specific NPSLE-manifestations and non-NPSLE-patients according to the NPSLE ACR model
NPSLE-manifestation	Serum S100A8/A9, ng/ml, median (range)	CSF S100A8/A9, pg/ml, median (range)
Autonomous neuropathy	1280 (280-2440) (n=10)	304 (<35-854) (n=6)
p-value	0.14	0.61
Cerebrovascular disease	1730 (740-2860) (n=5)	692 (n=1)
p-value	0.10	-
Headache	1300 (140-2860) (n=22)	252 (<35-854) (n=9)
p-value	0.058	0.37
Cranial neuropathy	1460 (280-2190) (n=7)	756 (692-854) (n=3)
p-value	0.087	-
Anxiety	1220 (260-2790) (n=12)	143 (<35-692) (n=6)
p-value	0.14	0.17
Cognitive dysfunction	1380 (260-3540) (n=26)	319 (<35-1703) (n=15)
p-value	0.013	0.36
Depression	1460 (140-2790) (n=13)	177 (<35-526) (n=6)
p-value	0.007	0.20
Less than five manifestations as well as less than five samples per group were not included in the analysis. P-values are for the comparison between specific NPSLE manifestations and the non-NPSLE patient group according to the ACR model. Values for non-NPSLE patients are demonstrated in Table 3. N: Number of analysed samples. ACR: American College of Rheumatology. CSF: cerebrospinal fluid.

NPSLE and clinical parameters, and biomarkers associated with SLE activity or inflammation

Table 3 demonstrates the differences of clinical and laboratory values between NPSLE and non-NPSLE patients using the three applied NPSLE attribution models. NPSLE patients did not differ significantly from non-NPSLE patients regarding their disease activity, disease duration or age. NPSLE patients according to SLICC A and B had higher degrees of SLE-related organ damage than non-NPSLE patients.

NPSLE patients according to the ACR model had a higher degree of fatigue, pain, depressive symptoms, lower HRQL, and worked less hours per week compared with non-NPSLE patients. NPSLE patients compared with non-NPSLE patients according to the SLICC models, did not have significantly higher levels of fatigue, pain and depressive scores for all scores in both models, however, both had lower HRQL. The majority of NPSLE patients according to the SLICC models did not work.

No differences were found regarding ongoing immunosuppressant treatment, except for belimumab which was more frequent among the non-NPSLE patients when applying the ACR model (NPSLE 5% and non-NPSLE 21%, p=0.049). As previously reported by us, NPSLE patients had more pronounced atrophy of the hippocampus bilaterally than non-NPSLE patients, whereas no significant differences were seen in the extension of white matter lesions between the groups (34). In this univariate analysis, NPSLE was also associated with higher CRP concentrations in all attribution models. NPSLE patients did not have lower complement levels or a higher frequency of anti-dsDNA positivity than non-NPSLE patients.

S100A8/A9 and correlations with clinical parameters

The correlations between serum S100A8/A9 concentrations in SLE patients and clinical parameters are depicted in Table 5. Higher serum S100A8/A9 concentrations correlated with higher VAS pain (r_s=0.27, p=0.021), higher VAS fatigue (r_s=0.31, p=0.008), lower HRQL (r_s=-0.29, p=0.014), and fewer work hours per week (r_s=-0.37, p=0.001) in SLE patients. Higher serum S100A8/A9 correlated with higher FSS in all individuals (r_s=0.24, p=0.018), although not in the SLE group alone (r_s=0.18, p=0.12).

Serum S100A8/A9 did not correlate with age (r_s=0.054, p=0.65), disease duration (r_s=-0.048, p=0.069), C-Reactive Protein (CRP) (r_s=0.20, p=0.099), SLEDAI (r_s=-0.048, p=0.069), SLICC/ACR-DI (r_s=0.14, p=0.26), the extent of white matter lesions (r_s=0.02, p=0.88), brain volumes or with neurocognitive test scores (CNS-VS) (data not shown). CSF S100A8/A9 concentrations did not correlate with serum S100A8/A9 (r_s=0.019, p=0.92), or with the abovementioned variables (data not shown). Increased CSF albumin quotient was present in 3 of 33 patients, and oligoclonal bands specific for CSF were present in 8 of 33 patients, of whom 3 had strong bands. No association was observed in the abovementioned analyses when excluding patients with increased CSF albumin quotient in the models (data not shown).

Table 5

Correlations between serum S100A8/A9 and clinical parameters in 72 SLE patients
Variable	Spearman’s rank correlation coefficient	p-value
Age at study for SLE patient	0.054	0.65
Disease duration	-0.048	0.69
SLICC/ACR-DI	0.14	0.26
SLEDAI	-0.048	0.69
C-Reactive Protein	0.20	0.099
VAS pain	0.27	0.021
MADRS-S	0.11	0.35
VAS fatigue	0.31	0.008
Fatigue Severity Scale	0.18	0.12
Work hours per week	-0.37	0.001
EQ5D	-0.29	0.014
SLICC: Systemic Lupus Erythematosus International Collaborating Clinics. ACR: American College of Rheumatology. DI: Damage Index. SLEDAI-2K: SLE Disease Activity Index 2000. FSS: Fatigue Severity Scale. VAS: Visual Analogue Scale 100mm. MADRS-S: Montgomery-Åsberg Depression Rating Scale – Self-rated version.

Multivariate analysis of NPSLE

A multivariate analysis comparing NPSLE with non-NPSLE patients was only performed for the ACR model due to the small group sizes of the SLICC A and B models. In this model serum S100A8/A9, CRP, scores of FSS and VAS pain were included as covariates. For CRP, values were added 1 and log-transformed to obtain less skewed data. In this analysis, NPSLE was not significantly associated with higher serum S100A8/A9 concentrations when adjusting for CRP, VAS pain, and FSS (OR 1.70, 95% CI 0.82-3.53, p=0.15). In addition, neither CRP (OR 5.93, 95% CI 0.69-50.8, p=0.11), fatigue (OR 1.02, 95% CI 0.98-1.06, p=0.26), nor pain scores (OR 1.02, 95% CI 1.00-1.04, p=0.14) remained associated with NPSLE. EQ5D and work hours per week were not included in the analysis since these variables were regarded as consequences rather than factors. MADRS-S and VAS fatigue were not included in the multivariate analysis due to multicollinearity. The depression scores were not independent of fatigue scores, and as expected VAS fatigue scores were strongly correlated with Fatigue Severity Scale as well as VAS pain scores. Furthermore, depression may be an NPSLE manifestation in itself, and depression scores (MADRS-S) were strongly correlated with NPSLE.

Although the exact role and underlying mechanisms of S100A8/A9 in CNS pathology remain unclear, increasing evidence has demonstrated that S100A8/A9 is closely related to certain CNS diseases (12, 18–25). S100A8/A9 has also been associated with SLE, and serum S100A8/A9 concentrations has been shown to be correlated with disease activity and decrease after immunosuppressive treatment (13–17). In this cross-sectional study we found an association between higher S100A8/A9 concentrations in serum and neuropsychiatric involvement in SLE, which was not seen when analysing anti-dsDNA or complement factor 3 and 4. S100A8/A9 was detectable in the CSF of SLE patients. Nevertheless, we could not find an association between CSF levels of S100A8/A9 and NPSLE. One possible explanation may be the difficulty of determining NPSLE, thus leading to misclassification, but also the probability that several pathogenetic mechanisms are operating in NPSLE, which would influence our findings. Patients with ongoing NP symptoms may not have active neuroinflammation, but would still be included in the NPSLE patient group. A study design assessing patients with new-onset NPSLE symptoms could possibly ameliorate this issue. Correspondingly, subclinical neuroinflammation may be present in non-NPSLE-patients. This may particularly be a confounding factor when applying the more stringent SLICC A and B application models for NPSLE. We did not have CSF samples from the healthy control group to compare with, and no reference to illustrate if the levels are high or low other than a study measuring S100A8/A9 in the CSF at concentrations around 100 pg/ml in healthy controls and 250 pg/ml in AD patients (19), which was lower than the median concentrations in this study.

S100A8/A9 in NPSLE immunopathogenesis

The possible role of S100A8/A9 in NPSLE immunopathogenesis remains unclear. Constantly, the immunologically privileged milieu in the CNS receives messages from the peripheral immune system, and cells of the CNS can become immunologically active upon stimulation. The subsequent cross-talking between microglia, oligodendrocytes, astrocytes and neurons can result in re-modulation or degeneration in “classical” neuroimmune diseases such as MS, but even in neurological and psychiatric disorders, which historically have not been associated with neuroinflammation such as depression, AD, Parkinson’s disease and stroke (35). In support of a pathogenetic role of S100A8/A9 in neuroinflammation, Wu et al demonstrated that local treatment with S100A8/A9 in murine brains induced the activation, proliferation and migration of microglial cells, and that the treated microglial cells in vitro switched from an anti-inflammatory activated phenotype to a pro-inflammatory activated phenotype, inducing the release of pro-inflammatory factors and chemokines via the NFκB-pathway, ultimately causing degeneration of brain tissue (26). Gong et al demonstrated that S100A8/A9 was upregulated in both the hippocampus and serum of mice after exposure to chronic stress which resulted in depressive behaviours. In addition, central administration of S100A8/A9 resulted in neuroinflammation via TLR4/NFκB-signaling leading to depressive behaviours, and pharmacological intervention with TLR4-inhibitors resulted in an attenuation of these effects (23). In our study, SLE patients with depression as an NPSLE manifestation according to the ACR model had higher S100A8/A9 concentrations in serum compared with non-NPSLE patients, supporting their suggestion of a pathogenetic contribution.

S100A8/A9 in the CNS may be a result of either local production from cells in the CNS or from systemically produced S100A8/A9 crossing the blood-brain barrier, possibly contributing to neuropsychiatric manifestations, although we could not confirm this hypothesis in this study. Neutrophils are the main source of S100A8/A9 in the circulation with concentrations 1000 times higher than CSF (10). The possibility that blood neutrophils from the lumbar puncture could affect CSF concentrations cannot be excluded. In MS, a disease that exclusively affects the CNS, patients had significantly higher serum levels of S100A8/A9 than healthy controls (medians 5150 ng/ml and 1482 ng/ml respectively), and MS patients with acute relapse had higher levels than MS patients with stable disease (18).

The elevated serum S100A8/A9 concentrations seen in our SLE patients with neuropsychiatric involvement may solely represent systemic SLE-related inflammation. Our study was cross-sectional and patients were consecutively asked to participate independently of neuropsychiatric symptoms or SLE activity. NPSLE manifestations were predominantly chronically active rather than new onset, and patients were on immunosuppressive treatment to a high degree. The higher serum S100A8/A9 levels in the NPSLE patients may thus indicate an ongoing SLE-related systemic inflammatory process, possibly contributing to neuropsychiatric symptoms. This is mirrored by the finding of an interferon signature also in clinically inactive SLE patients (36). Hypothetically, a potential indirect mechanism of S100A8/A9 in NPSLE is through its action on endothelial cells contributing to vasculopathy and atherosclerosis development (37). Serum S100A8/A9 concentrations did not correlate with disease activity assessed with SLEDAI-2K, a finding that has been demonstrated in two studies (13, 15). In our prevalent SLE patients, SLEDAI-2K was generally low, which would decrease the chance of detecting an association with disease activity. We also compared serum S100A8/A9 levels in SLE patients with healthy controls, and although our control group was small, the results were in line with previous studies, demonstrating significantly higher serum S100A8/A9 concentrations in SLE patients (14–16).

S100A8/A9 has previously been investigated in childhood-onset SLE with neurocognitive disorders. There was no significant difference of serum S100A8/A9 concentrations in these patients with or without neurocognitive disorders (mean 1540 ng/ml and 929 ng/ml respectively, p=0.25), however, only 9 and 31 patients respectively were included in this study (38). In the same study a significant correlation between serum S100A8/A9 levels and cognitive disability over time was demonstrated, which is consistent with our finding that patients with the NPSLE manifestation “cognitive dysfunction” according to the ACR model had higher serum levels of S100A8/A9 compared with non-NPSLE patients, although the degree of cognitive dysfunction in the majority of patients was mild. Longitudinal studies are needed to investigate the role of S100A8/A9 in the development of cognitive dysfunction in SLE.

S100A8/A9 and fatigue

Although not part of the ACR NPSLE case definitions, fatigue could potentially be an NP manifestation of SLE, and we found an association between fatigue scores and NPSLE. The aetiology of fatigue in SLE patients is likely multi-factorial, including medication, affective disorders, chronic pain, cognitive dysfunction and organ damage, albeit emerging evidence suggests that during chronic inflammation, various pathophysiological mechanisms negatively affect brain functioning in areas that are involved in fatigue by increasing glutaminergic and decreasing monoaminergic neurotransmission (7, 9, 39). Previous studies have demonstrated that depression, stress, anxiety and pain are independently associated with fatigue in SLE (40, 41). The majority of our SLE patients had clinically significant fatigue and the frequencies are consistent with previous SLE studies (32, 41, 42). Our results illustrate that fatigue often persists even after achieving low disease activity, which is observed in other chronic inflammatory diseases (7). Fatigue and pain scores were also positively correlated with higher serum S100A8/A9 levels, although the associations were not strong.

In our multivariate analysis neither serum S100A8/A9, CRP, pain, nor fatigue scores were independently associated with NPSLE. One possible explanation is that the included variables may not be unrelated to each other rendering estimates from a multivariate analysis unreliable. Furthermore, the size of the study precludes a firm conclusion from multivariate models. Fatigue may be part of subtle SLE activity systemically or in the CNS, although further studies are needed to investigate the association between S100A8/A9 and fatigue, and the mechanisms involved.

Despite similar treatment, low disease activity and low organ damage in both groups, SLE patients with neuropsychiatric involvement had lower HRQL and were less active in the work force. In particular, the majority of NPSLE patients according to the SLICC models did not work at all. These results are in line with previous studies and illustrates the consequences of living with NPSLE (2, 3).

Our study has limitations. The study is cross-sectional, which makes it impossible to determine causes and effects. The small sizes of the studied groups result in difficulties drawing firm conclusions and our findings need to be confirmed in larger studies, especially for individual NP manifestations. The methodology of solely including female patients with a certain age threshold increases the chance of finding significant associations, particularly since the NPSLE syndrome is heterogenous, however, the usefulness of the results is limited to that group. The study did not include disease controls with primary psychiatric or neurological disease, and therefore we were not able to draw conclusions on the discriminatory abilities of S100A8/A9 from other conditions. CSF was not obtained from the controls, and less than half of SLE patients consented to lumbar puncture. Most patients were in remission or had generally low clinically discernible activity in their SLE, thus the results are only applicable in this context. The study has strengths too. Considering the low prevalence of NPSLE, and the relatively small amount of literature on this topic, the aspects of NP-symptoms in SLE patients were described in detail involving a specialist in neurology, CSF and blood analysis, MRI assessments, and cognitive testing by a neuropsychologist.

In conclusion, NPSLE may be associated with higher serum S100A8/A9 concentrations. Higher serum S100A8/A9 may be associated with a higher degree of fatigue. The results suggest that S100A8/A9 may be of interest in neuropsychiatric involvement of SLE, although further investigations are warranted, including longitudinal studies.

SLE

Systemic lupus erythematosus

NPSLE

Neuropsychiatric systemic lupus erythematosus

CNS

Central nervous system

TLR4

Toll-like receptor 4

RAGE

Receptor of Advanced Glycation Endproducts

multiple sclerosis

CSF

cerebrospinal fluid

Alzheimer’s disease

HRQL

health-related quality of life

ACR

American College of Rheumatology

SLICC

Systemic Lupus Erythematosus International Collaborating Clinics

Damage index

SLEDAI-2K

SLE Disease Activity Index 2000

FSS

Fatigue Severity Scale

VAS

Visual Analogue Scale 100mm

MADRS-S

Montgomery-Åsberg Depression Rating Scale – Self-rated version

CNS VS

CNS Vital Signs

DMARD

disease-modifying anti-rheumatic drug

ANA

Antinuclear Antibody

Anti-dsDNA

Anti-double stranded DNA

Anti-Sm

Anti-Smith

Anti-PL

Anti-Phospholipid Antibody

CRP

C-Reactive Protein

Ethics approval and consent to participate: The study obtained ethics approval by the Lund Regional Ethics Board diary numbers 2012/254 & 2012/677. The study participants gave informed consent before taking part.

Consent to publication: Not applicable.

Availability of data and materials: The authors confirm that the data supporting the conclusions of this study are available within the article.

Competing Interests: The authors declare that they have no competing interests.

Funding: The work was supported by grants from the Medical Faculty of Lund University, Anna-Greta Crafoord Foundation, Greta and Johan Kock Foundation, King Gustav V 80^th Birthday Foundation, Alfred Österlund Foundation, Swedish Research Council, and Swedish Rheumatism Association.

Authors’ contributions: All authors drafted the article, revised it critically for important intellectual content, and approved the final version to be published. Study conception and design: KZ, AB, AJö. Acquisition of data: KZ, AJe, JN, BG, PN, PS, AB, AJö. Analysis and interpretation of data: KZ, AJö.

Acknowledgements: The research team would like to thank all involved patients for participating in this project.

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No competing interests reported.

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Serum S100A8/A9 Concentrations are Associated with Neuropsychiatric Involvement and Fatigue in Systemic Lupus Erythematosus: A Cross-Sectional Study

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Abstract

Background

Methods

Patients and data collection

Statistics

Results

Clinical characteristics

S100A8/A9 and NPSLE

NPSLE and clinical parameters, and biomarkers associated with SLE activity or inflammation

S100A8/A9 and correlations with clinical parameters

Multivariate analysis of NPSLE

Discussion

S100A8/A9 in NPSLE immunopathogenesis

S100A8/A9 and fatigue

Conclusions

Abbreviations

Declarations

References

Additional Declarations

Status:

Version 1