RT Journal Article
T1 Diagnostic model development for schizophrenia based on peripheral blood mononuclear cell subtype-specific expression of metabolic markers.
A1 Zaki, Jihan K
A1 Lago, Santiago G
A1 Rustogi, Nitin
A1 Gangadin, Shiral S
A1 Benacek, Jiri
A1 van Rees, Geertje F
A1 Haenisch, Frieder
A1 Broek, Jantine A
A1 Suarez-Pinilla, Paula
A1 Ruland, Tillmann
A1 Auyeung, Bonnie
A1 Mikova, Olya
A1 Kabacs, Nikolett
A1 Arolt, Volker
A1 Baron-Cohen, Simon
A1 Crespo-Facorro, Benedicto
A1 Drexhage, Hemmo A
A1 de Witte, Lot D
A1 Kahn, René S
A1 Sommer, Iris E
A1 Bahn, Sabine
A1 Tomasik, Jakub
AB 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.
YR 2022
FD 2022-10-30
LK http://hdl.handle.net/10668/19514
UL http://hdl.handle.net/10668/19514
LA en
DS RISalud
RD Apr 7, 2025