Improving response and remission rates in major depressive disorder (MDD) remains an important challenge. Matching patients to the treatment they will most likely respond to should be the ultimate ...goal. Even though numerous studies have investigated patient-specific indicators of treatment efficacy, no (bio)markers or empirical tests for use in clinical practice have resulted as of now. Therefore, clinical decisions regarding the treatment of MDD still have to be made on the basis of questionnaire- or interview-based assessments and general guidelines without the support of a (laboratory) test. We conducted a narrative review of current approaches to characterize and predict outcome to pharmacological treatments in MDD. We particularly focused on findings from newer computational studies using machine learning and on the resulting implementation into clinical decision support systems. The main issues seem to rest upon the unavailability of robust predictive variables and the lacking application of empirical findings and predictive models in clinical practice. We outline several challenges that need to be tackled on different stages of the translational process, from current concepts and definitions to generalizable prediction models and their successful implementation into digital support systems. By bridging the addressed gaps in translational psychiatric research, advances in data quantity and new technologies may enable the next steps toward precision psychiatry.
Childhood adversity (CA) as a significant stressor has consistently been associated with the development of mental disorders. The interaction between CA and genetic variants has been proposed to play ...a substantial role in disease etiology.
In this review, we focus on the gene by environment (GxE) paradigm, its background and interpretation and stress the necessity of its implementation in psychiatric research. Further, we discuss the findings supporting GxCA interactions, ranging from candidate gene studies to polygenic and genome-wide approaches, their strengths and limitations. To illustrate potential underlying epigenetic mechanisms by which GxE effects are translated, we focus on results from FKBP5 × CA studies and discuss how molecular evidence can supplement previous GxE findings.
In conclusion, while GxE studies constitute a valuable line of investigation, more harmonized GxE studies in large, deep-phenotyped, longitudinal cohorts, and across different developmental stages are necessary to further substantiate and understand reported GxE findings.
Different psychiatric disorders as well as exposure to adverse life events have individually been associated with multiple age-related diseases and mortality. Age acceleration in different epigenetic ...clocks can serve as biomarker for such risk and could help to disentangle the interplay of psychiatric comorbidity and early adversity on age-related diseases and mortality. We evaluated five epigenetic clocks (Horvath, Hannum, PhenoAge, GrimAge and DunedinPoAm) in a transdiagnostic psychiatric sample using epigenome-wide DNA methylation data from peripheral blood of 429 subjects from two studies at the Max Planck Institute of Psychiatry. Burden of psychiatric disease, represented by a weighted score, was significantly associated with biological age acceleration as measured by GrimAge and DunedinPoAm (R2-adj. 0.22 and 0.33 for GrimAge and DunedinPoAm, respectively), but not the other investigated clocks. The relation of burden of psychiatric disease appeared independent of differences in socioeconomic status and medication. Our findings indicate that increased burden of psychiatric disease may associate with accelerated biological aging. This highlights the importance of medical management of patients with multiple psychiatric comorbidities and the potential usefulness of specific epigenetic clocks for early detection of risk and targeted intervention to reduce mortality in psychiatric patients.
Reliable prediction models of treatment outcome in Major Depressive Disorder (MDD) are currently lacking in clinical practice. Data-driven outcome definitions, combining data from multiple modalities ...and incorporating clinician expertise might improve predictions.
We used unsupervised machine learning to identify treatment outcome classes in 1060 MDD inpatients. Subsequently, classification models were created on clinical and biological baseline information to predict treatment outcome classes and compared to the performance of two widely used classical outcome definitions. We also related the findings to results from an online survey that assessed which information clinicians use for outcome prognosis.
Three and four outcome classes were identified by unsupervised learning. However, data-driven outcome classes did not result in more accurate prediction models. The best prediction model was targeting treatment response in its standard definition and reached accuracies of 63.9 % in the test sample, and 59.5 % and 56.9 % in the validation samples. Top predictors included sociodemographic and clinical characteristics, while biological parameters did not improve prediction accuracies. Treatment history, personality factors, prior course of the disorder, and patient attitude towards treatment were ranked as most important indicators by clinicians.
Missing data limited the power to identify biological predictors of treatment outcome from certain modalities.
So far, the inclusion of available biological measures in addition to psychometric and clinical information did not improve predictive value of the models, which was overall low. Optimized biomarkers, stratified predictions and the inclusion of clinical expertise may improve future prediction models.
•Data-driven treatment outcome classes in depressed patients were created.•Outcome classes were not easier to predict than response/remission using baseline data.•Clinical and sociodemographic data were more predictive than biological measures.•Top predictors were compared to clinical expertise from an online survey.
Heterogeneity in the clinical presentation of major depressive disorder and response to antidepressants limits clinicians' ability to accurately predict a specific patient's eventual response to ...therapy. Validated depressive symptom profiles may be an important tool for identifying poor outcomes early in the course of treatment. To derive these symptom profiles, we first examined data from 947 depressed subjects treated with selective serotonin reuptake inhibitors (SSRIs) to delineate the heterogeneity of antidepressant response using probabilistic graphical models (PGMs). We then used unsupervised machine learning to identify specific depressive symptoms and thresholds of improvement that were predictive of antidepressant response by 4 weeks for a patient to achieve remission, response, or nonresponse by 8 weeks. Four depressive symptoms (depressed mood, guilt feelings and delusion, work and activities and psychic anxiety) and specific thresholds of change in each at 4 weeks predicted eventual outcome at 8 weeks to SSRI therapy with an average accuracy of 77% (p = 5.5E-08). The same four symptoms and prognostic thresholds derived from patients treated with SSRIs correctly predicted outcomes in 72% (p = 1.25E-05) of 1996 patients treated with other antidepressants in both inpatient and outpatient settings in independent publicly-available datasets. These predictive accuracies were higher than the accuracy of 53% for predicting SSRI response achieved using approaches that (i) incorporated only baseline clinical and sociodemographic factors, or (ii) used 4-week nonresponse status to predict likely outcomes at 8 weeks. The present findings suggest that PGMs providing interpretable predictions have the potential to enhance clinical treatment of depression and reduce the time burden associated with trials of ineffective antidepressants. Prospective trials examining this approach are forthcoming.
A major research finding in the field of Biological Psychiatry is that symptom-based categories of mental disorders map poorly onto dysfunctions in brain circuits or neurobiological pathways. Many of ...the identified (neuro) biological dysfunctions are "transdiagnostic", meaning that they do not reflect diagnostic boundaries but are shared by different ICD/DSM diagnoses. The compromised biological validity of the current classification system for mental disorders impedes rather than supports the development of treatments that not only target symptoms but also the underlying pathophysiological mechanisms. The Biological Classification of Mental Disorders (BeCOME) study aims to identify biology-based classes of mental disorders that improve the translation of novel biomedical findings into tailored clinical applications.
BeCOME intends to include at least 1000 individuals with a broad spectrum of affective, anxiety and stress-related mental disorders as well as 500 individuals unaffected by mental disorders. After a screening visit, all participants undergo in-depth phenotyping procedures and omics assessments on two consecutive days. Several validated paradigms (e.g., fear conditioning, reward anticipation, imaging stress test, social reward learning task) are applied to stimulate a response in a basic system of human functioning (e.g., acute threat response, reward processing, stress response or social reward learning) that plays a key role in the development of affective, anxiety and stress-related mental disorders. The response to this stimulation is then read out across multiple levels. Assessments comprise genetic, molecular, cellular, physiological, neuroimaging, neurocognitive, psychophysiological and psychometric measurements. The multilevel information collected in BeCOME will be used to identify data-driven biologically-informed categories of mental disorders using cluster analytical techniques.
The novelty of BeCOME lies in the dynamic in-depth phenotyping and omics characterization of individuals with mental disorders from the depression and anxiety spectrum of varying severity. We believe that such biology-based subclasses of mental disorders will serve as better treatment targets than purely symptom-based disease entities, and help in tailoring the right treatment to the individual patient suffering from a mental disorder. BeCOME has the potential to contribute to a novel taxonomy of mental disorders that integrates the underlying pathomechanisms into diagnoses.
Retrospectively registered on June 12, 2019 on ClinicalTrials.gov (TRN: NCT03984084).
Fear conditioning and extinction serve as a dominant model for the development and maintenance of pathological anxiety, particularly for phasic fear to specific stimuli or situations. The validity of ...this model would be supported by differences in the physiological or subjective fear response between patients with fear-related disorders and healthy controls, whereas the model's validity would be questioned by a lack of such differences.
We derived pupillometry, skin conductance response and startle electromyography as well as unconditioned stimulus expectancy in a two-day fear acquisition, immediate extinction and recall task and compared an unmedicated group of patients (n = 73) with phobias or panic disorder and a group of patients with posttraumatic stress disorder (PTSD, n = 21) to a group of carefully screened healthy controls (n = 35).
Bayesian statistics showed no convincing evidence for a difference in physiological and subjective responses between the groups during fear acquisition, extinction learning or recall. Only the PTSD subgroup had altered startle reactions during extinction learning.
Our data do not provide evidence for general differences in associative fear or extinction learning in fear-related pathologies and thereby question the diagnostic validity of the associative fear learning model of these disorders.
•Patients with fear disorders, PTSD, and controls were exposed to a fear acquisition, immediate extinction and recall task.•No robust differences in associative fear acquisition, extinction and recall between groups were detected.•There was anecdotal evidence for altered startle responses during extinction in patients with PTSD.•Results question the diagnostic validity of abnormalities in fear learning in patients with fear-related disorders.
Adverse experiences and chronic stress are well-known risk factors for the development of major depression, and an impaired stress response regulation is frequently observed in acute depression. ...Impaired glucocorticoid receptor (GR) signalling plays an important role in these alterations, and a restoration of GR signalling appears to be a prerequisite of successful antidepressant treatment. Variants in genes of the stress response regulation contribute to the vulnerability to depression in traumatized subjects. Consistent findings point to an important role of FKBP5, the gene expressing FK506-binding protein 51 (FKBP51), which is a strong inhibitor of the GR, and thus, an important regulator of the stress response. We investigated the role of FKBP5 and FKB51 expression with respect to stress response regulation and antidepressant treatment outcome in depressed patients. This study included 297 inpatients, who participated in the Munich Antidepressant Response Signature (MARS) project and were treated for acute depression. In this open-label study, patients received antidepressant treatment according to the attending doctor's choice. In addition to the
genotype, changes in blood FKBP51 expression during antidepressant treatment were analyzed using RT-PCR and ZeptoMARK
reverse phase protein microarray (RPPM). Stress response regulation was evaluated in a subgroup of patients using the combined dexamethasone (dex)/corticotropin releasing hormone (CRH) test. As expected, increased FKBP51 expression was associated with an impaired stress response regulation at baseline and after six weeks was accompanied by an elevated cortisol response to the combined dex/CRH test. Further, we demonstrated an active involvement of FKBP51 in antidepressant treatment outcome. While patients responding to antidepressant treatment had a pronounced reduction of
gene and FKBP51 protein expression, increasing expression levels were observed in nonresponders. This effect was moderated by the genotype of the
single nucleotide polymorphism (SNP) rs1360780, with carriers of the minor allele showing the most pronounced association. Our findings demonstrate that
and, specifically, its expression product FKBP51 are important modulators of antidepressant treatment outcome, pointing to a new, promising target for future antidepressant drug development.
Maladaptive stress responses are important risk factors in the etiology of mood and anxiety disorders, but exact pathomechanisms remain to be understood. Mapping individual differences of acute ...stress-induced neurophysiological changes, especially on the level of neural activation and functional connectivity (FC), could provide important insights in how variation in the individual stress response is linked to disease risk.
Using an established psychosocial stress task flanked by two resting states, we measured subjective, physiological, and brain responses to acute stress and recovery in 217 participants with and without mood and anxiety disorders. To estimate blockwise changes in stress-induced activation and FC, we used hierarchical mixed-effects models based on denoised time series within predefined stress-related regions. We predicted inter- and intraindividual differences in stress phases (anticipation vs. stress vs. recovery) and transdiagnostic dimensions of stress reactivity using elastic net and support vector machines.
We identified four subnetworks showing distinct changes in FC over time. FC but not activation trajectories predicted the stress phase (accuracy = 70%, pperm < .001) and increases in heart rate (R2 = 0.075, pperm < .001). Critically, individual spatiotemporal trajectories of changes across networks also predicted negative affectivity (ΔR2 = 0.075, pperm = .030) but not the presence or absence of a mood and anxiety disorder.
Spatiotemporal dynamics of brain network reconfiguration induced by stress reflect individual differences in the psychopathology dimension of negative affectivity. These results support the idea that vulnerability for mood and anxiety disorders can be conceptualized best at the level of network dynamics, which may pave the way for improved prediction of individual risk.
The gene product of the ABCB1 gene, the P-glycoprotein, functions as a custodian molecule in the blood-brain barrier and regulates the access of most antidepressants into the brain. Previous studies ...showed that ABCB1 polymorphisms predicted the response to antidepressants that are substrates of the P-gp, while the response to nonsubstrates was not influenced by ABCB1 polymorphisms. The aim of the present study was to evaluate the clinical application of ABCB1 genotyping in antidepressant pharmacotherapy.
Data came from 58 depressed inpatients participating in the Munich Antidepressant Response Signature (MARS) project, whose ABCB1 gene test results were implemented into the clinical decision making process. Hamilton Depression Rating Scale (HAM-D) scores, remission rates, and duration of hospital stay were documented with dose and kind of antidepressant treatment.
Patients who received ABCB1 genotyping had higher remission rates χ2(1) = 6.596, p = 0.005, 1-sided and lower Hamilton sores t(111) = 2.091, p = 0.0195, 1-sided at the time of discharge from hospital as compared to patients without ABCB1 testing. Among major allele homozygotes for ABCB1 single nucleotide polymorphisms (SNPs) rs2032583 and rs2235015 (TT/GG genotype), an increase in dose was associated with a shorter duration of hospital stay rho(28) = -0.441, p = 0.009, 1-sided, whereas other treatment strategies (eg, switching to a nonsubstrate) showed no significant associations with better treatment outcome. Discussion The implementation of ABCB1 genotyping as a diagnostic tool influenced clinical decisions and led to an improvement of treatment outcome. Patients carrying the TT/GG genotype seemed to benefit from an increase in P-gp substrate dose.
Results suggest that antidepressant treatment of depression can be optimized by the clinical application of ABCB1 genotyping.
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