[Establishment of diagnostic model for schizophrenia based on neurotrophic factor and other biomarkers].

ABSTRACT

Objective: To construct a diagnostic model of schizophrenia (SCZ) based on biomarkers such as serum neurotrophic factor. Methods: Patients of schizophrenia (SCZ group) and healthy controls (HC group) who were admitted to the First Affiliated Hospital of Zhengzhou University from January 2017 to December 2019 were prospectively selected. In the SCZ group, the mental symptoms were assessed by the positive and negative symptom scale (PANSS), cognitive function was assessed by the MATRICS consensus cognitive battery (MCCB), brain-derived neurotrophic factor (BDNF), glial cell derived neurotrophic factor (GDNF), fasting glucose (FGB) and fasting insulin (FINS) levels were detected, and insulin resistance (HOMA-IR) was calculated. The same methods were used to evaluate cognitive function, measure BDNF, GDNF, FGB and FINS levels, and calculate HOMA-IR in HC group. The indexes with statistically significant differences between the two groups were selected to be included in the model. The diagnostic model was constructed by machine learning and verified by cross-validation method, the receiver operating curve (ROC) was plotted, and the area under the curve (AUC), sensitivity and specificity were calculated. Results: (1) A total of 142 patients (70 males and 72 females) with schizophrenia were finally included, and aged (25±4) years. Meanwhile, 140 healthy controls (72 males and 68 females) were also enrolled, and aged (26±4) years. In SCZ group, scores in all areas of cognitive function were lower than those in HC group (all P<0.001), the levels of serum BDNF and GDNF [(6.7±1.8) ng/ml and (405±93) pg/ml] were also lower than those in HC group [(12.3±3.2) ng/ml and (574±139) pg/ml] (both P<0.001), but the levels of FINS and HOMA-IR [(8.4±0.8) µU/ml and 1.7±0.3] were higher than those in HC group [(6.7±0.9) µU/ml and 1.4±0.3] (both P<0.001). (2) Correlation analysis showed that the level of serum BDNF had a negative correlation with negative symptom scores and total scores (r=-0.31, P<0.001; r=-0.17, P=0.040), but had a positive correlation with attention/alertness (CPT-IP) T scores, working memory (WSM-Ⅲ) T scores and visual learning (BVMT) T scores in SCZ group (r=0.39, 0.37 and 0.29, all P<0.001). The level of serum GDNF also had a positive correlation with CPT-IP T scores, WSM-Ⅲ T scores and BVMT T scores (r=0.32, P<0.001; r=0.23, P=0.007; r=0.40, P<0.001). The values of HOMA-IR had a positive correlation with social cognition (MSCEIT) T scores in SCZ group (r=0.18, P=0.033). (3) AUC of the early diagnosis model constructed by combining BDNF, GDNF and HOMA-IR was 0.890 (95%CI 0.832-0.940), the accuracy was 0.89, the sensitivity and specificity was 0.94 and 0.82, respectively. Conclusion: The final diagnostic model based on biomarkers of serum neurotrophic factor has good diagnostic efficiency for SCZ, but large-scale independent sample verification is still needed.