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Bipolar Disorders May 2023Bipolar depression accounts for most of the disease duration in type I and type II bipolar disorder (BD), with few treatment options, often poorly tolerated. Many...
Treating bipolar depression with esketamine: Safety and effectiveness data from a naturalistic multicentric study on esketamine in bipolar versus unipolar treatment-resistant depression.
BACKGROUND
Bipolar depression accounts for most of the disease duration in type I and type II bipolar disorder (BD), with few treatment options, often poorly tolerated. Many individuals do not respond to first-line therapeutic options, resulting in treatment-resistant bipolar depression (B-TRD). Esketamine, the S-enantiomer of ketamine, has recently been approved for treatment-resistant depression (TRD), but no data are available on its use in B-TRD.
OBJECTIVES
To compare the efficacy of esketamine in two samples of unipolar and bipolar TRD, providing preliminary indications of its effectiveness in B-TRD. Secondary outcomes included the evaluation of the safety and tolerability of esketamine in B-TRD, focusing on the average risk of an affective switch.
METHODS
Thirty-five B-TRD subjects treated with esketamine nasal spray were enrolled and compared with 35 TRD patients. Anamnestic data and psychometric assessments (Montgomery-Asberg Depression Rating Scale/MADRS, Hamilton-depression scale/HAM-D, Hamilton-anxiety scale/HAM-A) were collected at baseline (T0), at one month (T1), and three months (T2) follow up.
RESULTS
A significant reduction in depressive symptoms was found at T1 and T2 compared to T0, with no significant differences in response or remission rates between subjects with B-TRD and TRD. Esketamine showed a greater anxiolytic action in subjects with B-TRD than in those with TRD. Improvement in depressive symptoms was not associated with treatment-emergent affective switch.
CONCLUSIONS
Our results supported the effectiveness and tolerability of esketamine in a real-world population of subjects with B-TRD. The low risk of manic switch in B-TRD patients confirmed the safety of this treatment.
Topics: Humans; Antidepressive Agents; Bipolar Disorder; Ketamine; Depression; Depressive Disorder, Treatment-Resistant
PubMed: 36636839
DOI: 10.1111/bdi.13296 -
Turk Psikiyatri Dergisi = Turkish... 2021Dear Editor, The chapter on mental, behavioural and neurodevelopmental disorders of the 11th revision of the International Classification of Diseases and Related Health...
Dear Editor, The chapter on mental, behavioural and neurodevelopmental disorders of the 11th revision of the International Classification of Diseases and Related Health Problems (ICD-11) has been now finalized. Reporting of health statistics by Member States to the World Health Organization (WHO) using the new diagnostic system will begin in 2022. The section on mood disorders of the ICD-11 is overall consistent with the corresponding section of the ICD-10. However, the definitions of a depressive and a manic episode have been slightly changed, making them consistent with the DSM-5 (see below), and an independent category of bipolar II disorder has been introduced. A significant effort has been made by the WHO and the American Psychiatric Association to harmonize the diagnostic systems they produce (the ICD-11 and the DSM-5). Indeed, the organizational framework ("metastructure") is now the same in the two systems. Nonetheless, several intentional differences between the two classifications remain, or have emerged as a consequence of changes made in the DSM- 5. Here we briefly summarize the convergences and the divergences between the ICD-11 and the DSM-5 regarding the section on mood disorders (see Table 1). A major convergence between the two diagnostic systems regards the minimum number of symptoms required for the diagnosis of major depression ("depressive episode" in the ICD-11). In the ICD-11, contrary to the ICD-10, the threshold for the diagnosis of depression is the same as in the DSM: at least five depressive symptoms. However, the ICD-11 requires at least five symptoms out of a list of ten (instead of nine as in the DSM-5). The additional symptom is "hopelessness", which has been found to outperform more than half of DSM symptoms in differentiating depressed from non-depressed people (McGlinchey et al. 2006). Table 1. Some Main Differences Between ICD-10, ICD-11 and DSM-5 Concerning the Diagnosis Of Mood Disorders ICD-10 ICD-11 DSM-5 Threshold for diagnosis of depressive episode At least four out of ten symptoms, two of which must be depressed mood, loss of interest and enjoyment, or increased fatigability At least five out of ten symptoms, one of which must be depressed mood or diminished interest or pleasure At least five out of nine symptoms, one of which must be depressed mood or diminished interest or pleasure The threshold for the diagnosis of depression is higher if the person is bereaved Not made explicit Yes No Antidepressant-related mania qualifies as a manic episode No Yes Yes Mixed episode is a separate diagnostic entity Yes Yes No Dysthymia is a separate diagnostic entity Yes Yes No Bipolar II disorder is a separate diagnostic entity No Yes Yes "Qualifiers" ("specifiers") for the diagnoses of mood disorders are provided No Yes Yes CONVERGENCES AND DIVERGENCES IN THE ICD-11 VS. DSM-5 CLASSIFICATION OF MOOD DISORDERS 294 The ICD-11 is also following the DSM-5 in requiring the presence of increased activity or a subjective experience of increased energy, in addition to euphoria (or irritability or expansiveness), for the diagnosis of a manic episode, in order to reduce the chance of false positive cases. The two diagnostic systems also converge in considering that a manic or hypomanic syndrome arising during antidepressant treatment, and enduring beyond the known physiological effects of that treatment, qualifies as a manic or hypomanic episode. Bipolar II disorder has become an independent category in the ICD-11 (it was just mentioned as an example of "other bipolar affective disorders" in the ICD-10). Furthermore, for the first time, the ICD follows the DSM in introducing "qualifiers" (corresponding to DSM-5 "specifiers") to the diagnoses of mood disorders, based on specific aspects of symptomatology or course. There are, however, three important aspects in which the two diagnostic systems diverge. All of them are a consequence of changes made in the DSM-5 that the relevant ICD-11 Committee has regarded as not sufficiently supported by the available research evidence. The first of these divergences concerns the issue of bereavement. In the ICD-11, in line with the DSM-IV and ICD-10 approach, it is stated that "a depressive episode should not be considered if the depressive symptoms are consistent with the normative response for grieving within the individual's religious and cultural context". However, the diagnosis of depression is not excluded if the person is bereaved; the diagnostic threshold is just raised, exactly as it happens in ordinary clinical practice. A depressive episode during bereavement is suggested by the persistence of symptoms for at least one month, and the presence of at least one symptom which is unlikely to occur in normal grief (such as extreme beliefs of low self-worth or guilt not related to the lost loved one, presence of psychotic symptoms, suicidal ideation, or psychomotor retardation). In contrast, the special status conferred by the DSM-IV to bereavement among life stressors has been eliminated in the DSM-5. However, two independent follow-up studies (Mojtabai 2011, Wakefield and Schmitz 2012) have reported that, in people with baseline bereavement-related depression, the risk for the occurrence of a further depressive episode during follow-up is significantly lower than in individuals with baseline non-bereavement-related depression, and not significantly different from the risk of people without a baseline history of depression to develop a first depressive episode during follow-up. This research evidence strongly supports the ICD-11 (and DSM-IV) approach. Furthermore, an intensive public debate has highlighted the consequences that the DSM-5 approach to the bereavement issue could have in several cultures, including a high rate of false positives and a trivialization of the concept of depression and consequently of mental disorder (Kleinman 2012). A second divergence between the ICD-11 and DSM-5 sections on mood disorders concerns mixed states. The category of mixed episode is kept in the ICD-11, defined by several prominent manic and depressive symptoms which either occur simultaneously or alternate very rapidly (from day to day or within the same day) during a period of at least two weeks. The mood state is altered throughout the episode (i.e., the mood should be depressed, dysphoric, euphoric or expansive for at least two weeks). When depressive symptoms predominate, common contrapolar symptoms are irritability, racing or crowded thoughts, increased talkativeness, and increased activity. When manic symptoms predominate, common contrapolar symptoms are dysphoric mood, expressed beliefs of worthlessness, hopelessness, and suicidal ideation. This definition is in line with the ICD-10 and completely consistent with both classic and recent research evidence, as well as with clinical experience. In contrast, the DSM-5 solution to eliminate the category of mixed episode and to introduce a specifier "with mixed features", applicable to manic, hypomanic and depressive episodes, has had the consequence to reduce the visibility of "mixity" in ordinary clinical practice (especially since the specifier is not codable, and is therefore at risk of not being recorded in clinical settings). Moreover, the DSM-5 definition of major depression with mixed features, requiring the presence of at least three "classic" manic symptoms (such as elevated mood, grandiosity, and increased involvement in risky activities) has been criticized for being inconsistent with the concept of mixed depression as delineated in both the classic and recent literature (e.g., Koukopoulos and Sani 2014). A third divergence between the two diagnostic systems consists in the fact that the ICD-11 has not followed the DSM-5 in combining dysthymic disorder and chronic major depressive disorder into a single category ("persistent depressive disorder"). In fact, the relevant ICD-11 Committee expert considered that the evidence that the two disorders represent the same condition, to be addressed therapeutically in the same way, is insufficient. The category of dysthymic disorder is kept in the ICD-11, while a qualifier "current episode persistent" is to be used when the diagnostic requirements for depressive episode have been met continuously for at least the past two years. For a discussion of other aspects of the classification of mood disorders, with the relevant therapeutic implications, as well as for information about the differences between the ICD-11 and the DSM-5 concerning other sections of the classification of mental disorders, we refer the reader to previous contributions (Demyttenaere et al. 2015, Fried et al. 2016, Haroz et al. 2017, Boschloo et al. 2019, Bryant 2019, Forbes et al. 2019, Fusar-Poli et al. 2019, Gureje et al. 2019, 295 Received: 13.09.2021, Accepted: 19.09.2021, Available Online Date: 30.11.2021 MD., University of Campania L. Vanvitelli, WHO Collaborating Centre for Research and Training in Mental Health, Naples, Italy. Dr. Arcangelo Di Cerbo, e-mail: [email protected] https://doi.org/10.5080/u26899 Reed et al. 2019, Kendall 2019, van Os et al. 2019, Cuijpers et al. 2020, Fava and Guidi 2020, Gaebel et al. 2019, 2020, Hasler 2020, Jarrett 2020, Kato et al. 2020, Maj et al. 2020, Reynolds 2020, Sanislow 2020, Stein et al. 2020). An International Advisory Group has been established to supervise the activities of translation, training of professionals and implementation of the ICD-11 chapter on mental disorders (see Giallonardo 2019, Pocai 2019, Perris 2020). The experience in the field will tell whether the above divergences from the DSM-5 in the ICD-11 classification of mood disorders are justified. Indeed, divergences in the description of the same mental health condition may sometimes be useful in order to allow the empirical comparison of different approaches to issues that are controversial. Arcangelo DI CERBO REFERENCES Boschloo L, Bekhuis E, Weitz ES et al (2019) The symptom-specific efficacy of antidepressant medication vs. cognitive behavioral therapy in the treatment of depression: results from an individual patient data meta-analysis. World Psychiatry 18:183-91. Bryant RA (2019) Post-traumatic stress disorder: a state-of-the-art review of evidence and challenges. World Psychiatry 18:259-69. Cuijpers P, Noma H, Karyotaki E et al (2020) A network meta-analysis of the effects of psychotherapies, pharmacotherapies and their combination in the treatment of adult depression. World Psychiatry 19:92-107. Demyttenaere K, Donneau AF, Albert A et al (2015) What is important in being cured from depression? Discordance between physicians and patients (1). J Affect Disord 174:390-6. Fava GA, Guidi J (2020) The pursuit of euthymia. World Psychiatry 19:40-50. Fried EI, Epskamp S, Nesse RM et al (2016) What are "good" depression symptoms? Comparing the centrality of DSM and non-DSM symptoms of depression in a network analysis. J Affect Disord 189:314-20. Forbes MK, Wright AGC, Markon KE et al (2019) The network approach to psychopathology: promise versus reality. World Psychiatry 18:272-3. Fusar-Poli P, Solmi M, Brondino N et al (2019) Transdiagnostic psychiatry: a systematic review. World Psychiatry 8:192-207. Gaebel W, Reed GM, Jakob R (2019) Neurocognitive disorders in ICD-11: a new proposal and its outcome. World Psychiatry 18:232-3. Gaebel W, Stricker J, Riesbeck M et al (2020) Accuracy of diagnostic classification and clinical utility assessment of ICD-11 compared to ICD-10 in 10 mental disorders: findings from a web-based field study. Eur Arch Psychiatry Clin Neurosci 270:281-9. Giallonardo V (2019) ICD-11 sessions within the 18th World Congress of Psychiatry. World Psychiatry 18:115-6. Gureje O, Lewis-Fernandez R, Hall BJ et al (2019) Systematic inclusion of culture-related information in ICD-11. World Psychiatry 18:357-8. Haroz EE, Ritchey M, Bass JK et al (2017) How is depression experienced around the world? A systematic review of qualitative literature. Soc Sci Med 183:151-62. Hasler G (2020) Understanding mood in mental disorders. World Psychiatry 19:56-7. Jarrett RB (2020) Can we help more? World Psychiatry 19:246-7. Kato TA, Kanba S, Teo AR (2020) Defining pathological social withdrawal: proposed diagnostic criteria for hikikomori. World Psychiatry 19:116-7. Kendall T (2019) Outcomes help map out evidence in an uncertain terrain, but they are relative. World Psychiatry 18:293-5. Kleinman A (2012) Culture, bereavement, and psychiatry. Lancet 379:608-9. Koukopoulos A, Sani G (2014) DSM-5 criteria for depression with mixed features: a farewell to mixed depression. Acta Psychiatr Scand 129:4-16. Kotov R, Jonas KG, Carpenter WT et al (2020) Validity and utility of Hierarchical Taxonomy of Psychopathology (HiTOP): I. Psychosis superspectrum. World Psychiatry 19:151-72. Maj M, Stein DJ, Parker G et al (2020) The clinical characterization of the adult patient with depression aimed at personalization of management. World Psychiatry 19:269-93. McGlinchey JB, Zimmerman M, Young D et al (2006) Diagnosing major depressive disorder VIII. Are some symptoms better than others? J Nerv Ment Dis 194:785-90. Mojtabai R (2011) Bereavement-related depressive episodes: characteristics, 3-year course, and implications for the DSM-5. Arch Gen Psychiatry 68:920-8. Perris F (2020) ICD-11 sessions at the 19th World Congress of Psychiatry. World Psychiatry 19:263-4. Pocai B (2019) The ICD-11 has been adopted by the World Health Assembly. World Psychiatry 18:371-2. Reed GM, First MB, Kogan CS et al (2019) Innovations and changes in the ICD-11 classification of mental, behavioural and neurodevelopmental disorders. World Psychiatry 18:3-19. Reynolds CF 3rd (2020) Optimizing personalized management of depression: the importance of real-world contexts and the need for a new convergence paradigm in mental health. World Psychiatry 19:266-8. Sanislow CA (2020) RDoC at 10: changing the discourse for psychopathology. World Psychiatry 19:311-2. Stein DJ, Szatmari P, Gaebel W et al (2020) Mental, behavioural and neurodevelopmental disorders in the OCD-11: an international perspective on key changes and controversies. BMC Med 18:21. van Os J, Guloksuz S, Vijn TW et al (2019) The evidence-based group-level symptom-reduction model as the organizing principle for mental health care: time for change? World Psychiatry 18:88-96. Wakefield JC, Schmitz MF (2012) Recurrence of bereavement-related depression: evidence for the validity of the DSM-IV bereavement exclusion from the Epidemiologic Catchment Area Study. J Ment Dis 200:480-5.
Topics: Adult; Depressive Disorder, Major; Diagnostic and Statistical Manual of Mental Disorders; Humans; International Classification of Diseases; Mood Disorders; Phobia, Social; Shame
PubMed: 34964106
DOI: 10.5080/u26899 -
Canadian Journal of Psychiatry. Revue... Apr 2020
Topics: Bipolar Disorder; Cannabidiol; Cannabinoid Receptor Modulators; Humans; Mood Disorders
PubMed: 31830820
DOI: 10.1177/0706743719895195 -
Trends in Pharmacological Sciences Oct 2023Mood disorders account for a significant global disease burden, and pharmacological innovation is needed as existing medications are suboptimal. A wide range of evidence... (Review)
Review
Mood disorders account for a significant global disease burden, and pharmacological innovation is needed as existing medications are suboptimal. A wide range of evidence implicates circadian and sleep dysfunction in the pathogenesis of mood disorders, and there is growing interest in these chronobiological pathways as a focus for treatment innovation. We review contemporary evidence in three promising areas in circadian-clock-based therapeutics in mood disorders: targeting the circadian system informed by mechanistic molecular advances; time-tailoring of medications; and personalizing treatment using circadian parameters. We also consider the limitations and challenges in accelerating the development of new circadian-informed pharmacotherapies for mood disorders.
Topics: Humans; Mood Disorders; Circadian Clocks; Biology
PubMed: 37648611
DOI: 10.1016/j.tips.2023.07.008 -
Translational Psychiatry May 2021Affective disorders are a group of neuropsychiatric disorders characterized by severe mood dysregulations accompanied by sleep, eating, cognitive, and attention... (Review)
Review
Affective disorders are a group of neuropsychiatric disorders characterized by severe mood dysregulations accompanied by sleep, eating, cognitive, and attention disturbances, as well as recurring thoughts of suicide. Clinical studies consistently show that affective disorders are associated with reduced size of brain regions critical for mood and cognition, neuronal atrophy, and synaptic loss in these regions. However, the molecular mechanisms that mediate these changes and thereby increase the susceptibility to develop affective disorders remain poorly understood. MicroRNAs (miRNAs or miRs) are small regulatory RNAs that repress gene expression by binding to the 3'UTR of mRNAs. They have the ability to bind to hundreds of target mRNAs and to regulate entire gene networks and cellular pathways implicated in brain function and plasticity, many of them conserved in humans and other animals. In rodents, miRNAs regulate synaptic plasticity by controlling the morphology of dendrites and spines and the expression of neurotransmitter receptors. Furthermore, dysregulated miRNA expression is frequently observed in patients suffering from affective disorders. Together, multiple lines of evidence suggest a link between miRNA dysfunction and affective disorder pathology, providing a rationale to consider miRNAs as therapeutic tools or molecular biomarkers. This review aims to highlight the most recent and functionally relevant studies that contributed to a better understanding of miRNA function in the development and pathogenesis of affective disorders. We focused on in vivo functional studies, which demonstrate that miRNAs control higher brain functions, including mood and cognition, in rodents, and that their dysregulation causes disease-related behaviors.
Topics: Animals; Gene Regulatory Networks; Humans; MicroRNAs; Mood Disorders; Neuronal Plasticity; RNA, Messenger
PubMed: 33941769
DOI: 10.1038/s41398-021-01379-7 -
Trends in Neurosciences Nov 2020Mood and anxiety disorders are complex heterogeneous syndromes that manifest in dysfunctions across multiple brain regions, cell types, and circuits. Biomarkers using... (Review)
Review
Mood and anxiety disorders are complex heterogeneous syndromes that manifest in dysfunctions across multiple brain regions, cell types, and circuits. Biomarkers using brain-wide activity patterns in humans have proven useful in distinguishing between disorder subtypes and identifying effective treatments. In order to improve biomarker identification, it is crucial to understand the basic circuitry underpinning brain-wide activity patterns. Leveraging a large repertoire of techniques, animal studies have examined roles of specific cell types and circuits in driving maladaptive behavior. Recent advances in multiregion recording techniques, data-driven analysis approaches, and machine-learning-based behavioral analysis tools can further push the boundary of animal studies and bridge the gap with human studies, to assess how brain-wide activity patterns encode and drive emotional behavior. Together, these efforts will allow identifying more precise biomarkers to enhance diagnosis and treatment.
Topics: Affect; Animals; Anxiety; Anxiety Disorders; Biomarkers; Brain; Humans; Mood Disorders
PubMed: 32917408
DOI: 10.1016/j.tins.2020.08.004 -
Journal of Neural Transmission (Vienna,... Sep 2023Mood disorders such as major depressive disorder (MDD) and bipolar disorder (BD) are often resistant to current pharmacological treatment. Therefore, various alternative... (Review)
Review
Mood disorders such as major depressive disorder (MDD) and bipolar disorder (BD) are often resistant to current pharmacological treatment. Therefore, various alternative therapeutic approaches including diets are, therefore, under investigation. Ketogenic diet (KD) is effective for treatment-resistant epilepsy and metabolic diseases, however, only a few clinical studies suggest its beneficial effect also for mental disorders. Animal models are a useful tool to uncover the underlying mechanisms of therapeutic effects. Women have a twice-higher prevalence of mood disorders but very little is known about sex differences in nutritional psychiatry. In this review, we aim to summarize current knowledge of the sex-specific effects of KD in mood disorders. Ketone bodies improve mitochondrial functions and suppress oxidative stress, inducing neuroprotective and anti-inflammatory effects which are both beneficial for mental health. Limited data also suggest KD-induced improvement of monoaminergic circuits and hypothalamus-pituitary-adrenal axis-the key pathophysiological pathways of mood disorders. Gut microbiome is an important mediator of the beneficial and detrimental effects of diet on brain functioning and mental health. Gut microbiota composition is affected in mood disorders but its role in the therapeutic effects of different diets, including KD, remains poorly understood. Still little is known about sex differences in the effects of KD on mental health as well as on metabolism and body weight. Some animal studies used both sexes but did not find differences in behavior, body weight loss or gut microbiota composition. More studies, both on a preclinical and clinical level, are needed to better understand sex-specific effects of KD on mental health.
Topics: Animals; Female; Male; Diet, Ketogenic; Depressive Disorder, Major; Bipolar Disorder; Epilepsy; Models, Animal
PubMed: 36943505
DOI: 10.1007/s00702-023-02620-x -
Depression and Anxiety Mar 2021There is consistent evidence that mood disorders often co-occur with anxiety disorders, however, the strength of the association of these two broad groups of disorders... (Meta-Analysis)
Meta-Analysis Review
There is consistent evidence that mood disorders often co-occur with anxiety disorders, however, the strength of the association of these two broad groups of disorders has been challenging to summarize across different studies. The aim was to conduct a meta-analysis of publications reporting on the pairwise comorbidity between mood and anxiety disorders after sorting into comparable study types. We searched MEDLINE, Embase, CINAHL, Web of Science, and the grey literature for publications between 1980 and 2017 regardless of geographical locations and languages. We meta-analyzed estimates from original articles after sorting by: (a) broad or narrow diagnostic criteria, (b) study time-frame, and (c) estimates with or without covariate adjustments. Over 43 000 unique studies were identified through electronic searches, of which 391 were selected for full-text review. Finally, 171 studies were eligible for inclusion, including 53 articles from additional snowball searching. In general, regardless of variations in diagnosis type, study time-frame, temporal order, or use of adjustments, there was substantial comorbidity between mood and anxiety disorders. Based on the entire 90 separate meta-analyses, the median OR was 6.1 (range 1.5-18.7). Of these estimates, all 90 were above 1, and 87 were significantly greater than 1 (i.e., the 95% confidence intervals did not include 1). Fourteen of the 90 pooled estimates had ORs that were greater than 10. This systematic review found robust and consistent evidence of comorbidity between broadly defined mood and anxiety disorders. Clinicians should be vigilant for the prompt identification and treatment of this common type of comorbidity.
Topics: Affect; Anxiety Disorders; Comorbidity; Humans; Mood Disorders; Morbidity
PubMed: 33225514
DOI: 10.1002/da.23113 -
Journal of the American Academy of... Feb 2021To identify the most appropriate threshold for disruptive mood dysregulation disorder (DMDD) diagnosis and the impact of potential changes in diagnostic rules on...
OBJECTIVE
To identify the most appropriate threshold for disruptive mood dysregulation disorder (DMDD) diagnosis and the impact of potential changes in diagnostic rules on prevalence levels in the community.
METHOD
Trained psychologists evaluated 3,562 preadolescents/early adolescents from the 2004 Pelotas Birth Cohort with the Development and Well-Being Behavior Assessment (DAWBA). The clinical threshold was assessed in 3 stages: symptomatic, syndromic, and clinical operationalization. The symptomatic threshold identified the response category in each DAWBA item, which separates normative misbehavior from a clinical indicator. The syndromic threshold identified the number of irritable mood and outbursts needed to capture preadolescents/early adolescents with high symptom levels. Clinical operationalization compared the impact of AND/OR rules for combining irritable mood and outbursts on impairment and levels of psychopathology.
RESULTS
At the symptomatic threshold, most irritable mood items were normative in their lowest response categories and clinically significant in their highest response categories. For outbursts, some indicated a symptom even when present at only a mild level, while others did not indicate symptoms at any level. At the syndromic level, a combination of 2 out of 7 irritable mood and 3 out of 8 outburst indicators accurately captured a cluster of individuals with high level of symptoms. Analysis combining irritable mood and outbursts delineated nonoverlapping aspects of DMDD, providing support for the OR rule in clinical operationalization. The best DMDD criteria resulted in a prevalence of 3%.
CONCLUSION
Results provide information for initiatives aiming to provide data-driven and clinically oriented operationalized criteria for DMDD.
Topics: Adolescent; Attention Deficit and Disruptive Behavior Disorders; Humans; Irritable Mood; Mood Disorders; Prevalence; Problem Behavior
PubMed: 32004697
DOI: 10.1016/j.jaac.2019.12.008 -
Ugeskrift For Laeger Apr 2021Alcohol use disorders (AUD) often occur together with other psychiatric disorders such as affective disorders, anxiety disorders, personality disorders, drug use... (Review)
Review
Alcohol use disorders (AUD) often occur together with other psychiatric disorders such as affective disorders, anxiety disorders, personality disorders, drug use disorders, attention deficit and hyperkinetic disorder, post-traumatic stress disorder and psychosis. This psychiatric comorbidity is a global health problem and often not recognised and successfully treated. Increased awareness of possible AUD among psychiatric patients is needed, e.g. by use of the screening tool The Alcohol Use Disorder Identification Test, which is described in this review.
Topics: Alcoholism; Anxiety Disorders; Comorbidity; Humans; Mood Disorders; Stress Disorders, Post-Traumatic; Substance-Related Disorders
PubMed: 33832553
DOI: No ID Found