Evidence, and replication thereof, that moleculargenetic and environmental risks for psychosis impact through an affective pathway Citation for published version (APA): van Os, J., Pries, L. K., ten Have, M., de Graaf, R., van Dorsselaer, S., Delespaul, P., Bak, M., Kenis, G., Lin, B. D., Luykx, J. J., Richards, A. L., Akdede, B., Binbay, T., Altinyazar, V., Yalincetin, B., Gumus-Akay, G., Cihan, B., Soygur, H., Ulas, H., ... Guloksuz, S. (2022). Evidence, and replication thereof, that molecular-genetic and environmental risks for psychosis impact through an affective pathway. Psychological Medicine, 52(10), 1910-1922. [0033291720003748]. https://doi.org/10.1017/S0033291720003748 Document status and date: Published: 01/07/2022 DOI: 10.1017/S0033291720003748 Document Version: Publisher's PDF, also known as Version of record Document license: Taverne Please check the document version of this publication: • A submitted manuscript is the version of the article upon submission and before peer-review. There can be important differences between the submitted version and the official published version of record. People interested in the research are advised to contact the author for the final version of the publication, or visit the DOI to the publisher's website. • The final author version and the galley proof are versions of the publication after peer review. • The final published version features the final layout of the paper including the volume, issue and page numbers. 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Psychological Medicine 52, 1910–1922. https://doi.org/10.1017/ S0033291720003748 Evidence, and replication thereof, that molecular-genetic and environmental risks for psychosis impact through an affective pathway Jim van Os1,2,3 , Lotta-Katrin Pries2, Margreet ten Have4, Ron de Graaf4, Saskia van Dorsselaer4, Philippe Delespaul2,5, Maarten Bak2,5, Gunter Kenis2, Bochao D. Lin6, Jurjen J. Luykx1,6,7, Alexander L. Richards8, Berna Akdede9, Tolga Binbay9, Vesile Altınyazar10, Berna Yalınçetin11, Güvem Gümüş-Akay12,13, Burçin Cihan14, Haldun Soygür15, Halis Ulaş16, Eylem Şahin Cankurtaran17, Semra Ulusoy Kaymak18, Marina M. Mihaljevic19,20, Sanja Andric Petrovic19,20, Received: 15 May 2020 Revised: 11 August 2020 Accepted: 22 September 2020 First published online: 19 October 2020 Tijana Mirjanic19,20, Miguel Bernardo21,22,23, Gisela Mezquida21,22,23, Key words: Affective pathway; childhood adversity; environment; genetics; psychosis José Luis Santos23,28, Estela Jiménez-López23,29, Manuel Arrojo30, Author for correspondence: Jim van Os, E-mail: j.j.vanos-2@umcutrecht.nl Mara Parellada23,33, Nadja P. Maric19,20, Cem Atbaşoğlu34, Alp Ucok35, Silvia Amoretti21,22,23, Julio Bobes23,24,25,26, Pilar A. Saiz23,24,25,26, María Paz García-Portilla23,24,25,26, Julio Sanjuan23,27, Eduardo J. Aguilar23,27, Angel Carracedo31,32, Gonzalo López23,33, Javier González-Peñas23,33, Köksal Alptekin9,11, Meram Can Saka34, Celso Arango23,33, Michael O’Donovan8, Bart P. F. Rutten2 and Sinan Guloksuz2,36 Abstract Background. There is evidence that environmental and genetic risk factors for schizophrenia spectrum disorders are transdiagnostic and mediated in part through a generic pathway of affective dysregulation. Methods. We analysed to what degree the impact of schizophrenia polygenic risk (PRS-SZ) and childhood adversity (CA) on psychosis outcomes was contingent on co-presence of affective dysregulation, defined as significant depressive symptoms, in (i) NEMESIS-2 (n = 6646), a representative general population sample, interviewed four times over nine years and (ii) EUGEI (n = 4068) a sample of patients with schizophrenia spectrum disorder, the siblings of these patients and controls. Results. The impact of PRS-SZ on psychosis showed significant dependence on co-presence of affective dysregulation in NEMESIS-2 [relative excess risk due to interaction (RERI): 1.01, p = 0.037] and in EUGEI (RERI = 3.39, p = 0.048). This was particularly evident for delusional ideation (NEMESIS-2: RERI = 1.74, p = 0.003; EUGEI: RERI = 4.16, p = 0.019) and not for hallucinatory experiences (NEMESIS-2: RERI = 0.65, p = 0.284; EUGEI: −0.37, p = 0.547). A similar and stronger pattern of results was evident for CA (RERI delusions and hallucinations: NEMESIS-2: 3.02, p < 0.001; EUGEI: 6.44, p < 0.001; RERI delusional ideation: NEMESIS-2: 3.79, p < 0.001; EUGEI: 5.43, p = 0.001; RERI hallucinatory experiences: NEMESIS-2: 2.46, p < 0.001; EUGEI: 0.54, p = 0.465). Conclusions. The results, and internal replication, suggest that the effects of known genetic and non-genetic risk factors for psychosis are mediated in part through an affective pathway, from which early states of delusional meaning may arise. Introduction © The Author(s) 2020. Published by Cambridge University Press Both genetic and environmental influences increase risk for psychotic disorder. One of the best replicated, non-proxy environmental effects with a relatively large effect size is childhood adversity (CA) (Varese et al., 2012). Molecular genetic analysis of schizophrenia case-control data allows for estimation of a model that predicts trait values from genetic variation, expressed as a polygenic risk score (PRS-SZ), providing a direct measure of schizophrenia genetic risk for analysis (Purcell et al., 2009). The risk associated with CA and PRS is not specific for psychotic disorder. Around two-thirds of genetic associations are common to schizophrenia, bipolar disorder and major depressive disorder and overlap also exists with genetic variants contributing to autism, https://doi.org/10.1017/S0033291720003748 Published online by Cambridge University Press 1911 Psychological Medicine attention-deficit/hyperactivity disorder and intellectual disabilities (Cross-Disorder Group of the Psychiatric Genomics et al., 2013; Cross-Disorder Group of the Psychiatric Genomics Consortium, 2019). Therefore, PRS of mental disorders to a large degree represent transdiagnostic risk for mental suffering, particularly the affective spectrum. The level of non-specificity seen for genetic risk also applies to environmental risk factors. CA thus is similarly broadly associated with a range of mental (affective) disorders (Green et al., 2010). The non-specificity of the most important genetic and environmental risks for psychotic disorder may indicate a shared mechanism of generic mental suffering. This is compatible with epidemiological studies on psychopathology, which have established that the earliest expression of psychosis typically arises within a transdiagnostic mix of symptoms (McGorry & van Os, 2013), particularly depression (Hafner et al., 2005), and that affective dysregulation, particularly depression, is strongly associated with the prevalence and incidence of subthreshold expression of psychotic phenomena in the general population (Guloksuz et al., 2020; van Os & Reininghaus, 2016) as well as with clinical psychotic syndromes (Herniman et al., 2019; Wilson, Yung, & Morrison, 2020). It has been suggested that affective processes are crucial in the causation of psychosis (Bebbington, 2015; Garety et al., 2005; Krabbendam & van Os, 2005; Upthegrove, Marwaha, & Birchwood, 2017), as predicted by network models of psychosis (Isvoranu, Borsboom, van Os, & Guloksuz, 2016), in which genetic and environmental influences impact each other in a dynamic fashion (Guloksuz et al., 2015; Isvoranu et al., 2017; Isvoranu et al., 2020). These data in combination suggest that although CA and PRS-SZ are strongly associated with schizophrenia spectrum disorder, the mechanism by which they increase risk may be transdiagnostic and mediated in part by a generic pathway of affective dysregulation. If this were true, the impact of CA and PRS on psychosis outcomes would show a degree of dependence on co-occurring affective dysregulation: i.e. is higher if there is additional evidence of affective dysregulation and is lower in the absence of affective dysregulation (Fig. 1). Recent work using indirect measures of genetic risk indeed suggest that this type of relationship exists between (proxy) genetic and environmental risks on the one hand and affective dysregulation on the other in their effects on psychosis outcomes (Pries et al., 2018; Radhakrishnan et al., 2019). However, the hypothesis remains to be tested with direct measures of genetic risk such as PRS-SZ. In this study, we examined, and attempted to replicate, the hypothesis that the association between PRS-SZ and CA on the one hand, and psychosis outcomes on the other, is contingent, to a degree, on co-presence of significant affective dysregulation. To this end, we examined the interacting contributions of PRS-SZ and CA on the one hand, and affective dysregulation on the other, in models of psychosis in (i) a large populationbased cohort (n = 6646) that was examined four times over period of 9 years; and (ii) a large schizophrenia-spectrum case-siblingcontrol study of 4068 participants. Given strong evidence that the terms making up the interactions, PRS-SZ and CA on the one hand, and affective dysregulation on the other, are associated with each other (Brainstorm et al., 2018; Kessler & Magee, 1993; Nivard et al., 2017), the theoretical model of how they work together to affect the outcome of psychosis was considered to be one of mediation, under the framework proposed by Kraemer and colleagues (Kraemer, https://doi.org/10.1017/S0033291720003748 Published online by Cambridge University Press Fig. 1. Evidence that genetic and environmental risks for psychosis are mediated by an affective pathway: effect sizes will be low if the psychosis outcome is not accompanied by affective dysregulation (left) and effect sizes will be high if affective dysregulation is co-present with the psychosis outcome. Stice, Kazdin, Offord, & Kupfer, 2001). According to this framework, statistical interaction is indicative of moderation if the terms of the interaction are not correlated with each other, and indicative of mediation if the terms of the interaction are correlated with each other. Conceptually, this means that mediation would explain values of Y (psychosis) as indirectly caused by values of X (genetic and non-genetic aetiology) over a pathway of affective dysregulation. Method Study populations Nemesis-2 All four waves of the Netherlands Mental Health Survey and Incidence Study-2 (NEMESIS-2) were used. NEMESIS-2 was conducted to study the prevalence, incidence, course and consequences of mental disorders in the Dutch general population. The baseline data of NEMESIS-2 were collected from 2007 to 2009, follow-up was until 2018. The study was approved by the Medical Ethics Review Committee for Institutions on Mental Health Care and written informed consent was collected from participants at each wave. To ensure representativeness of the sample in terms of age (between the ages of 18 and 65 at baseline), region and population density, a multistage random sampling procedure was applied. Dutch illiteracy was an exclusion criterion. Non-clinician, trained interviewers applied the Composite International Diagnostic Interview (CIDI) version 3.0 (Alonso et al., 2004; de Graaf, ten Have, Burger, & Buist-Bouwman, 2008) and additional questionnaires during home visits. Details of NEMESIS-2 are provided elsewhere (de Graaf, Ten Have, & van Dorsselaer, 2010; de Graaf, ten Have, van Gool, & van Dorsselaer, 2012). The first wave (T0) enrolled 6646 participants (response rate 65.1%; average interview duration: 95 min), who were followed up in three visits within 9 years: successive response rates at year 3 (T1), year 6 (T2) and year 9 (T3) were 80.4% (n = 5303; excluding those who deceased; interview duration: 84 min), 87.8% (n = 4618; interview duration: 83 min) and 86.8% (n = 4007; interview duration: 102 min), respectively. Rates at baseline reflect lifetime occurrence; rates at T1–T3 reflect interval (baseline–T1, T1–T2 and T2–T3) occurrence of ∼3 years. Attrition between T0 and T3 was not significantly associated with any of the mental disorders at T0, after controlling for sociodemographic characteristics (de Graaf, van Dorsselaer, Tuithof, & ten Have, 2018; Nuyen et al., 2020). 1912 https://doi.org/10.1017/S0033291720003748 Published online by Cambridge University Press Table 1. NEMESIS-2 sample characteristics, stratified by polygenic risk score risk set included for analysis (n = 9982 observations) or excluded from analysis (n = 9046 observations) Del/ Hal Del Hal Affective dysregulation Family history Adversity score Cannabis use Urbanicity Life events Living alone Married/ widowed Unemployed Income Edu-cation Age % Female Status % % % Mean % % % Mean Mean % % % mean mean mean % Excluded 0.1 0.06 0.05 0.37 0.52 0.54 0.02 2.98 0.72 0.22 0.63 0.13 6.9 2.99 49.15 0.54 1.35 0.92 2.48 0.9 12.91 S.D. 0.48 N 9046 8963 9032 9046 9046 9046 8785 9026 8949 9046 9045 9046 8597 9046 9046 9046 Included 0.09 0.06 0.05 0.35 0.52 0.52 0.02 3 0.7 0.19 0.64 0.11 7.03 3.06 48.43 0.56 1.34 0.9 2.42 0.89 12.92 S.D. 0.48 N 9982 9924 9965 9982 9982 9982 9706 9981 9961 9982 9982 9982 9668 9982 9982 9982 Total 0.09 0.06 0.05 0.36 0.52 0.53 0.02 2.99 0.71 0.2 0.63 0.12 6.97 3.03 48.77 0.55 1.34 0.91 2.45 0.89 12.92 19 007 18 910 18 265 19 028 19 028 S.D. N 0.48 19 028 18 887 18 997 19 028 19 028 19 028 18 491 19 028 19 027 19 028 19 028 Del/Hal: CIDI rating delusions or hallucinations. Del: CIDI rating delusions. Hal: CIDI rating hallucinations. Affective dysregulation: at least one of the CIDI 3.0 core symptoms of depressive episode. Family history: for participants who screened positive for the following psychiatric diagnoses, presence of the disorder in direct relatives was assessed: alcohol/drugs misuse, depression, mania and anxiety disorders (panic disorder, social phobia, agoraphobia, generalised anxiety disorder). Adversity score: total score NEMESIS-2 trauma questionnaire. Cannabis use: use of once or more per week during the lifetime period of most frequent use. Urbanicity: five levels based on the Dutch classification of increasing population density. Life events: total score on whether participants had experienced one of nine life events within the last 12 months (T0) or since the last interview (T1 to T3). Income: net annual household income, rated on a scale from 1 (lowest) to 14 (highest). Education: four-level continuous variable (higher level = higher educational level). Jim van Os et al. https://doi.org/10.1017/S0033291720003748 Published online by Cambridge University Press 4016 9.58 4.3 8.19 4022 3675 3695 3898 3930 4022 0.46 4022 4022 0.45 3661 3661 0.49 0.4 0.41 3676 0.39 3651 S.D. N 3658 3085 3088 33.9 0.68 3035 3014 11.98 0.12 2884 2886 48.55 0.31 3088 3088 0.31 0.38 3088 3038 1.49 0.66 3088 3085 0.36 Total 0.39 3088 3088 N 0.2 0.46 34 9.63 0.69 4.29 12.19 0.12 7.99 49.01 0.29 0.32 0.38 0.45 1.49 0.66 0.49 0.4 0.2 0.39 0.41 0.36 0.39 S.D. 934 934 623 0.44 0.48 573 573 588 563 0.36 0.42 0.38 S.D. N Included 8.7 934 789 811 884 4.27 895 934 9.41 931 0.45 33.56 0.64 11.25 0.11 46.86 0.37 0.28 0.35 1.48 0.63 0.4 0.35 Excluded 0.16 % Mean % Mean % Mean % % % Mean Mean % Mean Mean Status % Female Age In a relationship Years education Cannabis use Cognitive score % Patients % Siblings % Controls CTQ score Cape depression Cape any hallucination Cape delusions In NEMESIS-2, a psychosis add-on instrument based on the G section of previous CIDI versions was included. This add-on instrument consists of 20 psychotic symptoms corresponding to the symptoms assessed in a previous population survey in the Netherlands, NEMESIS, the precursor of NEMESIS-2 (Bijl, Ravelli, & van Zessen, 1998; de Graaf et al., 2010). Detailed descriptions of the specific psychotic experience (PE) items can be found in previous work using NEMESIS (Smeets et al., 2013) and NEMESIS-2 (van Nierop et al., 2012). At baseline, lifetime prevalence of PE was assessed. A clinician did a follow-up telephone interview when participants reported a psychotic symptom to assess whether this symptom was a true PE using questions from the Structured Clinical Interview for DSM-IV. At baseline, a total of 1081 participants (16.3%) endorsed at least one self-reported PE. Of these, 794 participated in clinical re-interview (73.5%), of whom 340 (42.8%) reported at least one clinically validated PE. At T1, 440 out of a total 5303 (8.3%) participants reported that at least one self-reported PE had occurred since the previous interview. Of these, 367 (83.4%) participants were available for clinical re-interview, of whom 172 (46.9%) reported at least one clinically validated PE. Cape positive Assessment of psychotic experiences Table 2. EUGEI sample characteristics, stratified by polygenic risk score risk set included for analysis (n = 3088 participants) or excluded from analysis (n = 934 participants) EUGEI The EUGEI project is a 25-centre, 15-country, EU-funded collaborative network studying the impact of genetic and environmental factors on the onset, course and neurobiology of psychosis spectrum disorder (European Network of National Networks studying Gene-Environment Interactions in Schizophrenia et al., 2014). Workpackage 6, entitled ‘Vulnerability and Severity’, focussed on the psychometric expression of genetic and environmental liability in the siblings of patients, who are at higher than average genetic and environmental risk compared to healthy comparison participants. The sample in Workpackage 6 was collected in Spain (5 centres), Turkey (3 centres) and Serbia (1 centre) and consisted of 1525 healthy comparison participants, 1261 patients with a diagnosis of psychosis spectrum disorder (average duration of illness since age of first contact with mental health services: 9.9 years) and 1282 siblings of these patients. Patients were diagnosed with schizophrenia spectrum disorder according to the DSM-IV-TR. This diagnosis was confirmed by the Operational Criteria Checklist for Psychotic and Affective Illness (McGuffin, Farmer, & Harvey, 1991). Exclusion criteria for all participants were diagnosis of psychotic disorders due to another medical condition, history of head injury with loss of consciousness and intelligence quotient <70. To achieve high quality and homogeneity in clinical, experimental and environmental assessments, standardised instruments were administered by psychiatrists, psychologists or trained research assistants who completed mandatory on-site training sessions and online training modules including interactive interview videos and self-assessment tools (European Network of National Networks studying Gene-Environment Interactions in Schizophrenia et al., 2014). Both on-site and online training sessions were repeated annually to maintain high inter-rater reliability throughout the study enrolment period (for details see: https://cordis.europa.eu/ result/rcn/175696_en.html). The EUGEI project was approved by the Medical Ethics Committees of all participating sites and conducted in accordance with the Declaration of Helsinki. All respondents provided written informed consent and, in the case of minors, such consent was also obtained from parents or legal guardian. Cape positive: Cape frequency score positive symptoms. Cape delusions: Cape frequency score delusion items. Cape any hallucination: any positive rating Cape hallucinations items. Cape depression: Cape depression frequency dimension. CTQ score: CTQ total score. Cognitive score: Z-score, expressed as T-score, of short version of the WAIS-III short form (digit symbol coding subtest, uneven items of the arithmetic subtest, uneven items of the block design subtest, every third item of the information subtest (Blyler, Gold, Iannone, & Buchanan, 2000; Velthorst et al., 2013; Wechsler, 1997). Cannabis use: use of once or more per week during the lifetime period of most frequent use. 1913 Psychological Medicine 1914 Jim van Os et al. Table 3. NEMESIS-2: risk of psychosis admixture as a function of combinations of binary schizophrenia polygenic risk (75th percentile cut-off) and binary affective dysregulation Phenotype Delusions and hallucinations Hallucinations Delusions Risk OR 95% CI PRS75 only 0.87 0.65 AD only 3.45 PRS75 + AD 4.34 RERI p N 1.18 0.369 9982 2.91 4.09 0.000 3.40 5.54 0.000 1.01 0.06 1.97 0.037 PRS75 only 0.94 0.63 1.41 0.778 AD only 3.35 2.67 4.20 0.000 PRS75 + AD 3.95 2.81 5.53 0.000 RERI 0.65 −0.54 1.85 0.284 PRS75 only 0.74 0.50 1.08 0.123 AD only 3.48 2.82 4.30 0.000 PRS75 + AD 4.96 3.78 6.51 0.000 RERI 1.74 0.58 2.91 0.003 9965 9924 OR, odds ratio; 95% CI, 95% confidence interval; N, number of observations in analysis; PRS75, polygenic risk score 75th percentile cut-off; AD, affective dysregulation (at least one of the two CIDI 3.0 core symptoms of depressive episode); RERI, relative excess risk due to interaction. At T2, 284 out of the total 4618 (6.2%) participants reported at least one self-reported PE since the previous interview. Of these, 230 (81.0%) participants were available for clinical re-interview, of which 135 (58.7%) reported at least one clinically validated PE. At T3, 222 out of the total 4007 (5.5%) participants reported at least one self-reported PE since the previous interview. Of these, 207 (93.2%) participants were available for clinical re-interview, of which 77 (37.2%) reported at least one clinically validated PE. Given similarities between CIDI self-reported and clinically validated PE, in terms of associations, predictive value and outcome (Bak et al., 2003; van der Steen et al., 2019; van Nierop et al., 2012), CIDI self-reported PE were used, thus increasing statistical power. PE were dichotomised consistent with previous work in NEMESIS and NEMESIS-2 (Pries et al., 2018; Radhakrishnan et al., 2019; van Rossum, Dominguez, Lieb, Wittchen, & van Os, 2011). Thus, the presence of delusions was defined as having at least one delusion endorsed and the presence of hallucinations was similarly defined. In EUGEI, the Community Assessment of Psychic Experiences (Cape; http://www.cape42.homestead.com) was developed to rate self-reports of lifetime psychotic experiences (Konings, Bak, Hanssen, van Os, & Krabbendam, 2006). The Cape includes dimensions of positive psychotic experiences, negative experiences and depressive experiences. Effect sizes for internal stability are high, as are correlations between Cape dimensions and conceptually similar dimensions of the Structured Interview for Schizotypy, Revised (Konings et al., 2006; Vollema & Ormel, 2000). Items are modelled on patient experiences as contained in the Present State Examination, 9th version (Wing, Cooper, & Sartorius, 1974), schedules assessing negative symptoms such as the Scale for the Assessment of Negative Symptoms (Andreasen, 1982) and the Subjective Experience of Negative Symptoms (Selten, Sijben, van den Bosch, Omloo Visser, & Warmerdam, 1993), and scales assessing depressive symptoms such as the Calgary Depression Scale (Addington, Addington, & Maticka-Tyndale, 1993). Items are scored on a four-point scale. In the current analyses, Cape dimensions of frequency of positive https://doi.org/10.1017/S0033291720003748 Published online by Cambridge University Press experiences (20 items), and depressive experiences (8 items) were included. A total score representing the mean of all items was calculated for each dimension. For the analyses, conform previous work in this area (Heins et al., 2011; van Dam et al., 2015; van Os, Marsman, van Dam, Simons, & Investigators, 2017), frequency of positive symptoms, dichotomised around the 80th percentile in the control group, were used as measures for delusions and hallucinations. Similarly, the frequency score of the 17 Cape delusion items, dichotomised around the 80th percentile in the control group, was used as the delusion outcome. Any presence of hallucinations, as measured by the three Cape hallucination items, was used as the binary hallucination outcome. Childhood adversity In NEMESIS-2, CA was assessed at T0 using a questionnaire based on the NEMESIS trauma questionnaire (de Graaf et al., 2010). Whenever a subject reported having experienced one of five types of CA before the age of 16 years [emotional neglect (not listened to, ignored or unsupported), physical abuse (kicked, hit, bitten or hurt with object or hot water), psychological abuse (yelled at, insulted, unjustly punished/treated, threatened, belittled or blackmailed), peer victimisation (bullying) and one time or more sexual abuse (any unwanted sexual experience)], they were asked to state how often it had occurred on a scale of 1 (once) to 5 (very often). Conforming with previous work in this area, the CA score was dichotomised at the 80th percentile (Heins et al., 2011; van Dam et al., 2015; van Os et al., 2017). In EUGEI, CA was assessed using the Childhood Trauma Questionnaire Short Form (CTQ) that consists of 28 items rated on a five-point Likert scale measuring five domains of maltreatment (emotional and physical neglect along with emotional, physical and sexual abuse) (Bernstein et al., 2003). The psychometric characteristics of the translated versions (Spanish, Turkish, Dutch and Serbian) of the CTQ have been comprehensively studied (Hernandez et al., 2013; Mitkovic-Voncina, Lecic-Tosevski, Pejovic-Milovancevic, & Popovic-Deusic, 2014; Sar, Akyuz, Kundakci, Kiziltan, & Dogan, 2004; Thombs, 1915 Psychological Medicine Consortium analysis (Schizophrenia Working Group of the Psychiatric Genomics Consortium, 2014). Conform previous analyses in these samples, statistical analyses were adjusted for three and 10 principal components in NEMESIS-2 and EIGEI, respectively (Guloksuz et al., 2019; Pries et al., 2020). Affective dysregulation A measure of affective dysregulation was constructed that was comparable across NEMESIS-2 and EUGEI. In NEMESIS-2, depressive symptoms were assessed with the CIDI version 3.0 (Alonso et al., 2004; de Graaf et al., 2008). Affective dysregulation was considered present if participants experienced at least one of the two CIDI 3.0 core symptoms of Depressive Episode, assessed at baseline (assessing lifetime occurrence) and each follow-up visit (assessing interval occurrence). The prevalence of affective dysregulation, thus defined, was 36%. In EUGEI, Cape frequency of depressive symptoms (eight items), dichotomised around the 80th control percentile, was used as the measure for affective dysregulation, conform previous work in this area (Heins et al., 2011; van Dam et al., 2015; van Os et al., 2017). Statistical analyses Fig. 2. NEMESIS-2: additive interaction effects of affective dysregulation (AD) and polygenic risk score for schizophrenia (PRS; 75% cut-off) in models of psychosis phenotypes; RERI – relative excess risk due to interaction. Bernstein, Lobbestael, & Arntz, 2009). Consistent with previous work in similar samples, CTQ score was modelled as a binary variable, calculated around the 80th percentile in the control group (Heins et al., 2011; van Dam et al., 2015; van Os et al., 2017). Polygenic risk score for schizophrenia For details of genotyping and calculation of PRS in NEMESIS-2 and EUGEI, we refer to recent papers detailing these procedures (Guloksuz et al., 2019; Pries et al., 2020). We used recent GWASs of schizophrenia (Pardinas et al., 2018) for PRS calculations (Choi, Mak, & O’Reilly, 2020). PRS-SZ was created, using the same genotyping platform for EUGEI and NEMESIS-2, from best-estimate genotypes at six different p-thresholds (0.5, 0.1, 0.05, 5 × 10−3, 5 × 10−5, 5 × 10−8). For our primary analyses, we used the p-threshold of <0.05, as this threshold explained most variation in the phenotype in the Psychiatric Genomics https://doi.org/10.1017/S0033291720003748 Published online by Cambridge University Press Risk set NEMESIS-2: For the CA analyses, data on CA, affective dysregulation and psychotic experiences were available for the entire sample with few missing values (n = 6643 at baseline). In the PRS analysis, material for DNA analysis of sufficient quality was available for 3104 individuals (47%) at T0 (Pries et al., 2020). Excluding individuals who at interview has been assessed as member of an ethnic minority, given lack of generalisability of PRS to this group, left 3052 for analysis. These 3052 individuals yielded 9982 observations with data on psychosis outcomes and affective dysregulation at least one of the four interviews. Values for important diagnostic, socio-demographic, familial and environmental risk variables were very similar in a comparison between the 9982 included and the 9046 non-included observations (Table 1). EUGEI: The EUGEI sample consisted of 4068 individuals. For the CA analysis, there were 3627 participants with complete data for psychosis outcomes, affective dysregulation and CA (1491 healthy comparison participants, 1137 relatives and 999 patients). For the PRS analysis, individuals of non-white ethnic group were excluded, as were individuals with missing GWAS information and missing data on psychosis outcomes and affective dysregulation, leaving 3088 participants (1186 healthy comparison participants, 1001 relatives and 901 patients) for the current analysis. Values for important diagnostic, socio-demographic, familial and environmental risk variables were very similar in a comparison between the 3088 included and the 934 non-included observations (Table 2). Analyses All analyses were performed using Stata, version 16 (StataCorp, 2019). p < 0.05 (2-tailed) was considered nominally statistically significant. Given that in each person contributed multiple observations so that observations were clustered within persons (NEMESIS-2), or that participants were clustered in families (EUGEI), the Stata cluster option was used to take into account intra-group correlations occasioned by clustering of observations 1916 Jim van Os et al. Table 4. NEMESIS-2: risk of psychosis admixture as a function of combinations of binary affective dysregulation and binary childhood adversity (80th percentile cut-of) Phenotype Delusions and hallucinations Hallucinations Delusions Risk OR 95% CI CA only 2.20 1.79 AD only 3.36 CA + AD 7.58 RERI 3.02 p N 2.71 0.000 20 574 2.97 3.80 0.000 6.51 8.83 0.000 2.04 4.01 0.000 CA only 2.76 2.11 3.60 0.000 AD only 3.38 2.84 4.01 0.000 CA + AD 7.59 6.20 9.30 0.000 RERI 2.46 1.21 3.71 0.000 CA only 1.91 1.47 2.48 0.000 AD only 3.54 3.04 4.12 0.000 CA + AD 8.24 6.92 9.80 0.000 RERI 3.79 2.59 5.00 0.000 20 537 20 409 OR, odds ratio; 95% CI, 95% confidence interval; N, number of observations in analysis; CA, childhood adversity; AD, affective dysregulation (at least one of the two CIDI 3.0 core symptoms of depressive episode); RERI, relative excess risk due to interaction. Table 5. EUGEI: risk of psychosis admixture as a function of combinations of binary schizophrenia polygenic risk (75th percentile cut-off) and binary affective dysregulation (Cape depression 80th percentile) Phenotype Delusions and hallucinationsa Risk Delusions p N 3088 0.95 0.71 1.27 0.714 AD only 7.34 5.70 9.45 0.000 10.67 7.68 14.84 0.000 RERI 3.39 0.03 6.75 0.048 PRS75 only 0.85 0.62 1.18 0.335 AD only 3.53 2.68 4.65 0.000 PRS75 + AD 3.01 2.10 4.32 0.000 −0.37 −1.57 0.83 0.547 RERI a 95% CI PRS75 only PRS75 + AD Hallucinationsa OR PRS75 only 0.97 0.71 1.31 0.838 AD only 7.32 5.67 9.46 0.000 11.45 8.27 15.85 0.000 4.16 0.69 7.63 0.019 PRS75 + AD RERI 3085 3088 a Delusions and hallucinations: Cape positive dimension 80% control cut-off; Delusions: Cape delusions 80% control cut-off; Hallucinations: any rating of Cape hallucinations. OR, odds ratio; 95% CI, 95% confidence interval; N, number of observations in analysis; PRS75, polygenic risk score 75th percentile cut-off; AD, affective dysregulation (Cape depression dimension 80% control cut-off); RERI, relative excess risk due to interaction. within individuals (NEMESIS-2) or families (EUGEI). Models including PRS were adjusted for three principal components (NEMESIS-2) or 10 principal components (EUGEI). Analyses using the EUGEI sample were additionally adjusted for country and for group (control, sibling, patient) using two dummies for siblings status and patient status. Given differences between delusions and hallucinations in their patterns of association with other variables (Bartels-Velthuis, van de Willige, Jenner, Wiersma, & van Os, 2012; Escher, Romme, Buiks, Delespaul, & van Os, 2002; Smeets et al., 2012), three psychosis phenotypes were examined as dependent variable in regression models: delusions and hallucinations (or: psychosis), hallucinations (with or without delusions) and delusions (with or without hallucinations). https://doi.org/10.1017/S0033291720003748 Published online by Cambridge University Press Regression models were fitted to examine the hypothesis that the association between affective dysregulation and psychosis would be stronger if PRS-SZ was high. To test this hypothesis, interactions between affective dysregulation and PRS-SZ/CA were tested in models of psychosis phenotypes. Consistent with previous epidemiological analyses with PRS-SZ in this sample, PRS-SZ was examined as a dichotomous variable, using the 75th percentile as cut-off (hereafter: PRS75), with sensitivity analyses using a range of cut-offs (50, 60, 70 80 and 90% percentile cut-offs) (Guloksuz et al., 2019). In NEMESIS-2, 75% cut-offs of the entire population were used; in EUGEI, 75% percentile cut-offs of the control values were used. 1917 Psychological Medicine Fig. 3. NEMESIS-2: additive interaction effects of affective dysregulation (AD) and childhood adversity (CA) in models of psychosis phenotypes; RERI – relative excess risk due to interaction. Logistic regression models, taking into account clustering of observations within participants or families as described above, were applied to test the association between binary affective dysregulation and PRS75 with the three psychosis phenotypes. In testing interaction, additive models were chosen over multiplicative models prior to genetic data collection (EUGEI consortium meeting, 14 December 2013). To test the joint effects of affective dysregulation and PRS, we entered the four states occasioned by the combination of binary affective dysregulation and binary PRS75 as independent variables (three dummy variables with no-risk state as the reference category), and psychosis phenotype as the dependent variable, in logistic regression models. We tested for departure from additivity using the interaction contrast ratio, also called the relative excess risk due to interaction (RERI). The RERI is considered the standard measure for interaction on the additive scale in case-control studies (Knol & VanderWeele, 2012). The RERI was estimated as (ORaffective dysregulation&PRS75−ORaffective dysregulation−ORPRS75 + 1) https://doi.org/10.1017/S0033291720003748 Published online by Cambridge University Press Fig. 4. EUGEI: additive interaction effects of affective dysregulation (AD; 80% cut-off) and polygenic risk score for schizophrenia (PRS; 75% cut-off) in models of psychosis phenotypes; RERI – relative excess risk due to interaction. (VanderWeele & Vansteelandt, 2014). A RERI greater than zero was defined as a positive deviation from additivity, and considered significant when the 95% confidence interval (CI) did not contain zero. Using the ORs derived from each model, the RERIs for each model were calculated using the delta method (Hosmer & Lemeshow, 1992). Results Distribution of demographic and risk variables are shown in Table 1 (NEMESIS-2) and Table 2 (EUGEI). In both NEMESIS and EUGEI, the terms making up the interactions in the models of psychosis outcomes were positively associated with each other (NEMESIS: PRS and affective dysregulation: p = 0.030; CA and affective dysregulation: p < 0.001; EUGEI: PRS and affective dysregulation: p = 0.025; CA and affective dysregulation: p < 0.001). In NEMESIS-2, there was evidence that the association between affective dysregulation and psychosis phenotypes was moderated by PRS. This was apparent for the phenotype of 1918 Jim van Os et al. Table 6. EUGEI: risk of psychosis admixture as a function of combinations of binary affective dysregulation (Cape depression 80th percentile) and childhood adversity (80th percentile cut-off) Phenotype Delusions and hallucinationsa Risk OR CA only 2.29 Hallucinations Delusionsa 1.77 p N 2.96 0.000 3627 AD only 6.95 5.44 8.87 0.000 CA + AD 14.67 11.38 18.92 0.000 6.44 3.10 9.78 0.000 RERI a 95% CI CA only 1.87 1.38 2.52 0.000 AD only 3.83 2.93 5.02 0.000 CA + AD 5.24 3.97 6.90 0.000 RERI 0.54 -0.90 1.97 0.465 CA only 2.30 1.77 2.99 0.000 AD only 7.40 5.78 9.46 0.000 CA + AD 14.13 10.96 18.21 0.000 5.43 2.25 8.61 0.001 RERI 3624 3627 OR, odds ratio; 95% CI, 95% confidence interval; N, number of observations in analysis; CA, childhood adversity; AD, affective dysregulation (Cape depression dimension 80% control cut-off); RERI, relative excess risk due to interaction. aDelusions and hallucinations: Cape positive dimension 80% control cut-off; Delusions: Cape delusions 80% control cut-off; Hallucinations: any rating of Cape hallucinations. delusions and hallucinations (RERI = 1.01; 95% CI 0.06–1.97), and for the phenotype of delusions (RERI = 1.74, 95% CI 0.58– 2.91) but not for hallucinations (RERI = 0.65, 95% CI −0.54 to 1.85) (Table 3, Fig. 2). Similar results were apparent in the EUGEI sample (RERI delusions and hallucinations: 3.39, 95% CI 0.03–6.75; RERI delusions: 4.16, 95% CI 0.69–7.63; RERI hallucinations: −0.37, 95% CI −1.57 to 0.83) (Table 4, Fig. 3). There was similar and stronger evidence that the association between affective dysregulation and psychosis phenotypes was moderated by CA. In NEMESIS-2, this was evident for all psychosis outcomes (RERI delusions and hallucinations: 3.02, 95% CI 2.04–4.01; RERI delusions: 3.79, 95% CI 2.59–5.00; RERI hallucinations: 2.46, 95% CI 1.21–3.71) (Table 5, Fig. 4). Similar results were apparent in EUGEI (RERI delusions and hallucinations: 6.44, 95% CI 3.10–9.78; RERI delusions: 5.43, 95% CI 2.25– 8.61; RERI hallucinations: 0.54, 95% CI −0.90 to 1.97) (Table 6, Fig. 5). Sensitivity analyses showed results with binary PRS measures were consistent across the different cut-off values (online Supplementary Figs S6 and S7). Discussion Findings We found, and replicated, that the association between PRS-SZ and CA on the one hand and psychosis outcomes on the other was contingent on the co-presence of affective dysregulation, which suggests that these risks may be mediated by an affective pathway, through which particularly delusional ideation may arise (Freeman et al., 2013; Garety et al., 2005; Krabbendam & van Os, 2005; Upthegrove et al., 2017). These findings may help explain the non-specific, transdiagnostic nature of the risk associated with PRS-SZ and CA and the strong connections between affective dysregulation and psychosis across the spectrum of psychotic disorders and the expression of subthreshold psychotic experiences (Hafner et al., 2005; Upthegrove et al., 2017; van Os & Reininghaus, 2016). The findings lend credence to the suggestion https://doi.org/10.1017/S0033291720003748 Published online by Cambridge University Press by Upthegrove and colleagues, that depression may be ‘more than comorbidity, and that increased effective therapeutic attention to mood symptoms will be needed to improve outcomes and to support prevention’ (Upthegrove et al., 2017). This suggestion concurs with a growing body of literature showing that psychosis arises as a result of worsening non-psychotic affective psychopathology (Guloksuz et al., 2015, 2016; van Rossum et al., 2011) and that the pathway from environmental risk to psychosis involves affective processes (Pries et al., 2018; Radhakrishnan et al., 2019; Reininghaus et al., 2016a, 2016b). Recent research has confirmed that high rates of affective symptoms in early psychosis require focussed attention on specific therapeutic options for these (Wilson et al., 2020). Potential therapeutic targets may be found in constructs that research suggest may lie at the interface of the dynamics between mood and psychosis such as emotion dysregulation (Liu, Chan, Chong, Subramaniam, & Mahendran, 2019), level of anticipatory pleasure for future experiences (Hallford & Sharma, 2019) and cognitive styles shaping response to early symptoms of affective dysregulation (Rauschenberg et al., 2020; Reininghaus et al., 2019). Affective dysregulation as a core feature of psychosis Much of the focus of research in clinical psychosis syndromes is on the 30% of patients with a relatively unfavourable prognosis, captured under the diagnosis of ‘schizophrenia’, of which cognitive dysfunction is considered a core feature (Guloksuz & van Os, 2018; Perala et al., 2007). However, like most measures of psychopathology, cognitive alterations are transdiagnostic (Millan et al., 2012), and cognition in patients with schizophrenia is more strongly associated with polygenic risk that indexes cognitive traits in the general population than polygenic risk from mental disorders (Richards et al., 2020). In other words, lower cognitive ability, distributed in the general population, may predict poorer outcome across mental disorders, which is why it would feature – somewhat tautologically – relatively prominently in the 30% of patients in the psychosis spectrum presenting with the poorest prognosis. Traditionally, affective dysregulation has 1919 Psychological Medicine Similarly, it is possible that other genetic influences may be more productively uncovered if analyses are stratified by other possible pathways, for example those involving cognitive and motivational factors, that research suggest may also moderate the impact of genetic risk on psychosis outcomes (Pries et al., 2018). Delusions, hallucinations and differential mediation by affective dysregulation Fig. 5. EUGEI: additive interaction effects of affective dysregulation (AD; 80th percentile cut-off) and childhood adversity (CA, 80th percentile cut-off) in models of psychosis phenotypes; RERI – relative excess risk due to interaction. received much less attention in research on diagnostic categories like schizophrenia (Garety, Kuipers, Fowler, Freeman, & Bebbington, 2001) even though it has a similar unfavourable effect on outcome (McGinty & Upthegrove, 2020). Review of treatment guidelines indicates a dearth of approaches other than prescription of antidepressant medications (Donde, Vignaud, Poulet, Brunelin, & Haesebaert, 2018). The current results concur with previous suggestions that affective dysregulation, in particular depression, may be a fundamental feature of psychosis rather than a comorbidity phenomenon (Upthegrove et al., 2017). Indeed, the findings suggest that psychosis spectrum may be best framed as an outcome of developmental vulnerability that can become associated with need for care through an affective pathway. Although this may not be the only pathway, a more formal acknowledgment of the role of affective dysregulation in psychosis would help to reposition diagnostic framing, treatment focus and research. The findings also have implications for research, as the association between PRS-SZ and psychosis outcomes may be more productively investigated if stratified by evidence of affective dysregulation. https://doi.org/10.1017/S0033291720003748 Published online by Cambridge University Press The results suggest that if psychosis aetiology in part depends on an affective pathway, this may apply to delusional ideation more than to hallucinatory experiences, particularly as regards genetic aetiology. Main effects were observed for affective dysregulation and CA for all three psychosis outcomes, but not for PRS. In addition, evidence for CA mediation by affective dysregulation was evident for both delusions and hallucinations (although not replicated across both samples), whereas for PRS this was only evident for delusions. These findings suggest a degree of dissociation between genetic and non-genetic aetiological factors in the degree of mediation by affective dysregulation, showing as divergence in results for hallucinations and delusions. It has been suggested that hallucinations may represent the ‘primary’ experience of aberrant salience that some suggest may be associated with underlying biological mechanisms (Howes & Murray, 2014). Delusional ideation may, to a degree, be secondary to hallucinatory experiences (Krabbendam et al., 2004; Maher, 2006), depending, amongst others, on the level of genetic and non-genetic-induced affective dysregulation (Howes & Murray, 2014; Smeets et al., 2012; Smeets, Lataster, Viechtbauer, & Delespaul, 2015). This may explain why for PRS, in the absence of a main effect on psychosis outcomes, mediation by affective dysregulation was limited to delusions, given the role of emotional biases in the onset of secondary delusions. For CA, the main effect on all psychosis outcomes may either depend more on affective dysregulation, or depend on it in a different fashion, causing it to differ from the pattern of results seen for PRS. However, more work is necessary to verify to what degree the level of affective mediation of genetic aetiology in models of psychosis truly differs between delusions and hallucinations, and what the possible underlying mechanisms of this divergence may be. It could also be argued that hallucinations are less prevalent than delusions, resulting in lower power to detect association and thus explaining the divergent findings. However, both in NEMESIS-2 as in EUGEI, the prevalence of delusions and hallucinations as defined for these analyses was approximately similar (NEMESIS-2: 6% and 5%, respectively; EUGEI: 25% and 20%, respectively). In addition, if lack of power was an issue, effect sizes for hallucinations might still be similar to those observed for delusions, which was not the case. Methodological issues Power was low for the analyses with PRS-SZ. The findings suggest that PRS-SZ effect sizes differ as a function of co-presence of affective dysregulation, but this effect was only significant for delusional ideation and effect sizes of PRS-SZ were low. Further replication is therefore required. Our measure of affective dysregulation was limited to measures of depression. Arguably measures of mania and/or anxiety could have been included, or examined separately for similar interactive effects in the models presented here. Future analyses may address this issue. 1920 Supplementary material. The supplementary material for this article can be found at https://doi.org/10.1017/S0033291720003748. Financial support. NEMESIS-2 is conducted by the Netherlands Institute of Mental Health and Addiction (Trimbos Institute) in Utrecht. Financial support has been received from the Ministry of Health, Welfare and Sport, with supplementary support from the Netherlands Organization for Health Research and Development (ZonMw). This work was supported by the European Community’s Seventh Framework Program under grant agreement No. HEALTH-F2-2009-241909 (Project EU-GEI). These funding sources had no further role in study design; in the collection, analysis and interpretation of data; in the writing of the report; or in the decision to submit the paper for publication. Bart PF Rutten was funded by a VIDI award number 91718336 from the Netherlands Scientific Organisation. Drs Guloksuz and van Os are supported by the Ophelia research project, ZonMw grant number: 636340001. Dr O’Donovan is supported by MRC programme grant (G08005009) and an MRC Centre grant (MR/L010305/1). Conflict of interest. None. 1 Department of Psychiatry, UMC Utrecht Brain Centre, University Medical Centre Utrecht, Utrecht University, Utrecht, The Netherlands; 2Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht University Medical Centre, Maastricht, The Netherlands; 3 Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, UK; 4Department of Epidemiology, Netherlands Institute of Mental Health and Addiction, Utrecht, The Netherlands; 5FACT, Mondriaan Mental Health, Maastricht, The Netherlands; 6Department of Translational Neuroscience, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands; 7GGNet Mental Health, Apeldoorn, The Netherlands; 8MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, UK; 9 Department of Psychiatry, Faculty of Medicine, Dokuz Eylul University, Izmir, Turkey; 10Department of Psychiatry, Faculty of Medicine, Adnan Menderes University, Aydin, Turkey; 11Department of Neuroscience, Graduate School of Health Sciences, Dokuz Eylul University, Izmir, Turkey; 12Department of Physiology, School of Medicine, Ankara University, Ankara, Turkey; 13Brain Research Center, Ankara University, Ankara, Turkey; 14Department of Psychology, Middle East Technical University, Ankara, Turkey; 15Turkish Federation of Schizophrenia Associations, Ankara, Turkey; 16Department of Psychiatry, Faculty of Medicine, Dokuz Eylul University, Izmir, Turkey (Discharged by statutory decree No:701 at 8 July 2018 because of signing ‘Peace Petition’); 17Güven Çayyolu Healthcare Campus, Ankara, Turkey; 18Atatürk Research and Training Hospital Psychiatry Clinic, Ankara, Turkey; 19Faculty of Medicine, University of Belgrade, Belgrade, Serbia; 20Institute of Mental Health, Belgrade, Serbia; 21Barcelona Clinic Schizophrenia Unit, Neuroscience Institute, Hospital Clinic of Barcelona, University of Barcelona, Barcelona, Spain; 22 Institut d’Investigacions Biomèdiques August Pi I Sunyer, Barcelona, Spain; 23 Biomedical Research Networking Centre in Mental Health (CIBERSAM), Spain; 24 Department of Psychiatry, School of Medicine, University of Oviedo, Oviedo, Spain; 25Instituto de Investigación Sanitaria del Principado de Asturias, Oviedo, Spain; 26Mental Health Services of Principado de Asturias, Oviedo, Spain; 27 Department of Psychiatry, Hospital Clínico Universitario de Valencia, School of Medicine, Universidad de Valencia, Valencia, Spain; 28Department of Psychiatry, Hospital Virgen de la Luz, Cuenca, Spain; 29Universidad de CastillaLa Mancha, Health and Social Research Center, Cuenca, Spain; 30Department of Psychiatry, Instituto de Investigación Sanitaria, Complejo Hospitalario Universitario de Santiago de Compostela, Santiago de Compostela, Spain; 31 Grupo de Medicina Genómica, Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), Universidad de Santiago de Compostela, Santiago de Compostela, Spain; 32Fundación Pública Galega de Medicina Xenómica (SERGAS), IDIS, Santiago de Compostela, Spain; 33Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, IiSGM, School of Medicine, Universidad Complutense, Madrid, Spain; 34Department of Psychiatry, School of Medicine, Ankara University, Ankara, Turkey; 35Department of Psychiatry, Faculty of Medicine, Istanbul University, Istanbul, Turkey; 36Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA https://doi.org/10.1017/S0033291720003748 Published online by Cambridge University Press Jim van Os et al. 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