SERUM NEUROFILAMENT LIGHT TO DISTINGUISH AND MONITOR ACTIVITY IN A COHORT OF NEUROPSYCHIATRIC SYSTEMIC LUPUS ERYTHEMATOSUS
Bibliographic record
Abstract
PV044 / #345 Poster Topic: AS05 - CNS Lupus Background/Purpose Neuropsychiatric systemic lupus erythematosus (NPSLE) is a poorly recognized entity leading to diagnostic and therapeutic delays. This is likely due to heterogeneity of manifestations complicating recognition, and the lack of markers that portend neuropsychiatric activity, with conventional serology, neuroimaging studies and CSF analysis often yielding unremarkable results. Serum levels of neurofilament light (NfL), a neuronal cytoskeletal protein, have been associated with other neurological conditions, eg, multiple sclerosis, suggesting utility as a noninvasive biomarker in neuroinflammatory pathologies. Studies to date assessing its utility in SLE have been limited by heterogeneously defined study populations of NPSLE.[1] We present serum NfL concentrations in an NPSLE cohort, highlighting the need for more novel modalities for assessment of neuropsychiatric involvement by SLE. Methods Subjects: 83 patients (70 female, 13 male) under the Department of Immunology at Blacktown and Westmead Hospitals, Sydney, Australia. All fulfilled the European League Against Rheumatism / American College of Rheumatology (ACR) 2019 Classification Criteria for SLE and were recruited at various treatment time points between 2014-2024 (disease duration 0-41 years). Seven were reassessed at a second timepoint (range: 0.2-3 years) due to a change in clinical activity or treatment. NPSLE: Classification based on 1999 ACR nomenclature and case definitions for NPSLE and Italian Society of Rheumatology 2015 attribution model for neuropsychiatric manifestations to SLE.[2] Classified at onset of neuropsychiatric manifestations, independent of activity during study recruitment. Serum NfL: Performed using Single Molecular Array technology, units expressed as pg/mL. Normal values increase with age,[3] therefore age-adjusted reference ranges were not utilized, rather comparing mean differences MRI: The following considered abnormal – atrophy, cerebrovascular disease or infarction, multiple high signal changes in white matter, demyelinating lesions, myelitis. Statistical analysis: Mann-Whitney U test. P values less than 0.05 considered significant. Results Sixty-five patients with non-NP SLE (ages 18-81 years [mean ± SD: 42 ± 14 years]) and 18 NPSLE (ages 21-60 years [37 ± 13 years]) were included. Six NPSLE patients had active whereas 12 had inactive neuropsychiatric manifestations. Serum NfL levels trended 2.5 times higher in NPSLE than non-NP SLE cohorts (mean ± standard error of mean: 64.28 ± 25.63 pg/mL vs 24.03 ± 4.543 pg/mL; p = 0.41) (Figure 1). NfL levels trended higher in those with active than inactive NPSLE (103.7 ± 55.18 pg/mL vs 44.58 ± 26.91 pg/mL; p = 0.7) (Figure 2). There was a trend toward a younger age in both NPSLE than non-NP SLE cohorts and active than inactive NPSLE cohorts, suggesting that patient age did not contribute to the measured difference between groups. Abnormal MRIs were seen in 45% of patients with NPSLE and 24% non-NP SLE (p = 0.4). There were no differences in seropositivity for anti-dsDNA nor antiphospholipid antibodies, nor in hypocomplementemia between the NPSLE and non-NP SLE groups. Six patients with NPSLE and 1 with non-NP SLE were followed up, 3 of whom improved with treatment with corresponding reductions in serum NfL concentrations, 2 of whom who had persistent active disease due to inadequate treatment with an increasing serial NfL concentrations, and 1 of who developed new neuropsychiatric involvement with a corresponding rise in serum NfL concentration. Figure 1. Serum NfL levels in NPSLE and non-NP SLE (mean & SEM pg/mL). Figure 2. Serum NfL levels in NPSLE patients with active neuropsychiatric manifestations and those with inactive neuropsychiatric manifestations (mean & SEM pg/mL). Conclusions Serum NfL levels may be a useful method for diagnosing, monitoring and prognosticating patients with NPSLE. References: [1.] Emerson J. Front Neurol 2023;14:1111769. [2.] Bortoluzzi A. Rheumatology (Oxford) 2015;54(5):891-8. [3.] Khalil M. Nat Rev Neurol 2018;14(10):577-89.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".