Body Mass Index and Atypical Balance Score as Predictors of Treatment Outcomes for Seasonal Affective Disorder

Efficacious treatments for winter seasonal affective disorder (SAD) include light therapy (LT) and cognitive-behavioral therapy (CBT-SAD); however, it is unknown whether patient baseline characteristics differentially predict treatment outcomes. The present study investigated body mass index (BMI) and atypical balance as prognostic and prescriptive predictors of SAD treatment outcomes using data from a parent study in which 177 adults diagnosed with Major Depression, Recurrent with Seasonal Pattern were randomized to either CBT-SAD (n = 88) or LT (n = 89). At pre-treatment, BMI was assessed and atypical balance was derived using the Structured Interview Guide for the Hamilton Rating Scale for Depression–Seasonal Affective Disorder Version (SIGH-SAD). Hierarchical linear and logistic regressions were used to investigate the main effects of treatment type, BMI, and atypical balance score and their interactive effects on SIGH-SAD depression outcomes at post-treatment and first and second winter follow-up. Linear mixed modeling was used to examine the effect of BMI and atypical balance on the rate of SIGH-SAD symptom improvement during the treatment period. BMI x treatment group interaction significantly predicted depression remission at second winter follow-up such that at BMI \u3c 26.1, the probability of depression remission was higher with CBT-SAD than LT. The atypical balance x treatment group interaction significantly predicted depression remission at second winter followup such that the probability of depression remission was higher with CBT-SAD than LT at atypical balance \u3c 40.3%. The linear mixed model analyses uncovered a significant interaction between time, BMI, and treatment group, indicating the rate of change in SIGH-SAD scores was slower in LT and faster in CBT-SAD as BMI increased. Taken together, results suggest that BMI and atypical balance are predictors of depression treatment outcomes, and thus may be useful in clinical decision making and efforts for precision medicine