Predictors of remission in depression to individual and combined treatments (PReDICT): study protocol for a randomized controlled trial

The utility of measuring daily hassles and uplifts in understanding outcomes to treatments for major depressive disorder

Little is known about the effects of common daily experiences in patients with major depressive disorder (MDD). The Daily Hassles and Uplifts Scale (HUPS) was assessed in 142 treatment-naïve adult MDD outpatients randomized to 12 weeks of treatment with either antidepressant medication (ADM) or Cognitive Behavior Therapy (CBT). Three HUPS measures were analyzed: hassle frequency (HF), uplift frequency (UF), and the mean hassle intensity to mean uplift intensity ratio (MHI:MUI). Remission after treatment was not predicted by these baseline HUPS measures and did not moderate outcomes by treatment type. In contrast, HUPS measures significantly changed with treatment and were impacted by remission status. Specifically, HF and MHI:MUI decreased and UF increased from baseline to week 12, with remission leading to significantly greater decreases in HF and MHI:MUI compared to non-remission. ADM-treated patients demonstrated significant improvements on all three HUPS measures regardless of remission status. In contrast, remitters to CBT demonstrated significant improvements in HF and MHI:MUI but not UF; among CBT non-remitters the only significant change was a reduction in HF. The changes in HUPS measures are consistent with how affective biases are impacted by treatments and support the potential value of increasing attention to positive events in CBT.

Metabolomics Signatures of serotonin reuptake inhibitor (Escitalopram), serotonin norepinephrine reuptake inhibitor (Duloxetine) and Cognitive Behavior Therapy on Key Neurotransmitter Pathways in Major Depressive Disorder

Metabolomics provides powerful tools that can inform about heterogeneity in disease and response to treatments. In this study, we employed an electrochemistry-based targeted metabolomics platform to assess the metabolic effects of three randomly-assigned treatments: escitalopram, duloxetine, and Cognitive Behavior Therapy (CBT) in 163 treatment-naïve outpatients with major depressive disorder. Serum samples from baseline and 12 weeks post-treatment were analyzed using targeted liquid chromatography-electrochemistry for metabolites related to tryptophan, tyrosine metabolism and related pathways. Changes in metabolite concentrations related to each treatment arm were identified and compared to define metabolic signatures of exposure. In addition, association between metabolites and depressive symptom severity (assessed with the 17-item Hamilton Rating Scale for Depression [HRSD17]) and anxiety symptom severity (assessed with the 14-item Hamilton Rating Scale for Anxiety [HRSA14]) were evaluated, both at baseline and after 12 weeks of treatment. Significant reductions in serum serotonin level and increases in tryptophan-derived indoles that are gut bacterially derived were observed with escitalopram and duloxetine arms but not in CBT arm. These include indole-3-propionic acid (I3PA), indole-3-lactic acid (I3LA) and Indoxyl sulfate (IS), a uremic toxin. Purine-related metabolites were decreased across all arms. Different metabolites correlated with improved symptoms in the different treatment arms revealing potentially different mechanisms between response to antidepressant medications and to CBT.

Brain Circuit-Derived Biotypes for Treatment Selection in Mood Disorders: A Critical Review and Illustration of a Functional Neuroimaging Tool for Clinical Translation

Although the lifetime burden due to major depressive disorder is increasing, we lack tools for selecting the most effective treatments for each patient. One-third to one-half of patients with major depressive disorder do not respond to treatment, and we lack strategies for selecting among available treatments or expediting access to new treatment options. This critical review concentrates on functional neuroimaging as a modality of measurement for precision psychiatry. We begin by summarizing the current landscape of how functional neuroimaging-derived circuit predictors can forecast treatment outcomes in depression. Then, we outline the opportunities and challenges in integrating circuit predictors into clinical practice. We highlight one standardized and reproducible approach for quantifying brain circuit function at an individual level, which could serve as a model for clinical translation. We conclude by evaluating the prospects and practicality of employing neuroimaging tools, such as the one that we propose, in routine clinical practice.

Promising new pharmacological targets for depression: The search for efficacy

Pharmacological treatment of major depressive disorder (MDD) still relies on the use of serotonergic drugs, despite their limited efficacy. A few mechanistically new drugs have been developed in recent years, but many fail in clinical trials. Several hypotheses have been proposed to explain MDD pathophysiology, indicating that physiological processes such as neuroplasticity, circadian rhythms, and metabolism are potential targets. Here, we review the current state of pharmacological treatments for MDD, as well as the preclinical and clinical evidence for an antidepressant effect of molecules that target non-serotonergic systems. We offer some insights into the challenges facing the development of new antidepressant drugs, and the prospect of finding more effectiveness for each target discussed.

Functional connectivity of salience and affective networks among remitted depressed patients predicts episode recurrence

Recurrent episodes in major depressive disorder (MDD) are common but the neuroimaging features predictive of recurrence are not established. Participants in the Predictors of Remission in Depression to Individual and Combined Treatments (PReDICT) study who achieved remission after 12 weeks of treatment withcognitive behavior therapy, duloxetine, or escitalopram were prospectively monitored for up to 21 months for recurrence. Neuroimaging markers predictive of recurrence were identified from week 12 functional magnetic resonance imaging scans by analyzing whole-brain resting state functional connectivity (RSFC) using seeds for four brain networks that are altered in MDD. Neuroimaging correlates of established clinical predictors of recurrence, including the magnitude of depressive (Hamilton Depression Rating Scale), anxiety (Hamilton Anxiety Rating Scale) symptom severity at time of remission, and a comorbid anxiety disorder were examined for their similarity to the neuroimaging predictors of recurrence. Of the 344 patients randomized in PReDICT, 61 achieved remission and had usable scans for analysis, 9 of whom experienced recurrence during follow-up. Recurrence was predicted by: 1) increased RSFC between subcallosal cingulate cortex (SCC) and right anterior insula, 2) decreased RSFC between SCC and bilateral primary visual cortex, and 3) decreased RSFC between insula and bilateral caudate. Week 12 depression and anxiety scores were negatively correlated with RSFC strength between executive control and default mode networks, but they were not correlated with the three RSFC patterns predicting recurrence. We conclude that altered RSFC in SCC and anterior insula networks are prospective risk factors associated with MDD recurrence, reflecting additional sources of risk beyond clinical measures.

Circulating metabolites modulated by diet are associated with depression

Metabolome reflects the interplay of genome and exposome at molecular level and thus can provide deep insights into the pathogenesis of a complex disease like major depression. To identify metabolites associated with depression we performed a metabolome-wide association analysis in 13,596 participants from five European population-based cohorts characterized for depression, and circulating metabolites using ultra high-performance liquid chromatography/tandem accurate mass spectrometry (UHPLC/MS/MS) based Metabolon platform. We tested 806 metabolites covering a wide range of biochemical processes including those involved in lipid, amino-acid, energy, carbohydrate, xenobiotic and vitamin metabolism for their association with depression. In a conservative model adjusting for life style factors and cardiovascular and antidepressant medication use we identified 8 metabolites, including 6 novel, significantly associated with depression. In individuals with depression, increased levels of retinol (vitamin A), 1-palmitoyl-2-palmitoleoyl-GPC (16:0/16:1) (lecithin) and mannitol/sorbitol and lower levels of hippurate, 4-hydroxycoumarin, 2-aminooctanoate (alpha-aminocaprylic acid), 10-undecenoate (11:1n1) (undecylenic acid), 1-linoleoyl-GPA (18:2) (lysophosphatidic acid; LPA 18:2) are observed. These metabolites are either directly food derived or are products of host and gut microbial metabolism of food-derived products. Our Mendelian randomization analysis suggests that low hippurate levels may be in the causal pathway leading towards depression. Our findings highlight putative actionable targets for depression prevention that are easily modifiable through diet interventions.

Candidate biomarkers in psychiatric disorders: state of the field

The field of psychiatry is hampered by a lack of robust, reliable and valid biomarkers that can aid in objectively diagnosing patients and providing individualized treatment recommendations. Here we review and critically evaluate the evidence for the most promising biomarkers in the psychiatric neuroscience literature for autism spectrum disorder, schizophrenia, anxiety disorders and post-traumatic stress disorder, major depression and bipolar disorder, and substance use disorders. Candidate biomarkers reviewed include various neuroimaging, genetic, molecular and peripheral assays, for the purposes of determining susceptibility or presence of illness, and predicting treatment response or safety. This review highlights a critical gap in the biomarker validation process. An enormous societal investment over the past 50 years has identified numerous candidate biomarkers. However, to date, the overwhelming majority of these measures have not been proven sufficiently reliable, valid and useful to be adopted clinically. It is time to consider whether strategic investments might break this impasse, focusing on a limited number of promising candidates to advance through a process of definitive testing for a specific indication. Some promising candidates for definitive testing include the N170 signal, an event-related brain potential measured using electroencephalography, for subgroup identification within autism spectrum disorder; striatal resting-state functional magnetic resonance imaging (fMRI) measures, such as the striatal connectivity index (SCI) and the functional striatal abnormalities (FSA) index, for prediction of treatment response in schizophrenia; error-related negativity (ERN), an electrophysiological index, for prediction of first onset of generalized anxiety disorder, and resting-state and structural brain connectomic measures for prediction of treatment response in social anxiety disorder. Alternate forms of classification may be useful for conceptualizing and testing potential biomarkers. Collaborative efforts allowing the inclusion of biosystems beyond genetics and neuroimaging are needed, and online remote acquisition of selected measures in a naturalistic setting using mobile health tools may significantly advance the field. Setting specific benchmarks for well-defined target application, along with development of appropriate funding and partnership mechanisms, would also be crucial. Finally, it should never be forgotten that, for a biomarker to be actionable, it will need to be clinically predictive at the individual level and viable in clinical settings.

Interplay of Metabolome and Gut Microbiome in Individuals With Major Depressive Disorder vs Control Individuals

Importance

Metabolomics reflect the net effect of genetic and environmental influences and thus provide a comprehensive approach to evaluating the pathogenesis of complex diseases, such as depression.

Objective

To identify the metabolic signatures of major depressive disorder (MDD), elucidate the direction of associations using mendelian randomization, and evaluate the interplay of the human gut microbiome and metabolome in the development of MDD.

Design, Setting and Participants

This cohort study used data from participants in the UK Biobank cohort (n = 500 000; aged 37 to 73 years; recruited from 2006 to 2010) whose blood was profiled for metabolomics. Replication was sought in the PREDICT and BBMRI-NL studies. Publicly available summary statistics from a 2019 genome-wide association study of depression were used for the mendelian randomization (individuals with MDD = 59 851; control individuals = 113 154). Summary statistics for the metabolites were obtained from OpenGWAS in MRbase (n = 118 000). To evaluate the interplay of the metabolome and the gut microbiome in the pathogenesis of depression, metabolic signatures of the gut microbiome were obtained from a 2019 study performed in Dutch cohorts. Data were analyzed from March to December 2021.

Main Outcomes and Measures

Outcomes were lifetime and recurrent MDD, with 249 metabolites profiled with nuclear magnetic resonance spectroscopy with the Nightingale platform.

Results

The study included 6811 individuals with lifetime MDD compared with 51 446 control individuals and 4370 individuals with recurrent MDD compared with 62 508 control individuals. Individuals with lifetime MDD were younger (median [IQR] age, 56 [49-62] years vs 58 [51-64] years) and more often female (4447 [65%] vs 2364 [35%]) than control individuals. Metabolic signatures of MDD consisted of 124 metabolites spanning the energy and lipid metabolism pathways. Novel findings included 49 metabolites, including those involved in the tricarboxylic acid cycle (ie, citrate and pyruvate). Citrate was significantly decreased (β [SE], -0.07 [0.02]; FDR = 4 × 10-04) and pyruvate was significantly increased (β [SE], 0.04 [0.02]; FDR = 0.02) in individuals with MDD. Changes observed in these metabolites, particularly lipoproteins, were consistent with the differential composition of gut microbiota belonging to the order Clostridiales and the phyla Proteobacteria/Pseudomonadota and Bacteroidetes/Bacteroidota. Mendelian randomization suggested that fatty acids and intermediate and very large density lipoproteins changed in association with the disease process but high-density lipoproteins and the metabolites in the tricarboxylic acid cycle did not.

Conclusions and Relevance

The study findings showed that energy metabolism was disturbed in individuals with MDD and that the interplay of the gut microbiome and blood metabolome may play a role in lipid metabolism in individuals with MDD.

Development of a Bayesian multimodal model to detect biomarkers in neuroimaging studies

In this article, we developed a Bayesian multimodal model to detect biomarkers (or neuromarkers) using resting-state functional and structural data while comparing a late-life depression group with a healthy control group. Biomarker detection helps determine a target for treatment intervention to get the optimal therapeutic benefit for treatment-resistant patients. The borrowing strength of the structural connectivity has been quantified for functional activity while detecting the biomarker. In the biomarker searching process, thousands of hypotheses are generated and tested simultaneously using our novel method to control the false discovery rate for small samples. Several existing statistical approaches, frequently used in analyzing neuroimaging data have been investigated and compared via simulation with the proposed approach to show its excellent performance. Results are illustrated with a live data set generated in a late-life depression study. The role of detected biomarkers in terms of cognitive function has been explored.

Shared and Unique Changes in Brain Connectivity Among Depressed Patients After Remission With Pharmacotherapy Versus Psychotherapy

OBJECTIVE

The authors sought to determine the shared and unique changes in brain resting-state functional connectivity (rsFC) between patients with major depressive disorder who achieved remission with cognitive-behavioral therapy (CBT) or with antidepressant medication.

METHODS

The Predictors of Remission in Depression to Individual and Combined Treatments (PReDICT) trial randomized adults with treatment-naive major depressive disorder to 12 weeks of treatment with CBT (16 1-hour sessions) or medication (duloxetine 30-60 mg/day or escitalopram 10-20 mg/day). Resting-state functional MRI scans were performed at baseline and at week 12. The primary outcome was change in the whole-brain rsFC of four seeded brain networks among participants who achieved remission.

RESULTS

Of the 131 completers with usable MRI data (74 female; mean age, 39.8 years), remission was achieved by 19 of 40 CBT-treated and 45 of 91 medication-treated patients. Three patterns of connectivity changes were observed. First, those who remitted with either treatment shared a pattern of reduction in rsFC between the subcallosal cingulate cortex and the motor cortex. Second, reciprocal rsFC changes were observed across multiple networks, primarily increases in CBT remitters and decreases in medication remitters. And third, in CBT remitters only, rsFC increased within the executive control network and between the executive control network and parietal attention regions.

CONCLUSIONS

Remission from major depression via treatment with CBT or medication is associated with changes in rsFC that are mostly specific to the treatment modality, providing biological support for the clinical practice of switching between or combining these treatment approaches. Medication is associated with broadly inhibitory effects. In CBT remitters, the increase in rsFC strength between networks involved in cognitive control and attention provides biological support for the theorized mechanism of CBT. Reducing affective network connectivity with motor systems is a shared process important for remission with both CBT and medication.

Temporal multi-step predictive modeling of remission in major depressive disorder using early stage treatment data; STAR*D based machine learning approach

BACKGROUND

Artificial intelligence is currently being used to facilitate early disease detection, better understand disease progression, optimize medication/treatment dosages, and uncover promising novel treatments and potential outcomes.

METHODS

Utilizing the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) dataset, we built a machine learning model to predict depression remission rates using same clinical data as features for each of the first three antidepressant treatment steps in STAR*D. We only used early treatment data (baseline and first follow up) in each STAR*D step to temporally analyze predictive features of remission at the end of the step.

RESULTS

Our model showed significant prediction performance across the three treatment steps, At step 1, Model accuracy was 66 %; sensitivity-65 %, specificity-67 %, positive predictive value (PPV)-65.5 %, and negative predictive value (NPV)-66.6 %. At step 2, model accuracy was 71.3 %, sensitivity-74.3 %, specificity-69 %, PPV-64.5 %, and NPV-77.9 %. At step 3, accuracy reached 84.6 %; sensitivity-69 %, specificity-88.8 %, PPV-67 %, and NPV-91.1 %. Across all three steps, the early Quick Inventory of Depressive Symptomatology-Self-Report (QIDS-SR) scores were key elements in predicting the final treatment outcome. The model also identified key sociodemographic factors that predicted treatment remission at different steps.

LIMITATIONS

The retrospective design, lack of replication in an independent dataset, and the use of "a complete case analysis" model in our analysis.

CONCLUSIONS

This proof-of-concept study showed that using early treatment data, multi-step temporal prediction of depressive symptom remission results in clinically useful accuracy rates. Whether these predictive models are generalizable deserves further study.

AI-based dimensional neuroimaging system for characterizing heterogeneity in brain structure and function in major depressive disorder: COORDINATE-MDD consortium design and rationale

BACKGROUND

Efforts to develop neuroimaging-based biomarkers in major depressive disorder (MDD), at the individual level, have been limited to date. As diagnostic criteria are currently symptom-based, MDD is conceptualized as a disorder rather than a disease with a known etiology; further, neural measures are often confounded by medication status and heterogeneous symptom states.

METHODS

We describe a consortium to quantify neuroanatomical and neurofunctional heterogeneity via the dimensions of novel multivariate coordinate system (COORDINATE-MDD). Utilizing imaging harmonization and machine learning methods in a large cohort of medication-free, deeply phenotyped MDD participants, patterns of brain alteration are defined in replicable and neurobiologically-based dimensions and offer the potential to predict treatment response at the individual level. International datasets are being shared from multi-ethnic community populations, first episode and recurrent MDD, which are medication-free, in a current depressive episode with prospective longitudinal treatment outcomes and in remission. Neuroimaging data consist of de-identified, individual, structural MRI and resting-state functional MRI with additional positron emission tomography (PET) data at specific sites. State-of-the-art analytic methods include automated image processing for extraction of anatomical and functional imaging variables, statistical harmonization of imaging variables to account for site and scanner variations, and semi-supervised machine learning methods that identify dominant patterns associated with MDD from neural structure and function in healthy participants.

RESULTS

We are applying an iterative process by defining the neural dimensions that characterise deeply phenotyped samples and then testing the dimensions in novel samples to assess specificity and reliability. Crucially, we aim to use machine learning methods to identify novel predictors of treatment response based on prospective longitudinal treatment outcome data, and we can externally validate the dimensions in fully independent sites.

CONCLUSION

We describe the consortium, imaging protocols and analytics using preliminary results. Our findings thus far demonstrate how datasets across many sites can be harmonized and constructively pooled to enable execution of this large-scale project.

The Precision in Psychiatry (PIP) study: Testing an internet-based methodology for accelerating research in treatment prediction and personalisation

BACKGROUND

Evidence-based treatments for depression exist but not all patients benefit from them. Efforts to develop predictive models that can assist clinicians in allocating treatments are ongoing, but there are major issues with acquiring the volume and breadth of data needed to train these models. We examined the feasibility, tolerability, patient characteristics, and data quality of a novel protocol for internet-based treatment research in psychiatry that may help advance this field.

METHODS

A fully internet-based protocol was used to gather repeated observational data from patient cohorts receiving internet-based cognitive behavioural therapy (iCBT) (N = 600) or antidepressant medication treatment (N = 110). At baseline, participants provided > 600 data points of self-report data, spanning socio-demographics, lifestyle, physical health, clinical and other psychological variables and completed 4 cognitive tests. They were followed weekly and completed another detailed clinical and cognitive assessment at week 4. In this paper, we describe our study design, the demographic and clinical characteristics of participants, their treatment adherence, study retention and compliance, the quality of the data gathered, and qualitative feedback from patients on study design and implementation.

RESULTS

Participant retention was 92% at week 3 and 84% for the final assessment. The relatively short study duration of 4 weeks was sufficient to reveal early treatment effects; there were significant reductions in 11 transdiagnostic psychiatric symptoms assessed, with the largest improvement seen for depression. Most participants (66%) reported being distracted at some point during the study, 11% failed 1 or more attention checks and 3% consumed an intoxicating substance. Data quality was nonetheless high, with near perfect 4-week test retest reliability for self-reported height (ICC = 0.97).

CONCLUSIONS

An internet-based methodology can be used efficiently to gather large amounts of detailed patient data during iCBT and antidepressant treatment. Recruitment was rapid, retention was relatively high and data quality was good. This paper provides a template methodology for future internet-based treatment studies, showing that such an approach facilitates data collection at a scale required for machine learning and other data-intensive methods that hope to deliver algorithmic tools that can aid clinical decision-making in psychiatry.

Biomarkers in Psychiatric Drug Development: From Precision Medicine to Novel Therapeutics

Burden of psychiatric disorders is compounded by their wide prevalence as well as the limited efficacy of currently available treatments and the current approaches for prescribing these treatments. The selection of treatments continues to be subjective and often results in a trial-and-error approach. Emerging research suggests that biological markers (or biomarkers) can be used to develop precision medicine approaches for psychiatric disorders. Furthermore, the biomarkers also promise to elucidate the underlying pathophysiological mechanisms which in turn can be used to develop novel therapeutic treatments. In this chapter we have focused on mood disorders and reviewed studies on electroencephalography (EEG), magnetic resonance imaging (MRI), and blood-based biomarkers that can guide selection of one treatment versus another (treatment-selection biomarker) as well as biomarkers that can guide the development of novel therapeutics. These studies suggest that the use of objective physiological data is poised to alter the landscape of psychiatric diagnosis and treatment. However, practical and economic barriers remain as major hurdles. The key to finding such translational diagnostic and therapeutic biomarkers is a better understanding of the underlying pathophysiology, and despite the tremendous advances in neuroscience, it is clear there remains much left to be elucidated.

Co-alteration Network Architecture of Major Depressive Disorder: A Multi-modal Neuroimaging Assessment of Large-scale Disease Effects

Major depressive disorder (MDD) exhibits diverse symptomology and neuroimaging studies report widespread disruption of key brain areas. Numerous theories underpinning the network degeneration hypothesis (NDH) posit that neuropsychiatric diseases selectively target brain areas via meaningful network mechanisms rather than as indistinct disease effects. The present study tests the hypothesis that MDD is a network-based disorder, both structurally and functionally. Coordinate-based meta-analysis and Activation Likelihood Estimation (CBMA-ALE) were used to assess the convergence of findings from 92 previously published studies in depression. An extension of CBMA-ALE was then used to generate a node-and-edge network model representing the co-alteration of brain areas impacted by MDD. Standardized measures of graph theoretical network architecture were assessed. Co-alteration patterns among the meta-analytic MDD nodes were then tested in independent, clinical T1-weighted structural magnetic resonance imaging (MRI) and resting-state functional (rs-fMRI) data. Differences in co-alteration profiles between MDD patients and healthy controls, as well as between controls and clinical subgroups of MDD patients, were assessed. A 65-node 144-edge co-alteration network model was derived for MDD. Testing of co-alteration profiles in replication data using the MDD nodes provided distinction between MDD and healthy controls in structural data. However, co-alteration profiles were not distinguished between patients and controls in rs-fMRI data. Improved distinction between patients and healthy controls was observed in clinically homogenous MDD subgroups in T1 data. MDD abnormalities demonstrated both structural and functional network architecture, though only structural networks exhibited between-groups differences. Our findings suggest improved utility of structural co-alteration networks for ongoing biomarker development.

Assessing in-session rumination and its effects on CBT for depression

The study evaluated if rumination of patients during therapy (i.e., in-session rumination) relates to whether or not they do less well in CBT treatment. We developed a reliably assessed in-session rumination observational measure and evaluated its relationship to depression over the course of CBT. Rated sessions came from 63 treatment-naïve patients with major depressive disorder who participated in CBT in the PReDICT study (Dunlop et al., 2017). In-session rumination was operationalized as repetitive, negative, and passive talking about depressive topics. Trained undergraduates rated the intensity and duration of in-session rumination occurring during 57 initial therapy sessions (i.e., session one) and 45 sessions in the middle of treatment (i.e., session eight). The observational ratings were sufficiently reliable (all ICCs > 0.69). Mixed model results indicated that greater intensity of in-session rumination during the initial treatment session predicted higher levels of subsequent clinician-rated depressive symptoms (p < .023). Regression results indicated that greater intensity and duration of in-session rumination at session 8 significantly predicted higher clinician-rated symptoms at end of treatment (p's < 0.02). In-session rumination intensity and duration were not, however, related to subsequent self-reported depressive symptoms. The results support efforts to identify which patients might benefit from rumination-specific interventions.

The Use of Psychotropic Medication in Pediatric Oncology for Acute Psychological and Psychiatric Problems: Balancing Risks and Benefits

Severe acute behavioral and emotional problems represent one of the most serious treatment-related adverse effects for children and adolescents who have cancer. The critical and severe nature of these symptoms often makes necessary the use of psychotropic drugs. A working group composed of experts in multiple disciplines had the task of creating an agreement regarding a management plan for severe acute behavioral and emotional problems (SABEPs) in children and adolescents treated for cancer. To obtain global information on the use of psychotropic drugs in pediatric oncology, the working group first developed and mailed a 15-item questionnaire to many Italian pediatric oncology centers. Overall, an evident lack of knowledge and education regarding the use of psychotropic medications for the treatment of SABEPs was found. Thus, by referring to an adapted version of the Delphi method of consensus and standard methods for the elaboration of clinical questions (PICOs), the working group elaborated evidence-based recommendations for psychotropic drugs in the pediatric oncology setting. Furthermore, based on a thorough multivariate analysis of needs and difficulties, a comprehensive management flow was developed to optimize therapeutic interventions, which allows more accurate and efficient matching of the acute needs of patients while guiding treatment options.

Gut Microbiome-Linked Metabolites in the Pathobiology of Major Depression With or Without Anxiety—A Role for Bile Acids

Background

The gut microbiome may play a role in the pathogenesis of neuropsychiatric diseases including major depressive disorder (MDD). Bile acids (BAs) are steroid acids that are synthesized in the liver from cholesterol and further processed by gut-bacterial enzymes, thus requiring both human and gut microbiome enzymatic processes in their metabolism. BAs participate in a range of important host functions such as lipid transport and metabolism, cellular signaling and regulation of energy homeostasis. BAs have recently been implicated in the pathophysiology of Alzheimer's and several other neuropsychiatric diseases, but the biochemical underpinnings of these gut microbiome-linked metabolites in the pathophysiology of depression and anxiety remains largely unknown.

Method

Using targeted metabolomics, we profiled primary and secondary BAs in the baseline serum samples of 208 untreated outpatients with MDD. We assessed the relationship of BA concentrations and the severity of depressive and anxiety symptoms as defined by the 17-item Hamilton Depression Rating Scale (HRSD₁₇) and the 14-item Hamilton Anxiety Rating Scale (HRSA-Total), respectively. We also evaluated whether the baseline metabolic profile of BA informs about treatment outcomes.

Results

The concentration of the primary BA chenodeoxycholic acid (CDCA) was significantly lower at baseline in both severely depressed (log₂ fold difference (LFD) = −0.48; p = 0.021) and highly anxious (LFD = −0.43; p = 0.021) participants compared to participants with less severe symptoms. The gut bacteria-derived secondary BAs produced from CDCA such as lithocholic acid (LCA) and several of its metabolites, and their ratios to primary BAs, were significantly higher in the more anxious participants (LFD's range = [0.23, 1.36]; p's range = [6.85E-6, 1.86E-2]). The interaction analysis of HRSD₁₇ and HRSA-Total suggested that the BA concentration differences were more strongly correlated to the symptoms of anxiety than depression. Significant differences in baseline CDCA (LFD = −0.87, p = 0.0009), isoLCA (LFD = −1.08, p = 0.016) and several BA ratios (LFD's range [0.46, 1.66], p's range [0.0003, 0.049]) differentiated treatment failures from remitters.

Conclusion

In patients with MDD, BA profiles representing changes in gut microbiome compositions are associated with higher levels of anxiety and increased probability of first-line treatment failure. If confirmed, these findings suggest the possibility of developing gut microbiome-directed therapies for MDD characterized by gut dysbiosis.

Metabolomic and inflammatory signatures of symptom dimensions in major depression

BACKGROUND

Major depressive disorder (MDD) is a highly heterogenous disease, both in terms of clinical profiles and pathobiological alterations. Recently, immunometabolic dysregulations were shown to be correlated with atypical, energy-related symptoms but less so with the Melancholic or Anxious distress symptom dimensions of depression in The Netherlands Study of Depression and Anxiety (NESDA) study. In this study, we aimed to replicate these immunometabolic associations and to characterize the metabolomic correlates of each of the three MDD dimensions.

METHODS

Using three clinical rating scales, Melancholic, and Anxious distress, and Immunometabolic (IMD) dimensions were characterized in 158 patients who participated in the Predictors of Remission to Individual and Combined Treatments (PReDICT) study and from whom plasma and serum samples were available. The NESDA-defined inflammatory index, a composite measure of interleukin-6 and C-reactive protein, was measured from pre-treatment plasma samples and a metabolomic profile was defined using serum samples analyzed on three metabolomics platforms targeting fatty acids and complex lipids, amino acids, acylcarnitines, and gut microbiome-derived metabolites among other metabolites of central metabolism.

RESULTS

The IMD clinical dimension and the inflammatory index were positively correlated (r = 0.19, p = 0.019) after controlling for age, sex, and body mass index, whereas the Melancholic and Anxious distress dimensions were not, replicating the previous NESDA findings. The three symptom dimensions had distinct metabolomic signatures using both univariate and set enrichment statistics. IMD severity correlated mainly with gut-derived metabolites and a few acylcarnitines and long chain saturated free fatty acids. Melancholia severity was significantly correlated with several phosphatidylcholines, primarily the ether-linked variety, lysophosphatidylcholines, as well as several amino acids. Anxious distress severity correlated with several medium and long chain free fatty acids, both saturated and polyunsaturated ones, sphingomyelins, as well as several amino acids and bile acids.

CONCLUSION

The IMD dimension of depression appears reliably associated with markers of inflammation. Metabolomics provides powerful tools to inform about depression heterogeneity and molecular mechanisms related to clinical dimensions in MDD, which include a link to gut microbiome and lipids implicated in membrane structure and function.

Network Diffusion Embedding Reveals Transdiagnostic Subnetwork Disruption and Potential Treatment Targets in Internalizing Psychopathologies

Network diffusion models are a common and powerful way to study the propagation of information through a complex system and they offer straightforward approaches for studying multimodal brain network data. We developed an analytic framework to identify brain subnetworks with perturbed information diffusion capacity using the structural basis that best maps to resting state functional connectivity and applied it towards a heterogeneous dataset of internalizing psychopathologies (IPs), a set of psychiatric conditions in which similar brain network deficits are found across the swath of the disorders, but a unifying neuropathological substrate for transdiagnostic symptom expression is currently unknown. This research provides preliminary evidence of a transdiagnostic brain subnetwork deficit characterized by information diffusion impairment of the right area 8BM, a key brain region involved in organizing a broad spectrum of cognitive tasks, which may underlie previously reported dysfunction of multiple brain circuits in the IPs. We also demonstrate that models of neuromodulation involving targeting this brain region normalize IP diffusion dynamics towards those of healthy controls. These analyses provide a framework for multimodal methods that identify both brain subnetworks with disrupted information diffusion and potential targets of these subnetworks for therapeutic neuromodulatory intervention based on previously well-characterized methodology.

Indoxyl sulfate, a gut microbiome-derived uremic toxin, is associated with psychic anxiety and its functional magnetic resonance imaging-based neurologic signature

It is unknown whether indoles, metabolites of tryptophan that are derived entirely from bacterial metabolism in the gut, are associated with symptoms of depression and anxiety. Serum samples (baseline, 12 weeks) were drawn from participants (n = 196) randomized to treatment with cognitive behavioral therapy (CBT), escitalopram, or duloxetine for major depressive disorder. Baseline indoxyl sulfate abundance was positively correlated with severity of psychic anxiety and total anxiety and with resting state functional connectivity to a network that processes aversive stimuli (which includes the subcallosal cingulate cortex (SCC-FC), bilateral anterior insula, right anterior midcingulate cortex, and the right premotor areas). The relation between indoxyl sulfate and psychic anxiety was mediated only through the metabolite's effect on the SCC-FC with the premotor area. Baseline indole abundances were unrelated to post-treatment outcome measures, and changes in symptoms were not correlated with changes in indole concentrations. These results suggest that CBT and antidepressant medications relieve anxiety via mechanisms unrelated to modulation of indoles derived from gut microbiota; it remains possible that treatment-related improvement stems from their impact on other aspects of the gut microbiome. A peripheral gut microbiome-derived metabolite was associated with altered neural processing and with psychiatric symptom (anxiety) in humans, which provides further evidence that gut microbiome disruption can contribute to neuropsychiatric disorders that may require different therapeutic approaches. Given the exploratory nature of this study, findings should be replicated in confirmatory studies.Clinical trial NCT00360399 "Predictors of Antidepressant Treatment Response: The Emory CIDAR" https://clinicaltrials.gov/ct2/show/NCT00360399 .

Toll-like receptor 4 methylation grade is linked to depressive symptom severity

This study explores potential associations between the methylation of promoter-associated CpG sites of the toll-like receptor (TLR)-family, plasma levels of pro-inflammatory proteins and depressive symptoms in young female psychiatric patients. Ratings of depressive symptoms and blood samples were obtained from 92 young women seeking psychiatric care. Methylation of 32 promoter-associated CpG sites in TLR1 to TLR10 was analysed using the Illumina Infinium Methylation EPIC BeadChip. Expression levels of 91 inflammatory proteins were determined by proximity extension assay. Statistical correlations between depressive state, TLR1-10 methylation and inflammatory proteins were investigated. Four additional cohorts were studied to evaluate the generalizability of the findings. In the discovery cohort, methylation grade of cg05429895 (TLR4) in blood was inversely correlated with depressive symptoms score in young adults. After correction for multiple testing, plasma levels of macrophage inflammatory protein 1β (MIP-1β/CCL4) were associated with both TLR4 methylation and depressive symptom severity. A similar inverse association between TLR4 methylation in blood and affective symptoms score was also found in a cohort of 148 both males and females (<40 years of age) from the Danish Twin Registry. These findings were not, however, replicated in three other external cohorts; which differed from the first two cohorts by a higher age and mixed ethnicities, thus limiting the generalizability of our findings. However, TLR4 methylation inversely correlated with TLR4 mRNA expression in the Danish Twin Study indicating a functional significance of methylation at this particular CpG. Higher depression scores in young Scandinavian adults was associated with decreased methylation of TLR4 in blood.

Combined effects of genotype and childhood adversity shape variability of DNA methylation across age

Lasting effects of adversity, such as exposure to childhood adversity (CA) on disease risk, may be embedded via epigenetic mechanisms but findings from human studies investigating the main effects of such exposure on epigenetic measures, including DNA methylation (DNAm), are inconsistent. Studies in perinatal tissues indicate that variability of DNAm at birth is best explained by the joint effects of genotype and prenatal environment. Here, we extend these analyses to postnatal stressors. We investigated the contribution of CA, cis genotype (G), and their additive (G + CA) and interactive (G × CA) effects to DNAm variability in blood or saliva from five independent cohorts with a total sample size of 1074 ranging in age from childhood to late adulthood. Of these, 541 were exposed to CA, which was assessed retrospectively using self-reports or verified through social services and registries. For the majority of sites (over 50%) in the adult cohorts, variability in DNAm was best explained by G + CA or G × CA but almost never by CA alone. Across ages and tissues, 1672 DNAm sites showed consistency of the best model in all five cohorts, with G × CA interactions explaining most variance. The consistent G × CA sites mapped to genes enriched in brain-specific transcripts and Gene Ontology terms related to development and synaptic function. Interaction of CA with genotypes showed the strongest contribution to DNAm variability, with stable effects across cohorts in functionally relevant genes. This underscores the importance of including genotype in studies investigating the impact of environmental factors on epigenetic marks.

Selective Serotonin Reuptake Inhibitor Pharmaco-Omics: Mechanisms and Prediction

Selective serotonin reuptake inhibitors (SSRIs) are a standard of care for the pharmacotherapy of patients suffering from Major Depressive Disorder (MDD). However, only one-half to two-thirds of MDD patients respond to SSRI therapy. Recently, a "multiple omics" research strategy was applied to identify genetic differences between patients who did and did not respond to SSRI therapy. As a first step, plasma metabolites were assayed using samples from the 803 patients in the PGRN-AMPS SSRI MDD trial. The metabolomics data were then used to "inform" genomics by performing a genome-wide association study (GWAS) for plasma concentrations of the metabolite most highly associated with clinical response, serotonin (5-HT). Two genome-wide or near genome-wide significant single nucleotide polymorphism (SNP) signals were identified, one that mapped near the TSPAN5 gene and another across the ERICH3 gene, both genes that are highly expressed in the brain. Knocking down TSPAN5 and ERICH3 resulted in decreased 5-HT concentrations in neuroblastoma cell culture media and decreased expression of enzymes involved in 5-HT biosynthesis and metabolism. Functional genomic studies demonstrated that ERICH3 was involved in clathrin-mediated vesicle formation and TSPAN5 was an ethanol-responsive gene that may be a marker for response to acamprosate pharmacotherapy of alcohol use disorder (AUD), a neuropsychiatric disorder highly co-morbid with MDD. In parallel studies, kynurenine was the plasma metabolite most highly associated with MDD symptom severity and application of a metabolomics-informed pharmacogenomics approach identified DEFB1 and AHR as genes associated with variation in plasma kynurenine levels. Both genes also contributed to kynurenine-related inflammatory pathways. Finally, a multiply replicated predictive algorithm for SSRI clinical response with a balanced predictive accuracy of 76% (compared with 56% for clinical data alone) was developed by including the SNPs in TSPAN5, ERICH3, DEFB1 and AHR. In summary, application of a multiple omics research strategy that used metabolomics to inform genomics, followed by functional genomic studies, identified novel genes that influenced monoamine biology and made it possible to develop a predictive algorithm for SSRI clinical outcomes in MDD. A similar pharmaco-omic research strategy might be broadly applicable for the study of other neuropsychiatric diseases and their drug therapy.

Treatment-Specific Hippocampal Subfield Volume Changes With Antidepressant Medication or Cognitive-Behavior Therapy in Treatment-Naive Depression

Background: Hippocampal atrophy has been consistently reported in major depressive disorder with more recent focus on subfields. However, literature on hippocampal volume changes after antidepressant treatment has been limited. The first-line treatments for depression include antidepressant medication (ADM) or cognitive-behavior therapy (CBT). To understand the differential effects of CBT and ADM on the hippocampus, we investigated the volume alterations of hippocampal subfields with treatment, outcome, and chronicity in treatment-naïve depression patients. Methods: Treatment-naïve depressed patients from the PReDICT study were included in this analysis. A total of 172 patients who completed 12 weeks of randomized treatment with CBT (n = 45) or ADM (n = 127) were included for hippocampal subfield volume analysis. Forty healthy controls were also included for the baseline comparison. Freesurfer 6.0 was used to segment 26 hippocampal substructures and bilateral whole hippocampus from baseline and week 12 structural MRI scans. A generalized linear model with covariates of age and gender was used for group statistical tests. A linear mixed model for the repeated measures with covariates of age and gender was used to examine volumetric changes over time and the contributing effects of treatment type, outcome, and illness chronicity. Results: Of the 172 patients, 85 achieved remission (63/127 ADM, 22/45 CBT). MDD patients showed smaller baseline volumes than healthy controls in CA1, CA3, CA4, parasubiculum, GC-ML-DG, Hippocampal Amygdala Transition Area (HATA), and fimbria. Over 12 weeks of treatment, further declines in the volumes of CA1, fimbria, subiculum, and HATA were observed regardless of treatment type or outcome. CBT remitters, but not ADM remitters, showed volume reduction in the right hippocampal tail. Unlike ADM remitters, ADM non-responders had a decline in volume in the bilateral hippocampal tails. Baseline volume of left presubiculum (regardless of treatment type) and right fimbria and HATA in CBT patients were correlated with a continuous measure of clinical improvement. Chronicity of depression had no effect on any measures of hippocampal subfield volumes. Conclusion: Two first-line antidepressant treatments, CBT and ADM, have different effects on hippocampal tail after 12 weeks. This finding suggests that remission achieved via ADM may protect against progressive hippocampal atrophy by altering neuronal plasticity or supporting neurogenesis. Studies with multimodal neuroimaging, including functional and structural analysis, are needed to assess further the impact of two different antidepressant treatments on hippocampal subfields.

Single-cell molecular profiling of all three components of the HPA axis reveals adrenal ABCB1 as a regulator of stress adaptation

Chronic activation and dysregulation of the neuroendocrine stress response have severe physiological and psychological consequences, including the development of metabolic and stress-related psychiatric disorders. We provide the first unbiased, cell type-specific, molecular characterization of all three components of the hypothalamic-pituitary-adrenal axis, under baseline and chronic stress conditions. Among others, we identified a previously unreported subpopulation of Abcb1b+ cells involved in stress adaptation in the adrenal gland. We validated our findings in a mouse stress model, adrenal tissues from patients with Cushing's syndrome, adrenocortical cell lines, and peripheral cortisol and genotyping data from depressed patients. This extensive dataset provides a valuable resource for researchers and clinicians interested in the organism's nervous and endocrine responses to stress and the interplay between these tissues. Our findings raise the possibility that modulating ABCB1 function may be important in the development of treatment strategies for patients suffering from metabolic and stress-related psychiatric disorders.

ERICH3: vesicular association and antidepressant treatment response

Selective serotonin reuptake inhibitors (SSRIs) are standard of care for major depressive disorder (MDD) pharmacotherapy, but only approximately half of these patients remit on SSRI therapy. Our previous genome-wide association study identified a single-nucleotide polymorphism (SNP) signal across the glutamate-rich 3 (ERICH3) gene that was nearly genome-wide significantly associated with plasma serotonin (5-HT) concentrations, which were themselves associated with SSRI response for MDD patients enrolled in the Mayo Clinic PGRN-AMPS SSRI trial. In this study, we performed a meta-analysis which demonstrated that those SNPs were significantly associated with SSRI treatment outcomes in four independent MDD trials. However, the function of ERICH3 and molecular mechanism(s) by which it might be associated with plasma 5-HT concentrations and SSRI clinical response remained unclear. Therefore, we characterized the human ERICH3 gene functionally and identified ERICH3 mRNA transcripts and protein isoforms that are highly expressed in central nervous system cells. Coimmunoprecipitation identified a series of ERICH3 interacting proteins including clathrin heavy chain which are known to play a role in vesicular function. Immunofluorescence showed ERICH3 colocalization with 5-HT in vesicle-like structures, and ERICH3 knock-out dramatically decreased 5-HT staining in SK-N-SH cells as well as 5-HT concentrations in the culture media and cell lysates without changing the expression of 5-HT synthesizing or metabolizing enzymes. Finally, immunofluorescence also showed ERICH3 colocalization with dopamine in human iPSC-derived neurons. These results suggest that ERICH3 may play a significant role in vesicular function in serotonergic and other neuronal cell types, which might help explain its association with antidepressant treatment response.

Connectome-based models can predict early symptom improvement in major depressive disorder

BACKGROUND

Major depressive disorder (MDD) is a debilitating mental illness with more than 50% of patients not achieving an adequate response using first-line treatments. Reliable models that predict antidepressant treatment outcome are needed to guide clinical decision making. We aimed to build predictive models of treatment improvement for MDD patients using machine learning approaches based on fMRI resting-state functional connectivity patterns.

METHODS

Resting-state fMRI data were acquired from 192 untreated MDD patients at recruitment, and their severity of depression was assessed by Hamilton Rating Scale for Depression (HAMD) at baseline. Patients were given medication after the initial MR scan and their symptoms were monitored through HAMD for a period of six months. Connectome-based predictive modeling (CPM) algorithms were implemented to predict the improvement in HAMD score at one month from resting-state connectivity at baseline. Additionally, by selectively combining the features from all leave-one-out iterations in the model building stage, we created a consensus model that could be generalized to predict improvement in HAMD score in samples of non-overlapping subjects at different time points.

RESULTS

Using baseline functional connectivity, CPM successfully predicted symptom improvement of depression at one month. In addition, a consensus 'MDD improvement model' could predict symptom improvement for novel individuals at the two-week, one-month, two-month and three-month time points after antidepressant treatment.

CONCLUSIONS

Individual pre-treatment functional brain networks contain meaningful information that can be gleaned to build predictors of treatment outcome. The identified MDD improvement networks could be an appropriate biomarker for predicting individual therapeutic response of antidepressant treatment. Replication and validation using other large datasets will be a key next step before these models can be used in clinical practice.

Psychotherapy or medication for depression? Using individual symptom meta-analyses to derive a Symptom-Oriented Therapy (SOrT) metric for a personalised psychiatry

BACKGROUND

Antidepressant medication (ADM) and psychotherapy are effective treatments for major depressive disorder (MDD). It is unclear, however, if treatments differ in their effectiveness at the symptom level and whether symptom information can be utilised to inform treatment allocation. The present study synthesises comparative effectiveness information from randomised controlled trials (RCTs) of ADM versus psychotherapy for MDD at the symptom level and develops and tests the Symptom-Oriented Therapy (SOrT) metric for precision treatment allocation.

METHODS

First, we conducted systematic review and meta-analyses of RCTs comparing ADM and psychotherapy at the individual symptom level. We searched PubMed Medline, PsycINFO, and the Cochrane Central Register of Controlled Trials databases, a database specific for psychotherapy RCTs, and looked for unpublished RCTs. Random-effects meta-analyses were applied on sum-scores and for individual symptoms for the Hamilton Rating Scale for Depression (HAM-D) and Beck Depression Inventory (BDI) measures. Second, we computed the SOrT metric, which combines meta-analytic effect sizes with patients' symptom profiles. The SOrT metric was evaluated using data from the Munich Antidepressant Response Signature (MARS) study (n = 407) and the Emory Predictors of Remission in Depression to Individual and Combined Treatments (PReDICT) study (n = 234).

RESULTS

The systematic review identified 38 RCTs for qualitative inclusion, 27 and 19 for quantitative inclusion at the sum-score level, and 9 and 4 for quantitative inclusion on individual symptom level for the HAM-D and BDI, respectively. Neither meta-analytic strategy revealed significant differences in the effectiveness of ADM and psychotherapy across the two depression measures. The SOrT metric did not show meaningful associations with other clinical variables in the MARS sample, and there was no indication of utility of the metric for better treatment allocation from PReDICT data.

CONCLUSIONS

This registered report showed no differences of ADM and psychotherapy for the treatment of MDD at sum-score and symptom levels. Symptom-based metrics such as the proposed SOrT metric do not inform allocation to these treatments, but predictive value of symptom information requires further testing for other treatment comparisons.

Graph theoretical measures of the uncinate fasciculus subnetwork as predictors and correlates of treatment response in a transdiagnostic psychiatric cohort

The internalizing psychopathologies (IP) are a highly prevalent group of disorders for which little data exists to guide treatment selection. We examine whether graph theoretical metrics from white matter connectomes may serve as biomarkers of disease and predictors of treatment response. We focus on the uncinate fasciculus subnetwork, which has been previously implicated in these disorders. We compared baseline graph measures from a transdiagnostic IP cohort with controls. Patients were randomized to either SSRI or cognitive behavioral therapy and we determined if graph theory metrics change following treatment, and whether these changes correlated with treatment response. Lastly, we investigated whether baseline metrics correlated with treatment response. Several baseline nodal graph metrics differed at baseline. Of note, right amygdala betweenness centrality was increased in patients relative to controls. In addition, white matter integrity of the uncinate fasciculus was decreased at baseline in patients versus controls. The SSRI and CBT cohorts had increased left frontal superior orbital betweenness centrality and left frontal medial orbital clustering coefficient, respectively, suggesting the presence of treatment specific neural correlates of treatment response. This study provides insight on shared white matter network features of IPs and elucidates potential biomarkers of treatment response that may be modality-specific.

Psychological therapies for people with borderline personality disorder

BACKGROUND

Over the decades, a variety of psychological interventions for borderline personality disorder (BPD) have been developed. This review updates and replaces an earlier review (Stoffers-Winterling 2012).

OBJECTIVES

To assess the beneficial and harmful effects of psychological therapies for people with BPD.

SEARCH METHODS

In March 2019, we searched CENTRAL, MEDLINE, Embase, 14 other databases and four trials registers. We contacted researchers working in the field to ask for additional data from published and unpublished trials, and handsearched relevant journals. We did not restrict the search by year of publication, language or type of publication.

SELECTION CRITERIA

Randomised controlled trials comparing different psychotherapeutic interventions with treatment-as-usual (TAU; which included various kinds of psychotherapy), waiting list, no treatment or active treatments in samples of all ages, in any setting, with a formal diagnosis of BPD. The primary outcomes were BPD symptom severity, self-harm, suicide-related outcomes, and psychosocial functioning. There were 11 secondary outcomes, including individual BPD symptoms, as well as attrition and adverse effects.

DATA COLLECTION AND ANALYSIS

At least two review authors independently selected trials, extracted data, assessed risk of bias using Cochrane's 'Risk of bias' tool and assessed the certainty of the evidence using the GRADE approach. We performed data analysis using Review Manager 5 and quantified the statistical reliability of the data using Trial Sequential Analysis.

MAIN RESULTS

We included 75 randomised controlled trials (4507 participants), predominantly involving females with mean ages ranging from 14.8 to 45.7 years. More than 16 different kinds of psychotherapy were included, mostly dialectical behaviour therapy (DBT) and mentalisation-based treatment (MBT). The comparator interventions included treatment-as-usual (TAU), waiting list, and other active treatments. Treatment duration ranged from one to 36 months. Psychotherapy versus TAU Psychotherapy reduced BPD symptom severity, compared to TAU; standardised mean difference (SMD) -0.52, 95% confidence interval (CI) -0.70 to -0.33; 22 trials, 1244 participants; moderate-quality evidence. This corresponds to a mean difference (MD) of -3.6 (95% CI -4.4 to -2.08) on the Zanarini Rating Scale for BPD (range 0 to 36), a clinically relevant reduction in BPD symptom severity (minimal clinical relevant difference (MIREDIF) on this scale is -3.0 points). Psychotherapy may be more effective at reducing self-harm compared to TAU (SMD -0.32, 95% CI -0.49 to -0.14; 13 trials, 616 participants; low-quality evidence), corresponding to a MD of -0.82 (95% CI -1.25 to 0.35) on the Deliberate Self-Harm Inventory Scale (range 0 to 34). The MIREDIF of -1.25 points was not reached. Suicide-related outcomes improved compared to TAU (SMD -0.34, 95% CI -0.57 to -0.11; 13 trials, 666 participants; low-quality evidence), corresponding to a MD of -0.11 (95% CI -0.19 to -0.034) on the Suicidal Attempt Self Injury Interview. The MIREDIF of -0.17 points was not reached. Compared to TAU, psychotherapy may result in an improvement in psychosocial functioning (SMD -0.45, 95% CI -0.68 to -0.22; 22 trials, 1314 participants; low-quality evidence), corresponding to a MD of -2.8 (95% CI -4.25 to -1.38), on the Global Assessment of Functioning Scale (range 0 to 100). The MIREDIF of -4.0 points was not reached. Our additional Trial Sequential Analysis on all primary outcomes reaching significance found that the required information size was reached in all cases. A subgroup analysis comparing the different types of psychotherapy compared to TAU showed no clear evidence of a difference for BPD severity and psychosocial functioning. Psychotherapy may reduce depressive symptoms compared to TAU but the evidence is very uncertain (SMD -0.39, 95% CI -0.61 to -0.17; 22 trials, 1568 participants; very low-quality evidence), corresponding to a MD of -2.45 points on the Hamilton Depression Scale (range 0 to 50). The MIREDIF of -3.0 points was not reached. BPD-specific psychotherapy did not reduce attrition compared with TAU. Adverse effects were unclear due to too few data. Psychotherapy versus waiting list or no treatment Greater improvements in BPD symptom severity (SMD -0.49, 95% CI -0.93 to -0.05; 3 trials, 161 participants), psychosocial functioning (SMD -0.56, 95% CI -1.01 to -0.11; 5 trials, 219 participants), and depression (SMD -1.28, 95% CI -2.21 to -0.34, 6 trials, 239 participants) were observed in participants receiving psychotherapy versus waiting list or no treatment (all low-quality evidence). No evidence of a difference was found for self-harm and suicide-related outcomes. Individual treatment approaches DBT and MBT have the highest numbers of primary trials, with DBT as subject of one-third of all included trials, followed by MBT with seven RCTs. Compared to TAU, DBT was more effective at reducing BPD severity (SMD -0.60, 95% CI -1.05 to -0.14; 3 trials, 149 participants), self-harm (SMD -0.28, 95% CI -0.48 to -0.07; 7 trials, 376 participants) and improving psychosocial functioning (SMD -0.36, 95% CI -0.69 to -0.03; 6 trials, 225 participants). MBT appears to be more effective than TAU at reducing self-harm (RR 0.62, 95% CI 0.49 to 0.80; 3 trials, 252 participants), suicidality (RR 0.10, 95% CI 0.04, 0.30, 3 trials, 218 participants) and depression (SMD -0.58, 95% CI -1.22 to 0.05, 4 trials, 333 participants). All findings are based on low-quality evidence. For secondary outcomes see review text.

AUTHORS' CONCLUSIONS

Our assessments showed beneficial effects on all primary outcomes in favour of BPD-tailored psychotherapy compared with TAU. However, only the outcome of BPD severity reached the MIREDIF-defined cut-off for a clinically meaningful improvement. Subgroup analyses found no evidence of a difference in effect estimates between the different types of therapies (compared to TAU) . The pooled analysis of psychotherapy versus waiting list or no treatment found significant improvement on BPD severity, psychosocial functioning and depression at end of treatment, but these findings were based on low-quality evidence, and the true magnitude of these effects is uncertain. No clear evidence of difference was found for self-harm and suicide-related outcomes. However, compared to TAU, we observed effects in favour of DBT for BPD severity, self-harm and psychosocial functioning and, for MBT, on self-harm and suicidality at end of treatment, but these were all based on low-quality evidence. Therefore, we are unsure whether these effects would alter with the addition of more data.

Comprehensive phenotyping of depression disease trajectory and risk: Rationale and design of Texas Resilience Against Depression study (T-RAD)

Depression has a chronic and recurrent course often with early onset and is the leading cause of disability worldwide. In contrast to diagnoses for other conditions which rely on precise medical tests, the diagnosis of depression still focuses exclusively on symptom reports. As a result, heterogeneous patient groups are included under broad categories. Furthermore, in the absence of companion diagnostic tests, choosing specific treatments for patients remains imprecise with only one-third of patients entering remission with initial treatment, with others requiring multiple intervention steps to achieve remission. In addition to improving treatment outcomes, disease prevention is essential to reduce overall disease burden. Adolescence is a critical window where complex emotional, social, familial, and biological shifts may predispose to lifelong depression. Thus, personalized medicine, integrating individual variability in genes, brain function, and clinical phenotypes, can offer a comprehensive approach to provide precise diagnosis, novel drug development, optimal treatment assignment, and prevention of illness and its associated burden. Texas Resilience Against Depression study (T-RAD) encompasses two natural history, longitudinal (10 + years), prospective studies (D2K and RAD), each enrolling 2500 participants. The D2K study follows participants (ages 10 years and older) who have a current or past diagnosis of depression or bipolar disorder. The RAD study follows participants aged 10-24 years who are at risk for depression but not yet suffering from the disease. The T-RAD study will help to uncover the socio-demographic, lifestyle, clinical, psychological, and neurobiological factors that contribute to mood disorder onset, recurrence, progression, and differential treatment response.

Neuroimaging as a Tool for Individualized Treatment Choice in Depression: the Past, the Present and the Future

Purpose of Review

This paper aims to review the findings on neuroimaging as a tool for facilitating individualized treatment choice in depression.

Recent Findings

Neuroimaging has allowed the exploration of neural candidates for response biomarkers. In less than two decades, the field has expanded from small single drug studies to large multisite initiatives testing multiple interventions; from simple analytical methods to employing artificial intelligence, with an aim of establishing models based on a variety of data, such as neuroimaging, biological, psychological and clinical measures.

Summary

Neural biomarkers of response may play an important role in treatment response prediction. It seems likely that they will need to be considered together with other types of data in complex models in order to achieve the high accuracy and generalizability of results necessary for clinical use.

Somatic symptoms in treatment-naïve Hispanic and non-Hispanic patients with major depression

BACKGROUND

Somatic complaints are a major driver of health care costs among patients with major depressive disorder (MDD). Some epidemiologic and clinical data suggest that Hispanic and non-Hispanic Black patients with MDD endorse higher levels of somatic symptoms than non-Hispanic White patients.

METHODS

Somatic symptoms in 102 Hispanic, 61 non-Hispanic Black, and 156 non-Hispanic White patients with treatment-naïve MDD were evaluated using the somatic symptom subscale of the Hamilton anxiety rating scale (HAM-A). The other seven items of the HAM-A comprise the psychic anxiety subscale, which was also evaluated across ethnicities.

RESULTS

Hispanic patients reported significantly greater levels of somatic symptoms than non-Hispanic patients, but levels of psychic anxiety symptoms did not differ by ethnicity. Levels of somatic symptoms did not significantly differ between Black and White non-Hispanic patients. Within the Hispanic sample, somatic symptom levels were higher only among those who were evaluated in Spanish; Hispanics who spoke English showed no significant differences versus non-Hispanics.

CONCLUSIONS

In this medically healthy sample of patients with MDD, monolingual Spanish-speaking Hispanic patients endorsed high levels of somatic symptoms. Clinicians should be mindful that the depressive experience may manifest somatically and be judicious in determining when additional medical work-up is warranted for somatic complaints.

Investigation of MORC1 DNA methylation as biomarker of early life stress and depressive symptoms

Early life stress (ELS) is associated with an increased risk of depression and this association may be mediated by epigenetic mechanisms. A previous epigenome-wide DNA methylation (DNAm) study investigating human newborns and two animal models of ELS suggested that the epigenetic regulator MORC1 is differentially methylated following ELS. The ELS-induced DNAm alterations were long-lasting in the animal models. However, whether this finding is also transferable to humans experiencing ELS in childhood was not investigated. Further, MORC1 may provide a link between ELS and adult depression, as MORC1 DNAm and genetic variants were found to be associated with depressive symptoms in humans. In the present study, we investigated the validity of MORC1 DNAm as a biomarker of ELS in humans and its role in linking ELS to depression later in life by studying childhood maltreatment. We analyzed whole blood MORC1 DNAm in an adult cohort (N = 151) that was characterized for both the presence of depressive symptoms and childhood maltreatment. Further, we investigated the association between MORC1 DNAm, depressive symptoms and childhood maltreatment in two additional cohorts (N = 299, N = 310). Overall, our data do not indicate an association of MORC1 DNAm with childhood maltreatment. An association of MORC1 DNAm with depressive symptoms was present in all cohorts, but was inconsistent in the specific CpG sites associated and the direction of effect (Tuebingen cohort: standardized β = 0.16, unstandardized β = 0.01, 95% CI [-0.0004, -0.0179], p = 0.061, PReDICT cohort: standardized β = -0.12, unstandardized β = -0.01, 95% CI [-0.0258, -0.0003], p = 0.045), Grady cohort: standardized β = 0.16, unstandardized β = 0.008, 95% CI [0.0019, 0.0143], p = 0.01). Our study thus suggests that peripheral MORC1 DNAm cannot serve as biomarker of childhood maltreatment in adults, but does provide further indication for the association of MORC1 DNAm with depressive symptoms.

Optimizing Outcomes of Treatment-Resistant Depression in Older Adults (OPTIMUM): Study Design and Treatment Characteristics of the First 396 Participants Randomized

OBJECTIVE

Evidence from clinical trials comparing effectiveness and safety of pharmacological strategies in older adults unresponsive to first-line antidepressants is limited. The study, Optimizing Outcomes of Treatment-Resistant Depression in Older Adults (OPTIMUM), tests three hypotheses concerning pharmacotherapy strategies for treatment-resistant late-life depression: 1) augmentation strategies will provide greater improvement than switching monotherapies; 2) augmentation strategies will have lower tolerability and more safety concerns than switching monotherapies; and 3) age will moderate the effectiveness and safety differences between treatment strategies. The authors describe the methodology, processes for stakeholder engagement, challenges, and lessons learned in the early phases of OPTIMUM.

METHODS

This pragmatic randomized clinical trial located in five North American regions will enroll 1,500 participants aged 60 years and older unresponsive to two or more antidepressant trials. The authors evaluate two strategies (medication augmentation versus switch) using four medications (aripiprazole, bupropion, lithium, and nortriptyline) via a stepwise, prespecified protocol. Primary outcomes include: 1) symptom remission (Montgomery Asberg Depression scale ≤10); 2) psychological well-being, comprising positive affect, general life satisfaction, and purpose; and 3) safety (rates of serious adverse events and prevalence of falls and fall-related injuries).

RESULTS

To date, 396 participants have been randomized. The authors report on four challenges: 1) engagement and recruitment; 2) increasing polypharmacy in older adults, resulting in potentially hazardous scenarios; 3) reporting adverse events and procedure standardization across sites; and 4) dissemination of results.

CONCLUSION

Solutions to these challenges, including early inclusion of stake holders, will inform future pragmatic studies in older adults with depression.

Pilot Study of Metabolomic Clusters as State Markers of Major Depression and Outcomes to CBT Treatment

Major depressive disorder (MDD) is a common and disabling syndrome with multiple etiologies that is defined by clinically elicited signs and symptoms. In hopes of developing a list of candidate biological measures that reflect and relate closely to the severity of depressive symptoms, so-called “state-dependent” biomarkers of depression, this pilot study explored the biochemical underpinnings of treatment response to cognitive behavior therapy (CBT) in medication-free MDD outpatients. Plasma samples were collected at baseline and week 12 from a subset of MDD patients (N = 26) who completed a course of CBT treatment as part of the Predictors of Remission in Depression to Individual and Combined Treatments (PReDICT) study. Targeted metabolomic profiling using the AbsoluteIDQ^® p180 Kit and LC-MS identified eight “co-expressed” metabolomic modules. Of these eight, three were significantly associated with change in depressive symptoms over the course of the 12-weeks. Metabolites found to be most strongly correlated with change in depressive symptoms were branched chain amino acids, acylcarnitines, methionine sulfoxide, and α-aminoadipic acid (negative correlations with symptom change) as well as several lipids, particularly the phosphatidlylcholines (positive correlation). These results implicate disturbed bioenergetics as an important state marker in the pathobiology of MDD. Exploratory analyses contrasting remitters to CBT versus those who failed the treatment further suggest these metabolites may serve as mediators of recovery during CBT treatment. Larger studies examining metabolomic change patterns in patients treated with pharmacotherapy or psychotherapy will be necessary to elucidate the biological underpinnings of MDD and the -specific biologies of treatment response.

Recall accuracy for the symptoms of a major depressive episode among clinical trial participants

For patients with major depressive disorder (MDD), approaches to treatment differ for those with a single versus recurrent episodes. Based on studies of community samples, however, accuracy is low for identifying past episodes. Recall accuracy among clinical samples with a well-defined major depressive episode (MDE) has not been examined previously. We evaluated episode recall accuracy in 79 MDD patients in follow-up of the Predictors of Remission in Depression to Individual and Combined Treatments (PReDICT) study at 12- and 24-month time-points after starting treatment. Using the Structured Clinical Interview for DSM-IV, patients were asked to recall whether they had been experiencing the nine criterion symptoms of an MDE at the time of their intake assessment. Accuracy of recall for the index MDE was high, with 95% of patients at month 12 and 85% at month 24 recalling sufficient symptoms to meet the diagnostic criteria. Recall accuracy for specific symptoms varied considerably, from >90% for dysphoria and anhedonia, to 55% for psychomotor and weight/appetite changes. For the thoughts of death/suicide criterion, patients with erroneous recall were significantly more likely to recall having had the symptom at the intake evaluation (though they had denied it at the time) than vice versa (p < .007). Patients who have participated in a clinical trial are likely to recall accurately a past MDE up to two years prior. Optimal vigilance for suicidal ideation for treatment-naïve patients should include a combination of self-report and clinician assessments.

Emotion-based brain mechanisms and predictors for SSRI and CBT treatment of anxiety and depression: a randomized trial

Mechanisms and predictors for the successful treatment of anxiety and depression have been elusive, limiting the effectiveness of existing treatments and curtailing the development of new interventions. In this study, we evaluated the utility of three widely used neural probes of emotion (experience, regulation, and perception) in their ability to predict symptom improvement and correlate with symptom change following two first-line treatments-selective serotonin reuptake inhibitors (SSRIs) and cognitive-behavioral therapy (CBT). Fifty-five treatment-seeking adults with anxiety and/or depression were randomized to 12 weeks of SSRI or CBT treatment (ClinicalTrials.gov identifier: NCT01903447). Functional magnetic resonance imaging (fMRI) was used to examine frontolimbic brain function during emotion experience, regulation, and perception, as probed by the Emotion Regulation Task (ERT; emotion experience and regulation) and emotional face assessment task (EFAT; emotion perception). Brain function was then related to anxiety and depression symptom change. Results showed that both SSRI and CBT treatments similarly attenuated insula and amygdala activity during emotion perception, and greater treatment-related decrease in insula and amygdala activity was correlated with greater reduction in anxiety symptoms. Both treatments also reduced amygdala activity during emotion experience but brain change did not correlate with symptom change. Lastly, greater pre-treatment insula and amygdala activity during emotion perception predicted greater anxiety and depression symptom improvement. Thus, limbic activity during emotion perception is reduced by both SSRI and CBT treatments, and predicts anxiety and depression symptom improvement. Critically, neural reactivity during emotion perception may be a non-treatment-specific mechanism for symptom improvement.

Suicidal ideation and other persisting symptoms after CBT or antidepressant medication treatment for major depressive disorder

BACKGROUND

Persisting symptoms after treatment for major depressive disorder (MDD) contribute to ongoing impairment and relapse risk. Whether cognitive behavior therapy (CBT) or antidepressant medications result in different profiles of residual symptoms after treatment is largely unknown.

METHODS

Three hundred fifteen adults with MDD randomized to treatment with either CBT or antidepressant medication in the Predictors of Remission in Depression to Individual and Combined Treatments (PReDICT) study were analyzed for the frequency of residual symptoms using the Montgomery Asberg Depression Rating Scale (MADRS) item scores at the end of the 12-week treatment period. Separate comparisons were made for treatment responders and non-responders.

RESULTS

Among treatment completers (n = 250) who responded to CBT or antidepressant medication, there were no significant differences in the persistence of residual MADRS symptoms. However, non-responders treated with medication were significantly less likely to endorse suicidal ideation (SI) at week 12 compared with those treated with CBT (non-responders to medication: 0/54, 0%, non-responders to CBT: 8/30, 26.7%; p = .001). Among patients who terminated the trial early (n = 65), residual MADRS item scores did not significantly differ between the CBT- and medication-treated groups.

CONCLUSIONS

Depressed adults who respond to CBT or antidepressant medication have similar residual symptom profiles. Antidepressant medications reduce SI, even among patients for whom the medication provides little overall benefit.

Multidimensional imaging techniques for prediction of treatment response in major depressive disorder

A large number of studies have attempted to use neuroimaging tools to aid in treatment prediction models for major depressive disorder (MDD). Most such studies have reported on only one dimension of function and prediction at a time. In this study, we used three different tasks across domains of function (emotion processing, reward anticipation, and cognitive control, plus resting state connectivity completed prior to start of medication to predict treatment response in 13-36 adults with MDD. For each experiment, adults with MDD were prescribed only label duloxetine (all experiments), whereas another subset were prescribed escitalopram. We used a KeyNet (both Task derived masks and Key intrinsic Network derived masks) approach to targeting brain systems in a specific match to tasks. The most robust predictors were (Dichter et al., 2010) positive response to anger and (Gong et al., 2011) negative response to fear within relevant anger and fear TaskNets and Salience and Emotion KeyNet (Langenecker et al., 2018) cognitive control (correct rejections) within Inhibition TaskNet (negative) and Cognitive Control KeyNet (positive). Resting state analyses were most robust for Cognitive control Network (positive) and Salience and Emotion Network (negative). Results differed by whether an -fwhm or -acf (more conservative) adjustment for multiple comparisons was used. Together, these results implicate the importance of future studies with larger sample sizes, multidimensional predictive models, and the importance of using empirically derived masks for search areas.

Benefits of Sequentially Adding Cognitive-Behavioral Therapy or Antidepressant Medication for Adults With Nonremitting Depression

OBJECTIVE

Adults with major depressive disorder frequently do not achieve remission with an initial treatment. Addition of psychotherapy for patients who do not achieve remission with antidepressant medication alone can target residual symptoms and protect against recurrence, but the utility of adding antidepressant medication after nonremission with cognitive-behavioral therapy (CBT) has received little study. The authors aimed to evaluate the acute and long-term outcomes resulting from both sequences of combination treatments.

METHODS

Previously untreated adults with major depression who were randomly assigned to receive escitalopram, duloxetine, or CBT monotherapy and completed 12 weeks of treatment without achieving remission entered an additional 12 weeks of combination treatment. For patients who did not achieve remission with CBT, escitalopram was added (CBT plus medication group) to their treatment, and for those who did not achieve remission with an antidepressant, CBT was added (medication plus CBT group) to their treatment. Patients who responded to the combination treatment entered an 18-month follow-up phase to assess risk of recurrence.

RESULTS

A total of 112 patients who did not achieve remission with a monotherapy entered combination treatment (41 who responded to monotherapy but did not achieve remission and 71 who did not respond to monotherapy). Overall, remission rates after subsequent combination therapy were significantly higher among patients who responded to monotherapy but did not achieve remission (61%) than among patients who did not respond to monotherapy (41%). Among patients who responded to monotherapy but did not achieve remission, the remission rate in the CBT plus medication group (89%) was higher than in the medication plus CBT group (53%). However, among patients whose depression did not respond to monotherapy, rates of response and remission were similar between the treatment arms. Higher levels of anxiety, both prior to monotherapy and prior to beginning combination treatment, predicted poorer outcomes for both treatment groups.

CONCLUSIONS

The order in which CBT and antidepressant medication were sequentially combined did not appear to affect outcomes. Addition of an antidepressant is an effective approach to treating residual symptoms for patients who do not achieve remission with CBT, as is adding CBT after antidepressant monotherapy. Patients who do not respond to one treatment modality warrant consideration for addition of the alternative modality.

Response rate profiles for major depressive disorder: Characterizing early response and longitudinal nonresponse

BACKGROUND

Definition of response is critical when seeking to establish valid predictors of treatment success. However, response at the end of study or endpoint only provides one view of the overall clinical picture that is relevant in testing for predictors. The current study employed a classification technique designed to group subjects based on their rate of change over time, while simultaneously addressing the issue of controlling for baseline severity.

METHODS

A set of latent class trajectory analyses, incorporating baseline level of symptoms, were performed on a sample of 344 depressed patients from a clinical trial evaluating the efficacy of cognitive behavior therapy and two antidepressant medications (escitalopram and duloxetine) in patients with major depressive disorder.

RESULTS

Although very few demographic and illness-related features were associated with response rate profiles, the aggregated effect of candidate genetic variants previously identified in large pharmacogenetic studies and meta-analyses showed a significant association with early remission as well as nonresponse. These same genetic scores showed a less compelling relationship with endpoint response categories. In addition, consistent nonresponse throughout the study treatment period was shown to occur in different subjects than endpoint nonresponse, which was verified by follow-up augmentation treatment outcomes.

CONCLUSIONS

When defining groups based on the rate of change, controlling for baseline depression severity may help to identify the clinically relevant distinctions of early response on one end and consistent nonresponse on the other.

Mapping depression rating scale phenotypes onto research domain criteria (RDoC) to inform biological research in mood disorders

BACKGROUND

Substantial research progress can be achieved if available clinical datasets can be mapped to the National Institute of Mental Health Research-Domain-Criteria (RDoC) constructs. This mapping would allow investigators to both explore more narrowly defined clinical phenotypes and the relationship of these phenotypes to biological markers and clinical outcomes approximating RDoC criteria.

METHODS

Using expert review and consensus, we defined four major depression phenotypes based on specific RDoC constructs. Having matched these constructs to individual items from the Hamilton Depression Rating Scale and Quick Inventory of Depressive Symptomatology, we identified subjects meeting criteria for each of these phenotypes from two large clinical trials of patients treated for major depression. In a post hoc analysis, we evaluated the overall treatment response based on the phenotypes: Core Depression (CD), Anxiety (ANX), and Neurovegetative Symptoms of Melancholia (NVSM) and Atypical Depression (NVSAD).

RESULTS

The phenotypes were prevalent (range 10.5-52.4%, 50% reduction range 51.9-82.9%) and tracked with overall treatment response. Although the CD phenotype was associated with lower rates of remission in both cohorts, this was mainly driven by baseline symptom severity. However, when controlling for baseline severity, patients with the ANX phenotype had a significantly lower rate of remission.

LIMITATIONS

The lack of replication between the studies of the phenotypes' treatment prediction value reflects important variability across studies that may limit generalizability.

CONCLUSION

Further work evaluating biological markers associated with these phenotypes is needed for further RDoC concept development.