PERIPHERAL BLOOD CELLS UNVEIL NEURAL AND SEX-RELATED SUBTYPES OF DEPRESSION: AN UNSUPERVISED MACHINE LEARNING APPROACH
Authors: Colombo *Federica , Manfredi Elena , Aggio Veronica , Lorenzi Cristina , Vai Benedetta , Poletti Sara , Benedetti Francesco 
Published: February 2025

Abstract

Background
Heterogeneity in depression results from interactions of multiple biological and environmental factors. For instance, exposure to early-adverse events amplifies the cross-talk between inflammation and brain activity, resulting in depressive symptomatology [1]. This brain-immune relationship is particularly crucial for females who are more vulnerable to inflammation and its mood effects compared to males [2]. However, only 30% of patients exhibit an abnormal immune profile, suggesting that inflamed depression might represent a distinct subtype [3]. Data-driven approaches are promising in yielding novel insights into depression neurobiology while disentangling heterogeneity [4].

Aims & Objectives
By applying an unsupervised machine learning approach, we aim to unveil immunophenotypes of depressed patients based on immune cell counts, also investigating sex, childhood trauma, and white matter differences.

Methods
Peripheral blood mononuclear cells (PBMC) from 145 depressed patients (92 Major Depressive Disorder [MDD], 53 Bipolar Disorder [BD]) were analysed using FlowJo v.10.8.1 (FlowJo LLC, Ashland, OR). An immune profile was generated on PBMC using a multiparametric flow cytometry. A 28-color flow cytometry panel was used to characterize lymphocyte phenotype and function. In a subsample of 108 patients (67 MDD, 41 BD), extracted tract-based fractional anisotropy and diffusivity indexes were computed from diffusion tensor images (TBSS, FSL). Exposure to childhood trauma was assessed through the Childhood Trauma Questionnaire (CTQ) [5], dichotomizing domains’ scores according to standard cut-offs [6]. A stability-based relative clustering validation approach [7] was applied to immune cell populations, considering K-means as clustering algorithm and support vector machine as classifier. Hyperparameters tuning was performed in a 2x10 repeated cross-validation, iterating over a number of clusters from 2 to 5. The solution that minimised the normalized stability (i.e., prediction error) were chosen as the best one. Statistical significance was assessed by 10,000 simulations from a single null Gaussian distribution. Differences in diagnosis, sex, extracted white matter diffusivity indices, and CTQ domains were assessed through chi-square and Mann-Whitney U tests.

Results
The unsupervised pipeline identified 2 clusters with a normalized stability of 0.28 (p&lt;0.0001). While one cluster (N=53) showed a higher proportion of MDD patients (p=0.008) with higher i) lymphocytes; ii) CD4+ and CD8+CD69+; iii) NK; iv) CD4+CD25highFoxP3+CD39+; and v) CM, EM and TEMRA CD8+Perf-Grz+ cells count (pFDR&lt;0.05), the other one (N=92) was characterized by higher F:M ratio (p=0.064) and higher i) CD4+CD25+; ii) CD4+CD25highFoxP3-CD39+; and iii) TEMRA, CM, EM CD8+Perf+GrzB- cells count (pFDR&lt;0.05). Higher axial, radial, and mean diffusivity values in the genu of the corpus callosum and left cingulum were found in the first cluster (pFDR&lt;0.05).

Discussion & Conclusions
Our results provided evidence of an immune cells-based stratification of depressed patients, suggesting the existence of 2 inflamed subgroups differentiated by distinct immune cell profiles and structural connectivity patterns. These findings foster our understanding of the neurobiological complexity of depression, which could have important implications in tailoring personalized treatments targeting the individual immune-biological profile.

References
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