Evidence map›Paper›PMID 41402866›Full record

ArticleLipids in health and disease2025

Exploration of the associations between violent and aggressive behaviors risk and NHHR, TyG in patients with schizophrenia: an SHAP interpretable machine learning prediction and FAERS pharmacovigilance analysis.

Guangwei Qing, Hanggai Taorigen, Yuxin Zhou, Gang Wang

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Article in Lipids in health and disease, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

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4 authors.

Guangwei Qing *Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China. qguangw@163.com.
Hanggai Taorigen *Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China.
Yuxin ZhouDepartment of Psychiatry, Jiangxi Mental Hospital & Affiliated Mental Hospital of Nanchang University, Nanchang, Jiangxi, China.
Gang WangBeijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China. gangwangdoc@ccmu.edu.cn.

Funding

Beijing Scholar 2021 No. 063
6 · The paper itself

Abstract

backgroundSchizophrenia (SCZ) patients have high violent and aggressive behavior (VAB) prevalence, causing harm and stigma. The non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio (NHHR) and triglyceride-glucose index (TyG) show promise for psychiatric disorder prediction, but their links to SCZ patients’ VAB risk remain understudied.

methodsA cross-sectional study included 102 hospitalized SCZ patients from May 2023–October 2024, stratified by Broset Violence Checklist (BVC) into low/medium/high VAB risk groups. Positive and Negative Syndrome Scale (PANSS) quantified psychiatric severity; NHHR and TyG were calculated from venous biochemical indices. Analyses included multivariate logistic regression, mediation analysis (PANSS Scores as mediator), 7 machine learning (ML) models with SHAP interpretation, and FDA Adverse Event Reporting System (FAERS) disproportionality analysis.

resultsIn fully adjusted models, NHHR and TyG were both negatively associated with VAB risk (NHHR: OR = 0.43, 95%CI:0.20–0.92, p = 0.0285; TyG: OR = 0.29, 95%CI:0.12–0.67, p = 0.0038); further analysis revealed an atypical inverted U-shaped correlation between NHHR and VAB risk, with the inflection point measured at 2.98. Meanwhile, PANSS scores partially mediated the associations between NHHR, TyG and VAB, and TyG had higher mediation proportions (54.2% for BVC scores, 57.8% for VAB risk) than NHHR. In ML-based VAB prediction, LASSO Logistic Regression was the optimal model (Accuracy = 0.8065, highest F1 Score), and SHAP analysis confirmed NHHR and TyG as core predictive features. Additionally, FAERS analysis identified positive metabolic abnormality signals for olanzapine (early-onset adverse events) and quetiapine (late-onset adverse events).

conclusionNHHR and TyG are potential VAB risk biomarkers for SCZ patients, with psychiatric severity mediating their effects. ML and FAERS provide clinical prediction and medication monitoring references.

Indexed as

AggressionBlood GlucoseCholesterol, HDLMachine LearningSchizophreniaTriglyceridesViolenceAdultAntipsychotic AgentsCross-Sectional StudiesFemaleHumansMaleMiddle AgedOlanzapinePharmacovigilanceAntipsychotic AgentsBlood GlucoseCholesterol, HDLOlanzapineTriglycerides

Identifiers

PMID41402866
PMCPMC12853584

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.