MRI BrainAGE demonstrates increased brain aging in systemic lupus erythematosus patients.

Introduction: Systemic lupus erythematosus (SLE) is an autoimmune connective tissue disease affecting multiple organs in the human body, including the central nervous system. Recently, an artificial intelligence method called BrainAGE (Brain Age Gap Estimation), defined as predicted age minus chronological age, has been developed to measure the deviation of brain aging from a healthy population using MRI. Our aim was to evaluate brain aging in SLE patients using a deep-learning BrainAGE model. Methods: Seventy female patients with a clinical diagnosis of SLE and 24 healthy age-matched control females, were included in this post-hoc analysis of prospectively acquired data. All subjects had previously undergone a 3 T MRI acquisition, a neuropsychological evaluation and a measurement of neurofilament light protein in plasma (NfL). A BrainAGE model with a 3D convolutional neural network architecture, pre-trained on the 3D-T1 images of 1,295 healthy female subjects to predict their chronological age, was applied on the images of SLE patients and controls in order to compute the BrainAGE. SLE patients were divided into 2 groups according to the BrainAGE distribution (high vs. low BrainAGE). Results: BrainAGE z-score was significantly higher in SLE patients than in controls (+0.6 [±1.1] vs. 0 [±1.0], p = 0.02). In SLE patients, high BrainAGE was associated with longer reaction times (p = 0.02), lower psychomotor speed (p = 0.001) and cognitive flexibility (p = 0.04), as well as with higher NfL after adjusting for age (p = 0.001). Conclusion: Using a deep-learning BrainAGE model, we provide evidence of increased brain aging in SLE patients, which reflected neuronal damage and cognitive impairment.

Plasma and cerebrospinal fluid neurofilament light concentrations reflect neuronal damage in systemic lupus Erythematosus.

BACKGROUND: Neuronal damage in systemic lupus erythematosus (SLE) is common, but the extent and mechanisms are unclear. Neurofilament light (NfL) concentrations rise in plasma and cerebrospinal fluid (CSF) during neuronal damage in various neurological disorders. In this cross-sectional study, plasma and CSF concentrations of NfL were explored as a marker of neuronal damage in SLE. METHODS: Seventy-two consecutive SLE out-patients and 26 healthy controls, all female, aged < 55 years, underwent magnetic resonance imaging (MRI) and neurocognitive testing. NfL concentrations in plasma from all individuals and in CSF from 32 patients were measured with single-molecule array technology. Patients were assessed by a rheumatologist and neurologist to define neuropsychiatric involvement (NPSLE) according to three attribution models: SLICC A, SLICC B and ACR. RESULTS: Plasma and CSF NfL concentrations correlated strongly (r = 0.72, p < 0.001). Both NPSLE and non-NPSLE patients in all attribution models had higher plasma NfL concentrations compared with healthy controls (log-NfL, pg/ml, mean (SD); healthy controls (0.71 (0.17)); SLICC A model: NPSLE (0.87 (0.13), p = 0.003), non-NPSLE (0.83 (0.18), p = 0.005); SLICC B model: NPSLE (0.87 (0.14), p = 0.001), non-NPSLE (0.83 (0.18), p = 0.008); ACR model: NPSLE (0.86 (0.16), p < 0.001), non-NPSLE (0.81 (0.17), p = 0.044)). Plasma and CSF NfL concentrations did not differ between NPSLE and non-NPSLE patients. Higher plasma NfL concentrations correlated with larger CSF volumes on MRI (r = 0.34, p = 0.005), and was associated with poorer cognitive performance in the domains of simple attention, psychomotor speed and verbal memory. SLICC/ACR-Damage Index ≥1 was independently associated with higher plasma NfL concentrations (ß = 0.074, p = 0.038). Higher plasma creatinine concentrations, anti-dsDNA-positivity, low complement C3 levels, or a history of renal involvement were associated with higher plasma NfL concentrations (ß = 0.003, p = 0.009; ß = 0.072, p = 0.031; ß = 0.077, p = 0.027; ß = 0.069, p = 0.047, respectively). CONCLUSIONS: Higher plasma NfL concentrations in NPSLE and non-NPSLE patients may indicate a higher degree of neuronal damage in SLE in general, corresponding to cognitive impairment and organ damage development. Furthermore, our results may indicate a higher degree of neuronal breakdown in patients with active SLE, also without overt clinical symptoms. NfL may serve as an indicator of neuronal damage in SLE in further studies.

Serum S100A8/A9 concentrations are associated with neuropsychiatric involvement in systemic lupus erythematosus: a cross-sectional study.

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), and depression (MADRS-S). 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 SLE patients compared with controls (medians 1230 ng/ml; 790 ng/ml, p = 0.023). The concentrations were higher in NPSLE patients compared with non-NPSLE patients when applying the SLICC A and ACR 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). 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) and VAS pain (r = 0.27, p = 0.021) in SLE patients. Serum S100A8/A9 was not independently associated with NPSLE when adjusting for scores of fatigue (FSS) and pain (VAS) (OR 1.86, 95% CI 0.93-3.73, p = 0.08). 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.

The red blood cell count and the erythrocyte sedimentation rate in the diagnosis of polycythaemia vera.

BACKGROUND: Iron deficiency in polycythaemia vera (PV) may impact the validity of the haematocrit (HCT), since HCT is red blood cell count (RBC) × mean corpuscular volume (MCV). OBJECTIVES: To investigate (a) the effect of microcytosis on HCT, (b) the erythrocyte sedimentation rate (ESR) as a possible additional diagnostic marker for PV. MATERIAL AND METHODS: This study included 182 subjects: 39 with PV, 27 with essential thrombocythemia (ET) and 116 suspected of myeloproliferative neoplasm (MPN) with a secondary cause for either thrombocytosis or erythrocytosis. RESULTS: Patients with PV had significantly lower ratio of MCV and serum ferritin compared to MPN suspects. A good correlation of RBC versus HCT was found for PV and MPN subjects when individuals with microcytosis were excluded (R2  = .87 in PV and R2  = .82 in MPN suspects). We found a specificity of 98% and a sensitivity of 37% for ESR <2 mm in the diagnosis of PV. CONCLUSION: The RBC may more precisely reflect the total red cell mass and accordingly the hypercoagulable state of the PV patient, which is integrated in the ESR. A combination of RBC and ESR is proposed as a novel tool to substitute the Hb concentration and the HCT in the diagnosis of PV.