Potential biomarkers of major depression diagnosis and chronicity

Self-Reported Mood and Lifestyle-Related Physical Activity of Young Adults With Major Depressive Disorder

We investigated whether mood and lifestyle-related indicators of physical health are differentially expressed according to self-reported levels of depressive symptoms among young adults with a current episode of major depression. In a cross-sectional study, we recruited 94 young adults (females = 67, 71.3%; males = 27, 28.7%; aged 18-35 years) with a current episode of major depression. We assessed their mood with the Profile of Mood States (POMS), and Beck Anxiety Inventory-(BAI), sleep with the Pittsburgh Sleep Quality Index (PSQI), physical activity with the Simple Physical Activity Questionnaire (SIMPAQ), and their cardiorespiratory fitness. Participants' depression levels were classified as follows using established cut-points: (a) Mild Depressive Symptoms (MIDS, BDI-II 14-19 points, n = 17), (b) Moderate Depressive Symptoms (MODS, BDI-II 20-28 points, n = 37) or (c) Severe Depressive Symptoms (SEDS, BDI-II 29-63 points, n = 40). As expected, we found that young adults with SEDS, when compared to those with MODS and MIDS, showed higher depressive mood on the POMS, and they exhibited greater anxiety symptoms, lower reported 'vigor' on physical activity measures, worse sleep quality as expressed by their global score sleep; daytime dysfunction; and sleep disturbance, and they showed lower cardiorespiratory fitness. Those with moderate depressive symptoms only differed from those with mild symptoms with respect to hostility, fatigue and mood disturbance. Although there was a gradient whereby worse mental and physical health indicators were more closely related to the SEDS depression categorization, while healthier indicators were associated with the MIDS category, some parameters were not different between the MDD severity groups, particularly when comparing MIDS and MODS. Clinicians treating patients with MDD should consider these factors when designing lifestyle-based interventions.

Epigenetic regulation in major depression and other stress-related disorders: molecular mechanisms, clinical relevance and therapeutic potential

Major depressive disorder (MDD) is a chronic, generally episodic and debilitating disease that affects an estimated 300 million people worldwide, but its pathogenesis is poorly understood. The heritability estimate of MDD is 30-40%, suggesting that genetics alone do not account for most of the risk of major depression. Another factor known to associate with MDD involves environmental stressors such as childhood adversity and recent life stress. Recent studies have emerged to show that the biological impact of environmental factors in MDD and other stress-related disorders is mediated by a variety of epigenetic modifications. These epigenetic modification alterations contribute to abnormal neuroendocrine responses, neuroplasticity impairment, neurotransmission and neuroglia dysfunction, which are involved in the pathophysiology of MDD. Furthermore, epigenetic marks have been associated with the diagnosis and treatment of MDD. The evaluation of epigenetic modifications holds promise for further understanding of the heterogeneous etiology and complex phenotypes of MDD, and may identify new therapeutic targets. Here, we review preclinical and clinical epigenetic findings, including DNA methylation, histone modification, noncoding RNA, RNA modification, and chromatin remodeling factor in MDD. In addition, we elaborate on the contribution of these epigenetic mechanisms to the pathological trait variability in depression and discuss how such mechanisms can be exploited for therapeutic purposes.

Poor subjective sleep reported by people living with HIV is associated with impaired working memory

Poor sleep can undermine health and may be especially disruptive to those with chronic conditions including HIV infection. Here, clinically well-described people living with HIV [PLWH] (74 men, 35 women) and healthy control (38 men, 35 women) participants were administered the Pittsburgh Sleep Quality Index (PSQI), a validated measure of subjective sleep with a global score ≥5 able to distinguish good from poor sleepers. In addition, participants completed a battery of neuropsychological tests. PLWH (6.8 ± 3.7) had higher global PSQI scores than healthy controls (4.1 ± 2.8): 39.7 % of uninfected controls and 68.8 % of PLWH had a PSQI≥5 indicative of poor sleep. There were no relations between the global PSQI score and any evaluated variables among uninfected individuals or with demographic or HIV-related variables in PLWH. Instead, a higher global PSQI score among PLWH was associated with worse "Quality of Life" scores [Global Assessment of Functioning (GAF, p=0.0007), Medical Outcomes Study survey (21-item short form, SF-21, p<0.0001), and Activities of Daily Living-Instrumental (ADL-I, p=0.0041)] and higher Beck Depression Index (BDI, p<0.0001) depressive symptoms. Further, in PLWH, higher global PSQI scores were associated with poor performance on a working memory task, the digit backward span (p=0.0036). In PLWH, the 5 variables together explained 32.3 % of the global PSQI score variance; only 3 variables - the SF-21, BDI, and digit backward scores - explained 30.6 % of the variance. To the extent that poor subjective sleep contributes to impaired working memory in HIV, we speculate that this impairment may be ameliorated by improved sleep health.

Optimizing the predictive power of depression screenings using machine learning

Objective

Mental health self-report and clinician-rating scales with diagnoses defined by sum-score cut-offs are often used for depression screening. This study investigates whether machine learning (ML) can detect major depressive episodes (MDE) based on screening scales with higher accuracy than best-practice clinical sum-score approaches.

Methods

Primary data was obtained from two RCTs on the treatment of depression. Ground truth were DSM 5 MDE diagnoses based on structured clinical interviews (SCID) and PHQ-9 self-report, clinician-rated QIDS-16, and HAM-D-17 were predictors. ML models were trained using 10-fold cross-validation. Performance was compared against best-practice sum-score cut-offs. Primary outcome was the Area Under the Curve (AUC) of the Receiver Operating Characteristic curve. DeLong's test with bootstrapping was used to test for differences in AUC. Secondary outcomes were balanced accuracy, precision, recall, F1-score, and number needed to diagnose (NND).

Results

A total of k = 1030 diagnoses (no diagnosis: k = 775; MDE: k = 255) were included. ML models achieved an AUCQIDS-16 = 0.94, AUCHAM-D-17 = 0.88, and AUCPHQ-9 = 0.83 in the testing set. ML AUC was significantly higher than sum-score cut-offs for QIDS-16 and PHQ-9 (ps ≤ 0.01; HAM_D-17: p = 0.847). Applying optimal prediction thresholds, QIDS-16 classifier achieved clinically relevant improvements (Δbalanced accuracy = 8%, ΔF1-score = 14%, ΔNND = 21%). Differences for PHQ_9 and HAM-D-17 were marginal.

Conclusions

ML augmented depression screenings could potentially make a major contribution to improving MDE diagnosis depending on questionnaire (e.g., QIDS-16). Confirmatory studies are needed before ML enhanced screening can be implemented into routine care practice.

Moderators of ayahuasca's biological antidepressant action

Introduction

The understanding of biological responses to psychedelics with antidepressant potential is imperative. Here we report how a set of acute parameters, namely emotional (depressive symptoms), cognitive (psychedelic experience), and physiological (salivary cortisol), recorded during an ayahuasca dosing session, modulated serum brain-derived neurotrophic factor (BDNF), serum cortisol (SC), serum interleukin 6 (IL-6), plasma C-reactive protein (CRP), and salivary cortisol awakening response (CAR).

Methods

Results were analyzed 2 days after the psychedelic intervention (ayahuasca) versus placebo in both patients with treatment-resistant depression and healthy volunteers. These measures were assessed as part of a randomized double-blinded, placebo-controlled trial (n = 72).

Results

Results revealed that larger reductions of depressive symptoms during the dosing session significantly moderated higher levels of SC in patients. Whereas lesser changes in salivary cortisol levels during the ayahuasca intervention were related to higher BDNF levels in patients with a larger clinical response in the reduction in depressive symptoms. No moderator was found for patient's CAR, IL-6, and CRP responses to ayahuasca and for all biomarker responses to ayahuasca in healthy controls and in the placebo group.

Discussion

In summary, some specific emotional and physiological parameters during experimental ayahuasca session were revealed as critical moderators of the improvement of major depression biomarkers, mainly BDNF and SC two days after ayahuasca intake. These findings contribute to paving the way for future studies investigating the biological antidepressant response to psychedelic therapy.

The emergence of psychoanalytical electrochemistry: the translation of MDD biomarker discovery to diagnosis with electrochemical sensing

The disease burden and healthcare costs of psychiatric diseases along with the pursuit to understand their underlying biochemical mechanisms have led to psychiatric biomarker investigations. Current advances in evaluating candidate biomarkers for psychiatric diseases, such as major depressive disorder (MDD), focus on determining a specific biomarker signature or profile. The origins of candidate biomarkers are heterogenous, ranging from genomics, proteomics, and metabolomics, while incorporating associations with clinical characterization. Prior to clinical use, candidate biomarkers must be validated by large multi-site clinical studies, which can be used to determine the ideal MDD biomarker signature. Therefore, identifying valid biomarkers has been challenging, suggesting the need for alternative approaches. Following validation studies, new technology must be employed to transition from biomarker discovery to diagnostic biomolecular profiling. Current technologies used in discovery and validation, such as mass spectroscopy, are currently limited to clinical research due to the cost or complexity of equipment, sample preparation, or measurement analysis. Thus, other technologies such as electrochemical detection must be considered for point-of-care (POC) testing with the needed characteristics for physicians' offices. This review evaluates the advantages of using electrochemical sensing as a primary diagnostic platform due to its rapidity, accuracy, low cost, biomolecular detection diversity, multiplexed capacity, and instrument flexibility. We evaluate the capabilities of electrochemical methods in evaluating current candidate MDD biomarkers, individually and through multiplexed sensing, for promising applications in detecting MDD biosignatures in the POC setting.

Elevated Serum Cortisol Levels in Patients with Focal Epilepsy, Depression, and Comorbid Epilepsy and Depression

Background: The hypothalamic-pituitary-adrenal (HPA) axis, inflammatory processes and neurotrophic factor systems are involved in pathogenesis of both epilepsy and depressive disorders. The study aimed to explore these systems in patients with focal epilepsy (PWE, n = 76), epilepsy and comorbid depression (PWCED n = 48), and major depressive disorder (PWMDD, n = 62) compared with healthy controls (HC, n = 78). Methods: Parameters of the HPA axis, neurotrophic factors, and TNF-α were measured in blood serum along with the hemogram. Results: Serum cortisol level was augmented in PWE, PWCED, and PWMDD compared with HC and was higher in PWMDD than in PWE. Serum cortisol negatively correlated with Mini–Mental State Examination (MMSE) score in PWE, and positively with depression inventory–II (BDI-II) score in PWMDD. Only PWMDD demonstrated elevated plasma ACTH. Serum TNF-α, lymphocytes, and eosinophils were augmented in PWMDD; monocytes elevated in PWE and PWCED, while neutrophils were reduced in PWE and PWMDD. Serum BDNF was decreased in PWE and PWCED, CNTF was elevated in all groups of patients. In PWE, none of above indices depended on epilepsy etiology. Conclusions: The results confirm the involvement of HPA axis and inflammatory processes in pathogenesis of epilepsy and depression and provide new insights in mechanisms of epilepsy and depression comorbidity.

Network Pharmacology-Based and Experimental Identification of the Effects of Paeoniflorin on Major Depressive Disorder

Objective: Major depressive disorder (MDD) is one of the most common psychiatric disorders, the diagnosis and treatment of MDD are major clinical issues. However, there is a lack of effective biomarkers and drugs diagnosis and therapeutics of MDD. In the present study, bioinformatics analysis combined with an experimental verification strategy was used to identify biomarkers and paeoniflorin targets for MDD diagnosis and treatment.

Methods: Based on network pharmacology, we obtained potential targets and pathways of paeoniflorin as an antidepressant through multiple databases. We then constructed a protein-protein interaction network and performed enrichment analyses. According to the results, we performed in vivo and in vitro experimental validation.

Results: The results showed that paeoniflorin may exert an antidepressant effect by regulating cell inflammation, synaptic function, NF-κB signaling pathway, and intestinal inflammation.

Conclusion: NPM1, HSPA8, HSPA5, HNRNPU, and TNF are the targets of paeoniflorin treatment. In addition, we demonstrated that paeoniflorin inhibits inflammatory cytokine production via the p38MAPK/NF-κB pathway and has neuroprotective effects on the synaptic structure. Our findings provide valuable evidence for the diagnosis and treatment of MDD.

BDNF and Cortisol in the Diagnosis of Cocaine-Induced Depression

BACKGROUND

Major depressive disorder (MDD) and cocaine use disorder (CUD) are related with disability and high mortality rates. The assessment and treatment of psychiatric comorbidity is challenging due to its high prevalence and its clinical severity, mostly due to suicide rates and the presence of medical comorbidities. The aim of this study is to investigate differences in brain derived neurotrophic factor (BDNF) and cortisol plasmatic levels in patients diagnosed with CUD-primary-MDD and CUD-induced-MDD and also to compare them to a sample of MDD patients (without cocaine use), a sample of CUD (without MDD), and a group of healthy controls (HC) after a stress challenge.

METHODS

A total of 46 subjects were included: MDD (n = 6), CUD (n = 15), CUD-primary-MDD (n = 16), CUD-induced-MDD (n = 9), and 21 HC. Psychiatric comorbidity was assessed with the Spanish version of the Psychiatric Research Interview for Substance and Mental Disorders IV (PRISM-IV), and depression severity was measured with the Hamilton Depression Rating Scale (HDRS). Patients were administered the Trier Social Stress Test (TSST) before and after the biological measures, including BDNF, and cortisol levels were obtained.

RESULTS

After the TSST, Cohen's d values between CUD-primary-MDD and CUD-induced-MDD increased in each assessment from 0.19 post-TSST to 2.04 post-90-TSST. Pairwise differences among CUD-induced-MDD and both MDD and HC groups had also a large effect size value in post-30-TSST and post-90-TSST. In the case of the BDNF concentrations, CUD-primary-MDD and CUD-induced-MDD in post-90-TSST (12,627.27 ± 5488.09 vs.17,144.84 ± 6581.06, respectively) had a large effect size (0.77).

CONCLUSION

Results suggest a different pathogenesis for CUD-induced-MDD with higher levels of cortisol and BDNF compared with CUD-primary-MDD. Such variations should imply different approaches in treatment.