Investigation of peripheral inflammatory biomarkers in association with violence in schizophrenia.
Inflammation
Schizophrenia
Violence
Journal
BMC psychiatry
ISSN: 1471-244X
Titre abrégé: BMC Psychiatry
Pays: England
ID NLM: 100968559
Informations de publication
Date de publication:
31 Jul 2024
31 Jul 2024
Historique:
received:
11
03
2024
accepted:
15
07
2024
medline:
1
8
2024
pubmed:
1
8
2024
entrez:
31
7
2024
Statut:
epublish
Résumé
Violent behavior carried out by patients with schizophrenia (SCZ) is a public health issue of increasing importance that may involve inflammation. Peripheral inflammatory biomarkers, such as the systemic immune inflammation index (SII), the neutrophil lymphocyte ratio (NLR), the platelet-lymphocyte ratio (PLR) and the monocyte lymphocyte ratio (MLR) are objective, easily accessible and cost-effective measures of inflammation. However, there are sparse studies investigating the role of peripheral inflammatory biomarkers in violence of patients with SCZ. 160 inpatients diagnosed with SCZ between January and December 2022 were recruited into this study. Violent behavior and positive symptoms of all participants were evaluated using Modified Overt Aggression Scale (MOAS) and Positive and Negative Syndrome Scale (PANSS), respectively. The partial correlation analysis was performed to examine the relationship of inflammatory indices and positive symptoms. Based on machine learning (ML) algorithms, these different inflammatory indices between groups were used to develop predictive models for violence in SCZ patients. After controlling for age, SII, NLR, MLR and PANSS positive scores were found to be increased in SCZ patients with violence, compared to patients without violence. SII, NLR and MLR were positively related to positive symptoms in all participants. Positive symptoms partially mediated the effects of peripheral inflammatory indices on violent behavior in SCZ. Among seven ML algorithms, penalized discriminant analysis (pda) had the best performance, with its an area under the receiver operator characteristic curve (AUC) being 0.7082. Subsequently, with the use of pda, we developed predictive models using four inflammatory indices, respectively. SII had the best performance and its AUC was 0.6613. These findings suggest that inflammation is involved in violent behavior of SCZ patients and positive symptoms partially mediate this association. The models built by peripheral inflammatory indices have a good median performance in predicting violent behavior in SCZ patients.
Sections du résumé
BACKGROUND
BACKGROUND
Violent behavior carried out by patients with schizophrenia (SCZ) is a public health issue of increasing importance that may involve inflammation. Peripheral inflammatory biomarkers, such as the systemic immune inflammation index (SII), the neutrophil lymphocyte ratio (NLR), the platelet-lymphocyte ratio (PLR) and the monocyte lymphocyte ratio (MLR) are objective, easily accessible and cost-effective measures of inflammation. However, there are sparse studies investigating the role of peripheral inflammatory biomarkers in violence of patients with SCZ.
METHODS
METHODS
160 inpatients diagnosed with SCZ between January and December 2022 were recruited into this study. Violent behavior and positive symptoms of all participants were evaluated using Modified Overt Aggression Scale (MOAS) and Positive and Negative Syndrome Scale (PANSS), respectively. The partial correlation analysis was performed to examine the relationship of inflammatory indices and positive symptoms. Based on machine learning (ML) algorithms, these different inflammatory indices between groups were used to develop predictive models for violence in SCZ patients.
RESULTS
RESULTS
After controlling for age, SII, NLR, MLR and PANSS positive scores were found to be increased in SCZ patients with violence, compared to patients without violence. SII, NLR and MLR were positively related to positive symptoms in all participants. Positive symptoms partially mediated the effects of peripheral inflammatory indices on violent behavior in SCZ. Among seven ML algorithms, penalized discriminant analysis (pda) had the best performance, with its an area under the receiver operator characteristic curve (AUC) being 0.7082. Subsequently, with the use of pda, we developed predictive models using four inflammatory indices, respectively. SII had the best performance and its AUC was 0.6613.
CONCLUSIONS
CONCLUSIONS
These findings suggest that inflammation is involved in violent behavior of SCZ patients and positive symptoms partially mediate this association. The models built by peripheral inflammatory indices have a good median performance in predicting violent behavior in SCZ patients.
Identifiants
pubmed: 39085826
doi: 10.1186/s12888-024-05966-y
pii: 10.1186/s12888-024-05966-y
doi:
Substances chimiques
Biomarkers
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
542Subventions
Organisme : Hefei Fourth People's Hospital
ID : HFSY2023YB12
Organisme : Department of Science and Technology of Anhui Province
ID : 201904a0702009
Informations de copyright
© 2024. The Author(s).
Références
McCutcheon RA, Reis Marques T, Howes OD. Schizophrenia-An Overview. JAMA Psychiatry. 2020;77(2):201–10.
doi: 10.1001/jamapsychiatry.2019.3360
pubmed: 31664453
Salim Z, Haddad C, Obeid S, Awad E, Hallit S, Haddad G. Command Voices and Aggression in a Lebanese sample patients with Schizophrenia. Psychiatr Danub. 2021;33(1):27–35.
doi: 10.24869/psyd.2021.27
pubmed: 33857037
Birchwood M, Peters E, Tarrier N, Dunn G, Lewis S, Wykes T, Davies L, Lester H, Michail M. A multi-centre, randomised controlled trial of cognitive therapy to prevent harmful compliance with command hallucinations. BMC Psychiatry. 2011;11:155.
doi: 10.1186/1471-244X-11-155
pubmed: 21961763
pmcid: 3191332
Guo Y, Yang X, Wang D, Fan R, Liang Y, Wang R, Xiang H, Liu Y, Liu X. Prevalence of violence to others among individuals with schizophrenia in China: a systematic review and meta-analysis. Front Psychiatry. 2022;13:939329.
doi: 10.3389/fpsyt.2022.939329
pubmed: 35935404
pmcid: 9354073
Whiting D, Gulati G, Geddes JR, Fazel S. Association of Schizophrenia Spectrum Disorders and violence perpetration in adults and adolescents from 15 countries: a systematic review and Meta-analysis. JAMA Psychiatry. 2022;79(2):120–32.
doi: 10.1001/jamapsychiatry.2021.3721
pubmed: 34935869
Deo RC. Machine learning in Medicine. Circulation. 2015;132(20):1920–30.
doi: 10.1161/CIRCULATIONAHA.115.001593
pubmed: 26572668
pmcid: 5831252
Parmigiani G, Barchielli B, Casale S, Mancini T, Ferracuti S. The impact of machine learning in predicting risk of violence: a systematic review. Front Psychiatry. 2022;13:1015914.
doi: 10.3389/fpsyt.2022.1015914
pubmed: 36532168
pmcid: 9751313
Wang KZ, Bani-Fatemi A, Adanty C, Harripaul R, Grifths J, Kolla N, Gerretsen P, Graf A, De Luca V. Prediction of physical violence in schizophrenia with machine learning algorithms. Psychiatry Res. 2020;289:112960.
doi: 10.1016/j.psychres.2020.112960
pubmed: 32361562
Yu T, Zhang X, Liu X, Xu C, Deng C. The prediction and influential factors of violence in male schizophrenia patients with machine learning algorithms. Front Psychiatry. 2022;13:799899.
doi: 10.3389/fpsyt.2022.799899
pubmed: 35360130
pmcid: 8962616
Sonnweber M, Lau S, Kirchebner J. Violent and non-violent ofending in patients with schizophrenia: exploring infuences and diferences via machine learning. Compr Psychiatry. 2021;107:152238.
doi: 10.1016/j.comppsych.2021.152238
pubmed: 33721584
Coccaro EF, Lee R, Gozal D. Elevated plasma oxidative stress markers in individuals with intermittent explosive disorder and correlation with aggression in humans. Biol Psychiatry. 2016;79(2):127–35.
doi: 10.1016/j.biopsych.2014.01.014
pubmed: 24582164
Das S, Deuri SK, Sarmah A, Pathak K, Baruah A, Sengupta S, Mehta S, Avinash PR, Kalita KN, Hazarika J. Aggression as an independent entity even in psychosis- the role of inflammatory cytokines. J Neuroimmunol. 2016;292:45–51.
doi: 10.1016/j.jneuroim.2016.01.012
pubmed: 26943958
Coccaro EF, Lee R, Coussons-Read M. Cerebrospinal fluid inflammatory cytokines and aggression in personality disordered subjects. Int J Neuropsychopharmacol. 2015;18(7):pyv001.
doi: 10.1093/ijnp/pyv001
pubmed: 25650410
pmcid: 4540103
Taşci G, Kaya Ş, Korkmaz S, Atmaca M. Peripheral inflammatory parameters in patients with antisocial personality disorder. Noro Psikiyatr Ars. 2021;59(1):38–43.
pubmed: 35317499
pmcid: 8895802
Fusar-Poli L, Natale A, Amerio A, Cimpoesu P, Grimaldi Filioli P, Aguglia E, Amore M, Serafini G, Aguglia A. Neutrophil-to-Lymphocyte, platelet-to-lymphocyte and monocyte-to-lymphocyte ratio in bipolar disorder. Brain Sci. 2021;11(1):58.
doi: 10.3390/brainsci11010058
pubmed: 33418881
pmcid: 7825034
Dionisie V, Filip GA, Manea MC, Movileanu RC, Moisa E, Manea M, Riga S, Ciobanu AM. Neutrophil-to-lymphocyte ratio, a novel inflammatory marker, as a predictor of bipolar type in depressed patients: a Quest for Biological markers. J Clin Med. 2021;10(9):1924.
doi: 10.3390/jcm10091924
pubmed: 33946871
pmcid: 8125288
Zhu X, Zhou J, Zhu Y, Yan F, Han X, Tan Y, Li R. Neutrophil/lymphocyte, platelet/lymphocyte and monocyte/lymphocyte ratios in schizophrenia. Australas Psychiatry. 2022;30(1):95–9.
doi: 10.1177/10398562211022753
pubmed: 34134532
Zhu X, Li R, Zhu Y, Zhou J, Huang J, Zhou Y, Tong J, Zhang P, Luo X, Chen S, Li Y, Tian B, Tan SP, Wang Z, Han X, Tian L, Li CR, Tan YL. Changes in inflammatory biomarkers in patients with Schizophrenia: a 3-Year retrospective study. Neuropsychiatr Dis Treat. 2023;19:1597–604.
doi: 10.2147/NDT.S411028
pubmed: 37465565
pmcid: 10350427
Mojadadi MS, Mahjour M, Fahimi H, Raoofi A, Shobeiri SS. Relationship between blood-based inflammatory indices and clinical score of schizophrenia patients: a cross-sectional study. Behav Brain Res. 2024;460:114807.
doi: 10.1016/j.bbr.2023.114807
pubmed: 38092259
Kaya S, Taşcı G, Kılıç N, Karadayı H, Özsoy F, Atmaca M. Examination of the relationship between peripheral inflammation markers and impulsivity and aggression in Schizophrenia patients involved and not involved in crime. J Pers Med. 2023;13(3):475.
doi: 10.3390/jpm13030475
pubmed: 36983657
pmcid: 10058134
Kay SR, Wolkenfeld F, Murrill LM. Profiles of aggression among psychiatric patients. I. Nature and prevalence. J Nerv Ment Dis. 1988;176(9):539–46.
doi: 10.1097/00005053-198809000-00007
pubmed: 3418327
Kay SR, Fiszbein A, Opler LA. The positive and negative syndrome scale (PANSS) for schizophrenia. Schizophr Bull. 1987;13(2):261–76.
doi: 10.1093/schbul/13.2.261
pubmed: 3616518
Huang ZH, Wang F, Chen ZL, Xiao YN, Wang QW, Wang SB, He XY, Migliorini C, Harvey C, Hou CL. Risk factors for violent behaviors in patients with schizophrenia: 2-year follow-up study in primary mental health care in China. Front Psychiatry. 2023;13:947987.
doi: 10.3389/fpsyt.2022.947987
pubmed: 36741582
pmcid: 9895824
Okasha TA, Omar AN, Elserafy D, Serry S, Rabie ES. Violence in relation to cognitive deficits and symptom severity in a sample of Egyptian patients with schizophrenia. Int J Soc Psychiatry. 2023;69(3):689–99.
doi: 10.1177/00207640221132706
pubmed: 36331135
Bo S, Abu-Akel A, Kongerslev M, Haahr UH, Simonsen E. Risk factors for violence among patients with schizophrenia. Clin Psychol Rev. 2011;31(5):711–26.
doi: 10.1016/j.cpr.2011.03.002
pubmed: 21497585
Keers R, Ullrich S, Destavola BL, Coid JW. Association of violence with emergence of persecutory delusions in untreated schizophrenia. Am J Psychiatry. 2014;171(3):332–9.
doi: 10.1176/appi.ajp.2013.13010134
pubmed: 24220644
Coid JW, Ullrich S, Kallis C, Keers R, Barker D, Cowden F, Stamps R. The relationship between delusions and violence: findings from the East London first episode psychosis study. JAMA Psychiatry. 2013;70(5):465–71.
doi: 10.1001/jamapsychiatry.2013.12
pubmed: 23467760
Carabellese F, Rocca G, Candelli C, Catanesi R. Mental illness, violence and delusional misidentifications: the role of Capgras’ syndrome in matricide. J Forensic Leg Med. 2014;21:9–13.
doi: 10.1016/j.jflm.2013.10.012
pubmed: 24365679
Drexhage RC, Knijff EM, Padmos RC, Lv H-N, Beumer W, Versnel MA, Drexhage HA. The mononuclear phagocyte system and its cytokine inflammatory networks in schizophrenia and bipolar disorder. Expert Rev Neurother. 2010;10(1):59–76.
doi: 10.1586/ern.09.144
pubmed: 20021321
Castle R, Bushell WC, Mills PJ, Williams MA, Chopra D, Rindfleisch JA. Global correlations between chronic inflammation and violent incidents: potential behavioral consequences of inflammatory illnesses across Socio-demographic levels. Int J Gen Med. 2021;14:6677–91.
doi: 10.2147/IJGM.S324367
pubmed: 34675629
pmcid: 8520436
Gou N, Xiang Y, Zhou J, Zhang S, Zhong S, Lu J, Liang X, Liu J, Wang X. Identifcation of violent patients with schizophrenia using a hybrid machine learning approach at the individual level. Psychiatry Res. 2021;306:114294.
doi: 10.1016/j.psychres.2021.114294
pubmed: 34823086
Yu T, Pei W, Xu C, Zhang X, Deng C. Prediction of violence in male schizophrenia using sMRI, based on machine learning algorithms. BMC Psychiatry. 2022;22(1):676.
doi: 10.1186/s12888-022-04331-1
pubmed: 36320010
pmcid: 9628088
Wei Y, Wang T, Li G, Feng J, Deng L, Xu H, Yin L, Ma J, Chen D, Chen J. Investigation of systemic immune-inflammation index, neutrophil/high-density lipoprotein ratio, lymphocyte/high-density lipoprotein ratio, and monocyte/high-density lipoprotein ratio as indicators of inflammation in patients with schizophrenia and bipolar disorder. Front Psychiatry. 2022;13:941728.
doi: 10.3389/fpsyt.2022.941728
pubmed: 35958647
pmcid: 9360542