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Abstract Number: 0777

Potential Biomarkers of Cognitive Impairment in the Context of Childhood-Onset Systemic Lupus Erythematosus

Santiago Arciniegas1, Sarah Mossad2, Tala El Tal3, Lawrence Ng4, Paris Moaf4, Helen Branson4, Adrienne Davis4, Linda Hiraki4, Deborah Levy4, Ashley Danguecan2 and Andrea Knight2, 1University of Toronto/The Hospital for Sick Children, Toronto, ON, Canada, 2Hospital for Sick Children, Toronto, ON, Canada, 3University of Toronto/Hospital for Sick Children, Toronto, ON, Canada, 4The Hospital for Sick Children, Toronto, ON, Canada

Meeting: ACR Convergence 2021

Keywords: Cognitive dysfunction, Neuroimaging, Pediatric rheumatology, Systemic lupus erythematosus (SLE)

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Session Information

Date: Sunday, November 7, 2021

Title: Pediatric Rheumatology – Clinical Poster II: SLE, JDM, & Juvenile Scleroderma (0764–0785)

Session Type: Poster Session B

Session Time: 8:30AM-10:30AM

Background/Purpose: Cognitive complaints are common in children with childhood-onset systemic lupus erythematosus (cSLE), but neuropsychiatric lupus (NPSLE) remains challenging to diagnose and treat. To increase understanding of contributing mechanisms, we examined the association between cognitive function, disease measures and structural neuroimaging metrics.

Methods: We examined a cross-sectional sample of 24 patients with cSLE (ages 12-17) meeting ACR or SLICC classification criteria. Patients completed standardized traditional neurocognitive tests quantifying domains of attention and inhibition (CPT-3, Conners’ Continuous Performance Test 3rd ed), working memory (WISC-V, Wechsler Intelligence Scale for Children 5th ed), and cognitive flexibility (D-KEFS, Delis-Kaplan Executive Function System). Cognitive impairment was defined as a score of 1.5 standard deviations below the mean in any domain. T1-weighted brain magnetic resonance images (MRI) were obtained using a 3T scanner. Advanced structural MRI analysis was used to extract volume, cortical thickness, and surface area metrics for brain segments. Demographic and disease measures were extracted from medical records. We used Partial Least-Squares Regression (PLS2), to examine the association between cognitive function (continuous outcome) and its potential predictors, comprised of structural brain metrics as well as disease and demographic measures. PLS2 analysis enables description of interactions between multivariate and potentially collinear data with a relatively small sample size. Each predictor’s relevance criteria (i.e., stability and significance) were based on the bootstrapped sample distribution of its variable importance in projection (VIP) value, which measures the relative weight of a predictor across all outcome variables.

Results: Cognitive impairment was present in 42% (10/24) of patients; only one subject had a diagnosis of NPSLE (Table 1). In PSL2 analysis (Figure 1), 52 predictors were found to be relevant in the estimation of cognitive function (CI = 95%, VIP > 1.18). Of these, 50 were brain structure variables, with the most highly associated brain measures deriving from the frontal lobe (n=19), temporal lobe (n=11), occipital lobe (n=9) and cingulate cortex (n=7). The surface area and volume of the mid-posterior corpus callosum, total left and bilateral cortical volumes, the level of CRP and the age of patients at study visit were also found to be relevant predictors of cognitive function.

Conclusion: Objective cognitive impairment was prevalent in >40% of patients with cSLE. Impairment was strongly associated with several structural brain metrics, most of which derived from the frontal lobe. Only one disease-related factor (CRP) and one demographic factor (patient age) were found to be relevant predictors of cognitive function. Our results suggest that computational modeling has the potential to enhance diagnosis of NPSLE. Further study is needed to identify robust disease biomarkers that can be linked to functional and structural brain metrics with the use of machine learning models.

Figure 1. Shown is the distribution of the most relevant structural predictors of cognitive function in cSLE patients. Predictors are grouped per category and reported as a percentage of the all relevant predictors found in partial least squares analysis. Relevant white matter predictors are the area and volume of the mid-posterior corpus callosum. Relevant grey matter predictors are the total left and bilateral cortical volumes. No relevant predictors were found in the parietal lobe, subcortical structures, cerebellum or brainstem.


Disclosures: S. Arciniegas, None; S. Mossad, None; T. El Tal, None; L. Ng, None; P. Moaf, None; H. Branson, None; A. Davis, None; L. Hiraki, Novartis, 6; D. Levy, amgen, 6, sobi, 1, roche, 1, janssen, 1, medexus, 6; A. Danguecan, None; A. Knight, None.

To cite this abstract in AMA style:

Arciniegas S, Mossad S, El Tal T, Ng L, Moaf P, Branson H, Davis A, Hiraki L, Levy D, Danguecan A, Knight A. Potential Biomarkers of Cognitive Impairment in the Context of Childhood-Onset Systemic Lupus Erythematosus [abstract]. Arthritis Rheumatol. 2021; 73 (suppl 9). https://acrabstracts.org/abstract/potential-biomarkers-of-cognitive-impairment-in-the-context-of-childhood-onset-systemic-lupus-erythematosus/. Accessed .
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