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Measuring brain glucose metabolism in order to predict response to antidepressant or placebo: A randomized clinical trial

There is critical need for a clinically useful tool to predict antidepressant treatment outcome in major depressive disorder (MDD) to reduce suffering and mortality. This analysis sought to build upon previously reported antidepressant treatment efficacy prediction from 2-[(18)F]-fluorodeoxyglucose...

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Autores principales: Hill, Kathryn R., Gardus, John D., Bartlett, Elizabeth A., Perlman, Greg, Parsey, Ramin V., DeLorenzo, Christine
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8551925/
https://www.ncbi.nlm.nih.gov/pubmed/34689056
http://dx.doi.org/10.1016/j.nicl.2021.102858
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author Hill, Kathryn R.
Gardus, John D.
Bartlett, Elizabeth A.
Perlman, Greg
Parsey, Ramin V.
DeLorenzo, Christine
author_facet Hill, Kathryn R.
Gardus, John D.
Bartlett, Elizabeth A.
Perlman, Greg
Parsey, Ramin V.
DeLorenzo, Christine
author_sort Hill, Kathryn R.
collection PubMed
description There is critical need for a clinically useful tool to predict antidepressant treatment outcome in major depressive disorder (MDD) to reduce suffering and mortality. This analysis sought to build upon previously reported antidepressant treatment efficacy prediction from 2-[(18)F]-fluorodeoxyglucose - Positron Emission Tomography (FDG-PET) using metabolic rate of glucose uptake (MRGlu) from dynamic FDG-PET imaging with the goal of translation to clinical utility. This investigation is a randomized, double-blind placebo-controlled trial. All participants were diagnosed with MDD and received an FDG-PET scan before randomization and after treatment. Hamilton Depression Rating Scale (HDRS-17) was completed in participants diagnosed with MDD before and after 8 weeks of escitalopram, or placebo. MRGlu (mg/(min*100 ml)) was estimated within the raphe nuclei, right insula, and left ventral Prefrontal Cortex in 63 individuals. Linear regression was used to examine the association between pretreatment MRGlu and percent decrease in HDRS-17. Additionally, the association between percent decrease in HDRS-17 and percent change in MRGlu between pretreatment scan and post-treatment scan was examined. Covariates were treatment type (SSRI/placebo), handedness, sex, and age. Depression severity decrease (n = 63) was not significantly associated with pretreatment MRGlu in the raphe nuclei [Formula: see text]  = -2.61e(-03) [-0.26, 0.25], p = 0.98), right insula [Formula: see text]  = 0.05 [-0.23, 0.32], p = 0.72), or ventral prefrontal cortex [Formula: see text]  = 0.06 [-0.23, 0.34], p = 0.68) where [Formula: see text] is the standardized estimated coefficient, with a 95% confidence interval, or in whole brain voxelwise analysis (family-wise error correction, alpha = 0.05). MRGlu percent change was not significantly associated with depression severity decrease (n = 58) before multiple comparison correction in the RN [Formula: see text]  = 0.20 [-0.07, 0.47], p = 0.15), right insula [Formula: see text]  = 0.24 [-0.03, 0.51], p = 0.08), or vPFC [Formula: see text]  = 0.22 [-0.06, 0.50], p = 0.12). We propose that FDG-PET imaging does not indicate a clinically relevant biomarker of escitalopram or placebo treatment response in heterogeneous major depressive disorder cohorts. Future directions include focusing on potential biologically-based subtypes of major depressive disorder by implementing biomarker stratified designs.
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spelling pubmed-85519252021-11-04 Measuring brain glucose metabolism in order to predict response to antidepressant or placebo: A randomized clinical trial Hill, Kathryn R. Gardus, John D. Bartlett, Elizabeth A. Perlman, Greg Parsey, Ramin V. DeLorenzo, Christine Neuroimage Clin Regular Article There is critical need for a clinically useful tool to predict antidepressant treatment outcome in major depressive disorder (MDD) to reduce suffering and mortality. This analysis sought to build upon previously reported antidepressant treatment efficacy prediction from 2-[(18)F]-fluorodeoxyglucose - Positron Emission Tomography (FDG-PET) using metabolic rate of glucose uptake (MRGlu) from dynamic FDG-PET imaging with the goal of translation to clinical utility. This investigation is a randomized, double-blind placebo-controlled trial. All participants were diagnosed with MDD and received an FDG-PET scan before randomization and after treatment. Hamilton Depression Rating Scale (HDRS-17) was completed in participants diagnosed with MDD before and after 8 weeks of escitalopram, or placebo. MRGlu (mg/(min*100 ml)) was estimated within the raphe nuclei, right insula, and left ventral Prefrontal Cortex in 63 individuals. Linear regression was used to examine the association between pretreatment MRGlu and percent decrease in HDRS-17. Additionally, the association between percent decrease in HDRS-17 and percent change in MRGlu between pretreatment scan and post-treatment scan was examined. Covariates were treatment type (SSRI/placebo), handedness, sex, and age. Depression severity decrease (n = 63) was not significantly associated with pretreatment MRGlu in the raphe nuclei [Formula: see text]  = -2.61e(-03) [-0.26, 0.25], p = 0.98), right insula [Formula: see text]  = 0.05 [-0.23, 0.32], p = 0.72), or ventral prefrontal cortex [Formula: see text]  = 0.06 [-0.23, 0.34], p = 0.68) where [Formula: see text] is the standardized estimated coefficient, with a 95% confidence interval, or in whole brain voxelwise analysis (family-wise error correction, alpha = 0.05). MRGlu percent change was not significantly associated with depression severity decrease (n = 58) before multiple comparison correction in the RN [Formula: see text]  = 0.20 [-0.07, 0.47], p = 0.15), right insula [Formula: see text]  = 0.24 [-0.03, 0.51], p = 0.08), or vPFC [Formula: see text]  = 0.22 [-0.06, 0.50], p = 0.12). We propose that FDG-PET imaging does not indicate a clinically relevant biomarker of escitalopram or placebo treatment response in heterogeneous major depressive disorder cohorts. Future directions include focusing on potential biologically-based subtypes of major depressive disorder by implementing biomarker stratified designs. Elsevier 2021-10-19 /pmc/articles/PMC8551925/ /pubmed/34689056 http://dx.doi.org/10.1016/j.nicl.2021.102858 Text en © 2021 The Authors https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Regular Article
Hill, Kathryn R.
Gardus, John D.
Bartlett, Elizabeth A.
Perlman, Greg
Parsey, Ramin V.
DeLorenzo, Christine
Measuring brain glucose metabolism in order to predict response to antidepressant or placebo: A randomized clinical trial
title Measuring brain glucose metabolism in order to predict response to antidepressant or placebo: A randomized clinical trial
title_full Measuring brain glucose metabolism in order to predict response to antidepressant or placebo: A randomized clinical trial
title_fullStr Measuring brain glucose metabolism in order to predict response to antidepressant or placebo: A randomized clinical trial
title_full_unstemmed Measuring brain glucose metabolism in order to predict response to antidepressant or placebo: A randomized clinical trial
title_short Measuring brain glucose metabolism in order to predict response to antidepressant or placebo: A randomized clinical trial
title_sort measuring brain glucose metabolism in order to predict response to antidepressant or placebo: a randomized clinical trial
topic Regular Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8551925/
https://www.ncbi.nlm.nih.gov/pubmed/34689056
http://dx.doi.org/10.1016/j.nicl.2021.102858
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