Latent class growth analyses reveal overrepresentation of dysfunctional fear conditioning trajectories in patients with anxiety-related disorders compared to controls

•Distinct latent fear conditioning trajectories exist among patients and controls.•Latent trajectory corresponding to impaired safety learning is overrepresented in patients with anxiety-related disorders.•Latent trajectory related to poor fear extinction is overrepresented in patients with anxiety-related disorders.•Maladaptive conditioning patterns are found in a subset of patients, and may be used in the future to predict treatment outcome. Recent meta-analyses indicated differences in fear acquisition and extinction between patients with anxiety-related disorders and comparison subjects. However, these effects are small and may hold for only a subsample of patients. To investigate individual trajectories in fear acquisition and extinction across patients with anxiety-related disorders (N = 104; before treatment) and comparison subjects (N = 93), data from a previous study (Duits et al., 2017) were re-analyzed using data-driven latent class growth analyses. In this explorative study, subjective fear ratings, shock expectancy ratings and startle responses were used as outcome measures. Fear and expectancy ratings, but not startle data, yielded distinct fear conditioning trajectories across participants. Patients were, compared to controls, overrepresented in two distinct dysfunctional fear conditioning trajectories: impaired safety learning and poor fear extinction to danger cues. The profiling of individual patterns allowed to determine that whereas a subset of patients showed trajectories of dysfunctional fear conditioning, a significant proportion of patients (≥50 %) did not. The strength of trajectory analyses as opposed to group analyses is that it allows the identification of individuals with dysfunctional fear conditioning. Results suggested that dysfunctional fear learning may also be associated with poor treatment outcome, but further research in larger samples is needed to address this question.