MicroRNAs signatures in small extracellular vesicles for psychological resilience in young adults using machine learning

Psychological resilience refers to an individual’s capacity to adapt to adverse events. MicroRNAs (miRNAs) play a crucial role in regulating post-transcriptional processes, while small extracellular vesicles (sEVs) act as transport vehicles. This study aimed to employ genome-wide profiling to identify and validate differences in the expression of resilience-associated sEV-miRNAs between low resilience (LR) and high resilience (HR) in young adults. Eighty participants were divided into LR or HR based on the Connor – Davidson Resilience Scale (CD-RISC). The expression levels of the target sEV-miRNAs in LR and HR were compared and analyzed. Expression analyses demonstrated significant differences in let-7b, miR-151b, miR-335, and miR-193a between LR and HR (<i>p</i> &lt; 0.01), with let-7b showing the highest discriminative ability. The AUC values for each sEV-miRNA ranged from 0.74 to 0.94, based on logistic regression and three machine learning models: random forest, support vector machine, and eXtreme gradient boosting. Based on leave-one-out cross-validation in different models, the combined four sEV-miRNAs demonstrated strong performance for detecting LR (AUC = 0.87–0.90). Sex-specific differences were also observed, with female participants showing more pronounced resilience signatures in targeted sEV-miRNAs. These findings suggest that sEV-miRNAs hold potential as biomarkers for psychological resilience in young adults. This study investigates the biological underpinnings of psychological resilience, the capacity of some young adults to adapt more effectively to stress than others. We scanned the entire set of genetic messages to find specific small molecules that regulate gene expression linked to how people handle stress. These specific molecules are packaged in tiny particles called small extracellular vesicles (sEVs), which protect them and help deliver them to other cells. Blood samples were collected from young adults with high or low resilience, based on a validated psychological scale. We then examined the levels of these specific molecules in the tiny particles. Four specific molecules showed significant differences between the groups. These specific molecules could help identify people with low resilience. We also used machine learning models to check and verify whether these specific molecules could distinguish low-resilience individuals. Notably, these specific molecules were more effective in females. The findings suggest that these specific molecules may have the potential to identify stress vulnerability and guide early psychological support strategies.

心理韧性（psychological resilience）指个体适应逆境事件的能力。微小RNA（microRNAs, miRNAs）在调控转录后过程（post-transcriptional processes）中发挥关键作用，而细胞外小囊泡（small extracellular vesicles, sEVs）则作为转运载体。本研究旨在通过全基因组表达谱分析（genome-wide profiling），鉴定并验证年轻成年人中低韧性（low resilience, LR）与高韧性（high resilience, HR）群体间韧性相关sEV-miRNAs的表达差异。研究人员依据康纳-戴维森韧性量表（Connor – Davidson Resilience Scale, CD-RISC）将80名受试者分为低韧性组与高韧性组，并对两组目标sEV-miRNAs的表达水平进行比较分析。表达分析结果显示，let-7b、miR-151b、miR-335及miR-193a在两组间存在显著差异（*p* < 0.01），其中let-7b的鉴别能力（discriminative ability）最强。基于逻辑回归（logistic regression）、随机森林（random forest）、支持向量机（support vector machine）及极限梯度提升（eXtreme gradient boosting）三种机器学习模型，各sEV-miRNA的曲线下面积（Area Under the Curve, AUC）值介于0.74至0.94之间。通过不同模型中的留一交叉验证（leave-one-out cross-validation），联合检测上述4种sEV-miRNAs可有效识别低韧性个体（AUC = 0.87–0.90）。研究还观察到性别特异性差异：女性受试者的目标sEV-miRNAs韧性特征更为显著。本研究结果表明，sEV-miRNAs有望成为年轻成年人心理韧性的生物标志物（biomarkers）。
本研究探讨了心理韧性的生物学基础——即部分年轻成年人比其他人更能有效适应压力的能力。我们对全部遗传信息进行扫描，以寻找与个体应对压力方式相关的、调控基因表达的特定小分子。这些特定小分子被包裹在名为细胞外小囊泡（sEVs）的微小颗粒中，这些颗粒可保护小分子并协助其被转运至其他细胞。研究人员基于经过验证的心理量表，从高韧性或低韧性的年轻成年人中采集血液样本，随后检测了这些微小颗粒中特定小分子的表达水平。4种特定小分子在两组间存在显著差异，可用于识别低韧性人群。我们还通过机器学习模型验证了这些特定小分子能否区分低韧性个体，值得注意的是，这类分子在女性群体中表现更为出色。本研究结果提示，这些特定小分子或可用于识别压力易感性人群，并为早期心理干预策略提供指导。