Correlation Between Psychological Resilience and Genetic, Inflammatory Indicators

18-24 years old is an important stage of psychological development and personality shaping of young people. The higher cortex of the brain, which is responsible for emotional regulation and environmental coping, enters a critical period of neuroplasticity development. Its psychological development is characterized by transition, closure and turbulence, etc. When facing the stressful environment such as college entrance, employment and rapid social development, depression, anxiety and other negative emotional experiences may occur. All kinds of psychological problems. According to the critical period theory of emotional development, emotional states and stress coping are regulated by the activities of the brain's higher cortex. At the same time, the occurrence of mental disorders such as adaptation disorders is also influenced by genetics, and people with high-risk genes are more likely to have disorders.

The purpose of this study is to focus on the evaluation and analysis of the influencing factors of the adaptive ability of young people, explore the impact of the interaction of environment and gene on the psychological adaptive ability of young people, incorporate the prediction model of the dynamic change of adaptive ability, build a standardized norm of young people's adaptive ability, and form a grading reference standard system.

Research contents: 1) According to the cognitive theory system of adaptive ability and the individual prediction model of adaptive ability, considering the environmental characteristics that affect the adaptive ability of young people, the evaluation items with high predictive power of the model are screened. 2) Test the adaptive ability evaluation system in the research objects, verify the reliability, validity and extensibility of the task, and verify the measurement reliability and ecological validity of the paradigm tool. 3) Representative populations with high and low adaptive ability were selected to conduct multi-dimensional data collection on genetics and brain nerve, study the genetic risk and risk gene loci of adaptive ability, analyze the brain image features corresponding to adaptive ability, and study the neural mechanism of adaptive ability.

This study was designed as a cross-sectional study, with young people aged 18-22 as the research objects. The inclusion criteria included 18-22 years old, high school graduation or above, consent to blood sample collection, and signed informed consent. Exclusion criteria include not agreeing to take blood samples, not signing informed consent forms, and having a major medical condition such as a malignant tumor. It is planned to recruit 400 subjects from January 1, 2024 to March 31, 2024, and collect basic information data, adaptive ability assessment data, genetic testing data, brain image scanning data, and inflammatory indicators data.

The subjects were divided into very low adaptive group, low adaptive group, high adaptive group and very high adaptive group according to the quartile of adaptive ability score. A variety of predictive modeling techniques (such as Logistic discriminant model, neural network model, decision tree, random forest model and support vector machine, etc.) are used to construct a dynamic change prediction model adapted to outcome risk. The internal validation of the model was carried out by using the ten-fold cross-validation method. The original data were divided into 10 groups, among which 9 groups were taken as the training set and 1 group was taken as the test set for cross-validation to ensure the stability of the model. ROC curve was drawn to evaluate the differentiation of evaluation models. The area under ROC curve synthesizes two indexes of sensitivity and specificity, and considers every possible threshold value, which can evaluate the diagnostic value of the model more objectively and accurately. By calculating the area AUC under ROC curve as a quantitative analysis of the diagnostic value of the model, the closer the AUC is to 1, the better the diagnostic effect is. The Hosmer-Lemeshow goodness of fit test was used to judge the degree of consistency between the predicted risk and the actual risk, so as to evaluate the calibration degree of the model.