Mary E Kelley1, Boadie W Dunlop2, Charles B Nemeroff3, Adriana Lori2, Tania Carrillo-Roa4, Elisabeth B Binder2,4, Michael H Kutner1, Vivianne Aponte Rivera5, W Edward Craighead2,6, Helen S Mayberg1,7. 1. Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, Georgia. 2. Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, Georgia. 3. Department of Psychiatry and Behavioral Sciences, University of Miami Miller School of Medicine, Miami, Florida. 4. Department of Translational Research in Psychiatry, Max-Planck Institute of Psychiatry, Munich, Germany. 5. Department of Psychiatry and Behavioral Sciences, Tulane University, New Orleans, Louisiana. 6. Department of Psychology, Emory University, Atlanta, Georgia. 7. Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, Georgia.
Abstract
BACKGROUND: Definition of response is critical when seeking to establish valid predictors of treatment success. However, response at the end of study or endpoint only provides one view of the overall clinical picture that is relevant in testing for predictors. The current study employed a classification technique designed to group subjects based on their rate of change over time, while simultaneously addressing the issue of controlling for baseline severity. METHODS: A set of latent class trajectory analyses, incorporating baseline level of symptoms, were performed on a sample of 344 depressed patients from a clinical trial evaluating the efficacy of cognitive behavior therapy and two antidepressant medications (escitalopram and duloxetine) in patients with major depressive disorder. RESULTS: Although very few demographic and illness-related features were associated with response rate profiles, the aggregated effect of candidate genetic variants previously identified in large pharmacogenetic studies and meta-analyses showed a significant association with early remission as well as nonresponse. These same genetic scores showed a less compelling relationship with endpoint response categories. In addition, consistent nonresponse throughout the study treatment period was shown to occur in different subjects than endpoint nonresponse, which was verified by follow-up augmentation treatment outcomes. CONCLUSIONS: When defining groups based on the rate of change, controlling for baseline depression severity may help to identify the clinically relevant distinctions of early response on one end and consistent nonresponse on the other.
BACKGROUND: Definition of response is critical when seeking to establish valid predictors of treatment success. However, response at the end of study or endpoint only provides one view of the overall clinical picture that is relevant in testing for predictors. The current study employed a classification technique designed to group subjects based on their rate of change over time, while simultaneously addressing the issue of controlling for baseline severity. METHODS: A set of latent class trajectory analyses, incorporating baseline level of symptoms, were performed on a sample of 344 depressedpatients from a clinical trial evaluating the efficacy of cognitive behavior therapy and two antidepressant medications (escitalopram and duloxetine) in patients with major depressive disorder. RESULTS: Although very few demographic and illness-related features were associated with response rate profiles, the aggregated effect of candidate genetic variants previously identified in large pharmacogenetic studies and meta-analyses showed a significant association with early remission as well as nonresponse. These same genetic scores showed a less compelling relationship with endpoint response categories. In addition, consistent nonresponse throughout the study treatment period was shown to occur in different subjects than endpoint nonresponse, which was verified by follow-up augmentation treatment outcomes. CONCLUSIONS: When defining groups based on the rate of change, controlling for baseline depression severity may help to identify the clinically relevant distinctions of early response on one end and consistent nonresponse on the other.
Authors: Luca Sforzini; Courtney Worrell; Melisa Kose; Ian M Anderson; Bruno Aouizerate; Volker Arolt; Michael Bauer; Bernhard T Baune; Pierre Blier; Anthony J Cleare; Philip J Cowen; Timothy G Dinan; Andrea Fagiolini; I Nicol Ferrier; Ulrich Hegerl; Andrew D Krystal; Marion Leboyer; R Hamish McAllister-Williams; Roger S McIntyre; Andreas Meyer-Lindenberg; Andrew H Miller; Charles B Nemeroff; Claus Normann; David Nutt; Stefano Pallanti; Luca Pani; Brenda W J H Penninx; Alan F Schatzberg; Richard C Shelton; Lakshmi N Yatham; Allan H Young; Roland Zahn; Georgios Aislaitner; Florence Butlen-Ducuing; Christine Fletcher; Marion Haberkamp; Thomas Laughren; Fanni-Laura Mäntylä; Koen Schruers; Andrew Thomson; Gara Arteaga-Henríquez; Francesco Benedetti; Lucinda Cash-Gibson; Woo Ri Chae; Heidi De Smedt; Stefan M Gold; Witte J G Hoogendijk; Valeria Jordán Mondragón; Eduard Maron; Jadwiga Martynowicz; Elisa Melloni; Christian Otte; Gabriela Perez-Fuentes; Sara Poletti; Mark E Schmidt; Edwin van de Ketterij; Katherine Woo; Yanina Flossbach; J Antoni Ramos-Quiroga; Adam J Savitz; Carmine M Pariante Journal: Mol Psychiatry Date: 2021-12-15 Impact factor: 13.437
Authors: Nili Solomonov; Jihui Lee; Samprit Banerjee; Christoph Flückiger; Dora Kanellopoulos; Faith M Gunning; Jo Anne Sirey; Conor Liston; Patrick J Raue; Thomas D Hull; Patricia A Areán; George S Alexopoulos Journal: Mol Psychiatry Date: 2020-07-10 Impact factor: 15.992
Authors: Riya Paul; Till F M Andlauer; Darina Czamara; David Hoehn; Susanne Lucae; Benno Pütz; Cathryn M Lewis; Rudolf Uher; Bertram Müller-Myhsok; Marcus Ising; Philipp G Sämann Journal: Transl Psychiatry Date: 2019-08-05 Impact factor: 6.222