Extracellular vesicles (EVs) transport biological content between cells to mediate physiological processes. The association between EVs and resilience, the ability to cope with stress, is unknown. Using unbiased machine learning approaches, we aimed to identify a biological profile of resilience. Twenty servicemen (27.8 ± 5.9 years) completed the Connor Davidson Resilience (CD-RISC) questionnaire and were exposed to daily physical and cognitive exertion with 48-hr sleep and caloric restriction. Blood samples from baseline and the second day of stress were analyzed for neuroendocrine biomarkers impacted by military stress. EVs were isolated from plasma and stained with antibodies associated with exosomes (CD63), microvesicles (VAMP3), and apoptotic bodies (THSD1). Individuals were separated into high (n = 10, CD-RISC > 90) and low (n = 10, CD-RISC < 79) resilience. EV features were stratified by size, then down-selected using regression trees and compared between groups. Diagnostic accuracy was assessed using receiver operating characteristic curves. Compared to low resilience, high resilience demonstrated a greater increase in variability of THSD1 local bright spot intensities among large-sized EVs in response to stress (p = 0.002, Hedges' g = 1.59). Among medium-sized EVs, high resilience exhibited a greater decrease in side scatter intensity (p = 0.014, Hedges' g = 1.17). Both features demonstrated high to moderate diagnostic accuracy for high resilience (AUC = 0.90 and 0.79). In contrast, neuroendocrine biomarker concentrations were similar between groups. The increase in variability among THSD1 + EVs in high, but not low, resilient individuals following stress may suggest high resilience is accompanied by stress-triggered apoptotic adaptations to the environment that are not detected in neuroendocrine biomarkers.
Keywords: decision trees; extracellular vesicles; machine learning; occupational stress; resilience.
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