Predicting self-reported depression after the onset of multiple sclerosis using genetic and non-genetic factors.

Background: Persons with multiple sclerosis (PwMS) are disproportionately burdened by depression compared to the general population. While several factors associated with depression and depression severity in PwMS have been identified, a prediction model for depression risk has not been developed. In addition, it is unknown if depression-related genetic variants, including Apolipoprotein E (), would be informative for predicting depression in PwMS.

Objective: To develop a depression prediction model for PwMS who did not have a history of depression prior MS onset.

Methods: The study population included 917 non-Hispanic white PwMS. An optimized multivariable Cox proportional hazards model for time to depression was generated using non-genetic variables, to which and a depression-related genetic risk score were included.

Results: Having a mother who had a history of depression, having obstructive pulmonary disease, obesity and other physical disorders at MS onset, and affect-related symptoms at MS onset predicted depression risk (hazards ratios (HRs): 1.6-2.3). Genetic variables improved the prediction model's performance. ε4/ε4 and ε2/x conferred increased (HR = 2.5,  = 0.026) and decreased (HR = 0.65,  = 0.046) depression risk, respectively.

Conclusion: We present a prediction model aligned with The Precision Medicine Initiative, which integrates genetic and non-genetic predictors to inform depression risk stratification after MS onset.