Development of the Korea-Polyenvironmental Risk Score for Psychosis

Abstract

Objective Comprehensive understanding of polyenvironmental risk factors for the development of psychosis is important. Based on a review of related evidence, we developed the Korea Polyenvironmental Risk Score (K-PERS) for psychosis. We investigated whether the K-PERS can differentiate patients with schizophrenia spectrum disorders (SSDs) from healthy controls (HCs). Methods We reviewed existing tools for measuring polyenvironmental risk factors for psychosis, including the Maudsley Environmental Risk Score (ERS), polyenviromic risk score (PERS), and Psychosis Polyrisk Score (PPS). Using odds ratios and relative risks for Western studies and the "population proportion" (PP) of risk factors for Korean data, we developed the K-PERS, and compared the scores thereon between patients with SSDs and HCs. In addition, correlation was performed between the K-PERS and Positive and Negative Syndrome Scale (PANSS). Results We first constructed the "K-PERS-I, " comprising five factors based on the PPS, and then the "K-PERS-II" comprising six factors based on the ERS. The instruments accurately predicted participants' status (case vs. control). In addition, the K-PERS-I and -II scores exhibited significant negative correlations with the negative symptom factor score of the PANSS. ConclusionThe K-PERS is the first comprehensive tool developed based on PP data obtained from Korean studies that measures polyenvironmental risk factors for psychosis. Using pilot data, the K-PERS predicted patient status (SSD vs. HC). Further research is warranted to examine the relationship of K-PERS scores with clinical outcomes of psychosis and schizophrenia.

The Translational Future of Stress Neurobiology and Psychosis Vulnerability: A Review of the Evidence

“…Better representation of interactions between risk factors in a multivariable approach ( e.g ., combining information on momentary stress with trauma history and cortisol levels) is essential for future research, particularly when considering multiple measures of stress, which will be highly correlated. Preliminary work has been encouraging [ 290 ], for example using polyenvironmental risk scores [ 285 , 286 , 291 ] but large, generalisable samples are needed to develop and validate these tools. External validation studies, where independent datasets are used to replicate model performance, are rare in psychiatry [ 288 ] but are needed in order to provide evidence that precision psychiatry tools can perform well in multiple settings and are viable for use in real-world clinical care [ 292 ].…”

Prenatal environmental exposures associated with sex differences in childhood obesity and neurodevelopment

“…In this study, we observed that the combination of the specific levels for the four exposures maximizes the differences in obesity risk between girls and boys. Previous studies have already suggested that better prediction of an outcome can be obtained from the aggregation of multiple environmental factors into risk scores [ 49 , 50 ] or the use of mixture models [ 51 ]. In line with this, we used causal inference for classifying the individuals in two environments (E0 and E1) based on the combination of the four exposures.…”