Significant shared heritability underlies suicide attempt and clinically predicted probability of attempting suicide.

Suicide accounts for nearly 800,000 deaths per year worldwide with rates of both deaths and attempts rising. Family studies have estimated substantial heritability of suicidal behavior; however, collecting the sample sizes necessary for successful genetic studies has remained a challenge. We utilized two different approaches in independent datasets to characterize the contribution of common genetic variation to suicide attempt. The first is a patient reported suicide attempt phenotype asked as part of an online mental health survey taken by a subset of participants (n = 157,366) in the UK Biobank. After quality control, we leveraged a genotyped set of unrelated, white British ancestry participants including 2433 cases and 334,766 controls that included those that did not participate in the survey or were not explicitly asked about attempting suicide. The second leveraged electronic health record (EHR) data from the Vanderbilt University Medical Center (VUMC, 2.8 million patients, 3250 cases) and machine learning to derive probabilities of attempting suicide in 24,546 genotyped patients. We identified significant and comparable heritability estimates of suicide attempt from both the patient reported phenotype in the UK Biobank (h2SNP = 0.035, p = 7.12 × 10-4) and the clinically predicted phenotype from VUMC (h2SNP = 0.046, p = 1.51 × 10-2). A significant genetic overlap was demonstrated between the two measures of suicide attempt in these independent samples through polygenic risk score analysis (t = 4.02, p = 5.75 × 10-5) and genetic correlation (rg = 1.073, SE = 0.36, p = 0.003). Finally, we show significant but incomplete genetic correlation of suicide attempt with insomnia (rg = 0.34-0.81) as well as several psychiatric disorders (rg = 0.26-0.79). This work demonstrates the contribution of common genetic variation to suicide attempt. It points to a genetic underpinning to clinically predicted risk of attempting suicide that is similar to the genetic profile from a patient reported outcome. Lastly, it presents an approach for using EHR data and clinical prediction to generate quantitative measures from binary phenotypes that can improve power for genetic studies.

Psychological primitives can make sense of biopsychosocial factor complexity in psychopathology.

BACKGROUND: Many agree that the biopsychosocial contributions to psychopathology are complex, yet it is unclear how we can make sense of this complexity. One approach is to reduce this complexity to a few necessary and sufficient biopsychosocial factors; although this approach is easy to understand, it has little explanatory power. Another approach is to fully embrace complexity, proposing that each instance of psychopathology is caused by a partially unique set of biopsychosocial factors; this approach has high explanatory power, but is impossible to comprehend. Due to deficits in either explanatory power or comprehensibility, both approaches limit our ability to make substantial advances in understanding, predicting, and preventing psychopathology. Thus, how can we make sense of biopsychosocial factor complexity? MAIN TEXT: There is a third possible approach that can resolve this dilemma, with high explanatory power and high comprehensibility. This approach involves understanding, predicting, and preventing psychopathology in terms of a small set of psychological primitives rather than biopsychosocial factors. Psychological primitives are the fundamental and irreducible elements of the mind, mediating all biopsychosocial factor influences on psychopathology. All psychological phenomena emerge from these primitives. Over the past decade, this approach has been successfully applied within basic psychological science, most notably affective science. It explains the sum of the evidence in affective science and has generated several novel research directions. This approach is equally applicable to psychopathology. The primitive-based approach does not eliminate the role of biopsychosocial factors, but rather recasts them as indeterminate causal influences on psychological primitives. In doing so, it reframes research away from factor-based questions (e.g., which situations cause suicide?) and toward primitive-based questions (e.g., how are suicidality concepts formed, altered, activated, and implemented?). This is a valuable shift because factor-based questions have indeterminate answers (e.g., infinite situations could cause suicide) whereas primitive-based questions have determinate answers (e.g., there are specific processes that undergird all concepts). CONCLUSION: The primitive-based approach accounts for biopsychosocial complexity, ties clinical science more directly to basic psychological science, and could facilitate progress in understanding, predicting, and preventing psychopathology.

Depression and hopelessness as risk factors for suicide ideation, attempts and death: meta-analysis of longitudinal studies.

BACKGROUND: Many studies have documented robust relationships between depression and hopelessness and subsequent suicidal thoughts and behaviours; however, much weaker and non-significant effects have also been reported. These inconsistencies raise questions about whether and to what degree these factors confer risk for suicidal thoughts and behaviours.AimsThis study aimed to evaluate the magnitude and clinical utility of depression and hopelessness as risk factors for suicide ideation, attempts and death. METHOD: We conducted a meta-analysis of published studies from 1971 to 31 December 2014 that included at least one longitudinal analysis predicting suicide ideation, attempt or death using any depression or hopelessness variable. RESULTS: Overall prediction was weaker than anticipated, with weighted mean odds ratios of 1.96 (1.81-2.13) for ideation, 1.63 (1.55-1.72) for attempt and 1.33 (1.18-1.49) for death. Adjusting for publication bias further reduced estimates. Effects generally persisted regardless of sample severity, sample age or follow-up length. CONCLUSIONS: Several methodological constraints were prominent across studies; addressing these issues would likely be fruitful moving forward.Declaration of interestNone.

Predicting suicide attempts in adolescents with longitudinal clinical data and machine learning.

BACKGROUND: Adolescents have high rates of nonfatal suicide attempts, but clinically practical risk prediction remains a challenge. Screening can be time consuming to implement at scale, if it is done at all. Computational algorithms may predict suicide risk using only routinely collected clinical data. We used a machine learning approach validated on longitudinal clinical data in adults to address this challenge in adolescents. METHODS: This is a retrospective, longitudinal cohort study. Data were collected from the Vanderbilt Synthetic Derivative from January 1998 to December 2015 and included 974 adolescents with nonfatal suicide attempts and multiple control comparisons: 496 adolescents with other self-injury (OSI), 7,059 adolescents with depressive symptoms, and 25,081 adolescent general hospital controls. Candidate predictors included diagnostic, demographic, medication, and socioeconomic factors. Outcome was determined by multiexpert review of electronic health records. Random forests were validated with optimism adjustment at multiple time points (from 1 week to 2 years). Recalibration was done via isotonic regression. Evaluation metrics included discrimination (AUC, sensitivity/specificity, precision/recall) and calibration (calibration plots, slope/intercept, Brier score). RESULTS: Computational models performed well and did not require face-to-face screening. Performance improved as suicide attempts became more imminent. Discrimination was good in comparison with OSI controls (AUC = 0.83 [0.82-0.84] at 720 days; AUC = 0.85 [0.84-0.87] at 7 days) and depressed controls (AUC = 0.87 [95% CI 0.85-0.90] at 720 days; 0.90 [0.85-0.94] at 7 days) and best in comparison with general hospital controls (AUC 0.94 [0.92-0.96] at 720 days; 0.97 [0.95-0.98] at 7 days). Random forests significantly outperformed logistic regression in every comparison. Recalibration improved performance as much as ninefold - clinical recommendations with poorly calibrated predictions can lead to decision errors. CONCLUSIONS: Machine learning on longitudinal clinical data may provide a scalable approach to broaden screening for risk of nonfatal suicide attempts in adolescents.

Understanding suicide risk within the Research Domain Criteria (RDoC) framework: A meta-analytic review.

BACKGROUND: The field is in need of novel and transdiagnostic risk factors for suicide. The National Institute of Mental Health's Research Domain Criteria (RDoC) provides a framework that may help advance research on suicidal behavior. METHOD: We conducted a meta-analytic review of existing prospective risk and protective factors for suicidal thoughts and behaviors (ideation, attempts, and deaths) that fall within one of the five RDoC domains or relate to a prominent suicide theory. Predictors were selected from a database of 4,082 prospective risk and protective factors for suicide outcomes. RESULTS: A total of 460 predictors met inclusion criteria for this meta-analytic review and most examined risk (vs. protective) factors for suicidal thoughts and behaviors. The overall effect of risk factors was statistically significant, but relatively small, in predicting suicide ideation (weighted mean odds ratio: wOR = 1.72; 95% CI: 1.59-1.87), suicide attempt (wOR = 1.66 [1.57-1.76), and suicide death (wOR = 1.41 [1.24-1.60]). Across all suicide outcomes, most risk factors related to the Negative Valence Systems domain, although effect sizes were of similar magnitude across RDoC domains. CONCLUSIONS: This study demonstrated that the RDoC framework provides a novel and promising approach to suicide research; however, relatively few studies of suicidal behavior fit within this framework. Future studies must go beyond the "usual suspects" of suicide risk factors (e.g., mental disorders, sociodemographics) to understand the processes that combine to lead to this deadly outcome.

Is externalizing psychopathology a robust risk factor for suicidal thoughts and behaviors? A meta-analysis of longitudinal studies.

OBJECTIVE: Our primary objective was to determine the potency of externalizing psychopathology as a risk factor for suicidal thoughts and behaviors (STBs). METHOD: We conducted a random effects meta-analysis of 174 prospective studies (839 unique statistical tests) examining externalizing psychopathology and suicidal thoughts and behaviors (STBs) published prior to December 8, 2017. The weighted mean odds ratios for the overall relationship between externalizing psychopathology and STBs were below 2.00 in magnitude, and all risk factor subcategories were also fairly modest predictors of STBs. Taking publication bias into account reduced the magnitude of these associations, particularly for death. Although externalizing psychopathology modestly predicts STBs, this may be due to design limitations of existing studies. Future research should employ shorter follow-up periods, consider risk factors in combination, and focus on forms of externalizing psychopathology that have not been studied extensively. RESULTS: The weighted mean odds ratios for the overall relationship between externalizing psychopathology and STBs were below 2.00 in magnitude, and all risk factor subcategories were also fairly modest predictors of STBs. Taking publication bias into account reduced the magnitude of these associations, particularly for death. Additionally, our results were mostly consistent regardless of sample age, sample severity, follow-up length, and predictor scale. CONCLUSIONS: Although externalizing psychopathology modestly predicts STBs, this may be due to design limitations of existing studies. Future research should employ shorter follow-up periods, consider risk factors in combination, and focus on forms of externalizing psychopathology that have not been studied extensively.

How to Implement Therapeutic Evaluative Conditioning in a Clinical Setting.

Self-injurious behaviors (SIBs), including both suicidal and nonsuicidal self-injury, are major public health problems that have been on the rise in recent decades. There are few effective SIB interventions, and those that are effective cannot reach most people who are in need of help-that is, these interventions are not scalable. To address this need, we recently developed a scalable, app-based treatment called Therapeutic Evaluative Conditioning (TEC) that preliminary studies have shown causes reductions in SIBs (Franklin et al., 2016). Although TEC was developed and evaluated as a standalone, self-administered intervention, it may also be a valuable therapeutic tool within traditional clinical settings. Here we provide a case illustration of a young adult female who presented at an outpatient clinic with a long history of self-injurious behaviors and multiple failed treatment attempts. In discussing this case, we describe how to implement TEC within such a setting and what might be expected as a result.

Evidence-based psychosocial treatments for self-injurious thoughts and behaviors in youth.

The purpose of this study was to review the current evidence base of psychosocial treatments for suicidal and nonsuicidal self-injurious thoughts and behaviors (SITBs) in youth. We reviewed major scientific databases (HealthSTAR, MEDLine, PsycINFO, PubMed) for relevant studies published prior to June 2013. The search identified 29 studies examining interventions for suicidal or nonsuicidal SITBs in children or adolescents. No interventions currently meet the Journal of Clinical Child and Adolescent Psychology standards for Level 1: well-established treatments. Six treatment categories were classified as Level 2: probably efficacious or Level 3: possibly efficacious for reducing SITBs in youth. These treatments came from a variety of theoretical orientations, including cognitive-behavioral, family, interpersonal, and psychodynamic theories. Common elements across efficacious treatments included family skills training (e.g., family communication and problem solving), parent education and training (e.g., monitoring and contingency management), and individual skills training (e.g., emotion regulation and problem solving). Several treatments have shown potential promise for reducing SITBs in children and adolescents. However, the probably/possibly efficacious treatments identified each have evidence from only a single randomized controlled trial. Future research should focus on replicating studies of promising treatments, identifying active treatment ingredients, examining mediators and moderators of treatment effects, and developing brief interventions for high-risk periods (e.g., following hospital discharge).

How developmental psychopathology theory and research can inform the research domain criteria (RDoC) project.

The recently proposed Research Domain Criteria (RDoC) project has the potential to stimulate new research and overcome many of the limitations of the Diagnostic and Statistical Manual of Mental Disorders taxonomy. In the present article we focus, in three main sections, on how theory and research from developmental psychopathology can inform RDoC. First, we discuss the ontology of mental illness and the potential advantages of the RDoC approach to understanding the nature of mental illness. Second, we note potential issues to consider when implementing the RDoC framework, including (a) integrating developmental processes, (b) classifying mental illness within a dimensional approach, and (c) avoiding problems associated with biological reductionism. Third, we describe how a developmental psychopathology perspective may inform each of these potential issues within RDoC. Finally, we highlight the study of emotion and the centrality of affective processes within the RDoC framework. Specifically, we describe how constructionist models of emotion are consistent with developmental psychopathology and how this perspective on emotion can help to guide RDoC research.

Chronic pain and suicide: understanding the association.

Chronic pain conditions are associated with an elevated risk for suicide. Of particular importance is the question of why pain conditions might be linked to increased suicide risk. We discuss the association between chronic pain and psychological pain, particularly in the context of depression, and the use of suicide as an attempt to escape from what is perceived as unbearable suffering. We also consider the role that chronic pain may play in increasing the capacity for suicide. Bridging across research areas and drawing on the interpersonal-psychological theory of suicide, we suggest that chronic pain may facilitate the development of a key risk factor for suicide: fearlessness about death. Given that chronic pain can lead to (and be exacerbated by) depression, engender hopelessness, facilitate a desire for escape through death, and erode the natural fear of dying, clinicians must be aware of psychological processes that can combine to create elevated suicide risk in patients with chronic pain, and they should also assess and treat suicide risk factors in these patients.

Feeling worse to feel better: pain-offset relief simultaneously stimulates positive affect and reduces negative affect.

Although pain itself induces negative affect, the removal (or offset) of pain induces a powerful state of relief. Despite being implicated in a wide range of psychological and behavioral phenomena, relief remains a poorly understood emotion. In particular, some theorists associate relief with increased positive affect, whereas others associate relief with diminished negative affect. In the present study, we examined the affective nature of relief in a pain-offset paradigm with psychophysiological measures that were specific to negative valence (startle eyeblink reactivity) and positive valence (startle postauricular reactivity). Results revealed that pain offset simultaneously stimulates positive affect and diminishes negative affect for at least several seconds. Results also indicated that pain intensity differentially affects the positive and negative valence aspects of relief. These findings clarify the affective nature of relief and provide insight into why people engage in both normal and abnormal behaviors associated with relief.

Nonsuicidal self-injury and diminished pain perception: the role of emotion dysregulation.

Nonsuicidal self-injury (NSSI) is the deliberate destruction of one's own body tissue in the absence of suicidal intent (e.g., cutting or burning the skin). Previous studies have found that people with a history of NSSI display diminished pain perception. However, it remains unclear why this effect occurs. In the present study, we used a sample of participants with (n = 25) and without (n = 47) a history of NSSI to test the hypothesis that emotion dysregulation partially explains why NSSI is associated with diminished pain perception. Pain perception was quantified as pain threshold, pain tolerance, and pain intensity ratings assessed during the cold pressor task. Nonsuicidal self-injury was associated with increased emotion dysregulation and diminished pain perception. Results showed that emotion dysregulation was correlated with diminished pain perception within both groups, demonstrating that this association exists regardless of NSSI history. Results also specified that emotion dysregulation partially accounted for the association between NSSI and pain tolerance but not other pain variables. Overall, results were consistent with the hypothesis that emotion dysregulation may increase NSSI risk in part by increasing the willingness to experience the pain involved in self-injury. Studies are needed to more directly investigate this hypothesis.

Longitudinal studies support the safety and ethics of virtual reality suicide as a research method.

Many have expressed concerns about the safety and ethics of conducting suicide research, especially intense suicide research methods that expose participants to graphic depictions of suicidality. We conducted two studies to evaluate the effects of one such method called virtual reality (VR) suicide. Study 1 tested the effects of VR suicide exposure over the course of one month in participants with (n = 56) and without a history of suicidality (n = 50). Study 2 exposed some participants to VR suicide scenarios (n = 79) and others to control scenarios (n = 80). Participants were invited to complete a follow-up assessment after an average of 2 years. For both studies, the presence of suicidality post exposure was the primary outcome, with closely related constructs (e.g., capability for suicide, agitation) as secondary outcomes. Study 1 found no pre-post increases in suicidality or related variables, but revealed several significant decreases associated with small to medium effect sizes in suicide-related constructs. In Study 2, VR suicide exposure did not cause any significant increases in suicidality or related variables. Together with prior research, these findings suggest that methods involving intense suicide stimuli appear safe and consistent with utilitarian ethics.

The Differences Between Individuals Engaging in Nonsuicidal Self-Injury and Suicide Attempt Are Complex (vs. Complicated or Simple).

BACKGROUND: Why do some people engage in nonsuicidal self-injury (NSSI) while others attempt suicide? One way to advance knowledge about this question is to shed light on the differences between people who engage in NSSI and people who attempt suicide. These groups could differ in three broad ways. First, these two groups may differ in a simple way, such that one or a small set of factors is both necessary and sufficient to accurately distinguish the two groups. Second, they might differ in a complicated way, meaning that a specific set of a large number of factors is both necessary and sufficient to accurately classify them. Third, they might differ in a complex way, with no necessary factor combinations and potentially no sufficient factor combinations. In this scenario, at the group level, complicated algorithms would either be insufficient (i.e., no complicated algorithm produces good accuracy) or unnecessary (i.e., many complicated algorithms produce good accuracy) to distinguish between groups. This study directly tested these three possibilities in a sample of people with a history of NSSI and/or suicide attempt. METHOD: A total of 954 participants who have either engaged in NSSI and/or suicide attempt in their lifetime were recruited from online forums. Participants completed a series of measures on factors commonly associated with NSSI and suicide attempt. To test for simple differences, univariate logistic regressions were conducted. One theoretically informed multiple logistic regression model with suicidal desire, capability for suicide, and their interaction term was considered as well. To examine complicated and complex differences, multiple logistic regression and machine learning analyses were conducted. RESULTS: No simple algorithm (i.e., single factor or small set of factors) accurately distinguished between groups. Complicated algorithms constructed with cross-validation methods produced fair accuracy; complicated algorithms constructed with bootstrap optimism methods produced good accuracy, but multiple different algorithms with this method produced similar results. CONCLUSIONS: Findings were consistent with complex differences between people who engage in NSSI and suicide attempts. Specific complicated algorithms were either insufficient (cross-validation results) or unnecessary (bootstrap optimism results) to distinguish between these groups with high accuracy.

The differences between suicide ideators and suicide attempters: Simple, complicated, or complex?

OBJECTIVE: Suicide ideators and suicide attempters might differ in 3 possible ways. First, they might differ in a simple way such that one or a small set of factors are both necessary and sufficient to distinguish between the 2 groups. Second, ideators and attempters might differ in a complicated way such that a specific combination of a large set of factors is necessary and sufficient for the distinction. Third, complex differences might exist: many possible combinations of a large set of factors may be sufficient to distinguish the 2 groups, but no combination may be necessary. This study empirically examined these possibilities. METHOD: Across 5 samples (total N = 3,869), univariate logistic regressions were conducted to test for simple differences. To test for complicated and complex differences, machine learning (ML) methods were used to identify the optimized algorithm with all variables. Subsequently, the same methods were repeated after removing the top 5 most important or discriminative variables, and a randomly selected 10% subset of variables. Multiple logistic regressions were conducted with all variables. RESULTS: Results were consistent across samples. Univariate logistic regressions on average yielded chance-level accuracy. ML algorithms with all variables showed good accuracy; substantial deviation from the optimized algorithms through the removal of variables did not result in significantly poorer performance. Multiple logistic regressions produced poor to fair accuracy. CONCLUSIONS: Differences between suicide ideators and attempters are complex. Findings suggest that their differences may be better understood on a psychological primitive level than a biopsychosocial factor level. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Anticipated consequences as the primary causes of suicidal behavior: Evidence from a laboratory study.

Due to the limitations of conducting experimental studies on suicide, little is known about its causes. Based on basic behavioral research, we hypothesized that the anticipated consequences of suicidal behavior (e.g., stress relief) are the primary causes of suicidal behavior, and experienced antecedents (e.g., stress) are secondary causes. We evaluated this general hypothesis by testing the causal effects of these two broad phenomena on virtual reality (VR) suicide in 497 participants across five groups. On their own, experienced antecedent manipulations (i.e., stress and rejection) did not significantly increase VR suicide rates relative to the control group. On its own, the anticipated consequence manipulation (i.e., instruction that engaging in VR suicide would allow one to avoid a future stressor) caused a large increase in the VR suicide rate. In the context of an experienced antecedent manipulation (i.e., stress), this anticipated consequence manipulation caused an even larger increase in the VR suicide rate. These findings suggest that the anticipated consequences of suicidal behavior (e.g., avoidance or escape from something unpleasant, attaining something pleasant) are the primary causes of suicidal behavior, and that experienced antecedents (e.g., stress) serve as secondary causes when they make an anticipated consequence of suicidal behavior seem more appealing.

Brain Differences Associated with Self-Injurious Thoughts and Behaviors: A Meta-Analysis of Neuroimaging Studies.

This meta-analysis aims to evaluate whether the extant literature justifies any definitive conclusions about whether and how SITBs may be associated with brain differences. A total of 77 papers (N = 4,903) published through January 1, 2019 that compared individuals with and without SITBs were included, resulting in 882 coordinates. A pooled meta-analysis assessing for general risk for SITBs indicated a lack of convergence on structural differences. When all types of control groups were considered, functional differences in the left posterior cingulate cortex (PCC), right amygdala, left hippocampus, and right thalamus were significant using multi-level kernel density analysis (pcorrected < 0.05) but nonsignificant using activation-likelihood estimation. These results suggest that a propensity for internally-oriented, emotional processing coupled with under-active pain processing could potentially underlie SITBs, but additional research is needed to test this possibility. Separate analyses for types of SITBs suggested that the brain differences associated with deliberate self-harm were consistent with the overall findings. Checkered moderator effects were detected. Overall, the meta-analytic evidence was not robust. More studies are needed to reach definitive conclusions about whether SITBs are associated with brain differences.

Interventions for suicide and self-injury: A meta-analysis of randomized controlled trials across nearly 50 years of research.

Self-injurious thoughts and behaviors (SITBs) are major public health concerns impacting a wide range of individuals and communities. Despite major efforts to develop and refine treatments to reduce SITBs, the efficacy of SITB interventions remains unclear. To provide a comprehensive summary of SITB treatment efficacy, we conducted a meta-analysis of published randomized controlled trials (RCTs) that have attempted to reduce SITBs. A total of 591 published articles from 1,125 unique RCTs with 3,458 effect sizes from the past 50 years were included. The random-effects meta-analysis yielded surprising findings: The overall intervention effects were small across all SITB outcomes; despite a near-exponential increase in the number of RCTs across five decades, intervention efficacy has not improved; all SITB interventions produced similarly small effects, and no intervention appeared significantly and consistently stronger than others; the overall small intervention effects were largely maintained at follow-up assessments; efficacy was similar across age groups, though effects were slightly weaker for child/adolescent populations and few studies focused on older adults; and major sample and study characteristics (e.g., control group type, treatment target, sample size, intervention length) did not consistently moderate treatment efficacy. This meta-analysis suggests that fundamental changes are needed to facilitate progress in SITB intervention efficacy. In particular, powerful interventions target the necessary causes of pathology, but little is known about SITB causes (vs. SITB correlates and risk factors). The field would accordingly benefit from the prioritization of research that aims to identify and target common necessary causes of SITBs. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

User-Centered Design of a Machine Learning Intervention for Suicide Risk Prediction in a Military Setting.

Primary care represents a major opportunity for suicide prevention in the military. Significant advances have been made in using electronic health record data to predict suicide attempts in patient populations. With a user-centered design approach, we are developing an intervention that uses predictive analytics to inform care teams about their patients' risk of suicide attempt. We present our experience working with clinicians and staff in a military primary care setting to create preliminary designs and a context-specific usability testing plan for the deployment of the suicide risk indicator.

Trait anger, anger expression, and suicide attempts among adolescents and young adults: a prospective study.

Previous studies of the relationship between anger, anger expression, and suicidal behavior have been largely cross-sectional and have yielded mixed findings. In a prospective, naturalistic study, we examined how trait anger and anger expression influenced the likelihood of suicide attempts among 180 adolescents followed for up to 13.3 years after discharge from an inpatient psychiatry unit. Results showed that higher trait anger and anger expressed outwardly over the follow-up was related to increased likelihood of suicide attempts among boys. For girls, trait anger and both the inward and outward expression of anger moderated the risk for suicide attempts associated with major depression. These results are interpreted in light of theory regarding behavioral activation and behavioral inhibition systems.