The Moderating Role of Genetic and Environmental Risk Factors for Schizophrenia on the Relationship between Autistic Traits and Psychosis Expression in the General Population
1 Department of Psychiatry, Karadeniz Eregli State Hospital, 67300 Zonguldak, Turkey
2 Department of Psychiatry, Faculty of Medicine, Ramathibodi Hospital, Mahidol University, 10400 Bangkok, Thailand
3 Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht University Medical Center, 6229 ER Maastricht, The Netherlands
4 Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht University Medical Center, 6229 ER Maastricht, The Netherlands
5 Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht University Medical Center, 6229 ER Maastricht, The Netherlands
6 Unit of Psychiatry and Eating Disorders, Department of Medicine (DMED), University of Udine, 33100 Udine, Italy
7 Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht University Medical Center, 6229 ER Maastricht, The Netherlands
8 Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht University Medical Center, 6229 ER Maastricht, The Netherlands
9 University Psychiatric Centre KU Leuven, 3000 Leuven, Belgium
10 Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht University Medical Center, 6229 ER Maastricht, The Netherlands
11 University Psychiatric Centre KU Leuven, 3000 Leuven, Belgium
12 Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht University Medical Center, 6229 ER Maastricht, The Netherlands
13 Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht University Medical Center, 6229 ER Maastricht, The Netherlands
14 University Psychiatric Centre KU Leuven, 3000 Leuven, Belgium
15 Centre of Human Genetics, University Hospitals Leuven, 3000 Leuven, Belgium
16 Department of Neurology, Ghent University Hospital, 9000 Ghent, Belgium
17 Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht University Medical Center, 6229 ER Maastricht, The Netherlands
18 Faculty of Psychology, Open University of the Netherlands, 6419 AT Heerlen, The Netherlands
19 Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht University Medical Center, 6229 ER Maastricht, The Netherlands
20 Brain Center Rudolf Magnus, UMC Utrecht, 3584 CX Utrecht, The Netherlands
21 Department of Psychosis Studies, Institute of Psychiatry, King’s Health Partners, King’s College London, SE5 8AF London, United Kingdom
22 Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht University Medical Center, 6229 ER Maastricht, The Netherlands
23 Department of Brain and Behavioral Sciences, University of Pavia, 27100 Pavia, Italy
24 Unit of Psychiatry and Eating Disorders, Department of Medicine (DMED), University of Udine, 33100 Udine, Italy
25 Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht University Medical Center, 6229 ER Maastricht, The Netherlands
26 Department of Psychiatry, Amsterdam University Medical Centre, 1105 AZ Amsterdam, The Netherlands
27 Department of Brain and Behavioral Sciences, University of Pavia, 27100 Pavia, Italy
28 Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht University Medical Center, 6229 ER Maastricht, The Netherlands
29 Department of Psychiatry, Yale School of Medicine, New Haven, CT 06510, United States
Abstract
Background
Psychosis-related environmental risks in autism, along with genetic overlaps between autism and psychosis, have been well-established. However, their moderating roles in the relationship between autistic traits (ATs) and psychotic experiences (PEs) remain underexplored.
Methods
First-wave data from 792 twins and siblings (mean age: 17.47 ± 3.6, 60.23% female) in the TwinssCan Project were analyzed. PEs and ATs were assessed using the Community Assessment of Psychic Experiences and the Autism-Spectrum Quotient, respectively. Polygenic risk scores for schizophrenia and psychosis-associated environmental factors (ie, childhood trauma (CT), bullying, negative life events, obstetric complications, cannabis use, winter birth, and hearing impairment) were tested for their independent effects on PEs and their interaction effects with ATs in moderating the relationship between ATs and PEs using separate multilevel linear regression models with Bonferroni’s correction.
Results
ATs, all CT subtypes, bullying, and negative life events were positively associated with PEs (all P < .004). Moderation analyses revealed that the association between ATs and PEs was amplified by emotional abuse (B:0.08, 95% CI, 0.05-0.11, P < .001), physical abuse (B:0.11, P = .001), sexual abuse (B:0.09, 95% CI, 0.03-0.15, P = .002), and physical neglect (B:0.06, 95% CI, 0.03-0.10, P = .001), emotional neglect (B:0.04, 95% CI, 0.01-0.07, P = .007), and negative life events (B:0.007, 95% CI, 0.0005-0.014, P = .04), although the latter 2 risks did not survive Bonferroni’s correction. No significant main or interacting effects of genetic and other risk factors were found.
Conclusions
People with high ATs were more likely to have PEs when exposed to CT. Trauma screening and early interventions might be warranted in this at-risk population.
Untitled section
Keywords: autistic traits, psychotic experiences, environmental risk factors, exposome, childhood trauma, polygenic risk scores
Article notes
Untitled section
Collection date 2025 Jan.
Introduction
Autism spectrum disorder (ASD) is a complex neurodevelopmental condition characterized by persistent deficits in social communication and social interaction along with restricted, repetitive patterns of behaviors, interests, and/or activities.1. The prevalence of ASD has recently been estimated to be 1 in 36 children in the United States.2 Psychiatric comorbidity is common in ASD. The prevalence of comorbid conditions has been estimated to be 28% for attention-deficit hyperactivity disorder, 20% for anxiety disorders, 11% for depressive disorders, and 9% for schizophrenia spectrum disorders (SSD).3 Recent meta-analyses consistently reported an increased prevalence of not only clinical psychosis (9.4%)4 but also subthreshold psychosis (24%)5 in autistic people. On the other hand, the prevalence of ASD is also high (9%) in patients with first-episode psychosis,6 and its symptoms are associated with poor cognitive and psychosocial function in people with SSD,7 highlighting an intricate, bidirectional link between psychosis expression and ASD.
In the past 2 decades, autism has increasingly been recognized as a true spectrum with varying degrees of traits existing on a continuum in the general population. According to such a concept, autistic traits (ATs), such as social communication difficulties and repetitive behaviors, could also be present among individuals who do not meet the formal diagnostic criteria for ASD. Similar to clinical ASD, ATs have been shown to be associated with an increased vulnerability to multiple psychopathologies, including subclinical psychosis expression.8–11 A recent meta-analysis has identified multiple demographic and clinical characteristics, such as older age, male gender, lower intellectual quotient, and less stereotyped interests/behaviors, as risk factors for developing clinical psychosis among autistic individuals.4 Nevertheless, evidence on risk factors for subclinical psychosis among people with subthreshold autism is lacking.
Considering that genetic and environmental risk factors for schizophrenia are shared across the psychosis continuum12 and throughout a neurodevelopmental trajectory also enclosing other conditions emerging earlier in life,13 it is likely that the risk factors may also influence psychosis expression in the context of the autism spectrum. Notably, childhood trauma (CT), bullying, stressors, and obstetric complications (OC) have been associated with a clinical diagnosis of ASD.14–16 Moreover, genetic studies have identified several direct and indirect links between autism and schizophrenia, highlighting shared vulnerabilities between these conditions.17,18 In the present study, we, therefore, aimed to examine the moderating role of polygenic risk and well-established environmental risk factors for schizophrenia (ie, CT, bullying, negative life events, OC, cannabis use, winter birth, and hearing impairment) on the relationship between ATs and psychotic experiences (PEs) in the general population of twins and nontwin siblings. We hypothesized that the presence of genetic and environmental risk factors for schizophrenia would strengthen the association between ATs and PEs and their influences on PEs would vary within the autism spectrum.
Methods
Participants
Analyses were conducted on the dataset obtained from the first wave of the TwinssCan Project. The details of participant enrollment and data collection have been previously described elsewhere.19 Briefly, the TwinssCan cohort comprises twins, nontwin siblings, and parents recruited from the East Flanders Prospective Twin Survey (EFPTS), a prospective population-based registry of multiple births from East Flanders, Belgium.20 The twins were between 15 and 18 years old upon enrollment, while their nontwin siblings were from 15 to 35 years old. The first assessment of the TwinssCan Project began in April 2010 and concluded in April 2014.21 Written informed consent was obtained from all participants. The exclusion criteria were: (1) lack of parental or caregiver consent for those under 18 years old and (2) having a pervasive mental disorder. The study was approved by the local ethics committee (Commissie Medische Ethiek van de Universitaire Ziekenhuizen KU Leuven, Nr. B32220107766). In this study the data of 821 twins and siblings were included. However, 29 participants were excluded due to missing data, leaving 792 participants for further analysis.
Measures
Psychotic Experiences.
Psychotic experiences were assessed with the Community Assessment of Psychic Experiences (CAPE), a widely used 42-item self-report questionnaire consisting of 3 subscales (ie, positive, negative, and depressive).22 The CAPE has demonstrated stability, reliability, and validity in evaluating PEs in the general population.23 Four-point Likert scales were used to measure the frequency and distress levels of the symptoms. The average scores from these scales give total and subscale scores (positive, negative, depressive). In our analysis, we specifically used the frequency scores as an indicator of PEs.
Autistic Traits.
The Autism-Spectrum Quotient (AQ), a 50-item self-report questionnaire, was used to assess ATs.24 It is widely used to measure ATs in the general population and has good psychometric properties through comprehensive research.24,25 The scale consists of 5 subdomains, each with 10 items: imagination, social skills, communication, attention to detail, and attention shifting. For items in line with ATs, “definitely agree” and “slightly agree” responses were scored 1, and “definitely disagree” and “slightly disagree” responses were scored 0.24 For the other items, “definitely disagree” and “slightly disagree” were reversely scored 1; otherwise, 0. The total score ranges from 0 to 50 with higher AQ scores indicating higher ATs.24
Childhood Trauma.
Childhood trauma was assessed using the Childhood Trauma Questionnaire (CTQ),26 a 28-item self-report instrument rated on a 5-point Likert scale to measure 5 subtypes of childhood adversity: emotional, physical, and sexual abuse, as well as emotional and physical neglect. In the manual of CTQ, 3 severity cutoff scores (low, moderate, and severe) were suggested for each CTQ subscale.27 Aligning with previous research,12,28 we binarized the variable into the presence or absence of each CT subtype by employing the low severity cutoff threshold based on the manual of the CTQ (≥9 for emotional abuse, ≥8 for physical abuse, ≥6 for sexual abuse, ≥10 for emotional neglect, and ≥8 for physical neglect). These cutoff scores were used to maintain consistency and increase comparability across studies.
Negative Life Events.
Negative life events were assessed with the Life Events Questionnaire, a 61-item self-report of major life events such as job loss, illness, death of a loved one, and financial changes.31 Participants indicated if these incidents had ever happened to them. The total score was calculated by summing the number of negative life events experienced over a lifetime.
Obstetric Complications.
Adverse events during pregnancy, labor, and postpartum period were measured with the McNeil-Sjöström Obstetric Complications (OC) Scale (MSS).32 The severity of OC was scored on a 6-point scale (1 = not harmful or relevant to 6 = very great harm or deviation in offspring). Aligning with prior research,33 a dichotomous OC variable was generated using an MSS cutoff score of 5 or more to indicate severe OC. In other words, severe OC was defined as (1) birth weight lower than 2 kg, (2) birth weight more than 20% lower than that of the sibling, (3) version extraction delivery mode, (4) face or forehead presentation of the fetus during delivery, or (5) umbilical cord complication.
Cannabis Use.
Lifetime cannabis use was assessed by the L section of Composite International Diagnostic Interview.34 A dichotomous variable was generated to indicate cannabis use over a lifetime.
Winter Birth.
The high-risk birth period was defined as the winter solstice (December–March) based on previous studies investigating the link between birth season and SSD in the Northern Hemisphere.35 A dichotomous variable was generated to indicate winter birth.
Hearing Impairment.
Hearing impairment was evaluated based on the participants’ self-reported current hearing status.
Genotyping and Quality Control.
As previously reported,12 genotypes of the twins and their siblings were generated on 2 platforms: the Infinium CoreExome-24 and Infinium PsychArray-24 kits. Quality control (QC) procedures were performed using PLINK v1.9 in both datasets separately.36 Single-nucleotide polymorphisms (SNPs) and participants with call rates below 95% and 98%, respectively, were removed. A strict SNP QC was conducted only for subsequent sample QC steps. This involved a minor allele frequency (MAF) threshold >10% and a Hardy–Weinberg equilibrium (HWE) P-value >10−5, followed by linkage disequilibrium (LD)-based SNP pruning (R2<0.5). This resulted in ~58K SNPs to assess sex errors (n = 8), heterozygosity [F < mean − 5× the SD, n = 3], homozygosity (F > mean + 5× SD), and relatedness by pairwise identity by descent values (monozygotic , dizygotic and full siblings: or , n = 5). The ancestry‐informed principal component (PC) analyses were conducted using EIGENSTRAT.37 The ethnic outliers of which the first 4 PCs diverged >10× SD from Utah residents with Northern and Western European ancestry from the Centre d’Etude du Polymorphisme Humain (CEPH) collection (Central EUrope, CEU) and Toscani in Italia (TSI) samples (n = 5), and > 3 × SD of the TwinssCan samples (n = 7) were excluded (see also Supplementary Figure 1). After removing these subjects, a regular SNP QC was performed (SNP call rate > 98%, HWE P-value > 1e-06, MAF > 1%, and strand ambiguous SNPs and duplicate SNPs were removed).
The 2 QCed datasets were imputed on the Michigan server,38 using the Haplotype Reference Consortium r1.1 2016 reference panel with European samples after phasing with Eagle v2.3. Postimputation QC involved removing SNPs with imputation quality (R2) < 0.8, with an MAF < 0.01, SNPs that had a discordant MAF compared to the reference panel (MAF difference with HRC reference > 0.15), as well as strand ambiguous AT/CG SNPs and multi-allelic SNPs. The 2 chips were merged, and an additional check for MAF > 0.01, HWE P > 1e-06 was executed, which resulted in 3 407 392 SNPs for 688 individuals. The general imputation quality is shown in Supplementary Figure 1.
PRS Calculation.
Polygenic risk score of schizophrenia (PRS-SZ) was calculated based on the genome-wide association study (GWAS) results for schizophrenia39 with the clumping and threshold (C + T) methods using PRSice2.40 To ensure the ethnicity match between training and target datasets, we selected the summary statistics from the European population. To calculate PRS-SCZ, the beta-values, effective allele, and P-values were extracted from all schizophrenia GWAS summary statistics. Insertions and deletions, ambiguous SNPs, SNPs with a MAF < 0.01 and imputation quality R2 < 0.9, as well as SNPs located in complex-LD regions and long-range LD regions33 were excluded from TwinssCan dataset (Supplementary Table 1). Overlapping SNPs between GWAS summary statistics (training dataset), 1000 genomes (reference), and our TwinssCan dataset (target) were selected. These SNPs were clumped in 2 rounds using PLINK’s clump function (round 1: --clump-kb 250 --clump-r2 0.5; round 2: --clump-kb 5000 --clump-r2 0.2). The numbers of alleles for PRS calculation are listed in Supplementary Table 2. We selected all independent SNPs at the P-threshold of < .05 to calculate PRS-SCZ since this threshold accounted for the most of the variation in the phenotype according to the analysis conducted by the Psychiatric Genomics Consortium.39
Statistical Analysis
All analyses were performed using Stata 16.41 Participants were clustered within twin pairs, necessitating the use of multilevel analyses. Multilevel regression models are recommended for analyzing data with observations at multiple levels, as they account for variability associated with each level of nesting.42 These models are particularly suitable for twin studies and have been commonly employed in twin studies using PRS analyses to adjust for relatedness.43–46 The number of predictors included was carefully considered to ensure adequate statistical power, adhering to the literature regarding sample size relative to the number of predictors.47–49 Firstly, to examine the main association between risk factors with PEs, multilevel linear regression analyses were performed using total CAPE as the dependent variable and AQ scores, PRS-SZ, CT subtypes, bullying, negative life events, OC, cannabis use, or winter birth as an independent variable tested in separate models. To examine whether the psychosis-associated genetic and environmental risk factors moderate the association between ATs and symptoms, a full interaction of total AQ and genetic or each environmental risk factor on total CAPE was tested using multilevel linear regression analyses. To facilitate the interpretation of the coefficients, AQ scores, negative life events, and PRS-SZ were standardized to have a mean of 0 and SD of 1. All models were adjusted for age and gender. In the PRS models, the first 2 ancestry PCs, capturing 82.4% of genetic variation related to population structure in the TwinssCan cohort, were included as covariates to adjust for major effects of population stratification in accordance with previous research in this sample.50,51 The statistical significance threshold was set at Bonferroni-corrected P < .004. In case of significant interaction, marginal effects analysis was performed to estimate the differential influences of the ATs (ie, mean, mean − 1SD, and mean + 1SD) at different levels of risk factors using the STATA “margins” command, and the results were visualized using the STATA “marginsplot” command.
As exploratory analyses, similar multilevel linear regression analyses were conducted to investigate potential differential effects of risk factors on psychotic subdomains (CAPE positive, negative, and depressive subscales as dependent variables) to capture the detailed relationships that may not be apparent in aggregate measures.
Results
Sample Characteristics
A total of 792 participants, including 274 monozygotic twins, 475 dizygotic twins, and 43 siblings, were included in the current analyses. Participant characteristics are displayed in Table 1.
| Characteristics | N = 792 |
|---|---|
| Age (years), M (SD) | 17.47 (3.60) |
| Gender, n (%) | |
| Female | 477 (60.23) |
| Male | 315 (39.77) |
| Zygosity, n (%) | |
| MZ | 274 (34.60) |
| DZ | 475 (59.97) |
| Sibling | 43 (5.43) |
| CAPE—frequency, M (SD) | |
| Total score | 1.56 (0.26) |
| Positive subscore | 1.41 (0.28) |
| Negative subscore | 1.67 (0.34) |
| Depressive subscore | 1.76 (0.36) |
| AQ, M (SD) | |
| Total score | 15.62 (5.69) |
| Imagination subscore | 2.77 (1.77) |
| Social skills subscore | 2.03 (1.93) |
| Communication subscore | 2.55 (1.82) |
| Attention to detail subscore | 4.44 (2.07) |
| Attention shifting subscore | 3.81 (1.85) |
| Childhood trauma, n (%) | |
| Emotional abuse | 248 (31.31) |
| Physical abuse | 35 (4.42) |
| Sexual abuse | 55 (6.94) |
| Emotional neglect | 340 (42.93) |
| Physical neglect | 129 (16.29) |
| Bullying, n (%) | |
| Severity ≥ 2 | 487 (61.88) |
| Severity ≥ 3 | 344 (43.71) |
| Severity ≥ 4 | 161 (20.46) |
| Winter birth, n (%) | 290 (36.62) |
| Cannabis use, n (%) | 39 (5.60) |
| Obstetric complications, n (%) | 162 (22.47) |
| Negative life events, M (SD) | 3.03 (1.86) |
| Hearing impairment, n (%) | 7 (0.88) |
Associations of ATs and Psychosis-Associated Risk Factors With PEs
Autistic traits were significantly associated with total CAPE (Table 2, B: 0.12, 95% CI, 0.10-0.14, P < .001). While PRS-SZ was not associated with total CAPE (B: 0.01, 95% CI, −0.01 to −0.03, P = 0.33), all 5 CT subtypes, bullying, and negative life events showed significant positive associations with total CAPE (all P < .001). No significant associations between other psychosis risk factors and total CAPE were found (Table 2).
| B (95% CI) | P* | |
|---|---|---|
| Autistic traits | 0.12 (0.10–0.14) | <.001 |
| PRS-SZ | 0.01 (−0.01 to 0.03) | .33 |
| Winter birth | 0.02 (−0.02 to 0.06) | .40 |
| Hearing impairment | 0.08 (−0.11 to 0.26) | .41 |
| Emotional abuse | 0.18 (0.14–0.22) | <.001 |
| Physical abuse | 0.22 (0.14–0.31) | <.001 |
| Sexual abuse | 0.21 (0.14–0.28) | <.001 |
| Emotional neglect | 0.08 (0.04–0.11) | <.001 |
| Physical neglect | 0.11 (0.07–0.16) | <.001 |
| Bullying | 0.09 (0.06–0.13) | <.001 |
| Obstetric complications | 0.01 (−0.03 to 0.06) | .54 |
| Negative life events | 0.07 (0.05–0.09) | <.001 |
| Cannabis use | 0.001 (−0.08 to 0.08) | .98 |
Similar to the main analysis, exploratory analyses of CAPE subscales showed significant positive associations of ATs, all CT subtypes, bullying, and negative life events with all 3 CAPE subscale scores (Supplementary Tables 3–5).
Moderations of Psychosis-Associated Risk Factors on the Associations between ATs and PEs
An interaction analysis revealed that PRS-SZ did not moderate the association between ATs and total CAPE. However, emotional abuse (B: 0.08, 95% CI, 0.05-0.11, P < .001), physical abuse (B: 0.11, 95% CI, 0.05-0.18, P = .001), sexual abuse (B: 0.09, 95% CI, 0.03-0.15, P = .002), and physical neglect (B: 0.06, 95% CI, 0.03-0.10, P = .001) significantly interacted with ATs in predicting total CAPE. Emotional neglect (B: 0.04, 95% CI, 0.01-0.07, P = .007) and negative life events (B: 0.007, 95% CI: 0.0005 to 0.014, P = 0.04) significantly interacted with ATs in predicting total CAPE at a nominal level, but the statistical significance was lost after correction for multiple testing (Table 3).
| B (95% CI) | P* | |
|---|---|---|
| PRS-SZ | −0.004 (−0.025 to 0.017) | .70 |
| Winter birth | 0.03 (−0.002 to 0.06) | .07 |
| Hearing impairment | 0.05 (−0.19 to 0.30) | .69 |
| Emotional abuse | 0.08 (0.05–0.11) | <.001 |
| Physical abuse | 0.11 (0.05–0.18) | .001 |
| Sexual abuse | 0.09 (0.03–0.15) | .002 |
| Emotional neglect | 0.04 (0.01–0.07) | .007 |
| Physical neglect | 0.06 (0.03–0.10) | .001 |
| Bullying | 0.02 (−0.01 to 0.05) | .22 |
| Obstetric complications | −0.001 (−0.040 to 0.038) | .94 |
| Negative life events | 0.007 (0.0005–0.014) | .04 |
| Cannabis use | −0.04 (−0.12 to 0.03) | .27 |
Marginal effects analysis revealed that the positive association between ATs and total CAPE increased in the presence of CT subtypes (Supplementary Table 6; Figure 1). No significant moderations of other psychosis risk factors on the relationship between ATs and total CAPE were found. Exploratory analyses of CAPE subscales showed similar significant interactions between CT subtypes and ATs for CAPE positive subscale (Supplementary Table 7). However, emotional abuse was the only risk factor significantly interacting with ATs in determining CAPE negative and depressive subscales (Supplementary Tables 8 and 9).
Discussion
The present study aimed to evaluate the role of psychosis-associated risk factors in moderating the association between ATs and PEs in a general population twin sample. Our primary analyses revealed that ATs significantly interacted with the CT subtypes, particularly emotional abuse, physical abuse, sexual abuse, and physical neglect, on the expression of psychosis, whereas genetic and the other tested environmental risk factors for psychosis did not. Exploratory analyses of psychosis subdomains yielded similar positive interactions between CT subtypes and ATs for positive symptoms, but only emotional abuse significantly interacted with ATs on the expression of depressive and negative symptoms.
We found that CT, but not bullying, significantly moderates the relationship between ATs and PEs. This is partially aligned with the previous research. To date, only a few studies have addressed the impact of CT and bullying on the risk of psychosis among individuals with elevated ATs. Two studies have consistently shown that both CT and bullying can mediate the relationship between ATs and PEs.52,53 Adding to prior evidence, our study suggests that CT, but not bullying, moderates the relationship between ATs and PEs. A possible explanation for this discrepancy between the impact of CT and bullying could be that the perpetrators of CT are usually parents, family members, or relatives,54 who could have a more profound impact on a child’s life and emotional development than the perpetrators of bullying, who are often peers. External support systems might also be available to help lessen the impact of bullying. Additionally, bullying tends to be more episodic, whereas chronic and insidious CT may involve prolonged exposure without immediate access to support, possibly leading to a more pervasive and enduring impact. Another speculation could be that CT might contribute to the development of maladaptive coping strategies such as avoidance and rumination,55 as well as an increased threat anticipation56 that could potentially lead to PEs, especially in autistic individuals who already tend to misinterpret threats. While bullying may also have similar effects, its impact may be more prevalent in certain social contexts compared to the broader and more internalized effect of CT.
Our exploratory analyses revealed significant interactions between various CT subtypes and ATs on positive symptoms similar to the overall psychosis expression. However, emotional abuse was the only form of CT that also interacted with ATs in determining negative and depressive symptoms. This is in line with the literature reporting that emotional abuse is a chronic, widespread type of CT and has been found to cause more severe developmental consequences into adulthood than those caused by other types of CT.57 Altogether, these findings implied that all forms of CT could amplify the risk of subclinical psychosis, especially positive symptoms in people with high ATs, whereas emotional abuse may exert a broader influence on multiple domains of psychosis expression in this population. Indeed, literature has acknowledged emotional abuse as the form of abuse most frequently linked to mental health problems.58
The number of negative life events did not interact with ATs despite its significant effect on PEs. A recent study found that exposure to family problems and conflicts with peers was associated with increased occurrence of psychotic/manic symptoms in autistic inpatients15; however, the relationship between ATs and life events was not studied in nonclinical samples. One possible explanation for this discrepancy is that individuals from our general population sample with high ATs levels, but do not meet the formal criteria for an ASD diagnosis, may cope better with negative life events than those from clinical samples. On the other hand, individuals with higher ATs might already have an elevated baseline of stress perception. Consequently, an increased number of negative life events may not significantly raise their stress levels or lead to PEs, resulting in a less pronounced effect of these events and a weaker association with ATs.
Winter birth, OC, hearing impairment, and cannabis use were not significantly associated with PEs and did not interact with ATs in predicting PEs. To the best of our knowledge, no studies have ever explored interactions of these factors with ATs on PEs before. First, while some studies suggest a seasonal effect on autism, the findings have been inconsistent, with various studies reporting different seasons of birth, such as fall59 or spring births60,61 as potential risk factors or no association at all.62,63Considering that the previously reported association between winter birth and psychosis expression is generally weak,64,65 such mixed findings might simply reflect the complex relationship between the season of birth and the risks for psychosis and autism. The level of complexity might require a larger study to detect more subtle effects. Similarly, our limited number of participants with hearing impairment (n = 7) and cannabis use (n = 39) might offer low statistical power. To this extent, given that ASD and hearing problems frequently co-occur, with a reported ASD prevalence of 9% in children with hearing impairment,66 further studies on the effect of hearing impairment on psychosis expression in the context of the autistic spectrum are warranted. Also, while existing research on cannabis use in ASD primarily focuses on medical use,67 the pro-psychotic impact of recreational cannabis exposure among autistic individuals and people with high ATs should not be overlooked due to disruptive epigenetic effects potentially implicated in pathophysiology of schizophrenia.68,69
We did not find a significant association between PRS-SZ and PEs, which might be attributed to the relatively small sample size of our study. Some large population-based studies such as Adolescent Brain Cognitive Development Study (n = 4650) and UK Biobank (n = 127 966) have found an association between PRS-SZ and PEs,70,71 while others, with smaller sample sizes (n = 2152 and n = 3483) have shown no significant relationships,72,73 that might be due to an inadequate statistical power to detect a potentially weak effect of PRS-SZ on PEs. Given that our sample size (n = 792) is even smaller, our study might arguably be underpowered to detect an association between PRS-SZ and PEs, such that the magnitude of the association of PRS-SZ with PEs was weaker than that with a clinical diagnosis for schizophrenia.73 In addition, subclinical psychotic symptoms often co-occur with other psychopathology, creating a transdiagnostic and overlapping symptom representation.74,75 Moreover, environmental factors may have a stronger influence than genetic predisposition as measured by PRS-SZ on the development of PEs. Methodological differences, particularly in the assessment protocol of PEs, might also explain the inconsistencies.
The interaction between ATs and PRS-SZ was not significantly associated with PEs, as well. To the best of our knowledge, this is the first study to specifically test PRS-SZ as a potential moderator between AT and PEs. Consequently, direct comparisons with prior research are limited. The lack of a significant interaction between ATs and PRS-SZ on PEs might be attributable to several factors, including the limitations mentioned above (small sample size, potential weaker impact of PRS-SZ on subclinical symptoms). Additionally, although several studies have demonstrated a substantial genetic overlap between schizophrenia and autism,18 it is important to note that PRS-SZ accounts only for common genetic variants. Other genetic factors, such as rare variants, copy number variations, and chromosomal anomalies, may also play a role in contributing to PEs in individuals with higher ATs.
Our findings hold important clinical and research implications. Screening for CT in individuals with elevated ATs could help identify those at a heightened risk of developing psychosis and facilitate early intervention. Trauma-focused therapies and resilience-building programs may provide effective strategies to decrease psychosis risk and improve long-term outcomes. However, research on the screening, treatment, and prevention of CT in autistic populations remains limited. Available research suggests that trauma screening, psychoeducation, and psychotherapy (such as trauma-focused cognitive behavioral therapy) should be adapted to meet the unique needs of autistic children.76 Future studies should explore this relationship, particularly focusing on how trauma contributes to psychosis and treatment of trauma-related symptoms in autism. To advance our understanding, future research should use longitudinal designs to investigate the causal pathways linking psychosis expression to environmental risk factors, particularly CT. Moreover, investigating potential intermediate mechanisms, such as affective dysregulation, cognitive biases or salience attribution, could help identify the shared vulnerabilities underlying these conditions. Additionally, studies involving larger and more diverse samples are essential to better understand the genetic and environmental mechanisms driving the autism psychosis continuum.
Limitations
The present study investigates, for the first time, the moderating role of schizophrenia-related genetic and environmental risk factors in the association between ATs and PEs, leveraging data from a large general population sample. However, several limitations should be considered. First, childhood adversities, bullying, and life events were evaluated using self-report measures, which may be subjected to recall bias. However, both retrospective and prospective reports of CT were demonstrated to be linked to mental health issues during adolescence and adulthood,77,78 supporting the validity of our findings based on retrospective reports. Second, our general population sample allowed us to investigate ATs and PEs as a continuum. However, it limits the generalization to those meeting the full criteria of ASD. Similar analyses in clinical samples should be conducted even though a validated tool for assessing PEs in autistic children remains to be developed.79 Furthermore, the generalizability of our findings might be influenced by the composition of the TwinssCan cohort, which primarily included twins. In this regard, it should be noted that twins experience unique genetic and shared environmental factors, such as specific prenatal conditions and family dynamics, that may differ from those of singletons and could shape the observed associations. Third, while the sample size of 792 participants provided sufficient power for detecting some significant moderate effects, the study may still be prone to type II error for certain risk factors with low prevalence or small effect sizes, including hearing impairment, cannabis use, and OC. The limited representation of these factors might have reduced our ability to identify the interaction effects, which are usually smaller than the main effects. Future research with larger cohorts is necessary to confirm these findings. Furthermore, our sample may have been underpowered to detect significant associations between PRS-SZ and PEs. Moreover, the age range of our sample may limit the detection of PEs that could emerge later in adulthood. However, it is important to note that the onset of psychopathology often occurs in adolescence and young adulthood, both in the general population,80 and among individuals with autism,81 supporting the significance of the findings from the current sample. Lastly, our study was a cross-sectional analysis, precluding a temporal relationship between the risk factors and PEs to be drawn. Therefore, further investigation using a longitudinal design is needed for causal inference.
Conclusions
Our findings underscore the significant role of environmental risks in shaping the complex continuum of autism and psychosis. Identifying individuals with higher ATs and screening them for childhood adversity could be a promising measure to safeguard them from further environmental risk exposure and facilitate early intervention to prevent psychosis expression.
Supplementary Material
Acknowledgments
T. P. received research training funding from the Faculty of Medicine Ramathibodi Hospital, Mahidol University.
Contributor Information
Melike Karacam Dogan, Department of Psychiatry, Karadeniz Eregli State Hospital, 67300 Zonguldak, Turkey.
Thanavadee Prachason, Department of Psychiatry, Faculty of Medicine, Ramathibodi Hospital, Mahidol University, 10400 Bangkok, Thailand.
Bochao Lin, Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht University Medical Center, 6229 ER Maastricht, The Netherlands.
Lotta-Katrin Pries, Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht University Medical Center, 6229 ER Maastricht, The Netherlands.
Angelo Arias-Magnasco, Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht University Medical Center, 6229 ER Maastricht, The Netherlands.
Riccardo Bortoletto, Unit of Psychiatry and Eating Disorders, Department of Medicine (DMED), University of Udine, 33100 Udine, Italy.
Claudia Menne-Lothmann, Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht University Medical Center, 6229 ER Maastricht, The Netherlands.
Jeroen Decoster, Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht University Medical Center, 6229 ER Maastricht, The Netherlands; University Psychiatric Centre KU Leuven, 3000 Leuven, Belgium.
Ruud van Winkel, Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht University Medical Center, 6229 ER Maastricht, The Netherlands; University Psychiatric Centre KU Leuven, 3000 Leuven, Belgium.
Dina Collip, Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht University Medical Center, 6229 ER Maastricht, The Netherlands.
Philippe Delespaul, Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht University Medical Center, 6229 ER Maastricht, The Netherlands.
Marc De Hert, University Psychiatric Centre KU Leuven, 3000 Leuven, Belgium.
Catherine Derom, Centre of Human Genetics, University Hospitals Leuven, 3000 Leuven, Belgium.
Evert Thiery, Department of Neurology, Ghent University Hospital, 9000 Ghent, Belgium.
Nele Jacobs, Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht University Medical Center, 6229 ER Maastricht, The Netherlands; Faculty of Psychology, Open University of the Netherlands, 6419 AT Heerlen, The Netherlands.
Jim van Os, Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht University Medical Center, 6229 ER Maastricht, The Netherlands; Brain Center Rudolf Magnus, UMC Utrecht, 3584 CX Utrecht, The Netherlands; Department of Psychosis Studies, Institute of Psychiatry, King’s Health Partners, King’s College London, SE5 8AF London, United Kingdom.
Bart Rutten, Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht University Medical Center, 6229 ER Maastricht, The Netherlands.
Natascia Brondino, Department of Brain and Behavioral Sciences, University of Pavia, 27100 Pavia, Italy.
Marco Colizzi, Unit of Psychiatry and Eating Disorders, Department of Medicine (DMED), University of Udine, 33100 Udine, Italy.
Jurjen Luykx, Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht University Medical Center, 6229 ER Maastricht, The Netherlands; Department of Psychiatry, Amsterdam University Medical Centre, 1105 AZ Amsterdam, The Netherlands.
Laura Fusar-Poli, Department of Brain and Behavioral Sciences, University of Pavia, 27100 Pavia, Italy.
Sinan Guloksuz, Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht University Medical Center, 6229 ER Maastricht, The Netherlands; Department of Psychiatry, Yale School of Medicine, New Haven, CT 06510, United States.
Funding
The East Flanders Prospective Twin Survey (EFPTS) received support from the Association for Scientific Research in Multiple Births (Belgium) and the TwinssCan project is funded by the European Community Seventh Framework Program (HEALTH-F2-2009-241909, Project EU-GEI). This work was further supported by the Scientific and Technological Research Council of Türkiye (TUBITAK), 2219 International Postdoctoral Research Fellowship (1059B192302449 to M.K.D.), by Ophelia research project, ZonMw (36340001 to J.V.O. and S.G.), the Netherlands Scientific Organisation Vidi award (91718336 to B.P.F.R.), the European Union’s Horizon Europe program, YOUTH-GEMs Project (01057182 to J.V.O., L.K.P., B.D.L., B.P.F.R., S.G., and A.A.M.) and by #NEXTGENERATIONEU (NGEU) from the Ministry of University and Research (MIUR), National Recovery and Resilience Plan (NRRP), project MNESYS (PE0000006)—A Multiscale integrated approach to the study of the nervous system in health and disease (DN. 1553 to L.F.P.).
Conflicts of Interest
Marco Colizzi has been a consultant/advisor to GW Pharma Limited, GW Pharma Italy SRL and F. Hoffmann-La Roche Limited, outside of this work.
References
Untitled section
References
- 1. American Psychiatric Association (APA). Diagnostic and Statistical Manual of Mental Disorders: DSM-5-TR (Fifth Edition, Text Revision). American Psychiatric Association Publishing; 2022.
- 2. Maenner MJ, Warren Z, Williams AR, et al. Prevalence and characteristics of autism spectrum disorder among children aged 8 years – autism and developmental disabilities monitoring network, 11 Sites, United States, 2020. MMWR Surveill Summ. 2023;72:1–14. https://doi.org/ 10.15585/MMWR.SS7202A1
- 3. Lai MC, Kassee C, Besney R, et al. Prevalence of co-occurring mental health diagnoses in the autism population: a systematic review and meta-analysis. Lancet Psychiatry. 2019;6:819–829. https://doi.org/ 10.1016/S2215-0366(19)30289-5
- 4. Varcin KJ, Herniman SE, Lin A, et al. Occurrence of psychosis and bipolar disorder in adults with autism: a systematic review and meta-analysis. Neurosci Biobehav Rev. 2022;134:104543. https://doi.org/ 10.1016/J.NEUBIOREV.2022.104543
- 5. Kiyono T, Morita M, Morishima R, et al. The prevalence of psychotic experiences in autism spectrum disorder and autistic traits: a systematic review and meta-analysis. Schizophr. Bull. Open. 2020;1:sgaa046. https://doi.org/ 10.1093/SCHIZBULLOPEN/SGAA046
- 6. Ferrara M, Curtarello EMA, Simonelli G, et al. First-episode psychosis and autism spectrum disorder: a scoping review and a guide to overcome diagnostic challenges. Int J Ment Health. 2024;53:4–35. https://doi.org/ 10.1080/00207411.2023.2276872
- 7. Nibbio G, Barlati S, Calzavara-Pinton I, et al. Assessment and correlates of autistic symptoms in Schizophrenia spectrum disorders measured with the PANSS autism severity score: a systematic review. Front Psychiatry. 2022;13:934005. https://doi.org/ 10.3389/FPSYT.2022.934005/BIBTEX
- 8. Bevan Jones R, Thapar A, Lewis G, Zammit S.. The association between early autistic traits and psychotic experiences in adolescence. Schizophr Res. 2012;135:164–169. https://doi.org/ 10.1016/j.schres.2011.11.037
- 9. Lundström S, Chang Z, Kerekes N, et al. Autistic-like traits and their association with mental health problems in two nationwide twin cohorts of children and adults. Psychol Med. 2011;41:2423–2433. https://doi.org/ 10.1017/S0033291711000377
- 10. Martinez AP, Wickham S, Rowse G, Milne E, Bentall RP.. Robust association between autistic traits and psychotic-like experiences in the adult general population: epidemiological study from the 2007 Adult Psychiatric Morbidity Survey and replication with the 2014 APMS. Psychol Med. 2021;51:2707–2713. https://doi.org/ 10.1017/S0033291720001373
- 11. Salmela L, Kuula L, Merikanto I, Räikkönen K, Pesonen AK.. Autistic traits and sleep in typically developing adolescents. Sleep Med. 2019;54:164–171. https://doi.org/ 10.1016/J.SLEEP.2018.09.028
- 12. Pries LK, Dal Ferro GA, Van Os J, et al. Examining the independent and joint effects of genomic and exposomic liabilities for schizophrenia across the psychosis spectrum. Epidemiol Psychiatr Sci. 2020;29:e182. https://doi.org/ 10.1017/S2045796020000943
- 13. Owen MJ, O’Donovan MC.. Schizophrenia and the neurodevelopmental continuum: evidence from genomics. World Psychiatry. 2017;16:227–235. https://doi.org/ 10.1002/WPS.20440
- 14. Kerns CM, Newschaffer CJ, Berkowitz SJ.. Traumatic childhood events and autism spectrum disorder. J Autism Dev Disord. 2015;45:3475–3486. https://doi.org/ 10.1007/S10803-015-2392-Y/METRICS
- 15. Bortoletto R, Bassani L, Garzitto M, et al. Risk of psychosis in autism spectrum disorder individuals exposed to psychosocial stressors: a 9-year chart review study. Autism Res. 2023;16:2139–2149. https://doi.org/ 10.1002/AUR.3042
- 16. Glasson EJ, Bower C, Petterson B, De Klerk N, Chaney G, Hallmayer JF.. Perinatal factors and the development of autism: a population study. Arch Gen Psychiatry. 2004;61:618–627. https://doi.org/ 10.1001/ARCHPSYC.61.6.618
- 17. Burbach JPH, van der Zwaag B.. Contact in the genetics of autism and schizophrenia. Trends Neurosci. 2009;32:69–72. https://doi.org/ 10.1016/J.TINS.2008.11.002
- 18. Carroll LS, Owen MJ.. Genetic overlap between autism, schizophrenia and bipolar disorder. Genome Med. 2009;1:102–107. https://doi.org/ 10.1186/GM102
- 19. Pries LK, Snijders C, Menne-Lothmann C, et al. TwinssCan – gene-environment interaction in psychotic and depressive intermediate phenotypes: risk and protective factors in a general population twin sample. Twin Res Hum Genet. 2019;22:460–466. https://doi.org/ 10.1017/THG.2019.96
- 20. Derom C, Thiery E, Rutten BPF, et al. The east Flanders prospective twin survey (EFPTS): 55 years later. Twin Res Hum Genet. 2019;22:454–459. https://doi.org/ 10.1017/THG.2019.64
- 21. Pries LK, Guloksuz S, Menne-Lothmann C, et al. White noise speech illusion and psychosis expression: an experimental investigation of psychosis liability. PLoS One. 2017;12:e0183695. https://doi.org/ 10.1371/JOURNAL.PONE.0183695
- 22. Stefanis NC, Hanssen M, Smirnis NK, et al. Evidence that three dimensions of psychosis have a distribution in the general population. Psychol Med. 2002;32:347–358. https://doi.org/ 10.1017/S0033291701005141
- 23. Konings M, Bak M, Hanssen M, Van Os J, Krabbendam L.. Validity and reliability of the CAPE: a self-report instrument for the measurement of psychotic experiences in the general population. Acta Psychiatr Scand. 2006;114:55–61. https://doi.org/ 10.1111/J.1600-0447.2005.00741.X
- 24. 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. https://doi.org/ 10.1023/A:1005653411471
- 25. Hoekstra RA, Bartels M, Cath DC, Boomsma DI.. Factor structure, reliability and criterion validity of the Autism-Spectrum Quotient (AQ): a study in Dutch population and patient groups. J Autism Dev Disord. 2008;38:1555–1566. https://doi.org/ 10.1007/S10803-008-0538-X
- 26. Bernstein DP, Stein JA, Newcomb MD, et al. Development and validation of a brief screening version of the childhood trauma questionnaire. Child Abuse Negl. 2003;27:169–190. https://doi.org/ 10.1016/S0145-2134(02)00541-0
- 27. Bernstein DP, Fink L.. Childhood Trauma Questionnaire: A Retrospective Self-Report: Manual. Psychological Corporation; 1998.
- 28. Guloksuz S, Pries LK, Delespaul P, et al. ; Genetic Risk and Outcome of Psychosis (GROUP) investigators. Examining the independent and joint effects of molecular genetic liability and environmental exposures in schizophrenia: results from the EUGEI study. World Psych. 2019;18:173–182. https://doi.org/ 10.1002/WPS.20629
- 29. Schäfer M, Korn S, Smith PK, et al. Lonely in the crowd: Recollections of bullying. Br J Dev Psychol. 2004;22(3):379–394. https://doi.org/ 10.1348/0261510041552756
- 30. Pries LK, Lage-Castellanos A, Delespaul P, et al. ; Genetic Risk and Outcome of Psychosis (GROUP) investigators. Estimating exposome score for schizophrenia using predictive modeling approach in two independent samples: the results from the EUGEI study. Schizophr Bull. 2019;45:960–965. https://doi.org/ 10.1093/SCHBUL/SBZ054
- 31. Paykel ES. The interview for recent life events. Psychol Med. 1997;27:301–310. https://doi.org/ 10.1017/S0033291796004424
- 32. McNeil TF. The McNeil–Sjöström OC Scale: A Comprehensive Scale for Measuring Obstetric Complications. Department of Psychiatry, Lund University, Malmö General Hospital; 1995: 2.
- 33. Guloksuz IS. Biological Mechanisms of Environmental Stressors in Psychiatry: The Role of the Immune System. Doctoral Thesis. Maastricht University; 2015. https://doi.org/ 10.26481/dis.20151202ig
- 34. Robins LN, Wing J, Wittchen HU, et al. The composite international diagnostic interview. an epidemiologic instrument suitable for use in conjunction with different diagnostic systems and in different cultures. Arch Gen Psychiatry. 1988;45:1069–1077. https://doi.org/ 10.1001/ARCHPSYC.1988.01800360017003
- 35. Davies G, Welham J, Chant D, Torrey EF, McGrath J.. A systematic review and meta-analysis of Northern Hemisphere season of birth studies in schizophrenia. Schizophr Bull. 2003;29:587–593. https://doi.org/ 10.1093/OXFORDJOURNALS.SCHBUL.A007030
- 36. Purcell S, Neale B, Todd-Brown K, et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet. 2007;81:559–575. https://doi.org/ 10.1086/519795
- 37. Price AL, Patterson NJ, Plenge RM, Weinblatt ME, Shadick NA, Reich D.. Principal components analysis corrects for stratification in genome-wide association studies. Nat Genet. 2006;38:904–909. https://doi.org/ 10.1038/NG1847
- 38. Das S, Forer L, Schönherr S, et al. Next-generation genotype imputation service and methods. Nat Genet. 2016;48:1284–1287. https://doi.org/ 10.1038/ng.3656
- 39. Trubetskoy V, Pardiñas AF, Qi T, et al. ; Indonesia Schizophrenia Consortium. Mapping genomic loci implicates genes and synaptic biology in schizophrenia. Nature. 2022;604:502–508. https://doi.org/ 10.1038/S41586-022-04434-5
- 40. Choi SW, Mak TSH, O’Reilly PF.. Tutorial: a guide to performing polygenic risk score analyses. Nat Protocols. 2020;15:2759–2772. https://doi.org/ 10.1038/s41596-020-0353-1
- 41. StataCorp. Stata Statistical Software: Release 16. StataCorp LLC; 2019.
- 42. Snijders TAB, Bosker RJ.. Multilevel Analysis: An Introduction to Basic and Advanced Multilevel Modeling. SAGE Publications; 2011. https://books.google.nl/books?id=N1BQvcomDdQC
- 43. Carlin JB, Gurrin LC, Sterne JAC, Morley R, Dwyer T.. Regression models for twin studies: a critical review. Int J Epidemiol. 2005;34:1089–1099. https://doi.org/ 10.1093/IJE/DYI153
- 44. Vink JM, Hottenga JJ, de Geus EJC, et al. Polygenic risk scores for smoking: Predictors for alcohol and cannabis use? Addiction. 2014;109:1141–1151. https://doi.org/ 10.1111/ADD.12491/SUPPINFO
- 45. Den Braber A, Zilhão NR, Fedko IO, et al. Obsessive–compulsive symptoms in a large population-based twin-family sample are predicted by clinically based polygenic scores and by genome-wide SNPs. Transl Psychiatry. 2016;6:e731–e731. https://doi.org/ 10.1038/tp.2015.223
- 46. Pain O, Dudbridge F, Cardno AG, et al. Genome-wide analysis of adolescent psychotic-like experiences shows genetic overlap with psychiatric disorders. Am J Med Genet B Neuropsychiatr Genet. 2018;177:416–425. https://doi.org/ 10.1002/AJMG.B.32630
- 47. Green SB. How many subjects does it take to do a regression analysis. Multivariate Behav Res. 1991;26:499–510. https://doi.org/ 10.1207/S15327906MBR2603_7
- 48. Harris RJ. A Primer of Multivariate Statistics. 2nd ed.Academic Press; 1985.
- 49. Wilson Van Voorhis CR, Morgan BL.. Understanding power and rules of thumb for determining sample sizes. Tutor Quant Methods Psychol. 2007;3:43–50. https://doi.org/ 10.20982/tqmp.03.2.p043
- 50. Pries LK, Klingenberg B, Menne-Lothmann C, et al. Polygenic liability for schizophrenia and childhood adversity influences daily‐life emotion dysregulation and psychosis proneness. Acta Psychiatr Scand. 2020;141:465–475. https://doi.org/ 10.1111/ACPS.13158
- 51. Klingenberg B, Guloksuz S, Pries LK, et al. Gene-environment interaction study on the polygenic risk score for neuroticism, childhood adversity, and parental bonding. Personal Neurosci. 2023;6:e5. https://doi.org/ 10.1017/PEN.2023.2
- 52. Dardani C, Schalbroeck R, Madley-Dowd P, et al. Childhood trauma as a mediator of the association between autistic traits and psychotic experiences: evidence from the avon longitudinal study of parents and children cohort. Schizophr Bull. 2023;49:364–374. https://doi.org/ 10.1093/schbul/sbac167
- 53. Stanyon D, Yamasaki S, Ando S, et al. The role of bullying victimization in the pathway between autistic traits and psychotic experiences in adolescence: data from the Tokyo teen cohort study. Schizophr Res. 2022;239:111–115. https://doi.org/ 10.1016/J.SCHRES.2021.11.015
- 54. U.S. Department of Health & Human Services, Administration for Children and Families, Administration on Children, Youth and Families, Children’s Bureau. Child Maltreatment. 2022. Accessed September 26, 2024. https://www.acf.hhs.gov/cb/data-research/child-maltreatment
- 55. O’Mahen HA, Karl A, Moberly N, Fedock G.. The association between childhood maltreatment and emotion regulation: two different mechanisms contributing to depression? J Affect Disord. 2015;174:287–295. https://doi.org/ 10.1016/J.JAD.2014.11.028
- 56. Reininghaus U, Gayer-Anderson C, Valmaggia L, et al. Psychological processes underlying the association between childhood trauma and psychosis in daily life: an experience sampling study. Psychol Med. 2016;46:2799–2813. https://doi.org/ 10.1017/S003329171600146X
- 57. Dye HL. Is emotional abuse as harmful as physical and/or sexual abuse? J Child Adolesc Trauma. 2019;13:399–407. https://doi.org/ 10.1007/S40653-019-00292-Y
- 58. Ackner S, Skeate A, Patterson P, Neal A.. Emotional abuse and psychosis: a recent review of the literature. J Aggres Maltreat Trauma. 2013;22:1032–1049. https://doi.org/ 10.1080/10926771.2013.837132
- 59. Lee BK, Gross R, Francis RW, et al. Birth seasonality and risk of autism spectrum disorder. Eur J Epidemiol. 2019;34:785–792. https://doi.org/ 10.1007/S10654-019-00506-5
- 60. Mouridsen SE, Nielsen S, Rich B, Isager T.. Season of birth in infantile autism and other types of childhood psychoses. Child Psychiatry Hum Dev. 1994;25:31–43. https://doi.org/ 10.1007/BF02251098
- 61. Hebert KJ, Miller LL, Joinson CJ.. Association of autistic spectrum disorder with season of birth and conception in a UK cohort. Autism Res. 2010;3:185–190. https://doi.org/ 10.1002/AUR.136
- 62. Atladóttir HO, Parner ET, Schendel D, Dalsgaard S, Thomsen PH, Thorsen P.. Variation in incidence of neurodevelopmental disorders with season of birth. Epidemiology. 2007;18:240–245. https://doi.org/ 10.1097/01.EDE.0000254064.92806.13
- 63. Kolevzon A, Weiser M, Gross R, et al. Effects of season of birth on autism spectrum disorders: fact or fiction? Am J Psychiatry. 2006;163:1288–1290. https://doi.org/ 10.1176/AJP.2006.163.7.1288
- 64. Hsu CW, Tseng PT, Tu YK, et al. Month of birth and mental disorders: a population-based study and validation using global meta-analysis. Acta Psychiatr Scand. 2021;144:153–167. https://doi.org/ 10.1111/ACPS.13313
- 65. Tochigi M, Nishida A, Shimodera S, Okazaki Y, Sasaki T.. Season of birth effect on psychotic-like experiences in Japanese adolescents. Eur Child Adolesc Psychiatry. 2013;22:89–93. https://doi.org/ 10.1007/S00787-012-0326-1
- 66. Do B, Lynch P, Macris EM, et al. Systematic review and meta-analysis of the association of autism spectrum disorder in visually or hearing impaired children. Ophthalmic Physiol Opt. 2017;37:212–224. https://doi.org/ 10.1111/OPO.12350
- 67. da Silva Junior EA, Medeiros WMB, Torro N, et al. Cannabis and cannabinoid use in autism spectrum disorder: a systematic review. Trends Psychiatry Psychother. 2022;44:e20200149. https://doi.org/ 10.47626/2237-6089-2020-0149
- 68. Colizzi M, Bortoletto R, Costa R, Bhattacharyya S, Balestrieri M.. The autism–psychosis continuum conundrum: exploring the role of the endocannabinoid system. Int J Environ Res Public Health. 2022;19:5616. https://doi.org/ 10.3390/IJERPH19095616
- 69. Bortoletto R, Colizzi M.. Cannabis use in autism: reasons for concern about risk for psychosis. Healthcare (Basel, Switzerland) 2022;10:1553. https://doi.org/ 10.3390/HEALTHCARE10081553
- 70. Karcher NR, Paul SE, Johnson EC, et al. Psychotic-like experiences and polygenic liability in the adolescent brain cognitive development study. Biol Psychiatry Cogn Neurosci Neuroimaging. 2022;7:45–55. https://doi.org/ 10.1016/J.BPSC.2021.06.012
- 71. Legge SE, Jones HJ, Kendall KM, et al. Association of genetic liability to psychotic experiences with neuropsychotic disorders and traits. JAMA Psych. 2019;76:1256–1265. https://doi.org/ 10.1001/JAMAPSYCHIATRY.2019.2508
- 72. Sieradzka D, Power RA, Freeman D, et al. Are genetic risk factors for psychosis also associated with dimension-specific psychotic experiences in adolescence? PLoS One. 2014;9:e94398. https://doi.org/ 10.1371/JOURNAL.PONE.0094398
- 73. Zammit S, Hamshere M, Dwyer S, et al. A population-based study of genetic variation and psychotic experiences in adolescents. Schizophr Bull. 2014;40:1254–1262. https://doi.org/ 10.1093/SCHBUL/SBT146
- 74. Van Os J, Reininghaus U.. Psychosis as a transdiagnostic and extended phenotype in the general population. World Psych. 2016;15:118–124. https://doi.org/ 10.1002/WPS.20310
- 75. McGorry P, Van Os J.. Redeeming diagnosis in psychiatry: timing versus specificity. Lancet. 2013;381:343–345. https://doi.org/ 10.1016/S0140-6736(12)61268-9
- 76. Peterson JL, Earl RK, Fox EA, et al. Trauma and autism spectrum disorder: review, proposed treatment adaptations and future directions. J Child Adolesc Trauma. 2019;12:529–547. https://doi.org/ 10.1007/S40653-019-00253-5/METRICS
- 77. Newbury JB, Arseneault L, Moffitt TE, et al. Measuring childhood maltreatment to predict early-adult psychopathology: comparison of prospective informant-reports and retrospective self-reports. J Psychiatr Res. 2018;96:57–64. https://doi.org/ 10.1016/J.JPSYCHIRES.2017.09.020
- 78. Reuben A, Moffitt TE, Caspi A, et al. Lest we forget: comparing retrospective and prospective assessments of adverse childhood experiences in the prediction of adult health. J Child Psychol Psychiatry. 2016;57:1103–1112. https://doi.org/ 10.1111/JCPP.12621
- 79. Hastings KN. The bullying of autistic children: a review of anti-bullying interventions, and a feasibility study examining the relationship between bullying victimisation and psychosis-like experiences. 2020. DClinPsy thesis, University of Sheffield.
- 80. Solmi M, Radua J, Olivola M, et al. Age at onset of mental disorders worldwide: large-scale meta-analysis of 192 epidemiological studies. Mol Psychiatry. 2021;27:281–295. https://doi.org/ 10.1038/s41380-021-01161-7
- 81. Fusar-Poli L, Avanzato C, Maccarone G, et al. The association between attention-deficit–hyperactivity disorder and autistic traits with psychotic-like experiences in sample of youths who were referred to a psychiatric outpatient service. Brain Sci. 2024;14:844. https://doi.org/ 10.3390/BRAINSCI14080844