Major depressive disorder: biomarkers and biosensors

Advances in biosensors for major depressive disorder diagnostic biomarkers

Depression is one of the most common mental disorders and is mainly characterized by low mood or lack of interest and pleasure. It can be accompanied by varying degrees of cognitive and behavioral changes and may lead to suicide risk in severe cases. Due to the subjectivity of diagnostic methods and the complexity of patients' conditions, the diagnosis of major depressive disorder (MDD) has always been a difficult problem in psychiatry. With the discovery of more diagnostic biomarkers associated with MDD in recent years, especially emerging non-coding RNAs (ncRNAs), it is possible to quantify the condition of patients with mental illness based on biomarker levels. Point-of-care biosensors have emerged due to their advantages of convenient sampling, rapid detection, miniaturization, and portability. After summarizing the pathogenesis of MDD, representative biomarkers, including proteins, hormones, and RNAs, are discussed. Furthermore, we analyzed recent advances in biosensors for detecting various types of biomarkers of MDD, highlighting representative electrochemical sensors. Future trends in terms of new biomarkers, new sample processing methods, and new detection modalities are expected to provide a complete reference for psychiatrists and biomedical engineers.

Establishing a research agenda for the study and assessment of opioid withdrawal

The opioid crisis is an international public health concern. Treatments for opioid use disorder centre largely on the management of opioid withdrawal, an aversive collection of signs and symptoms that contribute to opioid use disorder. Whereas in the past 50 years more than 90 medications have been developed for depression, only five medications have been developed for opioid use disorder during this period. We posit that underinvestment has occurred in part due to an underdeveloped understanding of opioid withdrawal syndrome. This Personal View summarises substantial gaps in our understanding of opioid withdrawal that are likely to continue to limit major advancements in its treatment. There is no firm consensus in the field as to how withdrawal should be precisely defined; 10-550 symptoms of withdrawal can be measured on 18 scales. The imprecise understanding of withdrawal is likely to result in overestimating or underestimating the severity of an individual's withdrawal syndrome or potential therapeutic effects of different candidate medications. The severity of the opioid crisis is not remitting, and an international research agenda for the study and assessment of opioid withdrawal is necessary to support transformational changes in withdrawal management and treatment of opioid use disorder. Nine actionable targets are delineated here: develop a consensus definition of opioid withdrawal; understand withdrawal symptomatology after exposure to different opioids (particularly fentanyl); understand precipitated opioid withdrawal; understand how co-exposure of other drugs (eg, xylazine and stimulants) influences withdrawal expression; examine individual variation in withdrawal phenotypes; precisely characterise the protracted withdrawal syndrome; identify biomarkers of opioid withdrawal severity; identify predictors of opioid withdrawal severity; and understand which symptoms are most closely associated with treatment attrition or relapse. The US Food and Drug Administration recently established a formal indication for opioid withdrawal that has invigorated interest in drug development for opioid withdrawal management. Action is now needed to support these interests and help industry identify new classes of medications so that real change can be achieved for people with opioid use disorder.

Omics approaches open new horizons in major depressive disorder: from biomarkers to precision medicine

Major depressive disorder (MDD) is a recurrent episodic mood disorder that represents the third leading cause of disability worldwide. In MDD, several factors can simultaneously contribute to its development, which complicates its diagnosis. According to practical guidelines, antidepressants are the first-line treatment for moderate to severe major depressive episodes. Traditional treatment strategies often follow a one-size-fits-all approach, resulting in suboptimal outcomes for many patients who fail to experience a response or recovery and develop the so-called "therapy-resistant depression". The high biological and clinical inter-variability within patients and the lack of robust biomarkers hinder the finding of specific therapeutic targets, contributing to the high treatment failure rates. In this frame, precision medicine, a paradigm that tailors medical interventions to individual characteristics, would help allocate the most adequate and effective treatment for each patient while minimizing its side effects. In particular, multi-omic studies may unveil the intricate interplays between genetic predispositions and exposure to environmental factors through the study of epigenomics, transcriptomics, proteomics, metabolomics, gut microbiomics, and immunomics. The integration of the flow of multi-omic information into molecular pathways may produce better outcomes than the current psychopharmacological approach, which targets singular molecular factors mainly related to the monoamine systems, disregarding the complex network of our organism. The concept of system biomedicine involves the integration and analysis of enormous datasets generated with different technologies, creating a "patient fingerprint", which defines the underlying biological mechanisms of every patient. This review, centered on precision medicine, explores the integration of multi-omic approaches as clinical tools for prediction in MDD at a single-patient level. It investigates how combining the existing technologies used for diagnostic, stratification, prognostic, and treatment-response biomarkers discovery with artificial intelligence can improve the assessment and treatment of MDD.

Multi-branch attention Raman network and surface-enhanced Raman spectroscopy for the classification of neurological disorders

Surface-enhanced Raman spectroscopy (SERS), a rapid, low-cost, non-invasive, ultrasensitive, and label-free technique, has been widely used in-situ and ex-situ biomedical diagnostics questions. However, analyzing and interpreting the untargeted spectral data remains challenging due to the difficulty of designing an optimal data pre-processing and modelling procedure. In this paper, we propose a Multi-branch Attention Raman Network (MBA-RamanNet) with a multi-branch attention module, including the convolutional block attention module (CBAM) branch, deep convolution module (DCM) branch, and branch weights, to extract more global and local information of characteristic Raman peaks which are more distinctive for classification tasks. CBAM, including channel and spatial aspects, is adopted to enhance the distinctive global information on Raman peaks. DCM is used to supplement local information of Raman peaks. Autonomously trained branch weights are applied to fuse the features of each branch, thereby optimizing the global and local information of the characteristic Raman peaks for identifying diseases. Extensive experiments are performed for two different neurological disorders classification tasks via untargeted serum SERS data. The results demonstrate that MBA-RamanNet outperforms commonly used CNN methods with an accuracy of 88.24% for the classification of healthy controls, mild cognitive impairment, Alzheimer's disease, and Non-Alzheimer's dementia; an accuracy of 90% for the classification of healthy controls, elderly depression, and elderly anxiety.

Effects of different chronic restraint stress periods on anxiety- and depression-like behaviors and tryptophan-kynurenine metabolism along the brain-gut axis in C57BL/6N mice

Chronic restraint stress (CRS) is a widely used stimulus to induce anxiety- and depression-like behaviors, linked to alterations in tryptophan-kynurenine (TRP-KYN) metabolism in animals. This study assessed the effects of different CRS periods on anxiety- or depression-like behaviors and TRP-KYN metabolism along brain-gut axis in C57BL/6N mice. Results showed that one-week CRS decreased the open arm entries of mice in elevated plus maze and delayed latency of feeding in novelty suppressed feeding test. Four-week CRS reduced sucrose preference, increases forced swimming immobility time, and also induced anxiety-like behaviors of mice. UPLC-MS/MS analysis revealed decreased levels of the neurotoxic 3-hydroxykynurenine (3-HK) and quinolinic acid (QA), and an increase in the neuroprotective kynurenic acid (KA) in the hippocampus of one-week CRS mice; meanwhile, four-week CRS mice displayed a reduction in KA and increases in 3-HK and QA. In the colon, both one-week and four-week CRS mice exhibited significant reductions in 3-HK and QA, with a marked increase of KA exclusively in four-week CRS mice. Briefly, one-week CRS only induced anxiety-like behaviors with hippocampal neuroprotection in TRP-KYN metabolism, whereas four-week CRS caused anxiety- and depression-like behaviors with neurotoxicity. In the colon, during both CRS periods, KYN was metabolized in the direction of NAD+ production. However, four-week CRS triggered intestinal inflammation risk with increased KA. Summarily, slightly short-term stress has beneficial effects on mice, while prolonged chronic stress can lead to pathological changes. This study offers valuable insights into stress-induced emotional disturbances.

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.

Biomarkers and optical based biosensors in cardiac disease detection: early and accurate diagnosis

Rapid and precise detection methods for the early-stage detection of cardiovascular irregularities are crucial to stopping and reducing their development. Cardiovascular diseases (CVDs) are the leading cause of death in the world. Hence, cardiac-related biomarkers are essential for monitoring and managing of process. The necessity for biomarker detection has significantly widened the field of biosensor development. Bio-sensing methods offer rapid detection, low cost, sensitivity, portability, and selectivity in the development of devices for biomarker detection. For the prediction of cardiovascular diseases, some biomarkers can be used, like C-reactive protein (CRP), troponin I or T, creatine kinase (CK-MB), B-type natriuretic peptide (BNP), myoglobin (Mb), suppression of tumorigenicity 2 protein (ST2) and galectin-3 (Gal3). In this review, recent research studies were covered for gaining insight into utilizing optical-based biosensors, including surface plasmon resonance (SPR), photonic crystals (PCs), fluorescence-based techniques, fiber optics, and also Raman spectroscopy biosensors for the ultrasensitive detection of cardiac biomarkers. The main goal of this review is to focus on the improvement of optical biosensors in the future for the diagnosis of heart diseases and to discuss how to enhance their properties for use in medicine. Some main data from each study reviewed are emphasized, including the CVD biomarkers and the response range of the optical-based devices and biosensors.

Apolipoprotein D as a Potential Biomarker in Neuropsychiatric Disorders

Neuropsychiatric disorders (NDs) are a diverse group of pathologies, including schizophrenia or bipolar disorders, that directly affect the mental and physical health of those who suffer from them, with an incidence that is increasing worldwide. Most NDs result from a complex interaction of multiple genes and environmental factors such as stress or traumatic events, including the recent Coronavirus Disease (COVID-19) pandemic. In addition to diverse clinical presentations, these diseases are heterogeneous in their pathogenesis, brain regions affected, and clinical symptoms, making diagnosis difficult. Therefore, finding new biomarkers is essential for the detection, prognosis, response prediction, and development of new treatments for NDs. Among the most promising candidates is the apolipoprotein D (Apo D), a component of lipoproteins implicated in lipid metabolism. Evidence suggests an increase in Apo D expression in association with aging and in the presence of neuropathological processes. As a part of the cellular neuroprotective defense machinery against oxidative stress and inflammation, changes in Apo D levels have been demonstrated in neuropsychiatric conditions like schizophrenia (SZ) or bipolar disorders (BPD), not only in some brain areas but in corporal fluids, i.e., blood or serum of patients. What is not clear is whether variation in Apo D quantity could be used as an indicator to detect NDs and their progression. This review aims to provide an updated view of the clinical potential of Apo D as a possible biomarker for NDs.

Potential Inflammatory Biomarkers for Major Depressive Disorder Related to Suicidal Behaviors: A Systematic Review

Major depressive disorder (MDD) is a highly prevalent psychiatric condition affecting an estimated 280 million individuals globally. Despite the occurrence of suicidal behaviors across various psychiatric conditions, MDD is distinctly associated with the highest risk of suicide attempts and death within this population. In this study, we focused on MDD to identify potential inflammatory biomarkers associated with suicidal risk, given the relationship between depressive states and suicidal ideation. Articles published before June 2023 were searched in PubMed, Embase, Web of Science, and the Cochrane Library to identify all relevant studies reporting blood inflammatory biomarkers in patients with MDD with suicide-related behaviors. Of 571 articles, 24 were included in this study. Overall, 43 significant biomarkers associated with MDD and suicide-related behaviors were identified. Our study provided compelling evidence of significant alterations in peripheral inflammatory factors in MDD patients with suicide-related behaviors, demonstrating the potential roles of interleukin (IL)-1β, IL-6, C-reactive protein, C-C motif chemokine ligand 2, and tumor necrosis factor-α as biomarkers. These findings underscore the intricate relationship between the inflammatory processes of these biomarkers and their interactions in MDD with suicidal risk.