Antidepressant Treatment Response Research Articles

Early-treatment cerebral blood flow change as a predictive biomarker of antidepressant treatment response: evidence from the EMBARC clinical trial.

Major depressive disorder (MDD) is one of the most prevalent and disabling illnesses worldwide. Treatment of MDD typically relies on trial-and-error to find an effective approach. Identifying early response-related biomarkers that predict response to antidepressants would help clinicians to decide, as early as possible, whether a particular treatment might be suitable for a given patient. Data were from the two-stage Establishing Moderators and Biosignatures of Antidepressant Response for Clinical Care (EMBARC) trial. A whole-brain, voxel-wise, mixed-effects model was applied to identify early-treatment cerebral blood flow (CBF) changes as biomarkers of treatment response. We examined changes in CBF measured with arterial spin labeling 1-week after initiating double-masked sertraline/placebo. We tested whether these early 1-week scans could be used to predict response observed after 8-weeks of treatment. Response to 8-week placebo treatment was associated with increased cerebral perfusion in temporal cortex and reduced cerebral perfusion in postcentral region captured at 1-week of treatment. Additionally, CBF response in these brain regions was significantly correlated with improvement in Hamilton Depression Rating Scale score in the placebo group. No significant associations were found for selective serotonin reuptake inhibitor treatment. We conclude that early CBF responses to placebo administration in multiple brain regions represent candidate neural biomarkers of longer-term antidepressant effects.

Serum brain-derived neurotrophic factor, Val66Met polymorphism and open-label SSRI treatment response in Major Depressive Disorder

BackgroundBrain-derived neurotrophic factor (BDNF) has been implicated in the therapeutic action of antidepressants and possibly in the pathophysiology of Major Depressive Disorder (MDD). Clinical studies of peripheral blood levels of BDNF in MDD have provided conflicting results, and there are also conflicting reports regarding the predictive value of peripheral BDNF levels for antidepressant treatment response. The present study investigated the association between serum BDNF levels, the BDNF Val66Met polymorphism (rs6265), clinical characteristics and SSRI treatment response. MethodsThis open-label clinical trial included 99 physically healthy, unmedicated MDD participants and 70 healthy controls. Following a baseline assessment, 53 of the MDD participants completed an eight-week, open-label course of SSRI antidepressant treatment. Serum BDNF levels and Hamilton Rating Scale for Depression (HDRS) ratings were examined at baseline and after eight weeks of treatment. Antidepressant response was defined as a decrease in HDRS ratings of > 50% from baseline to the end-of-treatment. Finally, serum BDNF levels and SSRI treatment response were compared between MDD participants who were heterozygous or homozygous for the Met allele (“Met-carriers”) and individuals homozygous for the Val allele. ResultsSerum BDNF levels at baseline were significantly higher in the unmedicated MDD participants compared to healthy controls (15.90 ng/ml vs 13.75 ng/ml, t (167) = −2.041, p = 0.043). In a post-hoc analysis, this difference was seen in the female but not male participants (16.85 ng/ml vs 14.06 ng/ml, t (91) = −2.067, p = 0.042; 14.86 ng/ml vs 13.31 ng/ml, t (74) = −0.923, p = 0.359). Baseline serum BDNF levels were not associated with treatment responder status or with absolute change in depression ratings over the course of 8-week SSRI treatment (p = 0.599). In both Responders and Non-responders, no significant changes in serum BDNF levels were found over the 8-week period of SSRI-treatment (16.32 ng/ml vs 16.23 ng/ml, t (18) = 0.060, p = 0.953; 16.04 ng/ml vs 15.61 ng/ml, t (29) = 0.438, p = 0.665, respectively). Further, no differences were found in serum BDNF levels prior to treatment between MDD Met-carriers and MDD Val/Val homozygotes (15.32 ng/ml vs 16.36 ng/ml, t (85) = 0.747, p = 0.457), and no differences were found in post-treatment serum BDNF (F1,42= 0.031, p = 0.862). However, MDD Val/Val homozygotes showed significantly greater antidepressant responses at week 8 than did MDD Met-carriers (F1,46 = 4.366, p = 0.043). ConclusionOur results do not support sufficient reliability of using peripheral BDNF to characterize depression or to predict antidepressant response in clinical use. The role of sex in moderating BDNF differences in depression, and the role of BDNF gene polymorphisms in predicting antidepressant response, remain to be further investigated. We conclude that, while central nervous system BDNF is likely involved in antidepressant efficacy and in aspects of MDD pathophysiology, its reflection in serum BDNF levels is of limited diagnostic or prognostic utility.

One size does not fit all: notable individual variation in brain activity correlates of antidepressant treatment response.

To date, no robust electroencephalography (EEG) markers of antidepressant treatment response have been identified. Variable findings may arise from the use of group analyses, which neglect individual variation. Using a combination of group and single-participant analyses, we explored individual variability in EEG characteristics of treatment response. Resting-state EEG data and Montgomery-Åsberg Depression Rating Scale (MADRS) symptom scores were collected from 43 patients with depression before, at 1 and 12 weeks of pharmacotherapy. Partial least squares (PLS) was used to: 1) identify group differences in EEG connectivity (weighted phase lag index) and complexity (multiscale entropy) between eventual medication responders and non-responders, and 2) determine whether group patterns could be identified in individual patients. Responders showed decreased alpha and increased beta connectivity, and early, widespread decreases in complexity over treatment. Non-responders showed an opposite connectivity pattern, and later, spatially confined decreases in complexity. Thus, as in previous studies, our group analyses identified significant differences between groups of patients with different treatment outcomes. These group-level EEG characteristics were only identified in ~40-60% of individual patients, as assessed quantitatively by correlating the spatiotemporal brain patterns between groups and individual results, and by independent raters through visualization. Our single-participant analyses suggest that substantial individual variation exists, and needs to be considered when investigating characteristics of antidepressant treatment response for potential clinical applicability. https://clinicaltrials.gov, identifier NCT00519428.

Atypical brain aging and its association with working memory performance in major depressive disorder

BackgroundPatients with major depressive disorder (MDD) can present with altered brain structure and deficits in cognitive function similar to aging. Yet, the interaction between age-related brain changes and brain development in MDD remains understudied. In a cohort of adolescents and adults with and without MDD, we assessed brain aging differences and associations through a newly developed tool quantifying normative neurodevelopmental trajectories. Methods304 MDD participants and 236 non-depressed controls were recruited and scanned from three studies under the Canadian Biomarker Integration Network for Depression. Volumetric data were used to generate brain centile scores, which were examined for: a) differences in MDD relative to controls; b) differences in individuals with versus without severe childhood maltreatment; and c) correlations with depressive symptom severity, neurocognitive assessment domains, or escitalopram treatment response. ResultsBrain centiles were significantly lower in the MDD group compared to controls. It was also significantly correlated with working memory in controls, but not the MDD group. No significant associations were observed in depression severity or antidepressant treatment response with brain centiles. Likewise, childhood maltreatment history did not significantly affect brain centiles. ConclusionsConsistent with prior work on machine learning models that predict “brain age”, brain centile scores differed in people diagnosed with MDD, and MDD was associated with differential relationships between centile scores and working memory. The results support the notion of atypical development and aging in MDD, with implications on neurocognitive deficits associated with aging-related cognitive function.

Omega-3 fatty acids for inflamed depression – A match/mismatch study

Despite decades of research on the pathophysiology of depression, the development of new therapeutic interventions has been slow, and no biomarkers of treatment response have been clinically implemented. Several lines of evidence suggest that the clinical and biological heterogeneity among patients with major depressive disorder (MDD) has hampered progress in this field. MDD with low-grade inflammation – “inflamed depression” – is a subtype of depression that may be associated with a superior antidepressant treatment response to anti-inflammatory compounds. Omega-3 fatty acid eicosapentaenoic acid (EPA) has anti-inflammatory properties, and preliminary data suggest that it may be particularly efficacious in inflamed depression. In this study we tested the hypothesis that add-on EPA has greater antidepressant efficacy in MDD patients with high baseline high-sensitivity C-reactive protein (hs-CRP) compared to MDD patients with low hs-CRP. All subjects received 2.2 g EPA, 400 mg docosahexaenoic acid and 800 mg of other fatty acids daily for 8 weeks, added to stable ongoing antidepressant treatment. The primary outcome was change in the 17-item Hamilton Depression Rating Scale (HAMD-17). Patients and raters were blind to baseline hs-CRP status. In an intention-to-treat analysis including all subjects with at least one post baseline visit (n = 101), ahs-CRPcut-off of ≥1 mg/L, but not ≥3 mg/L, was associated with a greater improvement in HAMD-17 total score. In addition to a general antidepressant effect among patients with hs-CRP ≥ 1 mg/L, adjuvant EPA treatment improved symptoms putatively related to inflamed depression such as fatigue and sleep difficulties. This adds to the mounting evidence that delineation of MDD subgroups based on inflammation may be clinically relevant to predict treatment response to anti-inflammatory interventions.

Pharmacogenomic overlap between antidepressant treatment response in major depression & antidepressant associated treatment emergent mania in bipolar disorder

There is increasing interest in individualizing treatment selection for more than 25 regulatory approved treatments for major depressive disorder (MDD). Despite an inconclusive efficacy evidence base, antidepressants (ADs) are prescribed for the depressive phase of bipolar disorder (BD) with oftentimes, an inadequate treatment response and or clinical concern for mood destabilization. This study explored the relationship between antidepressant response in MDD and antidepressant-associated treatment emergent mania (TEM) in BD. We conducted a genome-wide association study (GWAS) and polygenic score analysis of TEM and tested its association in a subset of BD-type I patients treated with SSRIs or SNRIs. Our results did not identify any genome-wide significant variants although, we found that a higher polygenic score (PGS) for antidepressant response in MDD was associated with higher odds of TEM in BD. Future studies with larger transdiagnostic depressed cohorts treated with antidepressants are encouraged to identify a neurobiological mechanism associated with a spectrum of depression improvement from response to emergent mania.

Personality traits predict treatment outcome of an antidepressant in untreated adolescents with depression: An 8-week, open-label, flexible-dose study

BackgroundAntidepressant response in adults with major depressive disorder (MDD) is probably influenced by personality dimensions. However, personality dimensions in depression and their association with antidepressant treatment in adolescents are relatively unknown. We sought to investigate whether personality traits (PTs) can influence antidepressant treatment response in adolescents with depression. MethodsEighty-two adolescents with MDD who had completed the 8 weeks of treatment with selective serotonin reuptake inhibitors (SSRI) were enrolled. The Revised NEO Five-Factor Inventory (NEO-FFI-R) was used to measure their personality at baseline, and the 17-item Hamilton Depression Rating Scale (HAMD-17) and Children's Depression Rating Scale-Revised (CDRS-R) were used to evaluate depressive symptoms at baseline and 8 weeks. Moreover, logistic regression was performed to investigate the relationship between personality dimensions and antidepressant response. Receiver operating characteristic analyses were employed to determine the accuracy of a PT-based model in predicting the antidepressant response rate. ResultsAdolescents with MDD had significantly different PTs at baseline. Multivariable logistic regression analysis showed that extroversion scores were associated with response to antidepressant treatment, the lower the extroversion score, the better the response to antidepressant treatment, after correcting for variables with significant differences and trends or all potential confounding variables. It was also found that the combination of disease duration, extraversion-gregariousness, and agreeableness-trust effectively predicted antidepressant response in adolescents with MDD, with a sensitivity of 79.4 % and specificity of 68.7 %. ConclusionPersonality dysfunction in adolescents is associated with MDD. The antidepressant treatment response is influenced by the degree of extroversion in adolescents with MDD.

Modeling elevated MAO-A activity in mice: role in emotionality and antidepressant treatment response

Modeling elevated MAO-A activity in mice: role in emotionality and antidepressant treatment response

Effects of Anxious Depression on Antidepressant Treatment Response.

Anxious depression represents a subtype of major depressive disorder and is associated with increased suicidality, severity, chronicity and lower treatment response. Only a few studies have investigated the differences between anxious depressed (aMDD) and non-anxious depressed (naMDD) patients regarding treatment dosage, serum-concentration and drug-specific treatment response. In our naturalistic and prospective study, we investigated whether the effectiveness of therapy including antidepressants (SSRI, SNRI, NaSSA, tricyclics and combinations) in aMDD patients differs significantly from that in naMDD patients. In a sample of 346 patients, we calculated the anxiety somatization factor (ASF) and defined treatment response as a reduction (≥50%) in the Hamilton Depression Rating Scale (HDRS)-21 score after 7 weeks of pharmacological treatment. We did not observe an association between therapy response and the baseline ASF-scores, or differences in therapy outcomes between aMDD and naMDD patients. However, non-responders had higher ASF-scores, and at week 7 aMDD patients displayed a worse therapy outcome than naMDD patients. In subgroup analyses for different antidepressant drugs, venlafaxine-treated aMDD patients showed a significantly worse outcome at week 7. Future prospective, randomized-controlled studies should address the question of a worse therapy outcome in aMDD patients for different psychopharmaceuticals individually.

Using Semi‐supervised Variational Autoencoder and Acute fMRI Changes to Predict Antidepressant Treatment Responses Among Older Adults with Depression

AbstractBackgroundLate‐life depression (LLD) has been consistently linked to an increased risk of dementia, with studies showing that older adults with depression have a two fold risk of developing dementia. Dementia has proven difficult to treat, yet depression has several viable treatment options, particularly antidepressants. However, the efficacy of antidepressants in LLD has proven modest as antidepressants typically achieve around a 50% remission rate and only after 4‐6 weeks of treatment. In this analysis, we investigate the effectiveness of combining fMRI data with deep learning for predicting antidepressant driven depression remission.MethodIn two LLD studies using venlafaxine (n = 51) and escitalopram or levomilnacipran (n = 29), resting state fMRI scans were collected at baseline and at day one of antidepressants. Region‐to‐region functional connectivity (FC) was calculated for each scan using the Shen50 atlas, and differential connectivity (DC) was calculated to compare baseline and day 1 FC. Clinical factors including age, race, education and cumulative illness burden and DC were used as input to a semi‐supervised variational autoencoder (VAE) to predict remission status at treatment end (Fig.1). Remission was encoded as a binary variable, defined as a final MADRS score of 10 or less after 12 weeks of treatment and subject to blinded clinician assessment. Monte Carlo cross‐validation over 30 repetitions was used to evaluate the model performance with 10% of the data held out for testing. The VAE was compared to a random forest classifier with the same cross validation methods using the area under the curve (AUC).ResultThe VAE achieved an average mean‐squared error of .005 and AUC of 0.87 on a held out test set during cross validation. The VAE outperformed the random forest classifier which achieved an average AUC of 0.68.ConclusionThe VAE using DC in fMRI scans between baseline and day one shows the potential in accurately identifying responders to antidepressants as early as in one day. Our results show that acute changes to functional networks triggered by a single dose of antidepressants are promising as a means to improve patient outcomes. These results can contribute to optimizing treatment for LLD in the future.

Transcriptional signatures of early-life stress and antidepressant treatment efficacy.

Individuals with a history of early-life stress (ELS) tend to have an altered course of depression and lower treatment response rates. Research suggests that ELS alters brain development, but the molecular changes in the brain following ELS that may mediate altered antidepressant response have not been systematically studied. Sex and gender also impact the risk of depression and treatment response. Here, we leveraged existing RNA sequencing datasets from 1) blood samples from depressed female- and male-identifying patients treated with escitalopram or desvenlafaxine and assessed for treatment response or failure; 2) the nucleus accumbens (NAc) of female and male mice exposed to ELS and/or adult stress; and 3) the NAc of mice after adult stress, antidepressant treatment with imipramine or ketamine, and assessed for treatment response or failure. We find that transcriptomic signatures of adult stress after a history of ELS correspond with transcriptomic signatures of treatment nonresponse, across species and multiple classes of antidepressants. Transcriptomic correspondence with treatment outcome was stronger among females and weaker among males. We next pharmacologically tested these predictions in our mouse model of early-life and adult social defeat stress and treatment with either chronic escitalopram or acute ketamine. Among female mice, the strongest predictor of behavior was an interaction between ELS and ketamine treatment. Among males, however, early experience and treatment were poor predictors of behavior, mirroring our bioinformatic predictions. These studies provide neurobiological evidence for molecular adaptations in the brain related to sex and ELS that contribute to antidepressant treatment response.

Exploring the human gut microbiota targets in relation to the use of contemporary antidepressants

BackgroundAntidepressants, specifically selective serotonin reuptake inhibitors (SSRIs) and serotonin-norepinephrine reuptake inhibitors (SNRIs), are commonly prescribed for depression treatment. Animal studies have shown that antidepressants can influence gut microbiota composition and specific bacterial taxa. We aimed to investigate the association between antidepressant use and human gut microbiota composition and functional pathway. MethodsWe collected information on antidepressant use, demographic, food patterns, and clinical characteristics through questionnaires and medical records. The gut microbiota profiles of 271 depressive patients were carried out through 16S rRNA gene sequencing. Patients were categorized based on different types of antidepressant use groups for gut microbiota comparisons. MaAsLin2 was performed to evaluate microbiota composition across groups. PICRUSt2 was used to predict microbiota functional pathways. ResultsPatients taking SSRIs or SNRIs had a lower microbiota diversity. We found seven taxa abundances (Turicibacter, Barnesiella, Lachnospiraceae_ND3007_group, Romboutia, Akkermansia, Dialister, Romboutia and Fusicatenibacter) differed in patients with various types of antidepressants compared with those without antidepressant treatments (p < 0.05). Turicibacter inversely correlated with depression severity in SSRIs or SNRI users (r = −0.43, p < 0.05). Top identified pathways were related to compound fermentation and biosynthesis in microbiota function. ConclusionAntidepressant usage, especially SSRIs and SNRIs, associates with changes in gut microbiota composition and specific taxa. Given our study's preliminary cross-sectional nature, further research is warranted to comprehend the relationship between antidepressant use, treatment response, and gut microbiota, aiming to enhance therapeutic interventions in the future.

SuPAR in major depression: Association with 26 weeks antidepressant response and 10-year depression outcomes

IntroductionInflammation has been associated with depression and differential antidepressant (AD) treatment response. Soluble urokinase plasminogen activator receptor (suPAR) is a novel measure of chronic inflammation. We investigated whether suPAR is associated with depression severity and AD response. MethodsWe included 90 patients with major depressive disorder (MDD) who participated in a part-randomized clinical trial of 26 weeks of treatment with escitalopram or nortriptyline. suPAR levels were measured in serum samples collected at baseline and after 8, 12 and 26 weeks. Mixed effects models for the association between suPAR levels and AD response were performed. By merging with Danish nationwide registers, we included information on psychiatric hospital contacts during ten years after the GENDEP trial. Cox regression analyses calculated the hazard rate ratios between suPAR levels and subsequent hospitalizations. ResultsAt baseline, higher suPAR levels were not associated with overall depression severity but with greater severity of neurovegetative depressive symptoms, specifically appetite and weight changes. 57 (63.3%) patients responded positively to treatment. Among 57 (63.3%) patients who achieved response, those who responded had significantly higher baseline suPAR levels levels, and response was associated with a significant decrease in suPAR during AD treatment. Remitters decreased from 3.1 ng/ml at baseline to 2.8 ng/ml after 26 weeks (p = 0.003) and responders from 3.0 to 2.8 ng/ml (p = 0.02), whereas non-remitters and non-responders showed unchanged suPAR levels. We found no correlation between a change in suPAR and a change in MADRS, but a lowering of suPAR correlated with a decrease in neurovegetative symptoms. We found no association between suPAR levels and 10-year risk for hospitalizations. DiscussionThe present study suggests that an elevated level of chronic inflammation, measured as the suPAR level, is associated with better response to AD treatment.

Neutrophil to lymphocyte ratio and antidepressant treatment response in patients with major depressive disorder: Effect of sex and hippocampal volume

Several factors may affect response to treatment in Major Depressive Disorder (MDD) including immune/inflammatory alterations and regional brain volumes, particularly in hippocampal regions which have shown to be influenced by inflammatory status. Neutrophil-to-lymphocyte ratio (NLR) is an inflammatory marker found to be elevated in depressed women in large population studies. Here we investigate the effect of NLR on treatment response in MDD patients, and the role of sex and hippocampal volume on influencing this relationship. A sample of 124 MDD depressed inpatients (F = 80) underwent MRI acquisition, admission NLR was calculated by dividing absolute neutrophil by absolute lymphocyte counts and depression severity was assessed at admission and discharge via the Hamilton Depression Rating Scale (HDRS). As a measure of treatment response, delta HDRS was calculated. We found a significant moderation effect of sex on the relationship between NLR and Delta HDRS: a negative relation was found in females and a positive one in males. NLR was found to negatively affect hippocampal volumes in females. Both left and right hippocampal volume positively associated with Delta HDRS. Finally, left hippocampal volume mediated the effect of NLR on Delta HDRS in females. Sex moderated the relation between inflammation and treatment response in line with previous reports linking inflammation to hampered antidepressant effect in females. Further, this effect is partially mediated by hippocampal volume, suggesting that antidepressant response may be hampered by the detrimental effect of inflammation on the brain.

Sex matters: acute functional connectivity changes as markers of remission in late-life depression differ by sex.

The efficacy of antidepressant treatment in late-life is modest, a problem magnified by an aging population and increased prevalence of depression. Understanding the neurobiological mechanisms of treatment response in late-life depression (LLD) is imperative. Despite established sex differences in depression and neural circuits, sex differences associated with fMRI markers of antidepressant treatment response are underexplored. In this analysis, we assess the role of sex on the relationship of acute functional connectivity changes with treatment response in LLD. Resting state fMRI scans were collected at baseline and day one of SSRI/SNRI treatment for 80 LLD participants. One-day changes in functional connectivity (differential connectivity) were related to remission status after 12 weeks. Sex differences in differential connectivity profiles that distinguished remitters from non-remitters were assessed. A random forest classifier was used to predict the remission status with models containing various combinations of demographic, clinical, symptomatological, and connectivity measures. Model performance was assessed with area under the curve, and variable importance was assessed with permutation importance. The differential connectivity profile associated with remission status differed significantly by sex. We observed evidence for a difference in one-day connectivity changes between remitters and non-remitters in males but not females. Additionally, prediction of remission was significantly improved in male-only and female-only models over pooled models. Predictions of treatment outcome based on early changes in functional connectivity show marked differences between sexes and should be considered in future MR-based treatment decision-making algorithms.

Pharmacomicrobiomics of Antidepressants in Depression: A Systematic Review.

This systematic review evaluated the animal and human evidence for pharmacomicrobiomics (PMx) interactions of antidepressant medications. Studies of gut microbiota effects on functional and behavioral effects of antidepressants in human and animal models were identified from PubMed up to December 2022. Risk of bias was assessed, and results are presented as a systematic review following PRISMA guidelines. A total of 28 (21 animal, 7 human) studies were included in the review. The reviewed papers converged on three themes: (1) Antidepressants can alter the composition and metabolites of gut microbiota, (2) gut microbiota can alter the bioavailability of certain antidepressants, and (3) gut microbiota may modulate the clinical or modeled mood modifying effects of antidepressants. The majority (n = 22) of studies had at least moderate levels of bias present. While strong evidence is still lacking to understand the clinical role of antidepressant PMx in human health, there is evidence for interactions among antidepressants, microbiota changes, microbiota metabolite changes, and behavior. Well-controlled studies of the mediating and moderating effects of baseline and treatment-emergent changes in microbiota on therapeutic and adverse responses to antidepressants are needed to better establish a potential role of PMx in personalizing antidepressant treatment selection and response prediction.

Association of albumin and neutrophil-to-lymphocyte ratio with hormone-mediated depression in prostate cancer.

e24214 Background: Androgen deprivation therapy (ADT) increases the risk of developing depression which can affect quality of life and adherence to treatment. Depression has been associated to inflammation in patients with cancer. Albumin and neutrophil-to-lymphocyte ratio (NLR) are inflammatory biomarkers that to our knowledge have not been evaluated in hormone mediated depression in prostate cancer (PC). The aim of this study is to evaluate the severity of hormone related depression and its association with inflammatory biomarkers, demographics, and cancer treatment variables and to determine antidepressant treatment response. Methods: A retrospective chart review of consecutive referrals to psycho-oncology of PC patients on ADT with new onset depression was conducted. Demographics, cancer and psychiatric history, and laboratories within 1 week of initial visit were collected. Assessments for depression included clinical interview and patient-rated scale [Beck Depression Inventory (BDI)]. These measures were done at baseline (before starting an antidepressant) and follow-up to determine treatment response. Successful antidepressant treatment response is defined as ≥50% reduction in baseline BDI score. Statistical analysis comparing the association of clinical variables between depression, inflammatory biomarkers, and demographics included Pearson’s correlation, chi-square, ANOVA, t-test, and binary logistic regression was used to determine associations with depression. Data was dichotomized using median split for inflammatory biomarkers due to skewness. Results: A sample of 26 PC patients on leuprolide (mean age 70 years; 13 Hispanic, 13 non-Hispanic) included 13 with mild depression (BDI£20) and 13 with moderate- to-severe depression (BDI&gt;20). Moderate-to-severe depression was associated with higher NLR (≥3, p= .041), stage IV PC (n=15, p= .001), and Hispanics ( p= .033). Lower albumin (£3.9) was associated with higher age ( p= .007), higher NLR ( p= .006), and non-Hispanics ( p= .049) but not depression. Over 54% (13/24) of patients did not respond to antidepressant treatment. In logistic regression only NLR and Stage IV PC remained significantly associated with depression after adjusting for confounding variables. Conclusions: NLR and stage IV PC were both associated with moderate-to-severe depression. These results are likely due to increased level of inflammation in advanced cancer. Inflammation has been associated with treatment resistant depression which could explain why 54% of patients did not respond to antidepressant treatment. NLR is an inexpensive routinely used test in cancer centers that has the potential to be used as a biomarker of severity of depression in PC patients.

Modeling the antidepressant treatment response to transcranial magnetic stimulation using an exponential decay function

Recovery from depression often demonstrates a nonlinear pattern of treatment response, where the largest reduction in symptoms is observed early followed by smaller improvements. This study investigated whether this exponential pattern could model the antidepressant response to repetitive transcranial magnetic stimulation (TMS). Symptom ratings from 97 patients treated with TMS for depression were collected at baseline and after every five sessions. A nonlinear mixed-effects model was constructed using an exponential decay function. This model was also applied to group-level data from several published clinical trials of TMS for treatment-resistant depression. These nonlinear models were compared to corresponding linear models. In our clinical sample, response to TMS was well modeled with the exponential decay function, yielding significant estimates for all parameters and demonstrating superior fit compared to a linear model. Similarly, when applied to multiple studies comparing TMS modalities as well as to previously identified treatment response trajectories, the exponential decay models yielded consistently better fits compared to linear models. These results demonstrate that the antidepressant response to TMS follows a nonlinear pattern of improvement that is well modeled with an exponential decay function. This modeling offers a simple and useful framework to inform clinical decisions and future studies.

Early antidepressant treatment response prediction in major depression using clinical and TPH2 DNA methylation features based on machine learning approaches

ObjectiveTo identify DNA methylation and clinical features, and to construct machine learning classifiers to assign the patients with major depressive disorder (MDD) into responders and non-responders after a 2-week treatment into responders and non-responders.MethodHan Chinese patients (291 in total) with MDD comprised the study population. Datasets contained demographic information, environment stress factors, and the methylation levels of 38 methylated sites of tryptophan hydroxylase 2 (TPH2) genes in peripheral blood samples. Recursive Feature Elimination (RFE) was employed to select features. Five classification algorithms (logistic regression, classification and regression trees, support vector machine, logitboost and random forests) were used to establish the models. Performance metrics (AUC, F-Measure, G-Mean, accuracy, sensitivity, specificity, positive predictive value and negative predictive value) were computed with 5-fold-cross-validation. Variable importance was evaluated by random forest algorithm.ResultRF with RFE outperformed the other models in our samples based on the demographic information and clinical features (AUC = 61.2%, 95%CI: 60.1-62.4%) / TPH2 CpGs features (AUC = 66.6%, 95%CI: 65.4-67.8%) / both clinical and TPH2 CpGs features (AUC = 72.9%, 95%CI: 71.8-74.0%).ConclusionThe effects of TPH2 on the early-stage antidepressant response were explored by machine learning algorithms. On the basis of the baseline depression severity and TPH2 CpG sites, machine learning approaches can enhance our ability to predict the early-stage antidepressant response. Some potentially important predictors (e.g., TPH2-10-60 (rs2129575), TPH2-2-163 (rs11178998), age of first onset, age) in early-stage treatment response could be utilized in future fundamental research, drug development and clinical practice.

Circadian rhythm biomarker from wearable device data is related to concurrent antidepressant treatment response

Major depressive disorder (MDD) is associated with circadian rhythm disruption. Yet, no circadian rhythm biomarkers have been clinically validated for assessing antidepressant response. In this study, 40 participants with MDD provided actigraphy data using wearable devices for one week after initiating antidepressant treatment in a randomized, double-blind, placebo-controlled trial. Their depression severity was calculated pretreatment, after one week and eight weeks of treatment. This study assesses the relationship between parametric and nonparametric measures of circadian rhythm and change in depression. Results show significant association between a lower circadian quotient (reflecting less robust rhythmicity) and improvement in depression from baseline following first week of treatment (estimate = 0.11, F = 7.01, P = 0.01). There is insufficient evidence of an association between circadian rhythm measures acquired during the first week of treatment and outcomes after eight weeks of treatment. Despite this lack of association with future treatment outcome, this scalable, cost-effective biomarker may be useful for timely mental health care through remote monitoring of real-time changes in current depression.