Department of Pharmacology, Jessenius Faculty of Medicine in Martin, Comenius University in Bratislava, Martin, Slovak Republic.
Physiol Res. 2020 Dec 31;69(Suppl 3):S523-S532. doi: 10.33549/physiolres.934590.
Mental disorders affect 10-20 % of the young population in the world. Major depressive disorder (MDD) is a common mental disease with a multifactorial and not clearly explained pathophysiology. Many cases remain undetected and untreated, which influences patients' physical and mental health and their quality of life also in adulthood. The aim of our pilot study was to assess the prediction value of selected potential biomarkers, including blood cell counts, blood cell ratios, and parameters like peroxiredoxin 1 (PRDX1), tenascin C (TNC) and type IV collagen (COL4) between depressive pediatric patients and healthy peers and to evaluate a short effect of antidepressant treatment. In this study, 27 young depressive patients and 26 non-depressed age-matched controls were included. Blood analyses and immunological assays using commercial kits were performed. Platelet count was the only blood parameter for which the case/control status was statistically significant (p=0.01) in a regression model controlling for the age and gender differences. The results from ELISA analyses showed that the case/control status is a significant predictor of the parameters PRDX1 (p=0.05) and COL4 (p=0.009) in respective regression model considering the age and gender differences between MDD patients and controls. A major finding of this study is that values of platelet count, monocyte to lymphocyte ratio, white blood cell, and monocyte counts were assessed by the Random Forest machine learning algorithm as relevant predictors for discrimination between MDD patients and healthy controls with a power of prediction AUC=0.749.
精神障碍影响全球 10-20%的年轻人。重度抑郁症(MDD)是一种常见的精神疾病,其病理生理学具有多因素且尚未明确解释的特点。许多病例未被发现和治疗,这会影响患者的身心健康和成年后的生活质量。我们的初步研究旨在评估包括血细胞计数、血细胞比、过氧化物酶 1(PRDX1)、-tenascin C(TNC)和 IV 型胶原(COL4)在内的选定潜在生物标志物对抑郁儿科患者和健康同龄人的预测价值,并评估抗抑郁治疗的短期效果。在这项研究中,我们纳入了 27 名年轻的抑郁患者和 26 名年龄匹配的非抑郁对照组。使用商业试剂盒进行血液分析和免疫测定。血小板计数是唯一在控制年龄和性别差异的回归模型中具有统计学意义的血液参数(p=0.01)。ELISA 分析结果表明,在考虑 MDD 患者和对照组之间的年龄和性别差异的情况下,病例/对照组的状态是 PRDX1(p=0.05)和 COL4(p=0.009)参数的显著预测因子。本研究的一个主要发现是,血小板计数、单核细胞与淋巴细胞比值、白细胞和单核细胞计数的值通过随机森林机器学习算法评估为区分 MDD 患者和健康对照者的相关预测因子,预测 AUC=0.749。