Predictors of neuropsychiatric manifestations in pediatric patients with lupus

doi:10.21203/rs.3.rs-4883579/v1

Early detection of neuropsychiatric systemic lupus erythematosus (NPSLE) in children remains a challenge. Previous studies have found an association between different autoantibodies and the occurrence of NPSLE. This study aimed to describe the autoantibodies, organ disorders, SLEDAI-2K score, and complement levels at diagnosis in systemic lupus erythematosus (SLE) and to investigate the predictors of NPSLE. We reviewed medical records and extracted information on the clinical features, serum autoantibodies, and laboratory test results of hospitalized children with SLE (< 18 years old). Multivariable logistic regression was used to determine the predictors of NPSLE and SLE without neuropsychiatric manifestations. The 22.8% children with NPSLE had higher ANA levels and SLEDAI-2K scores, lower C4 levels, and greater AMA-M2, β2GPI Abs, Anti-Rib-P Ab, ANCA, and LAC positivity at SLE diagnosis. They were also more likely to have fever and more easily developed digestive system complications, macrophage activation syndrome (MAS), and hypothyroidism. The predictors of NPSLE were β2GPI-Abs (OR = 4.603), anti-Rib-P Ab (OR = 4.153), and SLEDAI-2K score (OR = 1.215). The study indicates that the SLEDAI-2K score, β2GPI-Abs, and anti-Rib-P Ab were predictors of NPSLE. Our findings may have implications for NPSLE screening in patients with SLE.

Health sciences/Neurology

Health sciences/Rheumatology

systemic lupus erythematosus

neuropsychiatric systemic lupus erythematosus

autoantibodies

predictors

Systemic lupus erythematosus (SLE) is a chronic autoimmune disease that can involve any organ system, and it may lead to significant morbidity and mortality. Childhood-onset SLE is a lifelong autoimmune disease that follows a more aggressive course than adult-onset SLE, with greater disease activity at presentation and over time, consequently leading to greater morbidity and mortality than adult-onset SLE [1]. Patients with SLE, who have one or more neuropsychiatric symptoms, have a subcategory of SLE termed neuropsychiatric systemic lupus erythematosus (NPSLE). Between 22% and 95% of children with SLE have NPSLE, which seems to be more severe in children and most often affects the central nervous system (CNS), and peripheral and autonomic nervous system involvement is rare. NPSLE may be associated with higher rates of permanent organ damage in children than in adults [2–4].

The diagnosis and treatment of NPSLE are difficult in daily clinical practice, leading to severe irreversible neuropsychiatric (NP) damage [5]. Furthermore, the lack of specific biomarkers for diagnostic or prognostic purposes adds to the challenge of managing NPSLE, and a high index of clinical suspicion is key to recognizing its initial NP manifestations [6]. To address these problems, biomarkers, neuroimaging, and diagnostic algorithms have been proposed by many groups [7]. However, no definite procedure for the management of NPSLE has been established.

Therefore, it is crucial for clinicians to recognize the predictors of NPSLE in children. However, the literature on the risk factors for developing NPSLE in children is still few, which is crucial for diagnosing this disease and predicting its prognosis. Consequently, this study describes the characteristics and risk factors of pediatric NPSLE to provide a reference that clinicians can use to predict its occurrence and identify it early, thereby facilitating early intervention to improve its prognosis.

Patients

This is a retrospective cross-sectional multicenter study. The medical records of all patients with SLE who were admitted to the West China Second University Hospital, Sichuan University, Chengdu, China, and the Women and Children’s Hospital of Ningbo University, Ningbo, China, from January 2022 to December 2023 were reviewed.

SLE was diagnosed by a pediatric rheumatologist based on the American College of Rheumatology (ACR) 1997 criteria [8]. Patients were diagnosed with NPSLE according to the ACR nomenclature and case definitions published in 1999 [9]. A pediatric rheumatologist and a pediatric neurologist diagnosed and classified NPSLE. Many of the patients underwent neurological examinations, such as brain magnetic resonance imaging, computerized tomographic scanning of the head, electroencephalograms, and cerebrospinal fluid examination, as part of the diagnostic evaluation of the nervous system involvement. The effects of drugs, CNS infection, tumors, and metabolic derangements were excluded before a diagnosis of NPSLE was made.

The disease activity score was evaluated using the SLE disease activity index-2000 (SLEDAI-2K) [10] before treatment. The clinical manifestations and laboratory data of the patients were extracted from their medical records.

Data collection

Pediatric patient data were collected through the electronic medical record system. Relevant patient data at the time of SLE diagnosis, including demographic information (age, sex, and the duration between disease onset and diagnosis), clinical manifestations (fever, rash, organic disorder, and SLEDAI-2K), and laboratory test results (complement, autoantibodies, cerebrospinal fluid, and imaging test results), were extracted and collated in a standardized, anonymized data collection spreadsheet. The clinical findings in patients with NPSLE, including CNS manifestations and peripheral nervous system manifestations based on the 1999 ACR [9], were also collected.

Data analysis

All analyses were conducted using the SPSS software (version 26.0; IBM Corporation, Armonk, NY, USA). A Kolmogorov–Smirnov test was used to test for the normality of continuous numerical variables, and normally distributed data were expressed as mean and standard deviation (x ± s). For non-normally distributed continuous numerical variables, the data were expressed as the median with the upper and lower quartiles (M [P25, P75]), and the rank sum test was used for comparison. Count data were described as n (%), and a chi-square test was used to compare differences between groups. The Pearson chi-square test was used as the nonparametric test where the sample size was less than 40 and the theoretical frequency was less than 5. If the sample size was < 40 or the theoretical frequency was < 5, a Fisher’s exact probability test was performed. A value of 0.05 was used as the test level, and a P < 0.05 was considered statistically significant. Logistic regression analyses (univariate and multivariate analyses) were used to determine the risk factors for NPSLE.

Ethics approval and consent to participate

Ethical clearance was obtained from both the Research and Institutional Review Board of West China Second University Hospital of Sichuan University (approval NO. EC2024-156) and the Research and Institutional Review Board of Women and Children's Hospital of Ningbo University (approval NO. EC2024-091). Since it was a retrospective study, and every detail about the participants was kept private, and a strict code of ethics was enforced, the need for informed consent to participate was waived by Institutional Review Board in both two centers (West China Second University Hospital of Sichuan University and Women and Children's Hospital of Ningbo University). This study was conducted according to principles of the Declaration of Helsinki, and the research protocol conforms with the standards currently applied in China.

Patient characteristics

A total of 101 pediatric patients who were newly diagnosed with SLE were identified during the study period, and their demographics are shown in (Table 1). There were 85 females and 16 males, with a sex ratio of 84:16. The mean age at the onset of SLE was 10.76 ± 2.35 years, while the mean time from onset to diagnosis was 61.74 ± 97.51 days. Thirteen (12.9%), 17 (16.8%), and 71 (70.3%) patients had an SLEDAI-2K score of ≤ 6, 7–12, and > 12, respectively. The antinuclear antibody (ANA) level was 1:100, 1:320, 1:1000, and 1:3200 in 8 (7.9%), 16 (15.8%), 42 (41.6%), and 35 (34.7%) patients, respectively. We also examined the complement levels and found that the mean C3 level was 0.49 ± 0.38, while the mean C4 level was 0.08 ± 0.07. The proportion of patients who were positive for anti-double-strand DNA antibody (anti-dsDNA Ab), anti-Smith antibody (anti-Sm Ab), anti-centromere proteins antibody (anti-CENP Ab), anti-proliferating cell nuclear antigen antibody (anti-PCNA Ab), anti-nucleosome antibody (anti-NU Ab), anti-histone antibody (anti-HI Ab), anti-ribosomal-P antibody (anti-Rib-P Ab), anti-mitochondrial antibody-M2 (AMA-M2), anti-U1 small nuclear ribonucleoprotein antibody (anti-U1RNP Ab), anti-Sjogren’s syndrome A antibody (anti-SSA Ab), anti-Sjogren’s syndrome B antibody (anti-SSB Ab), anti-scleroderma-70 antibody (anti-Scl70 Ab), anti-polymyositis-sclerosis antibody (anti-PM Scl Ab), anti-neutrophil cytoplasmic antibodies (ANCA), lupus anticoagulant (LAC), anti-cardiolipin antibodies (aCL Abs), and anti-beta2-glycoprotein I antibodies (β2GPI Abs) were 61.4%, 32.7%, 3.0%, 2.0%, 59.4%, 51.5%, 29.7%, 28.7%, 41.6%, 38.6%, 20.8%, 4.0%, 12.9%, 46.5%, 32.7%, 21.8%, and 23.8%, respectively. We also evaluated the involvement of organs and systems in children with lupus at the time of diagnosis. There were 57.4% of children with a skin rash, 52.5% had a fever, and 4.0% had MAS. Among children with lupus, 73.3% had a renal disorder, 71.3% had a hematologic disorder, 14.9% had arthritis, 19.8% had a pulmonary disorder, 12.9% had a cardiac disorder, 30.7% had a digestive disorder, 35.6% had hypothyroidism, and 22.8% had a neurologic disorder. The raw data are shown in (Supplementary Table 1)

Clinical Features of NPSLE

Among the 101 children with SLE, 23 had NPSLE. Most children with NPSLE had a headache (34.8%), a mood disorder (26.1%), or an autonomic disorder (26.1%), and several had cerebrovascular disease (17.4%), movement disorders (13.0%), cognitive dysfunction (8.7%), seizure disorders (4.3%), and an acute confusional state (4.3%). Radiological examination revealed that 95.6% of them had abnormal brain magnetic resonance imaging results, and 17.4% had an abnormal electroencephalogram (Table 1).

NPSLE occurred in 23 (22.8%) children with SLE, aged 11.1 ± 2.02 years at diagnosis. Among those with NPSLE, 91.3% were females, whereas female patients accounted for 82.1% of those without NPSLE (P = 0.352).

When children with SLE alone were compared with those with NPSLE (Table 2), children with NPSLE were more likely to have a fever (44.9% vs. 78.3%, P = 0.01), a higher SLEDAI-2K score (14.46 ± 7.26 vs. 23.57 ± 6.77, P < 0.001), higher ANA levels (P = 0.025), and lower C4 levels (0.08 ± 0.08 vs. 0.05 ± 0.05, P = 0.041) than those of children with SLE alone. Anti-Rib-P Ab, AMA-M2, and ANCA were more frequently positive in children with NPSLE than in children with SLE alone (21.8% vs. 56.5%, P = 0.003; 23.1% vs. 47.8%, P = 0.041; and 39.7% vs. 69.6%, P = 0.022; respectively). For antiphospholipid antibody, the LAC and β2GPI Abs were more frequently positive in children with NPSLE than in those with SLE alone (24.4% vs. 60.9%, P = 0.002, and 17.9% vs. 43.5%, P = 0.025, respectively). Regarding organ and system involvement, children with NPSLE were more prone to digestive disorders (23.1% vs. 56.5%, P = 0.005), MAS (0 vs. 17.4%, P = 0.002), and hypothyroidism (29.5% vs. 56.5%, P = 0.033) than those with SLE alone (Table 2).

Risk Factors for NPSLE

We sought to identify associations between clinical or laboratory data and neurologic involvement at SLE diagnosis. In univariate logistic regression analyses, the presence of anti-Rib-P Ab (OR = 4.665, 95% CI 1.743–12.481; P = 0.002), anti-SSA Ab (OR = 3.168, 95% CI 1.197–8.384; P = 0.02), AMA-M2 (OR = 3.056, 95% CI 1.155–8.085; P = 0.024), anti-PM Scl Ab (OR = 3.58, 95% CI 1.065–12.03; P = 0.039), ANCA (OR = 3.465, 95% CI 1.278–9.394; P = 0.015), β2GPI Ab (OR = 3.516, 95% CI 1.285–9.626; P = 0.014), aCL Ab (OR = 5.231, 95% CI 1.815–15.074; P = 0.002), or LAC (OR = 4.83, 95% CI 1.805–12.924; P = 0.002) was a significant risk factor for NPSLE. Children with positive ANA (OR = 1.005, 95% CI 1.001–1.111; P = 0.003) and a high SLEDAI-2K score (OR = 1.204, 95% CI 1.101–1.318; P < 0.001) were also more likely to develop NPSLE. Fever (OR = 4.423, 95% CI 1.492–13.111; P = 0.007), digestive disorders (OR = 4.333, 95% CI 1.629–11.526; P = 0.003), and hypothyroidism (OR = 3.109, 95% CI 1.193–8.097; P = 0.02) were also risk factors for NPSLE in children with lupus. In contrast, high serum complement (C3 and C4) levels were protective against NPSLE (C3: OR = 0.503, 95% CI 0.118–0.938; P = 0.035 and C4: OR = 0.024, 95% CI 0.009–0.982; P = 0.035) (Table 3).

We performed multivariate analyses for the factors that were significantly different in the univariate analysis. This showed that a high SLEDAI-2K score (OR = 1.215, 95% CI 1.019–1.449; P = 0.03), the presence of β2GPI Ab (OR = 4.603, 95% CI 1.834–25.412; P = 0.04), and the presence of anti-Rib-P Ab (OR = 4.153, 95% CI 1.865–19.95; P = 0.038) were significantly associated with NPSLE (Table 4). The forest map was drawn for the results of multi-factor logistics regression analysis, and the OR value and its 95% confidence interval were visualized (Figure 1).

NP involvement is common in pediatric SLE and is a major cause of morbidity and mortality. Herein, we analyzed the correlation between clinical variables and NPSLE and identified the associated risk factors. The reported incidence of NP manifestations in patients (adults and children) with SLE varies greatly between studies, ranging from 12% to 95% [11]. Prevalence estimates vary widely across studies, but approximately 25% of pediatric patients with SLE appear to have NP involvement [4], which is consistent with the results of our study. Herein, NPSLE occurred in 22.8% of children with SLE. Previous studies [12, 13] found that headache was one of the most frequent NP manifestations in SLE patients. Furthermore, a meta-analysis of studies assessing NPSLE prevalence by Unterman et al. [14] indicated that the most frequent manifestations of NPSLE were headache (28.3%), mood disorders (20.7%), and cognitive dysfunction (19.7%). Our study showed that headache (34.8%) was the most common NP manifestation and mood disorders (26.1%) the second most common NP manifestation in children with NPSLE, which is consistent with the findings of previous studies. However, in our study, autonomic disorder (26.1%) tied for the second most common NP manifestation of NPSLE. This inconsistency with the results of previous studies may be because our study participants were all children who could not describe their symptoms precisely. Moreover, the manifestations of NPSLE may differ with age. In contrast to a previous study, 8.7% of children with NPSLE in our study had calculation and cognitive dysfunction, probably because it was difficult to assess cognition and calculation skills in very young children.

In our study, the lupus disease activity score (SLEDAI-2K score) and cumulative organ damage were higher in the NPSLE group. These results were in accordance with the findings of previous studies [5,15-17], which found more organ damage in the NPSLE group. This could be because of an association between SLE disease activity and concurrent diffuse and NP events that are attributable to SLE [18]. Fever is more common in younger patients with lupus, and NPSLE is more common in early-onset lupus [19, 20]. Our study found that children with NPSLE were more likely to have a fever than those with only SLE (P = 0.01). High ANA titers are frequently observed in patients with NPSLE [21], and our study showed that children with NPSLE had a higher level of ANA than those with only SLE (P = 0.025). Shatri H et al. [22] found that there was a negative correlation between anxiety and C3 or C4 levels in patients with SLE. We found that children with NPSLE had lower C3 and C4 levels (P = 0.444 and P = 0.041, respectively) than those with only SLE.

Becker Y et al. [23] found that in animal models, anti-mitochondrial RNA IgM titers were strongly correlated with β2GPI-Abs (P < 0.0001). In our study, AMA-M2 and β2GPI-Abs were more frequently positive in the NPSLE group than among those with only SLE. Pan Y et al. [24] found that the appearance of perinuclear ANCA in SLE indicated a high probability of lupus nephritis and a more severe condition. Moreover, patients with ANCA-positive lupus nephritis frequently have histological markers of severe disease [25]. In our study, ANCA was more positive in the NPSLE group than in the SLE alone group, suggesting that ANCA-positive patients had a higher disease activity and were more likely to have organ damage than ANCA-negative patients.

Anti-SSA antibodies are associated with subacute cutaneous lupus [26]. Novak GV et al. [27] reported that anti-Ro/SSA antibody was associated with childhood SLE with cutaneous and musculoskeletal involvements, whereas Higuera-Ortiz V et al. [28] reported that anti-Ro/SSA antibody may be involved in lupus-associated cardiac valve disease. The univariate logistic regression analysis in our study revealed that anti-SSA Ab was a risk factor for NPSLE. Previous studies suggest that anti-PM-Scl antibodies are associated with an increased frequency of Raynaud’s phenomenon in patients with SLE [29]. Another study showed that dysphagia is associated with anti-PM-Scl antibodies in SLE [30]. However, in our study, anti-PM Scl Ab was a risk factor for NPSLE in the univariate logistic regression analysis.

In addition, the anti-Rib-P Ab was more frequently positive in the NPSLE group than in the SLE alone group. Our result is consistent with those of previous studies [31], which reported that being anti-Rib-P antibody positive is associated with the NP manifestations of SLE. Many researchers believe that anti-Rib-P antibodies are biomarkers of NPSLE [32-37]. However, a study by Pradhan V et al. [38] showed no statistically significant difference in the incidence of Rib-P antibodies between patients with NPSLE and those with SLE alone. These differences may be due to differences in race, region, and age.

After using a chi-square test to compare the differences between SLE alone and NPSLE, we used univariate logistic regression to analyze the effect of single factors on NPSLE. The univariate logistic regression analyses showed that the presence of anti-Rib-P Ab, anti-SSA Ab, AMA-M2, anti-PM Scl Ab, ANCA, β2GPI Ab, aCL Ab, and LAC were significant risk factors for NPSLE. Fever, positive ANA levels and a high SLEDAI-2K score were also risk factors for NPSLE. High levels of C3 and C4 were the protective factors against the development of NPSLE. The results of the univariate logistic regression analyses were roughly consistent with those of the chi-square test. However, a logistic regression analysis better reflects the significance of the factor’s influence on the outcome variables [39].

A retrospective study in a Polish population by Pawlak-Buś K et al. [40] demonstrated that the SLEDAI‑2K score was significantly higher in patients with NPSLE than in those without NP symptoms attributable to SLE. Our study also showed similar results, with the SLEDAI-2K score being higher among those with NPSLE than in those with SLE alone (P < 0.001), and the SLEDAI-2K score was an independent risk factor for NPSLE. The NP domain scores of the EULAR/ACR criteria are positively correlated with the SLEDAI-2K score [41]. Toledano P et al. [42] found that a high SLEDAI score was associated with white matter lesions and myelopathy in patients with NPSLE, which might explain the symptoms of patients with NPSLE.

The pivotal role of antiphospholipid (aPL) antibodies, comprising LAC, aCL Abs, and β2GPI-Abs, in NPSLE and their value in predicting progression in NP damage are well known [43-46]. Recent research by Geng W et al. [47] found that β2GPI-Abs were more frequently observed in patients with mood disorders. In a previous meta-analysis by Ho RC et al. [48], β2GPI-Abs were found to be significantly associated with mood disorders (OR = 6.27). Seth G et al. [49] reported that among patients with NPSLE, β2GPI-Abs was more commonly present in those with a stroke, and Hawro T et al. [50] found that headaches and ischemic stroke were independently associated with β2GPI-Abs (OR = 5.6). All the above studies suggest that β2GPI-Abs may be a risk factor for NPSLE, which is consistent with the results of our study. In the multivariable analysis, β2GPI Ab was associated with a high risk of NPSLE. The early hypothesis and animal studies have shown that mood disorders may be due to neurotransmitter dysfunction [51], and recent research has revealed that mood disorders may be caused by vascular dysfunction [52]. Moreover, recent studies have demonstrated that patients with mood disturbances have cerebral hypoperfusion [53-55]. Based on the above studies, in patients with NPSLE, being positive for β2GPI-Abs results in cerebral blood flow dysfunction, which leads to the occurrence of mood disorders and stroke. However, the types of autoantibodies and the specific manifestations of NPSLE still need to be further studied.

A study with a large sample size by Mahler M et al. [56] found that anti-Rib-P Ab is a highly specific biomarker for SLE and is frequently associated with NPSLE. One meta-analysis by Shi ZR et al. [57] showed a strong association between anti-Rib-P Ab and NPSLE (OR = 2.72), and another meta-analysis by Ho RC et al. [48] showed that there was a significantly increased prevalence of anti-Rib-P Ab (OR = 2.29) in patients with NPSLE. The results of our study agree with those of these previous studies. Our multivariable analysis revealed that anti-Rib-P Ab are associated with a high risk of NPSLE. Abdel-Nasser AM et al. [34] found that a significantly higher proportion of patients with NPSLE were anti-Rib-P Ab positive than other patients, which has a high specificity for detecting depression. Previous studies have shown that anti-Rib-P Ab react with endothelial cells and thereby affect the integrity of the blood–brain barrier (BBB) [58, 59]. Another meta-analysis convincingly showed that the C-terminal 22-amino-acid peptide (C22), a key conserved epitope of the anti-Rib-P Ab, was associated with depression (OR = 3.96) and psychosis (OR = 3.99) in patients with NPSLE [60]. An animal study by Wang X et al. [61] indicated that anti-Rib-P Ab can directly induce dysfunction in auditory-evoked potentials in the brain, which can lead to neuropsychiatric dysfunction. Another animal study found that anti-Rib-P Ab acted on the surface of hippocampal neurons, leading to apoptosis or functional perturbations. Circulating anti-Rib-P Ab can access the hippocampus and impair memory when the BBB is disrupted, leading to cognitive impairment in SLE [62]. Furthermore, a previous in vitro study found that anti-Rib-P Ab enter neuronal cells via the neuronal growth-associated protein, thereby affecting neuronal cells. The above studies suggest that anti-Rib-P Ab not only causes BBB dysfunction by affecting the vascular endothelial cells but also affects the function of neuronal cells and the hippocampus, eventually leading to the manifestations of NPSLE, including depression, mental disorders, and cognitive impairment. To better elucidate the pathological mechanism of anti-Rib-P Ab in NPSLE, it is important to investigate its different NP manifestations separately in future studies with larger sample sizes. Our results have significance as a reference to guide early screening and intervention for NPSLE in children with SLE.

The main strength of our study is that it was a multi-center study that focused on different autoantibodies and different clinical manifestations associated with NPSLE in children. However, it has several limitations. First, several NP manifestations may not be fully reflected because of the small sample size. Second, we focused on the laboratory tests and clinical manifestations at the onset of the disease and did not follow-up with the patients for long. Third, due to the retrospective design of our study, data on some clinical confounding factors may have gone unnoticed. Subsequently, we will perform prospective studies to validate these findings, as well as further studies to elucidate the pathological mechanism that underlies NPSLE.

In conclusion, our results confirmed that high SLEDAI-2K score, positive of β2GPI-Abs and anti-Rib-P Ab at time of SLE diagnosis were as predictors of NPSLE. Neuropsychological assessment is essential in NPSLE, and the pathological mechanism of NPSLE in children still needs to be further explored.

Author contributions

WJ and XP contributed equally to this work. Study conception and design: WJ, XP, and LQD; data analysis: WJ and LW; data acquisition: XP, QL and LL; writing of original draft: XP; revising the article: WJ, YZD and LQD; revising and final approval of the version to be submitted: WJ, XP, and QL. All authors reviewed the manuscript.

Funding

This work was supported by the Science and Technology Project of Sichuan Provincial Health Commission (Clinical Research Special Project, 23LCYJ014)

Competing interest

The authors declare no competing interests.

Additional information

Correspondence and requests for materials should be addressed to XP.

Reprints and permissions information is available at www.nature.com/reprints.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Data availability

The datasets generated during and/or analyzed during the current study are available from corresponding authors upon reasonable request.

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Table 1. Demographic, clinical, and ancillary examination characteristics of all studied children with SLE

Characteristics	N = 101
Time from onset to diagnosis (days) mean (± SD)	61.74 ± 97.51
Age (years) mean (± SD)	10.76 ± 2.35
Sex n (%)
Male	16 (15.8)
Female	85 (84.2)
SLEDAI-2K, mean (± SD)	16.53 ± 8.09
SLEDAI-2K n (%) (≤ 6)	13 (12.9)
SLEDAI-2K n (%) (7–12)	17 (16.8)
SLEDAI-2K n (%) (> 12)	71 (70.3)
C3, mg/dl, mean (± SD)	0.49 ± 0.38
C4, mg/dl, mean (± SD)	0.08 ± 0.07
ANA positive, n (%)
1:100	8 (7.9)
1:320	16 (15.8)
1:1000	42 (41.6)
1:3200	35 (34.7)
Anti-dsDNA Ab n (%)	62 (61.4)
Anti-Sm Ab n (%)	33 (32.7)
Anti-CENP Ab n (%)	3 (3.0)
Anti-PCNA Ab n (%)	2 (2.0)
Anti-NU Ab n (%)	60 (59.4)
Anti-HI Ab n (%)	52 (51.5)
Anti-Rib-P Ab n (%)	30 (29.7)
AMA-M2 n (%)	29 (28.7)
Anti-U1RNP Ab n (%)	42 (41.6)
Anti-SSA Ab n (%)	39 (38.6)
Anti-SSB Ab n (%)	21 (20.8)
Anti-Scl70 Ab n (%)	4 (4.0)
Anti-PM Scl Ab n (%)	13 (12.9)
ANCA n (%)	47 (46.5)
LAC n (%)	33 (32.7)
aCL Abs n (%)	22 (21.8)
β2GPI Abs n (%)	24 (23.8)
Rash n (%)	58 (57.4)
Fever n (%)	53 (52.5)
MAS n (%)	4 (4.0)
Renal disorder n (%)	74 (73.3)
Hematologic disorder n (%)	72 (71.3)
Arthritis n (%)	15 (14.9)
Pulmonary disorder n (%)	20 (19.8)
Cardiac disorder n (%)	13 (12.9)
Digestive disorder n (%)	31 (30.7)
Hypothyroidism n (%)	36 (35.6)
Neurologic disorder n (%)	23 (22.8)

NP manifestations n (%)	23 (22.8)
NP characteristics
Headache	8 (34.8)
Seizure disorders	1 (4.3)
Cognitive dysfunction	2 (8.7)
Acute confusional state	1 (4.3)
Movement disorder	3 (13.0)
Mood disorder	6 (26.1)
Autonomic disorder	6 (26.1)
Cerebrovascular disease	4 (17.4)
NP auxiliary inspection
Abnormal brain MRI	22 (95.6)
Abnormal electroencephalogram	4 (17.4)

Unless otherwise indicated, values are expressed as numbers (percentages). All antibodies show the number of participants that were positive. ANA antinuclear antibody, SLEDAI-2K SLE disease activity index-2000, anti-dsDNA Ab anti-double-stranded DNA antibody, anti-Sm Ab anti-Smith antibody, anti-CENP Ab anti-centromere protein antibody, anti-PCNA Ab anti-proliferating cell nuclear antigen antibody, anti-NU Ab anti-nucleosome antibody, anti-HI Ab anti-histone antibody, anti-Rib-P Ab anti-ribosomal-P antibody, AMA-M2 anti-mitochondrial antibody-M2, anti-U1RNP Ab anti-U1 small nuclear ribonucleoprotein antibody, anti-SSA Ab anti-Sjogren’s syndrome A antibody, anti-SSB Ab anti-Sjogren’s syndrome B antibody, anti-Scl70 Ab anti-scleroderma-70 antibody, anti-PM Scl Ab anti-polymyositis-sclerosis antibodies, ANCA anti-neutrophil cytoplasmic antibody, LAC lupus anticoagulant, aCL Abs anti-cardiolipin antibodies, β2GPI Abs anti-beta2-glycoprotein I antibodies, MAS macrophage activation syndrome, MRI magnetic resonance imaging, NP neuropsychiatric disorders

Table 2. Clinical manifestations in patients with systemic lupus erythematosus (SLE) with or without neuropsychiatric (NP) manifestations

Variables	Without NP manifestations (n = 78)	With NP manifestations (n = 23)	P-value
Time from onset to diagnosis, days mean (± SD)	57.42 ± 91.42	76.39 ± 116.94	0.479
Age, years, mean (± SD)	10.65 ± 2.44	11.1 ± 2.02	0.376
Sex, n (%)			0.352
Male	14 (17.9)	2 (8.7)
Female	64 (82.1)	21 (91.3)
SLEDAI-2K score, mean (± SD)	14.46 ± 7.26	23.57 ± 6.77	< 0.001*
^aSLEDAI-2K score n (%)			0.007*
≤ 6	13 (16.7)	0 (0)
7–12	16 (20.5)	1 (4.3)
> 12	49 (62.8)	22 (95.7)
C3, mg/dl, mean (± SD)	0.51 ± 0.35	0.42 ± 0.49	0.444
C4, mg/dl, mean (± SD)	0.08 ± 0.08	0.05 ± 0.05	0.041*
^bANA positive n (%)			0.025*
1:100	7 (9)	1 (4.3)
1:320	15 (19.2)	1 (4.3)
1:1000	35 (44.9)	7 (30.4)
1:3200	21 (26.9)	14 (60.9)
Anti-dsDNA Ab n (%)	44 (56.4)	18 (78.3)	0.099
Anti-Sm Ab n (%)	25 (32.1)	8 (34.8)	1
Anti-CENP Ab n (%)	1 (1.3)	2 (8.7)	0.129
Anti-PCNA Ab n (%)	1 (1.3)	1 (4.3)	0.405
Anti-NU Ab n (%)	44 (56.4)	16 (69.6)	0.375
Anti-HI Ab n (%)	37 (47.4)	15 (65.2)	0.207
Anti-Rib-P Ab n (%)	17 (21.8)	13 (56.5)	0.003*
AMA-M2 n (%)	18 (23.1)	11 (47.8)	0.041*
Anti-U1RNP Ab n (%)	31 (39.7)	11 (47.8)	0.652
Anti-SSA Ab n (%)	31 (39.7)	8 (34.8)	0.853
Anti-SSB Ab n (%)	13 (16.7)	8 (34.8)	0.08
Anti-Scl70 Ab n (%)	3 (3.8)	1 (4.3)	1
Anti-PM Scl Ab n (%)	7 (9)	6 (26.1)	0.069
ANCA n (%)	31 (39.7)	16 (69.6)	0.022*
LAC n (%)	19 (24.4)	14 (60.9)	0.002*
aCL Abs n (%)	14 (17.9)	8 (34.8)	0.152
β2GPI Abs n (%)	14 (17.9)	10 (43.5)	0.025*
Rash n (%)	43 (55.1)	15 (65.2)	0.535
Fever n (%)	35 (44.9)	18 (78.3)	0.01*
MAS n (%)	0 (0)	4 (17.4)	0.002*
Renal disorder, n (%)	56 (71.8)	18 (78.3)	0.728
Hematologic disorder n (%)	52 (66.7)	20 (87)	0.104
Arthritis n (%)	9 (11.5)	6 (26.1)	0.101
Pulmonary disorder n (%)	13 (16.7)	7 (30.4)	0.231
Cardiac disorder n (%)	9 (11.5)	4 (17.4)	0.486
Digestive disorder n (%)	18 (23.1)	13 (56.5)	0.005*
Hypothyroidism n (%)	23 (29.5)	13 (56.5)	0.033*

*A P-value is significant if it is < 0.05. Values are presented as numbers (percentages) unless otherwise stated. The count data were described by n (%), and the chi-square test was used to compare variables between the groups. The sample size was > 40 and the theoretical frequency was > 5, and the Pearson chi-square test was used for nonparametric tests; for cases where the sample size was < 40 or the theoretical frequency was < 5, the Fisher exact probability method was used.

SLEDAI-2K SLE disease activity index-2000, ANA antinuclear antibody, anti-dsDNA Ab anti-double-stranded DNA antibody, anti-Sm Ab anti-Smith antibody, anti-CENP Ab anti-centromere protein antibody, anti-PCNA Ab anti-proliferating cell nuclear antigen antibody, anti-NU Ab anti-nucleosome antibody, anti-HI Ab anti-histone antibody, anti-Rib-P Ab anti-ribosomal-P antibody, AMA-M2 anti-mitochondrial antibody-M2, anti-U1RNP Ab anti-U1 small nuclear ribonucleoprotein antibody, anti-SSA Ab anti-Sjogren’s syndrome A antibody, anti-SSB Ab anti-Sjogren’s syndrome B antibody, anti-Scl70 Ab anti-scleroderma70 antibody, anti-PM Scl Ab anti-polymyositis-sclerosis antibodies, ANCA anti-neutrophil cytoplasmic antibody, LAC lupus anticoagulant, aCL Abs anti-cardiolipin antibodies, β2GPI Abs anti-beta2-glycoprotein I antibodies, MAS macrophage activation syndrome

^aA SLEDAI-2K score of ≤ 6 is classified as mild disease activity, 7–12 as moderate disease activity, and > 12 as severe disease activity.

^bPositive antinuclear antibodies with a titer of ≥ 1:100 on indirect immunofluorescence

Table 3. Univariate analyses of factors associated with NP dysfunction

Variable	OR	95% CI OR		P-value
Variable	OR	Lower limit	Upper limit	P-value
Time from onset to diagnosis, days	1.002	0.997	1.006	0.418
Age	1.091	0.884	1.346	0.419
Sex	2.297	0.482	10.946	0.297
SLEDAI-2K	1.204	1.101	1.318	< 0.001*
C3	0.503	0.118	0.938	0.035*
C4	0.024	0.009	0.982	0.035*
ANA	1.005	1.001	1.111	0.003*
Anti-dsDNA Ab	2.782	0.938	8.251	0.065
Anti-Sm Ab	1.131	0.424	3.016	0.806
Anti-CENP Ab	7.333	0.634	84.851	0.111
Anti-PCNA Ab	3.5	0.21	58.252	0.383
Anti-NU Ab	1.766	0.653	4.775	0.262
Anti-HI Ab	2.078	0.791	5.461	0.138
Anti-Rib-P Ab	4.665	1.743	12.481	0.002*
AMA-M2	3.056	1.155	8.085	0.024*
Anti-U1RNP Ab	1.39	0.545	3.542	0.49
Anti-SSA Ab	3.168	1.197	8.384	0.02*
Anti-SSB Ab	2.667	0.938	7.578	0.066
Anti-Scl70 Ab	1.136	0.113	11.477	0.914
Anti-PM Scl Ab	3.58	1.065	12.03	0.039*
ANCA	3.465	1.278	9.394	0.015*
LAC	4.83	1.805	12.924	0.002*
aCL Abs	5.231	1.815	15.074	0.002*
β2GPI Abs	3.516	1.285	9.626	0.014*
Rash	1.526	0.58	4.014	0.392
Fever	4.423	1.492	13.111	0.007*
Hematologic disorder	3.333	0.907	12.251	0.07
Renal disorder	1.414	0.468	4.278	0.539
MAS	6.543	0.001	95.245	0.999
Arthritis	2.706	0.847	8.643	0.093
Pulmonary disorder	2.187	0.751	6.373	0.151
Cardiac disorder	1.614	0.448	5.82	0.464
Digestive disorder	4.333	1.629	11.526	0.003*
Hypothyroidism	3.109	1.193	8.097	0.02*

*A P-value is significant if it is < 0.05. OR odds ratio, CI confidence interval, SLEDAI-2K SLE disease activity index-2000, ANA antinuclear antibody, anti-dsDNA Ab anti-double-stranded DNA antibody, anti-Sm Ab anti-Smith antibody, anti-CENP Ab anti-centromere protein antibody, anti-PCNA Ab anti-proliferating cell nuclear antigen antibody, anti-NU Ab anti-nucleosome antibody, anti-HI Ab anti-histone antibody, anti-Rib-P Ab anti-ribosomal-P antibody, AMA-M2 anti-mitochondrial antibody-M2, anti-U1RNP Ab anti-U1 small nuclear ribonucleoprotein antibody, anti-SSA Ab anti-Sjogren’s syndrome A antibody, anti-SSB Ab anti-Sjogren’s syndrome B antibody, anti-Scl70 Ab anti-scleroderma 70 antibody, anti-PM Scl Ab anti-polymyositis-sclerosis antibodies, ANCA anti-neutrophil cytoplasmic antibody, LAC lupus anticoagulant, aCL Abs anti-cardiolipin antibodies, β2GPI Abs anti-beta2-glycoprotein I antibodies, MAS macrophage activation syndrome

Table 4. Multivariate analyses of factors associated with NP dysfunction

Variable	OR	95% CI OR		P-value
Variable	OR	Lower limit	Upper limit	P-value
SLEDAI-2K	1.215	1.019	1.449	0.03*
C3	0.254	0.115	3.99	0.987
C4	0.347	0.113	5.572	0.242
ANA	1.000	0.999	1.001	0.771
Anti-Rib-P Ab	4.153	1.865	19.95	0.038*
AMA-M2	1.436	0.218	9.436	0.706
Anti-SSA Ab	2.964	0.491	17.894	0.236
Anti-PM Scl Ab	1.942	0.227	16.645	0.545
ANCA	1.095	0.213	5.616	0.914
LAC	3.226	0.752	13.832	0.115
aCL Abs	3.418	0.563	20.75	0.182
β2GPI Abs	4.603	1.834	25.412	0.04*
Fever	2.542	0.45	14.355	0.291
Digestive disorder	1.761	0.306	10.125	0.526
Hypothyroidism	0.542	0.087	3.364	0.511

*A P-value is significant if it is < 0.05. OR odds ratio, CI confidence interval, SLEDAI-2K SLE disease activity index-2000, ANA antinuclear antibody, anti-Rib-P Ab anti-ribosomal-P antibody, AMA-M2 anti-mitochondrial antibody-M2, anti-SSA Ab anti-Sjogren’s syndrome A antibody, anti-PM Scl Ab anti-polymyositis-sclerosis antibodies, ANCA anti-neutrophil cytoplasmic antibody, LAC lupus anticoagulant, aCL Abs anti-cardiolipin antibodies, β2GPI Abs anti-beta2-glycoprotein I antibodies

No competing interests reported.

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Supplementary Table 1. The raw data.

Predictors of neuropsychiatric manifestations in pediatric patients with lupus

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