A multivariate prediction model of schizophrenia

Univariate prediction models of schizophrenia may be adequate for hypothesis testing but are narrowly focused and limited in predictive efficacy. Therefore, we used a multivariate design to maximize the prediction of schizophrenia from premorbid measures and to evaluate the relative importance of various predictors. Two hundred twelve Danish subjects with at least one parent diagnosed in the schizophrenia spectrum (high risk) and 99 matched subjects with no such parent (low risk) were assessed on 25 premorbid variables in seven domains (genetic risk, birth factors, autonomic responsiveness, cognitive functioning, rearing environment, personality, and school behavior) when the subjects averaged 15 years of age. Twenty-five years later, 33 subjects had received lifetime diagnoses of schizophrenia. Discriminant function analyses were used to discriminate schizophrenia outcomes from no mental illness and nonschizophrenia outcomes on the basis of premorbid measures. Regardless of the comparison group used, schizophrenia was predicted by the interaction of genetic risk with rearing environment, and disruptive school behavior. Within the high-risk group, two-thirds of schizophrenia outcomes were correctly predicted by these premorbid measures; three-quarters of those with no mental illness were also correctly predicted. Prediction was enhanced among those with two schizophrenia spectrum parents, lending support to a multiplicative gene x environment model. Implications for early identification/primary prevention efforts are discussed.