Using biomarkers to predict treatment response in major depressive disorder: evidence from past and present studies

Immune-inflammatory effects of the multicomponent intervention FIBROWALK in outdoor and online formats for patients with fibromyalgia

The multicomponent intervention FIBROWALK integrates pain science education (PSE), therapeutic exercise, cognitive behavioral therapy (CBT), and mindfulness training for treating fibromyalgia (FM). This study investigated the effects of the FIBROWALK in online (FIBRO-On) and outdoor (FIBRO-Out) formats compared to treatment-as-usual (TAU) on core clinical variables along with serum immune-inflammatory biomarkers and brain-derived neurotrophic factor (BDNF). Furthermore, the predictive value of these biomarkers on clinical response to FIBROWALK was also evaluated. 120 participants were randomly divided into three groups: TAU, TAU + FIBRO-On or TAU + FIBRO-Out. Clinical and blood assessments were conducted pre-post treatment. Both FIBRO-Out and FIBRO-On showed effectiveness (vs TAU) by improving functional impairment and kinesiophobia. Individuals allocated to FIBRO-Out (vs TAU) additionally showed decreases in pain, fatigue, depressive symptoms, and serum IL-6 and IL-10 levels along with IL-6/IL-4 ratio; patients allocated to FIBRO-On only showed a less stepped increase in IL-6 compared to TAU. An exaggerated pro-inflammatory profile along with higher levels of BDNF at baseline predicted greater clinical improvements in both active treatment arms. Our results suggest that FIBROWALK -in online and outdoor formats- is effective in individuals with FM and has significant immune regulatory effects in FM patients, while immune-inflammatory pathways and BDNF levels may in part predict its clinical effectiveness. Trial registration number NCT05377567 (clinicaltrials.gov).

Insights for the Next Generation of Ketamine for the Treatment of Depressive Disorder

Treatment-resistant depression responds quickly to ketamine. As an N-methyl-d-aspartate receptor (NMDAR) antagonist, ketamine may affect prefrontal cortex (PFC) neurons. Recent investigations reveal that the (R)-enantiomer is the most effective and least abuseable antidepressant. The Food and Drug Administration approves only the (S)-enantiomer for medical usage. (2R,6R)-Hydroxynorketamine (HNK) inhibits mGlu2, linked to a Gi, in presynaptic glutamatergic neurons, increasing brain-derived neurotrophic factor (BDNF) release, which autocrinely activates Tropomyosin receptor kinase B (TrkB) and promotes synaptogenesis. Ketamine, originally an anesthetic, has garnered attention for its many pharmacological effects, including its potential as a rapid-acting antidepressant and recreational use. In this Perspective, we explore the synthesis, pharmacology, metabolism, and effects of ketamine and its metabolites in animal and human studies to explain the difference in the biological activity between the enantiomers.

Reinforcement learning processes as forecasters of depression remission

BACKGROUND

Aspects of reinforcement learning have been associated with specific depression symptoms and may inform the course of depressive illness.

METHODS

We applied support vector machines to investigate whether blood‑oxygen-level dependent (BOLD) responses linked with neural prediction error (nPE) and neural expected value (nEV) from a probabilistic learning task could forecast depression remission. We investigated whether predictions were moderated by treatment use or symptoms. Participants included 55 individuals (n = 39 female) with a depression diagnosis at baseline; 36 of these individuals completed standard cognitive behavioral therapy and 19 were followed during naturalistic course of illness. All participants were assessed for depression diagnosis at a follow-up visit.

RESULTS

Both nPE and nEV classifiers forecasted remission significantly better than null classifiers. The nEV classifier performed significantly better than the nPE classifier. We found no main or interaction effects of treatment status on nPE or nEV accuracy. We found a significant interaction between nPE-forecasted remission status and anhedonia, but not for negative affect or anxious arousal, when controlling for nEV-forecasted remission status.

LIMITATIONS

Our sample size, while comparable to that of other studies, limits options for maximizing and evaluating model performance. We addressed this with two standard methods for optimizing model performance (90:10 train and test scheme and bootstrapped sampling).

CONCLUSIONS

Results support nEV and nPE as relevant biobehavioral signals for understanding depression outcome independent of treatment status, with nEV being stronger than nPE as a predictor of remission. Reinforcement learning variables may be useful components of an individualized medicine framework for depression healthcare.

Challenges and Opportunities in Managing Geriatric Depression: The Role of Personalized Medicine and Age-Appropriate Therapeutic Approaches

The global aging population has experienced rapid growth in recent decades, leading to an increased prevalence of psychiatric disorders, particularly depression, among older adults. Depression in the geriatric population is often compounded by chronic physical conditions and various psychosocial factors, significantly impacting their quality of life. The main question raised in this review is as follows: how can personalized medicine and age-appropriate therapeutic approaches improve the management of geriatric depression? This paper explores the epidemiology of geriatric depression, highlighting the influence of gender, race, and socioeconomic status on its prevalence. The classification and diagnosis of geriatric depressive disorders, based on ICD-11 and DSM-5 criteria, reveal the complexity of managing these conditions in older adults. Personalized medicine (PM) emerges as a promising approach, focusing on tailoring treatments to the individual's genetic, clinical, and environmental characteristics. However, the application of PM in this demographic faces challenges, particularly in the context of pharmaceutical forms. The need for age-appropriate drug delivery systems is critical, given the prevalence of polypharmacy and issues such as dysphagia among the older patients. This study emphasizes the importance of developing patient-centric formulations to enhance the effectiveness of personalized therapy in geriatric patients.

Reward and Punishment Learning as Predictors of Cognitive Behavioral Therapy Response in Parkinson's Disease Comorbid with Clinical Depression

Depression is highly comorbid among individuals with Parkinson's Disease (PD), who often experience unique challenges to accessing and benefitting from empirically supported interventions like Cognitive Behavioral Therapy (CBT). Given the role of reward processing in both depression and PD, this study analyzed a subset (N = 25) of participants who participated in a pilot telemedicine intervention of PD-informed CBT, and also completed a Reward- and Punishment-Learning Task (RPLT) at baseline. At the conclusion of CBT, participants were categorized into treatment responders (n = 14) and non-responders (n = 11). Responders learned more optimally from negative rather than positive feedback on the RPLT, while this pattern was reversed in non-responders. Computational modeling suggested group differences in learning rate to negative feedback may drive the observed differences. Overall, the results suggest that a within-subject bias for punishment-based learning might help to predict response to CBT intervention for depression in those with PD.Plain Language Summary Performance on a Computerized Task may predict which Parkinson's Disease Patients benefit from Cognitive Behavioral Treatment of Clinical DepressionWhy was the study done? Clinical depression regularly arises in individuals with Parkinson's Disease (PD) due to the neurobiological changes with the onset and progression of the disease as well as the unique psychosocial difficulties associated with living with a chronic condition. Nonetheless, psychiatric disorders among individuals with PD are often underdiagnosed and likewise undertreated for a variety of reasons. The results of our study have implications about how to improve the accuracy and specificity of mental health treatment recommendations in the future to maximize benefits for individuals with PD, who often face additional barriers to accessing quality mental health treatment.What did the researchers do? We explored whether performance on a computerized task called the Reward- and Punishment-Learning Task (RPLT) helped to predict response to Cognitive Behavioral Therapy (CBT) for depression better than other predictors identified in previous studies. Twenty-five individuals with PD and clinical depression that completed a 10-week telehealth CBT program were assessed for: Demographics (Age, gender, etc.); Clinical information (PD duration, mental health diagnoses, levels of anxiety/depression, etc.); Neurocognitive performance (Memory, processing speed, impulse control, etc.); and RPLT performance.What did the researchers find? A total of 14 participants significantly benefitted from CBT treatment while 11 did not significantly benefit from treatment.There were no differences before treatment in the demographics, clinical information, and neurocognitive performance of those participants who ended up benefitting from the treatment versus those who did not.There were, however, differences before treatment in RPLT performance so that those individuals that benefitted from CBT seemed to learn better from negative feedback.What do the findings mean? Our results suggest that the CBT program benefitted those PD patients with clinical depression that seemed to overall learn best from avoiding punishment rather than obtaining reward which was targeted in CBT by focusing on increasing engagement in rewarding activities. The Reward- and Punishment-Learning Task hence may be a useful tool to help predict treatment response and provide more individualized recommendations on how to best maximize the benefits of psychotherapy for individuals with PD that may struggle to connect to mental health care. Caution is recommended about interpretating these results beyond this study as the overall number of participants was small and the data for this study were collected as part of a previous study so there was no opportunity to include additional measurements of interest.

Are There Biological Correlates of Response to Yoga-Based Interventions in Depression? A Critical Scoping Review

Depression is the most common mental disorder worldwide. Both antidepressants and psychotherapy are effective in treating depression, but the response to these treatments is often incomplete. Yoga-based interventions (YBIs) have been advocated by some researchers as a promising form of alternative treatment for depression. Recent research has attempted to identify the biological mechanisms associated with the antidepressant actions of YBIs. In this scoping review, conducted according to the PRISMA-ScR guidelines, the PubMed and Scopus databases were searched to retrieve research on biomarkers of response to YBIs in patients with depression. These studies were also critically reviewed to evaluate their methodological quality and any sources of bias. Nineteen studies were included in the review. Based on these studies, there is preliminary evidence that YBIs may be associated with increased serum brain-derived neurotrophic factor (BDNF) and reduced serum cortisol and interleukin-6 (IL-6) in patients with depression. However, many of these changes were also observed in the control arms, and the overall quality of the research was low. At present, it cannot be concluded that there are reliable biomarkers of response to YBIs in depression, though there are some potential biological correlates. Further advances in this field will depend critically on improvements in study design, particularly the minimization of sources of bias and the selection of more specific and sensitive biomarkers based on existing evidence from other treatment modalities.

A cross talk on the role of contemporary biomarkers in depression

Introduction: Biomarkers can be used to identify determinants of response to various treatments of mental disorders. Evidence to date demonstrates that markers of inflammatory, neurotransmitter, neurotrophic, neuroendocrine, and metabolic function can predict the psychological and physical consequences of depression in individuals, allowing for the development of new therapeutic targets with fewer side effects. Extensive research has included hundreds of potential biomarkers of depression, but their roles in depression, abnormal patients, and how bioinformatics can be used to improve diagnosis, treatment, and prognosis have not been determined or defined. To determine which biomarkers can and cannot be used to predict treatment response, classify patients for specific treatments, and develop targets for new interventions, proprietary strategies, and current research projects need to be tailored.Material and Methods: This review article focuses on - biomarker systems that would help in the further development and expansion of newer targets - which holds great promise for reducing the burden of depression.Results and Discussion: Further, this review point to the inflammatory response, metabolic marker, and microribonucleic acids, long non-coding RNAs, HPA axis which are - related to depression and can serve as future targets.

Optimal design for inference on the threshold of a biomarker

Enrichment designs with a continuous biomarker require the estimation of a threshold to determine the subpopulation benefitting from the treatment. This article provides the optimal allocation for inference in a two-stage enrichment design for treatment comparisons when a continuous biomarker is suspected to affect patient response. Several design criteria, associated with different trial objectives, are optimized under balanced or Neyman allocation and under equality of the first two empirical biomarker's moments. Moreover, we propose a new covariate-adaptive randomization procedure that converges to the optimum with the fastest available rate. Theoretical and simulation results show that this strategy improves the efficiency of a two-stage enrichment clinical trial, especially with smaller sample sizes and under heterogeneous responses.

Neural connectivity moderators and mechanisms of ketamine treatment among treatment-resistant depressed patients: a randomized controlled trial

BACKGROUND

Intravenous (IV) ketamine has emerged as a rapid and effective treatment for TRD. However, the specific neural mechanisms of ketamine's effects in humans remains unclear. Although neuroplasticity is implicated as a mechanism of action in animal models, relatively few randomized controlled trials (RCTs) in TRD patients have examined ketamine's impact on functional connectivity, a posited functional marker of neuroplasticity-particularly in the context of a mood-induction paradigm (termed miFC).

METHODS

152 adults with TRD (63% female; 37% male) were randomly allocated to receive a single infusion of ketamine or saline in a 2:1 ratio. We examined changes in connectivity (from baseline to 24-h post-infusion) that differed by treatment, and whether clinical treatment response at 24-h post-infusion was uniquely related (among patients allocated to ketamine relative to saline) to (1) pre-treatment connectivity and (2) changes in connectivity. We examined both miFC and rsFC, using prefrontal cortex and limbic seed regions. We also conducted a multiverse analysis to examine findings most robust against analytic decisions.

FINDINGS

Across both miFC and rsFC, ketamine was associated with greater in prefrontal/limbic connectivity compared to saline, and lower baseline connectivity of limbic and prefrontal regions predicted greater treatment response in patients receiving ketamine. Greater connectivity increases in participants receiving ketamine was uniquely related to greater treatment response. In addition, certain findings were identified as being reproducible against different analytic decisions in multiverse analyses.

INTERPRETATION

Our findings identify specific neural connectivity patterns impacted by ketamine and were uniquely related to outcomes following ketamine (relative to saline). These findings generally support prominent neuroplasticity models of ketamine's therapeutic efficacy. These findings lay new groundwork for understanding how to enhance and optimize ketamine treatments and develop novel rapid-acting treatments for depression.

FUNDING

This research was supported by NIH grant R01MH113857 and by the Clinical and Translational Sciences Institute at the University of Pittsburgh (UL1-TR-001857).

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.

Elevated serum leptin is associated with attenuated reward anticipation in major depressive disorder independent of peripheral C-reactive protein levels

Major depressive disorder (MDD) is associated with immunologic and metabolic alterations linked to central processing dysfunctions, including attenuated reward processing. This study investigated the associations between inflammation, metabolic hormones (leptin, insulin, adiponectin), and reward-related brain processing in MDD patients with high (MDD-High) and low (MDD-Low) C-reactive protein (CRP) levels compared to healthy comparison subjects (HC). Participants completed a blood draw and a monetary incentive delay task during functional magnetic resonance imaging. Although groups did not differ in insulin or adiponectin concentrations, both MDD-High (Wilcoxon p = 0.004, d = 0.65) and MDD-Low (Wilcoxon p = 0.046, d = 0.53) showed higher leptin concentrations than HC but did not differ from each other. Across MDD participants, higher leptin levels were associated with lower brain activation during reward anticipation in the left insula (r = - 0.30, p = 0.004) and left dorsolateral putamen (r = -- 0.24, p = 0.025). In contrast, within HC, higher leptin concentrations were associated with higher activation during reward anticipation in the same regions (insula: r = 0.40, p = 0.007; putamen: r = 0.37, p = 0.014). Depression may be characterized by elevated pro-inflammatory signaling via leptin concentrations through alternate inflammatory pathways distinct to CRP.

Integrated Module of Multidimensional Omics for Peripheral Biomarkers (iMORE) in patients with major depressive disorder: rationale and design of a prospective multicentre cohort study

INTRODUCTION

Major depressive disorder (MDD) represents a worldwide burden on healthcare and the response to antidepressants remains limited. Systems biology approaches have been used to explore the precision therapy. However, no reliable biomarker clinically exists for prognostic prediction at present. The objectives of the Integrated Module of Multidimensional Omics for Peripheral Biomarkers (iMORE) study are to predict the efficacy of antidepressants by integrating multidimensional omics and performing validation in a real-world setting. As secondary aims, a series of potential biomarkers are explored for biological subtypes.

METHODS AND ANALYSIS

iMore is an observational cohort study in patients with MDD with a multistage design in China. The study is performed by three mental health centres comprising an observation phase and a validation phase. A total of 200 patients with MDD and 100 healthy controls were enrolled. The protocol-specified antidepressants are selective serotonin reuptake inhibitors and serotonin-norepinephrine reuptake inhibitors. Clinical visits (baseline, 4 and 8 weeks) include psychiatric rating scales for symptom assessment and biospecimen collection for multiomics analysis. Participants are divided into responders and non-responders based on treatment response (>50% reduction in Montgomery-Asberg Depression Rating Scale). Antidepressants' responses are predicted and biomarkers are explored using supervised learning approach by integration of metabolites, cytokines, gut microbiomes and immunophenotypic cells. The accuracy of the prediction models constructed is verified in an independent validation phase.

ETHICS AND DISSEMINATION

The study was approved by the ethics committee of Shanghai Mental Health Center (approval number 2020-87). All participants need to sign a written consent for the study entry. Study findings will be published in peer-reviewed journals.

TRIAL REGISTRATION NUMBER

NCT04518592.

Effectiveness, cost-utility and physiological underpinnings of the FIBROWALK multicomponent therapy in online and outdoor format in individuals with fibromyalgia: Study protocol of a randomized, controlled trial (On&Out study)

Introduction: The On&Out study is aimed at assessing the effectiveness, cost-utility and physiological underpinnings of the FIBROWALK multicomponent intervention conducted in two different settings: online (FIBRO-On) or outdoors (FIBRO-Out). Both interventions have proved to be efficacious in the short-term but there is no study assessing their comparative effectiveness nor their long-term effects. For the first time, this study will also evaluate the cost-utility (6-month time-horizon) and the effects on immune-inflammatory biomarkers and Brain-Derived Neurotrophic Factor (BDNF) levels of both interventions. The objectives of this 6-month, randomized, controlled trial (RCT) are 1) to examine the effectiveness and cost-utility of adding FIBRO-On or FIBRO-Out to Treatment-As-Usual (TAU) for individuals with fibromyalgia (FM); 2) to identify pre-post differences in blood biomarker levels in the three study arms and 3) to analyze the role of process variables as mediators of 6-month follow-up clinical outcomes. Methods and analysis: Participants will be 225 individuals with FM recruited at Vall d'Hebron University Hospital (Barcelona, Spain), randomly allocated to one of the three study arms: TAU vs. TAU + FIBRO-On vs. TAU + FIBRO-Out. A comprehensive assessment to collect functional impairment, pain, fatigue, depressive and anxiety symptoms, perceived stress, central sensitization, physical function, sleep quality, perceived cognitive dysfunction, kinesiophobia, pain catastrophizing, psychological inflexibility in pain and pain knowledge will be conducted pre-intervention, at 6 weeks, post-intervention (12 weeks), and at 6-month follow-up. Changes in immune-inflammatory biomarkers [i.e., IL-6, CXCL8, IL-17A, IL-4, IL-10, and high-sensitivity C-reactive protein (hs-CRP)] and Brain-Derived Neurotrophic Factor will be evaluated in 40 participants in each treatment arm (total n = 120) at pre- and post-treatment. Quality of life and direct and indirect costs will be evaluated at baseline and at 6-month follow-up. Linear mixed-effects regression models using restricted maximum likelihood, mediational models and a full economic evaluation applying bootstrapping techniques, acceptability curves and sensitivity analyses will be computed. Ethics and dissemination: This study has been approved by the Ethics Committee of the Vall d'Hebron Institute of Research. The results will be actively disseminated through peer-reviewed journals, conference presentations, social media and various community engagement activities. Trial registration number NCT05377567 (clinicaltrials.gov).

Mental Activity as the Bridge between Neural Biomarkers and Symptoms of Psychiatric Illness

The Research Domain Criteria (RDoC) initiative challenges researchers to build neurobehavioral models of psychiatric illness with the hope that such models identify better targets that will yield more effective treatment. However, a guide for building such models was not provided and symptom heterogeneity within Diagnostic Statistical Manual categories has hampered progress in identifying endophenotypes that underlie mental illness. We propose that the best chance to discover viable biomarkers and treatment targets for psychiatric illness is to investigate a triangle of relationships: severity of a specific psychiatric symptom that correlates to mental activity that correlates to a neural activity signature. We propose that this is the minimal model complexity required to advance the field of psychiatry. With an understanding of how neural activity relates to the experience of the patient, a genuine understanding for how treatment imparts its therapeutic effect is possible. After the discovery of this three-fold relationship, causal testing is required in which the neural activity pattern is directly enhanced or suppressed to provide causal, instead of just correlational, evidence for the biomarker. We suggest using non-invasive brain stimulation (NIBS) as these techniques provide tools to precisely manipulate spatial and temporal activity patterns. We detail how this approach enabled the discovery of two orthogonal electroencephalography (EEG) activity patterns associated with anhedonia and anxiosomatic symptoms in depression that can serve as future treatment targets. Altogether, we propose a systematic approach for building neurobehavioral models for dimensional psychiatry.

Zebrafish as an Animal Model for Testing Agents with Antidepressant Potential

Depression is a serious mental disease that, according to statistics, affects 320 million people worldwide. Additionally, a current situation related to the COVID-19 pandemic has led to a significant deterioration of mental health in people around the world. So far, rodents have been treated as basic animal models used in studies on this disease, but in recent years, Danio rerio has emerged as a new organism that might serve well in preclinical experiments. Zebrafish have a lot of advantages, such as a quick reproductive cycle, transparent body during the early developmental stages, high genetic and physiological homology to humans, and low costs of maintenance. Here, we discuss the potential of the zebrafish model to be used in behavioral studies focused on testing agents with antidepressant potential.

Resting EEG theta connectivity and alpha power to predict repetitive transcranial magnetic stimulation response in depression: A non-replication from the ICON-DB consortium

OBJECTIVE

Our previous research showed high predictive accuracy at differentiating responders from non-responders to repetitive transcranial magnetic stimulation (rTMS) for depression using resting electroencephalography (EEG) and clinical data from baseline and one-week following treatment onset using a machine learning algorithm. In particular, theta (4-8 Hz) connectivity and alpha power (8-13 Hz) significantly differed between responders and non-responders. Independent replication is a necessary step before the application of potential predictors in clinical practice. This study attempted to replicate the results in an independent dataset.

METHODS

We submitted baseline resting EEG data from an independent sample of participants who underwent rTMS treatment for depression (N = 193, 128 responders) (Krepel et al., 2018) to the same between group comparisons as our previous research (Bailey et al., 2019).

RESULTS

Our previous results were not replicated, with no difference between responders and non-responders in theta connectivity (p = 0.250, Cohen's d = 0.1786) nor alpha power (p = 0.357, η_p² = 0.005).

CONCLUSIONS

These results suggest that baseline resting EEG theta connectivity or alpha power are unlikely to be generalisable predictors of response to rTMS treatment for depression.

SIGNIFICANCE

These results highlight the importance of independent replication, data sharing and using large datasets in the prediction of response research.

Assessment of a Prediction Model for Antidepressant Treatment Stability Using Supervised Topic Models

Importance

In the absence of readily assessed and clinically validated predictors of treatment response, pharmacologic management of major depressive disorder often relies on trial and error.

Objective

To assess a model using electronic health records to identify predictors of treatment response in patients with major depressive disorder.

Design, Setting, and Participants

This retrospective cohort study included data from 81 630 adults with a coded diagnosis of major depressive disorder from 2 academic medical centers in Boston, Massachusetts, including outpatient primary and specialty care clinics from December 1, 1997, to December 31, 2017. Data were analyzed from January 1, 2018, to March 15, 2020.

Exposures

Treatment with at least 1 of 11 standard antidepressants.

Main Outcomes and Measures

Stable treatment response, intended as a proxy for treatment effectiveness, defined as continued prescription of an antidepressant for 90 days. Supervised topic models were used to extract 10 interpretable covariates from coded clinical data for stability prediction. With use of data from 1 hospital system (site A), generalized linear models and ensembles of decision trees were trained to predict stability outcomes from topic features that summarize patient history. Held-out patients from site A and individuals from a second hospital system (site B) were evaluated.

Results

Among the 81 630 adults (56 340 women [69%]; mean [SD] age, 48.46 [14.75] years; range, 18.0-80.0 years), 55 303 reached a stable response to their treatment regimen during follow-up. For held-out patients from site A, the mean area under the receiver operating characteristic curve (AUC) for discrimination of the general stability outcome was 0.627 (95% CI, 0.615-0.639) for the supervised topic model with 10 covariates. In evaluation of site B, the AUC was 0.619 (95% CI, 0.610-0.627). Building models to predict stability specific to a particular drug did not improve prediction of general stability even when using a harder-to-interpret ensemble classifier and 9256 coded covariates (specific AUC, 0.647; 95% CI, 0.635-0.658; general AUC, 0.661; 95% CI, 0.648-0.672). Topics coherently captured clinical concepts associated with treatment response.

Conclusions and Relevance

The findings suggest that coded clinical data available in electronic health records may facilitate prediction of general treatment response but not response to specific medications. Although greater discrimination is likely required for clinical application, the results provide a transparent baseline for such studies.

Novel Treatment Targets Based on Insights in the Etiology of Depression: Role of IL-6 Trans-Signaling and Stress-Induced Elevation of Glutamate and ATP

Inflammation and psychological stress are risk factors for major depression and suicide. Both increase central glutamate levels and activate the hypothalamic-pituitary-adrenal axis and the sympathetic nervous system. Both factors also affect the function of the chloride transporters, Na-K-Cl-cotransporter-1 (NKCC1) and K-Cl-cotransporter-2 (KCC2), and provoke interleukin-6 (IL-6) trans-signaling. This leads to measurable increases in circulating corticosteroids, catecholamines, anxiety, somatic and psychological symptoms, and a decline in cognitive functions. Recognition of the sequence of pathological events allows the prediction of novel targets for therapeutic intervention. Amongst others, these include blockade of the big-K potassium channel, blockade of the P2X4 channel, TYK2-kinase inhibition, noradrenaline α2B-receptor antagonism, nicotinic α7-receptor stimulation, and the Sgp130Fc antibody. A better understanding of downstream processes evoked by inflammation and stress also allows suggestions for tentatively better biomarkers (e.g., SERPINA3N, MARCKS, or ¹³C-tryptophan metabolism).

Cardiopulmonary coupling analysis predicts early treatment response in depressed patients: A pilot study

This pilot study evaluated the effect of anti-depression treatment on sleep quality and symptoms of depression in patients with major depressive disorder, and identified cardiopulmonary coupling (CPC) indices for predicting early response. Forty-one Han Chinese patients with major depressive disorder were assessed for objective sleep quality before treatment (baseline) and at 2 weeks using CPC. Subjective sleep quality and depression levels were measured at baseline and 2 and 4 weeks after treatment, using the 24-item Hamilton Rating Scale for Depression (HAMD-24), Epworth Sleepiness Scale (ESS), and Pittsburgh Sleep Quality Index (PSQI). Objective and subjective sleep quality, and depression symptoms, improved after treatment. Significant correlations were found between CPC variables at baseline and depression symptom improvement after 2 weeks of treatment. Total sleep time at baseline significantly correlated with somnipathy score reduction at week 2. Total in-bed time at week 2 significantly correlated with reductions in anxiety/somatic symptoms and retardation score, and total HAMD-24 score at week 4. In binary logistic regression, the total in-bed time at baseline was significantly associated with treatment response. Our findings suggest that objective sleep quality measured by CPC analysis is useful for predicting treatment response to antidepressant treatment in patients with major depressive disorder.

Poor management of hypertension is an important precipitating factor for the development of acute aortic dissection

Hypertension is considered a key risk factor for acute aortic dissection (AAD). However, there is limited evidence demonstrating if hypertension management affects AAD development. The objective of this study was to investigate the role of hypertension management in AAD development in a Chinese population. A total of 825 AAD patients and 3300 age- and sex-matched controls were included. The authors analyzed data on demographics, chronic comorbidities, and hypertension management of all participants. Multiple logistic regression analysis was used to estimate the adjusted odds ratios (AOR) and 95% confidence intervals (95% CI) for the relationship between chronic comorbidities, as well as the management of hypertension and AAD risk. After adjusting for other related factors, multivariate logistic regression identified hypertension, chronic kidney disease, Marfan syndrome, history of cardiovascular surgery, and history of smoking as risk factors for AAD. Among the identified risk factors, hypertension was an important and controllable risk factor for AAD development. Thus, the authors further evaluated how hypertension management affects AAD development. A total of 848 controls and 585 AAD patients with hypertension were enrolled in this part of the study. Hypertensive patients with AAD had a longer history, higher stage, poorer medication compliance, and poor control rates of blood pressure, among which poor medication compliance (Irregular vs Regular P < 0.001; Never treated vs Regular P < 0.001) and uncontrolled hypertension (P < 0.001) significantly increased the risk of AAD development. In conclusion, uncontrolled hypertension and poor medication compliance are important precipitating and controllable factors for AAD development.

Therapy and prevention for mental health: What if mental diseases are mostly not brain disorders?

Neurobiology-based interventions for mental diseases and searches for useful biomarkers of treatment response have largely failed. Clinical trials should assess interventions related to environmental and social stressors, with long-term follow-up; social rather than biological endpoints; personalized outcomes; and suitable cluster, adaptive, and n-of-1 designs. Labor, education, financial, and other social/political decisions should be evaluated for their impacts on mental disease.

A systematic meta-review of predictors of antidepressant treatment outcome in major depressive disorder

INTRODUCTION

The heterogeneity of symptoms and complex etiology of depression pose a significant challenge to the personalization of treatment. Meanwhile, the current application of generic treatment approaches to patients with vastly differing biological and clinical profiles is far from optimal. Here, we conduct a meta-review to identify predictors of response to antidepressant therapy in order to select robust input features for machine learning models of treatment response. These machine learning models will allow us to learn associations between patient features and treatment response which have predictive value at the individual patient level; this learning can be optimized by selecting high-quality input features for the model. While current research is difficult to directly apply to the clinic, machine learning models built using knowledge gleaned from current research may become useful clinical tools.

METHODS

The EMBASE and MEDLINE/PubMed online databases were searched from January 1996 to August 2017, using a combination of MeSH terms and keywords to identify relevant literature reviews. We identified a total of 1909 articles, wherein 199 articles met our inclusion criteria.

RESULTS

An array of genetic, immune, endocrine, neuroimaging, sociodemographic, and symptom-based predictors of treatment response were extracted, varying widely in clinical utility.

LIMITATIONS

Due to heterogeneous sample sizes, effect sizes, publication biases, and methodological disparities across reviews, we could not accurately assess the strength and directionality of every predictor.

CONCLUSION

Notwithstanding our cautious interpretation of the results, we have identified a multitude of predictors that can be used to formulate a priori hypotheses regarding the input features for a computational model. We highlight the importance of large-scale research initiatives and clinically accessible biomarkers, as well as the need for replication studies of current findings. In addition, we provide recommendations for future improvement and standardization of research efforts in this field.

Hair cortisol and childhood trauma predict psychological therapy response in depression and anxiety disorders

OBJECTIVE

Around 30-50% of patients with depression and anxiety disorders fail to respond to standard psychological therapy. Given that cortisol affects cognition, patients with altered hypothalamic-pituitary-adrenal (HPA) axis functioning may benefit less from such treatments. To investigate this, reliable pretreatment cortisol measures are needed.

METHOD

N = 89 outpatients with depression and anxiety disorders were recruited before undergoing therapy within an Improving Access to Psychological Therapies (IAPT) service. Three-month hair cortisol was determined, and the Childhood Trauma Questionnaire was administered. Patients were classified as responders if they showed significant decreases in depression (>= 6 points on the Patient Health Questionnaire) or anxiety (>= 5 points on the Generalised Anxiety Disorder Scale).

RESULTS

Non-responders in terms of depression (57%) had lower pretreatment hair cortisol concentrations (P = 0.041) and reported more physical abuse (P = 0.024), sexual abuse (P = 0.010) and total trauma (P = 0.039) when compared to responders. Non-responders in terms of anxiety (48%) had lower pretreatment hair cortisol (P = 0.027), as well as higher levels of emotional abuse (P = 0.034), physical abuse (P = 0.042) and total trauma (P = 0.048).

CONCLUSION

If future research confirms hair cortisol to be a predictor of psychological therapy response, this may prove a useful clinical biomarker which identifies a subgroup requiring more intensive treatment.

GWAS-based machine learning approach to predict duloxetine response in major depressive disorder

Major depressive disorder (MDD) is one of the most prevalent psychiatric disorders and is commonly treated with antidepressant drugs. However, large variability is observed in terms of response to antidepressants. Machine learning (ML) models may be useful to predict treatment outcomes. A sample of 186 MDD patients received treatment with duloxetine for up to 8 weeks were categorized as "responders" based on a MADRS change >50% from baseline; or "remitters" based on a MADRS score ≤10 at end point. The initial dataset (N = 186) was randomly divided into training and test sets in a nested 5-fold cross-validation, where 80% was used as a training set and 20% made up five independent test sets. We performed genome-wide logistic regression to identify potentially significant variants related to duloxetine response/remission and extracted the most promising predictors using LASSO regression. Subsequently, classification-regression trees (CRT) and support vector machines (SVM) were applied to construct models, using ten-fold cross-validation. With regards to response, none of the pairs performed significantly better than chance (accuracy p > .1). For remission, SVM achieved moderate performance with an accuracy = 0.52, a sensitivity = 0.58, and a specificity = 0.46, and 0.51 for all coefficients for CRT. The best performing SVM fold was characterized by an accuracy = 0.66 (p = .071), sensitivity = 0.70 and a sensitivity = 0.61. In this study, the potential of using GWAS data to predict duloxetine outcomes was examined using ML models. The models were characterized by a promising sensitivity, but specificity remained moderate at best. The inclusion of additional non-genetic variables to create integrated models may improve prediction.

Understanding and Predicting Antidepressant Response: Using Animal Models to Move Toward Precision Psychiatry

There are two important gaps of knowledge in depression treatment, namely the lack of biomarkers predicting response to antidepressants and the limited knowledge of the molecular mechanisms underlying clinical improvement. However, individually tailored treatment strategies and individualized prescription are greatly needed given the huge socio-economic burden of depression, the latency until clinical improvement can be observed and the response variability to a particular compound. Still, individual patient-level antidepressant treatment outcomes are highly unpredictable. In contrast to other therapeutic areas and despite tremendous efforts during the past years, the genomics era so far has failed to provide biological or genetic predictors of clinical utility for routine use in depression treatment. Specifically, we suggest to (1) shift the focus from the group patterns to individual outcomes, (2) use dimensional classifications such as Research Domain Criteria, and (3) envision better planning and improved connections between pre-clinical and clinical studies within translational research units. In contrast to studies in patients, animal models enable both searches for peripheral biosignatures predicting treatment response and in depth-analyses of the neurobiological pathways shaping individual antidepressant response in the brain. While there is a considerable number of animal models available aiming at mimicking disease-like conditions such as those seen in depressive disorder, only a limited number of preclinical or truly translational investigations is dedicated to the issue of heterogeneity seen in response to antidepressant treatment. In this mini-review, we provide an overview on the current state of knowledge and propose a framework for successful translational studies into antidepressant treatment response.

The potential of predictive analytics to provide clinical decision support in depression treatment planning

PURPOSE OF REVIEW

To review progress developing clinical decision support tools for personalized treatment of major depressive disorder (MDD).

RECENT FINDINGS

Over the years, a variety of individual indicators ranging from biomarkers to clinical observations and self-report scales have been used to predict various aspects of differential MDD treatment response. Most of this work focused on predicting remission either with antidepressant medications versus psychotherapy, some antidepressant medications versus others, some psychotherapies versus others, and combination therapies versus monotherapies. However, to date, none of the individual predictors in these studies has been strong enough to guide optimal treatment selection for most patients. Interest consequently turned to decision support tools made up of multiple predictors, but the development of such tools has been hampered by small study sample sizes. Design recommendations are made here for future studies to address this problem.

SUMMARY

Recommendations include using large prospective observational studies followed by pragmatic trials rather than smaller, expensive controlled treatment trials for preliminary development of decision support tools; basing these tools on comprehensive batteries of inexpensive self-report and clinical predictors (e.g., self-administered performance-based neurocognitive tests) versus expensive biomarkers; and reserving biomarker assessments for targeted studies of patients not well classified by inexpensive predictor batteries.

Functional Recovery in Major Depressive Disorder: Providing Early Optimal Treatment for the Individual Patient

Major depressive disorder is an often chronic and recurring illness. Left untreated, major depressive disorder may result in progressive alterations in brain morphometry and circuit function. Recent findings, however, suggest that pharmacotherapy may halt and possibly reverse those effects. These findings, together with evidence that a delay in treatment is associated with poorer clinical outcomes, underscore the urgency of rapidly treating depression to full recovery. Early optimized treatment, using measurement-based care and customizing treatment to the individual patient, may afford the best possible outcomes for each patient. The aim of this article is to present recommendations for using a patient-centered approach to rapidly provide optimal pharmacological treatment to patients with major depressive disorder. Offering major depressive disorder treatment determined by individual patient characteristics (e.g., predominant symptoms, medical history, comorbidities), patient preferences and expectations, and, critically, their own definition of wellness provides the best opportunity for full functional recovery.

The Comparison of Effectiveness of Various Potential Predictors of Response to Treatment With SSRIs in Patients With Depressive Disorder

The substantial non-response rate in depressive patients indicates a continuing need to identify predictors of treatment outcome. The aim of this 6-week, open-label study was (1) to compare the efficacy of a priori defined predictors: ≥20% reduction in MADRS score at week 1, ≥20% reduction in MADRS score at week 2 (RM ≥ 20% W2), decrease of cordance (RC), and increase of serum and plasma level of brain-derived neurotrophic factor at week 1; and (2) to assess whether their combination yields higher efficacy in the prediction of response to selective serotonin re-uptake inhibitors (SSRIs) than when used singly. Twenty-one patients (55%) achieved a response to SSRIs. The RM ≥20% W2 (areas under curve-AUC = 0.83) showed better predictive efficacy compared to all other predictors with the exception of RC. The identified combined model (RM ≥ 20% W2 + RC), which predicted response with an 84% accuracy (AUC = 0.92), may be a useful tool in the prediction of response to SSRIs.

Neurocognitive performance as an endophenotype for mood disorder subgroups

BACKGROUND

There is growing evidence that neurocognitive function may be an endophenotype for mood disorders. The goal of this study is to examine the specificity and familiality of neurocognitive functioning across the full range of mood disorder subgroups, including Bipolar I (BP-I), Bipolar II (BP-II), Major Depressive Disorders (MDD), and controls in a community-based family study.

METHODS

A total of 310 participants from 137 families with mood spectrum disorders (n=151) and controls (n=159) completed the University of Pennsylvania's Computerized Neurocognitive Battery (CNB) that assessed the accuracy and speed of task performance across five domains. Mixed effects regression models tested association and familiality.

RESULTS

Compared to those without mood disorders, participants with BP-I had increased accuracy in complex cognition, while participants with MDD were more accurate in emotion recognition. There was also a significant familial association for accuracy of complex cognition. Mood disorder subgroups did not differ in performance speed in any of the domains.

LIMITATIONS

The small number of BP-I cases, and family size limited the statistical power of these analyses, and the cross-sectional assessment of neurocognitive function precluded our ability to determine whether performance precedes or post dates onset of disorder.

CONCLUSIONS

This is one of the few community-based family studies of potential neurocognitive endophenotypes that includes the full range of mood disorder subgroups. There were few differences in neurocognitive function except enhanced accuracy in specific domains among those with BP-I and MDD. The differential findings across specific mood disorder subgroups substantiate their heterogeneity in other biologic and endophenotypic domains.

Recent advances in predicting responses to antidepressant treatment

Major depressive disorder is one of the leading causes of disability in the world since depression is highly frequent and causes a strong burden. In order to reduce the duration of depressive episodes, clinicians would need to choose the most effective therapy for each individual right away. A prerequisite for this would be to have biomarkers at hand that would predict which individual would benefit from which kind of therapy (for example, pharmacotherapy or psychotherapy) or even from which kind of antidepressant class. In the past, neuroimaging, electroencephalogram, genetic, proteomic, and inflammation markers have been under investigation for their utility to predict targeted therapies. The present overview demonstrates recent advances in all of these different methodological areas and concludes that these approaches are promising but also that the aim to have such a marker available has not yet been reached. For example, the integration of markers from different systems needs to be achieved. With ongoing advances in the accuracy of sensing techniques and improvement of modelling approaches, this challenge might be achievable.

Biological profiling of prospective antidepressant response in major depressive disorder: Associations with (neuro)inflammation, fatty acid metabolism, and amygdala-reactivity

BACKGROUND

A better understanding of factors underlying antidepressant non-response may improve the prediction of which patients will respond to what treatment. Major depressive disorder (MDD) is associated with alterations in fatty acid metabolism, (neuro)inflammation and amygdala-reactivity. However, their mutual relations, and the extent to which they are associated with prospective antidepressant-response, remain unknown.

PURPOSE

To test (I) alterations in (neuro)inflammation and its associations with fatty acid metabolism and amygdala-reactivity in MDD-patients compared to controls, and (II) whether these alterations are associated with prospective paroxetine response.

METHODS

We compared 70 unmedicated MDD-patients with 51 matched healthy controls at baseline, regarding erythrocyte membrane omega-6 arachidonic acid (AA), inflammation [serum (high-sensitivity) C-reactive protein (CRP)], and in a subgroup amygdala-reactivity to emotional faces using functional magnetic resonance imaging (fMRI) (N=42). Subsequently, we treated patients with 12 weeks paroxetine, and repeated baseline measures after 6 and 12 weeks to compare non-responders, early-responders (response at 6 weeks), and late-responders (response at 12 weeks).

RESULTS

Compared to controls, MDD-patients showed higher CRP (p=0.016) and AA (p=0.019) after adjustment for confounders at baseline. AA and CRP were mutually correlated (p=0.043). In addition, patients showed a more negative relation between AA and left amygdala-reactivity (p=0.014). Moreover, AA and CRP were associated with antidepressant-response: early responders showed lower AA (p=0.018) and higher CRP-concentrations (p=0.008) than non-responders throughout the study.

CONCLUSION

Higher observed CRP and AA, their mutual association, and relation with amygdala-reactivity, are corroborative with a role for (neuro)inflammation in MDD. In addition, observed associations of these factors with prospective antidepressant-response suggest a potential role as biomarkers. Future studies in independent samples are needed to replicate and test the clinical applicability of these biological predictors for treatment response to result in a precision/personalized medicine approach for treatment.

Peripheral cytokine and chemokine alterations in depression: a meta-analysis of 82 studies

OBJECTIVE

To conduct a systematic review and meta-analysis of studies that measured cytokine and chemokine levels in individuals with major depressive disorder (MDD) compared to healthy controls (HCs).

METHOD

The PubMed/MEDLINE, EMBASE, and PsycINFO databases were searched up until May 30, 2016. Effect sizes were estimated with random-effects models.

RESULT

Eighty-two studies comprising 3212 participants with MDD and 2798 HCs met inclusion criteria. Peripheral levels of interleukin-6 (IL-6), tumor necrosis factor (TNF)-alpha, IL-10, the soluble IL-2 receptor, C-C chemokine ligand 2, IL-13, IL-18, IL-12, the IL-1 receptor antagonist, and the soluble TNF receptor 2 were elevated in patients with MDD compared to HCs, whereas interferon-gamma levels were lower in MDD (Hedge's g = -0.477, P = 0.043). Levels of IL-1β, IL-2, IL-4, IL-8, the soluble IL-6 receptor (sIL-6R), IL-5, CCL-3, IL-17, and transforming growth factor-beta 1 were not significantly altered in individuals with MDD compared to HCs. Heterogeneity was large (I² : 51.6-97.7%), and sources of heterogeneity were explored (e.g., age, smoking status, and body mass index).

CONCLUSION

Our results further characterize a cytokine/chemokine profile associated with MDD. Future studies are warranted to further elucidate sources of heterogeneity, as well as biosignature cytokines secreted by other immune cells.

Efficacy of Desvenlafaxine Compared With Placebo in Major Depressive Disorder Patients by Age Group and Severity of Depression at Baseline

PURPOSE

This post hoc meta-analysis evaluated the efficacy and safety of desvenlafaxine 50 and 100 mg versus placebo across age groups and severity of depression at baseline in patients with major depressive disorder.

METHODS

Data from placebo and desvenlafaxine 50-mg and 100-mg dose arms were pooled from 9 short-term, placebo-controlled, major depressive disorder studies (N = 4279). Effects of age (18-40 years, >40 to <55 years, 55-<65 years, and ≥65 years) and baseline depression severity (mild, 17-item Hamilton Rating Scale for Depression total score [HAM-D17] ≤18; moderate, HAM-D17 >18 to <25; severe, HAM-D17 ≥25) on desvenlafaxine efficacy were assessed using analysis of covariance for continuous end points and logistic regression for categorical end points.

FINDINGS

Desvenlafaxine-treated (50 or 100 mg/d) patients had significantly (P < 0.05, 2-sided) greater improvement in most measures of depression and function compared with placebo for patients 18 to 40 years, older than 40 to younger than 55 years, and 55 to younger than 65 years, with no significant evidence of an effect of age. Desvenlafaxine significantly improved most measures of depression and function in moderately and severely depressed patients. There was a significant baseline severity by treatment interaction for HAM-D17 total score only (P = 0.027), with a larger treatment effect for the severely depressed group.

IMPLICATIONS

Desvenlafaxine significantly improved depressive symptoms in patients younger than 65 years and in patients with moderate or severe baseline depression. Sample sizes were not adequate to assess desvenlafaxine efficacy in patients 65 years or older or with mild baseline depression.

Transcranial Direct Current Stimulation: Considerations for Research in Adolescent Depression

Adolescent depression is a prevalent disorder with substantial morbidity and mortality. Current treatment interventions do not target relevant pathophysiology and are frequently ineffective, thereby leading to a substantial burden for individuals, families, and society. During adolescence, the prefrontal cortex undergoes extensive structural and functional changes. Recent work suggests that frontolimbic development in depressed adolescents is delayed or aberrant. The judicious application of non-invasive brain stimulation techniques to the prefrontal cortex may present a promising opportunity for durable interventions in adolescent depression. Transcranial direct current stimulation (tDCS) applies a low-intensity, continuous current that alters cortical excitability. While this modality does not elicit action potentials, it is thought to manipulate neuronal activity and neuroplasticity. Specifically, tDCS may modulate N-methyl-d-aspartate receptors and L-type voltage-gated calcium channels and effect changes through long-term potentiation or long-term depression-like mechanisms. This mini-review considers the neurobiological rationale for developing tDCS protocols in adolescent depression, reviews existing work in adult mood disorders, surveys the existing tDCS literature in adolescent populations, reviews safety studies, and discusses distinct ethical considerations in work with adolescents.

Predisposition to treatment response in major depressive episode: A peripheral blood gene coexpression network analysis

Antidepressant efficacy is insufficient, unpredictable and poorly understood in major depressive episode (MDE). Gene expression studies allow for the identification of significantly dysregulated genes but can limit the exploration of biological pathways. In the present study, we proposed a gene coexpression analysis to investigate biological pathways associated with treatment response predisposition and their regulation by microRNAs (miRNAs) in peripheral blood samples of MDE and healthy control subjects. We used a discovery cohort that included 34 MDE patients that were given 12-week treatment with citalopram and 33 healthy controls. Two replication cohorts with similar design were also analyzed. Expression-based gene network was built to define clusters of highly correlated sets of genes, called modules. Association between each module's first principal component of the expression data and clinical improvement was tested in the three cohorts. We conducted gene ontology analysis and miRNA prediction based on the module gene list. Nine of the 59 modules from the gene coexpression network were associated with clinical improvement. The association was partially replicated in other cohorts. Gene ontology analysis demonstrated that 4 modules were associated with cytokine production, acute inflammatory response or IL-8 functions. Finally, we found 414 miRNAs that may regulate one or several modules associated with clinical improvement. By contrast, only 12 miRNAs were predicted to specifically regulate modules unrelated to clinical improvement. Our gene coexpression analysis underlines the importance of inflammation-related pathways and the involvement of a large miRNA program as biological processes predisposing associated with antidepressant response.

The establishment of the objective diagnostic markers and personalized medical intervention in patients with major depressive disorder: rationale and protocol

BACKGROUND

Major depressive disorders (MDD) is a common mental disorder with high prevalence, frequent relapse and associated with heavy disease burden. Heritability, environment and their interaction play important roles in the development of MDD. MDD patients usually display a wide variation in clinical symptoms and signs, while the diagnosis of MDD is relatively subjective. The treatment response varies substantially between different subtypes of MDD patients and only half respond adequately to the first antidepressant. This study aims to define subtypes of MDD, develop multi-dimension diagnostic test and combined predictors for improving the diagnostic accuracy and promoting personalized intervention in MDD patients.

METHODS/DESIGN

This is a multi-center, multi-stage and prospective study. The first stage of this study is a case-control study, aims to explore the risk factors for developing MDD and then define the subtypes of MDD using 1200 MDD patients and 1200 healthy controls with a set of questionnaire. The second stage is a diagnostic test, aims to indentify and replicate the potential indicators to assist MDD diagnosis using 600 MDD patients and 300 healthy controls from the first stage with a set of questionnaire, neuropsychological assessment and a series of biomarkers. The third stage is a 96-week longitudinal study, including 8-week acute period treatment and 88-week stable period treatment, aims to identify overall predictors of treatment effectiveness on MDD at week 8 post treatment and to explore the predictors on MDD prognosis in the following 2 years using 600 MDD patients from the first stage with a set of questionnaire, neuropsychological assessment and a series of biomarkers. The primary outcome measure is the change of the total score of 17-Item Hamilton Rating Scale for Depression.

DISCUSSION

This study will provide strong and suitable evidence for enhancing the accuracy of MDD diagnosis and promoting personalized treatment for MDD patients in clinical practice.

TRIAL REGISTRATION

ClinicalTrials.gov: NCT02023567 ; registration date: December 2013.

Telomere length as a predictor of response to Pioglitazone in patients with unremitted depression: a preliminary study

We studied peripheral leukocyte telomere length (LTL) as a predictor of antidepressant response to PPAR-γ agonist in patients with unremitted depression. In addition we examined correlation between LTL and the insulin resistance (IR) status in these subjects. Forty-two medically stable men and women ages 23-71 with non-remitted depression participated in double-blind placebo-controlled add-on of Pioglitazone to treatment-as-usual. Oral glucose tolerance tests were administered at baseline and at 12 weeks. Diagnostic evaluation of psychiatric disorders was performed at baseline and mood severity was followed weekly throughout the duration of the trial. At baseline, no differences in LTL were detected by depression severity, duration or chronicity. LTL was also not significantly different between insulin-resistant and insulin-sensitive subjects at baseline. Subjects with longer telomeres exhibited greater declines in depression severity in the active arm, but not in a placebo arm, P=0.005, r=-0.63, 95% confidence interval (95% CI)=(-0.84,-0.21). In addition, LTL predicted improvement in insulin sensitivity in the group overall and did not differ between intervention arms, P=0.036, r=-0.44, 95% CI=(-0.74,0.02) for the active arm, and P=0.026, r=-0.50, 95% CI=(-0.78,-0.03) for the placebo arm. LTL may emerge as a viable predictor of antidepressant response. An association between insulin sensitization and LTL regardless of the baseline IR status points to potential role of LTL as a non-specific moderator of metabolic improvement in these patients.

Studying depression using imaging and machine learning methods

Depression is a complex clinical entity that can pose challenges for clinicians regarding both accurate diagnosis and effective timely treatment. These challenges have prompted the development of multiple machine learning methods to help improve the management of this disease. These methods utilize anatomical and physiological data acquired from neuroimaging to create models that can identify depressed patients vs. non-depressed patients and predict treatment outcomes. This article (1) presents a background on depression, imaging, and machine learning methodologies; (2) reviews methodologies of past studies that have used imaging and machine learning to study depression; and (3) suggests directions for future depression-related studies.

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Past studies successfully studied depression using machine learning and imaging.

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Past studies have limitations in their methods.

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Methods for future studies can be improved.

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Future studies could yield more robust models to diagnosis and treat depression.