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Record W4414254099 · doi:10.3389/fpsyt.2025.1651246

Associations between suicide attempts and clinical, metabolic, and inflammatory markers in Chinese patients with long-term schizophrenia

2025· article· en· W4414254099 on OpenAlexaboutno aff
Lili Zhao, Lewei Liu, Xu Zhang, Xi Zhang, Xianhu Yao, Wenzheng Li, Lei Xia, Huanzhong Liu

Bibliographic record

VenueFrontiers in Psychiatry · 2025
Typearticle
Languageen
FieldNeuroscience
TopicTryptophan and brain disorders
Canadian institutionsnot available
Fundersnot available
KeywordsProinflammatory cytokineSchizophrenia (object-oriented programming)Psychological interventionContext (archaeology)InflammationDepression (economics)Suicide RiskMajor depressive disorder

Abstract

fetched live from OpenAlex

Background Suicide attempt (SA) is common among patients with long-term schizophrenia (SCZ), but the mechanisms underlying its occurrence remain incompletely understood. Thus, the purpose of this study was to investigate the associations between SA and clinical, metabolic, and inflammatory markers in Chinese patients with SCZ. Methods This study enrolled 299 patients with SCZ. SA, psychotic symptoms, depressive symptoms, and insomnia were evaluated through standardized questions, the Positive and Negative Syndrome Scale (PANSS), the Calgary Depression Scale for Schizophrenia (CDSS), and the Insomnia Severity Index (ISI), respectively. In addition, we measured metabolic parameters including total cholesterol (TC), triglycerides (TG), high-density lipoproteins (HDL) and low-density lipoproteins (LDL), fasting blood glucose (FBG), and fasting insulin (FI), along with inflammatory cytokine levels, including interleukin (IL)-1β, IL-6, IL-17A and tumor necrosis factor-α (TNF-α). Univariate analyses (chi-square test, the independent samples t-test or the Mann-Whitney U-test) were followed by multivariable logistic regression (“Forward: LR”) to identify independent risk factors for SA. Log10-transformed values were applied to inflammatory-cytokine data to approximate normal distribution. All analyses were performed in SPSS 23.0; P < 0.05 (two-sided) was considered statistically significant. Results The prevalence of SA in patients with SCZ was 22.7%. Patients in the SA group had a higher proportion of females, more severe depressive symptoms, and higher levels of TC, TG, Log IL-1β, and Log IL-6 (all P < 0.05). Logistic regression analyses showed that gender (OR = 0.239, 95% CI = 0.127 - 0.450, P < 0.001), CDSS total score (OR = 1.250, 95% CI = 1.146 - 1.364, P < 0.001), TC (OR = 1.682, 95% CI = 1.178 - 2.402, P = 0.004), and Log IL-1β (OR = 2.225, 95% CI = 1.114 - 4.564, P = 0.024) were independent correlates of SA. Conclusions Female gender, greater depressive severity, and elevated metabolic and proinflammatory markers (specifically TC and IL-1β) were independently associated with increased risk of SA in Chinese patients with long-term SCZ. These findings suggest that future interventions targeting metabolic and inflammatory pathways may hold promise for preventing SA in this population.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.009
GPT teacher head0.282
Teacher spread0.273 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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