Bart P. F. Rutten

People with severe mental illness (SMI), including schizophrenia (SCZ) and bipolar disorder (BD), show elevated risk for somatic conditions and premature mortality. Both genetic liability and modifiable lifestyle factors have been implicated in this comorbidity, yet large-scale studies examining their independent and combined effects across multiple somatic conditions are lacking. We therefore examined whether modifiable lifestyle factors (i.e. smoking, alcohol intake, diet, and exercise) and polygenic risk scores (PRS) for SCZ and BD are associated with the risk of developing somatic illnesses. Data were drawn from 315,313 unrelated individuals in the UK Biobank cohort, with a median follow up of 14 years. Cox proportional hazards models were used to estimate the independent, joint, and interaction effects of PRS-SCZ and PRS-BD and lifestyle factors on the incidence of nine chronic somatic illnesses. Healthy lifestyle was associated with lower risks of type 2 diabetes (T2D), cardiovascular disease (CVD), dementia, rheumatoid arthritis, asthma, chronic liver disease (CLD), and cancer (HRs ranging from 0.43 to 0.80). In addition, higher PRS for SCZ was associated with increased risks of asthma and dementia and lower risk of T2D, while higher PRS for BD was associated with increased risk of T2D. Moreover, likelihood ratio tests confirmed that PRS-SCZ and PRS-BD each contribute independent information to the joint Cox model for T2D, indicating that the opposing directions of their associations with T2D risk are not driven by shared genetic variance between the two scores. Finally, individuals with a healthy lifestyle consistently showed lower risks of CLD, T2D, CVD, and dementia across genetic risk strata; however, no significant additive interaction was observed between genetic risk and lifestyle factors. In conclusion, these findings indicate that genetic liabilities for SCZ and BD show outcome-specific associations with somatic illnesses, while a healthy lifestyle is associated with lower somatic illness risk regardless of genetic risk level.

Most genetic variants associated with complex heritability phenotypes lie in non-coding regions and are thought to influence disease risk by regulating gene expression. However, most transcriptome-wide association approaches primarily model local ( cis ) genetic effects, leaving much of gene regulation unexplained. Here, we show that incorporating distal ( trans ) regulatory effects improves the prediction of gene expression and the identification of disease-associated genes. Using RNA sequencing data from six human post-mortem brain regions, we developed INGENE and MODULE, two models capturing the combined influence of candidate trans- acting variants within gene coexpression networks. Integrating these models with conventional cis -based predictors improved gene expression imputation (maximum likelihood estimation, α = 0.05) for 18,744 genes across regions. Applying this framework to Psychiatric Genomics Consortium wave 3 genotypes identified 766 genes associated with schizophrenia ( P _FDR < 0.01), including 641 not previously reported by transcriptome-wide analyses. These findings highlight the contribution of distal regulatory mechanisms and gene network interactions to schizophrenia risk.

Background: The duration of untreated psychosis (DUP) is still considerably long in patients with psychotic disorders worldwide. Social determinants, such as the socioeconomic status, can influence DUP, exacerbating health inequalities in access to timely care. We investigated whether subpopulations with shared characteristics are associated with longer DUP. Methods: We performed latent class analyses to investigate whether classes with shared configurations of social and substance use-related risks can be identified in two large cohorts with psychotic disorders: N = 780 patients from the GROUP project and N = 847 patients from the EU-GEI project. Subsequently, we conducted survival analyses to analyze whether identified classes are associated with DUP. Results: We identified three classes in both samples. Membership of the class with predominantly younger men, higher proportion of cannabis use, and supported living was associated with longer DUP compared with a class with predominantly White ethnicity, higher education, and current employment in GROUP (HR = 1.28, 95% CI: 1.06–1.56, p = .011) and in EU-GEI (HR = 1.27, 95% CI: 1.07–1.51, p = .007). In GROUP, membership of a third class with predominantly White women, without cannabis use, was associated with the shortest DUP (HR = 0.78, 95% CI: 0.63–0.95, p = .016). Conclusions: Results suggest that specific populations differ in their risk distributions for prolonged DUP and highlight the importance of considering configurations of social determinants in context. Public mental health programs need to establish their differential impact for diverse populations and facilitate more targeted pathways to care.

Background: The vulnerability–stress framework guiding gene–environment interaction (GxE) research overlooks the role of positive experiences. The Differential Susceptibility (DS) model offers a broader perspective, suggesting that individuals vary in sensitivity to both negative and positive environments. This study aimed at replicating previous DS research by examining interactions between polygenic scores for environmental sensitivity (PGS-ES) and positive and negative early exposures on subclinical psychosis and internalizing psychopathology, functioning, and wellbeing. Methods: The sample consisted of 638 twins from the first wave of the TwinssCan study, a general population twin cohort. PGS-ES and adversity, bullying and positive experiences in childhood were collected, along with assessments of psychotic, affective, functioning, and positive mental health. GxE interactions were tested under a competitive–confirmatory approach. Results: DS effects were found for the interactions between PGS-ES and all environmental exposures on schizotypic eccentricity and functioning. Adolescents with high genetic sensitivity were rated as more eccentric and less functional under childhood adversity but were rated as less eccentric and better adjusted under childhood favorable conditions. DS also resulted from the interaction between PGS-ES and positive childhood on social coping. No significant models emerged for internalizing or wellbeing. Conclusion: Findings overall supported DS, indicating that genetic sensitivity to the environment operates in a “for better and for worse” manner depending on the quality of environmental exposures. It extends initial evidence that DS applies to nonclinical psychosis expression and highlights the importance of considering the full spectrum of environmental conditions to understand both risk and opportunity factors in GxE.

Non-coding genetic variants statistically associated with complex heritability phenotypes are thought to act primarily through transcriptome regulatory mechanisms. Predictions of gene expression in tissue like the human brain traditionally rely primarily on cis -eQTLs. Here, we introduce INGENE and MODULE, trans -eQTLs models designed to enhance the prediction of gene expression by capturing the collective impact of candidate trans -eQTLs acting within co-expression networks. Exploiting RNA-seq data in six post-mortem brain regions (amygdala, caudate nucleus, dorsal/subgenual anterior cingulate cortex, dorsolateral prefrontal cortex, and hippocampus), we validate our models on two testing datasets, demonstrating increased gene predictability compared to both an original cis -based model and to EpiXcan, the leading benchmark in cis -model performance. Integration of cis - and trans -predictions significantly improves gene-level expression imputation (MLE α= 0.05) for 18,744 genes across the six brain regions considered. Applying cis and trans models to PGC wave 3 genotypes identifies 766 SCZ-associated genes across brain regions (pFDR < .01), emphasizing the complementary nature of cis and trans predictions in trait association discovery. Of these genes, 641 represent novel transcriptome-wide associations with schizophrenia, highlighting the role of trans -heritability and genetic interactions underlying risk for this disorder, in addition to further supporting 125 previous candidates.

Background: Variation in the Apolipoprotein E (APOE) gene is a key genetic determinant of Alzheimer’s disease (AD) risk and is associated with AD-related neuroimaging changes. Lifestyle factors are also associated with AD risk and may interact with APOE to influence neuroimaging biomarkers. However, evidence on these interactions remains limited and inconsistent, particularly regarding composite healthy lifestyle scores in large-scale cohorts. Methods: We analyzed neuroimaging data from 21,014 UK Biobank participants (mean age: 55) to examine neuroimaging markers relevant to AD and assess the independent and interactive effects of APOE ε2/ ε4 dosage and healthy lifestyle risk factors. These biomarkers included diffusion tensor imaging (DTI) metrics-fractional anisotropy (FA) and mean diffusivity (MD), white matter hyperintensity (WMH) burden, and volumetric measures of medial temporal lobe regions such as the entorhinal cortex, parahippocampal region, amygdala, and hippocampus. Results: In the APOE dosage model, ε4 dosage was associated with lower bilateral hippocampal volume, left parahippocampal volume and lower WM integrity, particularly in the posterior thalamic radiation and cingulum-hippocampus and sagittal stratum. A higher healthy lifestyle score was broadly associated with lower WMH burden and higher WM integrity. No statistically significant evidence was found for effect modification by lifestyle on the association between APOE and MRI markers. Conclusions: Our results do not provide support for the idea that healthy lifestyle factors buffer or mitigate the detrimental effects of the APOE dosage on MRI markers, but rather support a primarily outright association between APOE ε4 dosage and worse MRI markers.

Purpose: To investigate sex-dependent effects of polygenic risk (PRS-SCZ) and exposome score (ES-SCZ) for schizophrenia, both independently and jointly, on distressing psychotic experiences (PEs) in early adolescents. Method: Baseline to 3-year follow-up data of the Adolescent Brain and Cognitive Development Study (ABCD) were used. PRS-SCZ and ES-SCZ were calculated to assess cumulative genetic and environmental (childhood adversity, cannabis use, hearing impairment, and winter births) risk for schizophrenia, respectively. The primary outcome was past-month distressing PEs at the 3-year follow-up. Secondary outcomes included distressing PEs across four yearly assessments: lifetime (≥1 wave), repeated (≥2 or ≥3 waves), and persisting (≥4 waves). Sex-stratified multilevel logistic regression models were used to test the independent and joint associations of binary modes (>75th percentile) of PRS-SCZ (PRS-SCZ ₇₅ ) and ES-SCZ (ES-SCZ ₇₅ ) on the outcomes. As sensitivity analysis, the sex-stratified analyses were repeated on a randomly selected unrelated sample, and the coefficients of males and females were compared. Results: PRS-SCZ ₇₅ was not associated with past-month distressing PEs in either sex but significantly associated with lifetime and repeated (≥2 waves) distressing PEs only in females. However, a significant sex difference was only detected for lifetime distressing PEs. ES-SCZ ₇₅ was significantly associated with past-month distressing PEs and all secondary outcomes without significant sex differences. No additive interaction between ES-SCZ ₇₅ and PRS-SCZ ₇₅ was detected for any PE outcomes. Conclusion: The influence of genomic risk for schizophrenia on distressing PEs might be sex-dependent, whereas that of the exposomic risk was universal in early adolescence. Further studies in larger samples are needed. Article Highlights: ▢ Genomic risks for schizophrenia were associated with distressing PEs only in female adolescents. ▢ Environmental risks for schizophrenia were associated with distressing PEs regardless of sex in early adolescence. ▢ Dose-response effects of the environmental risks on PE persistence support them as potential preventive targets. ▢ A sex-informed approach is needed to better understand genetic and environmental contributions to psychosis expression.

This study aimed to assess the independent and joint associations of genomic and exposomic liabilities for schizophrenia with distressing psychotic experiences (PEs) and their persistence in early adolescence. The Adolescent Brain and Cognitive Development Study data from children with European ancestry were used (N = 5122). The primary outcome was past-month distressing PEs at the 3-year follow-up. Secondary outcomes were distressing PEs at varying cutoffs of persistence. Multilevel logistic regression models were used to test the associations of binary modes (>75th percentile) of polygenic risk score for schizophrenia (PRS-SCZ₇₅) and exposome score for schizophrenia (ES-SCZ₇₅) on the outcomes. Relative excess risk due to interaction (RERI) calculation indicated additive interaction. When analyzed independently, PRS-SCZ₇₅ was not significantly associated with past-month distressing PEs but with lifetime (OR 1.29 [95% CI 1.08, 1.53]) and repeating distressing PEs ≥2 waves (OR 1.34 [95% CI 1.08, 1.65]); whereas, ES-SCZ₇₅ was consistently associated with all outcomes, with increasing strength of association as a function of PEs persistence (one wave: OR 2.77 [95% CI 2.31, 3.31]; two waves: OR 3.16 [95% CI 2.54, 3.93]; three waves: OR 3.93 [95% CI 2.86, 5.40]; four waves: OR 3.65 [95% CI 2.34, 5.70]). When considered jointly, ES-SCZ₇₅ and PRS-SCZ₇₅ did not additively interact to predict past-month distressing PEs but showed significant additive interactions for lifetime (RERI = 1.26 [95%CI 0.14, 2.38]) and repeating distressing PEs ≥2 waves (RERI = 1.79 [95%CI 0.35, 3.23]). Genomic and exposomic liabilities for schizophrenia were independently and jointly associated with distressing PEs and their persistence in early adolescence.