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This cross-sectional study analyzed biological markers and associated psychopathology in female patients with NSSI and healthy controls, using clinical data, saliva and blood, heart-rate variability, and pain sensitivity (n=149; n=40). Machine-learning classification tested oxytocin, DHEA, beta-endorphin, fT3, leukocytes, HRV, and pain sensitivity for outcomes in NSSI, depression, and borderline personality disorder symptoms. Moderate prediction emerged for NSSI (AUC 0.67–0.69), with low oxytocin, low pain sensitivity, and high leukocytes showing highest power.",{"@graph":69,"@context":123},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/the-quest-for-a-biological-phenotype-of-adolescent-non-suicidal-self-injury-a-machine-learning-approach/290317/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/the-quest-for-a-biological-phenotype-of-adolescent-non-suicidal-self-injury-a-machine-learning-approach/290317.png","ImageObject",300,407,{"name":92,"@type":93},"Olivia Brown","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-27","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",13,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"What is the main objective of the study?","Question",{"text":113,"@type":114},"To cross-sectionally investigate biological marker patterns underlying adolescent non-suicidal self-injury (NSSI) and their association with psychopathology.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"Which biological measures were used for machine-learning classification?",{"text":118,"@type":114},"Oxytocin, DHEA, beta-endorphin, free triiodothyronine (fT3), leukocytes, heart rate variability, and pain sensitivity were used alongside comprehensive clinical data.",{"name":120,"@type":111,"acceptedAnswer":121},"How well did the models predict clinical outcomes, particularly NSSI?",{"text":122,"@type":114},"For NSSI as an outcome, logistic regression and machine-learning models showed moderate predictive performance with an AUC between 0.67 and 0.69.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},290317,1789641319,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":34,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":130,"read_time":144,"preview_page_count":4},16904993612988,"https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd","Translational [Psychiatry](Psychiatry www.nature.com/tp)[ www.nature.com/tp](Psychiatry www.nature.com/tp)  \nARTICLE OPEN   \nThe quest for a biological phenotype of adolescent non-suicidal self-injury: a machine-learning approach  \nInes Mürner-Lavanchy1, Julian Koenig2, Corinna Reichl 1, Johannes Josi1, Marialuisa Cavelti 1 and Michael Kaess 1,3 ✉  \n© The Author(s) 2024  \n\n|  |  |  |\n| --- | --- | --- |\n|  | Non-suicidal self-injury (NSSI) is a transdiagnostic psychiatric symptom with high prevalence and relevance in child and adolescent psychiatry. Therefore, it is of great interest to identify a biological phenotype associated with NSSI. The aim of the present study was to cross-sectionally investigate patterns of biological markers underlying NSSI and associated psychopathology in a sample of female patients and healthy controls. Comprehensive clinical data, saliva and blood samples, heart rate variability and pain sensitivity, were collected in n = 149 patients with NSSI and n = 40 healthy participants. Using machine-based learning, we tested the extent to which oxytocin, dehydroepiandrosterone (DHEA), beta-endorphin, free triiodothyronine (fT3), leukocytes, heart rate variability and pain sensitivity were able to classify participants regarding their clinical outcomes in NSSI, depression and borderline personality disorder symptomatology. We evaluated the predictive performance of several models (linear and logistic regression, elastic net regression, random forests, gradient boosted trees) using repeated cross-validation. With NSSI as an outcome variable, both logistic regression and machine learning models showed moderate predictive performance (Area under the Receiver Operating Characteristic Curve between 0.67 and 0.69) . Predictors with the highest predictive power were low oxytocin (OR = 0.55; p = 0.002), low pain sensitivity (OR = 1.15; p = 0.021), and high leukocytes (OR = 1.67; p = 0.015) . For the psychopathological outcome variables, i.e., depression and borderline personality disorder symptomatology, models including the biological variables performed not better than the null model. A combination of hormonal and inflammatory markers, as well as pain sensitivity, were able to discriminate between participants with and without NSSI disorder. Based on this dataset, however, complex machine learning models were not able to detect non-linear patterns of associations between the biological markers. These findings need replication and future research will reveal the extent to which the respective biomarkers are useful for longitudinal prediction of |  |\n|  |  |  |\n|  |  |  |\n|  |  |  |\n|  |  |  |\n|  |  |  |\n|  |  |  |\n|  |  |  |\n|  |  |  |\n|  |  |  |\n|  |  |  |\n|  |  |  |\n|  |  |  |\n|  |  |  |\n|  |  |  |\n|  |  |  |\n|  |  |  |\n|  |  |  |\n|  | clinical outcomes or treatment response. |  |\n|  |  |  |\n|  |  |  |\n|  | Translational Psychiatry (2024)14:56; [https://doi.org/10.1038/s41398-024-02776-4](https://doi.org/10.1038/s41398-024-02776-4) |  |\n|  |  |  |\n\nINTRODUCTION  \nNon-suicidal self-injury (NSSI) denotes self-injurious behavior in the absence of suicidal intent (American Psychiatric Association, 2013), is highly prevalent in adolescents from the general population [1] as well as in clinical samples [2] and has been suggested as a transdiagnostic risk marker of psychopathology [3] . NSSI results from a complex interaction of psychological, social and biological mechanisms. Research on the biological factors contributing to the development and maintenance of NSSI is in its infancy. A recently advanced biological model of NSSI distinguishes distal from proximal biological traits as well as biological states [4] . While distal biological traits may include genetic, epigenetic or perinatal and early-life vulnerabilities increasing predisposition for NSSI, more proximal biological traits include moderately stable alterations in brain-circuitry, the stress response systems and pain processing, enhancing the risk for NSSI.","cbCaijspcsrTJssN","https://ap.wps.com/l/cbCaijspcsrTJssN","pdf",682237,"English","# Introduction\n## Biological traits, states, and stress systems\n## Neuroendocrine and autonomic evidence","[{\"question\":\"What is the main objective of the study?\",\"answer\":\"To cross-sectionally investigate biological marker patterns underlying adolescent non-suicidal self-injury (NSSI) and their association with psychopathology.\"},{\"question\":\"Which biological measures were used for machine-learning classification?\",\"answer\":\"Oxytocin, DHEA, beta-endorphin, free triiodothyronine (fT3), leukocytes, heart rate variability, and pain sensitivity were used alongside comprehensive clinical data.\"},{\"question\":\"How well did the models predict clinical outcomes, particularly NSSI?\",\"answer\":\"For NSSI as an outcome, logistic regression and machine-learning models showed moderate predictive performance with an AUC between 0.67 and 0.69.\"}]","The quest for a biological phenotype of adolescent non-suicidal self-injury: a machine-learning approach | PDF",18]