| Title: | Plasma proteomic profiles linked to suicidal behaviors |
| Journal: | Nature Mental Health |
| Published: | 23 Feb 2026 |
| DOI: | https://doi.org/10.1038/s44220-025-00582-5 |
Publication 17659
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Abstract
Suicidality is a major public health concern, and characterizing plasma proteomic profiles linked to suicidal behaviors (SBs, including suicide attempt and death by suicide) offers promising avenues for developing novel therapeutic targets. In this study, leveraging data from 53,026 UK Biobank participants with baseline measurements of 2,920 plasma proteins, we identified 421 proteins significantly associated with past SBs. Of these, 15 proteins were linked to an increased risk of future SBs. These SBs-associated proteins were predominantly enriched in inflammatory pathways, including cytokine-cytokine receptor interactions and tumor necrosis factor-receptor interactions. Further co-regulated network analysis revealed three distinct protein networks associated with SBs, which were involved in inflammatory and cell-cell adhesion pathways. Furthermore, these SBs-related proteins and co-regulated protein networks were correlated with the volume of brain regions implicated in emotion, including the medial and lateral orbitofrontal cortex, insula, middle temporal cortex and superior frontal cortex. Crucially, Mendelian randomization analysis identified one protein (GGH) as a potential causal factor for SBs, and this protein was also identified as a mediator of the effect of body mass index on SBs. Finally, machine-learning models incorporating plasma proteins and demographic data achieved moderate performance in identifying past SBs (area under the receiver operating characteristic curve = 0.79). This study provides valuable insights into the biological mechanisms underlying SBs and offers potential therapeutic targets.</p>
10 Authors
- Bei Zhang
- Jia You
- Edmund T. Rolls
- Peng Ren
- Yuzhu Li
- Wei Zhang
- Barbara J. Sahakian
- Fei Li
- Jianfeng Feng
- Wei Cheng
1 Application
| Application ID | Title |
|---|---|
| 19542 | Identifying multi-level biomarkers and disease mechanisms for major mental disorders |
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