Convergent neurobiological predictors of mood and anxiety symptoms and treatment response

Brain-Derived Neurotrophic Factor (BDNF) as a Predictor of Treatment Response in Major Depressive Disorder (MDD): A Systematic Review

Brain-derived neurotrophic factor (BDNF) has been studied as a biomarker of major depressive disorder (MDD). Besides diagnostic biomarkers, clinically useful biomarkers can inform response to treatment. We aimed to review all studies that sought to relate BDNF baseline levels, or BDNF polymorphisms, with response to treatment in MDD. In order to achieve this, we performed a systematic review of studies that explored the relation of BDNF with both pharmacological and non-pharmacological treatment. Finally, we reviewed the evidence that relates peripheral levels of BDNF and BDNF polymorphisms with the development and management of treatment-resistant depression.

Chronobiological parameters as predictors of early treatment response in major depression

BACKGROUND

Alterations in circadian system organization have been related to major depressive disorder manifestations. This study aimed to evaluate chronobiological parameters, such as sleep, levels of 6-sulfatoxymelatonin, and others derived from actimetry as potential predictors of adequate treatment response in MDD.

METHODS

98 adult women with confirmed diagnosis of MDD were included. Participants completed standard questionnaires (Hamilton Depression Rating Scale - HAM-D; Munich Chronotype Questionnaire - MCTQ) at baseline and after 4 weeks of treatment. Urinary samples for assessing 6-sulfatoxymelatonin were collected on the day before and immediately after pharmacological treatment administration, and 28 continuous days of actigraphy data were collected during the protocol. Participants were classified into Responder (R) or Non-responder (NR) to antidepressant treatment in 4 weeks (early responder), which was characterized by a ≥50 % decrease in the HAM-D score.

RESULTS

The following biological rhythms variables significantly predicted a better treatment response in a model controlling for age, sex, and previous treatments: higher levels of activity (M10 - average activity in the 10 most active hours within the 24 h-day) and an earlier center of the 10 most active hours (M10c), as well as lower intradaily variability (IV) of light exposure. Sleep parameters and 6-sulfatoxymelatonin levels did not associate with treatment response prediction.

LIMITATION

Actimetry data were not assessed before changing in the treatment plan.

CONCLUSION

Different patterns in activity and light exposure might be linked to early antidepressant response.

Weekly telephone mood monitoring is associated with decreased suicidality and improved sleep quality in a clinical sample

Sleep disturbances and suicidality are common presentations of mood and anxiety disorders. If not closely monitored post-discharge, patients may be at an increased risk of symptom worsening and completed suicide. We explored the associations between telephone mood monitoring, suicidality, and sleep quality in a clinical sample. Fifty inpatients (mean age = 39.49, SD = 11.17; female = 74%) with a mood and/or anxiety disorder were telephonically monitored weekly post-discharge for16 weeks for depression and mania. Suicidality and sleep quality were assessed at intake (pre-discharge), and at weeks 4, 8, 12, and 16 post-discharge. ANOVA indicated that suicidality significantly decreased, and sleep quality improved over 16 weeks. Linear regression analysis indicated that depression severity at week 1 post-discharge significantly predicted suicidality and sleep quality at week 16. Mania severity at week 1 post-discharge predicted sleep quality, but not suicidality, at week 16. Participants generally had positive experiences of the monitoring and perceived it as helpful. Monitoring of mood state, suicidality, and sleep quality post-discharge may allow for early detection of relapse when initiated at 1-week post-discharge. This is a potentially cost-effective intervention and may relieve the burden on the mental healthcare system, especially when face-to-face consultations are not possible.

Integrating biobehavioral information to predict mood disorder suicide risk

The will to live and the ability to maintain one's well-being are crucial for survival. Yet, almost a million people die by suicide globally each year (Aleman and Denys, 2014), making premature deaths due to suicide a significant public health problem (Saxena et al., 2013). The expression of suicidal behaviors is a complex phenotype with documented biological, psychological, clinical, and sociocultural risk factors (Turecki et al., 2019). From a brain disease perspective, suicide is associated with neuroanatomical, neurophysiological, and neurochemical dysregulations of brain networks involved in integrating and contextualizing cognitive and emotional regulatory behaviors. From a symptom perspective, diagnostic measures of dysregulated mood states like major depressive symptoms are associated with over sixty percent of suicide deaths worldwide (Saxena et al., 2013). This paper reviews the neurobiological and clinical phenotypic correlates for mood dysregulations and suicidal phenotypes. We further propose machine learning approaches to integrate neurobiological measures with dysregulated mood symptoms to elucidate the role of inflammatory processes as neurobiological risk factors for suicide.

Transcription Factor Motifs Associated with Anterior Insula Gene Expression Underlying Mood Disorder Phenotypes

Mood disorders represent a major cause of morbidity and mortality worldwide but the brain-related molecular pathophysiology in mood disorders remains largely undefined. Because the anterior insula is reduced in volume in patients with mood disorders, RNA was extracted from the anterior insula postmortem anterior insula of mood disorder samples and compared with unaffected controls for RNA-sequencing identification of differentially expressed genes (DEGs) in (a) bipolar disorder (BD; n = 37) versus (vs.) controls (n = 33), and (b) major depressive disorder (MDD n = 30) vs. controls, and (c) low vs. high axis I comorbidity (a measure of cumulative psychiatric disease burden). Given the regulatory role of transcription factors (TFs) in gene expression via specific-DNA-binding domains (motifs), we used JASPAR TF binding database to identify TF-motifs. We found that DEGs in BD vs. controls, MDD vs. controls, and high vs. low axis I comorbidity were associated with TF-motifs that are known to regulate expression of toll-like receptor genes, cellular homeostatic-control genes, and genes involved in embryonic, cellular/organ, and brain development. Robust imaging-guided transcriptomics by using meta-analytic imaging results to guide independent postmortem dissection for RNA-sequencing was applied by targeting the gray matter volume reduction in the anterior insula in mood disorders, to guide independent postmortem identification of TF motifs regulating DEG. Our findings of TF-motifs that regulate the expression of immune, cellular homeostatic-control, and developmental genes provide novel information about the hierarchical relationship between gene regulatory networks, the TFs that control them, and proximate underlying neuroanatomical phenotypes in mood disorders.

Precision Psychiatry: Biomarker-Guided Tailored Therapy for Effective Treatment and Prevention in Major Depression

Depression contributes greatly to global disability and is a leading cause of suicide. It has multiple etiologies and therefore response to treatment can vary significantly. By applying the concepts of personalized medicine, precision psychiatry attempts to optimize psychiatric patient care by better predicting which individuals will develop an illness, by giving a more accurate biologically based diagnosis, and by utilizing more effective treatments based on an individual's biological characteristics (biomarkers). In this chapter, we discuss the basic principles underlying the role of biomarkers in psychiatric pathology and then explore multiple biomarkers that are specific to depression. These include endophenotypes, gene variants/polymorphisms, epigenetic factors such as methylation, biochemical measures, circadian rhythm dysregulation, and neuroimaging findings. We also examine the role of early childhood trauma in the development of, and treatment response to, depression. In addition, we review how new developments in technology may play a greater role in the determination of new biomarkers for depression.