An analysis of the mediating factors of suicide risk in adolescents with depressive disorder based on machine learning

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Therefore, the purpose of this study was to investigate the role NSSI plays in suicide risk in patients with depressive disorder. Methods: A questionnaire survey was compiled using the HAMD-24, the Baker Suicide Risk Scale, and the NSSI Scale. The survey was administered to 113 adolescent patients with depressive disorder. The correlation between NSSI, depression, and suicide risk was analyzed using the gradient-lifting regression model. Results: NSSI had the highest incidence among high school students, regardless of gender. Furthermore, the incidence of NSSI was high among depressive patients with a history of smoking and drinking. NSSI partially mediated the risk of suicide in depressive patients. Conclusions: The findings show that NSSI increases the risk of suicide in patients with depressive disorder. Therefore, paying attention to NSSI in patients with depressive disorder and taking early mediating measures to minimize the effect will be conducive to reducing the risk of suicide. depressive disorder NSSI suicide risk mediating factors machine learning Figures Figure 1 Figure 2 Figure 3 INTRODUCTION Adolescent suicide is a serious public health concern. In recent years, with the continuous development of the social economy, the incidence of adolescent suicide has increased. The rate of youth suicide has gradually increased over the past 15 years, especially during the COVID-19 pandemic [ 1 ] . The motivation-volitional model of suicide holds that when an individual experiences persistent feelings of personal failure and other negative emotions, they find themselves trapped in an environmental and emotional state that leads to suicidal behavior [ 2 ] . It is well known that suicide is not an independent event but rather an endpoint that progresses from suicidal ideation to suicidal behavior [ 3 ] . Suicide risk, therefore, includes suicidal ideation and behavior. Suicide risk assessment is an important means of detecting and preventing suicidal behavior [ 4 ] . It is critical to identify suicide risk factors that can be targeted for treatment through prevention and early intervention programs [ 5 ] . Non-suicidal self-injury (NSSI) refers to suicidal behaviors that repeatedly, intentionally, and directly cause mild to moderate damage to tissue without the individual having suicidal motivation. This behavior is generally combated by society [ 6 ] . NSSI is a common problem among adolescents. The incidence of NSSI among adolescents worldwide ranges from 13.4–49.5% [7] . Previous literature found that the lifetime prevalence of NSSI was 24.7% among Chinese students, 32.8% among high school students, and 21.2% among college students [ 8 ] . This means that one in four Chinese students have experienced NSSI. Repeated self-harm is also a noteworthy risk factor for suicide among adolescents [ 9 ] . NSSI is likely to eventually lead to suicidal behavior. Therefore, NSSI is a predictor of suicide among adolescents [ 10 ] . Research has suggested that NSSI behavior may be a mode of emotional regulation in individuals with depressive disorder [ 11 ] Individuals use NSSI to release emotions or relieve distress pathways that influence suicide risk [ 12 ] . It may help people with depressive disorder avoid feelings of emptiness and loneliness, and avoid recalling sad events thereby reducing feelings of grief and loss. Consequently, NSSI is a coping mechanism for people with depression to alleviate negative thoughts and feelings. Domestic and foreign studies have found that patients with depressive disorder, who deliberately engage in negative behaviors such as self-harm and self-injury to deal with unpleasant emotions, are more prone to active or passive suicide [ 9 , 13 ] .However, the role of NSSI in the suicide process of patients with depressive disorder remains controversial [ 14 ] . Machine learning belongs to the field of artificial intelligence. It can select the most suitable algorithm from large and complex data. Machine learning algorithms have the unique advantage of automatically ranking the importance of each variable and integrating important variables into the final model. It can also detect and process nonlinearities and interactions between variables. Machine learning algorithms have been extensively used as efficient tools in clinical practice. However, few studies have investigated the mediating role of NSSI by combining machine learning with conventional clinical data [ 15 ] . Gradient Boosting Regressor (GBR) is a machine learning algorithm that iteratively trains fragile learners and combines their predictions to form a strong learner. In each iteration, GBR calculates the residuals for each sample and trains a new weak learner to minimize these residuals as much as possible. Finally, the predictions of all the weak learners were added to arrive at a final prediction [ 16 ] . This result indicates a higher prediction accuracy. Currently, there are no applications for predicting suicide risk. This study explored the mediating role of NSSI in suicide risk in patients with depressive disorder, based on a gradient lifting regression algorithm. MATERIALS AND METHODS Participant recruitment procedure All the objects used in this study were derived from the study Application of Machine Learning to Assist the Diagnosis of Depression Disorder on Dynamic Facial Features , which included 113 patients who visited the psychological outpatient department of Changzhou Second People's Hospital from January 2022 to December 2023. Informed consent was obtained from all patients or their guardians. This study was approved by the Ethics Committee of Changzhou No. 2 People's Hospital (2022YLJSA072). The inclusion criteria were as follows: (1) individuals aged ≥ 14 and ≤ 24 years; (2) diagnostic criteria of the DSM-5 for depressive disorder; (3) a HAMD score of 20 or above; and (4) first visit to the cardiology department. The exclusion criteria were as follows: (1) patients who could not cooperate with the information collection; (3) patients with mania, schizophrenia, and other mental illnesses; and (4) patients with connoisseur trauma or serious neurological disease history. Diagnostic criteria for NSSI and depressive disorder met the DSM-5 criteria [ 17 ] . An associate chief of neurology and an attending psychiatrist determined the inclusion and exclusion criteria. Research Methods Clinical characteristics: Demographic information collected included age, gender, and educational level. All data were registered, checked, and verified by two professionally trained graduate students. Assessment of the HAMD-24 Scale [ 18 ] : The HAMD-24 scale was administered and scored independently by two trained raters. Most items were scored on a 5-point scale (0–4), while a small number were scored on a 3-point scale (0–2). Items 8, 9, and 11 of the scale were scored based on observation of the patient. The remaining items were scored according to the patients' oral accounts. Item 1 requires a combination of the two. All interviewers completed the HAMD-24 consistency training. The intragroup correlation coefficients were all greater than 0.8. Zung Depression Self-Assessment [ 19 ] : The SDS consists of 20 items with an original score of 20–80. A self-assessment was conducted according to the patients' comprehensive understanding of the language of the guidelines. The standard score is an integral part of the original score multiplied by 1.25. Suicide Risk Scale [ 20 ] : The Beck Suicide Ideation Scale (Chinese version) was used to assess the most depressed or suicidal statuses of the previous week as well as in the past. Nineteen items were classified as suicidal ideation or tendencies. The higher the score, the higher the suicide risk. NSSI Scale: The NSSI Scale [ 21 ] was used in a large national sample NSSI survey and has good reliability and validity [ 22 ] . The scale comprises 12 items in two dimensions: NSSI with no obvious tissue injury and NSSI with tissue injury. Participants were asked to answer the following question, "In the past year, have you intentionally harmed yourself in any of the following ways without suicidal intent?" When participants answered "never," this was determined to be "none." And "yes" was determined when the participant answered "1" or "more than once". Statistical Analysis Data analysis was conducted using the SPSS 22.0 statistical software. Descriptive statistics were used to express the data as mean ± standard deviation (x̄±s). The t-test was used for normally distributed data and the Chi-square test was used for counting data. The mediating analysis was completed in three steps: the first step examined the correlation between the depression score and suicide risk, the second step examined the correlation between the depression score and non-suicidal NSSI, and the third step examined the association between the depression score and non-suicidal suicide with suicide risk(Fig. 1 ). RESULTS Baseline Characteristics of Participants A total of 113 patients with depressive disorder aged 14–24 years were included in this study, of which 39 were men and 74 women. The sample consisted of 17 middle school students, 32 high school students, and 64 college students. The incidence of non-suicidal self-injury was 33.33% for the males and 35.14% for the females. High school students had the highest incidence (62.50%), which gradually decreased in the college student group (18.75%). The incidence was higher in patients with depressive disorder who had a history of smoking or drinking (see Table 1 ). Table 1 Demographic characteristics of the study subjects variable NO(n = 74) NSSI(n = 39) T或X 2 p Age 21.04 ± 2.74 18.85 ± 2.67 5.133 0.000 Gender 0.140 0.708 Male 26 13 Female 48 26 Education 30.121 0.000 Junior high 10 7 High school (Vocational high school) 12 20 University (junior college) 52 12 Smoking 9 14 13.951 0.0002 Drinking 9 16 20.976 0.000 Related influencing factors of suicide risk The suicide risk score was positively correlated with the HAMD and self-rating depression scores. The NSSI score was also positively correlated with the HAMD and self-rated depression scores. There was a significant consistency between the HAMD and depression scale self-rating scores (see Table 2 ). Table 2 Correlation analysis of suicide risk Variable Suicide risk HAMD SDS NSSI Suicide risk 1.000 - - - HAMD 0.639 ✱✱ 1.000 - - SDS 0.671 ✱✱ 0.857 ✱✱ 1.000 - NSSI 0.849 ✱✱ 0.524 ✱✱ 0.536 ✱✱ 1.000 ✱✱ : p < 0.001 Gradient Boosting Regressor results The training and test sets were allocated in a 2:1 ratio, with an R2 of 0.980 for training set 2 and 0.717 for the test set. All indicators of the gradient lifting regression model were good (see Table 3 ), indicating that the model had an excellent fitting effect. There was no heteroscedasticity or missing model, and the errors followed a normal distribution (see Fig. 2 and Fig. 3 ). Table 3 Gradient Boosting Regressor Results Model MAE MSE RMSE R2 RMSLE MAPE Gradient Boosting Regressor 4.423 32.992 5.744 0.717 0.979 1.050 The mediating role of NSSI in the self-risk of patients with depressive disorder HAMD and the self-rated depression scores directly affected the suicide risk score. HAMD and the self-rated depression scores indirectly affected NSSI. The HAMD and self-rated depression scores partially influenced the suicide risk score through NSSI (see Table 4 ). Table 4 Results of direct, indirect, and total utility effects on mediators Effects B Standard Error Beta T p Direct impactions HAMD-Suicide risk 0.532 0.048 0.639 11.060 0.000 SDS-Suicide risk 0.346 0.028 0.677 12.247 0.000 Indirect impactions HAMD-NSSI 0.380 0.047 0.521 8.111 0.000 SDS-NSSI 0.256 0.028 0.571 9.264 0.000 Overall impactions HAMD-Suicide risk 0.213 0.032 0.256 6.554 0.000 NSSI-Suicide risk 0.840 0.044 0.737 18.911 0.000 SDS-Suicide risk 0.136 0.021 0.267 6.597 0.000 NSSI-Suicide risk 0.817 0.046 0.718 17.712 0.000 DISCUSSION In this study, non-suicidal self-injury had the highest incidence among high school students, regardless of sex, with a high incidence among depressive disorder patients with a history of smoking and drinking. NSSI partially mediated the risk of suicide in depressed patients. Epidemiological studies have shown that non-suicidal self-injury is a serious problem in adolescence, with a lifetime prevalence of 17–60%, with a peak in mid-adolescence (age 15–16) and a gradual decline in late adolescence (age 18 years). Adolescence is the most prevalent stage of NSSI, and the age distribution of NSSI shows an inverted V-shaped trend, with the highest reported rate of NSSI in the 15–16 age group [ 23 , 24 ] . One study investigated 1283 junior high school students in Wuhan City and found that the average age of first self-injury in students with NSSI was 12.35 ± 3.12 years, 57% of them were aged 12–15 years old. The incidence of NSSI gradually decreased with the increase of age. It was also believed that NSSI behavior would naturally ease with age in mature patients, and only adolescents with obvious emotional problems would require treatment. In this study, the incidence of NSSI was highest in high school or secondary school adolescent students (15–18 years old) and gradually decreased during the college years (after 19 years old). This problem is influenced by various factors, including social predisposition, interpersonal relationships, coping with stressful events, neurobiological characteristics, disorders of responsive regulation in childhood, and adverse experiences. Although there is a significant decline or cessation of non-suicidal self-injury behavior between late adolescence and early adulthood, adolescents who repeatedly engage in non-suicidal self-injury behavior are at an increased risk of long-term mental health problems, suicide, and risky behaviors. In terms of psychosocial factors, adolescence, a period of continuous brain development, is associated with increased impulse control and emotional overreactions. They are more likely to be influenced and instigated by their surroundings. Foreign research shows that the searches for non-suicidal self-injury behaviors on websites exceeded 40 million times in one year. Videos and photos of this behavior were viewed over one million times, which is significantly correlated with the annual increase in non-suicidal suicidal behaviors. If an individual’s relatives, classmates, or friends have carried out NSSI, the risk of the occurrence of the same behavior will greatly increase for such an individual, which may also be related to subcultural factors [ 25 ] . At present, it is difficult to determine whether sex differences influence the detection rate of NSSI behaviors at home and abroad. For example, Chinese researchers have found that the incidence of NSSI in males is higher than that in females. Huang et al. showed that the detection rates of NSSI in males and females were 23.5% and 19.6%, respectively [ 26 ] . Furthermore, a meta-analysis with a larger sample in China showed that male students were more likely to experience NSSI than female students. In contrast, other studies have found that the detection rate of NSSI is higher in females than in males [ 27 ] . Moreover, the results of other studies showed no statistically significant difference between men and women [ 9 , 28 ] . This finding is consistent with the results of the present study. Currently, there is no consensus on the effect of sex on the occurrence of NSSI. However, the age of the participants surveyed was related to NSSI. The incidence of NSSI was higher among women in middle and high school, whereas it was more common among men in college [ 9 , 29 ] . According to a survey conducted by Hasking on adolescents aged 12–18 years, there was no difference in gender NSSI behavior. However, when surveyed again 11.7 months later, they found that the detection rate in females was significantly higher than that in males [ 30 ] . Smoking and drinking, which are bad habits of adolescents, were more likely to occur in NSSI patients. Domestic and foreign studies have found that adolescents with NSSI have bad habits of smoking or drinking [ 31 ] . Drinking and smoking are common among adolescents because of their self-control difficulties [ 32 , 33 ] . In 2014, the U.S. Health Service reported that more than 600,000 middle school students and 3 million high school students smoked [ 34 ] . Moreover, a previous study reported that the proportions of former and current smokers in China were 33.83% and 7.93%, respectively [ 35 ] . Similarly, alcohol, a typical substance, is used by teenagers worldwide, and China is no exception. Approximately one-quarter of the teens reported drinking alcohol in the last 30 days of this study [ 36 ] . There is an established correlation between risky drinking and the manifestation of NSSI [ 37 ] . In China, the incidence of depression among young adults is increasing. This situation has become more severe during the COVID-19 pandemic [ 38 , 39 ] . Similarly, a survey conducted in the Yellowstone area of America found that the year 2021, the year of the COVID-19 epidemic, saw a marked increase in the incidence of depression among adolescents [ 40 ] . Suicide is a fatal outcome for people with depression and depression has worsened adolescent mental health and increased suicide rates globally since the pandemic [ 41 ] . A large case-control study in China confirmed that depression, particularly depression severity scores in the two weeks before death, are important predictors of suicide [ 42 ] . According to the interpersonal theory of suicide, suicidal intention and capacity are preconditions of suicide [ 43 ] . Suicidal intention comes from self-guilt and self-belonging frustration, which are clinical characteristics of patients with depression. Reducing self-guilt and self-belonging frustration in patients with depression can reduce the occurrence of suicidal behaviors [ 44 ] . The incidence of NSSI is the highest in adolescents with depression. The estimated incidence of NSSI among those diagnosed with depression was 62.6% (56.2–67.9%) [ 45 – 49 ] . In this study, the incidence of depressive disorder was 34.6%. This is lower than that in previous studies [ 45 – 49 ] , considering that some adolescents may be reluctant to report self-injury behavior in face-to-face scale assessments or there may be recall bias. Depressive mood is an important predictor of future NSSI development [ 45 ] . According to the theoretical model of NSSI, mood fluctuations are the initiation and recurrence factors of NSSI, particularly negative mood, which is a prerequisite for the occurrence of NSSI. Some self-reported studies have shown that individuals' depressive mood increases before NSSI, while their depressive mood is relieved, and new feelings are obtained after NSSI is concluded [ 50 ] . In addition, NSSI may be a predictor of new-onset depression in adolescents [ 51 ] . Depressive mood plays an important role in the occurrence and recurrence of NSSI. In this study, NSSI partially mediated the risk of suicide among patients with depressive disorder. This is consistent with the results of previous studies [ 52 ] . NSSI is not only directly related to depression severity scores, but also plays a partial mediating role in the regulation of suicide risk in patients with depressive disorder. Patients with high depression scores have a limited ability to withstand external stimuli or problem-solving coping strategies; therefore, depressed patients desire to quickly release negative emotions through NSSI negative coping strategies [ 53 ] . NSSI only satisfies depressed individuals for temporary and urgent self-regulation. According to the reward mechanism (acquired theory), a depressed person repeatedly uses this negative approach to achieve temporary satisfaction [ 54 ] . If the desired effect is not achieved, the depressed person will resort to more serious self-injury to achieve the desired effect and even commit suicide [ 55 ] . In addition, NSSI may aggravate depressed patients' negative self-perceptions such as low self-esteem and self-blame, which further increase the probability of suicide attempts [ 56 ] . This may explain why NSSI is an important predictor of suicide. LIMITATIONS Participants were enrolled as first-time patients in the Psychological Outpatient Department of the hospital. A selection bias may have limited the generalizability of the results. The results may have been influenced by the fact that adult psychology outpatient consultations with patients over 14 years of age (including 14 years of age) and patients under 14 years of age are required to attend pediatric outpatient consultations. There was only one association among the three and no causation. First, the sample size was small and limited to adolescents with depression. Possible session bias may have occurred due to self-reporting. Some adolescents may also have been reluctant to report self-harming behaviors. CONCLUSION The findings show that NSSI increases the risk of suicide in patients with depressive disorder. Therefore, paying attention to NSSI in patients with depressive disorder and taking early mediating measures to minimize the effect will be conducive to reducing the risk of suicide. Declarations DATA AVAILABILITY STATEMENT Publicly available datasets were analyzed in this study. The data are available at https://www.xxx ETHICS STATEMENT This study involved humans and was reviewed and approved by the National Center for Health Statistics Research Ethics Review Board. All the participants provided written informed consent to participate in this study. AUTHOR CONTRIBUTIONS GZD designed the study. XYZ conducted data analysis and wrote the manuscript. YC and YC directed all the work. All the authors contributed to the manuscript and approved the submitted version. FUNDING This study was supported by the Changzhou Medical Center, Nanjing Medical University Project (CMCC202216) and Jiangsu Province Traditional Chinese Medicine Science and Technology Development Plan Surface Project (MS2023087). References Bridge JA, Ruch DA, Sheftall AH, Hahm HC, O'Keefe VM, Fontanella CA, Brock G, Campo JV, Horowitz LM. Youth Suicide During the First Year of the COVID-19 Pandemic. Pediatrics. 2023 Mar 1;151(3):e2022058375. doi: 10.1542/peds.2022-058375. O'Connor RC, Kirtley OJ. 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Relationships among life events, emotional symptoms and non-suicidal self-injury behaviors in adolescents with depression. J Psychiatry. 2020;33(6):420–423. Wang YX, Lai JB, Hu CC, Meng HB, Hu SH, Lyu D. Non-suicidal self-harm is linked to suicidal thoughts in Chinese adolescents withmood disorders: a cross-sectional report. J Zhejiang Univ Sci B. 2021;22(3):233–240. Zheng YG, Xiao L, Wang HL, Chen ZH, Wang GH. A retrospective research on non-suicidal self-injurious behaviors among young patients diagnosed with mood disorders. Front Psychiatry. 2022;13: 895892. Wang L, Liu J, Yang Y, Zou H. Prevalence and risk factors for nonsuicidal self-injury among patients with depression or bipolar disorder in China. BMC Psychiatry. 2021;21(1):1–12. Andrewes HE, Hulbert C, Cotton SM, Betts J, Chanen AM. Ecological momentary assessment of nonsuicidal self-injury in youth with borderline personality disorder. Personal Disord. 2017 Oct;8(4):357-365. Wilkinson PO, Qiu T, Neufeld S, Jones PB, Goodyer IM. Sporadic and recurrent non-suicidal self-injury before age 14 and incident onset of psychiatric disorders by 17 years: prospective cohort study. Br J Psychiatry. 2018 Apr;212(4):222-226. Wang L, Cui Q, Liu J and Zou H (2021) Emotion Reactivity and Suicide Risk in Patients With Depression: The Mediating Role of Non-Suicidal Self-Injury and Moderating Role of Childhood Neglect. Front. Psychiatry 12:707181. Iskric A, Ceniti AK, Bergmans Y, McInerney S, Rizvi SJ. Alexithymia and self-harm: a review of nonsuicidal self-injury, suicidal ideation, and suicide attempts. Psychiatry Res. (2020) 288:112920. Chen X, Chen H, Liu J, Tang H, Zhou J, Liu P, Tian Y, Wang X, Lu F, Zhou J. Functional connectivity alterations in reward-related circuits associated with non-suicidal self-injury behaviors in drug-naïve adolescents with depression. J Psychiatr Res. 2023 Jul;163:270-277. Wester KL, Ivers N, Villalba JA, Trepal HC, Henson R. The relationship between nonsuicidal self-injury and suicidal ideation. J Couns Dev. (2016) 94:3–12. Perkins NM, Ortiz SN, Smith AR. Self-criticism longitudinally predicts nonsuicidal self-injury in eating disorders. Eat Disord. (2020) 28:157–70. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4217941","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":287861295,"identity":"d7e76fe1-4fa5-4904-9c91-35bce0cfaa16","order_by":0,"name":"Xuanyan Zhu","email":"","orcid":"","institution":"The Affiliated Changzhou No. 2 People’s Hospital of Nanjing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xuanyan","middleName":"","lastName":"Zhu","suffix":""},{"id":287861296,"identity":"2f8873f4-3a0f-482e-a497-0ae086ccd525","order_by":1,"name":"Yun Chen","email":"","orcid":"","institution":"The Affiliated Changzhou No. 2 People’s Hospital of Nanjing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yun","middleName":"","lastName":"Chen","suffix":""},{"id":287861297,"identity":"0bc88e4e-9a4b-4b41-b564-9e486cc249f0","order_by":2,"name":"Zhongyi Jiang","email":"","orcid":"","institution":"The Affiliated Changzhou No. 2 People’s Hospital of Nanjing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Zhongyi","middleName":"","lastName":"Jiang","suffix":""},{"id":287861298,"identity":"f4101886-39e8-4460-874d-699409390318","order_by":3,"name":"Ran Bi","email":"","orcid":"","institution":"The Affiliated Changzhou No. 2 People’s Hospital of Nanjing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Ran","middleName":"","lastName":"Bi","suffix":""},{"id":287861299,"identity":"730e6ae8-0c75-4405-911f-1f873b12908b","order_by":4,"name":"Qiaoyang Zhang","email":"","orcid":"","institution":"The Affiliated Changzhou No. 2 People’s Hospital of Nanjing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Qiaoyang","middleName":"","lastName":"Zhang","suffix":""},{"id":287861300,"identity":"b90a2d4d-3aa7-4303-a5df-3d978489858c","order_by":5,"name":"Yin Cao","email":"","orcid":"","institution":"The Affiliated Changzhou No. 2 People’s Hospital of Nanjing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yin","middleName":"","lastName":"Cao","suffix":""},{"id":287861301,"identity":"988ba0d0-541d-4ff4-b190-2ef2ba0e6759","order_by":6,"name":"Guanzhong Dong","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5UlEQVRIiWNgGAWjYDCCA2Cyxo6fvYHBAEmEoJZjyZI9B0jTwsy44UYCighuwHe89/DLL3/YmCVnvj1QdLONQY7vRgLj5wI8WiTPnEuzluGR4eOXzkswzm1jMJa8kcAsPQOPFoMbOWbGEhJAW2bnGIC0JAJdyMbMQ1CLAdAvN8+AtdQTo8X44YcEkPd5wFoSDAhpkTxzxoyZ4QAokIEOyzknYTjzzMNmaXxa+I73GH/88QcUlWfMjHPKbOT5jicf/IxPCxCwwcxkA0alBJBmbMCvARiNH39AGQ8IKR0Fo2AUjIKRCQCs0U29VKHxywAAAABJRU5ErkJggg==","orcid":"","institution":"The Affiliated Changzhou No. 2 People’s Hospital of Nanjing Medical University","correspondingAuthor":true,"prefix":"","firstName":"Guanzhong","middleName":"","lastName":"Dong","suffix":""}],"badges":[],"createdAt":"2024-04-04 12:37:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4217941/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4217941/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":54450781,"identity":"3598cb00-4ea1-4d77-8b87-f7432d34a8d5","added_by":"auto","created_at":"2024-04-10 17:47:50","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":104045,"visible":true,"origin":"","legend":"\u003cp\u003epresents the research method.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4217941/v1/8c66dc6ab5d0e181340d4c48.png"},{"id":54449693,"identity":"c9456341-6be2-4d47-9a28-73a3aeec0b70","added_by":"auto","created_at":"2024-04-10 17:39:50","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":28599,"visible":true,"origin":"","legend":"\u003cp\u003eResiduals for Gradient Boosting Regressor Model\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4217941/v1/8afbc23e1f80d29e0a7dc456.png"},{"id":54449694,"identity":"50f182c8-c1f8-487b-8540-7b80bf3be218","added_by":"auto","created_at":"2024-04-10 17:39:50","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":30823,"visible":true,"origin":"","legend":"\u003cp\u003ePrediction Error for the Gradient Boosting Regressor\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4217941/v1/db6688251f2649712371160d.png"},{"id":55265666,"identity":"68e6839c-32a8-423e-8b17-6d4c855a7a25","added_by":"auto","created_at":"2024-04-25 02:13:06","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":661563,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4217941/v1/bcc618d2-02ca-4308-8032-f0774d6b5b95.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"An analysis of the mediating factors of suicide risk in adolescents with depressive disorder based on machine learning","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eAdolescent suicide is a serious public health concern. In recent years, with the continuous development of the social economy, the incidence of adolescent suicide has increased. The rate of youth suicide has gradually increased over the past 15 years, especially during the COVID-19 pandemic\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. The motivation-volitional model of suicide holds that when an individual experiences persistent feelings of personal failure and other negative emotions, they find themselves trapped in an environmental and emotional state that leads to suicidal behavior\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. It is well known that suicide is not an independent event but rather an endpoint that progresses from suicidal ideation to suicidal behavior\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. Suicide risk, therefore, includes suicidal ideation and behavior. Suicide risk assessment is an important means of detecting and preventing suicidal behavior\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. It is critical to identify suicide risk factors that can be targeted for treatment through prevention and early intervention programs\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eNon-suicidal self-injury (NSSI) refers to suicidal behaviors that repeatedly, intentionally, and directly cause mild to moderate damage to tissue without the individual having suicidal motivation. This behavior is generally combated by society\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. NSSI is a common problem among adolescents. The incidence of NSSI among adolescents worldwide ranges from 13.4\u0026ndash;49.5%\u003csup\u003e[7]\u003c/sup\u003e. Previous literature found that the lifetime prevalence of NSSI was 24.7% among Chinese students, 32.8% among high school students, and 21.2% among college students\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. This means that one in four Chinese students have experienced NSSI. Repeated self-harm is also a noteworthy risk factor for suicide among adolescents\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. NSSI is likely to eventually lead to suicidal behavior. Therefore, NSSI is a predictor of suicide among adolescents\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eResearch has suggested that NSSI behavior may be a mode of emotional regulation in individuals with depressive disorder\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e Individuals use NSSI to release emotions or relieve distress pathways that influence suicide risk\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. It may help people with depressive disorder avoid feelings of emptiness and loneliness, and avoid recalling sad events thereby reducing feelings of grief and loss. Consequently, NSSI is a coping mechanism for people with depression to alleviate negative thoughts and feelings. Domestic and foreign studies have found that patients with depressive disorder, who deliberately engage in negative behaviors such as self-harm and self-injury to deal with unpleasant emotions, are more prone to active or passive suicide\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e .However, the role of NSSI in the suicide process of patients with depressive disorder remains controversial\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eMachine learning belongs to the field of artificial intelligence. It can select the most suitable algorithm from large and complex data. Machine learning algorithms have the unique advantage of automatically ranking the importance of each variable and integrating important variables into the final model. It can also detect and process nonlinearities and interactions between variables. Machine learning algorithms have been extensively used as efficient tools in clinical practice. However, few studies have investigated the mediating role of NSSI by combining machine learning with conventional clinical data\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. Gradient Boosting Regressor (GBR) is a machine learning algorithm that iteratively trains fragile learners and combines their predictions to form a strong learner. In each iteration, GBR calculates the residuals for each sample and trains a new weak learner to minimize these residuals as much as possible. Finally, the predictions of all the weak learners were added to arrive at a final prediction\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. This result indicates a higher prediction accuracy. Currently, there are no applications for predicting suicide risk.\u003c/p\u003e \u003cp\u003eThis study explored the mediating role of NSSI in suicide risk in patients with depressive disorder, based on a gradient lifting regression algorithm.\u003c/p\u003e"},{"header":"MATERIALS AND METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eParticipant recruitment procedure\u003c/h2\u003e \u003cp\u003eAll the objects used in this study were derived from the study \u003cem\u003eApplication of Machine Learning to Assist the Diagnosis of Depression Disorder on Dynamic Facial Features\u003c/em\u003e, which included 113 patients who visited the psychological outpatient department of Changzhou Second People's Hospital from January 2022 to December 2023. Informed consent was obtained from all patients or their guardians. This study was approved by the Ethics Committee of Changzhou No. 2 People's Hospital (2022YLJSA072).\u003c/p\u003e \u003cp\u003eThe inclusion criteria were as follows: (1) individuals aged\u0026thinsp;\u0026ge;\u0026thinsp;14 and \u0026le;\u0026thinsp;24 years; (2) diagnostic criteria of the DSM-5 for depressive disorder; (3) a HAMD score of 20 or above; and (4) first visit to the cardiology department.\u003c/p\u003e \u003cp\u003eThe exclusion criteria were as follows: (1) patients who could not cooperate with the information collection; (3) patients with mania, schizophrenia, and other mental illnesses; and (4) patients with connoisseur trauma or serious neurological disease history.\u003c/p\u003e \u003cp\u003eDiagnostic criteria for NSSI and depressive disorder met the DSM-5 criteria\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. An associate chief of neurology and an attending psychiatrist determined the inclusion and exclusion criteria.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eResearch Methods\u003c/h2\u003e \u003cp\u003eClinical characteristics: Demographic information collected included age, gender, and educational level. All data were registered, checked, and verified by two professionally trained graduate students.\u003c/p\u003e \u003cp\u003eAssessment of the HAMD-24 Scale\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e: The HAMD-24 scale was administered and scored independently by two trained raters. Most items were scored on a 5-point scale (0\u0026ndash;4), while a small number were scored on a 3-point scale (0\u0026ndash;2). Items 8, 9, and 11 of the scale were scored based on observation of the patient. The remaining items were scored according to the patients' oral accounts. Item 1 requires a combination of the two. All interviewers completed the HAMD-24 consistency training. The intragroup correlation coefficients were all greater than 0.8.\u003c/p\u003e \u003cp\u003eZung Depression Self-Assessment\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e: The SDS consists of 20 items with an original score of 20\u0026ndash;80. A self-assessment was conducted according to the patients' comprehensive understanding of the language of the guidelines. The standard score is an integral part of the original score multiplied by 1.25.\u003c/p\u003e \u003cp\u003eSuicide Risk Scale\u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e: The Beck Suicide Ideation Scale (Chinese version) was used to assess the most depressed or suicidal statuses of the previous week as well as in the past. Nineteen items were classified as suicidal ideation or tendencies. The higher the score, the higher the suicide risk.\u003c/p\u003e \u003cp\u003eNSSI Scale: The NSSI Scale\u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e was used in a large national sample NSSI survey and has good reliability and validity\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e. The scale comprises 12 items in two dimensions: NSSI with no obvious tissue injury and NSSI with tissue injury. Participants were asked to answer the following question, \"In the past year, have you intentionally harmed yourself in any of the following ways without suicidal intent?\" When participants answered \"never,\" this was determined to be \"none.\" And \"yes\" was determined when the participant answered \"1\" or \"more than once\".\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eData analysis was conducted using the SPSS 22.0 statistical software. Descriptive statistics were used to express the data as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (x̄\u0026plusmn;s). The t-test was used for normally distributed data and the Chi-square test was used for counting data. The mediating analysis was completed in three steps: the first step examined the correlation between the depression score and suicide risk, the second step examined the correlation between the depression score and non-suicidal NSSI, and the third step examined the association between the depression score and non-suicidal suicide with suicide risk(Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eBaseline Characteristics of Participants\u003c/h2\u003e \u003cp\u003eA total of 113 patients with depressive disorder aged 14\u0026ndash;24 years were included in this study, of which 39 were men and 74 women. The sample consisted of 17 middle school students, 32 high school students, and 64 college students. The incidence of non-suicidal self-injury was 33.33% for the males and 35.14% for the females. High school students had the highest incidence (62.50%), which gradually decreased in the college student group (18.75%). The incidence was higher in patients with depressive disorder who had a history of smoking or drinking (see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDemographic characteristics of the study subjects\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003evariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNO(n\u0026thinsp;=\u0026thinsp;74)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNSSI(n\u0026thinsp;=\u0026thinsp;39)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eT或X\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21.04\u0026thinsp;\u0026plusmn;\u0026thinsp;2.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.85\u0026thinsp;\u0026plusmn;\u0026thinsp;2.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.708\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e30.121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJunior high\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh school (Vocational high school)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUniversity (junior college)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13.951\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrinking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20.976\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eRelated influencing factors of suicide risk\u003c/h2\u003e \u003cp\u003eThe suicide risk score was positively correlated with the HAMD and self-rating depression scores. The NSSI score was also positively correlated with the HAMD and self-rated depression scores. There was a significant consistency between the HAMD and depression scale self-rating scores (see Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCorrelation analysis of suicide risk\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSuicide risk\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHAMD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSDS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNSSI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSuicide risk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHAMD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.639\u003csup\u003e✱✱\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSDS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.671\u003csup\u003e✱✱\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.857\u003csup\u003e✱✱\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNSSI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.849\u003csup\u003e✱✱\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.524\u003csup\u003e✱✱\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.536\u003csup\u003e✱✱\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003csup\u003e✱✱\u003c/sup\u003e: \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eGradient Boosting Regressor results\u003c/h2\u003e \u003cp\u003eThe training and test sets were allocated in a 2:1 ratio, with an R2 of 0.980 for training set 2 and 0.717 for the test set. All indicators of the gradient lifting regression model were good (see Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), indicating that the model had an excellent fitting effect. There was no heteroscedasticity or missing model, and the errors followed a normal distribution (see Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGradient Boosting Regressor Results\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMAE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRMSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eR2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRMSLE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMAPE\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGradient Boosting Regressor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.423\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32.992\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.744\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.717\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.979\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.050\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eThe mediating role of NSSI in the self-risk of patients with depressive disorder\u003c/h2\u003e \u003cp\u003eHAMD and the self-rated depression scores directly affected the suicide risk score. HAMD and the self-rated depression scores indirectly affected NSSI. The HAMD and self-rated depression scores partially influenced the suicide risk score through NSSI (see Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of direct, indirect, and total utility effects on mediators\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEffects\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStandard Error\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBeta\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDirect impactions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHAMD-Suicide risk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.532\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.639\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11.060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSDS-Suicide risk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.346\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.677\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e12.247\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndirect impactions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHAMD-NSSI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.380\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.521\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8.111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSDS-NSSI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.256\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.571\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9.264\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverall impactions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHAMD-Suicide risk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.213\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.256\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.554\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNSSI-Suicide risk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.840\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.737\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e18.911\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSDS-Suicide risk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.267\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.597\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNSSI-Suicide risk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.817\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.718\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e17.712\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eIn this study, non-suicidal self-injury had the highest incidence among high school students, regardless of sex, with a high incidence among depressive disorder patients with a history of smoking and drinking. NSSI partially mediated the risk of suicide in depressed patients.\u003c/p\u003e \u003cp\u003eEpidemiological studies have shown that non-suicidal self-injury is a serious problem in adolescence, with a lifetime prevalence of 17\u0026ndash;60%, with a peak in mid-adolescence (age 15\u0026ndash;16) and a gradual decline in late adolescence (age 18 years). Adolescence is the most prevalent stage of NSSI, and the age distribution of NSSI shows an inverted V-shaped trend, with the highest reported rate of NSSI in the 15\u0026ndash;16 age group\u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e. One study investigated 1283 junior high school students in Wuhan City and found that the average age of first self-injury in students with NSSI was 12.35\u0026thinsp;\u0026plusmn;\u0026thinsp;3.12 years, 57% of them were aged 12\u0026ndash;15 years old. The incidence of NSSI gradually decreased with the increase of age. It was also believed that NSSI behavior would naturally ease with age in mature patients, and only adolescents with obvious emotional problems would require treatment. In this study, the incidence of NSSI was highest in high school or secondary school adolescent students (15\u0026ndash;18 years old) and gradually decreased during the college years (after 19 years old). This problem is influenced by various factors, including social predisposition, interpersonal relationships, coping with stressful events, neurobiological characteristics, disorders of responsive regulation in childhood, and adverse experiences. Although there is a significant decline or cessation of non-suicidal self-injury behavior between late adolescence and early adulthood, adolescents who repeatedly engage in non-suicidal self-injury behavior are at an increased risk of long-term mental health problems, suicide, and risky behaviors. In terms of psychosocial factors, adolescence, a period of continuous brain development, is associated with increased impulse control and emotional overreactions. They are more likely to be influenced and instigated by their surroundings. Foreign research shows that the searches for non-suicidal self-injury behaviors on websites exceeded 40\u0026nbsp;million times in one year. Videos and photos of this behavior were viewed over one million times, which is significantly correlated with the annual increase in non-suicidal suicidal behaviors. If an individual\u0026rsquo;s relatives, classmates, or friends have carried out NSSI, the risk of the occurrence of the same behavior will greatly increase for such an individual, which may also be related to subcultural factors\u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAt present, it is difficult to determine whether sex differences influence the detection rate of NSSI behaviors at home and abroad. For example, Chinese researchers have found that the incidence of NSSI in males is higher than that in females. Huang et al. showed that the detection rates of NSSI in males and females were 23.5% and 19.6%, respectively\u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e. Furthermore, a meta-analysis with a larger sample in China showed that male students were more likely to experience NSSI than female students. In contrast, other studies have found that the detection rate of NSSI is higher in females than in males\u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e. Moreover, the results of other studies showed no statistically significant difference between men and women\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e. This finding is consistent with the results of the present study. Currently, there is no consensus on the effect of sex on the occurrence of NSSI. However, the age of the participants surveyed was related to NSSI. The incidence of NSSI was higher among women in middle and high school, whereas it was more common among men in college\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e. According to a survey conducted by Hasking on adolescents aged 12\u0026ndash;18 years, there was no difference in gender NSSI behavior. However, when surveyed again 11.7 months later, they found that the detection rate in females was significantly higher than that in males\u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eSmoking and drinking, which are bad habits of adolescents, were more likely to occur in NSSI patients. Domestic and foreign studies have found that adolescents with NSSI have bad habits of smoking or drinking\u003csup\u003e[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e. Drinking and smoking are common among adolescents because of their self-control difficulties\u003csup\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003e. In 2014, the U.S. Health Service reported that more than 600,000 middle school students and 3\u0026nbsp;million high school students smoked\u003csup\u003e[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/sup\u003e. Moreover, a previous study reported that the proportions of former and current smokers in China were 33.83% and 7.93%, respectively\u003csup\u003e[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/sup\u003e. Similarly, alcohol, a typical substance, is used by teenagers worldwide, and China is no exception. Approximately one-quarter of the teens reported drinking alcohol in the last 30 days of this study\u003csup\u003e[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/sup\u003e. There is an established correlation between risky drinking and the manifestation of NSSI\u003csup\u003e[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn China, the incidence of depression among young adults is increasing. This situation has become more severe during the COVID-19 pandemic\u003csup\u003e[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]\u003c/sup\u003e. Similarly, a survey conducted in the Yellowstone area of America found that the year 2021, the year of the COVID-19 epidemic, saw a marked increase in the incidence of depression among adolescents\u003csup\u003e[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]\u003c/sup\u003e. Suicide is a fatal outcome for people with depression and depression has worsened adolescent mental health and increased suicide rates globally since the pandemic\u003csup\u003e[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]\u003c/sup\u003e. A large case-control study in China confirmed that depression, particularly depression severity scores in the two weeks before death, are important predictors of suicide\u003csup\u003e[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]\u003c/sup\u003e. According to the interpersonal theory of suicide, suicidal intention and capacity are preconditions of suicide\u003csup\u003e[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]\u003c/sup\u003e. Suicidal intention comes from self-guilt and self-belonging frustration, which are clinical characteristics of patients with depression. Reducing self-guilt and self-belonging frustration in patients with depression can reduce the occurrence of suicidal behaviors\u003csup\u003e[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe incidence of NSSI is the highest in adolescents with depression. The estimated incidence of NSSI among those diagnosed with depression was 62.6% (56.2\u0026ndash;67.9%)\u003csup\u003e[\u003cspan additionalcitationids=\"CR46 CR47 CR48\" citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]\u003c/sup\u003e. In this study, the incidence of depressive disorder was 34.6%. This is lower than that in previous studies\u003csup\u003e[\u003cspan additionalcitationids=\"CR46 CR47 CR48\" citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]\u003c/sup\u003e, considering that some adolescents may be reluctant to report self-injury behavior in face-to-face scale assessments or there may be recall bias. Depressive mood is an important predictor of future NSSI development\u003csup\u003e[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]\u003c/sup\u003e. According to the theoretical model of NSSI, mood fluctuations are the initiation and recurrence factors of NSSI, particularly negative mood, which is a prerequisite for the occurrence of NSSI. Some self-reported studies have shown that individuals' depressive mood increases before NSSI, while their depressive mood is relieved, and new feelings are obtained after NSSI is concluded\u003csup\u003e[\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]\u003c/sup\u003e. In addition, NSSI may be a predictor of new-onset depression in adolescents\u003csup\u003e[\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]\u003c/sup\u003e. Depressive mood plays an important role in the occurrence and recurrence of NSSI.\u003c/p\u003e \u003cp\u003eIn this study, NSSI partially mediated the risk of suicide among patients with depressive disorder. This is consistent with the results of previous studies\u003csup\u003e[\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]\u003c/sup\u003e. NSSI is not only directly related to depression severity scores, but also plays a partial mediating role in the regulation of suicide risk in patients with depressive disorder. Patients with high depression scores have a limited ability to withstand external stimuli or problem-solving coping strategies; therefore, depressed patients desire to quickly release negative emotions through NSSI negative coping strategies\u003csup\u003e[\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]\u003c/sup\u003e. NSSI only satisfies depressed individuals for temporary and urgent self-regulation. According to the reward mechanism (acquired theory), a depressed person repeatedly uses this negative approach to achieve temporary satisfaction\u003csup\u003e[\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]\u003c/sup\u003e. If the desired effect is not achieved, the depressed person will resort to more serious self-injury to achieve the desired effect and even commit suicide\u003csup\u003e[\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]\u003c/sup\u003e. In addition, NSSI may aggravate depressed patients' negative self-perceptions such as low self-esteem and self-blame, which further increase the probability of suicide attempts\u003csup\u003e[\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]\u003c/sup\u003e. This may explain why NSSI is an important predictor of suicide.\u003c/p\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eLIMITATIONS\u003c/h2\u003e \u003cp\u003eParticipants were enrolled as first-time patients in the Psychological Outpatient Department of the hospital. A selection bias may have limited the generalizability of the results. The results may have been influenced by the fact that adult psychology outpatient consultations with patients over 14 years of age (including 14 years of age) and patients under 14 years of age are required to attend pediatric outpatient consultations. There was only one association among the three and no causation. First, the sample size was small and limited to adolescents with depression. Possible session bias may have occurred due to self-reporting. Some adolescents may also have been reluctant to report self-harming behaviors.\u003c/p\u003e \u003c/div\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eThe findings show that NSSI increases the risk of suicide in patients with depressive disorder. Therefore, paying attention to NSSI in patients with depressive disorder and taking early mediating measures to minimize the effect will be conducive to reducing the risk of suicide.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eDATA AVAILABILITY STATEMENT\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePublicly available datasets were analyzed in this study. The data are available at https://www.xxx\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eETHICS STATEMENT\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study involved humans and was reviewed and approved by the National Center for Health Statistics Research Ethics Review Board. All the participants provided written informed consent to participate in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAUTHOR CONTRIBUTIONS\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGZD designed the study. XYZ conducted data analysis and wrote the manuscript. YC and YC directed all the work. All the authors contributed to the manuscript and approved the submitted version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFUNDING\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the Changzhou Medical Center, Nanjing Medical University Project (CMCC202216) and Jiangsu Province Traditional Chinese Medicine Science and Technology Development Plan Surface Project (MS2023087).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eBridge JA, Ruch DA, Sheftall AH, Hahm HC, O\u0026apos;Keefe VM, Fontanella CA, Brock G, Campo JV, Horowitz LM. Youth Suicide During the First Year of the COVID-19 Pandemic. 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Psychiatry 12:707181.\u003c/li\u003e\n \u003cli\u003eIskric A, Ceniti AK, Bergmans Y, McInerney S, Rizvi SJ. Alexithymia and self-harm: a review of nonsuicidal self-injury, suicidal ideation, and suicide attempts. \u003cem\u003ePsychiatry Res.\u0026nbsp;\u003c/em\u003e(2020) 288:112920.\u003c/li\u003e\n \u003cli\u003eChen X, Chen H, Liu J, Tang H, Zhou J, Liu P, Tian Y, Wang X, Lu F, Zhou J. Functional connectivity alterations in reward-related circuits associated with non-suicidal self-injury behaviors in drug-na\u0026iuml;ve adolescents with depression. J Psychiatr Res. 2023 Jul;163:270-277.\u003c/li\u003e\n \u003cli\u003eWester KL, Ivers N, Villalba JA, Trepal HC, Henson R. The relationship between nonsuicidal self-injury and suicidal ideation. \u003cem\u003eJ Couns Dev.\u0026nbsp;\u003c/em\u003e(2016) 94:3\u0026ndash;12.\u003c/li\u003e\n \u003cli\u003ePerkins NM, Ortiz SN, Smith AR. Self-criticism longitudinally predicts nonsuicidal self-injury in eating disorders. \u003cem\u003eEat Disord.\u0026nbsp;\u003c/em\u003e(2020) 28:157\u0026ndash;70.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"depressive disorder, NSSI, suicide risk, mediating factors, machine learning","lastPublishedDoi":"10.21203/rs.3.rs-4217941/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4217941/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eAims: \u003c/strong\u003eThe role of non-suicidal self-injury (NSSI) in the suicide process of patients with depressive disorder remains controversial. Therefore, the purpose of this study was to investigate the role NSSI plays in suicide risk in patients with depressive disorder.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e A questionnaire survey was compiled using the HAMD-24, the Baker Suicide Risk Scale, and the NSSI Scale. The survey was administered to 113 adolescent patients with depressive disorder. The correlation between NSSI, depression, and suicide risk was analyzed using the gradient-lifting regression model.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e NSSI had the highest incidence among high school students, regardless of gender. Furthermore, the incidence of NSSI was high among depressive patients with a history of smoking and drinking. NSSI partially mediated the risk of suicide in depressive patients.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e The findings show that NSSI increases the risk of suicide in patients with depressive disorder. Therefore, paying attention to NSSI in patients with depressive disorder and taking early mediating measures to minimize the effect will be conducive to reducing the risk of suicide.\u003c/p\u003e","manuscriptTitle":"An analysis of the mediating factors of suicide risk in adolescents with depressive disorder based on machine learning","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-10 17:39:46","doi":"10.21203/rs.3.rs-4217941/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5b9b15aa-0ea4-4eda-99f2-0f1f219f0afb","owner":[],"postedDate":"April 10th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-04-23T15:51:57+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-10 17:39:46","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4217941","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4217941","identity":"rs-4217941","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}