Antipsychotic-induced weight gain in psychosis: causal mediation analysis and feasibility study of causal actionable prediction model development using counterfactuals to target obesity.

内容摘要

People with psychosis have a life expectancy that is reduced by 15 years, mainly owing to preventable physical illnesses of which obesity is a precursor. Obesity is three times more common in individuals with psychosis, and antipsychotics are an important cause. Prediction could individualise obesity treatment, but current models are not fully actionable for individuals. To test whether antipsychotic-induced weight increase at 1 year is causally mediated by weight change in the first 12 weeks of treatment, and then develop and internally validate a causal actionable prediction pathway to prevent antipsychotic-induced obesity. This was a post hoc analysis of a clinical trial of olanzapine versus haloperidol which recruited 263 participants with first-episode psychosis. We conducted two distinct analyses: causal mediation and prediction modelling, within which there were two sequential models (a baseline model to predict 12-week outcome and a 12-week model to predict 1-year outcome), followed by counterfactual prediction. In the first analysis, we used parallel causal mediation analysis to determine the natural direct and indirect and total effects of antipsychotic choice on weight in 97 participants, considering two mediators: weight change from 0 to 12 weeks, and weight change from 12 to 52 weeks. In the second analysis, we first developed a baseline causal actionable prediction model to predict weight gain at 12 weeks in 172 participants and then a 12-week model to predict obesity at 1 year in 97 of the participants. Finally, we demonstrated counterfactual prediction. Antipsychotic-induced weight gain at 1 year appeared to be causally mediated by weight change during the first 12 weeks of treatment (indirect effect 5.70; 95% CI 2.83 to 8.66). At internal validation, the discrimination c-statistic for the baseline causal actionable prediction model was 0.728 (95% CI 0.661 to 0.801), and the calibration slope was 0.768 (95% CI 0.436 to 1.21). For the 12-week model, the c-statistic was 0.904 (95% CI 0.820 to 0.961), and the calibration slope was 0.601 (95% CI -0.0633 to 1.21). We used the models to predict the counterfactual outcomes of antipsychotic choice and 12-week weight change. Our results show that it may be early rather than later weight change that causally mediates antipsychotic-induced weight gain at 1 year. They also demonstrate the potential for causal actionable prediction of counterfactuals for true precision medicine, although this is tempered by the feasibility scope of this study and small sample size. Our results are hypothesis-generating and not yet clinically deployable.