Comprehensive immunoprofiling of neurodevelopmental disorders suggests three distinct classes based on increased neurogenesis, Th-1 polarization or IL-1 signaling.

Attention Deficit Hyperactivity Disorder Autism Spectrum Disorder Biomarkers Inflammation Intellectual Disability Disorder Mental health Neurodevelopment Neurodevelopmental disorders Neuroscience Psychiatry Shared etiology

Journal

Brain, behavior, and immunity
ISSN: 1090-2139
Titre abrégé: Brain Behav Immun
Pays: Netherlands
ID NLM: 8800478

Informations de publication

Date de publication:
Jan 2024
Historique:
received: 10 07 2023
revised: 18 09 2023
accepted: 11 11 2023
pubmed: 17 11 2023
medline: 17 11 2023
entrez: 16 11 2023
Statut: ppublish

Résumé

Neurodevelopmental disorders (NDDs) are a spectrum of conditions with commonalities as well as differences in terms of phenome, symptomatome, neuropathology, risk factors and underlying mechanisms. Immune dysregulation has surfaced as a major pathway in NDDs. However, it is not known if neurodevelopmental disorders share a common immunopathogenetic mechanism. In this study, we explored the possibility of a shared immune etiology in three early-onset NDDs, namely Autism Spectrum Disorder (ASD), Attention Deficit Hyperactivity Disorder (ADHD) and Intellectual Disability Disorder (IDD). A panel of 48 immune pathway-related markers was assayed in 135 children with NDDs, represented by 45 children with ASD, ADHD and IDD in each group, along with 35 typically developing children. The plasma levels of 48 immune markers were analyzed on the Multiplex Suspension Assay platform using Pro Human cytokine 48-plex kits. Based on the cytokine/chemokine/growth factor levels, different immune profiles were computed. The primary characteristics of NDDs are depletion of the compensatory immune-regulatory system (CIRS) (z composite of IL-4, IL-10, sIL-1RA, and sIL-2R), increased interleukin (IL)-1 signaling associated with elevated IL-1α and decreased IL-1-receptor antagonist levels, increased neurogenesis, M1/M2 macrophage polarization and increased IL-4 as well as C-C Motif Chemokine Ligand 2 (CCL2) levels. With a cross-validated sensitivity of 81.8% and specificity of 94.4%, these aberrations seem specific for NDDs. Many immunological abnormalities are shared by ASD, ADHD and IDD, which are distinguished by minor differences in IL-9, IL-17 and CCL12. In contrast, machine learning reveals that NDD group consists of three immunologically distinct clusters, with enhanced neurogenesis, Th-1 polarization, or IL-1 signaling as the defining features. NDD is characterized by immune abnormalities that have functional implications for neurogenesis, neurotoxicity, and neurodevelopment. Using machine learning, NDD patients could be classified into subgroups with qualitatively distinct immune disorders that may serve as novel drug targets for the treatment of NDDs.

Identifiants

pubmed: 37972879
pii: S0889-1591(23)00346-X
doi: 10.1016/j.bbi.2023.11.013
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

505-516

Informations de copyright

Copyright © 2023. Published by Elsevier Inc.

Déclaration de conflit d'intérêts

Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Auteurs

Nikhitha Sreenivas (N)

Department of Human Genetics, National Institute of Mental Health and Neurosciences, Bangalore, India.

Michael Maes (M)

Department of Psychiatry, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, The Thai Red Cross Society, Bangkok 10330, Thailand; Department of Psychiatry, Medical University of Plovdiv, 4002 Plovdiv, Bulgaria; Research Center, Medical University of Plovdiv, 4002 Plovdiv, Bulgaria; Kyung Hee University, 26 Kyungheedae-ro, Dongdaemun-gu, Seoul 02447, Korea; Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu 610072, China; Key Laboratory of Psychosomatic Medicine, Chinese Academy of Medical Sciences, Chengdu, 610072, China.

Hansashree Padmanabha (H)

Department of Neurology, National Institute of Mental Health and Neurosciences, Bangalore, India.

Apoorva Dharmendra (A)

Department of Child and Adolescent Psychiatry, National Institute of Mental Health and Neurosciences, Bangalore, India.

Priyanka Chakkera (P)

Department of Neurology, National Institute of Mental Health and Neurosciences, Bangalore, India.

Saptamita Paul Choudhury (S)

Department of Human Genetics, National Institute of Mental Health and Neurosciences, Bangalore, India.

Fazal Abdul (F)

Department of Human Genetics, National Institute of Mental Health and Neurosciences, Bangalore, India.

Thrinath Mullapudi (T)

Department of Human Genetics, National Institute of Mental Health and Neurosciences, Bangalore, India.

Vykuntaraju K Gowda (VK)

Department of Paediatric Neurology, Indira Gandhi Institute of Child Health, Bangalore, India.

Michael Berk (M)

Deakin University, IMPACT Institute for Innovation in Physical and Mental Health and Clinical Translation, School of Medicine, Geelong, Australia; Orygen, The National Centre of Excellence in Youth Mental Health, The Department of Psychiatry, and the Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Parkville, Australia.

John Vijay Sagar Kommu (J)

Department of Child and Adolescent Psychiatry, National Institute of Mental Health and Neurosciences, Bangalore, India.

Monojit Debnath (M)

Department of Human Genetics, National Institute of Mental Health and Neurosciences, Bangalore, India. Electronic address: monojit-d@nimhans.ac.in.

Classifications MeSH