Peripheral inflammatory and metabolic markers as potential biomarkers in treatment-resistant schizophrenia: Insights from a Qatari Cohort.

IF 4.2 2区 医学 Q1 PSYCHIATRY Psychiatry Research Pub Date : 2025-02-01 Epub Date: 2024-11-30 DOI:10.1016/j.psychres.2024.116307
Mohamed Adil Shah Khoodoruth, Tarteel Hussain, Sami Ouanes, Nuzhah Widaad Chut-Kai Khoodoruth, Adel Hmissi, Samuel L Lachica, Mustafa Nissar Bankur, Abdul Waheed Khan, Mohamad Samir Makki, Yasser Saeed Khan, James Currie, Majid Alabdullah, Farhan Mohammad
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Abstract

Schizophrenia presents significant diagnostic and treatment challenges, particularly in distinguishing between treatment-resistant (TRS) and non-treatment-resistant schizophrenia (NTRS). This cross-sectional study analyzed routine laboratory parameters as potential biomarkers to differentiate TRS, NTRS, and healthy individuals within a Qatari cohort. The study included 31 TRS and 38 NTRS patients diagnosed with schizophrenia, alongside 30 control subjects from the Qatar Biobank. Key measurements included complete blood count, lipid panel, HbA1c, and ferritin levels. Our findings indicated elevated body mass index (BMI) and triglyceride (TG) levels in both patient groups compared to controls. The NTRS group also showed higher HbA1c levels. Variations in inflammatory markers were noted, with the NTRS group exhibiting a higher platelet/lymphocyte ratio (PLR). Multivariate analysis highlighted significant differences in platelet count, mean platelet volume (MPV), TG, HbA1c, BMI, neutrophil/lymphocyte ratio (NLR), monocyte/lymphocyte ratio (MLR), and ferritin among the groups. Linear regression analysis revealed that MLR and clozapine treatment were significantly correlated with the severity of schizophrenia symptoms. The Random Forest model, a supervised machine learning algorithm, efficiently differentiated between cases and controls and between TRS and NTRS, with accuracies of 86.87 % and 88.41 %, respectively. However, removing PANSS scores notably decreased the model's diagnostic effectiveness. These results suggest that accessible peripheral laboratory parameters can serve as useful biomarkers for schizophrenia, potentially aiding in the early identification of TRS, enhancing personalized treatment strategies, and contributing to precision psychiatry. Future longitudinal studies are necessary to confirm these findings and further explore the role of inflammation in schizophrenia pathophysiology and treatment response.

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外周炎症和代谢标志物作为治疗难治性精神分裂症的潜在生物标志物:来自卡塔尔队列的见解。
精神分裂症提出了重大的诊断和治疗挑战,特别是在区分治疗耐药(TRS)和非治疗耐药精神分裂症(NTRS)方面。本横断面研究分析了卡塔尔队列中常规实验室参数作为区分TRS、NTRS和健康个体的潜在生物标志物。该研究包括31名被诊断为精神分裂症的TRS和38名NTRS患者,以及来自卡塔尔生物银行的30名对照受试者。主要测量指标包括全血细胞计数、血脂、糖化血红蛋白和铁蛋白水平。我们的研究结果表明,与对照组相比,两组患者的体重指数(BMI)和甘油三酯(TG)水平均有所升高。NTRS组的HbA1c水平也较高。注意到炎症标志物的变化,NTRS组表现出更高的血小板/淋巴细胞比率(PLR)。多因素分析显示各组间血小板计数、平均血小板体积(MPV)、TG、HbA1c、BMI、中性粒细胞/淋巴细胞比值(NLR)、单核细胞/淋巴细胞比值(MLR)和铁蛋白均存在显著差异。线性回归分析显示,MLR和氯氮平治疗与精神分裂症症状严重程度显著相关。随机森林模型是一种有监督的机器学习算法,可以有效地区分病例和对照,TRS和NTRS,准确率分别为86.87%和88.41%。然而,去除PANSS评分显著降低了模型的诊断有效性。这些结果表明,可获得的外围实验室参数可以作为精神分裂症的有用生物标志物,可能有助于早期识别TRS,增强个性化治疗策略,并有助于精确精神病学。未来有必要进行纵向研究来证实这些发现,并进一步探索炎症在精神分裂症病理生理和治疗反应中的作用。
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来源期刊
Psychiatry Research
Psychiatry Research 医学-精神病学
CiteScore
17.40
自引率
1.80%
发文量
527
审稿时长
57 days
期刊介绍: Psychiatry Research offers swift publication of comprehensive research reports and reviews within the field of psychiatry. The scope of the journal encompasses: Biochemical, physiological, neuroanatomic, genetic, neurocognitive, and psychosocial determinants of psychiatric disorders. Diagnostic assessments of psychiatric disorders. Evaluations that pursue hypotheses about the cause or causes of psychiatric diseases. Evaluations of pharmacologic and non-pharmacologic psychiatric treatments. Basic neuroscience studies related to animal or neurochemical models for psychiatric disorders. Methodological advances, such as instrumentation, clinical scales, and assays directly applicable to psychiatric research.
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