Watson Kathleen T, Keller Jennifer, Spiro Caleb M, Satz Isaac B, Goncalves Samantha V, Pankow Heather, Kosti Idit, Lehallier Benoit, Sequeira Adolfo, Bunney William E, Rasgon Natalie L, Schatzberg Alan F
Department of Psychiatry and Behavioral Health, Stanford School of Medicine, Stanford, CA, USA.
Alkahest Inc, San Carlos, CA, USA.
Brain Behav Immun Health. 2024 Feb 7;36:100731. doi: 10.1016/j.bbih.2024.100731. eCollection 2024 Mar.
This study assessed the proteomic profiles of cytokines and chemokines in individuals with moderate to severe depression, with or without comorbid medical disorders, compared to healthy controls. Two proteomic multiplex platforms were employed for this purpose.
An immunofluorescent multiplex platform and an aptamer-based method were used to evaluate 32 protein analytes from 153 individuals with moderate to severe major depressive disorder (MDD) and healthy controls (HCs). The study focused on determining the level of agreement between the two platforms and evaluating the ability of individual analytes and principal components (PCs) to differentiate between the MDD and HC groups. Additionally, the study investigated the relationship between PCs consisting of chemokines and cytokines and comorbid inflammatory and cardiometabolic diseases.
Analysis revealed a small or moderate correlation between 47% of the analytes measured by the two platforms. Two proteomic profiles were identified that differentiated individuals with moderate to severe MDD from HCs. High eotaxin, age, BMI, IP-10, or IL-10 characterized profile 1. This profile was associated with several cardiometabolic risk factors, including hypertension, hyperlipidemia, and type 2 diabetes. Profile 2 is characterized by higher age, BMI, interleukins, and a strong negative loading for eotaxin. This profile was associated with inflammation but not cardiometabolic risk factors.
This study provides further evidence that proteomic profiles can be used to identify potential biomarkers and pathways associated with MDD and comorbidities. Our findings suggest that MDD is associated with distinct profiles of proteins that are also associated with cardiometabolic risk factors, inflammation, and obesity. In particular, the chemokines eotaxin and IP-10 appear to play a role in the relationship between MDD and cardiometabolic risk factors. These findings suggest that a focus on the interplay between MDD and comorbidities may be useful in identifying potential targets for intervention and improving overall health outcomes.
本研究评估了中度至重度抑郁症患者（无论有无合并内科疾病）与健康对照者细胞因子和趋化因子的蛋白质组学特征。为此采用了两种蛋白质组多重检测平台。
使用免疫荧光多重检测平台和基于适配体的方法，对153例中度至重度重度抑郁症（MDD）患者和健康对照者（HCs）的32种蛋白质分析物进行评估。该研究重点在于确定两种平台之间的一致性水平，并评估个体分析物和主成分（PCs）区分MDD组和HC组的能力。此外，该研究还调查了由趋化因子和细胞因子组成的主成分与合并的炎症和心脏代谢疾病之间的关系。
分析显示，两种平台检测的分析物中有47%存在小或中度相关性。确定了两种蛋白质组学特征，可区分中度至重度MDD患者与HCs。特征1以高嗜酸性粒细胞趋化因子、年龄、体重指数（BMI）、干扰素γ诱导蛋白10（IP-10）或白细胞介素10（IL-10）为特征。该特征与多种心脏代谢危险因素相关，包括高血压、高脂血症和2型糖尿病。特征2的特点是年龄、BMI、白细胞介素较高，且嗜酸性粒细胞趋化因子负荷为强阴性。该特征与炎症相关，但与心脏代谢危险因素无关。
本研究提供了进一步的证据，表明蛋白质组学特征可用于识别与MDD及其合并症相关的潜在生物标志物和途径。我们的研究结果表明，MDD与不同的蛋白质特征相关，这些特征也与心脏代谢危险因素、炎症和肥胖有关。特别是，趋化因子嗜酸性粒细胞趋化因子和IP-10似乎在MDD与心脏代谢危险因素之间的关系中起作用。这些研究结果表明，关注MDD与合并症之间的相互作用可能有助于识别潜在的干预靶点并改善整体健康结局。
