Domains of the autism phenotype, cognitive control, and rumination as transdiagnostic predictors of DSM-5 suicide risk

Darren Hedley; Mirko Uljarević; Ru Ying Cai; Simon M Bury; Mark A Stokes; David W Evans

doi:10.1371/journal.pone.0245562

. 2021 Jan 22;16(1):e0245562. doi: 10.1371/journal.pone.0245562

Domains of the autism phenotype, cognitive control, and rumination as transdiagnostic predictors of DSM-5 suicide risk

Darren Hedley ^1,^*, Mirko Uljarević ^2,³, Ru Ying Cai ⁴, Simon M Bury ¹, Mark A Stokes ⁵, David W Evans ⁶

Editor: Vincenzo De Luca⁷

PMCID: PMC7822649 PMID: 33482664

Abstract

Suicide is a global health problem affecting both normative and clinical populations. Theoretical models that examine mechanisms underlying suicide risk across heterogeneous samples are needed. The present study explored core characteristics associated with autism spectrum disorder (ASD), a sub-population at high risk of suicide, as well as two dimensional cognitive constructs, as potential transdiagnostic predictors of suicidal ideation in a clinically diverse sample. Participants (n = 1851, 62% female) aged 18 to 89 years completed online questionnaires assessing: social communication difficulties; insistence on sameness; cognitive control; and rumination. Forty-three percent of participants reported the presence of at least one neurodevelopmental or neuropsychiatric disorder. One third of the sample reported some suicidal ideation (SI), and 40 percent met the threshold for concern for depression. All hypothesized constructs were associated with SI and depression and, with the exception of rumination, contributed significantly to SI. Participants reporting SI returned significantly higher social communication difficulties and insistence on sameness, and lower levels of cognitive control than those reporting no-SI. The study was limited by the use of a cross-sectional sample assessed with self-report measures. All diagnoses were self-reported and the study was additionally limited by the use of a single item indicator of suicidal ideation. These findings support a role for constructs associated with the ASD phenotype and associated broad cognitive domains as potential risk factors underlying suicidal ideation in a large clinically diverse sample. Our findings suggest directions for future longitudinal research studies, along with specific targets for suicide prevention and clinical practice.

Introduction

Suicide and attempted suicide are major public health concerns, with suicide being a leading cause of death globally [1]. The World Health Organization estimates over 800,000 people die annually as a result of suicide, and it is the leading cause of death in youth aged 15 to 19 years [1]. Suicide is defined as the act of deliberately killing oneself, whereas suicide behavior refers to a range of behaviors including thinking about suicide (ideation), planning for suicide, non-fatal suicide attempt, as well as suicide [1]. To advance research in suicide prevention, O'Connor and Portzky, along with multiple international experts, identified key future developments and challenges in the field [2]. These included the need for more research into the testing and application of theoretical models of suicidal behavior, refining the understanding of sub-groups of people at risk in order to develop tailored interventions, and consideration of transdiagnostic theoretical frameworks and models that better address the heterogeneity between people who experience suicidal behavior.

In the present study we adopt a dimensional approach, based on principles derived from the National Institute of Mental Health Research Domain Criteria (RDoC) [3, 4], as well as the support for a continuum between autistic traits in clinical cases and the general population [5, 6], to explore the contribution of autistic traits to heightened suicidal ideation in a large, clinically diverse sample. Autistic traits were selected due to the heightened risk of suicide amongst clinical cases (i.e., a sub-group at risk) [2], as well as recent evidence of an association between elevated autistic-related traits and global suicide risk [7].

Suicide risk has traditionally been studied within the context of single disorders (e.g., most notably, depression) [8]. However, suicide rates are elevated across a wide range of psychiatric disorders, and are compounded by the presence of comorbidities [9–11]. In addition, suicide risk is elevated in those with subclinical traits who may not meet criteria for a formal diagnosis of a psychiatric disorder [8, 11, 12]. Better understanding of sub-groups at risk, embedded within a transdiagnostic framework, is suggested to be a useful approach to improving understanding of mechanisms that underpin elevated suicide risk [2]. Notably, recent research has highlighted particularly high risk for suicidal behavior––including death by suicide––among individuals with autism spectrum disorder (ASD) who also exhibit increased levels of other neuropsychiatric symptoms [13–17]. Furthermore, autistic traits are present in other (i.e., non-ASD) clinical groups [18] and in the general population [5, 6, 19, 20]. Thus, traits present in clinical cases of ASD (e.g., social communication difficulties, cognitive rigidity and insistence on sameness) might also underlie psychiatric difficulties across normative and clinical samples [5, 6]; for example, ASD traits are found to be associated with a range of negative outcomes (e.g., anxiety; depression) [21, 22], including suicide [23]. Based on these observations, a prudent approach towards better understanding of the suicide risk and personalization of treatment is to adopt a dimensional and individual differences approach [24, 25].

Indeed, it is now widely understood that a range of domains transect categorical diagnostic boundaries, and these domains might provide predictive value in explaining phenomena over and beyond, and independent from, specific diagnoses [4, 24–26]. A dimensional approach––which focuses on identifying specific risk and protective factors and attempts to understand underlying mechanisms––is therefore likely to provide a useful framework for understanding suicide risk and behavior across clinically diverse samples [24, 25, 27]. Recently, estimates of dimensional psychopathology derived from RDoC [3, 4] and applied to hospital discharge documentation were found to be associated with patient suicide and accidental death [27]; thereby demonstrating a potential application of dimensional frameworks to suicide prevention. However, to our knowledge, studies have yet to explore the nature of the interaction between constructs or traits associated with ASD, and transdiagnostic risk and resilience factors such as cognitive control and positive and negative valence, in predicting suicidality in a large community sample spanning normative and atypical development. Furthermore, we currently lack insight into how elevated traits associated with ASD alone, and in combination with other neuropsychiatric symptoms, relate to suicidality in community samples.

Autism phenotype as a risk factor for suicide

There is considerable evidence of heightened risk of suicidal behavior in people with ASD [13, 28–31], with suicide being the most significant predictor of premature mortality in individuals with ASD who do not have co-occurring intellectual disability (ID), as well as a significant risk factor in those with ID [14, 32]. Additionally, ASD trait severity is increased in adults with ASD who have planned or attempted suicide compared to those who do not have a lifetime history of planned or attempted suicide [13]. This suggests ASD trait severity may be a risk marker for suicide behavior in people with clinical ASD diagnoses. In terms of mechanisms, research with ASD clinical samples suggests ASD trait severity indirectly increases suicide risk through depression [30].

There is additional reason to believe that ASD traits may be an important risk factor for suicide behavior in broader clinical and non-clinical populations. From the standpoint of categorical diagnostic classifications, ASD commonly co-occurs with other neurodevelopmental and neuropsychiatric disorders [17], thereby compounding risk through the presence of multiple (vs. single) disorders. Traits or characteristics associated with ASD are normally distributed throughout the general population [33, 34] and, if viewed dimensionally, tend to be associated with elevated traits and symptoms of other disorders [35, 36]. There is emerging evidence that ASD traits are risk markers for suicide in people who do not have a diagnosis of ASD, including both non-clinical [7, 23, 28, 37, 38] and clinical (e.g., first episode psychosis) [17] populations.

Research with non-ASD populations suggests a direct relationship between ASD traits and suicidal behavior. In a non-clinical sample of young adults, Pelton and Cassidy [38] examined the relationship between ASD traits (broadly assessed with the Autism Spectrum Quotient, AQ) [33] and suicidal behavior within the context of the Interpersonal-Psychological Theory of Suicide (ITPS) [39]. ASD traits were found to significantly correlate with suicidal behavior, and this relationship was mediated by burdensomeness and thwarted belonging, suggesting a possible mechanism whereby social difficulties, which characterize ASD, may increase vulnerability to social risk factors for suicidal behavior. ASD traits also independently predicted variance in suicidal behavior in adults from the general population [28] and active military service members [23], supporting the hypothesis that heightened ASD traits increase risk for suicidal behavior in non-clinical populations. A study by Upthegrove et al. [17] examined the contribution of ASD traits to depression and suicide in a healthy, non-help seeking population, and in individuals experiencing first episode psychosis. Traits of ASD and psychosis were associated with increased levels of depressive symptoms in the non-help seeking population, and ASD traits and positive symptoms were associated with increased depressive symptoms, hopelessness, and suicidal ideation in the clinical sample. However, further research is required to assess the contribution of distinct constructs or characteristics directly associated with ASD, as well as broader but associated cognitive difficulties, to suicide risk.

Together, these studies suggest that traits associated with the ASD phenotype contribute to psychopathology. Moreover, it is plausible that ASD traits increase suicide risk either directly, or indirectly through depression and mediators such as hopelessness [17], loneliness, and low perceived social support [30], or burdensomeness and belonging [23, 38]. Furthermore, as has been demonstrated above, ASD traits may be present at the clinical or sub-clinical level thereby affecting a larger sector of the population. To gain better understanding of the association between ASD traits and suicidal behavior, two important gaps in the research must be addressed. First, research is needed to tease apart those aspects of the ASD phenotype that confer risk for suicidality; for example, social communication difficulties and insistence on sameness or perseveration are factors that have been linked to suicidal behavior [24], and are also defining characteristics of ASD [40]. Second, it is important to understand how different aspects of the ASD phenotype interact with other transdiagnostic domains (e.g., cognitive control, negative valence) to predict suicidal behavior. This is clinically important as some of these factors might be modifiable through targeted treatment.

Mechanisms underpinning suicide risk and the autistic phenotype

Diagnostically, ASD is characterized by persistent impairments in social communication and interaction, and the presence of restricted and repetitive patterns of behavior, interests and activities, inclusive of hyper- or hypo-reactivity to sensory stimuli [40]. Of these core symptoms, social communication difficulties and cognitive rigidity or insistence on sameness, that are also distributed across normative and clinical samples, may be particularly important risk factors for depression and suicidality [24, 25]. It is therefore important to understand how these factors individually, and collectively, interact to predict suicidal behavior within a clinically diverse, transdiagnostic sample.

Rumination and cognitive rigidity, which are associated with the restricted and repetitive domains of ASD [41], are not specific to ASD and are distributed across the general population, forming part of the RDoC Negative Valence system [3, 4]. Several studies have demonstrated a link between cognitive rigidity, which is associated with externalizing disorders [42], and suicide risk and behavior [42–44]. Rumination, on the other hand, is associated with internalizing symptoms [45] and depressive symptoms both in ASD [41, 45] and non-ASD populations [46]. Specifically, rumination has been found to predict the onset and duration of depression, and is associated with self-harm and suicidal ideation [47, 48].

In addition to the core symptoms, people with ASD often present with difficulties in broad cognitive domains including executive function and cognitive control [49–52], with these difficulties likely underpinning cognitive and behavioral rigidity, as well as social communication difficulties [53]. However, these deficits are not specific to ASD, but also feature across neurodevelopmental and neuropsychiatric disorders, as well as the general population, potentially leading to poor outcomes, which include suicide risk and behavior [54, 55].

The aims of the present study were to examine (1) the contribution of the two core clinical domains of ASD—social communication difficulties, insistence on sameness—on suicide risk (assessed using DSM-5 suicidal ideation; SI) and (2) the additional contribution and interaction of two key dimensional constructs––cognitive control and rumination. We predict that each of the identified constructs will independently contribute to SI, controlling for depression.

Materials and methods

Participants

Participants were 1851 (62.3% female) individuals aged 18–89 years (M = 37.09, SD = 12.28). The recruitment strategy followed that of previously published research and conducted recruitment online using Survey Sampling International (SSI; Shelton, CT) [56, 57], an online recruitment platform that specializes in recruiting demographically representative samples for scientific research in the United States and that is similar to other established and reliable commercial data recruitment platform (e.g., Prolific Academic, Amazon’s Mechanical Turk) [58–60]. Eligible participants were provided with a Qualtrics [61] link to the survey questionnaires. Participant demographics are provided in Table 1. The resultant sample was generally representative of the US population for race (although there were fewer Hispanics/Latinos in the study sample), income, education, and rural and urban populations [62], representing all 50 States as well as the District of Columbia (S1 Table). Given the sample consisted of a higher portion of females than males, demographic variables were explored between genders using Pearson’s chi-squared test (Table 1). Race was proportionately distributed across gender, except for a slightly higher proportion of Hispanic males relative to females. Females were also more likely to report having more than one racial identity than males. Male and female participants differed significantly on highest education level achieved, household income, and marital status. Because the sample was otherwise representative of the general population, lifetime presence of neurodevelopmental and neuropsychiatric disorders (43.5%) was consistent with that reported for the United States (46.4%) [63]. Females reported a relatively higher number of psychiatric diagnoses than males overall, including significantly more diagnoses of anxiety, depression, and approaching significance for post-traumatic stress disorder; however, relatively more males than females reported a diagnosis of schizophrenia. Approximately one quarter of the sample reported taking medications for their condition, the difference in medication use between males and females was not statistically significant.

Table 1. Demographics including neurodevelopmental and neuropsychiatric disorders, and gender comparisons.

		Male	Female	Total	General population data	Variable × Gender [95% BCa CI]
Variable	Label	n (%)			General population data	Variable × Gender [95% BCa CI]
n		698 (37.7%)	1153 (62.3%)	1851 (100%)	–
					2010 US Census, %
Race	White	510 (73.1%)	821 (71.2%)	1331 (71.9%)	72.4%	χ²(1) = 0.745, p = .388
	Black/African American	73 (10.5%)	129 (11.2%)	202 (10.9%)	12.6%	χ²(1) = 0.238, p = .626
	Hispanic	54 (7.7%)	62 (5.4%)	116 (6.3%)	16.4%	χ²(1) = 4.12, p = .042
	Asian	34 (4.9%)	44 (3.8%)	78 (4.2%)	4.8%	χ²(1) = 1.20, p = .274
	Native Hawaiian/Pacific Islander	2 (0.3%)	4 (0.3%)	6 (0.3%)	0.2%	χ²(1) = 0.049, p = .825
	Native American	3 (0.4%)	14 (1.2%)	17 (0.9%)	0.9%	χ²(1) = 2.94, p = .086
	More than one	21 (3%)	72 (6.2%)	93 (5%)	–	χ²(1) = 9.54, p = .002
	Other^a	1 (0.1%)	6 (0.5%)	7 (0.4%)	–	χ²(1) = 1.64, p = .200
Education	Less than high school	16 (2.3%)	31 (2.7%)	47 (2.5%)	–	χ²(7) = 125. 05, p < .001^d
	High school or GED	169 (24.2%)	316 (27.4%)	485 (26.2%)	–	–
	Some college	117 (16.8%)	320 (27.8%)	437 (23.6%)	–	–
	2-year college degree	67 (9.6%)	182 (15.8%)	249 (13.5%)	–	–
	4-year college degree (BA, BS)	207 (29.7%)	250 (21.7%)	457 (24.7%)	–	–
	Master’s degree (MA, MS)	72 (10.3%)	40 (3.5%)	112 (6.1%)	–	–
	Doctoral degree (PhD)	23 (3.3%)	4 (0.3%)	27 (1.5%)	–	–
	Professional degree (MD, JD)	23 (3.3%)	8 (0.7%)	31 (1.7%)	–	–
	Not reported	4 (0.6%)	2 (0.2%)	6 (0.3%)	–	–
					2014 Congressional, %
Income	< $10,000	29 (4.2%)	87 (7.5%)	116 (6.3%)	7.3%	χ²(11) = 293.37, p < .001^d
	$10,000–$19,999	35 (5%)	77 (6.7%)	112 (6.1%)	11.5%	–
	$20,000–$29,999	55 (7.9%)	131 (11.4%)	186 (10%)	10.9%	–
	$30,000–$39,999	52 (7.4%)	150 (13%)	202 (10.9%)	10%	–
	$40,000–$49,999	60 (8.6%)	126 (10.9%)	186 (10%)	8.9%	–
	$50,000–$59,999	55 (7.9%)	123 (10.7%)	178 (9.6%)	7.6%	–
	$60,000–$69,999	45 (6.4%)	87 (7.5%)	132 (7.1%)	6.8%	–
	$70,000–$79,999	73 (10.5%)	94 (8.2%)	167 (9%)	5.9%	–
	$80,000–$89,999	47 (6.7%)	38 (3.3%)	85 (4.6%)	4.9%	–
	$90,000–$99,999	57 (8.2%)	66 (5.7%)	123 (6.6%)	4%	–
	$100,000–$149,999	105 (15%)	119 (10.3%)	224 (12.1%)	12.4%	–
	≥ $150,000	76 (10.9%)	44 (3.8%)	120 (6.5%)	9.5%	–
	Not reported	9 (1.3%)	11 (1%)	20 (1.1%)	–	–
Marital status	Single, never married	142 (20.3%)	250 (21.7)	392 (21.2%)	–	χ²(4) = 40.85, p < .001^d
Marital status	Married	522 (74.8%)	752 (65.2%)	1274 (68.8%)	–	–
	Separated	7 (1%)	30 (2.6%)	37 (2%)	–	–
	Divorced	24 (3.4%)	87 (7.5%)	111 (6%)	–	–
	Widowed	1 (0.1%)	32 (2.8%)	33 (1.8%)	–	–
	Not reported	2 (0.3%)	2 (0.2%)	4 (0.2%)	–	–
Diagnosis^b	None	435 (62.3%)	610 (52.9%)	1045 (56.5%)	–	χ²(1) = 15.68, p = < .001
	Anxiety	117 (16.8%)	355 (30.8%)	472 (25.5%)	–	χ²(1) = 45.03, p = < .001
	Depression	107 (15.3%)	351 (30.4%)	458 (24.7%)	–	χ²(1) = 53.33, p = < .001
	ADD/ADHD	44 (6.3%)	82 (7.1%)	126 (6.8%)	–	χ²(1) = 0.448, p = .503
	Bipolar Disorder	32 (4.6%)	73 (6.3%)	105 (5.7%)	–	χ²(1) = 2.48, p = .115
	Obsessive Compulsive Disorder	25 (3.6%)	42 (3.6%)	67 (3.6%)	–	χ²(1) = 0.003, p = .960
	Autism Spectrum Disorder	8 (1.1%)	7 (0.6%)	15 (0.8%)	–	χ²(1) = 1.57, p = .210
	Tic Disorder	6 (0.9%)	4 (0.3%)	10 (0.5%)	–	χ²(1) = 2.13, p = .145
	Post-Traumatic Stress Disorder	1 (0.1%)	9 (0.8%)	10 (0.5%)	–	χ²(1) = 3.29, p = .070
	Schizophrenia	6 (0.9%)	2 (0.2%)	8 (0.4%)	–	χ²(1) = 4.76, p = .029
	Personality Disorder	1 (0.1%)	2 (0.2%)	3 (0.2%)	–	χ²(1) = 0.024, p = .876
	Other^c	35 (5%)	20 (1.7%)	55 (3%)	–	χ²(1) = 1.61, p = .205
Medication		158 (22.6%)	299 (25.9%)	457 (24.7%)	–	χ²(1) = 2.57, p = .109

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^aHebrew Israelite, Indigenous, German (all n = 1), mixed (n = 2), not reported (n = 2).

^bSum of diagnoses is more than total number of individuals due to selecting multiple options.

^cOther reported diagnoses were mostly non-psychiatric diagnoses and included anger/rage, arthritis, back/shoulder pain (n = 2), bronchitis, cancer (unspecified = 1, thyroid = 1), celiac disease, eczema, epilepsy (n = 2), diabetes (n = 3), gastroesophageal reflux disease (GERD), high blood pressure/cholesterol, human papillomavirus (HPV), insomnia, migraines (n = 3), Meniere’s disease, menopause, multiple sclerosis, obesity, trichotillomania, panic disorder, not reported (n = 28).

^dGroup comparison statistics are reported for the overall category only.

Procedures

The research was approved by Bucknell University, Institutional Review Board (DWE’s home institution). All participants reviewed an information document and were informed that participation was voluntary prior to agreeing to participate in the study. Online consent was received from all study participants.

Construct items

To measure the constructs, specific items or subscales were selected from a series of measures after careful review by the first and second authors. The second and last author are additionally authors of one of the measures, the Adult Routines Inventory (ARI) [57]. All individual scale items were further reviewed to minimize the risk of introduced covariance between constructs. Items for each construct along with scoring information are provided in S1 Appendix.

Social Communication Difficulties were assessed using items specifically designed to evaluate these difficulties in those with ASD. These were drawn from the Autism Spectrum Quotient, an instrument designed to detect ASD traits in people with average or above intelligence quotient (IQ) [64]. Higher scores reflect greater social communication difficulties. These items were not used for the presence of ASD, but simply only social communication difficulties. Similarly, Insistence on Sameness was assessed with items drawn from a measure that evaluates this in part, the ARI [57]. The selected items assess routines, habits, and “compulsive-like” restricted and repetitive behaviors often seen in disorders such as OCD and ASD. Higher scores reflect greater rigidity. Cognitive Control was assessed with the Attentional and Inhibitory Control scales of the Adult Temperament Questionnaire [65]. Higher scores indicate greater control. Rumination was assessed with all 3-items from the Penn State Worry Questionnaire, ultra-brief version (PSWQ-3), which assesses pathological worry [66]. Higher scores indicate increased worry. Depression and Suicidal Ideation were assessed with three items from the adult version of the DSM-5 Level 1 Cross-Cutting (CC) Symptom Measure [67, 68], a self-rated measure of mental health domains that was developed by the DSM-5 Task Force and Work Groups [69]. Depressive symptoms are indicated by two items and suicide risk is assessed with a single item which assesses SI. Respondents are asked to consider how much or how often they have been bothered by a specific symptom during the last two weeks. A score ≥2 on any item for depression and ≥1 for SI serve as a clinical guide for additional inquiry and follow up.

Data cleaning and analysis

No more than 1% (M = 0.303, SD = .19, Range = 0–1%) of data were missing for any questionnaire item overall, and Little’s MCAR test was not significant, p = .895 [70]. Thus, following Tabachnick and Fidell [71], cases with missing data on any of the questionnaires were deleted (n = 77, 3.8%). Where appropriate to do so, analyses were conducted using bootstrapping with 5000 resamples to provide more robust statistics, and 95% confidence intervals (BCa 95% CI) were used to interpret significance [71, 72]. Correlational analysis was used first to explore relationships between study variables. Bonferroni adjustment was used to account for multiple comparisons. Multiple linear regression was then run to identify factors contributing to suicidal ideation. Prior to performing the regression analysis the distribution of the residuals of the regression was reviewed for normality [73]. A Predicted Probability (P-P) plot was examined for normality with all constructs entered with suicidal ideation entered as the dependent variable. Examination of the P-P plot revealed that the residuals were normally distributed. All VIF values were below 10 (range 1.06–1.99).

Bootstrapped analysis of covariance (ANCOVA) controlling for age and depression were used to compare participants reporting no suicidal ideation (SI = 0) and those reporting presence of suicidal ideation (SI ≥ 1) on key study variables.

Results

The data that support the findings of the study are openly available at “OSF” at https://doi.org/10.17605/OSF.IO/C2AP3 [74].

The DSM-5 CC Symptom Measure was examined first to determine risk for depression and SI (Table 2). Overall, approximately 42–44% of the total sample met the ‘threshold for further inquiry’ for depression, and 33% met the threshold for follow-up for suicide risk due to presence of SI [67]. Means and standard deviations, and correlation coefficients between study variables are provided in Table 3. Given study variables were significantly correlated with age, partial correlations controlling for age were also examined although the pattern of results was unaffected. Social Communication Difficulties were significantly correlated with all variables, with effect sizes ranging from small to medium (r_p = .216–.365). Study variables were all significantly correlated with Depression and SI in the expected directions, with effect sizes in the small to large range (r_p = -.117–.590). Effect sizes for Social Communication Difficulties were in the medium range for Depression and SI and, as expected, SI was strongly correlated with Depression. In terms of the other variables, Insistence on Sameness, Rumination (both positively) and Attentional Control (negatively) were most strongly associated with Depression, and Attentional Control (negatively) was most strongly associated with SI. Thus, all of the hypothesized constructs were found to be significantly associated with SI thereby warranting their inclusion in the linear regression analysis.

Table 2. Distribution of scores on DSM-5 CC Symptom Measure, depression and suicidal ideation (N = 1851).

	n (%)
Score	Depression item 1^a	Depression item 2^b	Suicidal ideation
None (0)	574 (31%)	594 (32.1%)	1241 (67%)
Slight (1)	463 (25%)	478 (25.8%)	207 (11.2%)
Mild (2)	404 (21.8%)	384 (20.7%)	187 (10.1%)
Moderate (3)	258 (13.9%)	260 (14.0%)	143 (7.7%)
Severe (4)	152 (8.2%)	135 (7.3%)	73 (3.9%)
Threshold for further inquiry^c	814 (44%)	779 (42.1%)	610 (33%)

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^a“Little interest or pleasure in doing things”.

^b“Feeling down, depressed, or hopeless”.

^cDepression score ≥ 2, Suicidal ideation ≥ 1.

Table 3. Study variables (M, SD, range, normality) with Pearson's bootstrapped correlations (upper panel, shaded), and partial correlations (lower panel) controlling for age (n = 1851).

Variable	M	SD	Range	Shapiro-Wilk	2.	3.	4.	5.	6.	7.	8.
1. Age (years)	37.09	12.28	18–89	.912^*	-.094^* [-.14,-.05]	-.132^* [-.18,-.09]	.173^* [.13,.22]	.169^* [.12,.22]	-.139^* [-.18,-.09]	-.181^* [-.22,-.14]	-.199^* [-.24,-.15]
2. Social Communication Difficulties	9.96	2.61	5–20	.975^*	–	.115^* [.07,.16]	-.362^* [-.40,-.32]	-.280^* [-.33,-.23]	.152^* [.11,.20]	.206^* [.17,.25]	.318^* [.28,.36]
3. Insistence on Sameness	44.30	14.08	15–75	.989^*	.257^* [.21,.30]	–	-.432^* [-.47,-.39]	-.178^* [-.22,-.13]	.487^* [.45,.53]	.428^* [.39,.47]	.386^* [.35,.43]
4. Attentional Control	22.62	6.60	5–35	.983^*	-.364^* [-.40,-.32]	-.419^* [-.46,-.38]	–	.384^* [.34,.42]	-.587^* [-.62,-.55]	-.537^* [-.57,-.50]	-.436^* [-.47,-.40]
5. Inhibitory Control	30.40	6.17	11–49	.974^*	-.292^* [-.34,-.25]	-.159^* [-.20,-.11]	.365^* [.32,.41]	–	-.270^* [-.31,-.23]	-.227^* [-.27,-.18]	-.146^* [-.18,-.11]
6. Rumination	9.28	4.79	3–18	.929^*	.216^* [.17,.26]	.478^* [.44,.52]	-.578^* [-.61,-.54]	-.254^* [-.30,-.21]	–	.600^* [.57,.63]	.388^* [.35,.43]
7. Depression	2.82	2.36	0–8	.914^*	.276^* [.23,.32]	.414^* [.37,.46]	-.523^* [-.56,-.49]	-.202^* [-.25,-.16]	.590^* [.56,.62]	–	.602^* [.57,.64]
8. Suicidal ideation	.703	1.16	0–4	.652^*	.365^* [.33,.40]	.371^* [.33,.41]	-.416^* [-.45,-.38]	-.117^* [-.15,-.08]	.371^* [.33,.41]	.588^* [.55,.62]	–

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*p < .001.

Regression analysis

Table 4 presents the results of the linear regression model predicting SI. All hypothesized constructs were included in the model. Age was controlled for by including it in the model. The full model accounted for 43.3% of variance in SI scores, F(7, 1843) = 201.19, p < .001. Social Communication Difficulties significantly predicted SI, with the b-weight revealing that for each unit increase in Social Communication Difficulties, SI increased by 0.085 units. Similarly, Insistence on Sameness was also identified as a significant predictor of SI, with the b-weight revealing that for each unit increase in Insistence on Sameness, SI increased by 0.012 units. Attentional and Inhibitory Control both significantly predicted SI, with the b-weights revealing that for each unit increase in Attentional Control, SI decreased by 0.015 units, and for each unit increase in Inhibitory Control, SI increased by 0.015 units. Rumination was not a significant predictor of SI when entered in the model with the other variables, with each unit increase in Rumination associated with a decrease in SI of -0.010 units. Overall, Depression made the largest contribution to SI (β = 0.484). Comparing Social Communication to Insistence on Sameness; Social Communication Difficulties (β = 0.192) was relatively more important than Insistence on Sameness (β = 0.144). These two core variables shared some variance, but correlations in Table 3 reveal that these were largely independent contributions. Attentional Control (β = -0.085) and Inhibitory Control (β = 0.077) made similar, yet relatively smaller contributions to the model.

Table 4. Linear regression model of predictors of suicidal ideation.

	b	SEB^a	β	p-value	BCa 95% CI
Constant	-1.076	0.224	–	< .001	-1.508, -.617
Age	-0.007	0.002	-0.079	.001	-0.011, -0.004
Social Communication Difficulties	0.085	0.009	0.192	< .001	0.067, 0.104
Insistence on Sameness	0.012	0.002	0.144	< .001	0.009, 0.015
Attentional Control	-0.015	0.004	-0.085	.001	-0.023, -0.006
Inhibitory Control	0.015	0.004	0.077	< .001	0.008, 0.021
Rumination	-0.010	0.006	-0.042	.086	-0.022, 0.001
Depression	0.238	0.011	0.484	< .001	0.213, 0.264

Open in a new tab

R² = .433, F(7, 1843) = 201.19, p < .001. BCa 95% confidence intervals that do not cross zero are bolded.

^aSEB: the standard error for the unstandardized beta.

95% bias corrected and accelerated confidence intervals and standard errors based on 5000 bootstrap samples.

Suicidal ideation present versus not present comparisons

The sample was split into those reporting no suicidal ideation (SI = 0, n = 1241) and those reporting at least some ideation (SI ≥ 1, n = 610). Groups were compared on age, depression, and the main study variables. Results of these analyses are presented in Table 5. Groups differed significantly on age, with those reporting no SI being overall older than those reporting presence of SI. Cohen’s d effect size for the difference was in the small to moderate range. As would be expected, depression scores were also significantly higher in those reporting SI than the no SI group, with the difference returning a large effect size. Subsequently, bootstrapped ANCOVAs were used to compare the two groups on each of the main study variables, controlling for age and depression. Group membership did not have a significant effect on Rumination after controlling for age and depression in the model. There was a significant effect of group membership on core ASD related traits (i.e., Social Communication Difficulties, Insistence on Sameness) and cognitive variables (i.e., Attentional and Inhibitory Control). Thus, participants who reported some SI reported significantly greater Social Communication Difficulties, higher levels of Insistence on Sameness, and lower levels of Attentional and Inhibitory Control, than participants who did not report any SI.

Table 5. Means (SD) and bootstrapped ANCOVA comparisons between no-SI and SI groups on key variables controlling for age and depression.

	No-SI (n = 1241)		SI (n = 610)		F-statistic
Variable^a	M	SD	M	SD	df = 1, 1847	p-value^b	BCa 95% CI^c	Cohen’s d [95% CI]
Social communication difficulties	9.36	2.60	11.17	2.20	128.82	< .001	-1.99, -1.39	-0.73 [-0.88, -0.56]
Insistence on sameness	41.16	13.61	50.70	12.80	17.51	< .001	-4.66, -1.68	-0.72 [-1.47, 0.30]
Attentional control	24.51	6.30	18.79	5.45	36.53	< .001	1.34, 2.67	0.95 [0.60, 1.38]
Inhibitory control	31.23	6.76	28.71	4.29	7.62	.003	0.354, 1.67	0.42 [0.04, 0.76]
Rumination	8.07	4.55	11.72	4.30	1.07	.316	-0.714, 0.236	-0.82 [-1.07, -0.48]
Covariates					df = 1, 1849
Age	38.82	12.20	33.56	11.67	78.06	< .001	4.05, 6.44	0.44 [-0.24, 1.36]
Depression	1.89	1.93	4.72	1.99	864.60	< .001	-3.03, -2.63	-1.45 [-1.56, -1.29]

Open in a new tab

^aAge and depression entered as covariates in the model.

^b5000 samples bootstrapped p-value.

^cBCa 95% confidence intervals that do not cross zero are bolded.

Discussion

The present study aimed to examine the contribution of social communication difficulties and insistence on sameness, representative of core features of ASD, as well as cognitive control and ruminative thinking, to DSM-5 suicidal ideation [67, 68] in a large online recruited sample comprising normative and clinically diverse individuals. Cognitive control is a potential transdiagnostic risk factor for suicidal behavior that remains underexplored [24, 25], and is affected in ASD [49–51, 53, 75]. Similarly, rumination and cognitive rigidity/insistence on sameness have been shown to be associated with depression [46, 76] and suicidal ideation [47, 48], as well as ASD traits [45]. Social communication difficulties, which are associated with depression in non-ASD samples [77, 78], are relatively unexplored in terms of their contribution to suicide risk, but are core characteristics of the ASD phenotype. We were specifically interested to know whether each of these constructs provided a unique contribution to suicidal ideation after controlling for depressive symptoms.

One third of the sample met the DSM-5 CC threshold for further inquiry for suicide risk due to presence of suicidal ideation, with around 40 percent meeting the threshold for concern for depression. Correlational analyses revealed that higher scores on social communication difficulties, insistence on sameness and rumination, and lower scores on attentional and inhibitory control were all significantly associated with DSM-5 CC depression and suicidal ideation. Regression analysis controlling for depression revealed that all factors excepting rumination contributed significantly to DSM-5 CC suicidal ideation, with the full model accounting for 43 percent of variance in suicidal ideation scores. Comparison of participants reporting at least some versus no suicidal ideation, controlling for age and depression, revealed significantly higher levels of social communication difficulties and insistence of sameness, and lower levels of attentional and inhibitory control in the group reporting some suicidal ideation.

Our findings suggest a role for core ASD related traits in suicidal ideation, consistent with studies reporting a high level of risk in ASD clinical [13, 14, 28, 30] and non-ASD [15, 17, 23, 38] samples. Indeed, our findings indicate that social communication difficulties and insistence on sameness independently predicted suicidal ideation when controlling for cognitive risk factors and depression. Moreover, results of this study contribute to an emerging evidence base positing ASD traits as an important dimensional construct underlying suicide risk that cuts across diagnostic boundaries. Our study extends previous research [13, 17, 28, 30, 38] by deconstructing specific components (i.e., social communication difficulties, insistence on sameness) of the ASD phenotype that represent the two primary clinical domains of the disorder. Importantly, the identification of the role of domains associated with the ASD phenotype, representative of a clinical group at high risk of suicide, contributes to understanding and development of a transdiagnostic and dimensional framework for understanding suicide risk [8, 24, 25, 27]. The development of such a model pinpoints specific targets for intervention, and strengthens the call for assessing both individuals with clinical diagnoses of ASD, and those with high levels of ASD traits, for suicide risk [17].

Additional work is needed, however, to further clarify the processes (e.g., social, cognitive) whereby ASD traits contribute to suicide risk. Previous research has identified social relationships and loneliness as potentially important to depression and suicide in individuals with ASD [30, 79, 80], the present study extends this model by also examining cognitive control and negative valence domains.

Our findings provide support for the contribution of poor cognitive control, cognitive rigidity, and ruminative thinking style to suicidal ideation. However, it is interesting to note that the association between rumination and suicidal ideation was no longer significant when controlling for depression, suggesting overlap between these two factors. While depression is highly prevalent in people with ASD [81, 82], the mechanisms underlying increased suicide risk in this population may represent an interaction between traits associated with the core characteristics of the diagnosis, associated cognitive dysfunction, and co-occurring psychiatric conditions. Moreover, our findings suggest these mechanisms are not limited to clinical cases, but may constitute transdiagnostic risk factors. Together, our findings are significant in that they a) represent an attempt at unpacking the mechanisms associated with the ASD phenotype that might contribute to increased suicide risk and b) contribute to the growing literature concerning dimensional, transdiagnostic risk factors and mechanisms underlying suicide risk and behavior.

Strengths and limitations

This study was strengthened by our use of a large sample and theoretically informed constructs known to be associated with suicide risk. Our inclusion of constructs related to ASD traits, specifically social communication difficulties, RRBIs, and associated cognitive challenges represents a novel contribution to the literature. Nonetheless, the cross-sectional nature of the study limits our ability to infer causality. Findings are also limited due to reliance on self-report measures, including psychiatric diagnoses, and use of an online survey for data collection. Although our findings were generally consistent with the literature and theoretical background, it will be important to replicate our findings taking into consideration these methodological limitations. Future research will benefit from administration of cognitive assessments to better elucidate the effect of cognitive processes on suicide risk and more comprehensive, questionnaire and performance-based quantitative measures designed to capture strengths and weaknesses across different domains of social functioning, such as the SEL web [83] and the Stanford Social Dimensions Scale [84]. Suicide risk was assessed using the DSM-5 suicidal ideation screener incorporated into the cross-cutting symptom measure [67, 85], which was selected as it is relatively straightforward to administer using large scale online methodology, and because the presence of suicidal ideation has been shown to increase the likelihood of a suicide attempt [86]. However, the use of a single indicator or suicide risk limits our findings [87, 88]. Future research would benefit from a more comprehensive assessment of suicide risk and behavior.

Future directions

Executive dysfunction, including cognitive rigidity and poor decision-making, may be a trait vulnerability for suicide risk [89]. Our findings concerning rumination, depression, and suicidal ideation suggests an inter-relationship among these three constructs. Response Styles Theory [90] posits that rumination is a cognitive response to depressed mood. Our findings indicate that, although associated with depression and suicidal ideation, rumination itself is not predictive of suicidal ideation scores. Future research is required to disentangle the associations among these factors, and to understand better the potential contribution of repetitive thinking to suicide risk and behavior. Ultimately, this calls for the development of including genetic and biological, cognitive, and social elements (i.e., bio-psycho-social model) underlying suicide risk. The first step in this process is to identify those individual mechanisms and to demonstrate their links to suicide behavior.

Conclusions

Despite the aforementioned limitations, our study demonstrated the potential role of ASD traits, particularly social communication difficulties and cognitive rigidity/insistence on sameness, and difficulties in broad cognitive domains associated with the ASD phenotype, as potential transdiagnostic factors underlying suicide risk. Our approach represents a shift away from disorder-specific research in an attempt at uncovering common mechanisms and risk factors for suicide behavior in individuals with no psychiatric diagnosis, individuals with diagnoses of one or more common psychiatric disorders, and individuals who may not have a formal diagnosis but who present with subthreshold symptomatology. These findings provide a roadmap for further longitudinal research and identify potential targets for intervention and clinical practice.

Supporting information

S1 Appendix. Scale items and ratings.

Items for each of the constructs used in the study.

(DOCX)

Click here for additional data file.^{(17.3KB, docx)}

S1 Table. Participant distribution.

Distribution of participants between US States and the District of Columbia.

(DOCX)

Click here for additional data file.^{(13.1KB, docx)}

Acknowledgments

We thank the individuals who participated in this study. DWE and MU designed the survey and collected the data. DH completed the literature review. DH and MU conceived of the report and prepared the tables. DH completed the data analysis and interpretation. DH and MU wrote the report, incorporating comments from all authors. MAS and DWE critically revised the report. All authors reviewed and approved the final submitted version.

Data Availability

The data that support the findings of this study are openly available at “OSF” at https://doi.org/10.17605/OSF.IO/C2AP3. Additional correspondence or queries concerning the data sample should be directed to David W. Evans, PhD, Department of Psychology, Bucknell University, Lewisburg, PA, 17837; e-mail: dwevans@bucknell.edu.

Funding Statement

Research reported in this study was supported by Bucknell University Scholarly Development Grant awarded to DWE, a Suicide Prevention Australia National Suicide Prevention Research fellowship awarded to DH, and a Discovery Early Career Researcher Award from the Australian Research Council awarded to MU. The corresponding author had full access to all the data in the study and final responsibility for the decision to submit the report for publication. The content is solely the responsibility of the authors and has not been approved or endorsed by Suicide Prevention Australia, Bucknell University, or the Australian Research Council. The funder provided support in the form of salaries for authors DH and MU and research materials for DWE, but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of these authors are articulated in the ‘author contributions’ section.

References

1.World Health Organization. Preventing suicide: A global imperative (Vol. 2020). Geneva: World Health Organization; 2014. [Google Scholar]
2.O'Connor RC, Portzky G. Looking to the future: A synthesis of new developments and challenges in suicide research and prevention. Frontiers in Psychology. 2018;9:2139 10.3389/fpsyg.2018.02139 [DOI] [PMC free article] [PubMed] [Google Scholar]
3.Insel T, Cuthbert B, Garvey M, Heinssen R, Pine DS, Quinn K, et al. Research domain criteria (RDoC): toward a new classification framework for research on mental disorders. Am J Psychiatry. 2010;167:748–51. 10.1176/appi.ajp.2010.09091379 [DOI] [PubMed] [Google Scholar]
4.National Institute of Mental Health. Mechanisms underlying suicide risk: Integrating RDoC to inform novel and personalized intervention research. Maryland: National Institute of Mental Health; 2008. [Google Scholar]
5.Constantino JN, Todd RD. Autistic traits in the general population: a twin study. Archives of General Psychiatry. 2003;60:524–30. 10.1001/archpsyc.60.5.524 [DOI] [PubMed] [Google Scholar]
6.Evans DW, Leckman JF, Carter A, Reznick JS, Henshaw D, King RA, et al. Ritual, habit, and perfectionism: The prevalence and development of compulsive-like behavior in normal young children. Child Development. 1997;68:58–68. [PubMed] [Google Scholar]
7.Gallyer AJ, Stanley IH, Day TN, Joiner TE. Examining the interaction of autism spectrum disorder-related traits and unit cohesion on suicide risk among military personnel. J Affect Disord. 2020;271:59–65. 10.1016/j.jad.2020.03.092 [DOI] [PMC free article] [PubMed] [Google Scholar]
8.Eisner LR. A transdiagnostic model of suicidal ideation and suicide attempts. 2010. https://scholarlyrepoository.miami.edu/oa_dissertations/358. Accessed 24 Aug 2020. [Google Scholar]
9.Joiner TE, Brown JS, Wingate LR. The psychology and neurobiology of suicidal behavior. Annual Review of Psychology. 2005;56:287–314. 10.1146/annurev.psych.56.091103.070320 [DOI] [PubMed] [Google Scholar]
10.Kessler RC, Berglund P, Borges G, Nock M, Wang PS. Trends in suicide ideation, plans, gestures, and attempts in the United States, 1990–1992 to 2001–2003. JAMA. 2005;293:2487–95. 10.1001/jama.293.20.2487 [DOI] [PubMed] [Google Scholar]
11.Kessler RC, Chiu WT, Demler O, Merikangas KR, Walters EE. Prevalence, severity, and comorbidity of 12-month DSM-IV disorders in the National Comorbidity Survey Replication. Archives of General Psychiatry. 2005;62:617–27. 10.1001/archpsyc.62.6.617 [DOI] [PMC free article] [PubMed] [Google Scholar]
12.Hjelmeland H, Knizek BL. Suicide and mental disorders: A discourse of politics, power, and vested interests. Death Studies. 2017;41:481–92. 10.1080/07481187.2017.1332905 [DOI] [PubMed] [Google Scholar]
13.Cassidy S, Bradley P, Robinson J, Allison C, McHugh M, Baron-Cohen S. Suicidal ideation and suicide plans or attempts in adults with Asperger's syndrome attending a specialist diagnostic clinic: A clinical cohort study. Lancet Psychiatry. 2014;1:142–7. 10.1016/S2215-0366(14)70248-2 [DOI] [PubMed] [Google Scholar]
14.Hirvikoski T, Mittendorfer-Rutz E, Boman M, Larsson H, Lichtenstein P, Bölte S. Premature mortality in autism spectrum disorder. Br J Psychiatry. 2016;208:232–8. 10.1192/bjp.bp.114.160192 [DOI] [PubMed] [Google Scholar]
15.Pelton MK, Crawford H, Robertson AE, Rodgers J, Baron-Cohen S, Cassidy S. A measurement invariance analysis of the Interpersonal Needs Questionnaire and Acquired Capability for Suicide Scale in autistic and non-autistic adults. Autism in Adulthood. 2020; 2:193–203. 10.1089/aut.2019.0055 [DOI] [PMC free article] [PubMed] [Google Scholar]
16.Rodriguez-Seijas C, Gadow K D, Rosen T E., Kim H, Lerner MD, Eaton NR. A transdiagnostic model of psychiatric symptom co-occurrence and autism spectrum disorder. Autism Res. 2019;13:579–90. 10.1002/aur.2228 [DOI] [PubMed] [Google Scholar]
17.Upthegrove R, Abu-Akel A, Chisholm K, Lin A, Zahid S, Pelton M, et al. Autism and psychosis: Clinical implications for depression and suicide. Schizophr Res. 2018;195:80–5. 10.1016/j.schres.2017.08.028 [DOI] [PubMed] [Google Scholar]
18.De Crescenzo F, Postorino V, Siracusano M, Riccioni A, Armando M, Curatolo P, et al. Autistic symptoms in schizophrenia spectrum disorders: A systematic review and meta-analysis. Frontiers in Psychiatry. 2019;10:78–8. 10.3389/fpsyt.2019.00078 [DOI] [PMC free article] [PubMed] [Google Scholar]
19.Abu-Akel A, Allison C, Baron-Cohen S, Heinke D. The distribution of autistic traits across the autism spectrum: evidence for discontinuous dimensional subpopulations underlying the autism continuum. Mol Autism. 2019;10:e24 10.1186/s13229-019-0275-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
20.Robinson EB, St Pourcain B, Anttila V, Kosmicki JA, Bulik-Sullivan B, Grove J, et al. Genetic risk for autism spectrum disorders and neuropsychiatric variation in the general population. Nat Genet. 2016;48:552–555. 10.1038/ng.3529 [DOI] [PMC free article] [PubMed] [Google Scholar]
21.Rai D, Culpin I, Heuvelman H, Magnusson CMK, Carpenter P, Jones HJ, et al. (2018). Association of autistic traits with depression from childhood to age 18 years. JAMA Psychiatry. 2018;75:835–43. 10.1001/jamapsychiatry.2018.1323 [DOI] [PMC free article] [PubMed] [Google Scholar]
22.Rosbrook A, Whittingham K. Autistic traits in the general population: What mediates the link with depressive and anxious symptomatology? Res Autism Spectr Disord. 2010;4:415–24. [Google Scholar]
23.Stanley IH, Day TN, Gallyer AJ, Shelef L, Kalla C, Gutierrez PM, et al. Autism-related traits and suicide risk among active duty U.S. military service members. Psychol Serv. 2020. 10.1037/ser0000418 [DOI] [PubMed] [Google Scholar]
24.Glenn CR, Cha CB, Kleiman EM, Nock MK. Understanding suicide risk within the Research Domain Criteria (RDoC) Framework: Insights, challenges, and future research considerations. Clin Psychol Sci. 2017;5:568–92. 10.1177/2167702616686854 [DOI] [PMC free article] [PubMed] [Google Scholar]
25.Glenn CR, Kleiman EM, Cha CB, Deming CA, Franklin JC, Nock MK. Understanding suicide risk within the Research Domain Criteria (RDoC) framework: A meta-analytic review. Depress Anxiety. 2018;35:65–88. 10.1002/da.22686 [DOI] [PMC free article] [PubMed] [Google Scholar]
26.Cuthbert BN. The RDoC framework: facilitating transition from ICD/DSM to dimensional approaches that integrate neuroscience and psychopathology. World Psychiatry. 2014;13:28–35. 10.1002/wps.20087 [DOI] [PMC free article] [PubMed] [Google Scholar]
27.McCoy TH Jr, Pellegrini AM, Perlis RH. Research Domain Criteria scores estimated through natural language processing are associated with risk for suicide and accidental death. Depress Anxiety. 2019;36:392–99. 10.1002/da.22882 [DOI] [PMC free article] [PubMed] [Google Scholar]
28.Cassidy S, Bradley L, Shaw R, Baron-Cohen S. Risk markers for suicidality in autistic adults. Mol Autism. 2018;9:e42 10.1186/s13229-018-0226-4 [DOI] [PMC free article] [PubMed] [Google Scholar]
29.Hedley D, Uljarević M. Systematic review of suicide in Autism Spectrum Disorder: Current trends and implications. Current Developmental Disorders Reports. 2018;5:65–76. [Google Scholar]
30.Hedley D, Uljarević M, Foley KR, Richdale A, Trollor J. Risk and protective factors underlying suicidal ideation in Autism Spectrum Disorder. Depress Anxiety. 2018;35:648–57. 10.1002/da.22759 [DOI] [PubMed] [Google Scholar]
31.Zahid S, Upthegrove R. Suicidality in Autistic Spectrum Disorders: A systematic review. Crisis. 2017;8:237–46 [DOI] [PubMed] [Google Scholar]
32.Hand BN, Benevides TW, Carretta HJ. Suicidal ideation and self-inflicted injury in Medicare enrolled autistic adults with and without co-occurring intellectual disability. J Autism Dev Disord. 2020;50:3489–3495. 10.1007/s10803-019-04345-x [DOI] [PubMed] [Google Scholar]
33.Baron-Cohen S, Wheelwright S, Skinner R, Martin J, Clubley E. The autism-spectrum quotient (AQ): Evidence from Asperger syndrome/high-functioning autism, males and females, scientists and mathematicians. J Autism Dev Disord. 2001;31:5–17. 10.1023/a:1005653411471 [DOI] [PubMed] [Google Scholar]
34.Ruzich E, Allison C, Smith P, Watson P, Auyeung B, Ring H, et al. Measuring autistic traits in the general population: a systematic review of the Autism-Spectrum Quotient (AQ) in a nonclinical population sample of 6,900 typical adult males and females. Mol Autism. 2015;6:e2. [DOI] [PMC free article] [PubMed] [Google Scholar]
35.Colvert E, Tick B, McEwen F, Stewart C, Curran SR, Woodhouse E, et al. Heritability of Autism Spectrum Disorder in a UK population-based twin sample. JAMA Psychiatry. 2015;72:415–23. 10.1001/jamapsychiatry.2014.3028 [DOI] [PMC free article] [PubMed] [Google Scholar]
36.Stergiakouli E, Davey Smith G, Martin J, Skuse DH, Viechtbauer W, Ring SM, et al. Shared genetic influences between dimensional ASD and ADHD symptoms during child and adolescent development. Mol Autism. 2017;8:e18 10.1186/s13229-017-0131-2 [DOI] [PMC free article] [PubMed] [Google Scholar]
37.Cassidy S, Gould K, Townsend E, Pelton M, Robertson AE, Rodgers J. Is camouflaging autistic traits associated with suicidal thoughts and behaviours? Expanding the Interpersonal Psychological Theory of Suicide in an undergraduate student sample. J Autism Dev Disord. 2020;50:3638–48. 10.1007/s10803-019-04323-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
38.Pelton MK, Cassidy SA. Are autistic traits associated with suicidality? A test of the interpersonal-psychological theory of suicide in a non-clinical young adult sample. Autism Res. 2017;10:1891–904. 10.1002/aur.1828 [DOI] [PMC free article] [PubMed] [Google Scholar]
39.Joiner TE. Why people die by suicide Cambridge: Harvard University Press; 2005. [Google Scholar]
40.American Psychiatric Association. Diagnostic and statistical manual of mental disorders (5^th ed.). Washington: American Psychiatric Association; 2013. [Google Scholar]
41.Unruh KE, Bodfish JW, Gotham KO. Adults with autism and adults with depression show similar attentional biases to social-affective images. J Autism Dev Disord. 2020;50:2336–47. 10.1007/s10803-018-3627-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
42.Fazakas-DeHoog LL, Rnic K, Dozois, DJA. A cognitive distortions and deficits model of suicide ideation. Europe's Journal of Psychology. 2017;13:178–93. 10.5964/ejop.v13i2.1238 [DOI] [PMC free article] [PubMed] [Google Scholar]
43.Litinsky AM, Haslam N. Dichotomous thinking as a sign of suicide risk on the TAT. J Pers Assess. 1998;71:368–78. 10.1207/s15327752jpa7103_6 [DOI] [PubMed] [Google Scholar]
44.Pu S, Setoyama S, Noda T. Association between cognitive deficits and suicidal ideation in patients with major depressive disorder. Scientific Reports. 2017;7:e11637 10.1038/s41598-017-12142-8 [DOI] [PMC free article] [PubMed] [Google Scholar]
45.Keenan EG, Gotham K, Lerner MD. Hooked on a feeling: Repetitive cognition and internalizing symptomatology in relation to autism spectrum symptomatology. Autism. 2018;22:814–24. 10.1177/1362361317709603 [DOI] [PMC free article] [PubMed] [Google Scholar]
46.Nolen-Hoeksema S. The role of rumination in depressive disorders and mixed anxiety/depressive symptoms. J Abnorm Psychol. 2000;109:504–11. [PubMed] [Google Scholar]
47.Miranda R, Nolen-Hoeksema S. Brooding and reflection: rumination predicts suicidal ideation at 1-year follow-up in a community sample. Behav Res Ther. 2007;45:3088–95. 10.1016/j.brat.2007.07.015 [DOI] [PMC free article] [PubMed] [Google Scholar]
48.Smith JM, Alloy LB, Abramson LY. Cognitive vulnerability to depression, rumination, hopelessness, and suicidal ideation: multiple pathways to self-injurious thinking. Suicide and Life-Threatening Behavavior. 2006;36:443–54. [DOI] [PubMed] [Google Scholar]
49.Demurie E, Roeyers H, Baeyens D, Sonuga-Barke E. Temporal discounting of monetary rewards in children and adolescents with ADHD and autism spectrum disorders. Dev Sci. 2012;15:791–800. 10.1111/j.1467-7687.2012.01178.x [DOI] [PubMed] [Google Scholar]
50.Geurts HM, Verte S, Oosterlaan J, Roeyers H, Sergeant JA. How specific are executive functioning deficits in attention deficit hyperactivity disorder and autism? J Child Psychol Psychiatry. 2004;45:836–54. 10.1111/j.1469-7610.2004.00276.x [DOI] [PubMed] [Google Scholar]
51.Karalunas SL, Hawkey E, Gustafsson H, Miller M, Langhorst M, Cordova M, et al. Overlapping and distinct cognitive impairments in Attention-Deficit/Hyperactivity and Autism Spectrum Disorder without intellectual disability. J Abnorm Child Psychol. 2018;46:1705–16. 10.1007/s10802-017-0394-2 [DOI] [PMC free article] [PubMed] [Google Scholar]
52.Ozonoff S, Jensen J. Brief report: Specific executive function profiles in three neurodevelopmental disorders. J Autism Dev Disord. 1999;29:171–7. 10.1023/a:1023052913110 [DOI] [PubMed] [Google Scholar]
53.Bos DJ, Silverman MR, Ajodan EL, Martin C, Silver BM, Brouwer GJ, et al. Rigidity coincides with reduced cognitive control to affective cues in children with autism. J Abnorm Psychol. 2019;128:431–41. 10.1037/abn0000423 [DOI] [PMC free article] [PubMed] [Google Scholar]
54.Crandall A, Allsop Y, Hanson CL. The longitudinal association between cognitive control capacities, suicidality, and depression during late adolescence and young adulthood. J Adolesc. 2018;65:167–76. [DOI] [PubMed] [Google Scholar]
55.Stewart JG, Glenn CR, Esposito EC, Cha CB, Nock MK, Auerbach RP. Cognitive Control Deficits Differentiate Adolescent Suicide Ideators From Attempters. J Clin Psychiatry. 2017;78:614–21. 10.4088/JCP.16m10647 [DOI] [PubMed] [Google Scholar]
56.Evans DW, Lusk LG, Slane MM, Michael AM, Myers SM, Uljarević M, et al. Dimensional assessment of schizotypal, psychotic, and other psychiatric traits in children and their parents: Development and validation of the Childhood Oxford-Liverpool Inventory of Fellings and Experiences on a representative US sample. J Child Psychol Psychiatry. 2018;59:574–85. 10.1111/jcpp.12827 [DOI] [PMC free article] [PubMed] [Google Scholar]
57.Evans DW, Uljarević M, Lusk LG, Loth E, Frazier T. Development of two dimensional measures of restricted and repetitive behavior in parents and children. J Am Acad Child Adolesc Psychiatry. 2017;56:51–8. 10.1016/j.jaac.2016.10.014 [DOI] [PubMed] [Google Scholar]
58.Buhrmester M, Kwang T, Gosling SD. Amazon's Mechanical Turk: A new source of inexpensive, yet high-quality data? In: Kazdin AE, editor. Methodological issues and strategies in clinical research. Washington: American Psychological Association; 2016. p. 133–9. [DOI] [PubMed] [Google Scholar]
59.Buhrmester MD, Talaifar S, Gosling SD. An evaluation of Amazon’s mechanical Turk, its rapid rise, and its effective use. Perspect Psychol Sci. 2018;13:149–54. 10.1177/1745691617706516 [DOI] [PubMed] [Google Scholar]
60.Palan S, Schitter C. Prolific.ac–A subject pool for online research. Journal of Behavioral and Experimental Finance. 2018;17:22–7. [Google Scholar]
61.Qualtrics. Qualtrics version 2.16 [computer program]. Utah: Qualtrics; 2017.
62.United States Census Bureau. State and county QuickFacts 2010, 2014. United States Census Bureau. 2019. https://quickfacts.census.gov. Accessed Apr 16, 2020. [Google Scholar]
63.Kessler RC, Berglund P, Demler O, Jin R, Merikangas KR, Walters EE. Lifetime prevalence and age-of-onset distributions of DSM-IV disorders in the National Comorbidity Survey Replication. Archives of General Psychiatry. 2005;62:593–602. 10.1001/archpsyc.62.6.593 [DOI] [PubMed] [Google Scholar]
64.Allison C, Auyeung B, Baron-Cohen S. Toward brief "Red Flags" for autism screening: The Short Autism Spectrum Quotient and the Short Quantitative Checklist for Autism in toddlers in 1,000 cases and 3,000 controls [corrected]. J Am Acad Child Adolesc Psychiatry. 2012;51:202–12. 10.1016/j.jaac.2011.11.003 [DOI] [PubMed] [Google Scholar]
65.Evans DE, Rothbart MK. Developing a model for adult temperament. J Res Pers. 2007;41:868–88. [Google Scholar]
66.Berle D, Starcevic V, Moses K, Hannan A, Milicevic D, Sammut P. (2011). Preliminary validation of an ultra-brief version of the Penn State Worry Questionnaire. Clin Psychol Psychother. 2011;18:339–46. 10.1002/cpp.724 [DOI] [PubMed] [Google Scholar]
67.Narrow WE, Clarke DE, Kuramoto SJ, Kraemer HC, Kupfer DJ, Greiner L, et al. DSM-5 field trials in the United States and Canada, Part III: development and reliability testing of a cross-cutting symptom assessment for DSM-5. Am J Psychiatry. 2013;170:71–82. 10.1176/appi.ajp.2012.12071000 [DOI] [PubMed] [Google Scholar]
68.Narrow WE, Kuhl EA. Dimensional approaches to psychiatric diagnosis in DSM-5. J Ment Health Policy Econ. 2011;14:197–200. [PubMed] [Google Scholar]
69.Clarke DE, Kuhl EA. DSM-5 cross-cutting symptom measures: a step towards the future of psychiatric care? World Psychiatry. 2014;13:314–6. 10.1002/wps.20154 [DOI] [PMC free article] [PubMed] [Google Scholar]
70.Little RJA. A test of missing completely at random for multivariate data with missing values. J Am Stat Assoc. 1988;83:1198–202. [Google Scholar]
71.Tabachnick BG, Fidell LS. Using Multivariate Statistics (5th ed.). Massachusetts: Allyn & Bacon; 2007. [Google Scholar]
72.Efron B, Tibshirani R. An Introduction to the Bootstrap. Forida: Chapman & Hall/CRC; 1993. [Google Scholar]
73.Cohen J, Cohen P, West SG, Aiken LS. Applied multiple regression/correlation analysis for the behavioural sciences, 3rd ed. New Jersey: Lawrence Erlbaum Associates; 2013. [Google Scholar]
74.Hedley D, Uljarević M, Cai RY, Bury SM, Stokes MA, Evans DW. Transdiagnostic predictors of dsm-5 suicide risk [raw dataset, Version 1]. OSF. 2020. 10.17605/OSF.IO/C2AP3 [DOI] [PMC free article] [PubMed] [Google Scholar]
75.Nigg JT. On inhibition/disinhibition in developmental psychopathology: Views from cognitive and personality psychology and a working inhibition taxonomy. Psychology Bulletin. 2000;126:220–46. 10.1037/0033-2909.126.2.220 [DOI] [PubMed] [Google Scholar]
76.Nolen-Hoeksema S, Wisco BE, Lyubomirsky S. Rethinking rumination. Perspect Psychol Sci. 2008;3:400–24. 10.1111/j.1745-6924.2008.00088.x [DOI] [PubMed] [Google Scholar]
77.Knight MJ, Baune BT. Social cognitive abilities predict psychosocial dysfunction in major depressive disorder. Depress Anxiety. 2019;36:54–62. 10.1002/da.22844 [DOI] [PubMed] [Google Scholar]
78.LeMoult J, Joormann J, Sherdell L, Wright Y, Gotlib IH. Identification of emotional facial expressions following recovery from depression. J Abnorm Psychol. 2009;118:828–33. 10.1037/a0016944 [DOI] [PMC free article] [PubMed] [Google Scholar]
79.Hedley D, Uljarević M, Wilmot M, Richdale A, Dissanayake C. Social support, depression and suicidal ideation in adults with Autism Spectrum Disorder. J Autism Dev Disord. 2017;47:3669–77. 10.1007/s10803-017-3274-2 [DOI] [PubMed] [Google Scholar]
80.Hedley D, Uljarević M, Wilmot M, Richdale A, Dissanayake C. Understanding depression and thoughts of self-harm in autism: A potential mechanism involving loneliness. Res Autism Spectr Disord. 2018;46:1–7. [Google Scholar]
81.Hudson CC, Hall L, Harkness KL. Prevalence of depressive disorders in individuals with Autism Spectrum Disorder: A meta-analysis. J Abnorm Child Psychol. 2019;47:165–75. 10.1007/s10802-018-0402-1 [DOI] [PubMed] [Google Scholar]
82.Uljarević M, Hedley D, Foley KR, Magiati I, Cai RY, Dissanayake C, et al. Anxiety and depression from adolescence to old age in Autism Spectrum Disorder. J Autism Dev Disord. 2020;50:3155–65 10.1007/s10803-019-04084-z [DOI] [PubMed] [Google Scholar]
83.Russo-Ponsaran NM, Lerner MD, McKown C, Weber RJ, Karls A, Kang E, et al. Web-based assessment of social-emotional skills in school-aged youth with Autism Spectrum Disorder. Autism Res. 2019;12:1260–71. 10.1002/aur.2123 [DOI] [PubMed] [Google Scholar]
84.Phillips JM, Uljarevic M, Schuck RK, Frazier TW, Schapp SS, Solomon, EM, et al. Development of the Stanford Social Dimensions Scale (SSDS): Initial validation in autism spectrum disorder (ASD). Mol Autism. 2019;10:e48. [DOI] [PMC free article] [PubMed] [Google Scholar]
85.American Psychiatric Association. DSM-5 self-rated level 1 cross-cutting symptom measure-adult. 2013. https://www.psychiatry.org/psychiatrists/practice/dsm/educational-resources/assessment-measures. Accessed Aug 24, 2020. [Google Scholar]
86.Lewinsohn PM, Rohde P, Seeley JR. Adolescent suicidal ideation and attempts: Prevalence, risk factors, and clinical implications. Clin Psychol. 1996;3:25–46. [Google Scholar]
87.Hom MA, Joiner TE, Bernert RA. Limitations of a single-item assessment of suicide attempt history: Implications for standardized suicide risk assessment. Psychol Assess. 2016;28:1026–30. 10.1037/pas0000241 [DOI] [PMC free article] [PubMed] [Google Scholar]
88.Millner AJ, Lee MD, Nock MK. Single-item measurement of suicidal behaviors: validity and consequences of misclassification. PLoS ONE. 2015;10:e0141606 10.1371/journal.pone.0141606 [DOI] [PMC free article] [PubMed] [Google Scholar]
89.Weiner L, Flin A, Causin J-B, Weibe S, Bertschy G. (2019). A case study of suicidality presenting as a restricted interest in autism spectrum disorder. BMC Psychiatry. 2019;19:e126 10.1186/s12888-019-2122-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
90.Nolen-Hoeksema S. Responses to depression and their effects on the duration of depressive episodes. J Abnorm Psychol. 1991;100:569–82. 10.1037//0021-843x.100.4.569 [DOI] [PubMed] [Google Scholar]

PLoS One. doi: 10.1371/journal.pone.0245562.r001

Decision Letter 0

Vincenzo De Luca

26 Oct 2020

PONE-D-20-29676

Domains of the autism phenotype, cognitive control, and rumination as transdiagnostic predictors of DSM-5 suicide risk

PLOS ONE

Dear Dr. Hedley,

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[I have read the journal's policy and the authors of this manuscript have the following competing interests: DH is supported by a Suicide Prevention Australia National Suicide Prevention Research fellowship. MU is supported by a Discovery Early Career Researcher Award from the Australian Research Council (DE180100632). DWE declares that he receives funding from Roche Pharmaceutical for consulting on the use of the Childhood Routines Inventory-Revised (CRI-R). ].

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[Research reported in this study was supported by Bucknell University Scholarly Development Grant awarded to DWE, a Suicide Prevention Australia National Suicide Prevention Research fellowship awarded to DH, and a Discovery Early Career Researcher Award from the Australian Research Council awarded to MU. The corresponding author had full access to all the data in the study and final responsibility for the decision to submit the report for publication. The content is solely the responsibility of the authors and has not been approved or endorsed by Suicide Prevention Australia, Bucknell University, or the Australian Research Council. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. ].

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Reviewer #1: Partly

**********

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: No

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Reviewer #1: Yes

**********

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Reviewer #1: Yes

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5. Review Comments to the Author

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Reviewer #1: This paper examines the relationship between autistic traits and suicidal ideation. I think the topic of this research is very important, and the results of this research can help us better understand both autism and suicidal thoughts. In addition, the study had a very large sample size (n=1851) with participants from a diverse background. Such large representative data can give us accurate estimates of the population parameters of interest; thus, the authors should definitely find a channel to publish these data. However, there are several major problems with the paper that need to be addressed:

1. The data analyses of the paper are not well done. The authors claim that they did hierarchical regression analyses but based on the description of the analysis procedure, they actually did stepwise regression (see page 20). Stepwise regression is usually used in exploratory studies where researchers try to search for important predictors for the criterion variable. However, this paper is clearly a confirmatory study. I don’t see any justification for using the stepwise regression. I recommend the authors just use regular multiple regression analyses.

2. The results section of the paper is very poorly written. The results section merely introduced the tables for the results; it did not describe the patterns of the results at all. In the next revision, please provide a summary of the results of each table and describe the results patterns in words (i.e., do not just present the results in tables).

3. The authors included a lot of tables (e.g., Tables 1, 2 and 3) that are not essential for addressing the research goal. I recommend the authors put these tables in the supplementary materials.

In addition, there are few minor problems/mistakes in the paper:

• On page 6, there is a sentence that says “ASD traits were found to significantly correlate with suicidal behaviour, and the relationship was mediated by burdensomeness and thwarted belonging….. ” Does it mean burdensomeness and thwarted belonging are two of the underlying mechanisms explaining the relationship between autism and suicidal thoughts? If yes, then why this is not addressed in the next section of the paper, which is about “mechanisms underspinning suicide risk and the autistic phenotype”?

• There is a typo on page 8 under the “Participants” section. It says “…. condiucted recruitment online using…. ” “Condiucted” is a typo.

• On page 15, please provide a citation for Little’s MCAR test. Also on page 15, I am confused why the authors found out that the data are not MCAR but still used listwise deletion.

• On page 8, the sentence, “The aims of the present study were to examine (1) the contribution of two key clinical domains––social communication difficulties, insistence on sameness––usually considered core features of ASD, but also present across a range of other disorders, that have been associated with suicidal risk and behavior, thereby deconstructing the impact of specific ASD domains on suicide risk (assessed using DSM-5 suicidal ideation; SI)….”, is very confusing to read. Please simplify the sentence.

**********

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Reviewer #1: No

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Attachment

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PLoS One. 2021 Jan 22;16(1):e0245562. doi: 10.1371/journal.pone.0245562.r002

Author response to Decision Letter 0

28 Oct 2020

Dear editor,

Thank you for the opportunity to revise and re-submit our manuscript. Please find below a detailed response to all comments. We thank the reviewer and the editor for their suggestion leading to what we believe is an improved manuscript. Of note, we have revised the Results section and analyses as per the suggestions of the Review, and appreciate the suggestions.

1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at

https://journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and

https://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf

Response: We have carefully checked the manuscript against the PLOS ONE style templates and have revised the manuscript accordingly, including the reference list, title page, and table notes.

2.Please provide additional details regarding participant consent. In the ethics statement in the Methods and online submission information, please ensure that you have specified (1) whether consent was informed and (2) what type you obtained (for instance, written or verbal, and if verbal, how it was documented and witnessed). If your study included minors, state whether you obtained consent from parents or guardians. If the need for consent was waived by the ethics committee, please include this information. If you are reporting a retrospective study of medical records or archived samples, please ensure that you have discussed whether all data were fully anonymized before you accessed them and/or whether the IRB or ethics committee waived the requirement for informed consent. If patients provided informed written consent to have data from their medical records used in research, please include this information.

Response: The study was online. Participants read an information statement about the study and provided online consent. These additional details have been added to the Method section and the Ethics Statement in editorialmanager, and the Cover Letter. The consent process now reads:

“All participants reviewed an information document and were informed that participation was voluntary prior to agreeing to participate in the study. Online consent was received from all study participants.”

In your revised cover letter, please address the following prompts:

a) If there are ethical or legal restrictions on sharing a de-identified data set, please explain them in detail (e.g., data contain potentially sensitive information, data are owned by a third-party organization, etc.) and who has imposed them (e.g., an ethics committee). Please also provide contact information for a data access committee, ethics committee, or other institutional body to which data requests may be sent. b) If there are no restrictions, please upload the minimal anonymized data set necessary to replicate your study findings as either Supporting Information files or to a stable, public repository and provide us with the relevant URLs, DOIs, or accession numbers. For a list of acceptable repositories, please see http://journals.plos.org/plosone/s/data-availability#loc-recommended-repositories. We will update your Data Availability statement on your behalf to reflect the information you provide.

Response: All data are fully available without restrictions. Data are freely downloadable from Figshare, https://doi.org/10.26181/5e992fc659d7c. The reference for the dataset is:

“Hedley D, Uljarević M, Cai RY, Bury SM, Stokes MA, Evans DW. Transdiagnostic predictors of dsm-5 suicide risk [raw dataset, Version 2]. Figshare. 2020. https://doi.org/10.26181/5e992fc659d7c”

The statement in editorialmanager has been updated to indicate that data are openly available and that additional queries concerning the data sample can be directed to author DWE.

“The data that support the findings of this study are openly available in “figshare” at https://doi.org/10.26181/5e992fc659d7c [89]. Additional correspondence or queries concerning the data sample should be directed to David W. Evans, PhD, Department of Psychology, Bucknell University, Lewisburg, PA, 17837; e-mail: dwevans@bucknell.edu.”

4.Thank you for stating the following in the Competing Interests section:

Please confirm that this does not alter your adherence to all PLOS ONE policies on sharing data and materials, by including the following statement: "This does not alter our adherence to PLOS ONE policies on sharing data and materials.” (as detailed online in our guide for authors https://protect-au.mimecast.com/s/dHriC4QZgRFApQopfjbrgz?domain=journals.plos.org). If there are restrictions on sharing of data and/or materials, please state these. Please note that we cannot proceed with consideration of your article until this information has been declared.

Please include your updated Competing Interests statement in your cover letter; we will change the online submission form on your behalf.

Response: The Competing Interests statement has been update to include the additional information. It now reads:

“I have read the journal's policy and the authors of this manuscript have the following competing interests: DH is supported by a Suicide Prevention Australia National Suicide Prevention Research fellowship. MU is supported by a Discovery Early Career Researcher Award from the Australian Research Council (DE180100632). DWE declares that he receives funding from Roche Pharmaceutical for consulting on the use of the Childhood Routines Inventory-Revised (CRI-R). This does not alter our adherence to PLOS ONE policies on sharing data and materials.”

5.Thank you for stating the following financial disclosure:

1. Review your statements relating to the author contributions, and ensure you have specifically and accurately indicated the role(s) that these authors had in your study. These amendments should be made in the online form.

Response: The information relating to author contributions and roles on the online form are updated and correct.

2. Confirm in your cover letter that you agree with the following statement, and we will change the online submission form on your behalf:

Response: We confirm that funding organization did not play a role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript and only provided financial support in the form of authors' salaries and/or research materials. We confirm that we agree with the statement in [2]. The following statement has been added to the Cover Letter:

“The funder provided support in the form of salaries for authors DH and MU and research materials for DWE, but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of these authors are articulated in the ‘author contributions’ section.”

Reviewer comments

Response: The data analyses have been revised as suggested by the Reviewer. We now report the results of a regular multiple linear regression analysis.

Response: We have followed APA guidelines (www.apastyle.apa.org) for reporting the various analyses, and do summarize the results/findings for each of the main analyses. In line with the Reviewer’s recommendation, we have reviewed and revised Results reporting adding summary information where we felt it was appropriate to do so. We are happy to provide further information if the Review can please clearly identify what/how they would like the Results section to be changed. Examples of summaries for each of the main tables/analyses are provided below:

Table 2: “Overall, approximately 42–44% of the total sample met the ‘threshold for further inquiry’ for depression, and 33% met the threshold for follow-up for suicide risk due to presence of SI”.

Table 3: “Social communication difficulties were significantly correlated with all variables, with effect sizes ranging from small to medium (rp = .216–.365). Study variables were all significantly correlated with depression and SI in the expected directions, with effect sizes in the small to large range (rp = -.117–.590). Effect sizes for social communication difficulties were in the medium range for depression and SI and, as expected, SI was strongly correlated with depression. In terms of the other variables, insistence on sameness, rumination (both positively) and attentional control (negatively) were most strongly associated with depression, and attentional control (negatively) was most strongly associated with SI. Thus, all of the hypothesized constructs were found to be significantly associated with SI thereby warranting their inclusion in the linear regression analysis. ”

Table 4: “Table 4 presents the results of the linear regression model predicting SI. All hypothesized constructs were included in the model. Age was controlled for by including it in the model. The full model accounted for 43.3% of variance in SI scores, F(7, 1843) = 201.19, p < .001. In addition to depression (t = 20.81), social communication difficulties (t = 9.99), insistence on sameness (t = 6.95), attentional (t = -3.44) and inhibitory (t = 3.99) control were all identified as significant predictors of SI; however, rumination (t = -1.70) was not found to be a significant predictor of SI when entered in the model with the other variables.”

Table 5: “Thus, participants who reported some SI reported significantly greater social communication difficulties, higher levels of insistence on sameness, and lower levels of attentional and inhibitory control, than participants who did not report any SI.”

3. The authors included a lot of tables (e.g., Tables 1, 2 and 3) that are not essential for addressing the research goal. I recommend the authors put these tables in the supplementary materials.

Response: Table 1 presents sample demographics and is not presented in the results section, Table 2 summarizes clinical depression and suicidal ideation symptoms on the main symptom measure, and Table 3 presents means and correlations between the main study variables. We respectfully disagree with the Reviewer that these tables are better placed as Supplementary materials (which are only accessed through separate download), and our inclusion of them in the main body is consistent with standard APA style reporting. Furthermore, we do not agree that these data are not important to the research goal, and instead suggest that these data are important for informing the reader as to the nature of the sample, and the relationships between the main study variables. The inclusion of primary demographic and main variable data in the main body of the manuscript is also consistent with our own publications, and those of other studies published in PLoS One. We are therefore very hesitant and prefer not to remove these tables from the main manuscript.

4. On page 6, there is a sentence that says “ASD traits were found to significantly correlate with suicidal behaviour, and the relationship was mediated by burdensomeness and thwarted belonging….. ” Does it mean burdensomeness and thwarted belonging are two of the underlying mechanisms explaining the relationship between autism and suicidal thoughts? If yes, then why this is not addressed in the next section of the paper, which is about “mechanisms underpinning suicide risk and the autistic phenotype”?

Response: We agree with the Reviewer that the referenced study could have potentially been described in the section “mechanisms underpinning suicide risk and the autistic phenotype” as the study addresses a mechanism from ASD traits to suicidality based on Joiner’s (2005) theory. However, we pose that this study (Pelton & Cassidy, 2017) is better placed under the subheading “Autism phenotype as a risk factor for suicide”. First, the study examined the path from autistic traits through burdensomeness/belonging to suicidality, and therefore fits equally well within the section on the contribution of autistic traits to suicidality. Second, the next section, on mechanisms, builds an argument for the role of the autistic phenotype i.e., from those constructs associated with the diagnostic criteria of the disorder to suicidality directly. Neither belonginess nor burdensomeness fit this criteria. As examined by Pelton and Cassidy, the path from ASD traits to burdensomeness/belonging to suicidality is an indirect mediation pathway. The factors we have identified are those dimensional constructs that we think may directly predict suicidal risk. While a more complex model might be constructed that includes mediation or other pathways though factors such as belonginess/burdensomeness etc., this was beyond the scope of our study. Therefore, we believe that this section (i.e., mechanisms) is better focused on the specific constructs we test in the present study.

5. There is a typo on page 8 under the “Participants” section. It says “…. condiucted recruitment online using…. ” “Condiucted” is a typo.

Response: Thank you for pointing this out, now corrected.

6. On page 15, please provide a citation for Little’s MCAR test. Also on page 15, I am confused why the authors found out that the data are not MCAR but still used listwise deletion.

Response: The reference for MCAR is now provided. We note that there was an error in our reporting of MCAR – the non-significant p-value (p = .895) does indeed indicate that data are missing completely at random. This has now been corrected in text. We removed cases with missing data based on the recommendations by Tabachnick & Fidell. Given that data were missing at random, we have not altered this methodology and apologise for the error. The sentence now reads:

“Little’s MCAR test indicated that data were missing completely at random, p = .895 [70].”

7. On page 8, the sentence, “The aims of the present study were to examine (1) the contribution of two key clinical domains––social communication difficulties, insistence on sameness––usually considered core features of ASD, but also present across a range of other disorders, that have been associated with suicidal risk and behavior, thereby deconstructing the impact of specific ASD domains on suicide risk (assessed using DSM-5 suicidal ideation; SI)….”, is very confusing to read. Please simplify the sentence.

Response: We have significantly revised this sentence for clarity. The paragraph now reads:

“The aims of the present study were to examine (1) the contribution of the two core clinical domains of ASD—social communication difficulties, insistence on sameness—on suicide risk (assessed using DSM-5 suicidal ideation; SI) and (2) the additional contribution and interaction of two key dimensional constructs––cognitive control and rumination. We predict that each of the identified constructs will independently contribute to SI, controlling for depression.”

Attachment

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PLoS One. doi: 10.1371/journal.pone.0245562.r003

Decision Letter 1

Vincenzo De Luca

30 Nov 2020

PONE-D-20-29676R1

Domains of the autism phenotype, cognitive control, and rumination as transdiagnostic predictors of DSM-5 suicide risk

PLOS ONE

Dear Dr. Hedley,

Please submit your revised manuscript by Jan 14 2021 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

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A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

We look forward to receiving your revised manuscript.

Kind regards,

Vincenzo De Luca

Academic Editor

PLOS ONE

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: (No Response)

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: Partly

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: No

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #1: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #1: Yes

**********

6. Review Comments to the Author

Reviewer #1: The manuscript has improved. However, there are still a few things that need to be addressed.

First, on page 15, it says that "Little's MCAR test indicated that data were missing completely at random." Actually, non-significant test does NOT indicate the data are MCAR although it is a common misconception. A non-significance result only means that the covariance structure of the data across different missing data patterns are not heterogenous. Please refer to Yuan, Jamshidian and Kano (2018)'s paper about this. In the next revision, please correct this.

Second, please mention in your results section how to access your data (i.e., provide the "figshare" link).

Third, on page 20, I am not sure what those "t" values are. Please clarify.

Finally, on page 20, please also elaborate more on your results. Interpret the regression coefficients, and explain what each coefficient means based on your research purposes.

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

PLoS One. 2021 Jan 22;16(1):e0245562. doi: 10.1371/journal.pone.0245562.r004

Author response to Decision Letter 1

30 Nov 2020

Dear Editor,

Thank you for the opportunity to revise and re-submit our manuscript. Please find below a detailed response to all comments. We thank the reviewer for the additional suggestions and have revised the text accordingly. In particular, we have significantly elaborated on the interpretation of the regression analysis, as well as attending to the other three minor suggestions.

Response to Reviewer

1. First, on page 15, it says that "Little's MCAR test indicated that data were missing completely at random." Actually, non-significant test does NOT indicate the data are MCAR although it is a common misconception. A non-significance result only means that the covariance structure of the data across different missing data patterns are not heterogenous. Please refer to Yuan, Jamshidian and Kano (2018)'s paper about this. In the next revision, please correct this.

Response: The text has been revised.

Revised text: “No more than 1% (M = .303, SD = .19, Range = 0–1%) of data were missing for any questionnaire item overall, and Little’s MCAR test was not significant, p = .895 [70]. Thus, following Tabachnick and Fidell [71], cases with missing data on any of the questionnaires were deleted (n = 77, 3.8%).”

2. Second, please mention in your results section how to access your data (i.e., provide the "figshare" link).

Response: added.

Inserted text: “The data that support the findings of the study are openly available in “Figshare” at https://doi.org/10.26181/5e992fc659d7c [90].”

3. Third, on page 20, I am not sure what those "t" values are. Please clarify.

Author response: In response to the reviewer’s comment, t-statistics from the regression analysis have been deleted.

4. Finally, on page 20, please also elaborate more on your results. Interpret the regression coefficients, and explain what each coefficient means based on your research purposes.

Response: Results on p20 have been significantly elaborated upon.

Revised text: “Table 4 presents the results of the linear regression model predicting SI. All hypothesized constructs were included in the model. Age was controlled for by including it in the model. The full model accounted for 43.3% of variance in SI scores, F(7, 1843) = 201.19, p < .001. Social Communication Difficulties significantly predicted SI, with the b-weight revealing that for each unit increase in Social Communication Difficulties, SI increased by 0.085 units. Similarly, Insistence on Sameness was also identified as a significant predictor of SI, with the b-weight revealing that for each unit increase in Insistence on Sameness, SI increased by 0.012 units. Attentional and Inhibitory Control both significantly predicted SI, with the b-weights revealing that for each unit increase in Attentional Control, SI decreased by 0.015 units, and for each unit increase in Inhibitory Control, SI increased by 0.015 units. Rumination was not a significant predictor of SI when entered in the model with the other variables, with each unit increase in Rumination associated with a decrease in SI of -0.010 units. Overall, Depression made the largest contribution to SI (beta = 0.484). Comparing Social Communication to Insistence on Sameness; Social Communication Difficulties (beta = 0.192) was relatively more important than Insistence on Sameness (beta = 0.144). These two core variables shared some variance, but correlations in Table 3 reveal that these were largely independent contributions. Attentional Control (beta = -0.085) and Inhibitory Control (beta = 0.077) made similar, yet relatively smaller contributions to the model.”

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PLoS One. doi: 10.1371/journal.pone.0245562.r005

Decision Letter 2

Vincenzo De Luca

21 Dec 2020

PONE-D-20-29676R2

Domains of the autism phenotype, cognitive control, and rumination as transdiagnostic predictors of DSM-5 suicide risk

PLOS ONE

Dear Dr. Hedley,

Please submit your revised manuscript by Feb 04 2021 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

We look forward to receiving your revised manuscript.

Kind regards,

Vincenzo De Luca

Academic Editor

PLOS ONE

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

Reviewer #1: (No Response)

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #1: (No Response)

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #1: Yes

**********

6. Review Comments to the Author

Reviewer #1: First, when I clicked on the link for the data (https://doi.org/10.26181/5e992fc659d7c [90].), it led me to a page with an error message. Please upload your data on the Open Science Framework (OSF) and provide the appropriate link.

Second, please use correct APA notations for mathematical symbols. Don't just write "beta=0.144".

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

PLoS One. 2021 Jan 22;16(1):e0245562. doi: 10.1371/journal.pone.0245562.r006

Author response to Decision Letter 2

21 Dec 2020

Dear Editor,

Thank you for the opportunity to revise and re-submit our manuscript. Please find below a detailed response to all comments.

Response to Reviewer

1. First, when I clicked on the link for the data (https://doi.org/10.26181/5e992fc659d7c [90].), it led me to a page with an error message. Please upload your data on the Open Science Framework (OSF) and provide the appropriate link.

Response: It is unclear why the Reviewer was unable to access these data, which were hosted on the University OP[A]L Open framework. We wonder whether the inclusion of the reference number [90] in the link caused the error message? Alternatively, it is possible that the Reviewer was unable to open the file as it was in SPSS? Nonetheless, at the Reviewer’s request we have transferred the file to OSF and have replaced the OP[A]L link with the new OSF link. We have additionally saved the file in .spss, .xlsx and .csv formats, and updated the referencing and links. We have additionally double checked and confirmed the links are working and the files are readily downloadable from OSF. The new link is: https://doi.org/10.17605/OSF.IO/C2AP3

2. Second, please use correct APA notations for mathematical symbols. Don't just write "beta=0.144".

Response: Replaced.

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PLoS One. doi: 10.1371/journal.pone.0245562.r007

Decision Letter 3

Vincenzo De Luca

4 Jan 2021

Domains of the autism phenotype, cognitive control, and rumination as transdiagnostic predictors of DSM-5 suicide risk

PONE-D-20-29676R3

Dear Dr. Hedley,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

Kind regards,

Vincenzo De Luca

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

Reviewer #1: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #1: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #1: Yes

**********

6. Review Comments to the Author

Reviewer #1: (No Response)

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

PLoS One. doi: 10.1371/journal.pone.0245562.r008

Acceptance letter

Vincenzo De Luca

6 Jan 2021

PONE-D-20-29676R3

Domains of the autism phenotype, cognitive control, and rumination as transdiagnostic predictors of DSM-5 suicide risk

Dear Dr. Hedley:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

If we can help with anything else, please email us at plosone@plos.org.

Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Dr. Vincenzo De Luca

Academic Editor

PLOS ONE

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 Appendix. Scale items and ratings.

Items for each of the constructs used in the study.

(DOCX)

Click here for additional data file.^{(17.3KB, docx)}

S1 Table. Participant distribution.

Distribution of participants between US States and the District of Columbia.

(DOCX)

Click here for additional data file.^{(13.1KB, docx)}

Attachment

Submitted filename: Review of Manuscript from PLOS ONE.pdf

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Submitted filename: Response to reviewers.docx

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Data Availability Statement

[pone.0245562.ref001] 1.World Health Organization. Preventing suicide: A global imperative (Vol. 2020). Geneva: World Health Organization; 2014. [Google Scholar]

[pone.0245562.ref002] 2.O'Connor RC, Portzky G. Looking to the future: A synthesis of new developments and challenges in suicide research and prevention. Frontiers in Psychology. 2018;9:2139 10.3389/fpsyg.2018.02139 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0245562.ref003] 3.Insel T, Cuthbert B, Garvey M, Heinssen R, Pine DS, Quinn K, et al. Research domain criteria (RDoC): toward a new classification framework for research on mental disorders. Am J Psychiatry. 2010;167:748–51. 10.1176/appi.ajp.2010.09091379 [DOI] [PubMed] [Google Scholar]

[pone.0245562.ref004] 4.National Institute of Mental Health. Mechanisms underlying suicide risk: Integrating RDoC to inform novel and personalized intervention research. Maryland: National Institute of Mental Health; 2008. [Google Scholar]

[pone.0245562.ref005] 5.Constantino JN, Todd RD. Autistic traits in the general population: a twin study. Archives of General Psychiatry. 2003;60:524–30. 10.1001/archpsyc.60.5.524 [DOI] [PubMed] [Google Scholar]

[pone.0245562.ref006] 6.Evans DW, Leckman JF, Carter A, Reznick JS, Henshaw D, King RA, et al. Ritual, habit, and perfectionism: The prevalence and development of compulsive-like behavior in normal young children. Child Development. 1997;68:58–68. [PubMed] [Google Scholar]

[pone.0245562.ref007] 7.Gallyer AJ, Stanley IH, Day TN, Joiner TE. Examining the interaction of autism spectrum disorder-related traits and unit cohesion on suicide risk among military personnel. J Affect Disord. 2020;271:59–65. 10.1016/j.jad.2020.03.092 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0245562.ref008] 8.Eisner LR. A transdiagnostic model of suicidal ideation and suicide attempts. 2010. https://scholarlyrepoository.miami.edu/oa_dissertations/358. Accessed 24 Aug 2020. [Google Scholar]

[pone.0245562.ref009] 9.Joiner TE, Brown JS, Wingate LR. The psychology and neurobiology of suicidal behavior. Annual Review of Psychology. 2005;56:287–314. 10.1146/annurev.psych.56.091103.070320 [DOI] [PubMed] [Google Scholar]

[pone.0245562.ref010] 10.Kessler RC, Berglund P, Borges G, Nock M, Wang PS. Trends in suicide ideation, plans, gestures, and attempts in the United States, 1990–1992 to 2001–2003. JAMA. 2005;293:2487–95. 10.1001/jama.293.20.2487 [DOI] [PubMed] [Google Scholar]

[pone.0245562.ref011] 11.Kessler RC, Chiu WT, Demler O, Merikangas KR, Walters EE. Prevalence, severity, and comorbidity of 12-month DSM-IV disorders in the National Comorbidity Survey Replication. Archives of General Psychiatry. 2005;62:617–27. 10.1001/archpsyc.62.6.617 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0245562.ref012] 12.Hjelmeland H, Knizek BL. Suicide and mental disorders: A discourse of politics, power, and vested interests. Death Studies. 2017;41:481–92. 10.1080/07481187.2017.1332905 [DOI] [PubMed] [Google Scholar]

[pone.0245562.ref013] 13.Cassidy S, Bradley P, Robinson J, Allison C, McHugh M, Baron-Cohen S. Suicidal ideation and suicide plans or attempts in adults with Asperger's syndrome attending a specialist diagnostic clinic: A clinical cohort study. Lancet Psychiatry. 2014;1:142–7. 10.1016/S2215-0366(14)70248-2 [DOI] [PubMed] [Google Scholar]

[pone.0245562.ref014] 14.Hirvikoski T, Mittendorfer-Rutz E, Boman M, Larsson H, Lichtenstein P, Bölte S. Premature mortality in autism spectrum disorder. Br J Psychiatry. 2016;208:232–8. 10.1192/bjp.bp.114.160192 [DOI] [PubMed] [Google Scholar]

[pone.0245562.ref015] 15.Pelton MK, Crawford H, Robertson AE, Rodgers J, Baron-Cohen S, Cassidy S. A measurement invariance analysis of the Interpersonal Needs Questionnaire and Acquired Capability for Suicide Scale in autistic and non-autistic adults. Autism in Adulthood. 2020; 2:193–203. 10.1089/aut.2019.0055 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0245562.ref016] 16.Rodriguez-Seijas C, Gadow K D, Rosen T E., Kim H, Lerner MD, Eaton NR. A transdiagnostic model of psychiatric symptom co-occurrence and autism spectrum disorder. Autism Res. 2019;13:579–90. 10.1002/aur.2228 [DOI] [PubMed] [Google Scholar]

[pone.0245562.ref017] 17.Upthegrove R, Abu-Akel A, Chisholm K, Lin A, Zahid S, Pelton M, et al. Autism and psychosis: Clinical implications for depression and suicide. Schizophr Res. 2018;195:80–5. 10.1016/j.schres.2017.08.028 [DOI] [PubMed] [Google Scholar]

[pone.0245562.ref018] 18.De Crescenzo F, Postorino V, Siracusano M, Riccioni A, Armando M, Curatolo P, et al. Autistic symptoms in schizophrenia spectrum disorders: A systematic review and meta-analysis. Frontiers in Psychiatry. 2019;10:78–8. 10.3389/fpsyt.2019.00078 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0245562.ref019] 19.Abu-Akel A, Allison C, Baron-Cohen S, Heinke D. The distribution of autistic traits across the autism spectrum: evidence for discontinuous dimensional subpopulations underlying the autism continuum. Mol Autism. 2019;10:e24 10.1186/s13229-019-0275-3 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0245562.ref020] 20.Robinson EB, St Pourcain B, Anttila V, Kosmicki JA, Bulik-Sullivan B, Grove J, et al. Genetic risk for autism spectrum disorders and neuropsychiatric variation in the general population. Nat Genet. 2016;48:552–555. 10.1038/ng.3529 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0245562.ref021] 21.Rai D, Culpin I, Heuvelman H, Magnusson CMK, Carpenter P, Jones HJ, et al. (2018). Association of autistic traits with depression from childhood to age 18 years. JAMA Psychiatry. 2018;75:835–43. 10.1001/jamapsychiatry.2018.1323 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0245562.ref022] 22.Rosbrook A, Whittingham K. Autistic traits in the general population: What mediates the link with depressive and anxious symptomatology? Res Autism Spectr Disord. 2010;4:415–24. [Google Scholar]

[pone.0245562.ref023] 23.Stanley IH, Day TN, Gallyer AJ, Shelef L, Kalla C, Gutierrez PM, et al. Autism-related traits and suicide risk among active duty U.S. military service members. Psychol Serv. 2020. 10.1037/ser0000418 [DOI] [PubMed] [Google Scholar]

[pone.0245562.ref024] 24.Glenn CR, Cha CB, Kleiman EM, Nock MK. Understanding suicide risk within the Research Domain Criteria (RDoC) Framework: Insights, challenges, and future research considerations. Clin Psychol Sci. 2017;5:568–92. 10.1177/2167702616686854 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0245562.ref025] 25.Glenn CR, Kleiman EM, Cha CB, Deming CA, Franklin JC, Nock MK. Understanding suicide risk within the Research Domain Criteria (RDoC) framework: A meta-analytic review. Depress Anxiety. 2018;35:65–88. 10.1002/da.22686 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0245562.ref026] 26.Cuthbert BN. The RDoC framework: facilitating transition from ICD/DSM to dimensional approaches that integrate neuroscience and psychopathology. World Psychiatry. 2014;13:28–35. 10.1002/wps.20087 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0245562.ref027] 27.McCoy TH Jr, Pellegrini AM, Perlis RH. Research Domain Criteria scores estimated through natural language processing are associated with risk for suicide and accidental death. Depress Anxiety. 2019;36:392–99. 10.1002/da.22882 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0245562.ref028] 28.Cassidy S, Bradley L, Shaw R, Baron-Cohen S. Risk markers for suicidality in autistic adults. Mol Autism. 2018;9:e42 10.1186/s13229-018-0226-4 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0245562.ref029] 29.Hedley D, Uljarević M. Systematic review of suicide in Autism Spectrum Disorder: Current trends and implications. Current Developmental Disorders Reports. 2018;5:65–76. [Google Scholar]

[pone.0245562.ref030] 30.Hedley D, Uljarević M, Foley KR, Richdale A, Trollor J. Risk and protective factors underlying suicidal ideation in Autism Spectrum Disorder. Depress Anxiety. 2018;35:648–57. 10.1002/da.22759 [DOI] [PubMed] [Google Scholar]

[pone.0245562.ref031] 31.Zahid S, Upthegrove R. Suicidality in Autistic Spectrum Disorders: A systematic review. Crisis. 2017;8:237–46 [DOI] [PubMed] [Google Scholar]

[pone.0245562.ref032] 32.Hand BN, Benevides TW, Carretta HJ. Suicidal ideation and self-inflicted injury in Medicare enrolled autistic adults with and without co-occurring intellectual disability. J Autism Dev Disord. 2020;50:3489–3495. 10.1007/s10803-019-04345-x [DOI] [PubMed] [Google Scholar]

[pone.0245562.ref033] 33.Baron-Cohen S, Wheelwright S, Skinner R, Martin J, Clubley E. The autism-spectrum quotient (AQ): Evidence from Asperger syndrome/high-functioning autism, males and females, scientists and mathematicians. J Autism Dev Disord. 2001;31:5–17. 10.1023/a:1005653411471 [DOI] [PubMed] [Google Scholar]

[pone.0245562.ref034] 34.Ruzich E, Allison C, Smith P, Watson P, Auyeung B, Ring H, et al. Measuring autistic traits in the general population: a systematic review of the Autism-Spectrum Quotient (AQ) in a nonclinical population sample of 6,900 typical adult males and females. Mol Autism. 2015;6:e2. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0245562.ref035] 35.Colvert E, Tick B, McEwen F, Stewart C, Curran SR, Woodhouse E, et al. Heritability of Autism Spectrum Disorder in a UK population-based twin sample. JAMA Psychiatry. 2015;72:415–23. 10.1001/jamapsychiatry.2014.3028 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0245562.ref036] 36.Stergiakouli E, Davey Smith G, Martin J, Skuse DH, Viechtbauer W, Ring SM, et al. Shared genetic influences between dimensional ASD and ADHD symptoms during child and adolescent development. Mol Autism. 2017;8:e18 10.1186/s13229-017-0131-2 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0245562.ref037] 37.Cassidy S, Gould K, Townsend E, Pelton M, Robertson AE, Rodgers J. Is camouflaging autistic traits associated with suicidal thoughts and behaviours? Expanding the Interpersonal Psychological Theory of Suicide in an undergraduate student sample. J Autism Dev Disord. 2020;50:3638–48. 10.1007/s10803-019-04323-3 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0245562.ref038] 38.Pelton MK, Cassidy SA. Are autistic traits associated with suicidality? A test of the interpersonal-psychological theory of suicide in a non-clinical young adult sample. Autism Res. 2017;10:1891–904. 10.1002/aur.1828 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0245562.ref039] 39.Joiner TE. Why people die by suicide Cambridge: Harvard University Press; 2005. [Google Scholar]

[pone.0245562.ref040] 40.American Psychiatric Association. Diagnostic and statistical manual of mental disorders (5^th ed.). Washington: American Psychiatric Association; 2013. [Google Scholar]

[pone.0245562.ref041] 41.Unruh KE, Bodfish JW, Gotham KO. Adults with autism and adults with depression show similar attentional biases to social-affective images. J Autism Dev Disord. 2020;50:2336–47. 10.1007/s10803-018-3627-5 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0245562.ref042] 42.Fazakas-DeHoog LL, Rnic K, Dozois, DJA. A cognitive distortions and deficits model of suicide ideation. Europe's Journal of Psychology. 2017;13:178–93. 10.5964/ejop.v13i2.1238 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0245562.ref043] 43.Litinsky AM, Haslam N. Dichotomous thinking as a sign of suicide risk on the TAT. J Pers Assess. 1998;71:368–78. 10.1207/s15327752jpa7103_6 [DOI] [PubMed] [Google Scholar]

[pone.0245562.ref044] 44.Pu S, Setoyama S, Noda T. Association between cognitive deficits and suicidal ideation in patients with major depressive disorder. Scientific Reports. 2017;7:e11637 10.1038/s41598-017-12142-8 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0245562.ref045] 45.Keenan EG, Gotham K, Lerner MD. Hooked on a feeling: Repetitive cognition and internalizing symptomatology in relation to autism spectrum symptomatology. Autism. 2018;22:814–24. 10.1177/1362361317709603 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0245562.ref046] 46.Nolen-Hoeksema S. The role of rumination in depressive disorders and mixed anxiety/depressive symptoms. J Abnorm Psychol. 2000;109:504–11. [PubMed] [Google Scholar]

[pone.0245562.ref047] 47.Miranda R, Nolen-Hoeksema S. Brooding and reflection: rumination predicts suicidal ideation at 1-year follow-up in a community sample. Behav Res Ther. 2007;45:3088–95. 10.1016/j.brat.2007.07.015 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0245562.ref048] 48.Smith JM, Alloy LB, Abramson LY. Cognitive vulnerability to depression, rumination, hopelessness, and suicidal ideation: multiple pathways to self-injurious thinking. Suicide and Life-Threatening Behavavior. 2006;36:443–54. [DOI] [PubMed] [Google Scholar]

[pone.0245562.ref049] 49.Demurie E, Roeyers H, Baeyens D, Sonuga-Barke E. Temporal discounting of monetary rewards in children and adolescents with ADHD and autism spectrum disorders. Dev Sci. 2012;15:791–800. 10.1111/j.1467-7687.2012.01178.x [DOI] [PubMed] [Google Scholar]

[pone.0245562.ref050] 50.Geurts HM, Verte S, Oosterlaan J, Roeyers H, Sergeant JA. How specific are executive functioning deficits in attention deficit hyperactivity disorder and autism? J Child Psychol Psychiatry. 2004;45:836–54. 10.1111/j.1469-7610.2004.00276.x [DOI] [PubMed] [Google Scholar]

[pone.0245562.ref051] 51.Karalunas SL, Hawkey E, Gustafsson H, Miller M, Langhorst M, Cordova M, et al. Overlapping and distinct cognitive impairments in Attention-Deficit/Hyperactivity and Autism Spectrum Disorder without intellectual disability. J Abnorm Child Psychol. 2018;46:1705–16. 10.1007/s10802-017-0394-2 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0245562.ref052] 52.Ozonoff S, Jensen J. Brief report: Specific executive function profiles in three neurodevelopmental disorders. J Autism Dev Disord. 1999;29:171–7. 10.1023/a:1023052913110 [DOI] [PubMed] [Google Scholar]

[pone.0245562.ref053] 53.Bos DJ, Silverman MR, Ajodan EL, Martin C, Silver BM, Brouwer GJ, et al. Rigidity coincides with reduced cognitive control to affective cues in children with autism. J Abnorm Psychol. 2019;128:431–41. 10.1037/abn0000423 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0245562.ref054] 54.Crandall A, Allsop Y, Hanson CL. The longitudinal association between cognitive control capacities, suicidality, and depression during late adolescence and young adulthood. J Adolesc. 2018;65:167–76. [DOI] [PubMed] [Google Scholar]

[pone.0245562.ref055] 55.Stewart JG, Glenn CR, Esposito EC, Cha CB, Nock MK, Auerbach RP. Cognitive Control Deficits Differentiate Adolescent Suicide Ideators From Attempters. J Clin Psychiatry. 2017;78:614–21. 10.4088/JCP.16m10647 [DOI] [PubMed] [Google Scholar]

[pone.0245562.ref056] 56.Evans DW, Lusk LG, Slane MM, Michael AM, Myers SM, Uljarević M, et al. Dimensional assessment of schizotypal, psychotic, and other psychiatric traits in children and their parents: Development and validation of the Childhood Oxford-Liverpool Inventory of Fellings and Experiences on a representative US sample. J Child Psychol Psychiatry. 2018;59:574–85. 10.1111/jcpp.12827 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0245562.ref057] 57.Evans DW, Uljarević M, Lusk LG, Loth E, Frazier T. Development of two dimensional measures of restricted and repetitive behavior in parents and children. J Am Acad Child Adolesc Psychiatry. 2017;56:51–8. 10.1016/j.jaac.2016.10.014 [DOI] [PubMed] [Google Scholar]

[pone.0245562.ref058] 58.Buhrmester M, Kwang T, Gosling SD. Amazon's Mechanical Turk: A new source of inexpensive, yet high-quality data? In: Kazdin AE, editor. Methodological issues and strategies in clinical research. Washington: American Psychological Association; 2016. p. 133–9. [DOI] [PubMed] [Google Scholar]

[pone.0245562.ref059] 59.Buhrmester MD, Talaifar S, Gosling SD. An evaluation of Amazon’s mechanical Turk, its rapid rise, and its effective use. Perspect Psychol Sci. 2018;13:149–54. 10.1177/1745691617706516 [DOI] [PubMed] [Google Scholar]

[pone.0245562.ref060] 60.Palan S, Schitter C. Prolific.ac–A subject pool for online research. Journal of Behavioral and Experimental Finance. 2018;17:22–7. [Google Scholar]

[pone.0245562.ref061] 61.Qualtrics. Qualtrics version 2.16 [computer program]. Utah: Qualtrics; 2017.

[pone.0245562.ref062] 62.United States Census Bureau. State and county QuickFacts 2010, 2014. United States Census Bureau. 2019. https://quickfacts.census.gov. Accessed Apr 16, 2020. [Google Scholar]

[pone.0245562.ref063] 63.Kessler RC, Berglund P, Demler O, Jin R, Merikangas KR, Walters EE. Lifetime prevalence and age-of-onset distributions of DSM-IV disorders in the National Comorbidity Survey Replication. Archives of General Psychiatry. 2005;62:593–602. 10.1001/archpsyc.62.6.593 [DOI] [PubMed] [Google Scholar]

[pone.0245562.ref064] 64.Allison C, Auyeung B, Baron-Cohen S. Toward brief "Red Flags" for autism screening: The Short Autism Spectrum Quotient and the Short Quantitative Checklist for Autism in toddlers in 1,000 cases and 3,000 controls [corrected]. J Am Acad Child Adolesc Psychiatry. 2012;51:202–12. 10.1016/j.jaac.2011.11.003 [DOI] [PubMed] [Google Scholar]

[pone.0245562.ref065] 65.Evans DE, Rothbart MK. Developing a model for adult temperament. J Res Pers. 2007;41:868–88. [Google Scholar]

[pone.0245562.ref066] 66.Berle D, Starcevic V, Moses K, Hannan A, Milicevic D, Sammut P. (2011). Preliminary validation of an ultra-brief version of the Penn State Worry Questionnaire. Clin Psychol Psychother. 2011;18:339–46. 10.1002/cpp.724 [DOI] [PubMed] [Google Scholar]

[pone.0245562.ref067] 67.Narrow WE, Clarke DE, Kuramoto SJ, Kraemer HC, Kupfer DJ, Greiner L, et al. DSM-5 field trials in the United States and Canada, Part III: development and reliability testing of a cross-cutting symptom assessment for DSM-5. Am J Psychiatry. 2013;170:71–82. 10.1176/appi.ajp.2012.12071000 [DOI] [PubMed] [Google Scholar]

[pone.0245562.ref068] 68.Narrow WE, Kuhl EA. Dimensional approaches to psychiatric diagnosis in DSM-5. J Ment Health Policy Econ. 2011;14:197–200. [PubMed] [Google Scholar]

[pone.0245562.ref069] 69.Clarke DE, Kuhl EA. DSM-5 cross-cutting symptom measures: a step towards the future of psychiatric care? World Psychiatry. 2014;13:314–6. 10.1002/wps.20154 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0245562.ref070] 70.Little RJA. A test of missing completely at random for multivariate data with missing values. J Am Stat Assoc. 1988;83:1198–202. [Google Scholar]

[pone.0245562.ref071] 71.Tabachnick BG, Fidell LS. Using Multivariate Statistics (5th ed.). Massachusetts: Allyn & Bacon; 2007. [Google Scholar]

[pone.0245562.ref072] 72.Efron B, Tibshirani R. An Introduction to the Bootstrap. Forida: Chapman & Hall/CRC; 1993. [Google Scholar]

[pone.0245562.ref073] 73.Cohen J, Cohen P, West SG, Aiken LS. Applied multiple regression/correlation analysis for the behavioural sciences, 3rd ed. New Jersey: Lawrence Erlbaum Associates; 2013. [Google Scholar]

[pone.0245562.ref074] 74.Hedley D, Uljarević M, Cai RY, Bury SM, Stokes MA, Evans DW. Transdiagnostic predictors of dsm-5 suicide risk [raw dataset, Version 1]. OSF. 2020. 10.17605/OSF.IO/C2AP3 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0245562.ref075] 75.Nigg JT. On inhibition/disinhibition in developmental psychopathology: Views from cognitive and personality psychology and a working inhibition taxonomy. Psychology Bulletin. 2000;126:220–46. 10.1037/0033-2909.126.2.220 [DOI] [PubMed] [Google Scholar]

[pone.0245562.ref076] 76.Nolen-Hoeksema S, Wisco BE, Lyubomirsky S. Rethinking rumination. Perspect Psychol Sci. 2008;3:400–24. 10.1111/j.1745-6924.2008.00088.x [DOI] [PubMed] [Google Scholar]

[pone.0245562.ref077] 77.Knight MJ, Baune BT. Social cognitive abilities predict psychosocial dysfunction in major depressive disorder. Depress Anxiety. 2019;36:54–62. 10.1002/da.22844 [DOI] [PubMed] [Google Scholar]

[pone.0245562.ref078] 78.LeMoult J, Joormann J, Sherdell L, Wright Y, Gotlib IH. Identification of emotional facial expressions following recovery from depression. J Abnorm Psychol. 2009;118:828–33. 10.1037/a0016944 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0245562.ref079] 79.Hedley D, Uljarević M, Wilmot M, Richdale A, Dissanayake C. Social support, depression and suicidal ideation in adults with Autism Spectrum Disorder. J Autism Dev Disord. 2017;47:3669–77. 10.1007/s10803-017-3274-2 [DOI] [PubMed] [Google Scholar]

[pone.0245562.ref080] 80.Hedley D, Uljarević M, Wilmot M, Richdale A, Dissanayake C. Understanding depression and thoughts of self-harm in autism: A potential mechanism involving loneliness. Res Autism Spectr Disord. 2018;46:1–7. [Google Scholar]

[pone.0245562.ref081] 81.Hudson CC, Hall L, Harkness KL. Prevalence of depressive disorders in individuals with Autism Spectrum Disorder: A meta-analysis. J Abnorm Child Psychol. 2019;47:165–75. 10.1007/s10802-018-0402-1 [DOI] [PubMed] [Google Scholar]

[pone.0245562.ref082] 82.Uljarević M, Hedley D, Foley KR, Magiati I, Cai RY, Dissanayake C, et al. Anxiety and depression from adolescence to old age in Autism Spectrum Disorder. J Autism Dev Disord. 2020;50:3155–65 10.1007/s10803-019-04084-z [DOI] [PubMed] [Google Scholar]

[pone.0245562.ref083] 83.Russo-Ponsaran NM, Lerner MD, McKown C, Weber RJ, Karls A, Kang E, et al. Web-based assessment of social-emotional skills in school-aged youth with Autism Spectrum Disorder. Autism Res. 2019;12:1260–71. 10.1002/aur.2123 [DOI] [PubMed] [Google Scholar]

[pone.0245562.ref084] 84.Phillips JM, Uljarevic M, Schuck RK, Frazier TW, Schapp SS, Solomon, EM, et al. Development of the Stanford Social Dimensions Scale (SSDS): Initial validation in autism spectrum disorder (ASD). Mol Autism. 2019;10:e48. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0245562.ref085] 85.American Psychiatric Association. DSM-5 self-rated level 1 cross-cutting symptom measure-adult. 2013. https://www.psychiatry.org/psychiatrists/practice/dsm/educational-resources/assessment-measures. Accessed Aug 24, 2020. [Google Scholar]

[pone.0245562.ref086] 86.Lewinsohn PM, Rohde P, Seeley JR. Adolescent suicidal ideation and attempts: Prevalence, risk factors, and clinical implications. Clin Psychol. 1996;3:25–46. [Google Scholar]

[pone.0245562.ref087] 87.Hom MA, Joiner TE, Bernert RA. Limitations of a single-item assessment of suicide attempt history: Implications for standardized suicide risk assessment. Psychol Assess. 2016;28:1026–30. 10.1037/pas0000241 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0245562.ref088] 88.Millner AJ, Lee MD, Nock MK. Single-item measurement of suicidal behaviors: validity and consequences of misclassification. PLoS ONE. 2015;10:e0141606 10.1371/journal.pone.0141606 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0245562.ref089] 89.Weiner L, Flin A, Causin J-B, Weibe S, Bertschy G. (2019). A case study of suicidality presenting as a restricted interest in autism spectrum disorder. BMC Psychiatry. 2019;19:e126 10.1186/s12888-019-2122-7 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0245562.ref090] 90.Nolen-Hoeksema S. Responses to depression and their effects on the duration of depressive episodes. J Abnorm Psychol. 1991;100:569–82. 10.1037//0021-843x.100.4.569 [DOI] [PubMed] [Google Scholar]

PERMALINK

Domains of the autism phenotype, cognitive control, and rumination as transdiagnostic predictors of DSM-5 suicide risk

Darren Hedley

Mirko Uljarević

Ru Ying Cai

Simon M Bury

Mark A Stokes

David W Evans

Roles

Abstract

Introduction

Autism phenotype as a risk factor for suicide

Mechanisms underpinning suicide risk and the autistic phenotype

Materials and methods

Participants

Table 1. Demographics including neurodevelopmental and neuropsychiatric disorders, and gender comparisons.

Procedures

Construct items

Data cleaning and analysis

Results

Table 2. Distribution of scores on DSM-5 CC Symptom Measure, depression and suicidal ideation (N = 1851).

Table 3. Study variables (M, SD, range, normality) with Pearson's bootstrapped correlations (upper panel, shaded), and partial correlations (lower panel) controlling for age (n = 1851).

Regression analysis

Table 4. Linear regression model of predictors of suicidal ideation.

Suicidal ideation present versus not present comparisons

Table 5. Means (SD) and bootstrapped ANCOVA comparisons between no-SI and SI groups on key variables controlling for age and depression.

Discussion

Strengths and limitations

Future directions

Conclusions

Supporting information

Acknowledgments

Data Availability

Funding Statement

References

Decision Letter 0

Vincenzo De Luca

Roles

Author response to Decision Letter 0

Decision Letter 1

Vincenzo De Luca

Roles

Author response to Decision Letter 1

Decision Letter 2

Vincenzo De Luca

Roles

Author response to Decision Letter 2

Decision Letter 3

Vincenzo De Luca

Roles

Acceptance letter

Vincenzo De Luca

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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