AUTHOR=Kuchcinski Grégory , Rumetshofer Theodor , Zervides Kristoffer A. , Lopes Renaud , Gautherot Morgan , Pruvo Jean-Pierre , Bengtsson Anders A. , Hansson Oskar , Jönsen Andreas , Sundgren Pia C. Maly TITLE=MRI BrainAGE demonstrates increased brain aging in systemic lupus erythematosus patients JOURNAL=Frontiers in Aging Neuroscience VOLUME=15 YEAR=2023 URL=https://www.frontiersin.org/journals/aging-neuroscience/articles/10.3389/fnagi.2023.1274061 DOI=10.3389/fnagi.2023.1274061 ISSN=1663-4365 ABSTRACT=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.