Serum Cytokines as Biomarkers for Comorbid Anxiety in Postpartum Depression: A Machine Learning Approach.

Abstract

Background: This study aimed to investigate the serum levels of interleukin 2, interleukin 6 (IL-6), interleukin 10, and tumor necrosis factor-alpha in patients with postpartum depression (PPD) and to explore their potential as biomarkers for PPD and comorbid anxiety using machine learning techniques. Methods: Serum samples were collected from 53 patients diagnosed with PPD and 35 healthy controls. Cytokine levels were measured using a flow cytometer analyzer. Machine learning models, including Multinomial Logistic Regression, Decision Trees, Random Forest, and Support Vector Machines (SVMs), were developed to predict PPD and comorbid anxiety based on cytokine levels. Results: Patients with PPD exhibited significantly elevated serum levels of IL-6 compared to the control group. A positive correlation was found between psychological anxiety scores and IL-6 levels (r = 0.483, P < .001). Machine learning models, particularly the Random Forest and SVMs, demonstrated high accuracy in predicting PPD and comorbid anxiety, with IL-6 being identified as a key predictor. Conclusion: The activation of serum cytokines is evident in PPD patients, with IL-6 potentially serving as an auxiliary biomarker for the diagnosis of PPD and comorbid anxiety. The incorporation of machine learning techniques has enhanced the understanding of the complex relationships between cytokines and PPD, with IL-6 levels showing a correlation to the severity of clinical symptoms.