Right patient, right treatment, right time: biosignatures and precision medicine in depression

Reflections on the concept of optimality of single decision point treatment regimes

In many areas, applied researchers as well as practitioners have to choose between different solutions for a problem at hand; this calls for optimal decision rules to settle the choices involved. As a key example, one may think of the search for optimal treatment regimes (OTRs) in clinical research, that specify which treatment alternative should be administered to each patient under study. Motivated by the fact that the concept of optimality of decision rules in general and treatment regimes in particular has received so far relatively little attention and discussion, we will present a number of reflections on it, starting from the basics of any optimization problem. Specifically, we will analyze the search space and the to be optimized criterion function underlying the search of single decision point OTRs, along with the many choice aspects that show up in their specification. Special attention is paid to formal characteristics and properties as well as to substantive concerns and hypotheses that may guide these choices. We illustrate with a few empirical examples taken from the literature. Finally, we discuss how the presented reflections may help sharpen statistical thinking about optimality of decision rules for treatment assignment and to facilitate the dialogue between the statistical consultant and the applied researcher in search of an OTR.

Views on sharing mental health data for research purposes: qualitative analysis of interviews with people with mental illness

BACKGROUND

Improving the ways in which routinely-collected mental health data are shared could facilitate substantial advances in research and treatment. However, this process should only be undertaken in partnership with those who provide such data. Despite relatively widespread investigation of public perspectives on health data sharing more generally, there is a lack of research on the views of people with mental illness.

METHODS

Twelve people with lived experience of mental illness took part in semi-structured interviews via online video software. Participants had experience of a broad range of mental health conditions including anxiety, depression, schizophrenia, eating disorders and addiction. Interview questions sought to establish how participants felt about the use of routinely-collected health data for research purposes, covering different types of health data, what health data should be used for, and any concerns around its use.

RESULTS

Thematic analysis identified four overarching themes: benefits of sharing mental health data, concerns about sharing mental health data, safeguards, and data types. Participants were clear that health data sharing should facilitate improved scientific knowledge and better treatments for mental illness. There were concerns that data misuse could become another way in which individuals and society discriminate against people with mental illness, for example through insurance premiums or employment decisions. Despite this there was a generally positive attitude to sharing mental health data as long as appropriate safeguards were in place.

CONCLUSIONS

There was notable strength of feeling across participants that more should be done to reduce the suffering caused by mental illness, and that this could be partly facilitated by well-managed sharing of health data. The mental health research community could build on this generally positive attitude to mental health data sharing by following rigorous best practice tailored to the specific concerns of people with mental illness.

Sociodemographic, lifestyle and clinical characteristics of energy-related depression symptoms: A pooled analysis of 13,965 depressed cases in 8 Dutch cohorts

BACKGROUND

In a substantial subgroup of depressed patients, atypical, energy-related depression symptoms (e.g. increased appetite/weight, hypersomnia, loss of energy) tend to cluster with immuno-metabolic dysregulations (e.g. increased BMI and inflammatory markers). This clustering is proposed to reflect a more homogeneous depression pathology. This study examines to what extent energy-related symptoms are associated and share sociodemographic, lifestyle and clinical characteristics.

METHODS

Data were available from 13,965 participants from eight Dutch cohorts with DSM-5 lifetime major depression assessed by the Lifetime Depression Assessment Self-report (LIDAS) questionnaire. Information on four energy-related depression symptoms were extracted: energy loss, increased appetite, increased weight, and hypersomnia. Tetrachoric correlations between these symptoms, and associations of these symptoms with sociodemographic (sex, age, education), lifestyle (physical activity, BMI, smoking) and clinical characteristics (age of onset, episode duration, history, treatment and recency, and self-reported comorbidity) were computed.

RESULTS

Correlations between energy-related symptoms were overall higher than those with other depression symptoms and varied from 0.90 (increased appetite vs increased weight) to 0.11 (increased appetite vs energy loss). All energy-related symptoms were strongly associated with higher BMI and a more severe clinical profile. Patients with increased appetite were more often smokers, and only patients with increased appetite or weight more often had a self-reported diagnosis of PTSD (OR = 1.17, p = 2.91E-08) and eating disorder (OR = 1.40, p = 4.08E-17).

CONCLUSIONS

The symptom-specific associations may have consequences for a profile integrating these symptoms, which can be used to reflect immuno-metabolic depression. They indicate the need to study immuno-metabolic depression at individual symptom resolution as a starting point.

Key considerations in the pharmacological management of treatment-resistant depression

Introduction: Treatment-resistant depression (TRD) is a complex, multifactorial, and biologically heterogeneous disorder with debilitating outcomes. Understanding individual reasons why patients do not respond to treatment is necessary for improving clinical recommendations regarding medication regimens, augmentation strategies, and alternative treatments.Areas covered: This manuscript reviews evidence-based treatment strategies for the clinical management of TRD. Current developments in the field and potential future recommendations for personalized treatment of TRD are also discussed.Expert opinion: Treatment guidelines for TRD are limited by the heterogeneous nature of the disorder. Furthermore, current strategies reflect this heterogeneity by emphasizing disease characteristics as well as drug trial response or failure. Developing robust biomarkers that could one day be integrated into clinical practice has the potential to advance specific treatment targets and ultimately improve treatment and remission outcomes.

Can (immune and other) gene expression help us to treat depression?

It is becoming clearer that it might be a combination of different biological processes such as genetic, environmental, and psychological factors, together with immune system, stress response, brain neuroplasticity and the regulation of neurotransmitters, that leads to the development of major depressive disorder (MDD). A growing number of studies have tried to investigate the underlying mechanisms of MDD by analysing the expression levels of genes (mRNA) involved in such biological processes. In this review, I have highlighted a possible key role that gene expression might play in the treatment of MDD. This is critical because many patients do not respond to antidepressant treatment or can experience side effects, causing treatment to be interrupted. Unfortunately, selecting the best antidepressant for each individual is still largely a matter of making an informed guess.

Gene expression studies in Depression development and treatment: an overview of the underlying molecular mechanisms and biological processes to identify biomarkers

A combination of different risk factors, such as genetic, environmental and psychological factors, together with immune system, stress response, brain neuroplasticity and the regulation of neurotransmitters, is thought to lead to the development of major depressive disorder (MDD). A growing number of studies have tried to investigate the underlying mechanisms of MDD by analysing the expression levels of genes involved in such biological processes. These studies have shown that MDD is not just a brain disorder, but also a body disorder, and this is mainly due to the interplay between the periphery and the Central Nervous System (CNS). To this purpose, most of the studies conducted so far have mainly dedicated to the analysis of the gene expression levels using postmortem brain tissue as well as peripheral blood samples of MDD patients. In this paper, we reviewed the current literature on candidate gene expression alterations and the few existing transcriptomics studies in MDD focusing on inflammation, neuroplasticity, neurotransmitters and stress-related genes. Moreover, we focused our attention on studies, which have investigated mRNA levels as biomarkers to predict therapy outcomes. This is important as many patients do not respond to antidepressant medication or could experience adverse side effects, leading to the interruption of treatment. Unfortunately, the right choice of antidepressant for each individual still remains largely a matter of taking an educated guess.

Data-driven biological subtypes of depression: systematic review of biological approaches to depression subtyping

Research into major depressive disorder (MDD) is complicated by population heterogeneity, which has motivated the search for more homogeneous subtypes through data-driven computational methods to identify patterns in data. In addition, data on biological differences could play an important role in identifying clinically useful subtypes. This systematic review aimed to summarize evidence for biological subtypes of MDD from data-driven studies. We undertook a systematic literature search of PubMed, PsycINFO, and Embase (December 2018). We included studies that identified (1) data-driven subtypes of MDD based on biological variables, or (2) data-driven subtypes based on clinical features (e.g., symptom patterns) and validated these with biological variables post-hoc. Twenty-nine publications including 24 separate analyses in 20 unique samples were identified, including a total of ~ 4000 subjects. Five out of six biochemical studies indicated that there might be depression subtypes with and without disturbed neurotransmitter levels, and one indicated there might be an inflammatory subtype. Seven symptom-based studies identified subtypes, which were mainly determined by severity and by weight gain vs. loss. Two studies compared subtypes based on medication response. These symptom-based subtypes were associated with differences in biomarker profiles and functional connectivity, but results have not sufficiently been replicated. Four out of five neuroimaging studies found evidence for groups with structural and connectivity differences, but results were inconsistent. The single genetic study found a subtype with a distinct pattern of SNPs, but this subtype has not been replicated in an independent test sample. One study combining all aforementioned types of data discovered a subtypes with different levels of functional connectivity, childhood abuse, and treatment response, but the sample size was small. Although the reviewed work provides many leads for future research, the methodological differences across studies and lack of replication preclude definitive conclusions about the existence of clinically useful and generalizable biological subtypes.

[The current state and future prospects of depression research (clinical and classification problems)]

Despite decades of research, neurobiological studies of depression haven't achieved significant results. Many experts propose that one of the main reasons for this failure is current diagnostic standards not considering the heterogeneity and polymorphism of depression. Research is unable to identify specific neurobiological changes due to formal diagnosis 'major depressive disorder' and new diagnostic criteria are needed. RDoC (Research Domain Criteria) has intensified the confrontation between biological and clinical researchers and changes in approach to depressive psychopathology are discussed. A review presents the recent approaches used in studies of depressive disorders, the methodology they use, the scientific paradigms they rely on.

Validating pre-treatment body mass index as moderator of antidepressant treatment outcomes: Findings from CO-MED trial

BACKGROUND

Currently, there are no valid clinical or biological markers to personalize the treatment of depression. Recent evidence suggests that body mass index (BMI) may guide the selection of antidepressant medications with different mechanisms of action.

METHODS

Combining Medications to Enhance Depression Outcomes (CO-MED) trial participants with BMI measurement (n = 662) were categorized as normal- or underweight (<25), overweight (25-<30), obese I (30-<35), and obese II+ (≥35). Logistic regression analysis with remission as the dependent variable and treatment arm-by-BMI category interaction as the primary independent variable was used to evaluate if BMI differentially predicted response to escitalopram (SSRI) monotherapy, bupropion-escitalopram combination, or venlafaxine-mirtazapine combination, after controlling for gender and baseline depression severity.

RESULTS

Remission rates among the three treatment arms differed on the basis of pre-treatment BMI (chi-square=12.80, degrees of freedom=6, p = .046). Normal- or under-weight participants were less likely to remit with the bupropion-SSRI combination (26.8%) than SSRI monotherapy (37.3%, number needed to treat or NNT = 9.5) or venlafaxine-mirtazapine combination (44.4%, NNT = 5.7). Conversely, obese II+ participants were more likely to remit with bupropion-SSRI (47.4%) than SSRI monotherapy (28.6%, NNT = 5.3) or venlafaxine-mirtazapine combination (37.7%, NNT = 10.3). Remission rates did not differ among overweight and obese I participants.

LIMITATIONS

Secondary analysis, higher rates of obesity than the general population.

CONCLUSIONS

Antidepressant selection in clinical practice can be personalized with BMI measurements. Bupropion-SSRI combination should be avoided in normal- or under-weight depressed outpatients as compared to SSRI monotherapy and venlafaxine-mirtazapine combination and preferred in those with BMI≥35.

Personalized Antidepressant Selection and Pathway to Novel Treatments: Clinical Utility of Targeting Inflammation

Major depressive disorder (MDD) is a chronic condition that affects one in six adults in the US during their lifetime. The current practice of antidepressant medication prescription is a trial-and-error process. Additionally, over a third of patients with MDD fail to respond to two or more antidepressant treatments. There are no valid clinical markers to personalize currently available antidepressant medications, all of which have similar mechanisms targeting monoamine neurotransmission. The goal of this review is to summarize the recent findings of immune dysfunction in patients with MDD, the utility of inflammatory markers to personalize treatment selection, and the potential of targeting inflammation to develop novel antidepressant treatments. To personalize antidepressant prescription, a c-reactive protein (CRP)-matched treatment assignment can be rapidly implemented in clinical practice with point-of-care fingerstick tests. With this approach, 4.5 patients need to be treated for 1 additional remission as compared to a CRP-mismatched treatment assignment. Anti-cytokine treatments may be effective as novel antidepressants. Monoclonal antibodies against proinflammatory cytokines, such as interleukin 6, interleukin 17, and tumor necrosis factor α, have demonstrated antidepressant effects in patients with chronic inflammatory conditions who report significant depressive symptoms. Additional novel antidepressant strategies targeting inflammation include pharmaceutical agents that block the effect of systemic inflammation on the central nervous system. In conclusion, inflammatory markers offer the potential not only to personalize antidepressant prescription but also to guide the development of novel mechanistically-guided antidepressant treatments.

Pharmacogenomics and Biomarkers of Depression

The standard of care for antidepressant treatment in major depressive disorder (MDD) is a trial-and-error approach. Patients often have to undergo multiple medication trials for weeks to months before finding an effective treatment. Clinical factors such as severity of baseline symptoms and the presence of specific individual (anhedonia or insomnia) or cluster (atypical, melancholic, or anxious) of symptoms are commonly used without any evidence of their utility in selecting among currently available antidepressants. Genomic and proteomic biomarker have gained recent attention for their potential in informing antidepressant medication selection. In this report, we have reviewed some of the major pharmacogenomics studies along with individual genetic and proteomic biomarker of antidepressant response. Additionally, we have reviewed the blood-based protein biomarkers that can inform selection of one antidepressant over another. Among all currently available biomarkers, C-reactive protein (CRP) appears to be the most promising and pragmatic choice. Low CRP (<1 mg/L) in patients with MDD predicts better response to escitalopram while higher levels are associated with better response to noradrenergic/dopaminergic antidepressants. Future studies are needed to demonstrate the superiority of a CRP-based treatment assignment over high-quality measurement-based care in real-world clinical practices.

Platelet-Derived Growth Factor as an Antidepressant Treatment Selection Biomarker: Higher Levels Selectively Predict Better Outcomes with Bupropion-SSRI Combination

BACKGROUND

Platelet derived growth factor is integral to maintenance of blood brain barrier, increases in response to blood brain barrier disruption, and may reflect neuroinflammation. Based on previous reports of better outcomes with dopaminergic antidepressants in depressed patients with elevated inflammatory biomarkers, we hypothesize that elevated peripheral platelet derived growth factor levels can serve as a powerful biomarker for selecting dopaminergic antidepressants.

METHODS

Platelet derived growth factor, basic fibroblast growth factor, and granulocyte colony stimulating factor were measured as part of Bioplex Pro human cytokine 27-plex kit in participants of the Combining Medications to Enhance Depression Outcomes trial who provided baseline plasma (n=166) and were treated with either bupropion-plus-escitalopram, escitalopram-plus-placebo, or venlafaxine-plus-mirtazapine. Differential changes in overall symptom severity and anhedonia as well as side effects were tested with a treatment-arm-by-biomarker interaction in mixed model analyses. Effect of biomarkers with significant interaction was calculated in subsequent analyses stratified by treatment arm.

RESULTS

There was a significant treatment-arm-by-platelet derived growth factor interaction for depression severity (P=.03) and anhedonia (P=.008) but not for side effects (P=.44). Higher baseline platelet derived growth factor level was associated with greater reduction in depression severity (effect size=0.71, P=.015) and anhedonia (effect size=0.66, P=.02) in the bupropion- selective serotonin reuptake inhibitor but not the other two treatment arms. There was no significant treatment-arm-by-biomarker interaction for both depression severity and side effects with the other two biomarkers.

CONCLUSION

As compared with selective serotonin reuptake inhibitor monotherapy or venlafaxine-plus-mirtazapine, bupropion-plus-escitalopram selectively improves anhedonia, which in turn results in improved overall depression severity in depressed patients with elevated platelet derived growth factor levels.

Antidepressant prescribing in the precision medicine era: a prescriber's primer on pharmacogenetic tools

About half of people who take antidepressants do not respond and many experience adverse effects. These detrimental outcomes are in part a result of the impact of an individual's genetic profile on pharmacokinetics and pharmcodynamics. If known and made available to clinicians, this could improve decision-making and antidepressant therapy outcomes. This has spurred the development of numerous pharmacogenetic-based decision support tools. In this article, we provide an overview of pharmacogenetic decision support tools, with particular focus on tools relevant to antidepressants. We briefly describe the evolution and current state of antidepressant pharmacogenetic decision support tools in clinical practice, followed by the evidence-base for their use. Finally, we present a series of considerations for clinicians contemplating use of these tools and discuss the future of antidepressant pharmacogenetic decision support tools.