Incorporating Causal Inference Perspectives into Psychoneuroimmunology: A Simulation Study Highlighting Concerns about Controlling for Adiposity in Immunopsychiatry

Psychoneuroimmunology and immunopsychiatry are quickly approaching a critical point where the clinical translatability of our promising evidence base will be tested. To maximize our chances for translational success, we believe researchers must adopt causal inference techniques that augment the causal relevance of estimates given theorized causal structures. To illustrate the utility of incorporating causal inference perspectives into psychoneuroimmunological research, we applied the use of directed acyclic graphs and a combination of empirical and simulated data to demonstrate the consequences of controlling for adiposity when testing the hypothesized association between inflammation and depression under the plausible causal structure of increases in adipose tissue leading to increased inflammation that in turn promotes depression. Effect size estimates were pulled from a dataset combining the Midlife in the United States 2 (MIDUS-2) and MIDUS Refresher datasets. Data were extracted and then used to simulate data reflecting an adiposity -> inflammation -> depression causal structure. Next, a Monte Carlo simulation study with 1,000 iterations and three sample size scenarios (N=100, 250, and 500) was conducted testing whether controlling for adiposity when estimating the relation between inflammation and depression influenced the precision of this estimate. Across all three sample size scenarios controlling for adiposity reduced precision of the inflammation depression estimate, highlighting the importance of incorporating causal inference approaches into psychoneuroimmunological research.