Search

Background: Evidence suggests that the impact of adult stressful life events (SLE) on health depends on an individual’s environmental and genetic predisposition to psychopathology. However, the influence of the genomic and exposomic (the sum of exposures) liabilities for schizophrenia on the association between SLE and health outcomes in the general population has not been studied. The current longitudinal study therefore aims to evaluate whether the association of recent SLE with mental as well as physical health is moderated by polygenic risk score for schizophrenia (PRS-S) and the exposome score for schizophrenia (ES-S, an aggregated environmental exposure score, akin to PRS-S). Methods: Data from four waves of the Netherlands Mental Health Survey and Incidence Study-II (NEMESIS-II), a prospective survey in the Dutch general population aged 18 – 64, was used. The baseline data (T0) of NEMESIS-II, including 6646 participants, were collected from 2007 to 2009 and were followed up at year 3 (T1), year 6 (T2), and year 9 (T3). The Short-Form-36 Health Survey was used to repeatedly assess mental and physical health. Repeated assessment of SLE were based on the Brugha Life events section. Guided by our previous work, ES-S was constructed as the weighted sum of important baseline environmental factors, which were previously associated with schizophrenia. A subsample of 3099 participants (at T0) were genotyped and PRS-S was trained on the latest Psychiatric Genetics Consortium schizophrenia meta-analysis, adjusted for the first three population principal components. The analyses were conducted using random intercept multilevel regression models with age, sex, and education included as a priori covariates. Models were clustered for multiple assessments per individual, and the random intercept of mental or physical health at T-1 was added to the model to control for previous health status. We evaluated the main effects of SLE, ES-S, and PRS-S, and tested the moderating effects of ES-S and PRS-S on the impact of SLE on physical and mental health. As sensitivity analyses, we repeated the analyses using lagged SLE at T-1. Results: SLE and ES-S were associated with decreased mental (SLE: β = -2.53, P <0.001; ES-S: β = -2.93, P <0.001) and physical health (SLE: β = -2.62, P < 0.001; ES-S, β = -3.10, P < 0.001), while PRS-S was only associated with decreased mental health (β = -1.02, P <0.001) and not with physical health (β = -0.57, P = 0.061). ES-S moderated the association of SLE with mental health (β = -0.87, P = 0.002) but not with physical health (β = -0.60, P = 0.069). There was no evidence for gene–environment correlation between SLE and PRS-S (OR = 1.02, P = 0.527); and PRS-S did not moderate the association of SLE with mental (β = -0.38, P = 0.241) or physical health (β = -0.44, P = 0.252). The sensitivity analyses with lagged SLE at the previous time point converged with the results. SLE at T-1 was associated with mental (β = -0.90, P < 0.001) and physical health (β = -1.87, P < 0.001). ES-S moderated the association of SLE at T-1 with mental (β = -0.52 P = 0.029) but not physical health (β = -0.45, P = 0.116). PRS-S did not moderate the association of SLE at T-1 with mental (β = -0.03, P = 0.917) or physical health (β = 0.43, P = 0.233). Discussion: In support of the diathesis-stress model, we have provided novel evidence showing that exposure to adult SLE, particularly in individuals with high ES-S, is associated with decreased mental health. However, we did not find evidence for a gene-environment interaction in the context of SLExPRS-S. Our findings also add to the literature showing that genomic and exposomic liability for schizophrenia is pleiotropically associated with health outcomes in the general population. [ABSTRACT FROM AUTHOR]

Previously, we established an estimated exposome score for schizophrenia (ES-SCZ) as a cumulative measure of environmental liability for schizophrenia to use in gene-environment interaction studies and for risk stratification in population cohorts. Hereby, we examined the discriminative function of ES-SCZ for identifying individuals diagnosed with schizophrenia spectrum disorder in the general population by measuring the area under the receiver operating characteristic curve (AUC). Furthermore, we compared this ES-SCZ method to an environmental sum score (Esum-SCZ) and an aggregate environmental score weighted by the meta-analytical estimates (Emet-SCZ). We also estimated ORs and Nagelkerke's R2 for ES-SCZ in association with psychiatric diagnoses and other medical outcomes. ES-SCZ showed a good discriminative function (AUC = 0.84) and statistically significantly performed better than both Esum-SCZ (AUC = 0.80) and Emet-SCZ (AUC = 0.80). At optimal cut point, ES-SCZ showed similar performance in ruling out (LR- = 0.20) and ruling in (LR+ = 3.86) schizophrenia. ES-SCZ at optimal cut point showed also a progressively greater magnitude of association with increasing psychosis risk strata. Among all clinical outcomes, ES-SCZ was associated with schizophrenia diagnosis with the highest OR (2.76, P < .001) and greatest explained variance (R2 = 14.03%), followed by bipolar disorder (OR = 2.61, P < .001, R2 = 13.01%) and suicide plan (OR = 2.44, P < .001, R2 = 12.44%). Our findings from an epidemiologically representative general population cohort demonstrate that an aggregate environmental exposure score for schizophrenia constructed using a predictive modeling approach-ES-SCZ-has the potential to improve risk prediction and stratification for research purposes and may help gain insight into the multicausal etiology of psychopathology., (© The Author(s) 2020. Published by Oxford University Press on behalf of the Maryland Psychiatric Research Center.)

Identifying modifiable factors through environmental research may improve mental health outcomes. However, several challenges need to be addressed to optimize the chances of success. By analyzing the Netherlands Mental Health Survey and Incidence Study-2 data, we provide a data-driven illustration of how closely connected the exposures and the mental health outcomes are and how model and variable specifications produce "vibration of effects" (variation of results under multiple different model specifications). Interdependence of exposures is the rule rather than the exception. Therefore, exposure-wide systematic approaches are needed to separate genuine strong signals from selective reporting and dissect sources of heterogeneity. Pre-registration of protocols and analytical plans is still uncommon in environmental research. Different studies often present very different models, including different variables, despite examining the same outcome, even if consistent sets of variables and definitions are available. For datasets that are already collected (and often already analyzed), the exploratory nature of the work should be disclosed. Exploratory analysis should be separated from prospective confirmatory research with truly pre-specified analysis plans. In the era of big-data, where very low P values for trivial effects are detected, several safeguards may be considered to improve inferences, eg, lowering P-value thresholds, prioritizing effect sizes over significance, analyzing pre-specified falsification endpoints, and embracing alternative approaches like false discovery rates and Bayesian methods. Any claims for causality should be cautious and preferably avoided, until intervention effects have been validated. We hope the propositions for amendments presented here may help with meeting these pressing challenges.