H Oughli1, E J Lenze1, A E Locke2, M D Yingling1, Y Zhong3, J P Miller4, C F Reynolds5, B H Mulsant6, J W Newcomer7, T R Peterson2, D J Müller6, G E Nicol8. 1. Washington University School of Medicine, Department of Psychiatry, Healthy Mind Lab, St. Louis, MO, USA. 2. Washington University School of Medicine, Department of Internal Medicine, St. Louis, MO, USA. 3. University of Pittsburgh Graduate School of Public Health, Department of Epidemiology, Pittsburgh, PA, USA. 4. Washington University School of Medicine, Division of Biostatistics, St. Louis, MO, USA. 5. University of Pittsburgh Medical Center, Department of Psychiatry, Pittsburgh, PA, USA. 6. University of Toronto, Department of Psychiatry and Center for Addiction and Mental Health, Toronto, Canada. 7. Washington University School of Medicine, Department of Psychiatry, Healthy Mind Lab, St. Louis, MO, USA; Thriving Mind South Florida, Miami, FL, USA. 8. Washington University School of Medicine, Department of Psychiatry, Healthy Mind Lab, St. Louis, MO, USA. Electronic address: nicolg@wustl.edu.
Abstract
INTRODUCTION: All atypical antipsychotics are associated with some degree of weight gain. We applied a novel statistical approach to identify moderators of aripiprazole-induced fat gain using clinical and genetic data from a randomized clinical trial (RCT) of treatment resistant depression in older adults. MATERIALS AND METHODS:Adults aged ≥60 years with non-response to a prospective trial ofvenlafaxine were randomized to 12 weeks of aripiprazole augmentation (n = 91) or placebo (n = 90). Dual energy x-ray absorptiometry (DEXA) measured adiposity at baseline and 12 weeks. Independent moderators of total body fat gain were used to generate two combined multiple moderators, one including clinical data alone and one including both clinical and genetic data to characterize individuals who gained fat during aripiprazole augmentation. RESULTS: The value of the combined genetic + clinical multiple moderator (Mcg) was 0.57 [95% CI 0.46, 0.68] (effect size: 0.57), compared to the combined clinical moderator (Mc) value of 0.49 [0.34, 0.63] (effect size: 0.49). Individuals who gained adiposity in this study were more likely to be female and younger in age, have lower weight, fasting glucose and lipids at baseline and positive for the HTR2C polymorphism. DISCUSSION: These results demonstrate a combined multiple moderator approach, including both clinical and genetic moderators, can be applied to existing clinical trial data to understand adverse treatment effects. This method allowed for more specific characterization of individuals at risk for the outcome of interest. Further work is needed to identify additional genetic moderators and to validate the approach.
RCT Entities:
INTRODUCTION: All atypical antipsychotics are associated with some degree of weight gain. We applied a novel statistical approach to identify moderators of aripiprazole-induced fat gain using clinical and genetic data from a randomized clinical trial (RCT) of treatment resistant depression in older adults. MATERIALS AND METHODS: Adults aged ≥60 years with non-response to a prospective trial of venlafaxine were randomized to 12 weeks of aripiprazole augmentation (n = 91) or placebo (n = 90). Dual energy x-ray absorptiometry (DEXA) measured adiposity at baseline and 12 weeks. Independent moderators of total body fat gain were used to generate two combined multiple moderators, one including clinical data alone and one including both clinical and genetic data to characterize individuals who gained fat during aripiprazole augmentation. RESULTS: The value of the combined genetic + clinical multiple moderator (Mcg) was 0.57 [95% CI 0.46, 0.68] (effect size: 0.57), compared to the combined clinical moderator (Mc) value of 0.49 [0.34, 0.63] (effect size: 0.49). Individuals who gained adiposity in this study were more likely to be female and younger in age, have lower weight, fasting glucose and lipids at baseline and positive for the HTR2C polymorphism. DISCUSSION: These results demonstrate a combined multiple moderator approach, including both clinical and genetic moderators, can be applied to existing clinical trial data to understand adverse treatment effects. This method allowed for more specific characterization of individuals at risk for the outcome of interest. Further work is needed to identify additional genetic moderators and to validate the approach.
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