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

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. SommerSabine Bahn, Jakub Tomasik

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

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.
Original languageEnglish
Article number457
JournalTranslational Psychiatry
Early online date30 Oct 2022
Publication statusPublished - 2022

Keywords / Materials (for Non-textual outputs)

  • dagnostic markers
  • schizophrenia


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