Genetic risk for schizophrenia and autism, social impairment and developmental pathways to psychosis

Abstract

While psychotic experiences (PEs) are assumed to represent psychosis liability, general population studies have not been able to establish significant associations between polygenic risk scores (PRS) and PEs. Previous work suggests that PEs may only represent significant risk when accompanied by social impairment. Leveraging data from the large longitudinal IMAGEN cohort, including 2096 14-year old adolescents that were followed-up to age 18, we tested whether the association between polygenic risk and PEs is mediated by (increasing) impairments in social functioning and social cognitive processes. Using structural equation modeling (SEM) for the subset of participants (n = 643) with complete baseline and follow-up data, we examined pathways to PEs. We found that high polygenic risk for schizophrenia (p = 0.014), reduced brain activity to emotional stimuli (p = 0.009) and social impairments in late adolescence (p < 0.001; controlling for functioning in early adolescence) each independently contributed to the severity of PEs at age 18. The pathway between polygenic risk for autism spectrum disorder and PEs was mediated by social impairments in late adolescence (indirect pathway; p = 0.025). These findings point to multiple direct and indirect pathways to PEs, suggesting that different processes are in play, depending on genetic loading, and environment. Our results suggest that treatments targeting prevention of social impairment may be particularly promising for individuals at genetic risk for autism in order to minimize risk for psychosis.

Why Psychotropic Drugs Don't Cure Mental Illness—But Should They?

“…This is largely due to the fact that the descriptions of the relationships between the respective physical and mental phenomena, as they are classified, measured and understood by currently available methodologies, reflect only statistical associations between imperfectly aligned terminologies, and certainly do not satisfactorily explain the causal relations between them. For instance, recent path analyses from a large naturalistic cohort show that genetic, brain imaging and psychological (e.g., self-reports) measures obtained at age 14 have an independent (instead of linear) contribution to the development of psychotic experience at age 18 (10). In other words, the anticipated relation of genetic vulnerability leading to abnormalities of brain function leading to altered self-reports of behavior leading to psychotic experiences was not observed, suggesting that molecular, physiological and psychological phenomena are somewhat independent.…”

Clinical, Epidemiological and Experimental Approaches to Assess Adverse Health Outcomes of Indoor Biomass Smoke Exposure: Conclusions from An Indo-Swedish Workshop in Mysuru, January 2020

“…Although the phytochemical index provides only a rough approximation of the quantity or quality of phytochemical nutrition, it nonetheless could aid in exploring the health consequences of diets high in phytochemical-rich plant foods in the case of a BMS-exposed population. Assessing the phytochemical index [40,41] in the regular diet of a BMS-exposed population, which may be endemic to a particular region, may provide an important insight into plausible protective mechanisms. However, studies on the protective effects of phytochemicals on chronic lung diseases like COPD remains largely unexplored.…”

“…In studies related to examining the relationship of environmental exposures (e.g., BMS exposure), it is desirable to conduct other contemporaneous assessments of sociodemographic factors (like socioeconomic position—both individuals and neighborhoods, social mobility, cohesion, networks, and relationships) and lifestyle factors (like physical activity, use of alcohol, smoking, and dietary factors); blood tests (for glucose tolerance, insulin resistance, diabetes, dyslipidemia, anemia, vitamin B12 and folate deficiency, and hyperhomocysteinemia), physical health assessments (for hypertension, coronary heart disease, lung disorders, and stroke) and a genetic assay (e.g., for ApoE lipoprotein). These additional assessments will allow researchers to examine the mediating and/or confounding or interacting effects of a range of social, cardiometabolic, lung, genetic and life factors on the BMS exposure and cognitive function/decline by employing advanced statistical analyses like Structural Equation Modeling and Pathway Analysis [ 41 ].…”

Treatment-Resistant Schizophrenia: Insights From Genetic Studies and Machine Learning Approaches

“…In the last few years, PRS analysis has been successfully applied to the study of different psychiatric disorders (Bipolar Disorder and Schizophrenia Working Group of the Psychiatric Genomics Consortium, 2018; Kalman et al, 2018;Taylor et al, 2018; Richards et al, 2019). In the case of SCZ, PRS analysis has been used to evaluate how the polygenic burden can explain differences in specific symptoms (Wang et al, 2018; Anderson-Schmidt et al, 2019), functional and structural brain changes (Lieslehto et al, 2018; Ranlund et al, 2018; Velthorst et al, 2018), genetic overlap with other traits (International Consortium on Lithium Genetics (ConLi+Gen) et al, 2018), as well as gene co-expression networks in the brain (Radulescu et al, 2018). Studies investigating the potential value of PRS analysis to identify patients that are less likely to respond to treatment provided contrasting findings.…”