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Diagnostic model development for schizophrenia based on peripheral blood mononuclear cell subtype-specific expression of metabolic markers

Authors :
Jihan K. Zaki
Santiago G. Lago
Nitin Rustogi
Shiral S. Gangadin
Jiri Benacek
Geertje F. van Rees
Frieder Haenisch
Jantine A. Broek
Paula Suarez-Pinilla
Tillmann Ruland
Bonnie Auyeung
Olya Mikova
Nikolett Kabacs
Volker Arolt
Simon Baron-Cohen
Benedicto Crespo-Facorro
Hemmo A. Drexhage
Lot D. de Witte
René S. Kahn
Iris E. Sommer
Sabine Bahn
Jakub Tomasik
Source :
Translational Psychiatry, Vol 12, Iss 1, Pp 1-10 (2022)
Publication Year :
2022
Publisher :
Nature Publishing Group, 2022.

Abstract

Abstract A significant proportion of the personal and economic burden of schizophrenia can be attributed to the late diagnosis or misdiagnosis of the disorder. A novel, objective diagnostic approaches could facilitate the early detection and treatment of schizophrenia and improve patient outcomes. In the present study, we aimed to identify robust schizophrenia-specific blood biomarkers, with the goal of developing an accurate diagnostic model. The levels of selected serum and peripheral blood mononuclear cell (PBMC) markers relevant to metabolic and immune function were measured in healthy controls (n = 26) and recent-onset schizophrenia patients (n = 36) using multiplexed immunoassays and flow cytometry. Analysis of covariance revealed significant upregulation of insulin receptor (IR) and fatty acid translocase (CD36) levels in T helper cells (F = 10.75, P = 0.002, Q = 0.024 and F = 21.58, P = 2.8 × 10−5, Q = 0.0004, respectively), as well as downregulation of glucose transporter 1 (GLUT1) expression in monocytes (F = 21.46, P = 2.9 × 10−5, Q = 0.0004). The most robust predictors, monocyte GLUT1 and T helper cell CD36, were used to develop a diagnostic model, which showed a leave-one-out cross-validated area under the receiver operating characteristic curve (AUC) of 0.78 (95% CI: 0.66–0.92). The diagnostic model was validated in two independent datasets. The model was able to distinguish first-onset, drug-naïve schizophrenia patients (n = 34) from healthy controls (n = 39) with an AUC of 0.75 (95% CI: 0.64–0.86), and also differentiated schizophrenia patients (n = 22) from patients with other neuropsychiatric conditions, including bipolar disorder, major depressive disorder and autism spectrum disorder (n = 68), with an AUC of 0.83 (95% CI: 0.75–0.92). These findings indicate that PBMC-derived biomarkers have the potential to support an accurate and objective differential diagnosis of schizophrenia.

Details

Language :
English
ISSN :
21583188 and 71894144
Volume :
12
Issue :
1
Database :
Directory of Open Access Journals
Journal :
Translational Psychiatry
Publication Type :
Academic Journal
Accession number :
edsdoj.80de718941446dfb6c9665174400e1e
Document Type :
article
Full Text :
https://doi.org/10.1038/s41398-022-02229-w