[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-seo-272569-105":3,"doc-detail-272569-en":81,"detail-sidebar-cat-0-en-105":99},{"code":4,"msg":5,"data":6},0,"ok",{"site_id":7,"language":8,"slug":9,"title":10,"keywords":11,"description":12,"schema_data":13,"social_meta":74,"head_meta":76,"extra_data":78,"updated_unix":80},105,"en","discovery-and-validation-of-blood-biomarkers-for-suicidality","Discovery and validation of blood biomarkers for suicidality","","Suicidality remains a major cause of death in psychiatric patients and the broader community, yet objective and quantitative tools for tracking suicidal risk are still limited. This study identifies blood gene expression biomarkers for suicidality in a high-risk bipolar population, using both intrasubject and intersubject designs to detect differentially expressed genes. A Convergent Functional Genomics framework prioritizes candidates and external suicide-completer validation shows significant stepwise expression changes, highlighting biomarkers such as SAT1 and potential biological pathways involving stress, inflammation, and apoptosis.",{"@graph":14,"@context":73},[15,34,56],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/healthcare/","Healthcare",3,{"item":32,"name":10,"@type":21,"position":33},"https://docshare.wps.com/document/discovery-and-validation-of-blood-biomarkers-for-suicidality/272569/",4,{"url":32,"name":10,"@type":35,"image":36,"author":41,"headline":10,"publisher":44,"fileFormat":47,"inLanguage":8,"description":12,"dateModified":48,"datePublished":49,"encodingFormat":47,"isAccessibleForFree":50,"interactionStatistic":51},"DigitalDocument",{"url":37,"@type":38,"width":39,"height":40},"https://docshare.wps.com/thumbnails/discovery-and-validation-of-blood-biomarkers-for-suicidality/272569.png","ImageObject",300,407,{"name":42,"@type":43},"Aladdin","Person",{"url":19,"name":45,"@type":46},"DocShare","Organization","application/pdf","2026-10-19","2026-09-15",true,{"@type":52,"interactionType":53,"userInteractionCount":55},"InteractionCounter",{"@type":54},"ViewAction",9,{"@type":57,"mainEntity":58},"FAQPage",[59,65,69],{"name":60,"@type":61,"acceptedAnswer":62},"What is the main goal of the study on suicidality?","Question",{"text":63,"@type":64},"To develop and validate objective blood biomarkers for predicting and tracking suicidal states.","Answer",{"name":66,"@type":61,"acceptedAnswer":67},"How were candidate biomarkers identified and prioritized?",{"text":68,"@type":64},"Differential blood gene expression was determined between no and high suicidal ideation states, then prioritized using a Convergent Functional Genomics approach integrating multiple genomic evidence streams.",{"name":70,"@type":61,"acceptedAnswer":71},"Which biomarkers showed validation-related changes and future/past hospitalization relevance?",{"text":72,"@type":64},"SAT1 was the top candidate and showed differential patterns associated with future and past suicidality-related hospitalizations, with PTEN, MARCKS, and MAP3K3 also showing weaker but similar effects.","https://schema.org",{"og:url":32,"og:type":75,"og:title":10,"og:site_name":45,"og:description":12},"article",{"robots":77,"canonical":32},"index,follow",{"doc_id":79,"site_id":7},272569,1789975176,{"code":4,"msg":82,"data":83},"success",{"doc_id":79,"user_id":84,"nickname":42,"user_avatar":85,"doc_module":4,"category_id":86,"category_name":29,"doc_title":10,"doc_description":12,"doc_content":87,"file_id":88,"file_url":89,"file_type":90,"file_size":91,"view_count":55,"is_deleted":4,"is_public":22,"is_downloadable":22,"audit_status":22,"page_count":92,"language":93,"language_code":8,"site_id":7,"html_lang":8,"table_of_contents":94,"faqs":95,"seo_title":96,"seo_description":12,"update_tm":97,"read_time":98},2336478503145,"https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",7,"OPEN  \nMolecular Psychiatry (2013), 1–16  \n& 2013 Macmillan Publishers Limited All rights reserved 1359-4184/13  \n[www.nature.com/mp](www.nature.com/mp)  \nIMMEDIATE COMMUNICATION  \nDiscovery and validation of blood biomarkers for suicidality  \nH Le-Niculescu 1,2,7, DF Levey1,7, M Ayalew1, L Palmer 1, LM Gavrin 1, N Jain1, E Winiger 1, S Bhosrekar1, G Shankar3, M Radel2, E Bellanger2, H Duckworth2, K Olesek2, J Vergo2, R Schweitzer1,2, M Yard4, A Ballew5, A Shekhar 1, GE Sandusky4, NJ Schork6, SM Kurian6, DR Salomon6 and AB Niculescu III1,2,4  \n\n| Suicides are a leading cause of death in psychiatric patients, and in society at large. Developing more quantitative and objective ways (biomarkers) for predicting and tracking suicidal states would have immediate practical applications and positive societal implications. We undertook such an endeavor. First, building on our previous blood biomarker work in mood disorders and psychosis, we decided to identify blood gene expression biomarkers for suicidality, looking at differential expression of genes in the blood of subjects with a major mood disorder (bipolar disorder), a high-risk population prone to suicidality. We compared no suicidal ideation (SI) states and high SI states using a powerful intrasubject design, as well as an intersubject case–case design, to generate a list of differentially expressed genes. Second, we used a comprehensive Convergent Functional Genomics (CFG) approach to identify and prioritize from the list of differentially expressed gene biomarkers of relevance to suicidality. CFG integrates multiple independent lines of evidence—genetic and functional genomic data—as a Bayesian strategy for identifying and prioritizing findings, reducing the false-positives and false-negatives inherent in each individual approach. Third, we examined whether expression levels of the blood biomarkers identified by us in the live bipolar subject cohort are actually altered in the blood in an age-matched cohort of suicide completers collected from the coroner’s office, and report that 13 out of the 41 top CFG scoring biomarkers (32%) show step-wise significant change from no SI to high SI states, and then to the suicide completers group. Six out of them (15%) remained significant after strict Bonferroni correction for multiple comparisons. Fourth, we show that the blood levels of SAT1 (spermidine/spermine N1–acetyltransferase 1), the top biomarker identified by us, at the time of testing for this study, differentiated future as well as past hospitalizations with suicidality, in a live cohort of bipolar disorder subjects, and exhibited a similar but weaker pattern in a live cohort of psychosis (schizophrenia/schizoaffective disorder) subjects. Three other (phosphatase and tensin homolog (PTEN), myristoylated alanine-rich protein kinase C substrate (MARCKS), and mitogen-activated protein kinase kinase kinase 3 (MAP3K3)) of the six biomarkers that survived Bonferroni correction showed similar but weaker effects. Taken together, the prospective and retrospective hospitalization data suggests SAT1, PTEN, MARCKS and MAP3K3 might be not only state biomarkers but trait biomarkers as well. Fifth, we show how a multi-dimensional approach using SAT1 blood expression levels and two simple visual-analog scales for anxiety and mood enhances predictions of future hospitalizations for suicidality in the bipolar cohort (receiver-operating characteristic curve with area under the curve of 0.813) . Of note, this simple approach does not directly ask about SI, which some individuals may deny or choose not to share with clinicians. Lastly, we conducted bioinformatic analyses to identify biological pathways, mechanisms and medication targets. Overall, suicidality may be underlined, at least in part, by biological mechanisms related to stress, inflammation and apoptosis. |  |  |\n| --- | --- | --- |\n| Molecular Psychiatry advance online publication, 20 August 2013; doi:10 . 1038/mp.2013.95 |  | |\n| Keywords: b","cbCaiiU4nUfChxJO","https://ap.wps.com/l/cbCaiiU4nUfChxJO","pdf",3807833,49,"English","# Discovery and validation of blood biomarkers for suicidality\n## Identification strategy using differential gene expression\n## Convergent Functional Genomics prioritization\n## Validation in suicide completer cohort\n## Hospitalization prediction and clinical implication\n## Bioinformatic pathway and medication target analyses","[{\"question\":\"What is the main goal of the study on suicidality?\",\"answer\":\"To develop and validate objective blood biomarkers for predicting and tracking suicidal states.\"},{\"question\":\"How were candidate biomarkers identified and prioritized?\",\"answer\":\"Differential blood gene expression was determined between no and high suicidal ideation states, then prioritized using a Convergent Functional Genomics approach integrating multiple genomic evidence streams.\"},{\"question\":\"Which biomarkers showed validation-related changes and future/past hospitalization relevance?\",\"answer\":\"SAT1 was the top candidate and showed differential patterns associated with future and past suicidality-related hospitalizations, with PTEN, MARCKS, and MAP3K3 also showing weaker but similar effects.\"}]","Discovery and validation of blood biomarkers for suicidality | PDF",1789466147,123,{"code":4,"msg":82,"data":100},[101,105,109,113,118,123,126,131,135,138,142],{"id":22,"doc_module":4,"doc_module_name":25,"category_name":102,"show_sort_weight":103,"slug":104},"Story & Novel",90,"story-novel",{"id":26,"doc_module":4,"doc_module_name":25,"category_name":106,"show_sort_weight":107,"slug":108},"Literature",80,"literature",{"id":33,"doc_module":4,"doc_module_name":25,"category_name":110,"show_sort_weight":111,"slug":112},"Exam",70,"exam",{"id":114,"doc_module":4,"doc_module_name":25,"category_name":115,"show_sort_weight":116,"slug":117},5,"Comic",60,"comic",{"id":119,"doc_module":4,"doc_module_name":25,"category_name":120,"show_sort_weight":121,"slug":122},6,"Technology",50,"technology",{"id":86,"doc_module":4,"doc_module_name":25,"category_name":29,"show_sort_weight":124,"slug":125},40,"healthcare",{"id":127,"doc_module":4,"doc_module_name":25,"category_name":128,"show_sort_weight":129,"slug":130},8,"Research & Report",30,"research-report",{"id":55,"doc_module":4,"doc_module_name":25,"category_name":132,"show_sort_weight":133,"slug":134},"Religion & Spirituality",20,"religion-spirituality",{"id":133,"doc_module":4,"doc_module_name":25,"category_name":136,"show_sort_weight":133,"slug":137},"World Cup","world-cup",{"id":139,"doc_module":4,"doc_module_name":25,"category_name":140,"show_sort_weight":139,"slug":141},10,"Lifestyle","lifestyle",{"id":143,"doc_module":4,"doc_module_name":25,"category_name":144,"show_sort_weight":114,"slug":145},19,"General","general"]