Khoodoruth Mohamed Adil Shah, Hussain Tarteel, Ouanes Sami, Chut-Kai Khoodoruth Nuzhah Widaad, Hmissi Adel, Lachica Samuel L, Bankur Mustafa Nissar, Khan Abdul Waheed, Makki Mohamad Samir, Khan Yasser Saeed, Currie James, Alabdullah Majid, Mohammad Farhan
Department of Psychiatry, Hamad Medical Corporation, Qatar; Division of Genomics and Precision Medicine, College of Health and Life Sciences, Hamad Bin Khalifa University, Qatar; Division of Child & Adolescent Psychiatry, Department of Psychiatry, Schulich School of Medicine and Dentistry, Western University, Canada.
Department of Psychiatry, Hamad Medical Corporation, Qatar.
Psychiatry Res. 2025 Feb;344:116307. doi: 10.1016/j.psychres.2024.116307. Epub 2024 Nov 30.
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.
精神分裂症在诊断和治疗方面存在重大挑战,尤其是在区分难治性精神分裂症(TRS)和非难治性精神分裂症(NTRS)方面。这项横断面研究分析了常规实验室参数作为潜在生物标志物,以区分卡塔尔队列中的TRS、NTRS和健康个体。该研究纳入了31例被诊断为精神分裂症的TRS患者和38例NTRS患者,以及来自卡塔尔生物银行的30名对照受试者。关键测量指标包括全血细胞计数、血脂谱、糖化血红蛋白(HbA1c)和铁蛋白水平。我们的研究结果表明,与对照组相比,两个患者组的体重指数(BMI)和甘油三酯(TG)水平均升高。NTRS组的HbA1c水平也更高。注意到炎症标志物存在差异,NTRS组的血小板/淋巴细胞比率(PLR)更高。多变量分析突出了各组之间血小板计数、平均血小板体积(MPV)、TG、HbA1c、BMI、中性粒细胞/淋巴细胞比率(NLR)、单核细胞/淋巴细胞比率(MLR)和铁蛋白的显著差异。线性回归分析显示,MLR和氯氮平治疗与精神分裂症症状的严重程度显著相关。随机森林模型是一种监督式机器学习算法,能够有效地区分病例与对照以及TRS与NTRS,准确率分别为86.87%和88.41%。然而,去除阳性和阴性症状量表(PANSS)评分显著降低了模型的诊断有效性。这些结果表明,易于获取的外周实验室参数可作为精神分裂症的有用生物标志物,可能有助于TRS的早期识别,加强个性化治疗策略,并推动精准精神病学的发展。未来有必要进行纵向研究以证实这些发现,并进一步探索炎症在精神分裂症病理生理学和治疗反应中的作用。