Serum Levels of Tumor Necrosis Factor-α and Vascular Endothelial Growth Factor in the Subtypes of Clinical High Risk Individuals: A Prospective Cohort Study
Bibliographic record
Abstract
JiaYi Ye,1,* YanYan Wei,1,* JiaHui Zeng,1 YuQing Gao,1 XiaoChen Tang,1 LiHua Xu,1 YeGang Hu,1 XiaoHua Liu,1 HaiChun Liu,2 Tao Chen,3,4 ChunBo Li,1 LingYun Zeng,5,* JiJun Wang,1,6,7 TianHong Zhang1 1Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai Engineering Research Center of Intelligent Psychological Evaluation and Intervention, Shanghai Key Laboratory of Psychotic Disorders, Shanghai, 200030, People’s Republic of China; 2Department of Automation, Shanghai Jiao Tong University, Shanghai, 200240, People’s Republic of China; 3Big Data Research Lab, University of Waterloo, Ontario, Canada; 4Labor and Worklife Program, Harvard University, Cambridge, MA, USA; 5Department of Psychiatric Rehabilitation, Shenzhen Kangning Hospital, ShenZhen, GuangDong, People’s Republic of China; 6Center for Excellence in Brain Science and Intelligence Technology (CEBSIT), Chinese Academy of Science, Shanghai, People’s Republic of China; 7Institute of Psychology and Behavioral Science, Shanghai Jiao Tong University, Shanghai, People’s Republic of China*These authors contributed equally to this workCorrespondence: TianHong Zhang; JiJun Wang, Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai Engineering Research Center of Intelligent Psychological Evaluation and Intervention, Shanghai Key Laboratory of Psychotic Disorders, Shanghai, 200030, People’s Republic of China, Email zhang_tianhong@126.com; jijunwang27@163.comIntroduction: Numerous studies have established the roles of inflammation and angioneurins in the pathogenesis of schizophrenia (SCZ). This study aimed to compare the serum levels of tumour necrosis factor (TNF)-α and vascular endothelial growth factor (VEGF) in patients at clinical high risk (CHR) for psychosis or SCZ at baseline and one year after treatment.Methods: A total of 289 CHR participants from the Shanghai At Risk for Psychosis Extended Program (SHARP) were tracked for a year. They were divided into two and four subtypes based on symptom severity according to the Structured Interview for Prodromal Syndromes (SIPS) and received standard medical care. At baseline and one-year follow-up, TNF-α and VEGF were detected using enzyme-linked immunosorbent assay, and pathological features were assessed using the Global Assessment of Function (GAF) score.Results: Baseline TNF-α levels did not differ significantly, while VEGF levels were lower in patients with more severe symptoms. VEGF showed a negative correlation with negative features, both overall (r = − 0.212, p = 0.010) and in the subgroup with higher positive scores (r = − 0.370, p = 0.005). TNF-α was positively correlated with negative symptoms in the subgroup with higher negative scores (r = 0.352, p = 0.002). A three-way multivariate analysis of variance demonstrated that participants in Subtype 1 of positive or negative symptoms performed better than those in Subtype 2, with significant main effects and interactions of group and both cytokines.Discussion: TNF-α and VEGF levels are higher and lower, respectively, in CHR patients with more severe clinical symptoms, particularly negative symptoms, which point to a worsening inflammatory and vascular status in the brain.Keywords: ultra high risk, prodromal psychosis, inflammation, TNF-α, VEGF
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".