Subtypes Of Depression Research Articles

Aberrant interhemispheric functional connectivity in major depressive disorder with and without anhedonia

ObjectiveAnhedonia is a core feature of major depressive disorder (MDD), and as a subtype of depression, MDD with anhedonia may have exceptional neurobiological mechanisms. However, the neuropathology of anhedonia in MDD remains unclear. Thus, this study aimed to investigate the brain functional differences between MDD with and without anhedonia.MethodsA total of 62 individuals including 22 MDD patients with anhedonia, 20 MDD patients without anhedonia, and 20 healthy controls (HCs) were recruited for this study. All participants underwent 3.0-T functional magnetic resonance imaging scan. Voxel-mirrored homotopic connectivity (VMHC) was employed to quantitatively describe bilateral functional connectivity. Analyses of variance (ANOVA) were performed to obtain brain regions with significant differences among three groups and then post hoc tests were calculated for inter-group comparisons.ResultsThe ANOVA revealed significant VMHC differences among three groups in the bilateral middle temporal gyrus (MTG), superior frontal gyrus (SFG), and inferior parietal lobule (IPL) (F = 10.47 ~ 15.09, p < 0.05, AlphaSim corrected). Relative to HCs, MDD with anhedonia showed significantly decreased VMHC in the bilateral MTG (t = -5.368, p < 0.05, AlphaSim corrected), as well as increased VMHC in the bilateral SFG (t = -4.696, p < 0.05, AlphaSim corrected). Compared to MDD without anhedonia, MDD with anhedonia showed significantly decreased VMHC in the bilateral MTG and IPL (t = -5.629 ~ -4.330, p < 0.05, AlphaSim corrected), while increased VMHC in the bilateral SFG (t = 3.926, p < 0.05, AlphaSim corrected). However, no significant difference was found between MDD without anhedonia and HCs.ConclusionThe present findings suggest that MDD with and without anhedonia exhibit different patterns of interhemispheric connectivity. Anhedonia in MDD is related to aberrant interhemispheric connectivity within brain regions involved in the frontal–temporal-parietal circuit.

Depressive symptoms and mortality-findings from Helsinki birth cohort study.

Individuals with depression and depressive symptoms have a higher mortality rate than non-depressed individuals. The increased comorbidity and mortality associated with depression has remained largely unexplained. The underlying pathophysiological differences between depressive subtypes, melancholic and non-melancholic, may provide some explanation to this phenomenon. One thousand nine hundred and ninety five participants (mean age 61 years) from the Helsinki Birth Cohort Study were recruited for this prospective study and followed up for a mean of 14.1 years. Information regarding medical history, lifestyle, and biochemical parameters were obtained. Depressive symptoms were assessed using the Beck Depression Inventory. Standardized mortality ratios were calculated. Participants were followed up for a total of 28,044 person-years. The melancholic depressive group had an increased adjusted risk of mortality [HR 1.49 (95% CI: 1.02-2.20)] when compared to the non-depressive group. Comparing mortality to the whole population of Finland using standardized mortality ratios (SMR) both the non-melancholic [1.11 (95% CI: 0.85-1.44)] and melancholic depressive [1.26 (95% CI: 0.87-1.81)] groups had higher mortality than the non-depressive group [0.82 (95% CI: 0.73-0.93)]. Melancholic depressive symptoms are most strongly related to a higher mortality risk.

Transcranial Direct Current Stimulation (tDCS) for Major Depressive Disorder

Of all home-based and wearable neuromodulation techniques currently in use for treatment of major depressive disorder (MDD), transcranial direct current stimulation (tDCS) has the greatest evidence and efficacy for antidepressant response. The potential of tDCS, given that it is safe, low-cost, portable, and easy to use, is promising. We review clinical trials and meta-analyses on the efficacy of tDCS for MDD, and discuss standard protocols, safety, and side effects. The use of tDCS in bipolar disorder is also explored. Clinicians should fully understand their patient's profile before recommending tDCS, as certain medications, depression subtypes, and age can influence treatment planning and response. [ Psychiatr Ann . 2022;52(11):451–455.]

Depression Assessment in the German National Cohort (NAKO)

Abstract Background The mental health status of populations (public mental health) and its effect on societies has gained considerable attention in recent years, especially during the current pandemic. The measurement of depressive symptoms is at core of the assessment of mental health. The detailedness of this assessment defines the range of public mental health problems that can be answered. Methods Between 2014 and 2019 the German National Cohort (NAKO) recruited 205,000 participants aged 20-70 years into the baseline examination in 18 study centers. Depression and depressive symptoms were assessed by different instruments including a lifetime diagnosis of depression and current treatment, the Major Depressive Disorder (MDD) section of the Mini-International-Neuropsychiatric-Interview (M.I.N.I. 5.0) and the depression scale of the Patient Health Questionnaire (PHQ-9). These instruments include different time periods and interpretations. Associations between these depression outcomes and age, gender and education are analysed in linear and logistic regression models. Results A lifetime physician's diagnosis of depression was reported by 14.7% of participants with considerable regional variation and almost 50% of this group received treatment within the last 12 months. Based on PHQ-9 7.9% of the participants were classified as depressive according to the dimensional assessment (score≥10) and 3% of them as having a major depression subtype. In contrast 32.8% screened positive based on the MINI and 15.4% of those receiving the full MINI had a diagnosis MDD. Associations with important socioeconomic determinants for these different depression outcomes will be reported. Conclusions The large NAKO sample size and the detailed assessment of depression symptoms and status enables the analysis of a broad range of public mental health questions. The analysis of depression frequencies and the distribution of depressive symptoms allow the establishment of population references.

Toward biophysical markers of depression vulnerability.

A major difficulty with treating psychiatric disorders is their heterogeneity: different neural causes can lead to the same phenotype. To address this, we propose describing the underlying pathophysiology in terms of interpretable, biophysical parameters of a neural model derived from the electroencephalogram. We analyzed data from a small patient cohort of patients with depression and controls. Using DCM, we constructed biophysical models that describe neural dynamics in a cortical network activated during a task that is used to assess depression state. We show that biophysical model parameters are biomarkers, that is, variables that allow subtyping of depression at a biological level. They yield a low dimensional, interpretable feature space that allowed description of differences between individual patients with depressive symptoms. They could capture internal heterogeneity/variance of depression state and achieve significantly better classification than commonly used EEG features. Our work is a proof of concept that a combination of biophysical models and machine learning may outperform earlier approaches based on classical statistics and raw brain data.

The use of antidepressants is linked to bone loss: A systematic review and metanalysis.

Depression and antidepressants are among risk factors for osteoporosis. However, there are still inconsistencies in literature regarding bone consequences of antidepressant drugs and the role of age and the natural decline of bone health in patients with depression. To investigate the relationship between antidepressant and bone mineral density (BMD). We conducted a systematic review and metanalysis according to PRISMA guidelines searching on PubMed/Medline, Cochrane Database, and Scopus libraries and registered with PROSPERO (registration number CRD42021254006) using generic terms for antidepressants and BMD. Search was restricted to English language only and without time restriction from inception up to June 2021. Methodological quality was assessed with the Newcastle-Ottawa scale. Eighteen papers were included in the qualitative analysis and five in the quantitative analysis. A total of 42,656 participants affected by different subtypes of depression were identified. Among the included studies, 10 used serotonin reuptake inhibitors (SSRIs) only, 6 involved the use of SSRIs and tricyclic antidepressants, and 2 the combined use of more than two antidepressants. No significant studies meeting the inclusion criteria for other most recent categories of antidepressants, such as vortioxetine and esketamine. Overall, we observed a significant effect of SSRI on decrease of BMD with a mean effect of 0.28 (95% CI = 0.08, 0.39). Our data suggest that SSRIs are associated with a decrease of BMD. We aim to raise clinicians' awareness of the potential association between the use of antidepressants and bone fragility to increase monitoring of bone health.

Multi-dimensional relationships among dementia, depression and prescribed drugs in England and Wales hospitals

Background Dementia is a group of symptoms that largely affects older people. The majority of patients face behavioural and psychological symptoms (BPSD) during the course of their illness. Alzheimer’s disease (AD) and vascular dementia (VaD) are two of the most prevalent types of dementia. Available medications provide symptomatic benefits and provide relief from BPSD and associated health issues. However, it is unclear how specific dementia, antidepressant, antipsychotic, antianxiety, and mood stabiliser drugs, used in the treatment of depression and dementia subtypes are prescribed in hospital admission, during hospital stay, and at the time of discharge. To address this, we apply multi-dimensional data analytical approaches to understand drug prescribing practices within hospitals in England and Wales.MethodsWe made use of the UK National Audit of Dementia (NAD) dataset and pre-processed the dataset. We evaluated the pairwise Pearson correlation of the dataset and selected key data features which are highly correlated with dementia subtypes. After that, we selected drug prescribing behaviours (e.g. specific medications at the time of admission, during the hospital stay, and upon discharge), drugs and disorders. Then to shed light on the relations across multiple features or dimensions, we carried out multiple regression analyses, considering the number of dementia, antidepressant, antipsychotic, antianxiety, mood stabiliser, and antiepileptic/anticonvulsant drug prescriptions as dependent variables, and the prescription of other drugs, number of patients with dementia subtypes (AD/VaD), and depression as independent variables.ResultsIn terms of antidepressant drugs prescribed in hospital admission, during stay and discharge, the number of sertraline and venlafaxine prescriptions were associated with the number of VaD patients whilst the number of mirtazapine prescriptions was associated with frontotemporal dementia patients. During admission, the number of lamotrigine prescriptions was associated with frontotemporal dementia patients, and with the number of valproate and dosulepin prescriptions. During discharge, the number of mirtazapine prescriptions was associated with the number of donepezil prescriptions in conjunction with frontotemporal dementia patients. Finally, the number of prescriptions of donepezil/memantine at admission, during hospital stay and at discharge exhibited positive association with AD patients.ConclusionOur analyses reveal a complex, multifaceted set of interactions among prescribed drug types, dementia subtypes, and depression.

The relationship between depressive symptoms and subtypes of mild cognitive impairment in post-menopausal women: Results from the Women's Health Initiative Memory Study.

Depressive symptoms are associated with age-related cognitive impairment, but the relative risk of specific subtypes of mild cognitive impairment (MCI) conferred by depressive symptoms is unclear. The purpose of this exploratory study was to determine the longitudinal association between baseline depressive symptoms and incident cases of MCI subtypes (amnestic vs. non-amnestic) and probable dementia (PD) (Alzheimer's disease, vascular, mixed) among postmenopausal women. Depressive symptoms were assessed at study baseline using an 8-item Burnam algorithm in 7043 postmenopausal women who participated in the Women's Health Initiative Memory Study (WHIMS) and the WHIMS-Epidemiology of Cognitive Health Outcomes (WHIMS-ECHO) extension study. During the median 9.4-year follow-up interval, the presence of MCI and PD was classified by a central adjudication committee. Classification of participants by MCI subtype (amnestic single and multi-domain, non-amnestic single and multi-domain) was done algorithmically based on established criteria using data from annual cognitive testing. At baseline, 557 women (7.9%) had clinically significant depressive symptoms based on Burnam algorithm cut-point of 0.06. Depressive symptoms at baseline were associated with an increased risk of incident amnestic MCI (hazard ratio [HR]=1.91, 95% confidence interval [CI] 1.32-2.78, p<0.0001), but not non-amnestic MCI (HR=1.39, 95% CI 0.91-2.14, p=0.13) after controlling for demographic factors. This relationship between depressive symptoms and amnestic MCI remained consistent after controlling for lifestyle variables, cardiovascular risk factors, antidepressant use, and history of hormone therapy. There were no significant associations between depressive symptoms and incidence ofPD. Depressive symptoms at baseline among postmenopausal older women are associated with higher incidence of amnestic MCI, suggesting that they may be an independent risk factor or part of the early prodrome of dementia.

Exploring the Reasons behind Perceived Severity of Modern and Traditional Types of Depression in an American Undergraduate Sample

Aim: A subtype of depression called Modern-type depression (MTD) is typically viewed as less severe compared to the Traditional-type depression (TTD). The purpose of this study was to explore the reasons behind the differences of perceptions when assessing the severity of MTD and TTD. Methods: After reading two vignettes describing an individual who is experiencing symptoms of MTD or TTD, participants (N = 300, Mage= 20.36) were asked to rate the severity of the depression for each and rank, in order of importance, a set of sentences in each vignette that served as reasons why they chose that rating. Results: Overall, TTD was more likely to be rated as severe than MTD. Those who perceived high severity in TTD relied on the information of the individual experiencing mental and physical consequences a high workload. Those who perceived high severity in MTD also relied on the same information of X suffering a high workload; however, those who perceived low severity in MTD relied more on the information of the individual complaining about the workload. Conclusion: These results indicate that perceptions of each disorder may in part come from which information people pay attention to. Future studies should explore the possibility of other factors, such as familiarity with mental health disorders or certain trauma experiences that could lead some individuals to process circumstances and situations within each depression to reduce stigma of mental disorders.

Depression and bipolar disorder subtypes differ in their genetic correlations with biological rhythms

Major Depression and Bipolar Disorder Type I (BIP-I) and Type II (BIP-II), are characterized by depressed, manic, and hypomanic episodes in which specific changes of physical activity, circadian rhythm, and sleep are observed. It is known that genetic factors contribute to variation in mood disorders and biological rhythms, but unclear to what extent there is an overlap between their underlying genetics. In the present study, data from genome-wide association studies were used to examine the genetic relationship between mood disorders and biological rhythms. We tested the genetic correlation of depression, BIP-I, and BIP-II with physical activity (overall physical activity, moderate activity, sedentary behaviour), circadian rhythm (relative amplitude), and sleep features (sleep duration, daytime sleepiness). Genetic correlations of depression, BIP-I, and BIP-II with biological rhythms were compared to discover commonalities and differences. A gene-based analysis tested for associations of single genes and common circadian genes with mood disorders. Depression was negatively correlated with overall physical activity and positively with sedentary behaviour, while BIP-I showed associations in the opposite direction. Depression and BIP-II had negative correlations with relative amplitude. All mood disorders were positively correlated with daytime sleepiness. Overall, we observed both genetic commonalities and differences across mood disorders in their relationships with biological rhythms: depression and BIP-I differed the most, while BIP-II was in an intermediate position. Gene-based analysis suggested potential targets for further investigation. The present results suggest shared genetic underpinnings for the clinically observed associations between mood disorders and biological rhythms. Research considering possible joint mechanisms may offer avenues for improving disease detection and treatment.

Latent class analysis of behavior across dog breeds reveal underlying temperament profiles

Latent class analysis (LCA) is a type of modeling analysis approach that has been used to identify unobserved groups or subgroups within multivariate categorical data. LCA has been used for a wide array of psychological evaluations in humans, including the identification of depression subtypes or PTSD comorbidity patterns. However, it has never been used for the assessment of animal behavior. Our objective here is to identify behavioral profile-types of dogs using LCA. The LCA was performed on a C-BARQ behavioral questionnaire dataset from 57,454 participants representing over 350 pure breeds and mixed breed dogs. Two, three, and four class LCA models were developed using C-BARQ trait scores and environmental covariates. In our study, LCA is shown as an effective and flexible tool to classify behavioral assessments. By evaluating the traits that carry the strongest relevance, it was possible to define the basis of these grouping differences. Groupings can be ranked and used as levels for simplified comparisons of complex constructs, such as temperament, that could be further exploited in downstream applications such as genomic association analyses. We propose this approach will facilitate dissection of physiological and environmental factors associated with psychopathology in dogs, humans, and mammals in general.

What is Computational Psychiatry Good For?

It is rare in the field of biological psychiatry for hypotheses to be definitively refuted. Rather, topics of investigation drift into and out of fashion, often driven by the initial excitement of technological innovation, followed by the necessary corrective of nuanced or underwhelming clinical results. A well-known example of this is the association between depression and abnormal function of the hypothalamic-pituitary-adrenal axis, as measured using the dexamethasone suppression test (DSST) ( 1 Carroll B.J. Martin F.I. Davies B. Resistance to suppression by dexamethasone of plasma 11-O.H.C.S. levels in severe depressive illness. Br Med J. 1968; 3: 285-287 Crossref PubMed Scopus (219) Google Scholar ). This observation led to a great deal of work investigating whether the association might help us identify useful subtypes of depression ( 2 Schatzberg A.F. Rothschild A.J. Stahl J.B. Bond T.C. Rosenbaum A.H. Lofgren S.B. et al. The dexamethasone suppression test: Identification of subtypes of depression. Am J Psychiatry. 1983; 140: 88-91 Crossref PubMed Scopus (124) Google Scholar ) or help us predict treatment response ( 3 Rush A.J. Weissenburger J. Vasavada N. Orsulak P.J. Fairchild C.J. Dexamethasone suppression test status does not predict differential response to nortriptyline versus amitriptyline. J Clin Psychopharmacol. 1988; 8: 421-425 Crossref PubMed Scopus (1) Google Scholar ). As it turned out, the specificity and predictive value of the DSST was not thought to be of a level that would be useful clinically and the topic has gradually moved out of the spotlight. We are left in the familiar position of knowing that nonsuppression of the DSST is associated with depression, but not being sure how we can use this knowledge to help patients.

Post-Partum Depression Lactating Rat Model for Evaluating Ketamine's Safety as a Pharmacotherapeutic Treatment: Roles in Cardiac and Urinary Function.

Depression is one of the world’s most common and mentally disabling illnesses. Post-partum depression is a subtype of depression that affects one in seven women worldwide. Successful pharmacological treatment must consider the consequences for both, since the mother–child bond is fundamental for the well-being of both mother and infant as well as the general development of the newborn. Changes in maternal physiology and/or behavior can significantly influence the development of breastfed infants. Ketamine has been extensively studied for use as an antidepressant due to its mixed mechanisms of action. Safety and efficacy studies in the cardiovascular and urinary systems of a lactating postpartum depression animal model are essential for contributing toward ketamine’s clinical use in the respective patient population. Thus, this project aimed to study the implications of postpartum maternal exposure to ketamine during lactation on the cardiovascular system of female rats submitted to the depression induction model by maternal separation. This model promotes depressive effects through stress caused by the interruption of mother–infant bond early in the offspring’s life. To achieve depression, each dam was separated from her offspring for 3 h per day, from post-natal day 2 (PND2) to PND12. Experimental groups received daily treatment with either 5, 10, or 20 mg/kg of ketamine intraperitoneally during the lactation period, from PND2 to PND21. Behavioral tests consisted of the maternal and aggressive maternal behavior tests, the olfactory preference test, and the forced swim test. A technique for the detection of catecholamines and indoleamines in the heart muscle was developed for the experimental model groups. The histopathological evaluation was performed on these animals’ cardiac muscles and urinary bladders. Our findings suggest that ketamine is safe for use in postpartum depression and does not induce cardiovascular and/or urinary systems toxicity.

Effort and Appetitive Responding in Depression: Examining Deficits in Motivational and Consummatory Stages of Reward Processing Using the Effort-Doors Task

BackgroundReward sensitivity is a dimensional construct central to understanding the nature of depression. Psychophysiological research on this construct has primarily focused on the reward positivity, an event-related potential (ERP) that indexes consummatory reward sensitivity. This study extended prior research by focusing on ERPs that index the motivational component of reward. MethodsA novel effort-for-reward task was used to elicit motivational and consummatory ERPs. Groups consisting of 34 participants with depression and 32 participants without depression were compared across a range of reward-related ERPs. ResultsParticipants with depression exhibited reduced responsivity to effort completion cues following high effort expenditure, reduced anticipation of rewards after low effort expenditure (i.e., the stimulus preceding negativity), and reduced reward positivity following high effort expenditure. ERPs occurring prior to reward receipt accounted for unique variance in depression status and differentiated between subgroups of depressed individuals. ConclusionsFindings support the utility of leveraging multiple ERPs that index separate reward processing deficits to better characterize depression and depressive subtypes.

A-171 Dissociable Dimensions of Multiple Sclerosis-Related Depression

Abstract Objective: Multiple sclerosis (MS) is associated with increased risk of depression and suicide. Preliminary literature suggests that sadness and anhedonic aspects of depression may dissociate in MS. Based on this, we hypothesized that there would be three subtypes of depression experienced by individuals with MS: primarily sad, primarily anhedonic, and mixed (i.e., elevated anhedonia and sadness), and that certain dimensions would be associated with higher degrees of suicidal ideation. Method: Participants included 117 patients with MS [Mean (Standard Deviation): age = 47.48 (10.21) years; 78.9% female; 97.3% White non-Hispanic; disease duration = 7.86 (8.30) years; Expanded Disability Status Scale: Median = 3, Range = 0 to 8.0]. Depression was measured using the Beck Depression Inventory-II (BDI-II). Patients’ level of mood symptoms ranged from non-depressed to severely depressed [10.26 (9.35)]. Results: Principle components analysis of BDI-II item-level responses indicated that symptoms in patients with MS dissociated into three dimensions accounting for 59% of variance in total. Dimension 1 included eight items related to negative cognitions, including pessimism, loss of hope and suicidal thoughts. Dimension 2 consisted of eight items characterized by vegetative symptoms and anhedonia. Dimension 3 was comprised of five items pertaining to sadness and emotion regulation issues. Conclusion(s): There appear to be three dimensions of depression experienced by adults with MS, and the negative cognitions dimension is most strongly related to suicidality, highlighting the importance of screening for this aspect of depression in individuals with MS.

Common Methods of Suicide and Self-Inflicted Poisoning/Injury by Individuals With Major Depressive Disorder and Bipolar Disorder: A Nationwide Inpatient Sample Analysis.

Objective: Individuals with major depressive disorder (MDD) and bipolar disorder (BD) are at increased risk of suicide. The objective of this study was to examine the prevalence of different methods of suicide and self-inflicted poisoning/injury in individuals with MDD or BD. Methods: This study used a US National Inpatient Sample dataset from years 2012–2015. Data were collected from discharge records of patients aged ≥ 18 years admitted for MDD or BPD. The prevalence of different methods of suicide and self-inflicted poisoning/injury was evaluated. Highly prevalent subtypes of depression and bipolar disorder among the 2 groups were investigated, and psychiatric comorbidities were compared. Results: We identified 13,556 unweighted records of patients admitted to the hospital for MDD diagnosis, and 6,506 unweighted records of patients admitted for BD diagnosis. Suicide and self-inflicted poisoning/injury by cutting instrument was highly prevalent among both groups, with a greater prevalence among patients with BD compared to MDD (35.5% vs 30.8%, P < .001). Use of tranquilizers/other psychotropic agents (29.2% vs 29.4%, P = .72) and other specified drugs and medicinal substances (14.3% vs 14.0%, P = .546) was high among both groups; however, there was no significant difference. Analgesics (16.7% vs 11%, P = .000), other sedatives and hypnotics (4.9% vs 3.7%, P < .001), other and unspecified solid and liquid substances (3.9% vs 3.3%, P = .037), and hanging by strangulation and suffocation (3.7% vs 2.5%, P < .001) were more prevalent in patients in the MDD group. Conclusions: The results indicate that it is critical to investigate and understand the methods used by individuals with MDD and BD to commit suicide. Restricting access to the most prevalent forms of self-injury could be a practical suicide prevention approach.

Resolving heterogeneity in depression using individualized structural covariance network analysis.

Elucidating individual aberrance is a critical first step toward precision medicine for heterogeneous disorders such as depression. The neuropathology of depression is related to abnormal inter-regional structural covariance indicating a brain maturational disruption. However, most studies focus on group-level structural covariance aberrance and ignore the interindividual heterogeneity. For that reason, we aimed to identify individualized structural covariance aberrance with the help of individualized differential structural covariance network (IDSCN) analysis. T1-weighted anatomical images of 195 first-episode untreated patients with depression and matched healthy controls (n = 78) were acquired. We obtained IDSCN for each patient and identified subtypes of depression based on shared differential edges. As a result, patients with depression demonstrated tremendous heterogeneity in the distribution of differential structural covariance edges. Despite this heterogeneity, altered edges within subcortical-cerebellum network were often shared by most of the patients. Two robust neuroanatomical subtypes were identified. Specifically, patients in subtype 1 often shared decreased motor network-related edges. Patients in subtype 2 often shared decreased subcortical-cerebellum network-related edges. Functional annotation further revealed that differential edges in subtype 2 were mainly implicated in reward/motivation-related functional terms. In conclusion, we investigated individualized differential structural covariance and identified that decreased edges within subcortical-cerebellum network are often shared by patients with depression. The identified two subtypes provide new insights into taxonomy and facilitate potential clues to precision diagnosis and treatment of depression.

TNF-α, IL-6 and hsCRP in patients with melancholic, atypical and anxious depression: an antibody array analysis related to somatic symptoms

BackgroundThe association between inflammation and major depressive disorder (MDD) remains poorly understood, given the heterogeneity of patients with MDD.AimsWe investigated inflammatory markers, such as interleukin (IL)-6, high-sensitivity C reactive protein...

Unsupervised Machine Learning to Identify Depressive Subtypes.

ObjectivesThis study evaluated an unsupervised machine learning method, latent Dirichlet allocation (LDA), as a method for identifying subtypes of depression within symptom data.MethodsData from 18,314 depressed patients were used to create LDA models. The outcomes included future emergency presentations, crisis events, and behavioral problems. One model was chosen for further analysis based upon its potential as a clinically meaningful construct. The associations between patient groups created with the final LDA model and outcomes were tested. These steps were repeated with a commonly-used latent variable model to provide additional context to the LDA results.ResultsFive subtypes were identified using the final LDA model. Prior to the outcome analysis, the subtypes were labeled based upon the symptom distributions they produced: psychotic, severe, mild, agitated, and anergic-apathetic. The patient groups largely aligned with the outcome data. For example, the psychotic and severe subgroups were more likely to have emergency presentations (odds ratio [OR] = 1.29; 95% confidence interval [CI], 1.17–1.43 and OR = 1.16; 95% CI, 1.05–1.29, respectively), whereas these outcomes were less likely in the mild subgroup (OR = 0.86; 95% CI, 0.78–0.94). We found that the LDA subtypes were characterized by clusters of unique symptoms. This contrasted with the latent variable model subtypes, which were largely stratified by severity.ConclusionsThis study suggests that LDA can surface clinically meaningful, qualitative subtypes. Future work could be incorporated into studies concerning the biological bases of depression, thereby contributing to the development of new psychiatric therapeutics.

Psychological strain and suicidal ideation in young university students during Covid-19 outbreak: the mediating roles of rumination and depression.

The link between psychological strain and suicidal ideation is well embedded in literature, however, the underlying mechanism is rarely explored. Therefore, this study aimed to examine the relationship between psychological strain, suicidal ideation, rumination, and depression among Pakistani young adults during the Covid-19 outbreak. In addition, it will also try to determine the mediating role of ruminative subtypes and depression in the relationship between psychological strain and suicidal ideation. The sample consists of 400 university students (83 males and 289 females) with the age range from 18 to 30 years (M = 22.25, SD = 2.40). Psychological strain scale, Suicidal Ideation Scale, Beck Depression Inventory, and Ruminative Response Scale were administered to the participants. Results indicated a significant and positive association between the study variables. Psychological strain positively influenced suicidal ideation both directly, and indirectly via brooding and depression. Both variables were found to be serially mediated by the brooding subtype of rumination and depression and not with the reflective subtype. Strain theory of suicide and response style theory also postulated important insights regarding the association of these variables in young adults, among which, strain is serially related to first brooding and then depression, which is eventually related to suicidal ideation. The present study makes a significant contribution to the literature by providing a unique conceptual and theoretical framework to identify suicide at the ideation level before it proceeds to behavior during this unprecedented time of the pandemic outbreak.