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Identifying Neuro-Inflammatory Biomarkers of Generalized Anxiety Disorder from Lymphocyte Subsets Based on Machine
Jingjing Lu1,2, Weiwei Liang3, Lijun Cui1,2
1Sleep Medical Center of Huzhou Third Municipal Hospital, The Affiliated Hospital of Huzhou University, Huzhou, China.
Introduction:
Activation of the inflammatory response system is involved in the pathogenesis of generalized anxiety disorder (GAD). The purpose of this study was to identify and characterize inflammatory biomarkers in the diagnosis of GAD based on machine learning algorithms.
Methods:
The evaluation of peripheral immune parameters and lymphocyte subsets was performed on patients with GAD. Multivariable linear regression was used to explore the association between lymphocyte subsets and the severity of GAD. Receiver operating characteristic (ROC) analysis was used to determine the predictive value of these immunological parameters for GAD. Machine learning technology was applied to classify the collected data from patients in the GAD and healthy control groups.
Results:
Of the 340 patients enrolled, 171 were GAD patients, and 169 were non-GAD patients as healthy control. The levels of neutrophil, monocytes, and systemic immune-inflammation index (SII) were significantly elevated in GAD patients (p < 0.01), and the count and proportion of immune cells, including CD3+CD4+ T cells, CD3+CD8+ T cells, CD19+ B cells, and CD3-CD16+CD56+ NK cells (p < 0.001), have undergone large changes. The classification analysis conducted by machine learning using a weighted ensemble-L2 algorithm demonstrated an accuracy of 95.00 ± 2.04% in assessing the predictive value of these lymphocyte subsets in GAD. In addition, the feature importance analysis score is 0.255, which was much more predictive of GAD severity than for other lymphocyte subsets.
Conclusion:
In the presented work, we show the level of lymphocyte subsets altered in GAD. Lymphocyte subsets, specifically CD3+CD4+ T %, can serve as neuroinflammatory biomarkers for GAD diagnostics. Furthermore, the application of machine learning offers a highly efficient approach for investigating neuroinflammatory biomarkers and predicting GAD. Our research has provided novel insights into the involvement of cellular immunity in GAD and highlighted the potential predictive value and therapeutic targets of lymphocyte subsets in this disorder.
