Juvenile neuropsychiatric systemic lupus erythematosus: identification of novel central neuroinflammation biomarkers

International audienceIntroduction Juvenile systemic lupus erythematosus (j-SLE) is a rare chronic autoimmune disease affecting multiple organs. Ranging from minor features, such as headache or mild cognitive impairment, to serious and life-threatening presentations, j-neuropsychiatric SLE (j-NPSLE) is a therapeutic challenge. Thus, the diagnosis of NPSLE remains difficult, especially in pediatrics, with no specific biomarker of the disease yet validated. Objectives To identify central nervous system (CNS) disease biomarkers of j-NPSLE. Methods A 5-year retrospective tertiary reference monocentric j-SLE study. A combination of standardized diagnostic criteria and multidisciplinary pediatric clinical expertise was combined to attribute NP involvement in the context of j-SLE. Neopterin and interferon-alpha (IFN-α) protein levels in cerebrospinal fluid (CSF) were assessed, together with routine biological and radiological investigations. Results Among 51 patients with j-SLE included, 39% presented with j-NPSLE. J-NPSLE was diagnosed at onset of j-SLE in 65% of patients. No specific routine biological or radiological marker of j-NPSLE was identified. However, CSF neopterin levels were significantly higher in active j-NPSLE with CNS involvement than in j-SLE alone ( p = 0.0008). Neopterin and IFN-α protein levels in CSF were significantly higher at diagnosis of j-NPSLE with CNS involvement than after resolution of NP features (respectively p = 0.0015 and p = 0.0010) upon immunosuppressive treatment in all patients tested ( n = 10). Both biomarkers correlated strongly with each other ( R s = 0.832, p < 0.0001, n = 23 paired samples). Conclusion CSF IFN-α and neopterin constitute promising biomarkers useful in the diagnosis and monitoring of activity in j-NPSLE

Regulatory T lymphocytes/Th17 lymphocytes imbalance in autism spectrum disorders: evidence from a meta-analysis

International audienceBackground: Immune system dysfunction has been proposed to play a critical role in the pathophysiology of autism spectrum disorders (ASD). Conflicting reports of lymphocyte subpopulation abnormalities have been described in numerous studies of patients with ASD. To better define lymphocytes abnormalities in ASD, we performed a metaanalysis of the lymphocyte profiles from subjects with ASD. Methods: We used the PRISMA recommendations to query PubMed, Embase, PsychoINFO, BIOSIS, Science Direct, Cochrane CENTRAL, and Clinicaltrials.gov for terms related to clinical diagnosis of ASD and to lymphocytes' populations. We selected studies exploring lymphocyte subpopulations in children with ASD. The search protocol has been registered in the international Prospective Register of Systematic Reviews (CRD42019121473). Results: We selected 13 studies gathering 388 ASD patients and 326 healthy controls. A significant decrease in the CD4+ lymphocyte was found in ASD patients compared to controls [− 1.51 (95% CI − 2.99; − 0.04) p = 0.04] (I 2 = 96% [95% CI 94.6, 97.7], p < 0.01). No significant difference was found for the CD8+ T, B and natural killer lymphocytes. Considering the CD4+ subpopulation, there was a significant decrease in regulatory T lymphocytes (Tregs) in ASD patients (n = 114) compared to controls (n = 107) [− 3.09 (95% CI − 4.41; − 1.76) p = 0.0001]; (I 2 = 90.9%, [95% CI 76.2, 96.5], p < 0.0001) associated with an increase oin the Th17 lymphocytes (ASD; n = 147 controls; n = 128) [2.23 (95% CI 0.79; 3.66) p = 0,002] (I 2 = 95.1% [95% CI 90.4, 97.5], p < 0.0001). Limitations: Several factors inducing heterogeneity should be considered. First, differences in the staining method may be responsible for a part in the heterogeneity of results. Second, ASD population is also by itself heterogeneous, underlying the need of studying subgroups that are more homogeneous. Conclusion: Our meta-analysis indicates defects in CD4+ lymphocytes, specifically decrease oin Tregs and increase in Th17 in ASD patients and supports the development of targeted immunotherapies in the field of ASD

Natural language processing of multi-hospital electronic health records for public health surveillance of suicidality

Abstract There is an urgent need to monitor the mental health of large populations, especially during crises such as the COVID-19 pandemic, to timely identify the most at-risk subgroups and to design targeted prevention campaigns. We therefore developed and validated surveillance indicators related to suicidality: the monthly number of hospitalisations caused by suicide attempts and the prevalence among them of five known risks factors. They were automatically computed analysing the electronic health records of fifteen university hospitals of the Paris area, France, using natural language processing algorithms based on artificial intelligence. We evaluated the relevance of these indicators conducting a retrospective cohort study. Considering 2,911,920 records contained in a common data warehouse, we tested for changes after the pandemic outbreak in the slope of the monthly number of suicide attempts by conducting an interrupted time-series analysis. We segmented the assessment time in two sub-periods: before (August 1, 2017, to February 29, 2020) and during (March 1, 2020, to June 31, 2022) the COVID-19 pandemic. We detected 14,023 hospitalisations caused by suicide attempts. Their monthly number accelerated after the COVID-19 outbreak with an estimated trend variation reaching 3.7 (95%CI 2.1–5.3), mainly driven by an increase among girls aged 8–17 (trend variation 1.8, 95%CI 1.2–2.5). After the pandemic outbreak, acts of domestic, physical and sexual violence were more often reported (prevalence ratios: 1.3, 95%CI 1.16–1.48; 1.3, 95%CI 1.10–1.64 and 1.7, 95%CI 1.48–1.98), fewer patients died (p = 0.007) and stays were shorter (p < 0.001). Our study demonstrates that textual clinical data collected in multiple hospitals can be jointly analysed to compute timely indicators describing mental health conditions of populations. Our findings also highlight the need to better take into account the violence imposed on women, especially at early ages and in the aftermath of the COVID-19 pandemic

CATI: A Large Distributed Infrastructure for the Neuroimaging of Cohorts

The CATI ConsortiumInternational audienceThis paper provides an overview of CATI, a platform dedicated to multicenter neuroimaging. Initiated by the French Alzheimer’s plan (2008–2012), CATI is a research project called on to provide service to other projects like an industrial partner. Its core mission is to support the neuroimaging of large populations, providing concrete solutions to the increasing complexity involved in such projects by bringing together a service infrastructure, the know-how of its expert academic teams and a large-scale, harmonized network of imaging facilities. CATI aims to make data sharing across studies easier and promotes sharing as much as possible. In the last 4 years, CATI has assisted the clinical community by taking charge of 35 projects so far and has emerged as a recognized actor at the national and international levels