Functional biomarkers of depression: diagnosis, treatment, and pathophysiology

ElectroRetinoGraphy toward an exploration of the therapeutic potential of antidepressants in patients with major depressive disorder: a scoping review of the literature

Major Depressive Disorder (MDD) is characterized by at least one major depressive episode. It requires medical attention typically involving the prescription of antidepressants. Remission in MDD patients is often difficult to achieve because of the limited effectiveness of these drugs. Nowadays, numerous patients undergo various antidepressant treatments, with subjective changes in their personal experiences being regularly monitored. Therefore, it is essential to find clinical and objective tools that offer a more tailored approach to antidepressant selection. The neurochemistry of the retina being similar to the brain, one promising approach would be to use ElectroRetinoGraphy (ERG) measurements on MDD patients requiring antidepressant treatment. Thus, the aim of this scoping review is to highlight effects of different classes of antidepressants on retinal function evaluated by full-field ERG (ffERG), Pattern ERG (PERG) and multifocal ERG (mfERG) waveforms in MDD patients. These ERG measurements could serve as pivotal indicators in defining patient profiles, facilitating a more objective and personalized approach to therapeutic interventions, thereby advancing precision psychiatry.

Ghrelin/GHSR System in Depressive Disorder: Pathologic Roles and Therapeutic Implications

Depression is the most common chronic mental illness and is characterized by low mood, insomnia, and affective disorders. However, its pathologic mechanisms remain unclear. Numerous studies have suggested that the ghrelin/GHSR system may be involved in the pathophysiologic process of depression. Ghrelin plays a dual role in experimental animals, increasing depressed behavior and decreasing anxiety. By combining several neuropeptides and traditional neurotransmitter systems to construct neural networks, this hormone modifies signals connected to depression. The present review focuses on the role of ghrelin in neuritogenesis, astrocyte protection, inflammatory factor production, and endocrine disruption in depression. Furthermore, ghrelin/GHSR can activate multiple signaling pathways, including cAMP/CREB/BDNF, PI3K/Akt, Jak2/STAT3, and p38-MAPK, to produce antidepressant effects, given which it is expected to become a potential therapeutic target for the treatment of depression.

Digital biomarkers in depression: A systematic review and call for standardization and harmonization of feature engineering

BACKGROUND

General physicians misclassify depression in more than half of the cases. Researchers have explored the feasibility of leveraging passively collected data points, also called digital biomarkers, to provide more granular understanding of depression phenotypes as well as a more objective assessment of disease.

METHOD

This paper provides a systematic review following the PRISMA guidelines (Page et al., 2021) to understand which digital biomarkers might be relevant for passive screening of depression. Pubmed and PsycInfo were systematically searched for studies published from 2019 to early 2024, resulting in 161 records assessed for eligibility. Excluded were intervention studies, studies focusing on a different disease or those with a lack of passive data collection. 74 studies remained for a quality assessment, after which 27 studies were included.

RESULTS

The review shows that depressed participants' real-life behavior such as reduced communication with others can be tracked by passive data. Machine learning models for the classification of depression have shown accuracies up to 0.98, surpassing the quality of many standardized assessment methods.

LIMITATIONS

Inconsistency of outcome reporting of current studies does not allow for drawing statistical conclusions regarding effectiveness of individual included features. The Covid-19 pandemic might have impacted the ongoing studies between 2020 and 2022.

CONCLUSION

While digital biomarkers allow real-life tracking of participant's behavior and symptoms, further work is required to align the feature engineering of digital biomarkers. With shown high accuracies of assessments, connecting digital biomarkers with clinical practice can be a promising method of detecting symptoms of depression automatically.

Targeting GLP-1 receptors to reduce nicotine use disorder: Preclinical and clinical evidence

Nicotine use disorder (NUD) remains a leading cause of preventable death in the U.S. Unfortunately, current FDA-approved pharmacotherapies for smoking cessation have limited efficacy and are associated with high rates of relapse. One major barrier to long-term smoking abstinence is body weight gain during withdrawal. Nicotine withdrawal-induced body weight gain can also lead to development of chronic disease states like obesity and type II diabetes mellitus. Therefore, it is critical to identify novel pharmacotherapies for NUD that decrease relapse and nicotine withdrawal symptoms including body weight gain. Recent studies demonstrate that glucagon-like peptide-1 receptor (GLP-1R) agonists attenuate voluntary nicotine taking and seeking and prevent withdrawal-induced hyperphagia and body weight gain. Emerging evidence also suggests that GLP-1R agonists improve cognitive deficits, as well as depressive- and anxiety-like behaviors, which contribute to smoking relapse during withdrawal. While further studies are necessary to fully characterize the effects of GLP-1R agonists on NUD and understand the mechanisms by which GLP-1R agonists decrease nicotine withdrawal-mediated behaviors, the current literature supports GLP-1R-based approaches to treating NUD.

tiRNA-Gly-GCC-001 in major depressive disorder: Promising diagnostic and therapeutic biomarker

BACKGROUND AND PURPOSE

In major depressive disorder (MDD), exploration of biomarkers will be helpful in diagnosing the disorder as well as in choosing a treatment and predicting the treatment response. Currently, tRNA-derived small ribonucleic acids (tsRNAs) have been established as promising non-invasive biomarker candidates that may enable a more reliable diagnosis or monitoring of various diseases. Herein, we aimed to explore tsRNA expression together with functional activities in MDD development.

EXPERIMENTAL APPROACH

Serum samples were obtained from patients with MDD and healthy controls, and small RNA sequencing (RNA-Seq) was used to profile tsRNA expression. Dysregulated tsRNAs in MDD were validated by quantitative real-time polymerase chain reaction (qRT-PCR). The diagnostic utility of specific tsRNAs and the expression of these tsRNAs after antidepressant treatment were analysed.

KEY RESULTS

In total, 38 tsRNAs were significantly differentially expressed in MDD samples relative to healthy individuals (34 up-regulated and 4 down-regulated). qRT-PCR was used to validate the expression of six tsRNAs that were up-regulated in MDD (tiRNA-1:20-chrM.Ser-GCT, tiRNA-1:33-Gly-GCC-1, tRF-1:22-chrM.Ser-GCT, tRF-1:31-Ala-AGC-4-M6, tRF-1:31-Pro-TGG-2 and tRF-1:32-chrM.Gln-TTG). Interestingly, serum tiRNA-Gly-GCC-001 levels exhibited an area under the ROC curve of 0.844. Moreover, tiRNA-Gly-GCC-001 is predicted to suppress brain-derived neurotrophic factor (BDNF) expression. Furthermore, significant tiRNA-Gly-GCC-001 down-regulation was evident following an 8-week treatment course and served as a promising baseline predictor of patient response to antidepressant therapy.

CONCLUSION AND IMPLICATIONS

Our current work reports for the first time that tiRNA-Gly-GCC-001 is a promising MDD biomarker candidate that can predict patient responses to antidepressant therapy.

Are There Biological Correlates of Response to Yoga-Based Interventions in Depression? A Critical Scoping Review

Depression is the most common mental disorder worldwide. Both antidepressants and psychotherapy are effective in treating depression, but the response to these treatments is often incomplete. Yoga-based interventions (YBIs) have been advocated by some researchers as a promising form of alternative treatment for depression. Recent research has attempted to identify the biological mechanisms associated with the antidepressant actions of YBIs. In this scoping review, conducted according to the PRISMA-ScR guidelines, the PubMed and Scopus databases were searched to retrieve research on biomarkers of response to YBIs in patients with depression. These studies were also critically reviewed to evaluate their methodological quality and any sources of bias. Nineteen studies were included in the review. Based on these studies, there is preliminary evidence that YBIs may be associated with increased serum brain-derived neurotrophic factor (BDNF) and reduced serum cortisol and interleukin-6 (IL-6) in patients with depression. However, many of these changes were also observed in the control arms, and the overall quality of the research was low. At present, it cannot be concluded that there are reliable biomarkers of response to YBIs in depression, though there are some potential biological correlates. Further advances in this field will depend critically on improvements in study design, particularly the minimization of sources of bias and the selection of more specific and sensitive biomarkers based on existing evidence from other treatment modalities.

Serum Levels of Zinc, Albumin, Interleukin-6 and CRP in Patients with Unipolar and Bipolar Depression: Cross Sectional Study

Unipolar (UD) and bipolar depression (BDD) show a high degree of similarity in clinical presentations, which complicates the differential diagnosis of these disorders. The aim of this study was to investigate the serum levels of interleukin 6 (IL-6), C-reactive protein (CRP), albumin (Alb), and zinc (Zn) in patients with UD, BDD, and healthy controls (HC). A total of 211 samples were collected: 131 patient samples (65 UD and 68 BDD) and 80 HC. The Montgomery-Asberg Depression Rating Scale (MADRS), along with the Hamilton Depression Rating Scale (HAMD-17), were administered to patient groups to evaluate symptoms. A cross-sectional study was performed to analyse the serum levels of IL-6, CRP, albumin, and zinc. The concentration of CRP was determined using the immunoturbidimetry method, zinc using the colorimetric method, and albumin using the colorimetric method with bromocresol green on the Alinity c device. IL-6 cytokine concentration in serum samples was ascertained using a commercial enzyme immunoassay, ELISA. We found no significant differences in serum concentrations of zinc, albumin, CRP, and IL-6 between the groups of patients with unipolar and bipolar depression. There was a significant statistical difference (p < 0.001) between serum levels of all investigated parameters in both groups of depressed patients in comparison with HC. Furthermore, correlations with specific items on HAMD-17; (namely, hypochondrias, work and activities, somatic symptoms-general, and weight loss) and on MADRS (concentration difficulties, lassitude) were observed in both patient groups. These findings confirm the presence of low-grade inflammation in depression, thus adding better insight into the inflammation hypothesis directed to explain the aetiology of depressive disorders. Our results do not indicate potential biomarkers for distinguishing between unipolar and bipolar depression.

An Ensemble Classification Model for Depression Based on Wearable Device Sleep Data

Depression is one of the most common mental disorders, with sleep disturbances as typical symptoms. With the popularity of wearable devices increasing in recent years, more and more people wear portable devices to track sleep quality. Based on this, we believe that depression detection through wearable sleep data is more intelligent and economical. However, the majority of wearable devices face the problem of missing data during the data collection process. Otherwise, most existing studies of depression identification focus on the utilization of complex data, making it difficult to generalize and susceptible to noise interference. To address these issues, we propose a systematic ensemble classification model for depression (ECD). For the missing data problem of wearable devices, we design an improved GAIN method to further control the generation range of interpolated values, which can achieve a more reasonable treatment of missing values. Compared with the original GAIN approach, the improved method shows a 28.56% improvement when using MAE as the metric. For depression recognition, we use ensemble learning to construct a depression classification model which combines five classification models, including SVM, KNN, LR, CBR, and DT. Ensemble learning can improve the model's robustness and generalization. The voting mechanism is used in several places to improve noise immunity. The final classification model performed great on the dataset, with a precision of 92.55% and a recall of 91.89%. These results illustrate how efficient this method is in automatically detecting depression.

Metabolomics on depression: A comparison of clinical and animal research

BACKGROUND

Depression is a major cause of suicide and mortality worldwide. This study aims to conduct a systematic review to identify metabolic biomarkers and pathways for major depressive disorder (MDD), a prevalent subtype of clinical depression.

METHODS

We searched for metabolomics studies on depression published between January 2000 and January 2023 in the PubMed and Web of Science databases. The reported metabolic biomarkers were systematically evaluated and compared. Pathway analysis was implemented using MetaboAnalyst 5.0.

RESULTS

We included 26 clinical studies on MDD and 78 metabolomics studies on depressive-like animal models. A total of 55 and 77 high-frequency metabolites were reported consistently in two-thirds of clinical and murine studies, respectively. In the comparison between murine and clinical studies, we identified 9 consistently changed metabolites (tryptophan, tyrosine, phenylalanine, methionine, fumarate, valine, deoxycholic acid, pyruvate, kynurenic acid) in the blood, 1 consistently altered metabolite (indoxyl sulfate) in the urine and 14 disturbed metabolic pathways in both types of studies. These metabolic dysregulations and pathways are mainly implicated in enhanced inflammation, impaired neuroprotection, reduced energy metabolism, increased oxidative stress damage and disturbed apoptosis, laying solid molecular foundations for MDD.

LIMITATIONS

Due to unavailability of original data like effect-size results in many metabolomics studies, a meta-analysis cannot be conducted, and confounding factors cannot be fully ruled out.

CONCLUSIONS

This systematic review delineated metabolic biomarkers and pathways related to depression in the murine and clinical samples, providing opportunities for early diagnosis of MDD and the development of novel diagnostic targets.

High-Definition Transcranial Direct Current Stimulation-Primed Intermittent Theta Burst Stimulation in Treatment-Resistant Depression: A Controlled Study

OBJECTIVES

The aim of this study was to evaluate whether high-definition transcranial direct current stimulation (HDtDCS) priming improves the efficacy of intermittent theta burst stimulation (iTBS) in improving TRD.

METHODOLOGY

A prospective hospital-based, randomized control study where the participants were divided into active or sham HDtDCS-primed iTBS stimulation groups for a total of 10 sessions and were assessed on clinical parameters at baseline, end of week 1, and end of week 2 was done. Primary outcome of the study was the difference in Hamilton Depression Rating Scale (HDRS) scores over 2 weeks of HDtDCS-primed iTBS.

RESULT

A significant effect of time was seen over HDRS scores in both active and sham groups with a large effect size. Significant effect of time was also found over the Clinical Global Impressions-Severity Scale scores of patients with a large effect size. The difference in the improvement in depressive severity as measured using HDRS and Clinical Global Impressions-Severity Scale scores between active and sham groups was also found to be significant with large effect sizes.

CONCLUSION

High-definition tDCS-primed iTBS is superior to normal iTBS in patients with depression who have failed a trial of 2 antidepressants, whereas both mechanisms are of benefit to the patients.

Non-Targeted Metabolomics Investigation of a Sub-Chronic Variable Stress Model Unveils Sex-Dependent Metabolic Differences Induced by Stress

Depression is twice as prevalent in women as in men, however, most preclinical studies of depression have used male rodent models. This study aimed to examine how stress affects metabolic profiles depending on sex using a rodent depression model: sub-chronic variable stress (SCVS). The SCVS model of male and female mice was established in discovery and validation sets. The stress-induced behavioral phenotypic changes were similar in both sexes, however, the metabolic profiles of female plasma and brain became substantially different after stress, whereas those of males did not. Four stress-differential plasma metabolites-β-hydroxybutyric acid (BHB), L-serine, glycerol, and myo-inositol-could yield biomarker panels with excellent performance to discern the stressed individuals only for females. Disturbances in BHB, glucose, 1,5-anhydrosorbitol, lactic acid, and several fatty acids in the plasma of stressed females implied a systemic metabolic shift to β-oxidation in females. The plasma levels of BHB and corticosterone only in stressed females were observed not only in SCVS but also in an acute stress model. These results collectively suggest a sex difference in the metabolic responses by stress, possibly involving the energy metabolism shift to β-oxidation and the HPA axis dysregulation in females.

Mapping Changes in Glutamate with Glutamate-Weighted MRI in Forced Swim Test Model of Depression in Rats

Chemical exchange saturation transfer with glutamate (GluCEST) imaging is a novel technique for the non-invasive detection and quantification of cerebral Glu levels in neuromolecular processes. Here we used GluCEST imaging and 1H magnetic resonance spectroscopy (1H MRS) to assess in vivo changes in Glu signals within the hippocampus in a rat model of depression induced by a forced swim test. The forced swimming test (FST) group exhibited markedly reduced GluCEST-weighted levels and Glu concentrations when examined using 1H MRS in the hippocampal region compared to the control group (GluCEST-weighted levels: 3.67 ± 0.81% vs. 5.02 ± 0.44%, p < 0.001; and Glu concentrations: 6.560 ± 0.292 μmol/g vs. 7.133 ± 0.397 μmol/g, p = 0.001). Our results indicate that GluCEST imaging is a distinctive approach to detecting and monitoring Glu levels in a rat model of depression. Furthermore, the application of GluCEST imaging may provide a deeper insight into the neurochemical involvement of glutamate in various psychiatric disorders.

Serum proteomic biomarker investigation of vascular depression using data-independent acquisition: a pilot study

Background

Vascular depression (VaD) is a depressive disorder closely associated with cerebrovascular disease and vascular risk factors. It remains underestimated owing to challenging diagnostics and limited information regarding the pathophysiological mechanisms of VaD. The purpose of this study was to analyze the proteomic signatures and identify the potential biomarkers with diagnostic significance in VaD.

Methods

Deep profiling of the serum proteome of 35 patients with VaD and 36 controls was performed using liquid chromatography-tandem mass spectrometry (LC-MS/MS). Functional enrichment analysis of the quantified proteins was based on Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, and Reactome databases. Machine learning algorithms were used to screen candidate proteins and develop a protein-based model to effectively distinguish patients with VaD.

Results

There were 29 up-regulated and 31 down-regulated proteins in the VaD group compared to the controls (|log2FC| ≥ 0.26, p ≤ 0.05). Enrichment pathways analyses showed that neurobiological processes related to synaptic vesicle cycle and axon guidance may be dysregulated in VaD. Extrinsic component of synaptic vesicle membrane was the most enriched term in the cellular components (CC) terms. 19 candidate proteins were filtered for further modeling. A nomogram was developed with the combination of HECT domain E3 ubiquitin protein ligase 3 (HECTD3), Nidogen-2 (NID2), FTO alpha-ketoglutarate-dependent dioxygenase (FTO), Golgi membrane protein 1 (GOLM1), and N-acetylneuraminate lyase (NPL), which could be used to predict VaD risk with favorable efficacy.

Conclusion

This study offers a comprehensive and integrated view of serum proteomics and contributes to a valuable proteomics-based diagnostic model for VaD.

White matter integrity and medication response to antidepressants in major depressive disorder: a review of the literature

Major Depressive Disorder (MDD) is a severe psychiatric disorder characterized by selective impairments in mood regulation, cognition and behavior. Although it is well-known that antidepressants can effectively treat moderate to severe depression, the biochemical effects of these medications on white matter (WM) integrity are still unclear. Therefore, the aim of the study is to review the main scientific evidence on the differences in WM integrity in responders and non-responders to antidepressant medications. A record search was performed on three datasets (PubMed, Scopus and Web of Science) and ten records matched our inclusion criteria. Overall, the reviewed studies highlighted a good efficacy of antidepressants in MDD treatment. Furthermore, there were differences in WM integrity between responders and non-responders, mainly localized in cingulate cortices, hippocampus and corpus callosum, where the former group showed higher fractional anisotropy and lower axial diffusivity values. Modifications in WM integrity might be partially explained by branching and proliferation as well as neurogenesis of axonal fibers mediated by antidepressants, which in turn may have positively affected brain metabolism and increase the quantity of the serotonergic neurotransmitter within synaptic clefts. However, the reviewed studies suffer from some limitations, including the heterogeneity in treatment duration, antidepressant administration, medical posology, and psychiatric comorbidities. Therefore, future studies are needed to reduce confounding effects of antidepressant medications and to adopt longitudinal and multimodal approaches in order to better characterize the differences in WM integrity between responders and non-responders.

Combining Glial Fibrillary Acidic Protein and Neurofilament Light Chain for the Diagnosis of Major Depressive Disorder

Major depressive disorder (MDD) is a prevalent brain disorder affecting more than 2% of the world's population. Due to the lack of well-specific biomarkers, it is difficult to distinguish MDD from other diseases with similar clinical symptoms (such as Alzheimer's disease and cerebral thrombosis). In this work, we provided a strategy to address this issue by constructing a combinatorial biomarker of serum glial fibrillary acidic protein (GFAP) and neurofilament light chain (NFL). To achieve the convenient and sensitive detection of two proteins, we developed an electrochemical immunosandwich sensor using two metal-ion-doped carbon dots (Pb-CDs and Cu-CDs) as probes for signal output. Each probe contains approximately 300 Pb2+ or 200 Cu2+, providing excellent signal amplification. This method achieved detection limits of 0.3 pg mL-1 for GFAP and 0.2 pg mL-1 for NFL, lower than most of the reported detection limits. Analysis of real serum samples showed that the concentration ratio of GFAP to NFL, which is associated with the relative degree of brain inflammation and neurodegeneration, is suitable for not only distinguishing MDD from healthy individuals but also specifically distinguishing MDD from Alzheimer's disease and cerebral thrombosis. The good specificity gives the combinatorial GFAP/NFL biomarker broad application prospects in the screening, diagnosis, and treatment of MDD.

Cognitive impairment is associated with BDNF-TrkB signaling mediating synaptic damage and reduction of amino acid neurotransmitters in heart failure

Heart failure (HF) is often accompanied by cognitive impairment (CI). Brain-derived neurotrophic factor (BDNF) deficiency is closely associated with CI. However, the role and mechanism of BDNF in HF with CI is still not fully understood. Here, the case-control study was designed including 25 HF without CI patients (HF-NCI) and 50 HF with CI patients (HF-CI) to investigate the predictive value of BDNF in HF-CI while animal and cell experiments were used for mechanism research. Results found that BDNF levels in serum neuronal-derived exosomes were downregulated in HF-CI patients. There was no significant difference in serum BDNF levels among the two groups. HF rats showed obvious impairment in learning and memory; also, they had reduced thickness and length of postsynaptic density (PSD) and increased synaptic cleft width. Expression of BDNF, TrkB, PSD95, and VGLUT1 was significantly decreased in HF rats brain. In addition, compared with sham rats, amino acids were significantly reduced with no changes in the acetylcholine and monoamine neurotransmitters. Further examination showed that the number of synaptic bifurcations and the expression of BDNF, TrkB, PSD95, and VGLUT1 were all decreased in the neurons that interfered with BDNF-siRNA compared with those in the negative control neurons. Together, our results demonstrated that neuronal-derived exosomal BDNF act as effective biomarkers for prediction of HF-CI. The decrease of BDNF in the brain triggers synaptic structural damage and a decline in amino acid neurotransmitters via the BDNF-TrkB-PSD95/VGLUT1 pathway. This discovery unveils a novel pathological mechanism underlying cognitive impairment following heart failure.

Impaired eating behaviors but intact metabolic hormone levels in individuals with major depressive disorder and generalized anxiety disorder

BACKGROUND

Major depressive disorder (MDD) and generalized anxiety disorder (GAD) contribute significantly to global health burdens. Identifying disease markers for these comorbid disorders can increase understanding of pathogenesis and improve screening and intervention strategies. This study examined the association of physical health factors with MDD and MDD + GAD, across sexes.

METHODS

Two samples of participants from the Tulsa-1000 study (exploratory cohort: N = 136; confirmatory cohort: N = 185) completed body composition measurements, eating behavior (Three Factor Eating Questionnaire [TFEQ], Eating Disorder Diagnostic Scale [EDDS]), exercise questionnaires, and a blood draw. Metabolic hormone concentrations (leptin, insulin, and adiponectin) were analyzed from blood samples. Within each cohort, a two-way analysis of variance compared three groups (MDD, MDD + GAD, and healthy controls [HC]), sex, and their interaction on dependent variables. Hedges g was calculated to reflect effect size magnitude.

RESULTS

Medium-to-large group main effects across cohorts indicated that compared to HC: (1) MDD (g = 1.71/0.57) and MDD + GAD (g = 0.93/0.69) reported higher TFEQ Disinhibition scores; (2) MDD endorsed higher TFEQ Hunger scores (g = 0.66/0.48); and (3) MDD (g = 1.60/1.30) and MDD + GAD (g = 0.92/1.72) reported greater EDDS scores. Large sex main effects across cohorts indicated that females exhibited higher levels than males for percent body fat (g = 1.07/1.17), leptin (g = 1.27/1.12), and adiponectin (g=0.82/0.88).

LIMITATIONS

The power to detect group*sex interactions was limited due to a greater number of females (than males) in the study, and over half of clinical participants were taking medications.

CONCLUSIONS

Individuals with MDD and MDD + GAD demonstrate difficulties in regulating eating behaviors, potentially contributing to functional impairment and increased disease burden.

Discovery and validation of a protein biomarker for the diagnosis and classification of disease severity of major depressive disorder

BACKGROUND AND AIMS

Diagnosis and classification of disease severity of major depressive disorder (MDD) are determined through a doctor's consultation and questionnaire-based rating scale. This study aimed to identify and validate a serum protein biomarker for diagnosing and classifying the disease severity of MDD.

MATERIALS AND METHODS

Based on the Hamilton Depression Rating Scale (HAMD) score, participants were divided into control, mild, moderate, and severe groups. Samples prepared from collected sera were analyzed using non-targeted qualitative and targeted quantitative tools to identify potential biomarkers.

RESULTS

Four proteins were selected as biomarker candidates, which showed statistically significant consistent tendencies depending on MDD severity. Among them, tetranectin was the only validated protein in the quantitative analysis that showed the same decreasing tendency as that in the qualitative analysis. Furthermore, tetranectin showed fair discrimination performance between the control and MDD group.

CONCLUSIONS

Tetranectin may be a novel potential biomarker for diagnosing and classifying the severity of MDD, though further verification and validation studies of its efficacy are needed.

A review of new insights into existing major depressive disorder biomarkers

As major depressive disorder (MDD) is such a diverse condition, there are currently no clear ways for determining its severity, endophenotype, or therapy response. The distinctive nature of depression, the variability of analysis in literature and the large number of conceptually complicated biomarkers are some of the many reasons for the lack of progress. Markers are involved in the process of neurotrophic, metabolic, and inflammation as well as neuroendocrine and neurotransmitter systems' components. Some clinical indicators are strong enough so that can be measured using assessments of proteomic, genetic, metabolomics, neuroimaging, epigenetic and transcriptomic. Markers of oxidative stress, endocrine, inflammatory, proteomic, and growth indicators are currently among the promising biologic systems/markers identified in this analysis. This narrative review examines succinct studies which investigated cytokines of inflammatory factors, peripheral factors of development, metabolic and endocrine markers as pathophysiological biomarkers of MDD, and treatment responses. Endocrine and metabolic alterations have also been linked to MDD in various studies. So, this study summarizes all of the numerous biomarkers that are significant in the detection or treatment of MDD patients. The paper also provides an overview of various biomarkers which are important for the regulation and its effects on MDD.

Awareness and related factors of depressive symptoms in breastfeeding people in South Korea: a survey-based cross-sectional study

OBJECTIVES

This study identifies depressive symptoms and the factors that could explain its presence in breastfeeding people.

DESIGN

This study is a cross-sectional study from national survey data.

SETTING AND PARTICIPANTS

Data were derived from the 2019 Korean Community Health Survey. The study subjects were breastfeeding people under the age of 50.

PRIMARY OUTCOME MEASURES

Depressive symptoms in breastfeeding people were classified according to the Patient Health Questionnaire-9 (PHQ-9) score. Physical and health behaviours were considered as factors related to depressive symptoms. A multilevel logistic regression analysis was used.

RESULTS

Among 497 participants, 19.4% (n=97) of breastfeeding people were depressed. We found that depressive symptoms were associated with age (31-35, OR: 0.79, 95% CI: 0.67 to 0.94; 35-49, OR: 0.42, 95% CI: 0.32 to 0.56), rural setting (OR: 0.62, 95% CI: 0.51 to 0.76), economic activity (OR: 0.75, 95% CI: 0.61 to 0.91) and physical health (diabetus mellitus or hypertension, OR: 5.17, 95% CI: 3.78 to 7.06).

CONCLUSIONS

This study implies that socioeconomic factors, physical health and health behaviours may influence depressive symptoms in breastfeeding people. These findings should be used as descriptive data to support the development of education programmes to help breastfeeding people.

Current development of biosensing technologies towards diagnosis of mental diseases

The biosensor is an instrument that converts the concentration of biomarkers into electrical signals for detection. Biosensing technology is non-invasive, lightweight, automated, and biocompatible in nature. These features have significantly advanced medical diagnosis, particularly in the diagnosis of mental disorder in recent years. The traditional method of diagnosing mental disorders is time-intensive, expensive, and subject to individual interpretation. It involves a combination of the clinical experience by the psychiatrist and the physical symptoms and self-reported scales provided by the patient. Biosensors on the other hand can objectively and continually detect disease states by monitoring abnormal data in biomarkers. Hence, this paper reviews the application of biosensors in the detection of mental diseases, and the diagnostic methods are divided into five sub-themes of biosensors based on vision, EEG signal, EOG signal, and multi-signal. A prospective application in clinical diagnosis is also discussed.

Cellular and Molecular Evidence of Multiple Sclerosis Diagnosis and Treatment Challenges

Multiple sclerosis (MS) is a chronic autoimmune disease that impacts the central nervous system and can result in disability. Although the prevalence of MS has increased in India, diagnosis and treatment continue to be difficult due to several factors. The present study examines the difficulties in detecting and treating multiple sclerosis in India. A lack of MS knowledge among healthcare professionals and the general public, which delays diagnosis and treatment, is one of the significant issues. Inadequate numbers of neurologists and professionals with knowledge of MS management also exacerbate the situation. In addition, MS medications are expensive and not covered by insurance, making them inaccessible to most patients. Due to the absence of established treatment protocols and standards for MS care, India's treatment techniques vary. In addition, India's population diversity poses unique challenges regarding genetic variations, cellular and molecular abnormalities, and the potential for differing treatment responses. MS is more difficult to accurately diagnose and monitor due to a lack of specialized medical supplies and diagnostic instruments. Improved awareness and education among healthcare professionals and the general public, as well as the development of standardized treatment regimens and increased investment in MS research and infrastructure, are required to address these issues. By addressing these issues, it is anticipated that MS diagnosis and treatment in India will improve, leading to better outcomes for those affected by this chronic condition.

A Study of Individualized Diagnosis and Treatment for Depression with Atypical Features (iDoT-AFD): study protocol for a randomized clinical trial and prognosis study

BACKGROUND

Major depressive disorder (MDD) with atypical features, namely depression with atypical features (AFD), is one of the most common clinical specifiers of MDD, closely associated with bipolar disorder (BD). However, there is still a lack of clinical guidelines for the diagnosis, treatment, and prognosis of AFD. Our study mainly focuses on three issues about how to identify AFD, what is the appropriate individualized treatment for AFD, and what are the predictive biomarkers of conversion to BD.

METHODS

The Study of Individualized Diagnosis and Treatment for Depression with Atypical Features (iDoT-AFD) is a multicenter, prospective, open-label study consisting of a 12-week randomized controlled trial (RCT) and a continued follow-up until 4 years or reaching the study endpoint. It is enrolling 480 patients with AFD (120 per treatment arm), 100 patients with BD, and 100 healthy controls (HC). Multivariate dimension information is collected including clinical features, cognitive function, kynurenine pathway metabolomics, and multimodal magnetic resonance imaging (MRI) data. Firstly, multivariate informatics analyses are performed to recognize patients with AFD from participants including the first-episode and recurrent atypical depression, patients with BD, and patients with HC. Secondly, patients with atypical depression are randomly allocated to one of the four treatment groups including "single application of selective serotonin reuptake inhibitor (SSRI) or serotonin-noradrenaline reuptake inhibitor (SNRI)", "SSRI/SNRI combined with mood stabilizer," "SSRI/SNRI combined with quetiapine (≥ 150 mg/day)," or "treatment as usual (TAU)" and then followed up 12 weeks to find out the optimized treatment strategies. Thirdly, patients with atypical depression are followed up until 4 years or switching to BD, to explore the risk factors of conversion from atypical depression to BD and eventually build the risk warning model of conversion to BD.

DISCUSSION

The first enrolment was in August 2019. The iDoT-AFD study explores the clinical and biological markers for the diagnosis, treatment, and prognosis of AFD and further provides evidence for clinical guidelines of AFD.

TRIAL REGISTRATION

ClinicalTrials.gov NCT04209166. Registered on December 19, 2019.

Th17 cells sense microbiome to promote depressive-like behaviors

BACKGROUND

Microbiome alterations have been associated with depression, and fecal transfer of depressed patients' microbiomes is sufficient to enhance despair behaviors in rodents. Yet little is known about the potential mechanisms, whereby microbes modulate depressive-like behaviors.

RESULTS

In this study, we showed that certain bacteria known to induce Th17 cells are increased in depressed patients and mice exhibiting learned helplessness. Fecal transfers of human depressed patients' microbiomes into germ-free-like mice were sufficient to decrease sociability and increased susceptibility to the learned helplessness paradigm, confirming that the microbiome is sufficient to confer depressive-like behaviors. This microbial effect was dependent on the presence of Th17 cells in the recipient, as germ-free-like recipient mice deficient in Th17 cells were resistant to the behavioral changes induced by the microbiome of depressed patients.

CONCLUSION

Altogether, these findings suggest a crucial role of the microbiome/Th17 cell axis in regulating depressive-like behaviors. Video Abstract.

Integrating functional neuroimaging and serum proteins improves the diagnosis of major depressive disorder

BACKGROUND

The lack of effective objective diagnostic biomarkers for major depressive disorder (MDD) leads to high misdiagnosis. Compared with healthy controls (HC), abnormal brain functions and protein levels are often observed in MDD. However, it is unclear whether combining these changed multidimensional indicators could help improve the diagnosis of MDD.

METHODS

Sixty-three MDD and eighty-one HC subjects underwent resting-state fMRI scans, among whom 37 MDD and 45 HC provided blood samples. Amplitudes of low-frequency fluctuation (ALFF), regional homogeneity (ReHo), and serum levels of brain-derived neurotrophic factor (BDNF), cortisol, and multiple cytokines were measured and put into the linear discriminant analysis (LDA) to construct corresponding MDD diagnostic models. The area under the receiver operating characteristic curve (AUC) of 5-fold cross-validation was calculated to evaluate each model's performance.

RESULTS

Compared with HC, MDD patients' spontaneous brain activity, serum BDNF, cortisol, interleukin (IL)-4, IL-6, and IL-10 levels changed significantly. The combinations of unidimensional multi-indicator had better diagnostic performance than a single one. The model consisted of multidimensional multi-indicator further exhibited conspicuously superior diagnostic efficiency than those constructed with unidimensional multi-indicator, and its AUC, sensitivity, specificity, and accuracy of 5-fold cross-validation were 0.99, 92.0 %, 100.0 %, and 96.3 %, respectively.

LIMITATIONS

This cross-sectional study consists of relatively small samples and should be replicated in larger samples with follow-up data to optimize the diagnostic model.

CONCLUSIONS

MDD patients' neuroimaging features and serum protein levels significantly changed. The model revealed by LDA could diagnose MDD with high accuracy, which may serve as an ideal diagnostic biomarker for MDD.

Inhibition of Microglial GSK3β Activity Is Common to Different Kinds of Antidepressants: A Proposal for an In Vitro Screen to Detect Novel Antidepressant Principles

Depression is a major public health concern. Unfortunately, the present antidepressants often are insufficiently effective, whilst the discovery of more effective antidepressants has been extremely sluggish. The objective of this review was to combine the literature on depression with the pharmacology of antidepressant compounds, in order to formulate a conceivable pathophysiological process, allowing proposals how to accelerate the discovery process. Risk factors for depression initiate an infection-like inflammation in the brain that involves activation microglial Toll-like receptors and glycogen synthase kinase-3β (GSK3β). GSK3β activity alters the balance between two competing transcription factors, the pro-inflammatory/pro-oxidative transcription factor NFκB and the neuroprotective, anti-inflammatory and anti-oxidative transcription factor NRF2. The antidepressant activity of tricyclic antidepressants is assumed to involve activation of GS-coupled microglial receptors, raising intracellular cAMP levels and activation of protein kinase A (PKA). PKA and similar kinases inhibit the enzyme activity of GSK3β. Experimental antidepressant principles, including cannabinoid receptor-2 activation, opioid μ receptor agonists, 5HT2 agonists, valproate, ketamine and electrical stimulation of the Vagus nerve, all activate microglial pathways that result in GSK3β-inhibition. An in vitro screen for NRF2-activation in microglial cells with TLR-activated GSK3β activity, might therefore lead to the detection of totally novel antidepressant principles with, hopefully, an improved therapeutic efficacy.

Measuring stress: a review of the current cortisol and dehydroepiandrosterone (DHEA) measurement techniques and considerations for the future of mental health monitoring

Psychological stress and its inevitable trajectory toward mental health deteriorations such as clinical and major depression has become an unprecedented global burden. The diagnostic procedures involved in the characterization of mental illnesses commonly follow qualitative and subjective measures of stress, often leading to greater socioeconomic burdens due to misdiagnosis and poor understanding of the severity of such illnesses, further fueled by the stigmatization surrounding mental health. In recent years, the application of cortisol and stress hormone measurements has given rise to an alternative, quantifiable approach for the psychological evaluation of stress and depression. This review comprehensively evaluates the current state-of-the-art technology for measuring cortisol and dehydroepiandrosterone (DHEA) and their applications within stress monitoring in humans. Recent advancements in these fields have shown the importance of measuring stress hormones for the characterization of stress manifestation within the human body, and its relevance in mental health decline. Preliminary results from studies considering multimodal approaches toward stress monitoring have showcased promising developments, emphasizing the need for further technological advancement in this field, which consider both neurochemical and physiological biomarkers of stress, for global benefit.

Heterogeneity in major depressive disorder: The need for biomarker-based personalized treatments

Major Depressive Disorder (MDD) or depression is a pathological mental condition affecting millions of people worldwide. Identification of objective biological markers of depression can provide for a better diagnostic and intervention criteria; ultimately aiding to reduce its socioeconomic health burden. This review provides a comprehensive insight into the major biomarker candidates that have been implicated in depression neurobiology. The key biomarker categories are covered across all the "omics" levels. At the epigenomic level, DNA-methylation, non-coding RNA and histone-modifications have been discussed in relation to depression. The proteomics system shows great promise with inflammatory markers as well as growth factors and neurobiological alterations within the endocannabinoid system. Characteristic lipids implicated in depression together with the endocrine system are reviewed under the metabolomics section. The chapter also examines the novel biomarkers for depression that have been proposed by studies in the microbiome. Depression affects individuals differentially and explicit biomarkers identified by robust research criteria may pave the way for better diagnosis, intervention, treatment, and prediction of treatment response.

Chronic jet lag-like conditions dysregulate molecular profiles of neurological disorders in nucleus accumbens and prefrontal cortex

Background

Patients with neurological disorders often display altered circadian rhythms. The disrupted circadian rhythms through chronic jetlag or shiftwork are thought to increase the risk and severity of human disease including, cancer, psychiatric, and related brain diseases.

Results

In this study, we investigated the impact of shiftwork or chronic jetlag (CJL) like conditions on mice's brain. Transcriptome profiling based on RNA sequencing revealed that genes associated with serious neurological disorders were differentially expressed in the nucleus accumbens (NAc) and prefrontal cortex (PFC). According to the quantitative PCR (qPCR) analysis, several key regulatory genes associated with neurological disorders were significantly altered in the NAc, PFC, hypothalamus, hippocampus, and striatum. Serotonin levels and the expression levels of serotonin transporters and receptors were significantly altered in mice treated with CJL.

Conclusion

Overall, these results indicate that CJL may increase the risk of neurological disorders by disrupting the key regulatory genes, biological functions, serotonin, and corticosterone. These molecular linkages can further be studied to investigate the mechanism underlying CJL or shiftwork-mediated neurological disorders in order to develop treatment strategies.

The Association of Biochemical and Genetic Biomarkers in VEGF Pathway with Depression

VEGF is an important neurotrophic and vascular factor involved in mental disorders. The objective of this study was to verify the effect of genetic polymorphisms in the VEGF pathway on the risk for depression, symptom intensity, and suicide attempts. To examine the association between the VEGF pathway and depression, we genotyped polymorphisms and measured the plasma concentrations of VEGF, KDR, and FLT1 proteins. The participants were 160 patients with depression and 114 healthy controls. The questionnaires that assessed the clinical profile of the patients were the MINI-International Neuropsychiatric Interview, GRID-HAMD₂₁, CTQ, BSI, and the number of suicide attempts. Genotyping of participants was performed using the real-time PCR and protein measurements were performed using the enzyme-linked immunosorbent assay (ELISA). VEGF and its inhibitors were reduced in depression. Individuals with depression and displaying the homozygous AA of the rs699947 polymorphism had higher plasma concentrations of VEGF (p-value = 0.006) and were associated with a greater number of suicide attempts (p-value = 0.041). Individuals with depression that were homozygous for the G allele of the FLT1 polymorphism rs7993418 were associated with lower symptom severity (p-value = 0.040). Our results suggest that VEGF pathway polymorphisms are associated with the number of suicide attempts and the severity of depressive symptoms.

The Role of Equilibrium between Free Radicals and Antioxidants in Depression and Bipolar Disorder

Background: Increasing evidence suggests that the presence of oxidative stress and disorders of the antioxidant defense system are involved in a wide range of neuropsychiatric disorders, such as bipolar disorder, schizophrenia and major depression, but the exact mechanism remains unknown. This review focuses on a better appreciation of the contribution of oxidative stress to depression and bipolar disorder. Methods: This review was conducted by extracting information from other research and review studies, as well as other meta-analyses, using two search engines, PubMed and Google Scholar. Results: As far as depression is concerned, there is agreement among researchers on the association between oxidative stress and antioxidants. In bipolar disorder, however, most of them observe strong lipid peroxidation in patients, while regarding antioxidant levels, opinions are divided. Nevertheless, in recent years, it seems that on depression, there are mainly meta-analyses and reviews, rather than research studies, unlike on bipolar disorder. Conclusions: Undoubtedly, this review shows that there is an association among oxidative stress, free radicals and antioxidants in both mental disorders, but further research should be performed on the exact role of oxidative stress in the pathophysiology of these diseases.

The effect of a Mediterranean diet on the symptoms of depression in young males (the "AMMEND: A Mediterranean Diet in MEN with Depression" study): a randomized controlled trial

BACKGROUND

Depression is a common mental health condition that affects 1 in 8 males each year, especially young adults. Young adulthood offers an opportunity for early dietary interventions, with research suggesting that a Mediterranean diet (MD) could be beneficial in treating depression.

OBJECTIVES

This study aimed to determine if an MD can improve depressive symptoms in young males with clinical depression.

METHODS

A 12-wk, parallel-group, open-label, randomized controlled trial was conducted to assess the effect of an MD intervention in the treatment of moderate to severe depression in young males (18-25 y). Befriending therapy was chosen for the control group. Assessments were taken at baseline, week 6, and week 12. MD adherence was measured with the Mediterranean Diet Adherence Score (MEDAS). The primary outcome measure was the Beck Depression Inventory Scale-version II (BDI-II) and secondary outcome was quality of life (QoL).

RESULTS

A total of 72 participants completed the study. After 12 wk, the MEDAS scores were significantly higher in the MD group compared with the befriending group (mean difference: 7.8; 95% CI: 7.23, 8.37; P < 0.001). The mean change in BDI-II score was significantly higher in the MD group compared with the befriending group at week 12 (mean difference: 14.4; 95% CI: 11.41, 17.39; P < 0.001). The mean change in QoL score was also significantly higher in the MD group compared with the befriending group at week 12 (mean difference: 12.7; 95% CI: 7.92, 17.48; P < 0.001).

CONCLUSIONS

Our results demonstrate that compared with befriending, an MD intervention leads to significant increases in MEDAS, decreases in BDI-II score, and increases in QoL scores. These results highlight the important role of nutrition for the treatment of depression and should inform advice given by clinicians to this specific demographic population.The trial was registered with Australia and New Zealand Clinical Trials Registry (trial ID ACTRN12619001545156) and has also been registered with the WHO International Clinical Trials Registry Platform (Universal Trial Number U1111-1242-5215).

Prospective associations of multimodal serum biomarkers with 12-week and 12-month remission in patients with depressive disorders receiving stepwise psychopharmacotherapy

Prognostic biomarkers for depression treatment outcomes have yet to be elucidated. This study sought to evaluate whether a multi-modal serum biomarker panel was prospectively associated with 12-week and 12-month remission in outpatients with depressive disorders receiving stepwise psychopharmacotherapy. At baseline, 14 serum biomarkers and socio-demographic/clinical characteristics were evaluated in 1094 patients. They received initial antidepressant monotherapy followed, as required by a protocol of successive alternative pharmacological strategies administered in 3-week steps during the acute (3-12 week) phase (N = 1086), and in 3-month steps during the continuation (6-12 month) phase (N = 884). Remission was defined as a Hamilton Depression Rating Scale score of ≤ 7. Remission was achieved in 490 (45.1%) over the 12-week, and in 625 (70.7%) over the 12-month, treatment periods. Combination scores of four serum biomarkers (high-sensitivity C-reactive protein, interleukin-1 beta, interleukin-6, and leptin) were prospectively associated with 12-week remission; and four (high-sensitivity C-reactive protein, tumor necrosis factor-alpha, interleukin-1 beta, and brain-derived neurotrophic factor) were prospectively associated with 12-month remission in a clear gradient manner (P-values < 0.001) and after adjustment for relevant covariates. These associations were evident after the Step 1 treatment monotherapy but weakened with increasing treatment steps, falling below statistical significance after 4 + treatment steps. Application of combined multiple serum biomarkers, particularly on inflammatory markers, could improve predictability of remission at acute and continuation treatment phases for depressive disorders. Patients with unfavourable biomarkers might require alternative treatment regimes for better outcomes.

Prognostic Significance of Blood-Based Baseline Biomarkers in Treatment-Resistant Depression: A Literature Review of Available Studies on Treatment Response

Major depressive disorder is a leading cause of disability worldwide and a major contributor to the overall global burden of disease. While there are several options for antidepressant treatment, only about 40–60% of patients respond to initial monotherapy, while 30–40% of patients may even show resistance to treatment. This article offers a narrative review of those studies evaluating the predictive properties of various blood-based baseline biomarkers regarding treatment responses to the pharmacological, stimulation, or behavioral treatment of patients with treatment-resistant depression (TRD). Our results show that overall, there is only a very limited number of studies assessing baseline peripheral biomarkers regarding treatment response in TRD. Although there is some evidence for the predictive significance of particular biomarkers (e.g., IL-6, CRP, BDNF), the majority of the results are either single-study reports or studies with conflicting results. This may contribute to the wide variety of treatment protocols and different TRD definition criteria, the small number of patients included, and the existence of different biological phenotypes of the disorder used within the various studies. Taken together, there does not yet appear to be any specific baseline peripheral biomarker with sufficient discriminative predictive validity that can be used in the routine clinical practice of TRD. The discovery of new biomarkers and the better clinical characterization of known biomarkers could support the better classification and staging of TRD, the development of personalized treatment algorithms with higher rates of remission and fewer side effects, and the development of new precision drugs for specific subgroups of patients.

Depression in Adolescence and Brain-Derived Neurotrophic Factor

The incidence of depression among adolescents has been rapidly increasing in recent years. Environmental and genetic factors have been identified as important risk factors for adolescent depression. However, the mechanisms underlying the development of adolescent depression that are triggered by these risk factors are not well understood. Clinical and preclinical studies have focused more on adult depression, and differences in depressive symptoms between adolescents and adults make it difficult to adequately diagnose and treat adolescent depression. Brain-derived neurotrophic factor (BDNF) is known to play a critical role in the pathophysiology of many psychiatric disorders, including depression. However, there are still few studies on adolescent depression. Therefore, in this review paper, the causes and treatment of adolescent depression and the function of BDNF are investigated.

A tale of two transmitters: serotonin and histamine as in vivo biomarkers of chronic stress in mice

Background

Stress-induced mental illnesses (mediated by neuroinflammation) pose one of the world’s most urgent public health challenges. A reliable in vivo chemical biomarker of stress would significantly improve the clinical communities’ diagnostic and therapeutic approaches to illnesses, such as depression.

Methods

Male and female C57BL/6J mice underwent a chronic stress paradigm. We paired innovative in vivo serotonin and histamine voltammetric measurement technologies, behavioral testing, and cutting-edge mathematical methods to correlate chemistry to stress and behavior.

Results

Inflammation-induced increases in hypothalamic histamine were co-measured with decreased in vivo extracellular hippocampal serotonin in mice that underwent a chronic stress paradigm, regardless of behavioral phenotype. In animals with depression phenotypes, correlations were found between serotonin and the extent of behavioral indices of depression. We created a high accuracy algorithm that could predict whether animals had been exposed to stress or not based solely on the serotonin measurement. We next developed a model of serotonin and histamine modulation, which predicted that stress-induced neuroinflammation increases histaminergic activity, serving to inhibit serotonin. Finally, we created a mathematical index of stress, S_i and predicted that during chronic stress, where S_i is high, simultaneously increasing serotonin and decreasing histamine is the most effective chemical strategy to restoring serotonin to pre-stress levels. When we pursued this idea pharmacologically, our experiments were nearly identical to the model’s predictions.

Conclusions

This work shines the light on two biomarkers of chronic stress, histamine and serotonin, and implies that both may be important in our future investigations of the pathology and treatment of inflammation-induced depression.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12974-022-02508-9.

Association of Depression and Anxiety With the Accumulation of Chronic Conditions

IMPORTANCE

Longitudinal associations between comorbid depression and anxiety with the accumulation of chronic illnesses are unclear, and questions remain about the contributions associated with each condition in the increasing prevalence of multimorbidity.

OBJECTIVE

To compare the risk and rate of accumulating chronic conditions in people with depression, anxiety, and comorbid depression and anxiety vs individuals with neither depression nor anxiety.

DESIGN, SETTING, AND PARTICIPANTS

This cohort study used the Rochester Epidemiology Project medical records-linkage system to identify residents of Olmsted County, Minnesota, from January 1, 2005, to December 31, 2014, with follow-up ending December 31, 2017. The sample was divided into cohorts anchored at birthday ages of 20, 40, and 60 years. Individuals were classified at anchoring birthday age as having depression alone, anxiety alone, comorbid depression and anxiety, or neither depression nor anxiety (reference group), using electronically extracted diagnosis codes from the International Classification of Diseases, Ninth Revision (ICD-9) in the 5 years before each anchoring birthday. Data were analyzed from August 2020 through November 2021.

EXPOSURES

Depression alone, anxiety alone, comorbid depression and anxiety, or neither depression nor anxiety (reference group).

MAIN OUTCOMES AND MEASURES

The main outcome was sex-specific risk, calculated as hazard ratios (HRs) and rates of accumulation, calculated as mean annual incidence rates per 100 person-years, of 15 common chronic conditions within each birthday age cohort through the end of study.

RESULTS

Among the 40 360 individuals included across all 3 age cohorts, 21 516 (53.3%) were women. After balancing cohorts on race, Hispanic ethnicity, education level, body mass index, smoking status, and calendar year at index birthday, the risk of accumulating chronic conditions was significantly increased among women with depression alone (cohort aged 20 years: HR, 1.20 [95% CI, 1.02-1.42]; cohort aged 40 years: HR, 1.20 [95% CI, 1.10-1.31]; cohort aged 60 years: HR, 1.09 [95% CI, 1.02-1.16]) and women with comorbid depression and anxiety (cohort aged 20 years: HR, 1.60 [95% CI, 1.28-1.99]; cohort aged 40 years: HR, 1.41 [95% CI, 1.21-1.65]; cohort aged 60 years: HR, 1.29 [95% CI, 1.15-1.44]) compared with referent women in the same birthday cohorts and in men with comorbid depression and anxiety compared with referent men in the cohort aged 20 years (HR, 1.77 [95% CI, 1.08-2.91]). For women, the rates of accumulation of conditions were significantly higher across birthday cohorts in the comorbid depression and anxiety group compared with the depression alone group (eg, cohort aged 20 years: difference, 1.2 [95% CI, 0.2-2.1] per 100 person-years) and reference group (eg, cohort aged 20 years: difference, 1.7 [95% CI, 0.9-2.6] per 100 person-years). For men, compared with the reference group, the rates of accumulation of conditions were significantly higher in men with comorbid depression and anxiety in the cohort aged 20 years (difference, 1.4 [95% CI, 0.1-2.6] per 100 person-years) and in men with depression in the cohort aged 40 years (difference, 2.0 [95% CI, 0.8-3.2] per 100 person-years).

CONCLUSIONS AND RELEVANCE

In this cohort study, the risk of accumulating chronic conditions was increased with depression and comorbid depression and anxiety in women across the age span and in younger men with comorbid depression and anxiety. Compared with women without depression or anxiety, there was a more rapid rate of accumulation of chronic conditions in women with depression and anxiety individually and an even higher rate when depression and anxiety cooccurred.

Retinoic acid-induced protein 14 controls dendritic spine dynamics associated with depressive-like behaviors

Dendritic spines are the central postsynaptic machinery that determines synaptic function. The F-actin within dendritic spines regulates their dynamic formation and elimination. Rai14 is an F-actin-regulating protein with a membrane-shaping function. Here, we identified the roles of Rai14 for the regulation of dendritic spine dynamics associated with stress-induced depressive-like behaviors. Rai14-deficient neurons exhibit reduced dendritic spine density in the Rai14^+/- mouse brain, resulting in impaired functional synaptic activity. Rai14 was protected from degradation by complex formation with Tara, and accumulated in the dendritic spine neck, thereby enhancing spine maintenance. Concurrently, Rai14 deficiency in mice altered gene expression profile relevant to depressive conditions and increased depressive-like behaviors. Moreover, Rai14 expression was reduced in the prefrontal cortex of the mouse stress model, which was blocked by antidepressant treatment. Thus, we propose that Rai14-dependent regulation of dendritic spines may underlie the plastic changes of neuronal connections relevant to depressive-like behaviors.

Utility of extracellular vesicles as a potential biological indicator of physiological resilience during military operational stress

Extracellular vesicles (EVs) transport biological content between cells to mediate physiological processes. The association between EVs and resilience, the ability to cope with stress, is unknown. Using unbiased machine learning approaches, we aimed to identify a biological profile of resilience. Twenty servicemen (27.8 ± 5.9 years) completed the Connor Davidson Resilience (CD‐RISC) questionnaire and were exposed to daily physical and cognitive exertion with 48‐hr sleep and caloric restriction. Blood samples from baseline and the second day of stress were analyzed for neuroendocrine biomarkers impacted by military stress. EVs were isolated from plasma and stained with antibodies associated with exosomes (CD63), microvesicles (VAMP3), and apoptotic bodies (THSD1). Individuals were separated into high (n = 10, CD‐RISC > 90) and low (n = 10, CD‐RISC < 79) resilience. EV features were stratified by size, then down‐selected using regression trees and compared between groups. Diagnostic accuracy was assessed using receiver operating characteristic curves. Compared to low resilience, high resilience demonstrated a greater increase in variability of THSD1 local bright spot intensities among large‐sized EVs in response to stress (p = 0.002, Hedges’ g = 1.59). Among medium‐sized EVs, high resilience exhibited a greater decrease in side scatter intensity (p = 0.014, Hedges’ g = 1.17). Both features demonstrated high to moderate diagnostic accuracy for high resilience (AUC = 0.90 and 0.79). In contrast, neuroendocrine biomarker concentrations were similar between groups. The increase in variability among THSD1 + EVs in high, but not low, resilient individuals following stress may suggest high resilience is accompanied by stress‐triggered apoptotic adaptations to the environment that are not detected in neuroendocrine biomarkers.

Fluoxetine inhibited the activation of A1 reactive astrocyte in a mouse model of major depressive disorder through astrocytic 5-HT2BR/β-arrestin2 pathway

Background

Fluoxetine, a selective serotonin reuptake inhibitor, has been reported to directly bind with 5-HT2B receptor (5-HT2BR), but the precise mechanisms, whereby fluoxetine confers the anti-depressive actions via 5-HT2BR is not fully understood. Although neuroinflammation-induced A1 astrocytes are involved in neurodegenerative diseases, the role of A1 astrocyte in the pathogenesis and treatment of major depressive disorder (MDD) remains unclear.

Methods

Mice were subjected to chronic mild stress (CMS) for 6 weeks and subsequently treated with fluoxetine for 4 weeks. The depressive-like and anxiety-like behaviors and the activation of A1 reactive astrocyte in hippocampus and cortex of mice were measured. Primary astrocytes were stimulated with A1 cocktail (tumor necrosis factor (TNF)-α, interleukin (IL)-1α and C1q), activated (LPS) microglia-conditioned medium (MCM) or IL-6 for 24 h and the expression of A1-special and A2-special markers were determined using RT-qPCR and western blot. The role of 5-HT2BR in the effects of fluoxetine on A1 reactive astrocyte was measured using 5-HT2BR inhibitor and siRNA in vitro and AAVs in vivo. The functions of downstream signaling Gq protein and β-arrestins in the effects of fluoxetine on the activation of A1 astrocyte were determined using pharmacological inhibitor and genetic knockout, respectively.

Results

In this study, we found that fluoxetine inhibited the activation of A1 reactive astrocyte and reduced the abnormal behaviors in CMS mice, as well as ameliorated A1 astrocyte reactivity under three different stimulators in primary astrocytes. We also showed that astrocytic 5-HT2BR was required in the inhibitory effects of fluoxetine on A1 reactive astrocyte in MDD in vivo and in vitro. We further found that the functions of fluoxetine in the activation of A1 astrocyte were independent of either Gq protein or β-arrestin1 in vitro. β-arrestin2 pathway was the downstream signaling of astrocytic 5-HT2BR mediated the inhibitory effects of fluoxetine on A1 astrocyte reactivity in primary astrocytes and CMS mice, as well as the improved roles of fluoxetine in behavioral impairments of CMS mice.

Conclusions

These data demonstrate that fluoxetine restricts reactive A1 astrocyte via astrocytic 5-HT2BR/β-arrestin2 pathway in a mouse model of MDD and provide a novel therapeutic avenue for MDD.

Neurobiological Highlights of Cognitive Impairment in Psychiatric Disorders

This review is focused on several psychiatric disorders in which cognitive impairment is a major component of the disease, influencing life quality. There are plenty of data proving that cognitive impairment accompanies and even underlies some psychiatric disorders. In addition, sources provide information on the biological background of cognitive problems associated with mental illness. This scientific review aims to summarize the current knowledge about neurobiological mechanisms of cognitive impairment in people with schizophrenia, depression, mild cognitive impairment and dementia (including Alzheimer's disease).The review provides data about the prevalence of cognitive impairment in people with mental illness and associated biological markers.

Depressive symptoms and chronic lung disease in middle-aged and older Chinese adults: Prospective bidirectional association and mediation analysis

BACKGROUND

We investigated the prospective bidirectional association between depressive symptoms and chronic lung disease (CLD) and explored biologically and behaviorally relevant mediators in this bidirectional association among Chinese middle-aged and older population in the China Health and Retirement Longitudinal Study.

METHODS

Multivariable-adjusted Cox proportional hazard models were used to examine baseline depressive symptoms in relation to incident CLD risk among 12,546 adults and examine CLD condition in association with incidence of elevated depressive symptoms among 6,929 participants from 2011 to 2018. Elevated depressive symptoms were assessed with the 10-item Center for Epidemiologic Studies Depression scale and CLD was determined by self-reported physician diagnosis. Causal mediation analysis was performed to examine the direct and indirect effects of a priori selected nine blood biomarkers and four lifestyle factors in the bidirectional association.

RESULTS

Elevated depressive symptoms significantly increased CLD risk by 68% (HR=1.68, 95%CI=1.46-1.93) after a mean follow-up of 5.9 years and the strong positive association was consistently shown in almost all the subgroups. Having positive CLD status at baseline was associated with 17% increased risk of developing elevated depressive symptoms (HR=1.17, 95%CI=1.01-1.35) during an average of 4.6 years follow-up period. Significant inflammatory, metabolic or pulmonary function related mediators were not identified.

LIMITATIONS

Inadequate follow-up time and limited mediator variable information may reduce chance of identifying significant mediators.

CONCLUSIONS

Elevated depressive symptoms and CLD were mutual risk factors in middle-aged and older Chinese adults. Early screening and treatment of depression is needed to reduce CLD risk and related comorbidities including new-onset depression so as to relieve substantial disease burdens of CLD and depression in China.

Biological Diagnosis of Depression: A Biomarker Panel from Several Nonspecial Indicators Instead of the Specific Biomarker(s)

It is a consensus that the diagnosis efficiency of depression is rather low in clinic. The traditional way of diagnosing depression by symptomatology is flawed. Recent years, a growing body of evidence has underlined the importance of physiological indicators in the diagnosis of depression. However, the diagnosis of depression is difficult to be like some common clinical diseases, which have clear physiological indicators. A single physiological index provides limited information to clinicians and is of little help in the diagnosis of depression. Thus, it is more rational and practical to diagnose depression with a biomarker panel, which covers a few non-specific indicators, such as hormones, cytokines, and neurotrophins. This open review suggested that biomarker panel had a bright future in creating a new model of depression diagnosis or at least providing a reference to the existing depression criteria. The viewpoint is also the future of other psychiatric diagnosis.

The applicability of the Beck Depression Inventory and Hamilton Depression Scale in the automatic recognition of depression based on speech signal processing

Depression is a growing problem worldwide, impacting on an increasing number of patients, and also affecting health systems and the global economy. The most common diagnostical rating scales of depression are self-reported or clinician-administered, which differ in the symptoms that they are sampling. Speech is a promising biomarker in the diagnostical assessment of depression, due to non-invasiveness and cost and time efficiency. In our study, we try to achieve a more accurate, sensitive model for determining depression based on speech processing. Regression and classification models were also developed using a machine learning method. During the research, we had access to a large speech database that includes speech samples from depressed and healthy subjects. The database contains the Beck Depression Inventory (BDI) score of each subject and the Hamilton Rating Scale for Depression (HAMD) score of 20% of the subjects. This fact provided an opportunity to compare the usefulness of BDI and HAMD for training models of automatic recognition of depression based on speech signal processing. We found that the estimated values of the acoustic model trained on BDI scores are closer to HAMD assessment than to the BDI scores, and the partial application of HAMD scores instead of BDI scores in training improves the accuracy of automatic recognition of depression.

Neuroinflammation as a pathophysiological factor in the development and maintenance of functional seizures: A hypothesis

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Early-life stress may be a priming neuroinflammatory factor for later development of FS.

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Secondary trauma has emerged as an important predisposing factor for FS initiation.

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We propose an explanatory, two-hit hypothesis for FS development.

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The proposed hypothesis is based on findings from neuroimaging and biomarker studies.

The neurobiological underpinnings of functional seizure (FS) development and maintenance represent an active research area. Recent work has focused on hardware (brain structure) and software (brain function and connectivity). However, understanding whether FS are an adaptive consequence of changes in brain structure, function, and/or connectivity is important for identifying a causative mechanism and for FS treatment and prevention. Further, investigation must also uncover what causes these structural and functional phenomena. Pioneering work in the field of psychoneuroimmunology has established a strong, consistent link between psychopathology, immune dysfunction, and brain structure/function. Based on this and recent FS biomarker findings, we propose a new etiologic model of FS pathophysiology. We hypothesize that early-life stressors cause neuroinflammatory and neuroendocrine changes that prime the brain for later FS development following secondary trauma (e.g., traumatic brain injury or psychological trauma). This framework coalesces existing knowledge regarding brain aberrations underlying FS and established neurobiological theories on the pathophysiology of underlying psychiatric disorders. We also propose brain temperature mapping as a way of indirectly visualizing neuroinflammation in patients with FS, particularly in emotion regulation, fear processing, and sensory-motor integration circuits. We offer a foundation on which future research can be built, with clear recommendations for future studies.