The identification of symptom-based subtypes of depression: A nationally representative cohort study

Symptoms and prevalence of common mental disorders in a heterogenous outpatient sample: an investigation of clinical characteristics and latent subgroups

BACKGROUND

Patient-reported outcome measures (PROM) provide clinicians with information about patients' perceptions of distress. When linked with treatment and diagnostic registers, new information on common mental health disorders (CMHD) and service use, may be obtained, which might be useful clinically and for policy decision-making. This study reports the prevalence of CMHD and their association with PROM severity. Further, subgroups of self-reported symptoms of depression and anxiety were examined, and their association with clinician-assessed mental disorders, functional impairment, and service use.

METHODS

In a cohort study of 2473 (63% female) outpatients, CMHD was examined with pre-treatment scores of self-reported depression and anxiety, and the number of assessments and psychotherapy appointments one year after treatment start. Factor mixture modelling (FMM) of anxiety and depression was used to examine latent subgroups.

RESULTS

Overall, 22% of patients with a CMHD had an additional comorbid mood/anxiety disorder, making the prevalence lower than expected. This comorbid group reported higher symptoms of anxiety and depression compared to patients with non-comorbid disorders. FMM revealed three classes: "anxiety and somatic depression" (33%), "mixed depression and anxiety" (40%), and "cognitive depression" (27%). The anxiety and somatic depression class was associated with older age, being single and on sick leave, higher probability of depressive-, anxiety-, and comorbid disorders, having more appointments and higher functional impairment. Although the cognitive depression class had less somatic distress than the mixed depression and anxiety class, they reported more functional impairment and had higher service use.

CONCLUSION

The results show that higher levels of somatic symptoms of depression could both indicate higher and lower levels of functional impairment and service use. A group of patients with high somatic depression and anxiety was identified, with severe impairment and high service needs. By gaining insights into CMHD factors' relation with clinical covariates, self-reported risk factors of depression and anxiety could be identified for groups with different levels of aggravating life circumstances, with corresponding service needs. These could be important symptom targets in different groups of patients.

Trace Elements Levels in Major Depressive Disorder-Evaluation of Potential Threats and Possible Therapeutic Approaches

The multifactorial etiology of major depressive disorder (MDD) includes biological, environmental, genetic, and psychological aspects. Recently, there has been an increasing interest in metallomic studies in psychiatry, aiming to evaluate the role of chosen trace elements in the MDD etiology as well as the progression of symptoms. This narrative review aims to summarize the available literature on the relationship between the concentration of chosen elements in the serum of patients with MDD and the onset and progression of this psychiatric condition. The authors reviewed PubMed, Web of Science, and Scopus databases searching for elements that had been investigated so far and further evaluated them in this paper. Ultimately, 15 elements were evaluated, namely, zinc, magnesium, selenium, iron, copper, aluminium, cadmium, lead, mercury, arsenic, calcium, manganese, chromium, nickel, and phosphorus. The association between metallomic studies and psychiatry has been developing dynamically recently. According to the results of current research, metallomics might act as a potential screening tool for patients with MDD while at the same time providing an assessment of the severity of symptoms. Either deficiencies or excessive amounts of chosen elements might be associated with the progression of depressive symptoms or even the onset of the disease among people predisposed to MDD.

Subgroups of depressive symptoms in Korean police officers: A latent class analysis

The prevalence of depressive symptoms is common among police officers; however, studies that identify the patterns of depressive symptoms in police officers and occupational characteristics related to the specific subgroups of depressive symptoms are scarce. A total of 493 police officers in South Korea participated in this descriptive cross-sectional study between October and December 2019. Depressive symptoms were measured using the Patient Health Questionnaire-9. Latent class analysis was used to identify the subgroups of depressive symptoms. To identify the characteristics and predictors of the subgroup, χ2 tests, analysis of variance, and multinomial logistic regression analysis were performed. Four latent classes of depressive symptoms were identified: "at-risk" (10.8%), "anhedonic" (21.5%), "somatic" (17.2%), and "minimal" (50.5%). Compared to the minimal group, drinking behaviors were higher in the at-risk group (odds ratio [OR] = 1.10, 95% confidence interval [CI] [1.03, 1.11]), and working hours were greater in the somatic group (OR = 1.01, 95% CI [1.00, 1.02]). Additionally, sleep quality (OR = 1.35, 95% CI [0.82, 2.22]) and fatigue (OR = 1.02, 95% CI [1.00, 1.04]) were found to be related in the anhedonic group. This study identified the heterogeneity of depressive symptoms among police officers. It is necessary to accurately identify the factors associated with the depression subgroups of police officers to develop support strategies and prevent an increase in their depression severity. The association between risk factors such as working hours and drinking behaviors might inform strategies to reduce depression in police offers.

Elevated serum leptin is associated with attenuated reward anticipation in major depressive disorder independent of peripheral C-reactive protein levels

Major depressive disorder (MDD) is associated with immunologic and metabolic alterations linked to central processing dysfunctions, including attenuated reward processing. This study investigated the associations between inflammation, metabolic hormones (leptin, insulin, adiponectin), and reward-related brain processing in MDD patients with high (MDD-High) and low (MDD-Low) C-reactive protein (CRP) levels compared to healthy comparison subjects (HC). Participants completed a blood draw and a monetary incentive delay task during functional magnetic resonance imaging. Although groups did not differ in insulin or adiponectin concentrations, both MDD-High (Wilcoxon p = 0.004, d = 0.65) and MDD-Low (Wilcoxon p = 0.046, d = 0.53) showed higher leptin concentrations than HC but did not differ from each other. Across MDD participants, higher leptin levels were associated with lower brain activation during reward anticipation in the left insula (r = - 0.30, p = 0.004) and left dorsolateral putamen (r = -- 0.24, p = 0.025). In contrast, within HC, higher leptin concentrations were associated with higher activation during reward anticipation in the same regions (insula: r = 0.40, p = 0.007; putamen: r = 0.37, p = 0.014). Depression may be characterized by elevated pro-inflammatory signaling via leptin concentrations through alternate inflammatory pathways distinct to CRP.

Course of Subtypes of Late-Life Depression Identified by Bipartite Network Analysis During Psychosocial Interventions

Importance

Approximately half of older adults with depression remain symptomatic at treatment end. Identifying discrete clinical profiles associated with treatment outcomes may guide development of personalized psychosocial interventions.

Objective

To identify clinical subtypes of late-life depression and examine their depression trajectory during psychosocial interventions in older adults with depression.

Design, Setting, and Participants

This prognostic study included older adults aged 60 years or older who had major depression and participated in 1 of 4 randomized clinical trials of psychosocial interventions for late-life depression. Participants were recruited from the community and outpatient services of Weill Cornell Medicine and the University of California, San Francisco, between March 2002 and April 2013. Data were analyzed from February 2019 to February 2023.

Interventions

Participants received 8 to 14 sessions of (1) personalized intervention for patients with major depression and chronic obstructive pulmonary disease, (2) problem-solving therapy, (3) supportive therapy, or (4) active comparison conditions (treatment as usual or case management).

Main Outcomes and Measures

The main outcome was the trajectory of depression severity, assessed using the Hamilton Depression Rating Scale (HAM-D). A data-driven, unsupervised, hierarchical clustering of HAM-D items at baseline was conducted to detect clusters of depressive symptoms. A bipartite network analysis was used to identify clinical subtypes at baseline, accounting for both between- and within-patient variability across domains of psychopathology, social support, cognitive impairment, and disability. The trajectories of depression severity in the identified subtypes were compared using mixed-effects models, and time to remission (HAM-D score ≤10) was compared using survival analysis.

Results

The bipartite network analysis, which included 535 older adults with major depression (mean [SD] age, 72.7 [8.7] years; 70.7% female), identified 3 clinical subtypes: (1) individuals with severe depression and a large social network; (2) older, educated individuals experiencing strong social support and social interactions; and (3) individuals with disability. There was a significant difference in depression trajectories (F2,2976.9 = 9.4; P < .001) and remission rate (log-rank χ22 = 18.2; P < .001) across clinical subtypes. Subtype 2 had the steepest depression trajectory and highest likelihood of remission regardless of the intervention, while subtype 1 had the poorest depression trajectory.

Conclusions and Relevance

In this prognostic study, bipartite network clustering identified 3 subtypes of late-life depression. Knowledge of patients' clinical characteristics may inform treatment selection. Identification of discrete subtypes of late-life depression may stimulate the development of novel, streamlined interventions targeting the clinical vulnerabilities of each subtype.

Towards automatic text-based estimation of depression through symptom prediction

Major Depressive Disorder (MDD) is one of the most common and comorbid mental disorders that impacts a person's day-to-day activity. In addition, MDD affects one's linguistic footprint, which is reflected by subtle changes in speech production. This allows us to use natural language processing (NLP) techniques to build a neural classifier to detect depression from speech transcripts. Typically, current NLP systems discriminate only between the depressed and non-depressed states. This approach, however, disregards the complexity of the clinical picture of depression, as different people with MDD can suffer from different sets of depression symptoms. Therefore, predicting individual symptoms can provide more fine-grained information about a person's condition. In this work, we look at the depression classification problem through the prism of the symptom network analysis approach, which shifts attention from a categorical analysis of depression towards a personalized analysis of symptom profiles. For that purpose, we trained a multi-target hierarchical regression model to predict individual depression symptoms from patient-psychiatrist interview transcripts from the DAIC-WOZ corpus. Our model achieved results on par with state-of-the-art models on both binary diagnostic classification and depression severity prediction while at the same time providing a more fine-grained overview of individual symptoms for each person. The model achieved a mean absolute error (MAE) from 0.438 to 0.830 on eight depression symptoms and showed state-of-the-art results in binary depression estimation (73.9 macro-F1) and total depression score prediction (3.78 MAE). Moreover, the model produced a symptom correlation graph that is structurally identical to the real one. The proposed symptom-based approach provides more in-depth information about the depressive condition by focusing on the individual symptoms rather than a general binary diagnosis.

Network and Exploratory Factorial Analysis of the Depression Clinical Evaluation Test

Depression is a highly prevalent disorder with a wide range of symptomatology. Existing instruments for its assessment have only a few items for each factor. The Depression Clinical Evaluation Test (DCET) has been created to cover all depression symptoms at different times (month, year, and always) with several items for each facet. The content validity of this instrument has been judged by experts and, in this paper, we analyse its factorial structure and make a network analysis of it. The test (196 items) was administered to 602 adults without psychological disorders (M_age = 24.7, SD = 8.38, 72% women) both online and on paper. A network was estimated for each time point, using the absolute minimum selection and shrinkage operator. From the factor analysis, 12 factors were established for month, 11 for year, and 10 for always, leaving 94 items. The network analysis showed that the facets of depressive mood, anhedonia, and thoughts of Death, are central to all the estimated networks. The DCET is proposed as a valid and reliable multifactorial instrument to detect the variability of depressive symptoms in adults, guaranteeing its diagnostic usefulness.

Predicting non-response to multimodal day clinic treatment in severely impaired depressed patients: a machine learning approach

A considerable number of depressed patients do not respond to treatment. Accurate prediction of non-response to routine clinical care may help in treatment planning and improve results. A longitudinal sample of N = 239 depressed patients was assessed at admission to multi-modal day clinic treatment, after six weeks, and at discharge. First, patient’s treatment response was modelled by identifying longitudinal trajectories using the Hamilton Depression Rating Scale (HDRS-17). Then, individual items of the HDRS-17 at admission as well as individual patient characteristics were entered as predictors of response/non-response trajectories into the binary classification model (eXtremeGradient Boosting; XGBoost). The model was evaluated on a hold-out set and explained in human-interpretable form by SHapley Additive explanation (SHAP) values. The prediction model yielded a multi-class AUC = 0.80 in the hold-out set. The predictive power for the binary classification yielded an AUC = 0.83 (sensitivity = .80, specificity = .77). Most relevant predictors for non-response were insomnia symptoms, younger age, anxiety symptoms, depressed mood, being unemployed, suicidal ideation and somatic symptoms of depressive disorder. Non-responders to routine treatment for depression can be identified and screened for potential next-generation treatments. Such predictors may help personalize treatment and improve treatment response.

Cognitive Considerations in Major Depression: Evaluating the Effects of Pharmacotherapy and ECT on Mood and Executive Control Deficits

Deficits in the executive control of attention greatly impact the quality of life of patients diagnosed with major depressive disorder (MDD). However, attentional deficits are often underemphasized in clinical contexts compared with mood-based symptoms, and a comprehensive approach for specifically evaluating and treating them has yet to be developed. The present study evaluates the efficacy of bifrontal electroconvulsive therapy (ECT) combined with drug therapy (DT) in alleviating mood-related symptomatology and executive control deficits in drug-refractory MDD patients and compares these effects with those observed in MDD patients undergoing DT only. The Hamilton Rating Scale for Depression and the Lateralized Attentional Network Test-Revised were administered across two test sessions to assess treatment-related changes in mood-based symptoms and conflict processing, respectively, in patients undergoing ECT + DT (n = 23), patients undergoing DT (n = 33), and healthy controls (n = 40). Although both groups showed an improvement in mood-based symptoms following treatment and a deficit in conflict processing estimated on error rate, a post-treatment reduction of an executive control deficit estimated on RT was solely observed in the ECT + DT patient group. Furthermore, Bayesian correlational analyses confirmed the dissociation of mood-related symptoms and of executive control measures, supporting existing literature proposing that attentional deficits and mood symptoms are independent aspects of MDD. The cognitive profile of MDD includes executive control deficits, and while both treatments improved mood-based symptoms, only ECT + DT exerted an effect on both measures of the executive control deficit. Our findings highlight the importance of considering the improvement in both mood and cognitive deficits when determining the efficacy of therapeutic approaches for MDD.

Subtyping late-life depression according to inflammatory and metabolic dysregulation: a prospective study

BACKGROUND

Inflammation and metabolic dysregulation are age-related physiological changes and are associated with depressive disorder. We tried to identify subgroups of depressed older patients based on their metabolic-inflammatory profile and examined the course of depression for these subgroups.

METHODS

This clinical cohort study was conducted in a sample of 364 depressed older (⩾60 years) patients according to DSM-IV criteria. Severity of depressive symptoms was monitored every 6 months and a formal diagnostic interview repeated at 2-year follow-up. Latent class analyses based on baseline metabolic and inflammatory biomarkers were performed. Adjusted for confounders, we compared remission of depression at 2-year follow-up between the metabolic-inflammatory subgroups with logistic regression and the course of depression severity over 2-years by linear mixed models.

RESULTS

We identified a 'healthy' subgroup (n = 181, 49.7%) and five subgroups characterized by different profiles of metabolic-inflammatory dysregulation. Compared to the healthy subgroup, patients in the subgroup with mild 'metabolic and inflammatory dysregulation' (n = 137, 37.6%) had higher depressive symptom scores, a lower rate of improvement in the first year, and were less likely to be remitted after 2-years [OR 0.49 (95% CI 0.26-0.91)]. The four smaller subgroups characterized by a more specific immune-inflammatory dysregulation profile did not differ from the two main subgroups regarding the course of depression.

CONCLUSIONS

Nearly half of the patients with late-life depressions suffer from metabolic-inflammatory dysregulation, which is also associated with more severe depression and a worse prognosis. Future studies should examine whether these depressed older patients benefit from a metabolic-inflammatory targeted treatment.

A Data-Driven Clustering Method for Discovering Profiles in the Dynamics of Major Depressive Disorder Using a Smartphone-Based Ecological Momentary Assessment of Mood

BACKGROUND

Although major depressive disorder (MDD) is characterized by a pervasive negative mood, research indicates that the mood of depressed patients is rarely entirely stagnant. It is often dynamic, distinguished by highs and lows, and it is highly responsive to external and internal regulatory processes. Mood dynamics can be defined as a combination of mood variability (the magnitude of the mood changes) and emotional inertia (the speed of mood shifts). The purpose of this study is to explore various distinctive profiles in real-time monitored mood dynamics among MDD patients in routine mental healthcare.

METHODS

Ecological momentary assessment (EMA) data were collected as part of the cross-European E-COMPARED trial, in which approximately half of the patients were randomly assigned to receive the blended Cognitive Behavioral Therapy (bCBT). In this study a subsample of the bCBT group was included (n = 287). As part of bCBT, patients were prompted to rate their current mood (on a 1-10 scale) using a smartphone-based EMA application. During the first week of treatment, the patients were prompted to rate their mood on three separate occasions during the day. Latent profile analyses were subsequently applied to identify distinct profiles based on average mood, mood variability, and emotional inertia across the monitoring period.

RESULTS

Overall, four profiles were identified, which we labeled as: (1) "very negative and least variable mood" (n = 14) (2) "negative and moderate variable mood" (n = 204), (3) "positive and moderate variable mood" (n = 41), and (4) "negative and highest variable mood" (n = 28). The degree of emotional inertia was virtually identical across the profiles.

CONCLUSIONS

The real-time monitoring conducted in the present study provides some preliminary indications of different patterns of both average mood and mood variability among MDD patients in treatment in mental health settings. Such varying patterns were not found for emotional inertia.

Characteristics and subtypes of depressive symptoms in Chinese female breast cancer patients of different ages: a cross-sectional study

PURPOSE

To identify the characteristics and subtypes of depressive symptoms and explore the relationship between depressive subtypes and age among Chinese female breast cancer patients.

METHOD

In this cross-sectional study, 566 breast cancer patients were recruited from three tertiary comprehensive hospital in Shandong Province, China through convenient sampling from April 2013 to June 2019. Depressive symptoms were measured using the Patient Health Questionnaire-9 (PHQ-9). Data analyses included descriptive analyses, latent class analysis.

RESULTS

There were significant differences in specific depressive symptoms by age group, but no significant difference in total scores on PHQ-9. The depressive subtypes were severe (Class 4), relatively severe (Class 3; with lower psychomotor agitation/retardation and suicidal ideation), moderate (Class 2; with higher psychomotor agitation/retardation and suicidal ideation), and mild depressive symptoms (Class 1). The distribution of depression subtypes is different in various age groups. In the 45-59 age groups, severe symptoms subtype showed the highest ratios (i.e. 50.3%).

CONCLUSION

This is the first study that analyses depressive symptom characteristics and identifies depressive subtypes in Chinese women with breast cancer across ages to explore symptom heterogeneity. Our findings can contribute to identifying the mechanisms behind these relationships and developing targeted interventions for patients with specific depressive subtypes.

Depression symptom profiles and long-term response to cognitive behavioral therapy plus contingency management for smoking cessation

BACKGROUND

Depression is heterogeneous in nature and using diagnostic categories limits insight into understanding psychopathology and its impact on treatment efficacy. This secondary analysis sought to: 1) identify distinct subpopulations of cigarette users with depression, and 2) examine their response to cognitive-behavioral treatment (CBT) + contingency management (CM) for smoking cessation at one year.

METHOD

The sample comprised 238 (74 % females) adults who smoke receiving CBT only or CBT + CM. A latent class analysis was conducted on baseline depressive symptoms measured using the Beck Depression Inventory-II. Generalized estimating equations assessed the main and interactive effects of class, time, treatment, and sex on smoking abstinence.

RESULTS

Three distinct classes were identified: C1 (n= 76/238), characterized by mild depression, loss of energy, pessimism, and criticism, C2 (n= 100/238) presenting moderate severity and decreased appetite, and C3 (n= 62/238) showing severe depression, increased appetite, and feelings of punishment. There was a significant cluster × treatment interaction, which indicated additive effects of CM over CBT alone for Class 1 and 2. Persons in Class 1 and 2 were 3.60 [95 % CI: 1.62, 7.97] and 2.65 [95 % CI: 1.19, 5.91] times more likely to be abstinent if CBT + CM was delivered rather than CBT only. No differential sex effects were observed on treatment response according to cluster.

CONCLUSIONS

Profiling depression symptom subtypes of cigarette users may be more informative to improve CM treatment response than merely focusing on total scores.

Discovering different profiles in the dynamics of depression based on real-time monitoring of mood: a first exploration

BACKGROUND

Although depression is typically characterized by a persistent depressed mood, mood dynamics do seem to vary across a depressed population. Heterogeneity of mood variability (magnitude of changes) and emotional inertia (speed at which mood shifts) is seen in clinical practice. However, studies investigating the heterogeneity of these mood dynamics are still scarce. The aim of the present study is to explore different distinctive profiles in real-time monitored mood dynamics among depressed persons.

METHODS

After completing baseline measures, mildly-to-moderately depressed persons (n = 37) were prompted to rate their current mood (1-10 scale) on their smartphones, 3 times a day for 7 consecutive days. Latent profile analyses were applied to identify profiles based on average mood, variability of mood and emotional inertia as reported by the participants.

RESULTS

Two profiles were identified in this sample. The overwhelming majority of the sample belonged to profile 1 (n = 31). Persons in profile 1 were characterized by a mood just above the cutoff for positive mood (M = 6.27), with smaller mood shifts (lower variability [SD = 1.05]) than those in profile 2 (n = 6), who displayed an overall negative mood (M = 4.72) and larger mood shifts (higher variability [SD = 1.95]) but at similar speed (emotional inertia) (AC = 0.19, AC = 0.26, respectively).

CONCLUSIONS

The present study provides preliminary indications for patterns of average mood and mood variability, but not emotional inertia, among mildly-to-moderately depressed persons.

Associations between depression subtype and functional impairment and treatment utilization in a national sample of adolescents

BACKGROUND

Prior studies have characterized distinct major depressive episode (MDE) subtypes among adults, yet limited evidence exists regarding variation in MDE during adolescence.

METHODS

Using 2008-2016 National Survey of Drug Use and Health data, latent class analysis (LCA) was used to characterize depression subtypes (based on symptom presentation) among 9,896 youth ages 12-17 with recent first-onset MDE. Logistic regression was used to estimate associations of MDE subtype with functional outcomes and treatment utilization, adjusting for demographic characteristics and depression severity (i.e., number of MDE diagnostic criteria and recurrence status) RESULTS: A 5-class LCA model provided optimal fit. Three distinct categories of MDE symptoms generally clustered together, which we termed "somatic," "cognitive," and "self-worth;" classes were differentiated by distinct combinations of symptoms across these 3 categories. Subtypes were characterized as: Highly Symptomatic (39% of youth); Somatic & Cognitive (24%), Somatic (22%), Diffuse Symptoms (8%), and Somatic & Self-Worth (6%). The majority of youth reported at least moderate impairment across multiple domains; subtype was a significant predictor of functional impairment. Only 34% of youth received any past-year depression-related treatment; treatment utilization was significantly higher for MDE subtypes with the highest prevalences of suicidal ideation.

LIMITATIONS

Due to cross-sectional data, we cannot establish causal directionality.

CONCLUSIONS

Subtype was significantly predictive of functional impairment and treatment utilization, above and beyond number of MDE diagnostic criteria or recurrence status. Understanding distinct profiles of adolescent depression, as well as potential differential associations with impairment, can inform prevention, diagnosis, and treatment of depression among youth.

The concept of anorexia of aging in late life depression: A cross-sectional analysis of a cohort study

INTRODUCTION

Anorexia of aging (AA) is classically associated with depression. However, robust evidence is lacking regarding general clinic populations. Our aim was to evaluate the association between AA and major depressive disorder (MDD) in geriatric outpatients from a middle-income country.

METHODS

We conducted a cross-sectional analysis of a cohort study. MDD diagnosis was assessed with a psychiatric interview (SCID-5-CV) according to DSM-5 criteria. Depressive symptomatology was assessed by a 15-items Geriatric Depression Scale (GDS) and the PHQ-9 questionnaire. Appetite was measured with the Simple Nutrition Appetite Questionnaire (SNAQ), whereas AA was defined as a SNAQ score ≤13 points). Linear and logistic regression analysis adjusted for potential confounders were applied to assess the association between depressive symptomatology, MDD and AA.

RESULTS

Of the total 339 participants, MDD was present in 65. AA was more frequent in patients with MDD compared to non-depressed patients (30.7 versus 7.7%; p<0.001). The SNAQ score was lower in depressed patients (14.5 vs. 16.6, p<0.001). Adjusted for confounding, linear and logistic regression showed a significant association between the GDS score, PHQ-9 score and MDD with the SNAQ score (p<0.001) and cut-off representing AA (p<0.001), respectively. Moreover, MDD and AA interacted significantly with their association with weight loss (p<0.001).

CONCLUSIONS

Depression scales (even without somatic complaints) and MDD were associated with AA in geriatric outpatients. AA is associated with weight loss in MDD. Prospective studies should expand these findings.

Latent Class Analysis of Adolescent Psychosocial Functioning and Course of Major Depression

There are few studies on the predictors of long-term course of major depressive disorder (MDD) with an onset in childhood and adolescence. Studies have relied on variable-centered methods, utilizing psychosocial and clinical characteristics to predict depression outcomes. However, fewer studies have used person-centered approaches that rely on profiles of functioning to predict course and outcomes of depression. This study examined the long-term course and outcome of early onset depression as a function of profiles of psychosocial and clinical characteristics in adolescence. Participants from the Oregon Adolescent Depression Project with a history of MDD by study entry (M_age = 16.29 years) and who had follow-up assessments at age 30 were included (n = 215). Psychosocial and clinical constructs, including domains of internalizing problems, externalizing problems, correlates of internalizing problems, adolescent stress, and social support, were assessed in adolescence. Latent profile analyses found a 3-class solution with Low Negative Cognitive Style (LNCS; 27.9%); Internalizing and High Negative Cognitive Style (INT/HNCS; 53.9%); and Internalizing and High Negative Cognitive Style plus Poor Interpersonal Functioning and High Stress (INT/HNCS+ ; 18.1%). Overall, classes differed in depression morbidity, such that the INT/HNCS+ class had the greatest depression morbidity across follow-up assessments. Social adjustment differed between all classes, with the INT/HNCS+ class showing the worst functioning, the LNCS class showing the best functioning, and the INT/HNCS class falling in the middle. Patterns of clinical and psychosocial functioning were differentially associated with long-term depression and social adjustment among youth with depression.

Latent Class Analysis in Depression, Including Clinical and Functional Variables: Evidence of a Complex Depressive Subtype in Primary Care in Chile

OBJECTIVE

To establish differentiated depressive subtypes using a latent class analysis (LCA), including clinical and functional indicators in a sample of depressed patients consulted in Chilean Primary Health Care.

METHODS

A LCA was performed on a sample of 297 depressed patients consulted in Chilean PHC. The Mini International Neuropsychiatric Interview, the Hamilton Depression Rating Scale, the Outcome Questionnaire -social role, and interpersonal subscales were as instruments. A regression analysis of the different subtypes with sociodemographic and adverse life experiences was performed.

RESULTS

In a sample characterized by 87.5% of women, two, three, and four latent class models were obtained. The three-class model likely represents the best clinical implications. In this model, the classes were labeled: "complex depression" (CD) (58% of the sample), "recurrent depression" (RD) (34%), and "single depression episode" (SD) (8%). Members of CD showed a higher probability of history of suicide attempts, interpersonal, and social dysfunction. Psychiatric comorbidities differentiated the RD from SD. According to a multinomial regression model, childhood trauma experiences, recent stressful life experiences, and intimate partner violence events were associated with the CD class (p < 0.01). Limitations. The vast majority of participants were females from Chile and the sample studied was not random. So, the results may not necessarily represent outpatient clinics.

CONCLUSIONS

This study can provide additional evidence that depression, specifically in female gender, could be better understood as a complex heterogeneous disorder when clinical and functional indicators are studied. Furthermore, adverse life experiences starting in childhood could lead to a differentiated complex depressive subtype.

Appetite changes reveal depression subgroups with distinct endocrine, metabolic, and immune states

There exists little human neuroscience research to explain why some individuals lose their appetite when they become depressed, while others eat more. Answering this question may reveal much about the various pathophysiologies underlying depression. The present study combined neuroimaging, salivary cortisol, and blood markers of inflammation and metabolism collected prior to scanning. We compared the relationships between peripheral endocrine, metabolic, and immune signaling and brain activity to food cues between depressed participants experiencing increased (N = 23) or decreased (N = 31) appetite and weight in their current depressive episode and healthy control participants (N = 42). The two depression subgroups were unmedicated and did not differ in depression severity, anxiety, anhedonia, or body mass index. Depressed participants experiencing decreased appetite had higher cortisol levels than subjects in the other two groups, and their cortisol values correlated inversely with the ventral striatal response to food cues. In contrast, depressed participants experiencing increased appetite exhibited marked immunometabolic dysregulation, with higher insulin, insulin resistance, leptin, CRP, IL-1RA, and IL-6, and lower ghrelin than subjects in other groups, and the magnitude of their insulin resistance correlated positively with the insula response to food cues. These findings provide novel evidence linking aberrations in homeostatic signaling pathways within depression subtypes to the activity of neural systems that respond to food cues and select when, what, and how much to eat. In conjunction with prior work, the present findings strongly support the existence of pathophysiologically distinct depression subtypes for which the direction of appetite change may be an easily measured behavioral marker.

Subtypes of Depression: Latent Class Analysis in Spanish Old People with Depressive Symptoms

Major depressive disorder (MDD) is one of the most disabling disorders and the one that most contributes to disability. When it occurs in older people, it is an additional burden to their potential physical and cognitive deficiencies, making MDD an important public health problem that supposes a large investment in health. There is a clear lack of consistency between the subtypes of depression found in the literature, ranging from two to seven classes, with three being the most commonly found non-melancholic, melancholic and psychotic, or putative psychotics. The aim of this research is to add knowledge to the profiles of depressive symptoms in a representative sample of older Spanish people, and to study the possible relationship of these symptom profiles with variables that have traditionally been related to depression. Spanish data from the sixth wave of SHARE were used, with 612 Spanish older adults living in Spain. A routine of several LCAs with a different number of classes was performed to answer this first aim to classify Spanish adults with depression symptoms. The results pointed out the presence of three different classes among the participants in the study: psychosomatic (11.12%), melancholic (14.21%), and anhedonic (74.67%). This work represents a step forward to understand the heterogeneity of major depressive disorder, facilitating the diagnosis, and subsequent treatment of older adults.

Predictability of temperaments and negative experiences on higher-order symptom-based subtypes of depression

BACKGROUND

The identification of subtypes of depression based on higher-order symptoms of emotional, thought, and behavioral dysfunction will broaden understanding of the heterogeneity in depression. Furthermore, exploring the ability of temperaments and negative experiences to predict each subtype is an effective way of facilitating treatment decisions.

METHODS

Participants were 417 patients diagnosed with depressive disorder at the psychiatry department of a major medical hospital in Seoul, Korea. A latent profile analysis was performed based on three higher-order scales of the MMPI-2-RF: Emotional/Internalizing Dysfunction, Thought Dysfunction, and Behavioral/Externalizing Dysfunction. Four temperament dimensions were assessed by the Temperament and Character Inventory-Revised-Short, and negative experiences including recent negative life events, number of lifetime traumatic events, and severity of maltreatment, were used as covariates in a multinomial regression analysis.

RESULTS

Four classes were obtained from the latent profile analysis: a "severe mood class" (39.8%), a "moderate mood class" (37.4%), a "mild mood class" (11.3%), and a "severe mood/thought class" (11.5%). Among temperament dimensions, high harm avoidance and low persistence significantly predicted more severe mood classes. Low reward dependence, number of lifetime traumatic events, and severity of maltreatment in negative experiences were significant predictors of the severe mood/thought class.

LIMITATIONS

This study could not explain the more detailed heterogeneity within depression because of over-inclusiveness of the higher-order scales.

CONCLUSIONS

This study identified three latent classes that differed in emotional severity and one other class with thought problems. The distinct dimensions of temperament and different types of negative experiences predicted the identified subtypes.

Finding intestinal fortitude: Integrating the microbiome into a holistic view of depression mechanisms, treatment, and resilience

Depression affects at least 322 million people globally, or approximately 4.4% of the world's population. While the earnestness of researchers and clinicians to understand and treat depression is not waning, the number of individuals suffering from depression continues to increase over and above the rate of global population growth. There is a sincere need for a paradigm shift. Research in the past decade is beginning to take a more holistic approach to understanding depression etiology and treatment, integrating multiple body systems into whole-body conceptualizations of this mental health affliction. Evidence supports the hypothesis that the gut microbiome, or the collective trillions of microbes inhabiting the gastrointestinal tract, is an important factor determining both the risk of development of depression and persistence of depressive symptoms. This review discusses recent advances in both rodent and human research that explore bidirectional communication between the gut microbiome and the immune, endocrine, and central nervous systems implicated in the etiology and pathophysiology of depression. Through interactions with circulating inflammatory markers and hormones, afferent and efferent neural systems, and other, more niche, pathways, the gut microbiome can affect behavior to facilitate the development of depression, exacerbate current symptoms, or contribute to treatment and resilience. While the challenge of depression may be the direst mental health crisis of our age, new discoveries in the gut microbiome, when integrated into a holistic perspective, hold great promise for the future of positive mental health.

The importance of identifying functional Val158Met polymorphism in catechol-O- Methyltransferase when assessing MRI-based volumetric measurements in major depressive disorder

Many studies have shown volumetric differences in the hippocampus between COMT gene polymorphisms and other studies have shown differences between depressed patients and controls; yet, few studies have been completed to identify the volumetric differences when taking both factors into consideration. Using voxel-based morphology (VBM) we investigated, in major depressive disorder (MDD) patients and healthy controls, the relationship between COMT gene polymorphism and volumetric abnormalities. Data from 60 MDD patients and 25 healthy controls were included in this study. Volumetric measurements and genotyping of COMTval158met polymorphism were conducted to determine its impact on gray matter volume (GMV) in the hippocampus and amygdala using a Met dominant model (Val/Val vs Met/Val & Met/Met). In the analysis, a significant difference in the right hippocampus (p = 0.015), right amygdala (p = 0.003) and entire amygdala (p = 0.019) was found between the interaction of diagnosis and genotype after MRI scanner, age and sex correction. Healthy controls (HC) with the Met dominant genotype exhibited a larger right hippocampal, right amygdalar and entire amydgalar volume than MDD patients with the Met dominant genotype. Conversely, HC with the Val/Val genotype displayed a lower right hippocampal, right amygdalar and entire amygdalar volume than their MDD counterparts. This study shows that COMT polymorphism and depression may have a confounding effect on neuroimaging studies.

Chronic Stress Induces Activity, Synaptic, and Transcriptional Remodeling of the Lateral Habenula Associated with Deficits in Motivated Behaviors

Chronic stress (CS) is a major risk factor for the development of depression. Here, we demonstrate that CS-induced hyperactivity in ventral tegmental area (VTA)-projecting lateral habenula (LHb) neurons is associated with increased passive coping (PC), but not anxiety or anhedonia. LHb→VTA neurons in mice with increased PC show increased burst and tonic firing as well as synaptic adaptations in excitatory inputs from the entopeduncular nucleus (EP). In vivo manipulations of EP→LHb or LHb→VTA neurons selectively alter PC and effort-related motivation. Conversely, dorsal raphe (DR)-projecting LHb neurons do not show CS-induced hyperactivity and are targeted indirectly by the EP. Using single-cell transcriptomics, we reveal a set of genes that can collectively serve as biomarkers to identify mice with increased PC and differentiate LHb→VTA from LHb→DR neurons. Together, we provide a set of biological markers at the level of genes, synapses, cells, and circuits that define a distinctive CS-induced behavioral phenotype.

Do Relational and Self-Definitional Traits Influence Deep Brain Stimulation Device Preference?

Personality psychodynamics have been shown to influence individual responses to psychiatric treatments, including medication. Increasingly, neuromodulation therapies have become available for severe and treatment-resistant depression. This study aims to evaluate patient response to an implanted neurostimulator battery within the framework of relational versus self-definitional personality traits. Relational development is interpersonally oriented and disruptions along this pathway lead to dependency on others for a sense of security and self-worth. Self-definitional development is characterized by autonomy strivings and disruptions lead to self-critical feelings of failing to meet expectations. Patients drawn from a larger study of deep brain stimulation (DBS) for treatment-resistant depression were switched from a non-rechargeable to a rechargeable battery type to maintain stimulation therapy. This switch entailed taking greater personal responsibility for device maintenance and allowed for fewer battery replacement surgeries. Twenty-six patients completed the Depressive Experiences Questionnaire (DEQ) and a questionnaire surveying their preference for DBS battery type. Results show that the DEQ dependency subscale, and more specifically the neediness component of the subscale, is associated with patient preference for the non-rechargeable battery. This suggests that individuals with higher relational needs prefer treatment options that increase contact with and need for medical caregivers and may prioritize this aspect of an intervention over alternative considerations. In contrast, individuals with more self-critical personality traits did not have a battery type preference, indicating that self-definitional needs were not predictive of battery preference. The link between an individual's personality psychodynamics and response to biomedical interventions, including neuromodulation and treatments that incorporate medical devices, deserves further attention.

Subtypes of treatment-resistant depression determined by a latent class analysis in a Chinese clinical population

BACKGROUND

This study aimed to explore subtypes of treatment-resistant depression (TRD).

METHODS

Latent class analysis (LCA) was performed on clinical and demographic data collected from 375 patients with TRD. Clinical variables were compared across subtypes. Treatment outcomes across subtypes of TRD were compared separately for those within each subtype with anxiety (those with a HRSD-17 anxiety/somatization factor score ≥ 7) and those without anxiety. LCA subtypes were compared using Cochran's and Mantel-Haenszel χ² test, respectively. Unordered multinomial logistic regression was used to assess clinical correlates of TRD subtypes.

RESULTS

Three categories were detected: severe depression (66%), moderate depression with anxiety (9%) and mild depression with anxiety/somatization (25%). Gender, age, age at first onset, family monthly income, number of hospitalizations, HRSD-17 and clinical global impression-severity (CGI) scores were significantly different across the three groups. Remission rates were significantly different among anxious cases with severe (43.75%), moderate (22.73%) and mild (26.25%) depression subtypes. Compared to cases in the mild depression group, those in the severe depression group had a greater likelihood of being male, having a later age of first onset, higher numbers of hospitalization, higher HRSD-17 and CGI total scores, and lower family income. Those in the moderate depression group were more likely to be male and have lower family income than those in the mild depression group.

LIMITATIONS

Representative bias, relatively small sample size, unbalanced group size and incomplete indicator variables might have a negative effect on the validity and generalization of the findings.

CONCLUSIONS

Depression severity could be a basis for subtype classification of patients with TRD. The classification of latent class of TRD observed in our study was similar to the structure found in MDD. Longitudinal research into the stability of the latent structure of TRD across illness course is merited as is research into treatment outcomes for TRD subtypes.

Clinical Predictors of Response to Magnetic Seizure Therapy in Depression: A Preliminary Report

OBJECTIVES

Magnetic seizure therapy (MST) is a novel convulsive brain stimulation method in clinical testing, which is used as an alternative for electroconvulsive therapy in patients with treatment-resistant depression (TRD). Preliminary studies have suggested that MST leads to fewer cognitive adverse effects than electroconvulsive therapy but has similar efficacy. However, the clinical predictors of response to MST have not been evaluated yet. This study aimed to investigate whether these predictors can be identified in patients with TRD.

METHODS

Thirty-eight patients with TRD were included. As clinical predictors for treatment response, we used the diagnosis, sex, age, family history, and severity of depression, as well as the melancholic, psychotic, anxiety, and atypical depression symptoms. A response was defined as an improvement higher than 50% on the 28-item Hamilton Rating Scale for Depression. The binary logistic regression, stepwise linear regression, and effect sizes were calculated.

RESULTS

We found that 68.4% of the patients responded to MST. The responders had significantly fewer previous depressive episodes, less severe depression, and fewer melancholic (anhedonia) and anxiety symptoms than the nonresponders. In addition, responders were more likely to have a positive family history of depression than nonresponders. In particular, the number of previous episodes and a family history of depression were significant predictors of the response to MST.

CONCLUSIONS

We demonstrate that the chronicity, severity, and family history of depression, as well as the presence of melancholic and anxiety symptoms, can serve as clinical predictors of the response to MST. Further research with a larger sample size will be required to verify these preliminary findings.

Course of subthreshold depression into a depressive disorder and its risk factors

BACKGROUND

Information on the natural course of subthreshold depression and risk factors for the development of a full-blown depressive disorder in the general population is scarce. This information is crucial to understand the development of depression and to advance indicated depression prevention.

METHODS

Using longitudinal data from a representative population-based study (the Netherlands Mental Health Survey and Incidence Study-2) we assessed 3-year course of subthreshold depression (depressive symptoms causing clinically significant distress for at least 2 weeks, or for 3 days per month for a year; n = 120), compared to an asymptomatic group (n = 4111) and a depressive disorder group (major depression or dysthymia; n = 294). Next, risk factors for the development of a depressive disorder among adults with subthreshold depression were determined.

RESULTS

Twelve percent of the subthreshold cases developed a full-blown depressive disorder during 3-year follow-up. Risk factors were lower social support, having recurrent short episodes of depressive symptomatology, remitted and current anxiety disorder, remitted substance use disorder, lifetime suicide thoughts, a chronic physical disorder and diminished mental and physical functioning.

LIMITATIONS

The number of subjects with subthreshold depression that developed a depressive disorder was small. This limits the possibility to detect significant risk factors.

CONCLUSION

Only a minority of the subthreshold cases developed a full-blown depressive disorder over three years. This shows that subthreshold depression does not, by itself, carry an a priori risk to warrant focusing indicated prevention. The identified risk factors could help to detect those subthreshold cases in whom depression prevention is economically and practically viable.

Cluster analysis with MOODS-SR illustrates a potential bipolar disorder risk phenotype in young adults with remitted major depressive disorder

OBJECTIVES

Delays in the diagnosis and detection of bipolar disorder can lead to adverse consequences, including improper treatment and increased suicide risk. The Mood Spectrum Self-Report Measure (MOODS-SR) was designed to capture the full spectrum of lifetime mood symptomology with factor scores for depression and mania symptom constellations. The utility of the MOODS-SR as a tool to investigate homogeneous subgroups was examined, with particular focus on a possible bipolar risk subgroup. Moreover, potential patterns of differences in MOODS-SR subtypes were probed using cognitive vulnerabilities, neuropsychological functioning, and ventral striatum connectivity.

METHODS

K-mean cluster analysis based on factor scores of MOODS-SR was used to determine homogeneous subgroupings within a healthy and remitted depressed young adult sample (N = 86). Between-group comparisons (based on cluster subgroupings) were conducted on measures of cognitive vulnerabilities, neuropsychological functioning, and ventral striatum rs-fMRI connectivity.

RESULTS

Three groups of participants were identified: one with minimal symptomology, one with moderate primarily depressive symptomology, and one with more severe manic and depressive symptomology. Differences in impulsivity, neuroticism, conscientiousness, facial perception accuracy, and rs-fMRI connectivity exist between moderate and severe groups.

CONCLUSIONS

Within a sample of people with and without depression histories, a severe subgroup was identified with potentially increased risk of developing bipolar disorder through use of the MOODS-SR. This small subgroup had higher levels of lifetime depression and mania symptoms. Additionally, differences in traits, affective processing, and connectivity exist between those with a more prototypic unipolar subgrouping and those with potential risk for developing bipolar disorder.

Functioning before and after a major depressive episode: pre-existing vulnerability or scar? A prospective three-wave population-based study

BACKGROUND

The vulnerability hypothesis suggests that impairments after remission of depressive episodes reflect a pre-existing vulnerability, while the scar hypothesis proposes that depression leaves residual impairments that confer risk of subsequent episodes. We prospectively examined vulnerability and scar effects in mental and physical functioning in a representative Dutch population sample.

METHODS

Three waves were used from the Netherlands Mental Health Survey and Incidence Study-2, a population-based study with a 6-years follow-up. Mental and physical functioning were assessed with the Medical Outcomes Study Short Form (SF-36). Major depressive disorder (MDD) was assessed with the Composite International Diagnostic Interview 3.0. Vulnerability effects were examined by comparing healthy controls (n = 2826) with individuals who developed a first-onset depressive episode during first follow-up but did not have a lifetime diagnosis of MDD at baseline (n = 181). Scarring effects were examined by comparing pre- and post-morbid functioning in individuals who developed a depressive episode after baseline that was remitted at the third wave (n = 108).

RESULTS

Both mental (B = -5.4, s.e. = 0.9, p < 0.001) and physical functioning (B = -8.2, s.e. = 1.1, p < 0.001) at baseline were lower in individuals who developed a first depressive episode after baseline compared with healthy controls. This effect was most pronounced in people who developed a severe episode. No firm evidence of scarring in mental or physical functioning was found. In unadjusted analyses, physical functioning was still lowered post-morbidly (B = -5.1, s.e. = 2.1, p = 0.014), but this effect disappeared in adjusted analyses.

CONCLUSIONS

Functional impairments after remission of depression seem to reflect a pre-existing vulnerability rather than a scar.

The use of latent class analysis for identifying subtypes of depression: A systematic review

Depression is a significant public health problem but symptom remission is difficult to predict. This may be due to substantial heterogeneity underlying the disorder. Latent class analysis (LCA) is often used to elucidate clinically relevant depression subtypes but whether or not consistent subtypes emerge is unclear. We sought to critically examine the implementation and reporting of LCA in this context by performing a systematic review to identify articles detailing the use of LCA to explore subtypes of depression among samples of adults endorsing depression symptoms. PubMed, PsycINFO, CINAHL, Scopus, and Google Scholar were searched to identify eligible articles indexed prior to January 2016. Twenty-four articles reporting 28 LCA models were eligible for inclusion. Sample characteristics varied widely. The majority of articles used depression symptoms as the observed indicators of the latent depression subtypes. Details regarding model fit and selection were often lacking. No consistent set of depression subtypes was identified across studies. Differences in how models were constructed might partially explain the conflicting results. Standards for using, interpreting, and reporting LCA models could improve our understanding of the LCA results. Incorporating dimensions of depression other than symptoms, such as functioning, may be helpful in determining depression subtypes.

The influence of comorbid anxiety on the effectiveness of Cognitive Therapy and Interpersonal Psychotherapy for Major Depressive Disorder

BACKGROUND

Anxious depression is an important subtype of Major Depressive Disorder (MDD) defined by both syndromal (anxiety disorders) and dimensional (anxiety symptoms) criteria. A debated question is how anxiety affects MDD treatment. This study examined the impact of comorbid anxiety disorders and symptoms on the effectiveness of and dropout during Cognitive Therapy (CT) and Interpersonal Psychotherapy (IPT) for MDD.

METHODS

Depressed individuals were randomized to CT (n = 76) or IPT (n = 75). Outcome was depression severity measured with the Beck Depression Inventory-II (BDI-II) at the start of each therapy session, post treatment, and monthly up to five months follow-up. Anxiety disorders were assessed with the Structured Clinical Interview for DSM-IV Axis I disorders, (phobic) anxiety symptoms were assessed with Brief Symptom Inventory subscales.

RESULTS

Approximately one third of participants had a comorbid anxiety disorder. Comorbid anxiety disorders and anxiety symptoms were associated with less favorable depression change during IPT as compared to CT in the treatment phase, but not in the trial follow-up phase. Individuals with a comorbid anxiety disorder had significantly higher treatment dropout during both treatments.

LIMITATIONS

Not all therapists and participants were blind to the assessment of comorbid anxiety disorders and the assessments were performed by one rater.

CONCLUSIONS

A preference for CT over IPT for MDD is justifiable when comorbid anxiety is present, although long-term differences are not established and replication of this finding is needed. Clinicians should be aware of the risk of dropout for depressed individuals with an anxiety disorder.

A Review on the General Stability of Mood Disorder Diagnoses Along the Lifetime

PURPOSE OF REVIEW

The purpose of this review is to review the most recent literature regarding diagnostic stability of mood disorders, focusing on epidemiological, clinical-psychopathological, and neurobiological data for unipolar and bipolar affective disorders.

RECENT FINDINGS

Unipolar depression follows a chronic course in at least half of all cases and presents a considerable diagnostic stability across all age ranges. Studies using latent class analysis are allowing improved profiling of depressive subtypes and assessment of their prevalence. Advances have been made in our understanding of the neurobiological underpinnings of depression, with data highlighting the roles of amyloid deposits, the ApoE4 allele, and atrophy of the anterior hippocampus or frontal cortex. The diagnostic instability of bipolar disorder is manifest in the early years, seen in both the extent of diagnostic delay and the high rate of diagnostic conversion from unipolar depression. Regarding disruptive mood dysregulation disorder, we have little data to date, but those which exist indicate a high rate of comorbidity and minimal diagnostic stability for this disorder. Diagnostic stability varies substantially among mood disorders, which would be related to the validity of current diagnostic categories and our diagnostic accuracy.

Associations of mindful eating domains with depressive symptoms and depression in three European countries

OBJECTIVE

To examine associations of mindful eating domains with depressive symptoms and depression in three European countries. Moderation by change in appetite-with increased appetite as marker for depression with atypical features - was also tested.

METHODS

Data were collected in Denmark (n = 1522), Spain (n = 1512) and the Netherlands (n = 1439). Multiple linear and logistic regression analyses segregated by country were used to test associations of four mindful eating domains (Mindful Eating Behaviour Scale; MEBS) with depressive symptoms (continuous score on the Center for Epidemiologic Studies Depression Scale; CES-D) and depression (score above the CES-D cut-off value, and/or use of antidepressants, and/or psychological treatment). Moderation by change in appetite was tested with bias-corrected bootstrap confidence intervals.

RESULTS

The domains Focused Eating, Eating with Awareness and Eating without Distraction were significantly negatively associated with depressive symptoms and depression in all three countries (e.g. Focused Eating Denmark: B = - 0.71, 95% CI: - 0.87, - 0.54; OR = 0.89, 95% CI: 0.86, 0.93). The domain Hunger and Satiety Cues (only measured in the Netherlands) was significantly positively associated with depressive symptoms in the adjusted models (B = 0.09, 95% CI: 0.02, 0.16), but not with depression (OR = 1.02, 95% CI: 0.98, 1.05). These associations were found for both people with and without increased appetite.

LIMITATIONS

The cross-sectional design, which makes it impossible to draw causal conclusions.

CONCLUSIONS

The present study indicates that higher scores on three mindful eating domains are consistently associated with a lower level of depressive symptoms and a lower likelihood of having depression in three European countries.

Sample heterogeneity in unipolar depression as assessed by functional connectivity analyses is dominated by general disease effects

OBJECTIVES

Combinations of resting-state fMRI and machine-learning techniques are increasingly employed to develop diagnostic models for mental disorders. However, little is known about the neurobiological heterogeneity of depression and diagnostic machine learning has mainly been tested in homogeneous samples. Our main objective was to explore the inherent structure of a diverse unipolar depression sample. The secondary objective was to assess, if such information can improve diagnostic classification.

MATERIALS AND METHODS

We analyzed data from 360 patients with unipolar depression and 360 non-depressed population controls, who were subdivided into two independent subsets. Cluster analyses (unsupervised learning) of functional connectivity were used to generate hypotheses about potential patient subgroups from the first subset. The relationship of clusters with demographical and clinical measures was assessed. Subsequently, diagnostic classifiers (supervised learning), which incorporated information about these putative depression subgroups, were trained.

RESULTS

Exploratory cluster analyses revealed two weakly separable subgroups of depressed patients. These subgroups differed in the average duration of depression and in the proportion of patients with concurrently severe depression and anxiety symptoms. The diagnostic classification models performed at chance level.

LIMITATIONS

It remains unresolved, if subgroups represent distinct biological subtypes, variability of continuous clinical variables or in part an overfitting of sparsely structured data.

CONCLUSIONS

Functional connectivity in unipolar depression is associated with general disease effects. Cluster analyses provide hypotheses about potential depression subtypes. Diagnostic models did not benefit from this additional information regarding heterogeneity.

Moving Forward: Challenges and Directions for Psychopathological Network Theory and Methodology

Since the introduction of mental disorders as networks of causally interacting symptoms, this novel framework has received considerable attention. The past years have resulted in over 40 scientific publications and numerous conference symposia and workshops. Now is an excellent moment to take stock of the network approach: What are its most fundamental challenges, and what are potential ways forward in addressing them? After a brief conceptual introduction, we first discuss challenges to network theory: (1) What is the validity of the network approach beyond some commonly investigated disorders such as major depression? (2) How do we best define psychopathological networks and their constituent elements? And (3) how can we gain a better understanding of the causal nature and real-life underpinnings of associations among symptoms? Next, after a short technical introduction to network modeling, we discuss challenges to network methodology: (4) heterogeneity of samples studied with network analytic models, and (5) a lurking replicability crisis in this strongly data-driven and exploratory field. Addressing these challenges may propel the network approach from its adolescence into adulthood and promises advances in understanding psychopathology both at the nomothetic and idiographic level.

Duration of major and minor depressive episodes and associated risk indicators in a psychiatric epidemiological cohort study of the general population

OBJECTIVE

Hardly any studies exist on the duration of major depressive disorder (MDD) and factors that explain variations in episode duration that lack biases. This limits clinical decision-making and leaves patients wondering when they will recover.

METHOD

Data were used from the Netherlands Mental Health Survey and Incidence Study-2, a psychiatric epidemiological cohort study among a nationally representative adult population. Respondents with a newly originated depressive episode were selected: 286 MDD and 107 minor depressive disorder (MinDD) cases. DSM-IV diagnoses were assessed with the Composite International Diagnostic Interview 3.0 and episode duration with the Life Chart Interview.

RESULTS

Among MDD cases, median episode duration was 6 months, mean duration was 10.7 months, and 12% had not recovered at 36 months. Longer duration was associated with comorbid dysthymia, anxiety disorder, psychotropic medication use (i.e. antidepressants or benzodiazepines prescribed by a mental health professional), mental health care use and suicidal behaviour. Better physical and mental functioning before depression onset predicted shorter duration. Among MinDD cases, shorter median duration (3 months) but similar mean duration (8.7 months), risk of chronicity (10% not recovered at 36 months) and risk indicators for episode duration were found.

CONCLUSION

As the risk of chronicity was similar for MDD and MinDD, MinDD cannot be dismissed as a merely brief mood state.

Depressive subtypes in an elderly cohort identified using latent class analysis

BACKGROUND

Clinical findings indicate heterogeneity of depressive disorders, stressing the importance of subtyping depression for research and clinical care. Subtypes of the common late life depression are however seldom studied. Data-driven methods may help provide a more empirically-based classification of late-life depression.

METHODS

Data were used from the Netherlands Study of Depression in Older People (NESDO) derived from 359 persons, aged 60 years or older, with a current diagnosis of major depressive disorder. Latent class analysis (LCA) was used to identify subtypes of depression, using ten CIDI-based depression items. Classes were then characterized using various sociodemographic and clinical characteristics.

RESULTS

The most prevalent class, as identified by LCA, was a moderate-severe class (prevalence 46.5%), followed by a severe melancholic class (prevalence 38.4%), and a severe atypical class (prevalence 15.0%). The strongest distinguishing features between the three classes were appetite and weight and, to a lesser extent, psychomotor symptoms and loss of interest. Compared with the melancholic class, the severe atypical class had the highest prevalence of females, the lowest mean age, the highest BMI, and highest prevalence of both cardiovascular disease, and metabolic syndrome.

LIMITATIONS

The strongest distinguishing symptoms, appetite and weight, could be correlated. Further, only longitudinal studies could demonstrate whether the identified classes are stable on the long term.

DISCUSSION

In older persons with depressive disorders, three distinct subtypes were identified, similar to subtypes found in younger adults. The strongest distinguishing features were appetite and weight; moreover, classes differed strongly on prevalence of metabolic syndrome and cardiovascular disease. These findings suggest differences in the involvement of metabolic pathways across classes, which should be considered when investigating the pathogenesis and (eventually) treatment of depression in older persons.

A personality-based latent class typology of outpatients with major depressive disorder: association with symptomatology, prescription pattern and social function

BACKGROUND

While major depressive disorder (MDD) is considered to be a heterogeneous disorder, the nature of the heterogeneity remains unclear. Studies have attempted to classify patients with MDD using latent variable techniques, yet the empirical approaches to symptom-based subtyping of MDD have not provided conclusive evidence. Here we aimed to identify homogeneous classes of MDD based on personality traits, using a latent profile analysis.

METHODS

We studied 238 outpatients with DSM-IV MDD recruited from our specialized depression outpatient clinic and assessed their dimensional personality traits with the Temperament and Character Inventory. Latent profile analysis was conducted with 7 dimensions of the Temperament and Character Inventory as indicators. Relationships of the identified classes with symptomatology, prescription pattern, and social function were then examined.

RESULTS

The latent profile analysis indicated that a 3-class solution best fit the data. Of the sample, 46.2% was classified into a "neurotic" group characterized by high harm avoidance and low self-directedness; 30.3% into an "adaptive" group characterized by high self-directedness and cooperativeness; and 23.5% into a "socially-detached" group characterized by low reward dependence and cooperativeness and high self-transcendence. The 2 maladaptive groups, namely neurotic and socially-detached groups, demonstrated unique patterns of symptom expression, different classes of psychotropic medication use, and lower social functioning.

LIMITATIONS

Generalizability of the findings was limited since our patients were recruited from the specialized depression outpatient clinic.

CONCLUSIONS

Our personality-based latent profile analysis identified clinically meaningful 3 MDD groups that were markedly different in their personality profiles associated with distinct symptomatology and functioning.

Using a hybrid subtyping model to capture patterns and dimensionality of depressive and anxiety symptomatology in the general population

BACKGROUND

Researchers have tried to identify more homogeneous subtypes of major depressive disorder (MDD) with latent class analyses (LCA). However, this approach does no justice to the dimensional nature of psychopathology. In addition, anxiety and functioning-levels have seldom been integrated in subtyping efforts. Therefore, this study used a hybrid discrete-dimensional approach to identify subgroups with shared patterns of depressive and anxiety symptomatology, while accounting for functioning-levels.

METHODS

The Comprehensive International Diagnostic Interview (CIDI) 1.1 was used to assess previous-year depressive and anxiety symptoms in the Netherlands Mental Health Survey and Incidence Study-1 (NEMESIS-1; n=5583). The data were analyzed with factor analyses, LCA and hybrid mixed-measurement item response theory (MM-IRT) with and without functioning covariates. Finally, the classes' predictors (measured one year earlier) and outcomes (measured two years later) were investigated.

RESULTS

A 3-class MM-IRT model with functioning covariates best described the data and consisted of a 'healthy class' (74.2%) and two symptomatic classes ('sleep/energy' [13.4%]; 'mood/anhedonia' [12.4%]). Factors including older age, urbanicity, higher severity and presence of 1-year MDD predicted membership of either symptomatic class vs. the healthy class. Both symptomatic classes showed poorer 2-year outcomes (i.e. disorders, poor functioning) than the healthy class. The odds of MDD after two years were especially increased in the mood/anhedonia class.

LIMITATIONS

Symptoms were assessed for the past year whereas current functioning was assessed.

CONCLUSIONS

Heterogeneity of depression and anxiety symptomatology are optimally captured by a hybrid discrete-dimensional subtyping model. Importantly, accounting for functioning-levels helps to capture clinically relevant interpersonal differences.

maLPA1-null mice as an endophenotype of anxious depression

Anxious depression is a prevalent disease with devastating consequences and a poor prognosis. Nevertheless, the neurobiological mechanisms underlying this mood disorder remain poorly characterized. The LPA1 receptor is one of the six characterized G protein-coupled receptors (LPA1-6) through which lysophosphatidic acid acts as an intracellular signalling molecule. The loss of this receptor induces anxiety and several behavioural and neurobiological changes that have been strongly associated with depression. In this study, we sought to investigate the involvement of the LPA1 receptor in mood. We first examined hedonic and despair-like behaviours in wild-type and maLPA1 receptor null mice. Owing to the behavioural response exhibited by the maLPA1-null mice, the panic-like reaction was assessed. In addition, c-Fos expression was evaluated as a measure of the functional activity, followed by interregional correlation matrices to establish the brain map of functional activation. maLPA1-null mice exhibited anhedonia, agitation and increased stress reactivity, behaviours that are strongly associated with the psychopathological endophenotype of depression with anxiety features. Furthermore, the functional brain maps differed between the genotypes. The maLPA1-null mice showed increased limbic-system activation, similar to that observed in depressive patients. Antidepressant treatment induced behavioural improvements and functional brain normalisation. Finally, based on validity criteria, maLPA1-null mice are proposed as an animal model of anxious depression. Here, for we believe the first time, we have identified a possible relationship between the LPA1 receptor and anxious depression, shedding light on the unknown neurobiological basis of this subtype of depression and providing an opportunity to explore new therapeutic targets for the treatment of mood disorders, especially for the anxious subtype of depression.