Serum proteomic profiling of major depressive disorder

Putative Risk Biomarkers of Bipolar Disorder in At-risk Youth

Bipolar disorder is a highly heritable and functionally impairing disease. The recognition and intervention of BD especially that characterized by early onset remains challenging. Risk biomarkers for predicting BD transition among at-risk youth may improve disease prognosis. We reviewed the more recent clinical studies to find possible pre-diagnostic biomarkers in youth at familial or (and) clinical risk of BD. Here we found that putative biomarkers for predicting conversion to BD include findings from multiple sample sources based on different hypotheses. Putative risk biomarkers shown by perspective studies are higher bipolar polygenetic risk scores, epigenetic alterations, elevated immune parameters, front-limbic system deficits, and brain circuit dysfunction associated with emotion and reward processing. Future studies need to enhance machine learning integration, make clinical detection methods more objective, and improve the quality of cohort studies.

Elevated levels of apolipoprotein A4 in umbilical cord serum from the maternal major depressive disorder

AIM

Prenatal maternal depression is known to affect the neurodevelopment of offspring. This study aimed to investigate the profile of umbilical cord serum in mothers with major depressive disorder (MDD).

METHODS

Liquid chromatography-tandem mass spectrometry (LC-MS) was conducted using umbilical cord serum from mothers with MDD (n = 5) and controls (control, n = 5). The levels of several differentially expressed proteins in umbilical cord serum were compared between the MDD (n = 10) and control groups (n = 10) by enzyme-linked immunosorbent assay.

RESULTS

The proteomic profiles in the umbilical cord serum were different between the MDD and control groups, including the pathways of regulation of plasma lipoprotein particle levels, and synapse organization. Only apolipoprotein A4 (APOA4) was significantly higher in the cord blood of MDD group. APOA4 levels in maternal serum were also significantly higher in the MDD group than those in the control group. The APOA4 levels in the umbilical cord serum were higher than that in the maternal serum.

CONCLUSIONS

The levels of APOA4, a biomarker of depression, in the umbilical cord serum at birth were elevated in the neonates of MDD mothers. It is, therefore, likely that fetuses of MDD mothers were exposed to higher APOA4 levels in utero and this could have developmental and mental health implications for the offspring.

Dynamic Human Gut Microbiome and Immune Shifts During an Immersive Psychosocial Therapeutic Program

Background

Depression is a leading cause of disability worldwide yet its underlying factors, particularly microbial associations, are poorly understood.

Methods

We examined the longitudinal interplay between the microbiome and immune system in the context of depression during an immersive psychosocial intervention. 142 multi-omics samples were collected from 52 well-characterized participants before, during, and three months after a nine-day inquiry-based stress reduction program.

Results

We found that depression was associated with both an increased presence of putatively pathogenic bacteria and reduced microbial beta-diversity. Following the intervention, we observed reductions in neuroinflammatory cytokines and improvements in several mental health indicators. Interestingly, participants with a Prevotella-dominant microbiome showed milder symptoms when depressed, along with a more resilient microbiome and more favorable inflammatory cytokine profile, including reduced levels of CXCL-1.

Conclusions

Our findings reveal a protective link between the Prevotella-dominant microbiome and depression, associated with a less inflammatory environment and moderated symptoms. These insights, coupled with observed improvements in neuroinflammatory markers and mental health from the intervention, highlight potential avenues for microbiome-targeted therapies in depression management.

Comparative analysis of the nucleus accumbens transcriptional features in multiple depressive animal models

Chronic stress is deemed a significant clinical contributor to depression. The use of animal models of chronic stress can fully reveal the complex pathological mechanisms and their changing trends in the pathogenesis of depression, which is crucial for both disease prevention and therapy. It is also unknown how various forms of stress differ in their impact on animal physiology and behavior. The nucleus accumbens (NAc), an essential brain area for the pathophysiology of depression, and its underlying neural mechanisms remain unclear. Here, we systematically compared transcriptional signatures in the NAc of four chronic stress models in rats: chronic unpredictable mild stress (CUMS), chronic social defeat stress (CSDS), learned helplessness (LH), chronic restraint stress (CRS). The majority of differentially expressed genes (DEGs) were unique to a single depression model, while the rank-rank hypergeometric overlap analysis showed that the CSDS and CRS models had the greatest overlap, and the CRS and CUMS models had the least. Then, we performed pathway analysis of the differential genes and found that the neuroactive ligand-receptor interaction pathway was significantly enriched not only in the LH, CRS and CSDS stress models, but also significantly enriched in stress genes that were also altered in at least two stress models. Finally, we found three hub genes (Dcx, Tnc and Wdfy4) by constructing co-expression networks for stress genes. In summary, our research has the potential to offer fresh insights into the molecular mechanisms underlying depression induced by different types of stress, highlighting both their similarities and differences. It may provide valuable clues for understanding the pathogenesis of depression.

Translational bioinformatics and data science for biomarker discovery in mental health: an analytical review

Translational bioinformatics and data science play a crucial role in biomarker discovery as it enables translational research and helps to bridge the gap between the bench research and the bedside clinical applications. Thanks to newer and faster molecular profiling technologies and reducing costs, there are many opportunities for researchers to explore the molecular and physiological mechanisms of diseases. Biomarker discovery enables researchers to better characterize patients, enables early detection and intervention/prevention and predicts treatment responses. Due to increasing prevalence and rising treatment costs, mental health (MH) disorders have become an important venue for biomarker discovery with the goal of improved patient diagnostics, treatment and care. Exploration of underlying biological mechanisms is the key to the understanding of pathogenesis and pathophysiology of MH disorders. In an effort to better understand the underlying mechanisms of MH disorders, we reviewed the major accomplishments in the MH space from a bioinformatics and data science perspective, summarized existing knowledge derived from molecular and cellular data and described challenges and areas of opportunities in this space.

Genome-wide association study meta-analysis of dizygotic twinning illuminates genetic regulation of female fecundity

STUDY QUESTION

Which genetic factors regulate female propensity for giving birth to spontaneous dizygotic (DZ) twins?

SUMMARY ANSWER

We identified four new loci, GNRH1, FSHR, ZFPM1, and IPO8, in addition to previously identified loci, FSHB and SMAD3.

WHAT IS KNOWN ALREADY

The propensity to give birth to DZ twins runs in families. Earlier, we reported that FSHB and SMAD3 as associated with DZ twinning and female fertility measures.

STUDY DESIGN, SIZE, DURATION

We conducted a genome-wide association meta-analysis (GWAMA) of mothers of spontaneous dizygotic (DZ) twins (8265 cases, 264 567 controls) and of independent DZ twin offspring (26 252 cases, 417 433 controls).

PARTICIPANTS/MATERIALS, SETTING, METHODS

Over 700 000 mothers of DZ twins, twin individuals and singletons from large cohorts in Australia/New Zealand, Europe, and the USA were carefully screened to exclude twins born after use of ARTs. Genetic association analyses by cohort were followed by meta-analysis, phenome wide association studies (PheWAS), in silico and in vivo annotations, and Zebrafish functional validation.

MAIN RESULTS AND THE ROLE OF CHANCE

This study enlarges the sample size considerably from previous efforts, finding four genome-wide significant loci, including two novel signals and a further two novel genes that are implicated by gene level enrichment analyses. The novel loci, GNRH1 and FSHR, have well-established roles in female reproduction whereas ZFPM1 and IPO8 have not previously been implicated in female fertility. We found significant genetic correlations with multiple aspects of female reproduction and body size as well as evidence for significant selection against DZ twinning during human evolution. The 26 top single nucleotide polymorphisms (SNPs) from our GWAMA in European-origin participants weakly predicted the crude twinning rates in 47 non-European populations (r = 0.23 between risk score and population prevalence, s.e. 0.11, 1-tail P = 0.058) indicating that genome-wide association studies (GWAS) are needed in African and Asian populations to explore the causes of their respectively high and low DZ twinning rates. In vivo functional tests in zebrafish for IPO8 validated its essential role in female, but not male, fertility. In most regions, risk SNPs linked to known expression quantitative trait loci (eQTLs). Top SNPs were associated with in vivo reproductive hormone levels with the top pathways including hormone ligand binding receptors and the ovulation cycle.

LARGE SCALE DATA

The full DZT GWAS summary statistics will made available after publication through the GWAS catalog (https://www.ebi.ac.uk/gwas/).

LIMITATIONS, REASONS FOR CAUTION

Our study only included European ancestry cohorts. Inclusion of data from Africa (with the highest twining rate) and Asia (with the lowest rate) would illuminate further the biology of twinning and female fertility.

WIDER IMPLICATIONS OF THE FINDINGS

About one in 40 babies born in the world is a twin and there is much speculation on why twinning runs in families. We hope our results will inform investigations of ovarian response in new and existing ARTs and the causes of female infertility.

STUDY FUNDING/COMPETING INTEREST(S)

Support for the Netherlands Twin Register came from the Netherlands Organization for Scientific Research (NWO) and The Netherlands Organization for Health Research and Development (ZonMW) grants, 904-61-193, 480-04-004, 400-05-717, Addiction-31160008, 911-09-032, Biobanking and Biomolecular Resources Research Infrastructure (BBMRI.NL, 184.021.007), Royal Netherlands Academy of Science Professor Award (PAH/6635) to DIB, European Research Council (ERC-230374), Rutgers University Cell and DNA Repository (NIMH U24 MH068457-06), the Avera Institute, Sioux Falls, South Dakota (USA) and the National Institutes of Health (NIH R01 HD042157-01A1) and the Genetic Association Information Network (GAIN) of the Foundation for the National Institutes of Health and Grand Opportunity grants 1RC2 MH089951. The QIMR Berghofer Medical Research Institute (QIMR) study was supported by grants from the National Health and Medical Research Council (NHMRC) of Australia (241944, 339462, 389927, 389875, 389891, 389892, 389938, 443036, 442915, 442981, 496610, 496739, 552485, 552498, 1050208, 1075175). L.Y. is funded by Australian Research Council (Grant number DE200100425). The Minnesota Center for Twin and Family Research (MCTFR) was supported in part by USPHS Grants from the National Institute on Alcohol Abuse and Alcoholism (AA09367 and AA11886) and the National Institute on Drug Abuse (DA05147, DA13240, and DA024417). The Women's Genome Health Study (WGHS) was funded by the National Heart, Lung, and Blood Institute (HL043851 and HL080467) and the National Cancer Institute (CA047988 and UM1CA182913), with support for genotyping provided by Amgen. Data collection in the Finnish Twin Registry has been supported by the Wellcome Trust Sanger Institute, the Broad Institute, ENGAGE-European Network for Genetic and Genomic Epidemiology, FP7-HEALTH-F4-2007, grant agreement number 201413, National Institute of Alcohol Abuse and Alcoholism (grants AA-12502, AA-00145, AA-09203, AA15416, and K02AA018755) and the Academy of Finland (grants 100499, 205585, 118555, 141054, 264146, 308248, 312073 and 336823 to J. Kaprio). TwinsUK is funded by the Wellcome Trust, Medical Research Council, Versus Arthritis, European Union Horizon 2020, Chronic Disease Research Foundation (CDRF), Zoe Ltd and the National Institute for Health Research (NIHR) Clinical Research Network (CRN) and Biomedical Research Centre based at Guy's and St Thomas' NHS Foundation Trust in partnership with King's College London. For NESDA, funding was obtained from the Netherlands Organization for Scientific Research (Geestkracht program grant 10000-1002), the Center for Medical Systems Biology (CSMB, NVVO Genomics), Biobanking and Biomolecular Resources Research Infrastructure (BBMRI-NL), VU University's Institutes for Health and Care Research (EMGO+) and Neuroscience Campus Amsterdam, University Medical Center Groningen, Leiden University Medical Center, National Institutes of Health (NIH, ROI D0042157-01A, MH081802, Grand Opportunity grants 1 RC2 Ml-1089951 and IRC2 MH089995). Part of the genotyping and analyses were funded by the Genetic Association Information Network (GAIN) of the Foundation for the National Institutes of Health. Computing was supported by BiG Grid, the Dutch e-Science Grid, which is financially supported by NWO. Work in the Del Bene lab was supported by the Programme Investissements d'Avenir IHU FOReSIGHT (ANR-18-IAHU-01). C.R. was supported by an EU Horizon 2020 Marie Skłodowska-Curie Action fellowship (H2020-MSCA-IF-2014 #661527). H.S. and K.S. are employees of deCODE Genetics/Amgen. The other authors declare no competing financial interests.

TRIAL REGISTRATION NUMBER

N/A.

Precision Medicine in Depression: The Role of Proteomics and Metabolomics in Personalized Treatment Approaches

Depression, or major depressive disorder (MDD), is a widespread mental health condition marked by enduring feelings of sorrow and loss of interest. Treatment of depression frequently combines psychotherapy, medication, and lifestyle modifications. However, the occurrence of treatment resistance in certain individuals makes it difficult for physicians to effectively manage this disorder, calling for the implementation of alternative therapeutic strategies. Recently, precision medicine has gained increased attention in the field of mental health, paving the way for more personalized and effective therapeutic interventions in depression. Also known as personalized medicine, this approach relies on genetic composition, molecular profiles, and environmental variables to customize therapies to individual patients. In particular, precision medicine has offered novel viewpoints on depression through two specific domains: proteomics and metabolomics. On one hand, proteomics is the thorough study of proteins in a biological system, while metabolomics focuses on analyzing the complete set of metabolites in a living being. In the past few years, progress in research has led to the identification of numerous depression-related biomarkers using proteomics and metabolomics techniques, allowing for early identification, precise diagnosis, and improved clinical outcome. However, despite significant progress in these techniques, further efforts are required for advancing precision medicine in the diagnosis and treatment of depression. The overarching goal of this chapter is to provide the current state of knowledge regarding the use of proteomics and metabolomics in identifying biomarkers related to depression. It also highlights the potential of proteomics and metabolomics in elucidating the intricate processes underlying depression, opening the door for tailored therapies that could eventually enhance clinical outcome in depressed patients. This chapter finally discusses the main challenges in the use of proteomics and metabolomics and discusses potential future research directions.

Upregulation of S100A8 in peripheral blood mononuclear cells from patients with depression treated with SSRIs: a pilot study

BACKGROUND

Major depressive disorder (MDD) affects more than 350 million people worldwide, and there is currently no laboratory test to diagnose it. This pilot study aimed to identify potential biomarkers in peripheral blood mononuclear cells (PBMCs) from MDD patients.

METHODS

We used tandem mass tagging coupled to synchronous precursor selection (mass spectrometry) to obtain the differential proteomic profile from a pool of PBMCs from MDD patients and healthy subjects, and quantitative PCR to assess gene expression of differentially expressed proteins (DEPs) of our interest.

RESULTS

We identified 247 proteins, of which 133 had a fold change ≥ 2.0 compared to healthy volunteers. Using pathway enrichment analysis, we found that some processes, such as platelet degranulation, coagulation, and the inflammatory response, are perturbed in MDD patients. The gene-disease association analysis showed that molecular alterations in PBMCs from MDD patients are associated with cerebral ischemia, vascular disease, thrombosis, acute coronary syndrome, and myocardial ischemia, in addition to other conditions such as inflammation and diabetic retinopathy.

CONCLUSIONS

We confirmed by qRT-PCR that S100A8 is upregulated in PBMCs from MDD patients and thus could be an emerging biomarker of this disorder. This report lays the groundwork for future studies in a broader and more diverse population and contributes to a deeper characterization of MDD.

Machine learning models of plasma proteomic data predict mood in chronic stroke and tie it to aberrant peripheral immune responses

Post-stroke depression is common, long-lasting and associated with severe morbidity and death, but mechanisms are not well-understood. We used a broad proteomics panel and developed a machine learning algorithm to determine whether plasma protein data can predict mood in people with chronic stroke, and to identify proteins and pathways associated with mood. We used Olink to measure 1,196 plasma proteins in 85 participants aged 25 and older who were between 5 months and 9 years after ischemic stroke. Mood was assessed with the Stroke Impact Scale mood questionnaire (SIS3). Machine learning multivariable regression models were constructed to estimate SIS3 using proteomics data, age, and time since stroke. We also dichotomized participants into better mood (SIS3 > 63) or worse mood (SIS3 ≤ 63) and analyzed candidate proteins. Machine learning models verified that there is indeed a relationship between plasma proteomic data and mood in chronic stroke, with the most accurate prediction of mood occurring when we add age and time since stroke. At the individual protein level, no single protein or set of proteins predicts mood. But by using univariate analyses of the proteins most highly associated with mood we produced a model of chronic post-stroke depression. We utilized the fact that this list contained many proteins that are also implicated in major depression. Also, over 80% of immune proteins that correlate with mood were higher with worse mood, implicating a broadly overactive immune system in chronic post-stroke depression. Finally, we used a comprehensive literature review of major depression and acute post-stroke depression. We propose that in chronic post-stroke depression there is over-activation of the immune response that then triggers changes in serotonin activity and neuronal plasticity leading to depressed mood.

Chronic exposure to multiple stressors alters the salivary proteome of piglets

Monitoring chronic stress in pigs is not only essential in view of animal welfare but is also important for the farmer, given that stress influences the zootechnical performance of the pigs and increases their susceptibility to infectious diseases. To investigate the use of saliva as a non-invasive, objective chronic stress monitoring tool, twenty-four 4-day-old piglets were transferred to artificial brooders. At the age of 7 days, they were assigned to either the control or the stressed group and reared for three weeks. Piglets in the stressed group were exposed to overcrowding, absence of cage enrichment, and frequent mixing of animals between pens. Shotgun analysis using an isobaric labelling method (iTRAQ) for tandem mass spectrometry performed on saliva samples taken after three weeks of chronic stress identified 392 proteins, of which 20 proteins displayed significantly altered concentrations. From these 20 proteins, eight were selected for further validation using parallel reaction monitoring (PRM). For this validation, saliva samples that were taken one week after the start of the experiment and samples that were taken at the end of the experiment were analysed to verify the profile over time. We wanted to investigate whether the candidate biomarkers responded fast or rather slowly to the onset of chronic exposure to multiple stressors. Furthermore, this validation could indicate whether age influenced the baseline concentrations of these salivary proteins, both in healthy and stressed animals. This targeted PRM analysis confirmed that alpha-2-HS-glycoprotein was upregulated in the stressed group after one and three weeks, while odorant-binding protein, chitinase, long palate lung and nasal epithelium protein 5, lipocalin-1, and vomeromodulin-like protein were present in lower concentrations in the saliva of the stressed pigs, albeit only after three weeks. These results indicate that the porcine salivary proteome is altered by chronic exposure to multiple stressors. The affected proteins could be used as salivary biomarkers to identify welfare problems at the farm and facilitate research to optimise rearing conditions.

Dissection of depression heterogeneity using proteomic clusters

BACKGROUND

The search for relevant biomarkers of major depressive disorder (MDD) is challenged by heterogeneity; biological alterations may vary in patients expressing different symptom profiles. Moreover, most research considers a limited number of biomarkers, which may not be adequate for tagging complex network-level mechanisms. Here we studied clusters of proteins and examined their relation with MDD and individual depressive symptoms.

METHODS

The sample consisted of 1621 subjects from the Netherlands Study of Depression and Anxiety (NESDA). MDD diagnoses were based on DSM-IV criteria and the Inventory of Depressive Symptomatology questionnaire measured endorsement of 30 symptoms. Serum protein levels were detected using a multi-analyte platform (171 analytes, immunoassay, Myriad RBM DiscoveryMAP 250+). Proteomic clusters were computed using weighted correlation network analysis (WGCNA).

RESULTS

Six proteomic clusters were identified, of which one was nominally significantly associated with current MDD (p = 9.62E-03, Bonferroni adj. p = 0.057). This cluster contained 21 analytes and was enriched with pathways involved in inflammation and metabolism [including C-reactive protein (CRP), leptin and insulin]. At the individual symptom level, this proteomic cluster was associated with ten symptoms, among which were five atypical, energy-related symptoms. After correcting for several health and lifestyle covariates, hypersomnia, increased appetite, panic and weight gain remained significantly associated with the cluster.

CONCLUSIONS

Our findings support the idea that alterations in a network of proteins involved in inflammatory and metabolic processes are present in MDD, but these alterations map predominantly to clinical symptoms reflecting an imbalance between energy intake and expenditure.

Plasma biomarker panel for major depressive disorder by quantitative proteomics using ensemble learning algorithm: A preliminary study

Major depressive disorder (MDD) is a major international public health issue; thus, investigating its underlying mechanisms and identifying suitable biomarkers to enable its early detection are imperative. Using data-independent acquisition-mass spectrometry-based proteomics, the plasma of 44 patients with MDD and 25 healthy controls was studied to detect differentially expressed proteins. Bioinformatics analyses, such as Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analysis, Protein-Protein Interaction network, and weighted gene co-expression network analysis were employed. Moreover, an ensemble learning technique was used to build a prediction model. A panel of two biomarkers, L-selectin and an isoform of the Ras oncogene family was identified. With an area under the receiver operating characteristic curve of 0.925 and 0.901 for the training and test sets, respectively, the panel was able to distinguish MDD from the controls. Our investigation revealed numerous potential biomarkers and a diagnostic panel based on several algorithms, which may contribute to the future development of a plasma-based diagnostic approach and better understanding of the molecular mechanisms of MDD.

Plausible Role of Stem Cell Types for Treating and Understanding the Pathophysiology of Depression

Major Depressive Disorder (MDD), colloquially known as depression, is a debilitating condition affecting an estimated 3.8% of the population globally, of which 5.0% are adults and 5.7% are above the age of 60. MDD is differentiated from common mood changes and short-lived emotional responses due to subtle alterations in gray and white matter, including the frontal lobe, hippocampus, temporal lobe, thalamus, striatum, and amygdala. It can be detrimental to a person’s overall health if it occurs with moderate or severe intensity. It can render a person suffering terribly to perform inadequately in their personal, professional, and social lives. Depression, at its peak, can lead to suicidal thoughts and ideation. Antidepressants manage clinical depression and function by modulating the serotonin, norepinephrine, and dopamine neurotransmitter levels in the brain. Patients with MDD positively respond to antidepressants, but 10–30% do not recuperate or have a partial response accompanied by poor life quality, suicidal ideation, self-injurious behavior, and an increased relapse rate. Recent research shows that mesenchymal stem cells and iPSCs may be responsible for lowering depression by producing more neurons with increased cortical connections. This narrative review discusses the plausible functions of various stem cell types in treating and understanding depression pathophysiology.

Chronic exposure to metam sodium-based pesticide in mice during adulthood elevated anxiety and depression-like behaviors: Involvement of serotoninergic depletion and gut microbiota dysbiosis

Metam sodium-based pesticide (MS-BP) is widely used in agriculture and public health. We have previously demonstrated that maternal exposure to MS-BP resulted in sensorimotor alterations in mice offspring with long-lasting deficits including anxiety- and depression-like behaviors. Here, we project to verify whether these two neurobehavioral effects occur during adulthood following direct exposure to MS-BP and whether it results in changes in the serotoninergic system and gut microbiota. Our findings showed that chronic exposure to MS-BP increased anxiety- and depression-like behaviors, accompanied by a depletion of serotonin-like neurons within the dorsal raphe nucleus and a reduction in serotoninergic terminals in the infralimbic cortex and the basolateral amygdala. In addition, all MS-BP-exposed animals exhibited a reduced total bacterial number and diversity of gut microbiota. Taken together, our data demonstrated that MS-BP-induced behavioral changes could be related to the impairment of the serotoninergic system and gut microbiota dysbiosis.

Altered circulating GDF-15 level predicts sex hormone imbalance in males with major depressive disorder

BACKGROUND

It has been hypothesized that higher growth differentiation factor 15 (GDF15) level and lower testosterone/ estradiol (T/E) ratio are associated with major depressive disorder (MDD), yet the underlying effect of serum GDF15 on hinting the T/E ratio imbalance is not fully understood. We observed the correlation between serum T/E ratio and circulating GDF15 in male depressed cohort.

METHODS

The sample consisted of participants (aged 18 ~ 65 years) from the Renmin Hospital of Wuhan University with MDD (n = 412) defined according to a Structured Clinical Interview for DSM-V (SCID), and male healthy controls (n = 137). Serum levels of testosterone, estradiol, and depression risk biomarkers (thyroid hormone, lipids, hs-CRP, Tenascin-C [TNC], GDF15, KLF4, Gas6, and sgp130) were measured. The associations among log-transformed T/E ratio and these biomarkers were analyzed using univariate correlation analysis, category analyses, and linear regression adjusting for standard risk factors.

RESULTS

Of the sample, 36.89% had lower T/E ratio (< 10:1) and 10.20% had higher T/E ratio (> 20:1). After multivariable adjustment, T/E ratio was negatively associated with GDF15 (-0.095 [95% CI -0.170 ~ -0.023] standard deviation [SD] change per SD increase in lg[T/E], P = 0.015) and inversely related to TNC (-0.085 [95% CI -0.167 ~ 0.003] standard deviation [SD] change per SD increase in lg[T/E], P = 0.048). Serum T/E ratio was negatively associated with GDF15 level in both FT3, TSH and HDL strata, whereas this association was not observed in TNC. In T/E ratio strata analyses, there is a significant and negative correlation among T/E ratio and GDF15 in depressive patients with sex hormone imbalance, yet this relationship was not investigated in patients with sex hormone balance.

CONCLUSION

In our community-based observation, circulating GDF-15 level was greatly and inversely associated with serum T/E ratio, indicating that higher GDF-15 alerts sex hormone imbalance in patients with MDD.

A randomized clinical trial to test efficacy of chamomile and saffron for neuroprotective and anti-inflammatory responses in depressive patients

Depression is one of the common psychiatric problems in growing world population caused by long-term stressful events that may trigger the down regulation of neurogenesis. The pathogenesis of depression initially relies on serotonin deficiency which is associated with depressive feelings. Tryptophan (TRP) depletion participate crucial role in inducing depressive symptoms. Long-term reduction of 5-HT may disseminate to high sensitivity of MDD and alters the level of BDNF. Some studies have also revealed the strong association between excessive neuroinflammation and BDNF levels, due the release of pro-inflammatory cytokines. The treatment approach through FDA approved medicine has their own merits and drawbacks. Therefore, herbal alternatives have recently garnered attention for their effectiveness against depression. However, evidence-based synergic effects of antidepressant with different herbal agents are limited. The purpose of this study was to assess the synergistic effects of two well-known herbs, chamomile and saffron, as an adjuvant therapy in patients with mild to moderate depression. The present study was study randomized, open, blinded trial and comprised of 120 participants randomly allocated to control (n = 60) and test (n = 60). After consent, the patient health questionnaire- 9 (PHQ-9) was filled to obtain depression scores. The test participants were received herbal tea sachets twice a day for one month (20 mg Chamomile and 1 mg Saffron/sachet) along with routine medicines, while control participants were received only allopathic medications. Blood samples were taken before and after the treatment. The depressive symptoms improved significantly with both treatments. The effect of herbs enhanced the efficacy of medications and significantly improved PHQ-9 scale and BDNF while reduced the inflammatory markers (CRP) and TRP level in plasma thereby increased the availability of TRP in brain. It has been concluded that the herbal adjuvant therapy produced long term improvement against depression and enhanced the efficacy of allopathic treatment.

The Importance of CXCL1 in Physiology and Noncancerous Diseases of Bone, Bone Marrow, Muscle and the Nervous System

This review describes the role of CXCL1, a chemokine crucial in inflammation as a chemoattractant for neutrophils, in physiology and in selected major non-cancer diseases. Due to the vast amount of available information, we focus on the role CXCL1 plays in the physiology of bones, bone marrow, muscle and the nervous system. For this reason, we describe its effects on hematopoietic stem cells, myoblasts, oligodendrocyte progenitors and osteoclast precursors. We also present the involvement of CXCL1 in diseases of selected tissues and organs including Alzheimer’s disease, epilepsy, herpes simplex virus type 1 (HSV-1) encephalitis, ischemic stroke, major depression, multiple sclerosis, neuromyelitis optica, neuropathic pain, osteoporosis, prion diseases, rheumatoid arthritis, tick-borne encephalitis (TBE), traumatic spinal cord injury and West Nile fever.

Investigating data-driven biological subtypes of psychiatric disorders using specification-curve analysis

BACKGROUND

Cluster analyses have become popular tools for data-driven classification in biological psychiatric research. However, these analyses are known to be sensitive to the chosen methods and/or modelling options, which may hamper generalizability and replicability of findings. To gain more insight into this problem, we used Specification-Curve Analysis (SCA) to investigate the influence of methodological variation on biomarker-based cluster-analysis results.

METHODS

Proteomics data (31 biomarkers) were used from patients (n = 688) and healthy controls (n = 426) in the Netherlands Study of Depression and Anxiety. In SCAs, consistency of results was evaluated across 1200 k-means and hierarchical clustering analyses, each with a unique combination of the clustering algorithm, fit-index, and distance metric. Next, SCAs were run in simulated datasets with varying cluster numbers and noise/outlier levels to evaluate the effect of data properties on SCA outcomes.

RESULTS

The real data SCA showed no robust patterns of biological clustering in either the MDD or a combined MDD/healthy dataset. The simulation results showed that the correct number of clusters could be identified quite consistently across the 1200 model specifications, but that correct cluster identification became harder when the number of clusters and noise levels increased.

CONCLUSION

SCA can provide useful insights into the presence of clusters in biomarker data. However, SCA is likely to show inconsistent results in real-world biomarker datasets that are complex and contain considerable levels of noise. Here, the number and nature of the observed clusters may depend strongly on the chosen model-specification, precluding conclusions about the existence of biological clusters among psychiatric patients.

Relationships Between Circulating Tenascin-C Levels and Gonadal Hormones in Male Patients with Depressive Disorder: A Retrospective, Cross-Sectional Study

OBJECTIVE

Tenascin-C (TNC) is an extracellular matrix glycoprotein closely associated with the progression of psychiatric disorders. The present study was performed to investigate the possible association between serum gonadal hormones and TNC levels in male patients with depressive disorder.

MATERIALS AND METHODS

We measured serum TNC levels by enzyme-linked immunosorbent assay. In addition, we investigated the influence of testosterone (T) and estradiol (E2) on TNC levels in primary neuronal cultures.

RESULTS

Patients with depression had lower levels of T, free tri-iodothyronine (FT3), thyroid-stimulating hormone (TSH), and the T/E2 ratio than healthy control patients. Levels of TNC and high-sensitivity C-reactive protein were significantly higher in patients than in healthy volunteers. Serum TNC concentrations were negatively associated with levels of E2 and T and with the T/E2 ratio. Levels of TNC, TSH, and FT3 and the T/E2 ratio were predictors of depression. Among men with depression, TNC was negatively associated with T levels and with the T/E2 ratio. Incubating pheochromocytoma 12 cells with the combination of T and E2 greatly decreased TNC levels in the culture medium.

CONCLUSION

Increased TNC levels may predict imbalance between T and E2 in patients with depression, and gonadal hormones may modulate TNC expression in vivo.

Exploring low grade inflammation by soluble urokinase plasminogen activator receptor levels in schizophrenia: a sex-dependent association with depressive symptoms

BACKGROUND

There is evidence of increased low grade inflammation (LGI) in schizophrenia patients. However, the inter-individual variation is large and the association with demographic, somatic and psychiatric factors remains unclear. Our aim was to explore whether levels of the novel LGI marker soluble urokinase plasminogen activator receptor (suPAR) were associated with clinical factors in schizophrenia and if such associations were sex-dependent.

METHOD

In this observational study a total of 187 participants with schizophrenia (108 males, 79 females) underwent physical examination and assessment with clinical interviews (Positive and Negative Syndrome Scale (PANSS), Calgary Depression Scale for Schizophrenia (CDSS), Alcohol Use Disorder Identification Test (AUDIT), and Drug Use Disorder Identification Test (DUDIT)). Blood levels of suPAR, glucose, lipids, and high sensitivity C-reactive protein (hsCRP) were determined and body mass index (BMI) calculated. Multivariable linear regression analyses were used adjusting for confounders, and sex interaction tested in significant variables.

RESULTS

Adjusting for sex, age, current tobacco smoking and BMI, we found that levels of hsCRP and depressive symptoms (CDSS) were positively associated with levels of suPAR (p < 0.001). The association between suPAR and CDSS score was significant in females (p < 0.001) but not in males. Immune activation measured by hsCRP was not associated with depressive symptoms after adjusting for BMI.

CONCLUSION

Our findings indicate that increased suPAR levels are associated with depressive symptoms in females with schizophrenia, suggesting aberrant immune activation in this subgroup. Our results warrant further studies, including longitudinal follow-up of suPAR levels in schizophrenia and experimental studies of mechanisms.

Blood-Brain Barrier Dysfunction in the Pathogenesis of Major Depressive Disorder

Major depression represents a complex and prevalent psychological disease that is characterized by persistent depressed mood, impaired cognitive function and complicated pathophysiological and neuroendocrine alterations. Despite the multifactorial etiology of depression, one of the most recent factors to be identified as playing a critical role in the development of depression is blood-brain barrier (BBB) disruption. The occurrence of BBB integrity disruption contributes to the disturbance of brain homeostasis and leads to complications of neurological diseases, such as stroke, chronic neurodegenerative disorders, neuroinflammatory disorders. Recently, BBB associated tight junction disruption has been shown to implicate in the pathophysiology of depression and contribute to increased susceptibility to depression. However, the underlying mechanisms and importance of BBB damage in depression remains largely unknown. This review highlights how BBB disruption regulates the depression process and the possible molecular mechanisms involved in development of depression-induced BBB dysfunction. Moreover, insight on promising therapeutic targets for treatment of depression with associated BBB dysfunctions are also discussed.

Predictive protein markers for depression severity in mood disorders: A preliminary trans-diagnostic approach study

Depression is a common symptom of many mental disorders, especially major depressive disorder (MDD) and bipolar disorder (BD). Previous studies have reported that these diseases share common pathophysiological pathways; therefore, this study elucidated whether the plasma levels of protein markers related to common depressive symptoms differed between patients with BD and those with MDD. Plasma samples of 71 patients with mood disorders and clinical manifestations were analyzed in this study. After depleting the abundant proteins, liquid chromatography-tandem mass spectrometry and label-free quantification were performed. Five proteins, viz., cholesteryl ester transfer protein (CETP), apolipoprotein D (APOD), mannan-binding lectin serine protease 2 (MASP2), Ig lambda chain V-II region BO (IGLV2-8) and Ig kappa chain V-III region NG9 (IGKV3-20) were negatively associated with the total scores of the Hamilton depression rating scale (HAM-D), after adjusting for the covariates. CETP and APOD also showed significant negative correlations with the anhedonia/retardation and guilt/agitation scores of the HAM-D. Four proteins, namely, Ig kappa chain V-II region TEW (IGKC; IGKV2D-28), Ig lambda variable 5-45 (IGLV5-45), complement factor H (CFH) and attractin (ATRN), showed significant associations with anhedonia/retardation after adjusting for covariates. Proteins that significantly correlated with the symptoms could predict the remission state of depression (area under the curve [AUC], 0.83) and anhedonia/retardation (AUC, 0.80). Bioinformatics analysis revealed that complement activation, immune response, and lipid metabolism were significantly enriched pathways. Although our study design was cross-sectional and no controls were included, protein markers identified in this preliminary study will be further investigated in our subsequent longitudinal study.

Machine Learning-Based Behavioral Diagnostic Tools for Depression: Advances, Challenges, and Future Directions

The psychiatric diagnostic procedure is currently based on self-reports that are subject to personal biases. Therefore, the diagnostic process would benefit greatly from data-driven tools that can enhance accuracy and specificity. In recent years, many studies have achieved promising results in detecting and diagnosing depression based on machine learning (ML) analysis. Despite these favorable results in depression diagnosis, which are primarily based on ML analysis of neuroimaging data, most patients do not have access to neuroimaging tools. Hence, objective assessment tools are needed that can be easily integrated into the routine psychiatric diagnostic process. One solution is to use behavioral data, which can be easily collected while still maintaining objectivity. The current paper summarizes the main ML-based approaches that use behavioral data in diagnosing depression and other psychiatric disorders. We classified these studies into two main categories: (a) laboratory-based assessments and (b) data mining, the latter of which we further divided into two sub-groups: (i) social media usage and movement sensors data and (ii) demographic and clinical information. The paper discusses the advantages and challenges in this field and suggests future research directions and implementations. The paper's overarching aim is to serve as a first step in synthetizing existing knowledge about ML-based behavioral diagnosis studies in order to develop interventions and individually tailored treatments in the future.

Elevated Epidermal Growth Factor (EGF) as Candidate Biomarker of Mood Disorders-Longitudinal Study in Adolescent and Young Adult Patients

Bipolar disorder (BD) is a chronic mental disorder that affects more than 1% of the population worldwide. Over 65% of patients experience early onset of the disease. Most cases of juvenile bipolar disorder begin with a depressed mood episode, and up to 50% of youth initially diagnosed with major depression go onto developing a BD. Our study aimed to find biomarkers of diagnosis conversion in young patients with mood disorders. We performed a two-year follow-up study on 79 adolescent patients diagnosed with MDD or BD, with a detailed clinical assessment at five visits. We monitored diagnosis change from MDD to BD. The control group consisted of 31 healthy youths. According to the neurodevelopmental and neuroimmunological hypotheses of mood disorders, we analyzed serum levels of brain-derived neurotrophic factor (BDNF), proBDNF, epidermal growth factor (EGF), migration inhibitory factor (MIF), stem cell factor (SCF), and correlations with clinical factors. We detected a significant disease-dependent increase in EGF level in MDD and BP patients at baseline exacerbation of depressive or hypomanic/manic episodes as well as in euthymic state compared to healthy controls. No potential biological predictors of disease conversion were found. Replication studies on a larger cohort of patients are needed.

Tianeptine modulates synaptic vesicle dynamics and favors synaptic mitochondria processes in socially isolated rats

Deregulation of synaptic function and neurotransmission has been linked with the development of major depression disorder (MDD). Tianeptine (Tian) has been used as antidepressant with anxiolytic properties and recently as a nootropic to improve cognitive performance, but its mechanism of action is unknown. We conducted a proteomic study on the hippocampal synaptosomal fractions of adult male Wistar rats exposed to chronic social isolation (CSIS, 6 weeks), an animal model of depression and after chronic Tian treatment in controls (nootropic effect) and CSIS-exposed rats (lasting 3 weeks of 6-week CSIS) (therapeutic effect). Increased expression of Syn1 and Camk2-related neurotransmission, vesicle transport and energy processes in Tian-treated controls were found. CSIS led to upregulation of proteins associated with actin cytoskeleton, signaling transduction and glucose metabolism. In CSIS rats, Tian up-regulated proteins involved in mitochondrial energy production, mitochondrial transport and dynamics, antioxidative defense and glutamate clearance, while attenuating the CSIS-increased glycolytic pathway and cytoskeleton organization proteins expression and decreased the expression of proteins involved in V-ATPase and vesicle endocytosis. Our overall findings revealed that synaptic vesicle dynamics, specifically exocytosis, and mitochondria-related energy processes might be key biological pathways modulated by the effective nootropic and antidepressant treatment with Tian and be a potential target for therapeutic efficacy of the stress-related mood disorders.

Serum Proteomic Analysis of Cannabis Use Disorder in Male Patients

Cannabis use has been growing recently and it is legally consumed in many countries. Cannabis has a variety of phytochemicals including cannabinoids, which might impair the peripheral systems responses affecting inflammatory and immunological pathways. However, the exact signaling pathways that induce these effects need further understanding. The objective of this study is to investigate the serum proteomic profiling in patients diagnosed with cannabis use disorder (CUD) as compared with healthy control subjects. The novelty of our study is to highlight the differentially changes proteins in the serum of CUD patients. Certain proteins can be targeted in the future to attenuate the toxicological effects of cannabis. Blood samples were collected from 20 male individuals: 10 healthy controls and 10 CUD patients. An untargeted proteomic technique employing two-dimensional difference in gel electrophoresis coupled with mass spectrometry was employed in this study to assess the differentially expressed proteins. The proteomic analysis identified a total of 121 proteins that showed significant changes in protein expression between CUD patients (experimental group) and healthy individuals (control group). For instance, the serum expression of inactive tyrosine protein kinase PEAK1 and tumor necrosis factor alpha-induced protein 3 were increased in CUD group. In contrast, the serum expression of transthyretin and serotransferrin were reduced in CUD group. Among these proteins, 55 proteins were significantly upregulated and 66 proteins significantly downregulated in CUD patients as compared with healthy control group. Ingenuity pathway analysis (IPA) found that these differentially expressed proteins are linked to p38MAPK, interleukin 12 complex, nuclear factor-κB, and other signaling pathways. Our work indicates that the differentially expressed serum proteins between CUD and control groups are correlated to liver X receptor/retinoid X receptor (RXR), farnesoid X receptor/RXR activation, and acute phase response signaling.

Integrating the monoamine and cytokine hypotheses of depression: Is histamine the missing link?

Psychiatric diseases, like depression, largely affect the central nervous system (CNS). While the underlying neuropathology of depressive illness remains to be elucidated, several hypotheses have been proposed as molecular underpinnings for major depressive disorder, including the monoamine hypothesis and the cytokine hypothesis. The monoamine hypothesis has been largely supported by the pharmaceuticals that target monoamine neurotransmitters as a treatment for depression. However, these antidepressants have come under scrutiny due to their limited clinical efficacy, side effects, and delayed onset of action. The more recent, cytokine hypothesis of depression is supported by the ability of immune-active agents to induce "sickness behaviour" akin to that seen with depression. However, treatments that more selectively target inflammation have yielded inconsistent antidepressive results. As such, neither of these hypotheses can fully explain depressive illness pathology, implying that the underlying neuropathological mechanisms may encompass aspects of both theories. The goal of the current review is to integrate these two well-studied hypotheses and to propose a role for histamine as a potential unifying factor that links monoamines to cytokines. Additionally, we will focus on stress-induced depression, to provide an updated perspective of depressive illness research and thereby identify new potential targets for the treatment of major depressive disorder.

Serum proteomic analysis of major depressive disorder patients and their remission status: Novel biomarker set of zinc-alpha-2-glycoprotein and keratin type II cytoskeletal 1

Major depressive disorder (MDD) is the most common mood disorder, and causes various mental, physical and cognitive symptoms. Clinicians diagnose MDD using multiple interviews and overall impression during the interviews, which makes MDD diagnosis highly subjective. To overcome this, we investigated novel protein biomarker for MDD. Serum from each subject were analyzed using nano liquid chromatography-triple time-of-flight mass spectrometry. We identified two proteins, zinc-alpha-2-glycoprotein (ZA2G) and keratin type II cytoskeletal 1 (K2C1), as final biomarkers. These biomarkers were downregulated during depression (p < 0.05, AUC of ROC >0.7). ZA2G is related to tryptophan metabolism, which is a main serotonin synthesis pathway. K2C1 is involved in the kinin-kallikrein system, which produces bradykinin, an anti-inflammatory mediator in the brain. Our results suggest that the two protein candidates are related to inflammation and that MDD is highly associated with inflammation. Finally, since all subjects in the two groups were taking antidepressants, our results suggest that the identified biomarkers could determine the presence or absence of illness and could be used to monitor therapeutic effects.

Cohort profile of the longitudinal Netherlands Study of Depression and Anxiety (NESDA) on etiology, course and consequences of depressive and anxiety disorders

INTRODUCTION

The Netherlands Study of Depression and Anxiety (NESDA, www.nesda.nl) is a longitudinal, multi-site, naturalistic, case-control cohort study set up to examine the etiology, course and consequences of depressive and anxiety disorders. This paper presents a cohort profile of NESDA.

METHODS AND RESULTS

The NESDA sample recruited initially 2329 persons with a remitted or current DSM-IV based depressive (major depressive disorder, dysthymia) and/or anxiety disorder (panic disorder, social phobia, agoraphobia, generalized anxiety disorder), 367 of their siblings and 652 healthy controls, yielding a total of 3348 participants. Half-day face-to-face assessments of participants started in 2004 and since then have been repeated six times over a period of 9 years. A 13-year follow-up assessment is ongoing, at what time we also recruit offspring of participants. Retention rates are generally high, ranging from 87.1% (after 2 years) to 69.4% (after 9 years). Psychiatric diagnostic interviews have been administered at all face-to-face assessments, as was monitoring of clinical characteristics, psychosocial functioning and somatic health. Assessed etiological factors include e.g. early and current environmental risk factors, psychological vulnerability and resilience factors as well as (neuro)biology through hypothesis-driven biomarker assessments, genome-wide and large-scale '-omics' assessments, and neuroimaging assessments.

LIMITATIONS

The naturalistic design allows research into course and consequences of affective disorders but is limited in treatment response interpretation.

CONCLUSIONS

NESDA provides a strong research infrastructure for research into depressive and/or anxiety disorders. Its data have been used for many scientific papers describing either NESDA-based analyses or joint collaborative consortia-projects, and are in principle available to researchers outside the NESDA consortium.

Quantitative Proteomic Approach for Discriminating Major Depressive Disorder and Bipolar Disorder by Multiple Reaction Monitoring-Mass Spectrometry

Because major depressive disorder (MDD) and bipolar disorder (BD) manifest with similar symptoms, misdiagnosis is a persistent issue, necessitating their differentiation through objective methods. This study was aimed to differentiate between these disorders using a targeted proteomic approach. Multiple reaction monitoring-mass spectrometry (MRM-MS) analysis was performed to quantify protein targets regarding the two disorders in plasma samples of 270 individuals (90 MDD, 90 BD, and 90 healthy controls (HCs)). In the training set (72 MDD and 72 BD), a generalizable model comprising nine proteins was developed. The model was evaluated in the test set (18 MDD and 18 BD). The model demonstrated a good performance (area under the curve (AUC) >0.8) in discriminating MDD from BD in the training (AUC = 0.84) and test sets (AUC = 0.81) and in distinguishing MDD from BD without current hypomanic/manic/mixed symptoms (90 MDD and 75 BD) (AUC = 0.83). Subsequently, the model demonstrated excellent performance for drug-free MDD versus BD (11 MDD and 10 BD) (AUC = 0.96) and good performance for MDD versus HC (AUC = 0.87) and BD versus HC (AUC = 0.86). Furthermore, the nine proteins were associated with neuro, oxidative/nitrosative stress, and immunity/inflammation-related biological functions. This proof-of-concept study introduces a potential model for distinguishing between the two disorders.

Discovery of Screening Biomarkers for Major Depressive Disorder in Remission by Proteomic Approach

Major depressive disorder (MDD) is a common disorder involving depressive mood and decreased motivation. Due to its high heterogeneity, novel biomarkers are required to diagnose MDD. In this study, a proteomic method was used to identify a new MDD biomarker. Using sequential window acquisition of all theoretical mass spectra acquisitions and multiple reaction monitoring analysis via mass spectrometry, relative and absolute quantification of proteins in the sera was performed. The results of the relative quantitation by sequential window acquisition for all theoretical mass spectra data showed that seven proteins were significantly differently expressed between MDD patients and other patients with remission status. However, absolute quantification by multiple reaction monitoring analysis identified prothrombin as the only significantly upregulated protein in the depressive state compared to remission (p < 0.05) and was, thus, subsequently selected as an MDD biomarker. The area under the curve for prothrombin was 0.66. Additionally, increased prothrombin/thrombin induced hyper-activation of platelets via activating protease-activated receptors, a feature associated with MDD; specifically, activated platelets secrete various molecules related to MDD, including brain-derived neurotropic factors and serotonin. Therefore, prothrombin is a potential screening, prognostic, and diagnostic marker for MDD.

Associations Between Tenascin-C and Testosterone Deficiency in Men with Major Depressive Disorder: A Cross-Sectional Retrospective Study

Background

Elevated levels of tenascin-C are linked to increased risk and severity of major depressive disorder (MDD), while testosterone shows a protective effect. The present study explored associations between serum levels of tenascin-C and testosterone in Chinese men with MDD.

Methods

Testosterone and tenascin-C levels were measured in sera of 412 men with MDD and 237 age- and sex-matched controls. Serum levels of thyroid hormone, lipids, and high-sensitivity C-reactive protein (hs-CRP) were also quantified. Potential associations were examined using covariance, subgroup analysis, and multivariate linear regression analyses.

Results

Significantly higher concentrations of tenascin-C were detected in sera of subjects with MDD than in controls. Among subjects with MDD, testosterone concentrations inversely correlated with tenascin-C levels. This relationship was observed when patients were stratified by age at onset; duration or severity of depression; or concentration of thyroid hormones, low- or high-density lipoprotein, or hs-CRP. The negative association remained even when the statistical model was adjusted for age, smoking status, alcohol use, and body mass index. Linear regression with bootstrap resampling confirmed that high tenascin-C levels inversely correlated with testosterone levels.

Conclusion

In men with MDD, high tenascin-C concentrations correlate with testosterone deficiency. The combination of elevated tenascin-C and testosterone deficiency may be associated with MDD progression.

Effects of Alpha-2-HS-glycoprotein on cognitive and emotional assessment in prediabetic and diabetic subjects

BACKGROUND

High concentrations of Alpha-2-HS-glycoprotein, also called Fetuin-A (Fet-A), are associated with insulin resistance, obesity, non-alcoholic fatty liver disease, type 2 diabetes and polycystic ovary syndrome. Moreover, Fet-A is able to cross the bloodbrain barrier into ischemic brain tissue in adult humans. Although the brain is an important target of insulin action, there is little evidence associating serum levels of Fet-A with psychiatric conditions such as depression and cognitive decline, and no reports about the presence and degree of anxiety disorders.

METHODS

We have examined cognitive and emotional alterations in a Caucasian population of 94 subjects.

RESULTS

Our data confirmed that, irrespective of insulin sensitivity status, circulating Fet-A levels are positively associated with an increased risk of showing signs of depression according to the BDI-II test, and have reported new evidences of a positive association between Fet-A and state- and trait- anxiety, as measured by the STAI questionnaires. In contrast, no association was observed between Fet-A levels and cognitive performance on the MMSE.

LIMITATIONS

Although the study includes a well-characterized population, the small sample size and cross sectional nature are important limitations, and this results should not be considered definitive. The data are based only on Caucasian subjects and their generalizability to other ethnic groups should be done with caution.

CONCLUSION

Overall, these data suggest for the first time a role of Fet-A as an independent risk factor in the development of symptoms of anxiety and depression in prediabetic and diabetic subjects.

Alteration of transthyretin and thyroxine-binding globulin in major depressive disorder: multiple reaction monitoring-based proteomic analysis

Background

Major depressive disorder (MDD), common mental disorder, lacks objective diagnostic and prognosis biomarkers. The objective of this study was to perform proteomic analysis to identify proteins with changed expression levels after antidepressant treatment and investigate differences in protein expression between MDD patients and healthy individuals.

Methods

A total of 111 proteins obtained from literature review were subjected to multiple reaction monitoring (MRM)-based protein quantitation. Finally, seven proteins were quantified for plasma specimens of 10 healthy controls and 78 MDD patients (those at baseline and at 6 weeks after antidepressant treatment of either selective serotonin reuptake inhibitors (SSRIs) or mirtazapine).

Results

Among 78 MDD patients, 35 patients were treated with SSRIs and 43 patients were treated with mirtazapine. Nineteen (54.3%) and 16 (37.2%) patients responded to SSRIs and mirtazapine, respectively. Comparing MDD patients with healthy individuals, alteration of transthyretin was observed in MDD (P = 0.026). A few differences were observed in protein levels related to SSRIs treatment, although they were not statistically significant. Plasma thyroxine-binding globulin (TBG) was different between before and after mirtazapine treatment only in responders (P = 0.007).

Conclusions

In proteomic analysis of plasma specimens from MDD patients, transthyretin and TBG levels were altered in MDD and changed after antidepressant treatment.

Depression Augments Plasma APOA4 without Changes of Plasma Lipids and Glucose in Female Adolescents Carrying G Allele of APOA4 rs5104

The association of apolipoprotein AIV (APOA4) with depression or plasma levels of lipids and glucose has been inconsistently reported. However, interplays between APOA4 and depression on the levels have not been explored yet. The present study aimed to investigate plasma levels of APOA4, lipids, and glucose in adolescents with different genotypes of APOA4 rs5104 and with or without depression. Depressive symptoms were assessed in 631 adolescents by Beck Depression Inventory (BDI). A total score of 14 was defined as the cutoff point for depression. Plasma levels of triglycerides (TG), total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), glucose, and insulin were measured by routine methods, and APOA4 by enzyme-linked immunosorbent assays. Genotyping was performed by polymerase chain reaction-restriction fragment length polymorphism analyses and verified by DNA sequencing. Female adolescents had higher prevalence of depression than male subjects only in G allele carriers (p = 0.015), but not in AA homozygotes. Risk factors of depression and predictors of depression severity were different between G allele carriers and AA homozygotes. Lower levels of glucose (p = 0.003) were observed in male G allele carriers than those in male AA homozygotes and increased TG levels (p = 0.008) in female G allele carriers when compared with those in female AA homozygotes. When both APOA4 rs5104 and depression were taken into account, subjects with depression had higher levels of plasma APOA4 than adolescents without depression only in female G allele carriers (p = 0.043), but no significant changes of plasma lipids and glucose. Depression augments plasma APOA4 levels without changes of plasma lipids and glucose in female adolescents carrying G allele of APOA4 rs5104. These results may provide a novel explanation for the inconsistent relationship between depression, APOA4, and plasma levels of lipids and glucose in the literature.

Development and Internal Validation of a Novel Model to Identify Inflammatory Biomarkers of a Response to Escitalopram in Patients With Major Depressive Disorder

Objective: The aim of our study was to identify immune- and inflammation-related factors with clinical utility to predict the clinical efficacy of treatment for depression. Study Design: This was a follow-up study. Participants who met the entry criteria were administered with escitalopram (5-10 mg/day) as an initial treatment. Self-evaluation and observer valuations were arranged at the end of weeks 0, 4, 8, and 12, with blood samples collected at baseline and during weeks 2 and 12. Multivariable logistic regression analysis was then carried out by incorporating three cytokines selected by the Least Absolute Shrinkage and Selection Operator (LASSO) regression model. Internal validation was estimated using the bootstrap method with 1,000 repetitions. Results: A total of 85 patients with Major Depressive Disorder (MDD), including 62 responders and 23 non-responders, were analyzed. Monocyte chemoattractant protein-1 (MCP-1), vascular cell adhesion molecule-1 (VCAM-1), and lipocalin-2 were selected by the LASSO regression model. The area under the curve (AUC) from the logistic model was 0.811 and was confirmed as 0.7887 following bootstrapping validation. Conclusions: We established and validated a good prediction model to facilitate the individualized prediction of escitalopram treatment for MDD and created a personalized approach to treatment for patients with depression.

Potential major depressive disorder biomarkers in pediatric population - a pilot study

Mental disorders affect 10-20 % of the young population in the world. Major depressive disorder (MDD) is a common mental disease with a multifactorial and not clearly explained pathophysiology. Many cases remain undetected and untreated, which influences patients' physical and mental health and their quality of life also in adulthood. The aim of our pilot study was to assess the prediction value of selected potential biomarkers, including blood cell counts, blood cell ratios, and parameters like peroxiredoxin 1 (PRDX1), tenascin C (TNC) and type IV collagen (COL4) between depressive pediatric patients and healthy peers and to evaluate a short effect of antidepressant treatment. In this study, 27 young depressive patients and 26 non-depressed age-matched controls were included. Blood analyses and immunological assays using commercial kits were performed. Platelet count was the only blood parameter for which the case/control status was statistically significant (p=0.01) in a regression model controlling for the age and gender differences. The results from ELISA analyses showed that the case/control status is a significant predictor of the parameters PRDX1 (p=0.05) and COL4 (p=0.009) in respective regression model considering the age and gender differences between MDD patients and controls. A major finding of this study is that values of platelet count, monocyte to lymphocyte ratio, white blood cell, and monocyte counts were assessed by the Random Forest machine learning algorithm as relevant predictors for discrimination between MDD patients and healthy controls with a power of prediction AUC=0.749.

Evidence for an enhanced procoagulant state in remitted major depression

OBJECTIVES

Hypercoagulability is one mechanism to explain the increased risk of incident atherothrombotic disease in patients with major depressive disorder (MDD). We examined whether patients with remitted MDD show an enhanced procoagulant state.

METHODS

63 individuals (median age 35 years, 59% women), 40 with a DSM-IV diagnosis of remitted MDD, made by a clinical interview, and 23 healthy controls provided blood samples for the measurement of fibrinogen, D-dimer, von Willebrand factor, and plasminogen activator inhibitor-1. Standardised z-scores of plasma levels of these haemostatic factors were added to form a procoagulant index (PCI) as the primary outcome variable. Self-ratings of residual depressive symptoms and trait anxiety were also obtained.

RESULTS

Compared with controls, remitted MDD patients had higher PCI (p = 0.013, Cohen's d = 0.69) and fibrinogen (p = 0.001, d = 0.91), controlling for age, sex, body mass index, smoking and C-reactive protein. There were no significant associations of the PCI and individual haemostatic molecules with age of MDD onset, time since the last MDD episode, the number of previous MDD episodes and residual depressive symptoms. Additional adjustment for anxiety symptoms did not change these results.

CONCLUSIONS

Remitted MDD is associated with an enhanced procoagulant state. Hypercoagulability seems more a trait than a state characteristic of depression.

Endothelial dysfunction in neuroprogressive disorders-causes and suggested treatments

BACKGROUND

Potential routes whereby systemic inflammation, oxidative stress and mitochondrial dysfunction may drive the development of endothelial dysfunction and atherosclerosis, even in an environment of low cholesterol, are examined.

MAIN TEXT

Key molecular players involved in the regulation of endothelial cell function are described, including PECAM-1, VE-cadherin, VEGFRs, SFK, Rho GEF TRIO, RAC-1, ITAM, SHP-2, MAPK/ERK, STAT-3, NF-κB, PI3K/AKT, eNOS, nitric oxide, miRNAs, KLF-4 and KLF-2. The key roles of platelet activation, xanthene oxidase and myeloperoxidase in the genesis of endothelial cell dysfunction and activation are detailed. The following roles of circulating reactive oxygen species (ROS), reactive nitrogen species and pro-inflammatory cytokines in the development of endothelial cell dysfunction are then described: paracrine signalling by circulating hydrogen peroxide, inhibition of eNOS and increased levels of mitochondrial ROS, including compromised mitochondrial dynamics, loss of calcium ion homeostasis and inactivation of SIRT-1-mediated signalling pathways. Next, loss of cellular redox homeostasis is considered, including further aspects of the roles of hydrogen peroxide signalling, the pathological consequences of elevated NF-κB, compromised S-nitrosylation and the development of hypernitrosylation and increased transcription of atherogenic miRNAs. These molecular aspects are then applied to neuroprogressive disorders by considering the following potential generators of endothelial dysfunction and activation in major depressive disorder, bipolar disorder and schizophrenia: NF-κB; platelet activation; atherogenic miRs; myeloperoxidase; xanthene oxidase and uric acid; and inflammation, oxidative stress, nitrosative stress and mitochondrial dysfunction.

CONCLUSIONS

Finally, on the basis of the above molecular mechanisms, details are given of potential treatment options for mitigating endothelial cell dysfunction and activation in neuroprogressive disorders.

Reduced plasma Fetuin-A is a promising biomarker of depression in the elderly

Depression affects 7% of the elderly population, and it often remains misdiagnosed or untreated. Peripheral biomarkers might aid clinicians by allowing more accurate and well-timed recognition of the disease. We sought to determine if plasma protein levels predict the severity of depressive symptomatology or distinguish patients from healthy individuals. The severity of depressive symptoms and global cognitive functioning were assessed by the Geriatric Depression Scale (GDS) and Mini-Mental State Examination (MMSE) in 152 elderly subjects, 76 of which with major depressive disorder (MDD). Plasma levels of 24 proteins were measured by multiplexing and analyzed as continuous predictors or dichotomized using the median value. The association between individual plasma proteins and MDD risk or depressive symptoms severity was investigated using multiple logistic and linear regressions including relevant covariates. Sensitivity analyses were performed excluding cognitively impaired individuals or non-acute patients with MDD. After adjusting for possible confounders and false discovery rate (FDR) correction, we found lower Fetuin-A levels in MDD patients vs. controls (p_FDR = 1.95 × 10^-6). This result was confirmed by the sensitivity and dichotomized analyses. Lower prolactin (PRL) levels predicted more severe depressive symptoms in acute MDD patients (p_FDR = 0.024). Fetuin-A is a promising biomarker of MDD in the elderly as this protein was negatively associated with the disorder in our sample, regardless of the global cognitive functioning. Lower PRL levels may be a peripheral signature of impaired neuroprotective processes and serotoninergic neurotransmission in more severely depressed patients.

The Discovery of Clinically Applicable Biomarkers for Bipolar Disorder: A Review of Candidate and Proteomic Approaches

Bipolar disorder (BD) is a severe psychiatric condition which affects innumerable people across the globe. The etiopathogenesis of BD is multi-faceted with genetic, environmental and psychosocial factors playing a role. Hitherto, the diagnosis and management of BD are purely on empirical grounds as we lack confirmed biomarkers for this condition. In this regard, hypothesis-driven investigations have been unable to identify clinically applicable biomarkers, steering the field towards newer technologies. Innovative, state-of-the-art techniques like multiplex immunoassays and mass spectrometry can potentially investigate the entire proteome. By detecting up or down regulated proteins, novel biomarkers are identified and new postulates about the etiopathogenesis of BD are specified. Hence, biological pathways are uncovered which are involved in the initiation and advancement of the disease and new therapeutic targets are identified. In this manuscript, the extant literature is thoroughly reviewed and the latest findings on candidate BD biomarkers are provided, followed by an overview of the proteomic approaches. It was found that due to the heterogeneous nature of BD no single biomarker is feasible, instead a panel of tests is more likely to be useful. With the application of latest technologies, it is expected that validated biomarkers will be discovered which will be useful as diagnostic tools and help in the delivery of individually tailored therapies to the patients.

Depression Heterogeneity and Its Biological Underpinnings: Toward Immunometabolic Depression

Epidemiological evidence indicates the presence of dysregulated homeostatic biological pathways in depressed patients, such as increased inflammation and disrupted energy-regulating neuroendocrine signaling (e.g., leptin, insulin). Alterations in these biological pathways may explain the considerable comorbidity between depression and cardiometabolic conditions (e.g., obesity, metabolic syndrome, diabetes) and represent a promising target for intervention. This review describes how immunometabolic dysregulations vary as a function of depression heterogeneity by illustrating that such biological dysregulations map more consistently to atypical behavioral symptoms reflecting altered energy intake/expenditure balance (hyperphagia, weight gain, hypersomnia, fatigue, and leaden paralysis) and may moderate the antidepressant effects of standard or novel (e.g., anti-inflammatory) therapeutic approaches. These lines of evidence are integrated in a conceptual model of immunometabolic depression emerging from the clustering of immunometabolic biological dysregulations and specific behavioral symptoms. The review finally elicits questions to be answered by future research and describes how the immunometabolic depression dimension could be used to dissect the heterogeneity of depression and potentially to match subgroups of patients to specific treatments with higher likelihood of clinical success.

The Association Between Vascular Inflammation and Depressive Disorder. Causality, Biomarkers and Targeted Treatment

Diabetes, obesity, atherosclerosis, and myocardial infarction are frequently co-morbid with major depressive disorder. In the current review, it is argued that vascular inflammation is a factor that is common to all disorders and that an endothelial dysfunction of the blood-brain barrier could be involved in the induction of depression symptoms. Biomarkers for vascular inflammation include a high plasma level of C-reactive protein, soluble cell-adhesion molecules, von Willebrand factor, aldosterone, and proinflammatory cytokines like interleukin-6 or tumor necrosis factor α. A further possible biomarker is flow-mediated dilation of the brachial artery. Treatment of vascular inflammation is expected to prevent or to reduce symptoms of depression. Several tentative treatments for this form of depression can be envisioned: eicosapentaenoic acid (EPA), valproate, Vagus-nerve stimulation, nicotinic α7 agonists, and agonists of the cannabinoid CB₂-receptor.

Comparison of serum protein profiles between major depressive disorder and bipolar disorder

BACKGROUND

Major depressive disorder and bipolar disorder are prevalent and debilitating psychiatric disorders that are difficult to distinguish, as their diagnosis is based on behavioural observations and subjective symptoms. Quantitative protein profile analysis might help to objectively distinguish between these disorders and increase our understanding of their pathophysiology. Thus, this study was conducted to compare the peripheral protein profiles between the two disorders.

METHODS

Serum samples were collected from 18 subjects with major depressive disorder and 15 subjects with bipolar disorder. After depleting abundant proteins, liquid chromatography-tandem mass spectrometry (LC-MS/MS) and label-free quantification were performed. Data-dependent acquisition data were statistically analysed from the samples of 15 subjects with major depressive disorder and 10 subjects with bipolar disorder who were psychotropic drug-free. Two-sided t-tests were performed for pairwise comparisons of proteomes to detect differentially-expressed proteins (DEPs). Ingenuity Pathway Analysis of canonical pathways, disease and functions, and protein networks based on these DEPs was further conducted.

RESULTS

Fourteen DEPs were significant between subjects with major depressive disorder and those with bipolar disorder. Ras-related protein Rab-7a (t = 5.975, p = 4.3 × 10^- 6) and Rho-associated protein kinase 2 (t = 4.782, p = 8.0 × 10^- 5) were significantly overexpressed in subjects with major depressive disorder and Exportin-7 (t = -4.520, p = 1.5 × 10^- 4) was significantly overexpressed in subjects with bipolar disorder after considering multiple comparisons. Bioinformatics analysis showed that cellular functions and inflammation/immune pathways were significantly different.

CONCLUSIONS

Ras-related protein Rab-7a, Rho-associated protein kinase 2, and Exportin-7 were identified as potential peripheral protein candidates to distinguish major depressive disorder and bipolar disorder. Further large sample studies with longitudinal designs and validation processes are warranted.

Proinflammatory Cytokines Predict Brain Metabolite Concentrations in the Anterior Cingulate Cortex of Patients With Bipolar Disorder

Bipolar disorder (BD) is a severe psychiatric illness characterized by abnormalities in the immune/inflammatory function and in brain metabolism. Evidences suggest that inflammation may affect the levels of brain metabolites as measured by single-proton magnetic resonance spectroscopy (¹H-MRS). The aim of the study was to investigate whether a wide panel of inflammatory markers (i.e., cytokines, chemokines, and growth factors) can predict brain metabolite concentrations of glutamate, myo-inositol, N-acetylaspartate, and glutathione in a sample of 63 bipolar patients and 49 healthy controls. Three cytokines influenced brain metabolite concentrations: IL-9 positively predicts glutamate, IL-1β positively predicts Myo-inositol, and CCL5 positively predicts N-acetylaspartate concentrations. Furthermore, patients showed higher concentrations of glutamate, Myo-inositol, and glutathione and lower concentrations of N-acetylaspartate in respect to healthy controls. Our results confirm that inflammation in BD alters brain metabolism, through mechanisms possibly including the production of reactive oxygen species and glia activation.

Integrating proteomic, sociodemographic and clinical data to predict future depression diagnosis in subthreshold symptomatic individuals

Individuals with subthreshold depression have an increased risk of developing major depressive disorder (MDD). The aim of this study was to develop a prediction model to predict the probability of MDD onset in subthreshold individuals, based on their proteomic, sociodemographic and clinical data. To this end, we analysed 198 features (146 peptides representing 77 serum proteins (measured using MRM-MS), 22 sociodemographic factors and 30 clinical features) in 86 first-episode MDD patients (training set patient group), 37 subthreshold individuals who developed MDD within two or four years (extrapolation test set patient group), and 86 subthreshold individuals who did not develop MDD within four years (shared reference group). To ensure the development of a robust and reproducible model, we applied feature extraction and model averaging across a set of 100 models obtained from repeated application of group LASSO regression with ten-fold cross-validation on the training set. This resulted in a 12-feature prediction model consisting of six serum proteins (AACT, APOE, APOH, FETUA, HBA and PHLD), three sociodemographic factors (body mass index, childhood trauma and education level) and three depressive symptoms (sadness, fatigue and leaden paralysis). Importantly, the model demonstrated a fair performance in predicting future MDD diagnosis of subthreshold individuals in the extrapolation test set (AUC = 0.75), which involved going beyond the scope of the model. These findings suggest that it may be possible to detect disease indications in subthreshold individuals up to four years prior to diagnosis, which has important clinical implications regarding the identification and treatment of high-risk individuals.

Major depression and enhanced molecular senescence abnormalities in young and middle-aged adults

Recent evidence suggests a significant overlap in biological changes between major depression and aging across the lifespan. We aim to evaluate the impact of a major depressive episode on the Senescence-Associated Secretory Phenotype (SASP) index, a dynamic secretory molecular pattern indicative of cellular senescence. We also tested the potential moderators of the association between major depression and the SASP index. We included 1165 young and middle-aged adults (527 with a current major depressive episode (cMDE) and 638 with no lifetime history of depression) from a community-based cohort from the Netherlands. We calculated the SASP index based on a previously developed composite index involving 19 biomarkers. cMDE had higher SASP index values (t₍₁₁₆₃₎ = 2.93, p = 0.003) compared to controls in the univariate analysis. After controlling for sociodemographic and somatic health covariates, there was no significant association between cMDE and SASP index (F_(1,1158) = 1.09, p = 0.29). Those with the most severe depressive episodes had significantly higher SASP indices compared to those with mild-to-moderate cMDE and controls (F_(2,1162) = 6.73, p = 0.001). We found a significant interaction between cMDE and overweight (F_(1,1164) = 5.1, p = 0.028): those with comorbid cMDE and overweight had the highest SASP index. Our study demonstrated a complex interaction between cMDE and medical morbidity, especially overweight, on the SASP index, suggesting that their coexistence aggravate age-related biological processes. Moreover, higher SASP index can be a biomarker for more severe depressive episodes.

The difference in serum proteomes in schizophrenia and bipolar disorder

BACKGROUND

Purpose of study is revealing significant differences in serum proteomes in schizophrenia and bipolar disorder (BD).

RESULTS

Quantitative mass-spectrometry based proteomic analysis was used to quantify proteins in the blood serum samples after the depletion of six major blood proteins. Comparison of proteome profiles of different groups revealed 27 proteins being specific for schizophrenia, and 18 - for BD. Protein set in schizophrenia was mostly associated with immune response, cell communication, cell growth and maintenance, protein metabolism and regulation of nucleic acid metabolism. Protein set in BD was mostly associated with immune response, regulating transport processes across cell membrane and cell communication, development of neurons and oligodendrocytes and cell growth. Concentrations of ankyrin repeat domain-containing protein 12 (ANKRD12) and cadherin 5 in serum samples were determined by ELISA. Significant difference between three groups was revealed in ANKRD12 concentration (p = 0.02), with maximum elevation of ANKRD12 concentration (median level) in schizophrenia followed by BD. Cadherin 5 concentration differed significantly (p = 0.035) between schizophrenic patients with prevailing positive symptoms (4.78 [2.71, 7.12] ng/ml) and those with prevailing negative symptoms (1.86 [0.001, 4.11] ng/ml).

CONCLUSIONS

Our results are presumably useful for discovering the new pathways involved in endogenous psychotic disorders.