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.