Estimating Exposome Score for Schizophrenia Using Predictive Modeling Approach in Two Independent Samples: The Results from the EUGEI Study

Exposures constitute a dense network of the environment: exposome. Here, we argue for embracing the exposome paradigm to investigate the sum of nongenetic "risk" and show how predictive modeling approaches can be used to construct an exposome score (ES; an aggregated score of exposures) for schizophrenia. The training dataset consisted of patients with schizophrenia and controls, whereas the independent validation dataset consisted of patients, their unaffected siblings, and controls. Binary exposures were cannabis use, hearing impairment, winter birth, bullying, and emotional, physical, and sexual abuse along with physical and emotional neglect. We applied logistic regression (LR), Gaussian Naive Bayes (GNB), the least absolute shrinkage and selection operator (LASSO), and Ridge penalized classification models to the training dataset. ESs, the sum of weighted exposures based on coefficients from each model, were calculated in the validation dataset. In addition, we estimated ES based on meta-analyses and a simple sum score of exposures. Accuracy, sensitivity, specificity, area under the receiver operating characteristic, and Nagelkerke's R² were compared. The ES_{Meta-analyses} performed the worst, whereas the sum score and the ES_GNB were worse than the ES_LR that performed similar to the ES_LASSO and ES_RIDGE. The ES_LR distinguished patients from controls (odds ratio [OR] = 1.94, P <. 001), patients from siblings (OR = 1.58, P <. 001), and siblings from controls (OR = 1.21, P =. 001). An increase in ES_LR was associated with a gradient increase of schizophrenia risk. In reference to the remaining fractions, the ES_LR at top 30%, 20%, and 10% of the control distribution yielded ORs of 3.72, 3.74, and 4.77, respectively. Our findings demonstrate that predictive modeling approaches can be harnessed to evaluate the exposome.