Proteomic profiles of cytokines and chemokines in moderate to severe depression: Implications for comorbidities and biomarker discovery

Kathleen T Watson; Jennifer Keller; Caleb M Spiro; Isaac B Satz; Samantha V Goncalves; Heather Pankow; Idit Kosti; Benoit Lehallier; Adolfo Sequeira; William E Bunney; Natalie L Rasgon; Alan F Schatzberg

doi:10.1016/j.bbih.2024.100731

Proteomic profiles of cytokines and chemokines in moderate to severe depression: Implications for comorbidities and biomarker discovery

Brain Behav Immun Health. 2024 Feb 7:36:100731. doi: 10.1016/j.bbih.2024.100731. eCollection 2024 Mar.

Authors

Affiliations

¹ Department of Psychiatry and Behavioral Health, Stanford School of Medicine, Stanford, CA, USA.
² Alkahest Inc, San Carlos, CA, USA.
³ Department of Psychiatry & Human Behavior, University of California, Irvine, CA, USA.
⁴ School of Medicine, Irvine, CA, USA.

Abstract

Objective: 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.

Metods: 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.

Findings: 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.

Conclusion: 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.

Keywords: Biomarkers; Chemokines; Cytokines; Inflammation; Major depressive disorder; Proteomics.