Poster No:
410
Submission Type:
Abstract Submission
Authors:
Bei Zhang1, Jia You1, Edmund Rolls2, Wei Cheng1, JianFeng Feng1
Institutions:
1Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China, 2Department of Computer Science, University of Warwick, Coventry, UK
First Author:
Bei Zhang
Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University
Shanghai, China
Co-Author(s):
Jia You
Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University
Shanghai, China
Edmund Rolls
Department of Computer Science, University of Warwick
Coventry, UK
Wei Cheng
Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University
Shanghai, China
JianFeng Feng
Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University
Shanghai, China
Introduction:
Suicide is a critical public health challenge, and identifying biomarkers associated with suicidal behaviors (SBs) holds significant promise for improving risk assessment and advancing therapeutic strategies.
Methods:
This cohort study analyzed data from the UK Biobank, encompassing baseline measurements of 2,920 plasma proteins and clinical data from 53,026 participants, with follow-up extending up to 15 years. Logistic regression and Cox proportional hazards models were employed to identify proteins associated with past and future SBs, respectively. Co-regulated network analyses explored protein networks related to SBs. Mendelian Randomization evaluated potential causal relationships between proteins, protein networks, and SBs. Machine-learning models were used to assess the predictive capability of plasma proteins for SBs.
Results:
Among the 53,026 participants, 268 (0.51%) reported a history of SBs at baseline, and 202 (0.38%) experienced SBs during the follow-up period. Of the 2,920 plasma proteins analyzed, 222 were significantly linked to SBs, with 15 proteins showing a strong association with an increased risk of future SBs. These proteins were enriched in inflammatory pathways, including TNF-receptor binding, with IL-6 identified as a central hub protein. Co-regulated network analyses revealed two protein networks associated with SBs, one of which was linked to inflammatory responses. These proteins and networks correlated with the volume of brain regions involved in emotion, such as the medial and lateral orbitofrontal cortex, insula, precuneus, and superior frontal cortex. Mendelian Randomisation analyses supported a potential causal effect of liability to one specific protein (GGH) and one protein network, both of which implicated inflammation, on SBs. Machine-learning models demonstrated strong performance in identifying SBs (AUC = 0.79) and moderate accuracy in predicting future occurrences (AUC = 0.57).
Conclusions:
This study, the largest and most comprehensive to date, identifies plasma proteomic profiles associated with SBs, some associated with inflammation, and highlights potential pathways for targeted prevention and intervention strategies.
Disorders of the Nervous System:
Psychiatric (eg. Depression, Anxiety, Schizophrenia) 1
Physiology, Metabolism and Neurotransmission:
Physiology, Metabolism and Neurotransmission Other 2
Keywords:
Blood
Machine Learning
MRI
Other - Suicide; Plasma proteins; Co-regulated network
1|2Indicates the priority used for review
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Please indicate below if your study was a "resting state" or "task-activation” study.
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Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
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Was this research conducted in the United States?
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Were any human subjects research approved by the relevant Institutional Review Board or ethics panel?
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Please indicate which methods were used in your research:
Neurophysiology
Structural MRI
Behavior
For human MRI, what field strength scanner do you use?
3.0T
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SPM
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