Reducing Suicidality Through Insomnia Treatment: Critical Next Steps in Suicide Prevention

Hamilton, Jessica L.; Buysse, Daniel J.

doi:10.1176/appi.ajp.2019.19080888

Cited by 18 publications

(12 citation statements)

References 17 publications

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“…For GPS generation, we selected 24 target outcomes that are thought to be related to suicidality. These include personality, cognitive, psychological traits, and psychiatric disorders that are known to be broadly related to suicidality: general happiness, 12,13 insomnia, 14 depression, 15 risk behaviors, 16 risk tolerance, educational attainment, 17,18 cognitive performance, 16,17 snoring, 19 worry, 20 IQ, 17 behaviors: cannabis usage, 21,22 drink per week, 23 smoker, 24 Attention deficit hyperactivity disorder (ADHD), 25 Autism spectrum disorder (ASD), 26 major depressive disorder (MDD), 27 schizophrenia, 28 bipolar disorder, 29 post-traumatic stress disorder (PTSD). 30 For general happiness, four kinds of GPS were built and tested for the study: the participants were asked with different mental health questionnaires for measuring the level of subjective well-being, such as (1) two different GWAS of “how happy are you in general”, (2) “how happy are you with your health in general”, and (3) “to what extent do you feel your life to be meaningful”.…”

Section: Methodsmentioning

confidence: 99%

Genome-Wide Polygenic Scores for Common Traits and Psychiatric Disorders Identify Young Children with Risk for Suicides

Joo

Moon

Wang

et al. 2020

Preprint

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BackgroundSuicide is the leading cause of death in youth worldwide.1 Identifying children with high risk for suicide remains challenging.2 Here we test the extents to which genome-wide polygenic scores (GPS) for common traits and psychiatric disorders are linked to the risk for suicide in young children.MethodsWe constructed GPSs of 24 traits and psychiatric disorders broadly related to suicidality from 8,212 US children with ages of 9 to 10 from the Adolescent Brain Cognitive Development study. We performed multiple logistic regression to test the association between childhood suicidality, defined as suicidal ideation or suicidal attempt, and the GPSs. Machine learning techniques were used to test the predictive utility of the GPSs and other phenotypic outcomes on suicide and suicidal behaviors in the youth.OutcomesWe identified three GPSs significantly associated with childhood suicidality: Attention deficit hyperactivity disorder (ADHD) (P = 2.83×10−4; odds ratio (OR) = 1.12, FDR correction), general happiness with belief that own life is meaningful (P = 1.30×10−3; OR = 0.89) and autism spectrum disorder (ASD) (P = 1.81×10−3; OR = 1.14). Furthermore, the ASD GPS showed significant interaction with ELS such that a greater polygenic score in the presence of a greater ELS has even greater likelihood of suicidality (with active suicidal ideation, P = 1.39×10−2, OR = 1.11). In machine learning predictions, the cross validated and optimized model showed an ROC-AUC of 0.72 and accuracy of 0.756 for the hold-out set of overall suicidal ideation prediction, and showed an ROC-AUC of 0.765 and accuracy of 0.750 for the hold-out set of suicidal attempts.InterpretationOur results show that childhood suicidality is linked to the GPSs for psychiatric disorders, ADHD and ASD, and for a common trait, general happiness, respectively; and that GPSs for ASD and insomnia, respectively, have synergistic effects on suicidality via an interaction with early life stress. By providing the quantitative account of the polygenic and environmental factors of childhood suicidality in a large, representative population, this study shows the potential utility of the GPS in investigation of childhood suicidality for early screening, intervention, and prevention.

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Section: Methodsmentioning

confidence: 99%

Genome-Wide Polygenic Scores for Common Traits and Psychiatric Disorders Identify Young Children with Risk for Suicides

Joo

Moon

Wang

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…For GPS generation, we selected 25 psychiatric and common traits that are known to be related to suicidality and have their GWAS summary statistics data publicly available, including personality, cognitive, psychological traits, and psychiatric disorders that are known to be broadly related to suicidality: general happiness, 34,35 insomnia, 36 depression, 37 risk behaviors, 38 risk tolerance, educational attainment, 39,40 cognitive performance, 38,39 snoring, 41 worry, 42 IQ, 39 cannabis usage, 43,44 drink per week, 45 smoker, 46 Attention de cit hyperactivity disorder (ADHD), 47 Autism spectrum disorder (ASD), 48 major depressive disorder (MDD), 49 schizophrenia, 50 bipolar disorder, 51 post-traumatic stress disorder (PTSD) 52 and Alzheimer's disease 53 For general happiness, four GPS were built and tested for the study: the participants were asked with different questionnaires about their level of subjective well-being, such as (1) two different GWAS of "how happy are you in general", (2) "how happy are you with your health in general", and (3) "to what extent do you feel your life to be meaningful". All the GWAS summary statistics of the aforementioned traits are publicly available and have been collected for GPS generation.…”

Section: Construction Of Genome-wide Polygenic Scores (Gps)mentioning

confidence: 99%

Genome-wide Polygenic Scores for Multiple Psychiatric and Common Traits Identify Preadolescent Youth with Risk for Suicide

Joo

Moon

Wang

et al. 2021

Preprint

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Importance. Suicide is the second leading cause of death in children worldwide but no available means exist to identify the risk in youth. Objective. To predict the risk of suicide in children and to investigate whether and to what extents genetic factors and a major environmental risk factor, early life stress(ELS), influence youth suicide. Design, Setting and Participants. We analyzed the genotype-phenotype data of 11,869 preadolescent children ages 9- to 10-year-old from the Adolescent Brain and Cognitive Development (ABCD) study. We estimated genome-wide polygenic scores (GPSs) of 25 complex traits to investigate their phenome-wide associations and predictive utility with suicidality (suicidal ideation and attempt) with machine learning approaches. Predictors. GPSs of 25 traits including psychiatric disorders, personality, cognitive capacity, and psychological traits. Parent Child Behavior Checklist to measure ELS in youth and Youth Family Environment Scale to assess family environment. Main outcomes and Measures. Records of suicidal ideation and attempt of the participants were derived from the computerized version of Kiddie Schedule for Affective Disorders and Schizophrenia (K-SADS). Results. We identified three GPSs associated with youth suicidality in multiethnic (n = 7,206) and European-ancestry (n = 5,749) participants: ADHD (P = 3.48x10− 4; odds ratio = 1.13 in multiethnic participants, P = 5.60x10− 5, OR = 1.25 in European-ancestry participants), general happiness (P = 1.43x10− 3; OR = 0.89 in multiethnic, P = 8.61x10− 4, OR = 0.89 in European) and autism spectrum disorder(ASD) (P = 1.81x10− 3; OR = 1.15 in multiethnic, P = 1.26x10− 3, OR = 1.18 in European). We also found a significant GPS-by-environment interaction between the effects of genetic risk factors for ASD and the level of ELS in increasing the risk for suicidal ideation (P = 1.36x10− 2, OR = 1.12 in multiethnic, P = 1.39x10− 3, OR = 1.19 in European). A machine learning model trained on the same data showed moderately accurate prediction of children with overall suicidal ideation with a test ROC-AUC of 0.727 (0.746 in European), and with suicidal attempts with a test ROC-AUC of 0.641 (0.975 in European) in held-out samples. Conclusions and Relevance. This study provides the first quantitative account of polygenic and environmental factors of suicidality in a large, representative population of preadolescent youth. It thus shows the potential utility of the GPSs in identifying a child with high risk for suicidality for early screening, intervention, and prevention.

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“…For GPS generation, we selected psychiatric and common traits that are known to be related to suicidality including personality, cognitive, psychological traits, and psychiatric disorders that are known to be broadly related to suicidality: general happiness, 29,30 insomnia, 31 depression, 32 risk behaviors, 33 risk tolerance, educational attainment, 34,35 cognitive performance, 33,34 snoring, 36 worry, 37 IQ, 34 cannabis usage, 38,39 drink per week, 40 smoker, 41 Attention deficit hyperactivity disorder (ADHD), 42 Autism spectrum disorder (ASD), 43 major depressive disorder (MDD), 44 schizophrenia, 45 bipolar disorder, 46 posttraumatic stress disorder (PTSD). 47 For general happiness, four GPS were built and tested for the study: the participants were asked with different questionnaires about their level of subjective well-being, such as (1) two different GWAS of "how happy are you in general", (2) "how happy are you with your health in general", and 3"to what extent do you feel your life to be meaningful".…”

Section: Construction Of Genomewide Polygenic Scores (Gps)mentioning

confidence: 99%

Genome-wide Polygenic Scores for Multiple Psychiatric and Common Traits Identify Preadolescent Youth with Risk for Suicide

Joo¹,

Moon²,

Wang³

et al. 2020

Preprint

View full text Add to dashboard Cite

Suicide is a leading cause of death in youth worldwide, but identifying which youth are at high risk for suicide remains challenging. We constructed genome-wide polygenic scores (GPSs) from 24 psychiatric disorders and common traits from 8,212 US preadolescent children ages 9 to 10 and investigated their associations and predictive utility with suicidality (suicidal ideation and attempt). We identified three GPSs significantly associated with youth suicidality: ADHD (P=2.83x10-4; odds ratio=1.12), general happiness with a belief that life is meaningful (P=1.30x10-3; odds ratio=0.89) and autism spectrum disorder (ASD) (P=1.81x10-3; odds ratio=1.14). We also found a significant gene-by-environment interaction such that the GPS of ASD in the context of early life stress substantially increased suicidal ideation (P=1.39x10-2, odds ratio=1.11). Machine learning models showed, in predicting suicidal ideation, a receiver operators characteristics-area under the curve (ROC-AUC) of 0.72, and, in suicidal attempts, a ROC-AUC of 0.765. By providing the first quantitative account of the polygenic and environmental factors of suicidality in a large, representative population of preadolescent youth, this study shows the potential utility of the GPSs in investigating youth suicidality for early screening, intervention, and prevention.

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Reducing Suicidality Through Insomnia Treatment: Critical Next Steps in Suicide Prevention

Cited by 18 publications

References 17 publications

Genome-Wide Polygenic Scores for Common Traits and Psychiatric Disorders Identify Young Children with Risk for Suicides

Genome-Wide Polygenic Scores for Common Traits and Psychiatric Disorders Identify Young Children with Risk for Suicides

Genome-wide Polygenic Scores for Multiple Psychiatric and Common Traits Identify Preadolescent Youth with Risk for Suicide

Genome-wide Polygenic Scores for Multiple Psychiatric and Common Traits Identify Preadolescent Youth with Risk for Suicide

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