Diagnostic Issues of Depressive Disorders from Kraepelinian Dualism to the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition

The clinical utility and relevance in clinical practice of DSM-5 specifiers for major depressive disorder: A Delphi expert consensus study

Major depressive disorder (MDD) is a heterogeneous syndrome, associated with different levels of severity and impairment on the personal functioning for each patient. Classification systems in psychiatry, including ICD-11 and DSM-5, are used by clinicians in order to simplify the complexity of clinical manifestations. In particular, the DSM-5 introduced specifiers, subtypes, severity ratings, and cross-cutting symptom assessments allowing clinicians to better describe the specific clinical features of each patient. However, the use of DSM-5 specifiers for major depressive disorder in ordinary clinical practice is quite heterogeneous. The present study, using a Delphi method, aims to evaluate the consensus of a representative group of expert psychiatrists on a series of statements regarding the clinical utility and relevance of DSM-5 specifiers for major depressive disorder in ordinary clinical practice. Experts reached an almost perfect agreement on statements related to the use and clinical utility of DSM-5 specifiers in ordinary clinical practice. In particular, a complete consensus was found regarding the clinical utility for ordinary clinical practice of using DSM-5 specifiers. The use of specifiers is considered a first step toward a "dimensional" approach to the diagnosis of mental disorders.

Gender difference in the relationship between clinical symptoms, thyroid hormones, and metabolic parameters in young, first-episode and drug-naïve major depressive disorder patients with suicide attempts: A network analysis perspective

BACKGROUND

Suicide attempts are one of the most serious comorbidities in patients with major depressive disorder (MDD), and the prevalence of suicide attempts is higher in younger people compared to older people, with significant gender differences. This study aimed to investigate the relationship between suicide attempts, clinical symptoms, thyroid hormones, and metabolic parameters in young first-episode and drug-naïve (FEND) MDD patients of different genders.

METHODS

A total of 1289 FEND MDD patients were recruited. Depression, anxiety, and psychotic symptoms were assessed using the Hamilton Depression Rating Scale (HAMD), Hamilton Anxiety Rating Scale (HAMA), and the Positive and Negative Syndrome Scale (PANSS) positive subscale, respectively. Thyroid hormones and glucolipid metabolism indicators were also tested. Network analysis was employed to delineate the interplay between thyroid dysfunction, clinical symptoms, and metabolic disorders.

RESULTS

Among young FEND MDD patients, the rate of suicide attempts was 17.4% in males and 19.8% in females, showing no significant gender difference in the incidence of suicide attempts (χ2 = 1.06, p = 0.303). In the network model, PANSS positive subscale (Expected Influence = 0.578) and HAMD scores (Expected Influence = 0.576) were identified as the individual symptoms that most affected male patients, whereas TSH (Thyroid-Stimulating Hormone) (Expected Influence = 0.972) and PANSS positive subscale (Expected Influence = 0.937) were identified as the individual symptoms that most affected female patients. In addition, we found that TSH (Expected Influence = 0.438) was a pivotal node connecting metabolic disturbances and clinical symptoms.

CONCLUSION

Our findings emphasize the important role of psychotic symptoms in young MDD patients with suicide attempts. Moreover, our results highlight the pivotal role of serum TSH levels in the pathophysiology of young female MDD patients with suicide attempts.

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.

Machine Learning-Based Definition of Symptom Clusters and Selection of Antidepressants for Depressive Syndrome

The current polythetic and operational criteria for major depression inevitably contribute to the heterogeneity of depressive syndromes. The heterogeneity of depressive syndrome has been criticized using the concept of language game in Wittgensteinian philosophy. Moreover, “a symptom- or endophenotype-based approach, rather than a diagnosis-based approach, has been proposed” as the “next-generation treatment for mental disorders” by Thomas Insel. Understanding the heterogeneity renders promise for personalized medicine to treat cases of depressive syndrome, in terms of both defining symptom clusters and selecting antidepressants. Machine learning algorithms have emerged as a tool for personalized medicine by handling clinical big data that can be used as predictors for subtype classification and treatment outcome prediction. The large clinical cohort data from the Sequenced Treatment Alternatives to Relieve Depression (STAR*D), Combining Medications to Enhance Depression Outcome (CO-MED), and the German Research Network on Depression (GRND) have recently began to be acknowledged as useful sources for machine learning-based depression research with regard to cost effectiveness and generalizability. In addition, noninvasive biological tools such as functional and resting state magnetic resonance imaging techniques are widely combined with machine learning methods to detect intrinsic endophenotypes of depression. This review highlights recent studies that have used clinical cohort or brain imaging data and have addressed machine learning-based approaches to defining symptom clusters and selecting antidepressants. Potentially applicable suggestions to realize machine learning-based personalized medicine for depressive syndrome are also provided herein.

Patients with first-episode untreated schizophrenia who experience concomitant visual disturbances and auditory hallucinations exhibit co-impairment of the brain and retinas-a pilot study

There are limited structural brain and retina alteration data from schizophrenia patients who experience visual disturbances (VDs) with or without auditory hallucinations (AHs). We compared brain and retina alterations between first-episode untreated schizophrenia patients with VDs (FUSCH-VDs) with versus without AHs, and between patients and healthy controls (HCs)(N = 30/group). VDs, AHs, gray matter volumes (GMVs), and retinal thicknesses were evaluated with the Bonn Scale for Assessment of Basic Symptoms (BSABS) scale, the Auditory Hallucinations Rating Scale (AHRS), magnetic resonance imaging (MRI), and optical coherence tomography (OCT), respectively. Compared to HCs, FUSCH-VDs had reduced GMVs, mainly in dorsal V3/V3A and V5 regions, the fusiform gyrus, and ventral V4 and V8 regions. Most FUSCH-VDs (85.0%; 51/60) had primary visual cortex-retina co-impairments. FUSCH-VDs with AHs had more serious and larger scope GMV reductions than FUSCH-VDs without AHs. FUSCH-VDs with AHs had significant retinal thickness reductions compared to HCs. Primary visual cortex-retina co-impairments were found to be more common, and more pronounced when present, in FUSCH-VDs with AHs than in FUSCH-VDs without AHs. The present findings support the notion that VDs and AHs may have reciprocal deteriorating actions in patients with schizophrenia.

An alternative approach to future diagnostic standards for major depressive disorder

During the period extending from 1780 to 1880, the conceptualization of melancholia changed from an intellectual to a mood model. The modern view of depression, based on Kraepelinian dualism, has reflected changes in opinion on psychiatric taxonomy of individual melancholia. From the point of view of an "operational revolution," the diagnostic criteria for major depressive disorder in the Diagnostic and Statistical Manual of Mental Disorders, 3rd edition (DSM-III) were based on a neoKraepelinian approach rooted in disease essentialism. In the revision process from the DSM-IV to the DSM-5, a combined dimensional and categorial approach was used. In the DSM-5, the diagnostic criteria for major depressive disorder are polythetic and operational in approach reflecting the heterogeneity of major depressive disorder. Although 227 different symptom combinations fulfilling the diagnostic criteria for major depressive disorder can be theoretically calculated, certain symptom combinations are more prevalent than others in real clinical situations. The heterogeneity of these operational criteria for major depressive disorder have been criticized in a manner informed by the Wittgensteinian analogy of the language game. Herein, our network analysis proposes a novel perspective on the psychopathology of major depressive disorder. The novel approach suggested here may lay the foundation for a redefinition of the traditional taxonomy of depression.

Challenges and Strategies for Current Classifications of Depressive Disorders: Proposal for Future Diagnostic Standards

The Diagnostic and Statistical Manual of Mental Disorder, Fourth Edition (DSM-IV) was revised based on a combination of a categorical and a dimensional approach such that in the DSM, Fifth Edition (DSM-5), depressive disorders have been separated as a distinctive disease entity from bipolar disorders, consistent with the deconstruction of Kraepelinian dualism. Additionally, the diagnostic thresholds of depressive disorders may be reduced due to the addition of "hopelessness" to the subjective descriptors of depressed mood and the removal of the "bereavement exclusion." Manic/hypomanic, psychotic, and anxious symptoms in major depressive disorder (MDD) and other depressive disorders are described using the transdiagnostic specifiers of "with mixed features," "with psychotic features," and "with anxious distress," respectively. Additionally, due to the polythetic and operational characteristics of the DSM-5 diagnostic criteria, the heterogeneity of MDD is inevitable. Thus, 227 different symptom combinations fulfill the DSM-5 diagnostic criteria for MDD. This heterogeneity of MDD is criticized in view of the Wittgensteinian analogy of language game. Depression subtypes determined by disturbances in monoamine levels and the severity of the disease have been identified in the literature. According to a review of the Gottesman and Gould criteria, neuroticism, morning cortisol, cortisol awakening response, asymmetry in frontal cortical activity on electroencephalography (EEG), and probabilistic reward learning, among other variables, are evidenced as endophenotypes for depressive disorders. Network analysis has been proposed as a potential method to compliment the limitations of current diagnostic criteria and to explore the pathways between depressive symptoms, as well as to identify novel and interesting relationships between depressive symptoms. Based on the literature on network analysis in this field, no differences in the centrality index of the DSM and non-DSM symptoms were repeatedly present among patients with MDD. Furthermore, MDD and other depressive syndromes include two of the Research Domain Criteria (RDoC), including the Loss construct within the Negative Valence Systems domains and various Reward constructs within the Positive Valence Systems domain.

Network Analysis of the Symptoms of Depressive Disorders Over the Course of Therapy: Changes in Centrality Measures

OBJECTIVE

Network analysis can be used in terms of a novel psychopathological approach for depressive syndrome. We aimed to estimate the successive network structures of depressive symptoms in patients with depressive disorder using data from the Clinical Research Center for Depression study.

METHODS

We enrolled 1,152 South Korean adult patients with depressive disorders who were beginning treatment for first-onset or recurrent depressive episodes. We examined the network structure of the severities of the items on the Hamilton Depression Rating Scale (HAMD) at baseline and at weeks 2, 12, 25, and 52. The node strength centrality of all the HAMD items at baseline and at week 2, 12, 25, and 52 in terms of network analysis.

RESULTS

In the severity networks, the anxiety (psychic) item was the most centrally situated in the initial period (baseline and week 2), while loss of weight was the most centrally situated item in the later period (weeks 25 and 52). In addition, the number of strong edges (i.e., edges representing strong correlations) increased in the late period compared to the initial period.

CONCLUSION

Our findings support a period-specific and symptom-focused therapeutic approach that can provide complementary information to the unidimensional total HAMD score.

The Centrality of Depression and Anxiety Symptoms in Major Depressive Disorder Determined Using a Network Analysis

BACKGROUND

Comorbid anxiety symptoms are highly prevalent and closely linked with poorer treatment outcomes, chronicity, and hospitalization in major depressive disorder (MDD). Our study aimed to estimate the network of depression and anxiety symptoms that was developed based on a sample of MDD patients.

METHODS

We constructed a network of the 21 Beck Depression Inventory (BDI) symptoms and 21 Beck Anxiety Inventory (BAI) symptoms in 223 patients with MDD who were beginning psychiatric treatment. In addition, each of the depression and anxiety symptoms was considered to be an ordered categorical variable ranging in value from 0 to 3.

RESULTS

The three depression symptoms including loss of energy, loss of interest, and worthlessness and the seven anxiety symptoms including faintness or lightheadedness, feeling of choking, feeling scared, fear of the worst happening, nervousness, inability to relax, and feeling shaky were identified as the ten most central nodes within a network of depression and anxiety symptoms. The inter-connection between irritability and nervousness was a strong trans-diagnostic edge within the network of depression and anxiety symptoms.

LIMITATIONS

Because our study was designed in a cross-sectional manner, the networks were estimated undirectionally.

CONCLUSIONS

Our findings show that depression symptoms are not more central than anxiety symptoms within an estimated network structure of symptoms in patients with MDD. Moreover, the inter-connection between irritability and nervousness may suggests a probable trans-diagnostic association in MDD symptomatology.

Network analysis of the depressive symptom profiles in Asian patients with depressive disorders: Findings from the Research on Asian Psychotropic Prescription Patterns for Antidepressants (REAP-AD)

AIM

We aimed to estimate the network structures of depressive symptoms using network analysis and evaluated the geographic regional differences in theses network structures among Asian patients with depressive disorders.

METHODS

Using data from the Research on Asian Psychotropic Prescription Patterns for Antidepressants (REAP-AD), the network of the ICD-10 diagnostic criteria for depressive episode was estimated from 1174 Asian patients with depressive disorders. The node strength centrality of all ICD-10 diagnostic criteria for a depressive episode was estimated using a community-detection algorithm. In addition, networks of depressive symptoms were estimated separately among East Asian patients and South or Southeast Asian patients. Moreover, networks were estimated separately among Asian patients from high-income countries and those from middle-income countries.

RESULTS

Persistent sadness, fatigue, and loss of interest were the most centrally situated within the network of depressive symptoms in Asian patients with depressive disorders overall. A community-detection algorithm estimated that when excluding psychomotor disturbance as an outlier, the other nine symptoms formed the largest clinically meaningful cluster. Geographic and economic variations in networks of depressive symptoms were evaluated.

CONCLUSION

Our findings demonstrated that the typical symptoms of the ICD-10 diagnostic criteria for depressive episode are the most centrally situated within the network of depressive symptoms. Furthermore, our findings suggested that cultural influences related to geographic and economic distributions of participants could influence the estimated depressive symptom network in Asian patients with depressive disorders.

Symptom-based selection of antidepressants

Most evidence-based pharmacological guidelines recommend selective serotonin reuptake inhibitors, serotoninnorepinephrine reuptake inhibitors, norepinephrine-dopamine reuptake inhibitors or norepinephrine and specific serotonin antidepressants as the first-line treatment for major depression. Since the clinical factors associated with treating patients with depression are relatively complex, it can be challenging to apply the recommendations of evidence-based medicine verbatim. Furthermore, the diagnostic criteria of major depressive disorders, which are defined in a polythetic and operational manner, inevitably result in their heterogeneity. Studies have inferred that depressive syndrome may be connected with “family resemblance” rather than being shared with a neurobiological essence. Therefore, the symptom-based selection of antidepressants can be supported by a network analysis that provides a novel perspective on the symptom structure of major depression. The symptom-based treatment algorithm suggests treatment options that can be applied to the symptoms that are included in and excluded from the diagnosis criteria of major depressive disorder. The symptom-based selection of antidepressants and other psychotropic agents involves matching the deconstructed symptoms of depression to the specific neuroanatomical regions and neurotransmitters. This ensures timely and optimized treatment options for patients with depression.