Pre-treatment allostatic load and metabolic dysregulation predict SSRI response in major depressive disorder: a preliminary report

2020 ◽  
pp. 1-9
Author(s):  
Christina M. Hough ◽  
F. Saverio Bersani ◽  
Synthia H. Mellon ◽  
Alexandra E. Morford ◽  
Daniel Lindqvist ◽  
...  
Keyword(s):  
Major Depressive Disorder ◽  
Depressive Disorder ◽  
Treatment Outcomes ◽  
Allostatic Load ◽  
Antidepressant Treatment ◽  
Depression Severity ◽  
Major Depressive ◽  
Metabolic Dysregulation ◽  
Pre Treatment ◽  
Ssri Treatment

Abstract Background Major depressive disorder (MDD) is associated with increased allostatic load (AL; a measure of physiological costs of repeated/chronic stress-responding) and metabolic dysregulation (MetD; a measure of metabolic health and precursor to many medical illnesses). Though AL and MetD are associated with poor somatic health outcomes, little is known regarding their relationship with antidepressant-treatment outcomes. Methods We determined pre-treatment AL and MetD in 67 healthy controls and 34 unmedicated, medically healthy MDD subjects. Following this, MDD subjects completed 8-weeks of open-label selective serotonin reuptake inhibitor (SSRI) antidepressant treatment and were categorized as ‘Responders’ (⩾50% improvement in depression severity ratings) or ‘Non-responders’ (<50% improvement). Logistic and linear regressions were performed to determine if pre-treatment AL or MetD scores predicted SSRI-response. Secondary analyses examined cross-sectional differences between MDD and control groups. Results Pre-treatment AL and MetD scores significantly predicted continuous antidepressant response (i.e. absolute decreases in depression severity ratings) (p = 0.012 and 0.014, respectively), as well as post-treatment status as a Responder or Non-responder (p = 0.022 and 0.040, respectively), such that higher pre-treatment AL and MetD were associated with poorer SSRI-treatment outcomes. Pre-treatment AL and MetD of Responders were similar to Controls, while those of Non-responders were significantly higher than both Responders (p = 0.025 and 0.033, respectively) and Controls (p = 0.039 and 0.001, respectively). Conclusions These preliminary findings suggest that indices of metabolic and hypothalamic-pituitary-adrenal-axis dysregulation are associated with poorer SSRI-treatment response. To our knowledge, this is the first study to demonstrate that these markers of medical disease risk also predict poorer antidepressant outcomes.

1005. Pre-Treatment Allostatic Load and Metabolic Dysregulation Predict Antidepressant Response in Major Depressive Disorder

2017 ◽  
Vol 81 (10) ◽  
pp. S406-S407
Author(s):  
Christina Hough ◽  
Alexandra Morford ◽  
Elissa Epel ◽  
Daniel Lindqvist ◽  
Francesco Saverio Bersani ◽  
...  
Keyword(s):  
Major Depressive Disorder ◽  
Depressive Disorder ◽  
Allostatic Load ◽  
Antidepressant Response ◽  
Major Depressive ◽  
Metabolic Dysregulation ◽  
Pre Treatment

scholarly journals Changes in the neural correlates of implicit emotional face processing during antidepressant treatment in major depressive disorder

2013 ◽  
Vol 16 (10) ◽  
pp. 2195-2208 ◽  
Author(s):  
Teresa A. Victor ◽  
Maura L. Furey ◽  
Stephen J. Fromm ◽  
Arne Öhman ◽  
Wayne C. Drevets
Keyword(s):  
Major Depressive Disorder ◽  
Depressive Disorder ◽  
Treatment Condition ◽  
Functional Neuroimaging ◽  
Antidepressant Treatment ◽  
Antidepressant Drugs ◽  
Anterior Cingulate ◽  
Major Depressive ◽  
Pre Treatment ◽  
Emotional Face

Abstract An emerging hypothesis regarding the mechanisms underlying antidepressant pharmacotherapy suggests that these agents benefit depressed patients by reversing negative emotional processing biases (Harmer, 2008). Neuropsychological indices and functional neuroimaging measures of the amygdala response show that antidepressant drugs shift implicit and explicit processing biases away from the negative valence and toward the positive valence. However, few studies have explored such biases in regions extensively connected with the amygdala, such as the pregenual anterior cingulate cortex (pgACC) area, where pre-treatment activity consistently has predicted clinical outcome during antidepressant treatment. We used functional magnetic resonance imaging (fMRI) to investigate changes in haemodynamic response patterns to positive vs. negative stimuli in patients with major depressive disorder (MDD) under antidepressant treatment. Participants with MDD (n = 10) underwent fMRI before and after 8 wk sertraline treatment; healthy controls (n = 10) were imaged across an equivalent interval. A backward masking task was used to elicit non-conscious neural responses to sad, happy and neutral face expressions. Haemodynamic responses to emotional face stimuli were compared between conditions and groups in the pgACC. The response to masked-sad vs. masked-happy faces (SN-HN) in pgACC in the depressed subjects was higher in the pre-treatment condition than in the post-treatment condition and this difference was significantly greater than the corresponding change across time in the controls. The treatment-associated difference was attributable to an attenuated response to sad faces and an enhanced response to happy faces. Pre-treatment pgACC responses to SN-HN correlated positively with clinical improvement during treatment. The pgACC participates with the amygdala in processing the salience of emotional stimuli. Treatment-associated functional changes in this limbic network may influence the non-conscious processing of such stimuli by reversing the negative processing bias extant in MDD.

Optimizing patient expectancy in the pharmacologic treatment of major depressive disorder

2018 ◽  
Vol 49 (14) ◽  
pp. 2414-2420 ◽  
Author(s):  
Sigal Zilcha-Mano ◽  
Patrick J. Brown ◽  
Steven P. Roose ◽  
Kiley Cappetta ◽  
Bret R. Rutherford
Keyword(s):  
Major Depressive Disorder ◽  
Treatment Outcome ◽  
Depressive Symptoms ◽  
Depressive Disorder ◽  
Antidepressant Treatment ◽  
Subsequent Treatment ◽  
Major Depressive ◽  
Treatment Patient ◽  
Treatment Parameter ◽  
Pre Treatment

AbstractBackgroundPatient expectancy is an important source of placebo effects in antidepressant clinical trials, but all prior studies measured expectancy prior to the initiation of medication treatment. Little is known about how expectancy changes during the course of treatment and how such changes influence clinical outcome. Consequently, we undertook the first analysis to date of in-treatment expectancy during antidepressant treatment to identify its clinical and demographic correlates, typical trajectories, and associations with treatment outcome.MethodsData were combined from two randomized controlled trials of antidepressant medication for major depressive disorder in which baseline and in-treatment expectancy assessments were available. Machine learning methods were used to identify pre-treatment clinical and demographic predictors of expectancy. Multilevel models were implemented to test the effects of expectancy on subsequent treatment outcome, disentangling within- and between-patient effects.ResultsRandom forest analyses demonstrated that whereas more severe depressive symptoms predicted lower pre-treatment expectancy, in-treatment expectancy was unrelated to symptom severity. At each measurement point, increased in-treatment patient expectancy significantly predicted decreased depressive symptoms at the following measurement (B = −0.45, t = −3.04, p = 0.003). The greater the gap between expected treatment outcomes and actual depressive severity, the greater the subsequent symptom reductions were (B = 0.49, t = 2.33, p = 0.02).ConclusionsGreater in-treatment patient expectancy is associated with greater subsequent depressive symptom reduction. These findings suggest that clinicians may benefit from monitoring and optimizing patient expectancy during antidepressant treatment. Expectancy may represent another treatment parameter, similar to medication compliance and side effects, to be regularly monitored during antidepressant clinical management.

scholarly journals Intrinsic connectomes are a predictive biomarker of remission in major depressive disorder

2019 ◽  
Vol 25 (7) ◽  
pp. 1537-1549 ◽  
Author(s):  
Mayuresh S. Korgaonkar ◽  
Andrea N. Goldstein-Piekarski ◽  
Alexander Fornito ◽  
Leanne M. Williams
Keyword(s):  
Major Depressive Disorder ◽  
Functional Connectivity ◽  
Depressive Disorder ◽  
Treatment Response ◽  
Antidepressant Treatment ◽  
Predictive Biomarker ◽  
Major Depressive ◽  
Attention Networks ◽  
Pre Treatment

Abstract Although major depressive disorder (MDD) is associated with altered functional coupling between disparate neural networks, the degree to which such measures are ameliorated by antidepressant treatment is unclear. It is also unclear whether functional connectivity can be used as a predictive biomarker of treatment response. Here, we used whole-brain functional connectivity analysis to identify neural signatures of remission following antidepressant treatment, and to identify connectomic predictors of treatment response. 163 MDD and 62 healthy individuals underwent functional MRI during pre-treatment baseline and 8-week follow-up sessions. Patients were randomized to escitalopram, sertraline or venlafaxine-XR antidepressants and assessed at follow-up for remission. Baseline measures of intrinsic functional connectivity between each pair of 333 regions were analyzed to identify pre-treatment connectomic features that distinguish remitters from non-remitters. We then interrogated these connectomic differences to determine if they changed post-treatment, distinguished patients from controls, and were modulated by medication type. Irrespective of medication type, remitters were distinguished from non-remitters by greater connectivity within the default mode network (DMN); specifically, between the DMN, fronto-parietal and somatomotor networks, the DMN and visual, limbic, auditory and ventral attention networks, and between the fronto-parietal and somatomotor networks with cingulo-opercular and dorsal attention networks. This baseline hypo-connectivity for non-remitters also distinguished them from controls and increased following treatment. In contrast, connectivity for remitters was higher than controls at baseline and also following remission, suggesting a trait-like connectomic characteristic. Increased functional connectivity within and between large-scale intrinsic brain networks may characterize acute recovery with antidepressants in depression.

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