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

Background: People with psychosis have a reduced life expectancy by 15 years, mainly due to preventable physical illnesses for which obesity is a precursor. Obesity is three times more common in psychosis and antipsychotics are an important cause. Prediction could individualise obesity treatment but current models are not fully actionable for individuals.Aims: We test whether antipsychotic-induced weight increase at 1 year is causally mediated by weight change in the first 12 weeks of treatment, rather than the following 40 weeks. We then develop and internally validate a causal actionable prediction pathway to prevent antipsychotic-induced obesity.Methods: We use parallel causal mediation analysis to determine the natural direct and indirect, and total effects of antipsychotic choice on weight in a trial of olanzapine versus haloperidol in 97 participants. We develop a baseline causal actionable prediction model to predict weight gain at 12 weeks in 172 participants, and a 12-week model to predict obesity at 1 year in 97 of the participants, and then demonstrate counterfactual prediction.Results: Antipsychotic-induced weight gain at 1 year appears to be causally mediated by weight change during the first 12 weeks 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 0.732 and the calibration slope was 0.768. For the 12-week model, the c-statistic was 0.914 and the calibration slope was 0.601. We use the models to predict the counterfactual outcomes of antipsychotic choice and 12-week weight change.Conclusions: Our study shows it may be early, rather than later weight change, which causally mediates antipsychotic-induced weight gain at 1 year. We also demonstrate the potential for causal prediction of counterfactuals targeting obesity for true precision medicine. Further research in larger samples is necessary before causal prediction models could be applied clinically.